From 32a6ec0438afd844e8a2b17dbd2702d207e8afd6 Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Fri, 3 Apr 2026 23:33:40 +0800
Subject: [PATCH 1/9] docs(knowledge): add rag service extraction design

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+---
+sidebar_position: 1
+---
+
+# RAG Service 独立化设计
+
+## 背景
+
+`docs/plans/2026-03-24-rag-service-split-plan.md` 给出了“Backend 保留控制面，RAG 数据面逐步拆出”的总体方向。
+
+`docs/specs/knowledge/2026-03-31-rag-modular-data-plane-design.md` 已完成进程内模块边界收敛，当前稳定基础包括：
+
+- `RuntimeSpec`
+- `RagGateway`
+- `LocalRagGateway`
+- `local_data_plane`
+
+在该基础上，下一步不再是继续做进程内重构，而是将 RAG 数据面执行能力进一步抽为可独立启动的 `rag_service`，同时保留灰度与回退能力。
+
+本设计聚焦：
+
+- 将解析、切分、embedding、索引、检索、删索引迁移到独立 `rag_service`
+- Backend 保持唯一控制面
+- 不将 `direct injection` 与 `restricted mediation` 下沉到 `rag_service`
+- 为 `summary_vector_index` 和后续多索引族检索路线预留稳定扩展点
+
+## 状态快照
+
+当前代码中已经具备以下基础：
+
+- Backend internal retrieval 已收敛到 `backend/app/api/endpoints/internal/rag.py`
+- `chat_shell` 中 `knowledge_base_search` 通过 Backend internal RAG API 完成检索
+- `backend/app/services/rag/runtime_specs.py` 已提供 `IndexRuntimeSpec` 与 `QueryRuntimeSpec`
+- `backend/app/services/rag/gateway.py` 已定义 `RagGateway`
+- `backend/app/services/rag/local_gateway.py` 已将本地执行路径封装到 `LocalRagGateway`
+
+当前知识库 MCP 尚未提供 `search` 能力，但本轮不将其纳入拆分主线，只在后续工作中说明。
+
+## 问题
+
+### 1. Backend 仍承载重数据面执行
+
+虽然控制面与数据面边界已有所收敛，但当前实际执行仍在 Backend 进程内完成：
+
+- 文档解析
+- chunking
+- embedding
+- 向量索引写入
+- 检索执行
+
+这意味着 Backend 镜像、依赖、启动成本和故障域仍然被重 RAG 能力绑定。
+
+### 2. 首版服务化如果边界错误，返工成本会更高
+
+如果直接将现有执行逻辑整体通过 HTTP 暴露，而不先明确稳定 contract，会把以下不稳定点固化进服务协议：
+
+- 文件内容如何获取
+- 执行面是否关心权限与 CRD
+- `direct injection` 是否属于检索服务职责
+- `restricted mediation` 是否属于 RAG 服务职责
+- 未来 `summary_vector_index`、`tableRAG` 如何接入
+
+### 3. 文件内容传输方式会影响服务职责
+
+索引链路中的解析、切分、embedding 都依赖原始文档内容。
+
+如果 Backend 在每次索引时直接把文件 bytes 推给 `rag_service`：
+
+- Backend 会重新成为大文件中转站
+- 异步重试与 reindex 模型会变差
+- 同一份内容会重复经过“上传存储”和“服务推送”两条链路
+
+因此，首版必须优先稳定“内容引用 contract”，而不是只追求“是否能传得动文件”。
+
+## 目标
+
+- 将 RAG 数据面执行迁移为可独立启动的 `rag_service`
+- 保持 Backend 作为唯一 control plane
+- 保持 `chat_shell` 和其他消费者继续只面向 Backend 调用
+- 通过 `RagGateway` 支持 local / remote 双实现切换
+- 建立统一 remote contract，使 `LocalRagGateway` 与 `RemoteRagGateway` 语义一致
+- 以 `index_family` 预留 `summary_vector_index` 扩展能力
+
+## 非目标
+
+- 本轮不实现知识库 MCP `search`
+- 本轮不让 `rag_service` 直接访问 Backend 数据库
+- 本轮不把附件存储实现复制到 `rag_service`
+- 本轮不将 `direct injection` 下沉到 `rag_service`
+- 本轮不将 `restricted mediation` 下沉到 `rag_service`
+- 本轮不决定 `tableRAG` 的具体底层存储和协议
+
+## 方案对比
+
+### 方案 1：一次性切换到远程 `rag_service`
+
+做法：
+
+- Backend 直接移除本地执行主路径
+- 索引、检索、删除全部切到独立 `rag_service`
+
+优点：
+
+- 目标形态直接
+- Backend 能最快变轻
+
+缺点：
+
+- 首版风险最大
+- 出问题时回退困难
+- 远程 contract、内容获取、鉴权、回写、观测会在同一轮同时收敛
+
+### 方案 2：双实现网关，先落远程能力，再灰度切换
+
+做法：
+
+- 保留 `LocalRagGateway`
+- 新增 `RemoteRagGateway`
+- Backend 继续解析 `RuntimeSpec`
+- 通过配置决定索引/检索/删除走 local 或 remote
+
+优点：
+
+- 与现有代码边界最一致
+- 支持渐进灰度
+- 允许快速回退到 local
+
+缺点：
+
+- 过渡期会同时维护 local / remote 两套执行入口
+
+### 方案 3：仅先拆索引，检索继续留在本地
+
+做法：
+
+- 解析、切分、embedding、索引迁到 `rag_service`
+- 检索仍由本地 data-plane 执行
+
+优点：
+
+- 优先拆出最重的文件处理链路
+
+缺点：
+
+- 会形成“写远程、读本地”的阶段性混合边界
+- 后续还要再做一次检索远程化收敛
+
+## 选型
+
+采用方案 2。
+
+原因：
+
+- 当前 `RuntimeSpec + RagGateway` 已经形成天然过渡层
+- 可以先稳定远程 contract，再决定何时切换默认路径
+- 能在不打断现有 `chat_shell` 调用面的前提下完成服务化
+- 风险和收益平衡最好
+
+## 总体设计
+
+目标调用链如下：
+
+```text
+chat_shell / public API / future MCP consumers
+  -> Backend knowledge control plane
+     -> RagRuntimeResolver
+     -> RagGateway
+        -> LocalRagGateway | RemoteRagGateway
+           -> local_data_plane | rag_service
+```
+
+### Backend 职责
+
+Backend 保留：
+
+- 权限、多租户、namespace、group / personal 规则
+- `KnowledgeBase` / `KnowledgeDocument` 元数据
+- Retriever / Embedding / Summary 等 CRD 解析
+- 任务调度、状态机、失败回写、摘要触发
+- `direct injection` 路由决策
+- `restricted mediation`
+- 对 `chat_shell` 和其他消费者暴露统一 API
+
+Backend 不负责：
+
+- 文档解析实现
+- chunking / embedding / 索引写入执行
+- 远程数据面中的向量检索执行
+
+### rag_service 职责
+
+`rag_service` 只负责执行面：
+
+- 文档内容拉取
+- 文档解析
+- splitter
+- embedding
+- index family 写入
+- query 执行
+- document index 删除
+
+`rag_service` 不负责：
+
+- 权限判断
+- CRD 查询与解析
+- ORM / DB session 处理
+- Backend 元数据回写
+- `direct injection` 消费编排
+- `restricted mediation`
+
+### 稳定边界原则
+
+真正稳定的边界不是“文件存在哪里”，而是：
+
+- Backend 传递 runtime contract 和 content reference
+- `rag_service` 只消费这些 contract，不理解数据库语义
+
+## 内容获取设计
+
+### 为什么不采用 Backend 直接推送文件 bytes
+
+即使多数文件只有几 MB，直接 push 仍存在结构性问题：
+
+- Backend 重新成为数据面中转站
+- 异步重试与 reindex 难以围绕统一引用建模
+- 同一份内容需要经过两次主链路搬运
+- 长请求、流控、超时、补偿更复杂
+
+因此，首版不以“直推 bytes”作为主路径。
+
+### 采用 content_ref 拉取模式
+
+索引请求只传内容引用，不直接传大文件内容。
+
+建议定义：
+
+```text
+content_ref
+  -> backend_attachment_stream
+  -> presigned_url
+```
+
+#### `backend_attachment_stream`
+
+适用于当前 MySQL 或仅 Backend 可读的附件存储。
+
+行为：
+
+- Backend 生成内部可鉴权下载 URL
+- `rag_service` 按引用回源拉取内容
+
+意义：
+
+- 保证兼容当前所有附件存储后端
+- 不需要把附件存储实现复制到 `rag_service`
+
+#### `presigned_url`
+
+适用于 S3 / MinIO / 其他对象存储。
+
+行为：
+
+- Backend 生成预签名 URL
+- `rag_service` 直接读取对象存储
+
+意义：
+
+- 减少 Backend 在对象存储场景下的数据流参与
+- 为后续更彻底解耦铺路
+
+### 首版解耦目标
+
+首版不追求“`rag_service` 完全不依赖 Backend 提供内容入口”。
+
+首版追求的是：
+
+- `rag_service` 不理解附件存储实现
+- Backend 不承担长期 bytes push 中转职责
+- 索引重试可围绕统一 `content_ref` 复用
+
+## Runtime Contract 与 Remote Contract
+
+### 本地 contract 保持不变
+
+Backend 内部继续使用：
+
+- `IndexRuntimeSpec`
+- `QueryRuntimeSpec`
+
+### 引入远程 payload 映射层
+
+`RemoteRagGateway` 负责将本地 runtime contract 转换为远程请求：
+
+- `RemoteIndexRequest`
+- `RemoteQueryRequest`
+- `RemoteDeleteDocumentIndexRequest`
+
+这样 `local` 与 `remote` 的差异只体现在 transport，而不体现在业务语义。
+
+### RemoteIndexRequest
+
+建议包含：
+
+- `knowledge_base_id`
+- `document_id`
+- `index_owner_user_id`
+- `retriever_config`
+- `embedding_model_config`
+- `splitter_config`
+- `index_families`
+- `content_ref`
+- `trace_context`
+
+说明：
+
+- Backend 在发请求前完成 retriever / embedding 配置展开
+- `rag_service` 不再按 name / namespace 回查 CRD
+
+### RemoteQueryRequest
+
+建议尽量贴近 `QueryRuntimeSpec`，包含：
+
+- `knowledge_base_ids`
+- `query`
+- `max_results`
+- `document_ids`
+- `user_name`
+- `enabled_index_families`
+- `retrieval_policy`
+
+其中：
+
+- `retrieval_policy` 是稳定的远程执行语义，不应直接暴露 chat-specific 的消费策略
+- 当前 `QueryRuntimeSpec` 中已有的 `route_mode` / `direct_injection_budget` 可以在 Backend 内部继续保留，作为 control-plane 路由输入
+- 首版 `RemoteQueryRequest` 不把 `direct injection` 决策语义固化进服务 contract
+- `retrieval_policy` 用于未来扩展多索引族路线
+
+### RemoteDeleteDocumentIndexRequest
+
+建议包含：
+
+- `knowledge_base_id`
+- `document_ref`
+- `index_owner_user_id`
+- `retriever_config`
+- `enabled_index_families`
+
+## rag_service API 形态
+
+首期仅提供三类内部执行接口：
+
+- `POST /internal/rag/index`
+- `POST /internal/rag/query`
+- `POST /internal/rag/delete-document-index`
+
+设计约束：
+
+- 不接收 DB session、ORM、CRD ref 查找请求
+- 不直接回写 Backend 数据库
+- 返回结构尽量与本地 gateway 一致
+
+## 多索引族扩展设计
+
+### 将 index family 作为一等概念
+
+不能将未来扩展继续围绕单一 `chunk_vector` 路径硬编码。
+
+从本轮开始，需将索引执行与检索执行都按 `index_family` 预留。
+
+### 首期默认 family
+
+首期默认：
+
+- `chunk_vector`
+
+未来扩展：
+
+- `summary_vector`
+- 其他 family，例如 `table_rag`
+
+### IndexRuntimeSpec 扩展方向
+
+建议保持：
+
+- `index_families: ["chunk_vector"]`
+
+未来可扩：
+
+- `["chunk_vector", "summary_vector"]`
+
+### QueryRuntimeSpec 扩展方向
+
+建议新增或保留扩展位：
+
+- `enabled_index_families`
+- `retrieval_policy`
+
+推荐 `retrieval_policy` 面向未来支持：
+
+- `chunk_only`
+- `summary_first`
+- `summary_then_chunk_expand`
+- `hybrid`
+
+### 结果来源标记
+
+`rag_service` 返回结果时应明确记录来源族，例如：
+
+- `index_family = chunk_vector`
+- `index_family = summary_vector`
+
+这样 Backend 才能在未来做：
+
+- 多路线编排
+- 调试与观测
+- 不同消费方的结果解释
+
+## direct injection 与 restricted mediation 边界
+
+### direct injection 保留在 Backend
+
+`direct injection` 不是纯检索执行，而是消费编排决策。
+
+它依赖：
+
+- 当前会话上下文预算
+- 模型窗口与输出保留
+- 消费方的 prompt 注入方式
+
+因此首版不将其作为 `rag_service` 的主职责。
+
+允许的边界是：
+
+- `rag_service` 提供执行原语，例如常规 query 或 all-chunks 风格执行
+- Backend 决定是否走 direct injection
+- Backend 决定如何包装结果供 `chat_shell` 消费
+
+### restricted mediation 保留在 Backend
+
+`restricted mediation` 明确属于安全与权限策略，而不是检索能力。
+
+因此：
+
+- `rag_service` 返回原始检索候选
+- Backend 负责决定是否：
+  - 直接返回
+  - 做 safe-summary / restricted artifact
+  - 拒绝输出
+
+不允许 `rag_service` 承担 policy decision。
+
+## 三条主链路
+
+### 索引链路
+
+```text
+Backend
+  -> 权限校验 / 元数据创建 / 任务调度
+  -> RagRuntimeResolver 构造 IndexRuntimeSpec
+  -> RemoteRagGateway 映射 RemoteIndexRequest
+  -> 生成 content_ref
+  -> rag_service 拉取内容并执行解析、切分、embedding、索引
+  -> 返回执行结果
+  -> Backend 回写 document 状态与 chunks，并触发摘要
+```
+
+Backend 回写内容包括：
+
+- `KnowledgeDocument.index_status`
+- `KnowledgeDocument.is_active`
+- `KnowledgeDocument.chunks`
+- 摘要任务触发
+
+### 检索链路
+
+```text
+chat_shell / other consumers
+  -> Backend API
+  -> 权限与目标解析
+  -> QueryRuntimeSpec
+  -> RagGateway(local|remote)
+  -> rag_service 执行 query
+  -> Backend 保持 direct injection / restricted / persistence 编排
+```
+
+首版中，检索远程化不改变以下责任归属：
+
+- direct injection routing：Backend
+- restricted mediation：Backend
+- retrieval persistence：Backend
+
+### 删除索引链路
+
+```text
+Backend
+  -> 基于文档与 KB 元数据决定删索引
+  -> 组装 remote delete request
+  -> rag_service 删除 index family 中的 document 数据
+  -> Backend 回写状态与后续清理
+```
+
+## Gateway 与迁移策略
+
+### 双实现网关
+
+保留：
+
+- `LocalRagGateway`
+
+新增：
+
+- `RemoteRagGateway`
+
+建议引入选择层：
+
+- `ConfigurableRagGateway`
+- 或 factory based gateway selector
+
+### 切换粒度
+
+建议按操作独立切换：
+
+- `index_mode = local | remote`
+- `query_mode = local | remote`
+- `delete_mode = local | remote`
+
+原因：
+
+- 索引与检索风险模型不同
+- 灰度与回退更细粒度
+
+### 推荐迁移顺序
+
+1. 落 `rag_service` 可启动骨架与 remote contract
+2. 实现 `RemoteRagGateway`，默认仍走 local
+3. 先灰度索引链路到 remote
+4. 索引稳定后灰度检索链路
+5. 最后灰度删除链路
+6. 稳定后评估是否降低 local data-plane 主路径权重
+
+### 回退策略
+
+- 任一操作异常时，可通过配置切回 local
+- 对上层 API 不做改动
+- 不引入双写、双查等高复杂度补偿逻辑
+
+## 观测与诊断
+
+切换期必须显式区分 local / remote：
+
+- `gateway_mode`
+- `operation = index | query | delete`
+- 远程请求耗时
+- 内容拉取耗时
+- 解析耗时
+- embedding 耗时
+- 向量写入耗时
+- 错误类型
+- 回退次数
+
+`rag_service` 返回错误时，建议区分：
+
+- 内容获取失败
+- 解析失败
+- embedding 调用失败
+- 向量存储失败
+- 非法 contract
+
+## 测试策略
+
+### Backend
+
+- `RagRuntimeResolver` contract 测试
+- `RemoteRagGateway` payload 映射测试
+- gateway 切换配置测试
+- 索引 / 检索 / 删除的回写测试
+
+### rag_service
+
+- `content_ref` 解析测试
+- `backend_attachment_stream` 拉取测试
+- `presigned_url` 拉取测试
+- index / query / delete 接口测试
+- 多 index family executor 测试
+
+### 集成测试
+
+- local 模式回归
+- remote 模式端到端
+- remote 失败后切回 local
+
+## 风险
+
+### 1. MySQL 附件场景仍依赖 Backend 在线
+
+这是首版有意识接受的现实约束。
+
+但该依赖只体现在：
+
+- Backend 作为内容流入口
+
+不应扩散为：
+
+- `rag_service` 直接依赖 Backend 数据库
+- Backend 重新承担 bytes push 中转职责
+
+### 2. contract 如果只围绕单一路径设计，未来会返工
+
+如果远程 query contract 仍默认“只有 chunk_vector”，则 `summary_vector_index` 接入时会再次重做协议。
+
+因此本轮必须将：
+
+- `index_family`
+- `retrieval_policy`
+- 结果来源标记
+
+作为一等扩展位写入设计。
+
+### 3. chat-specific 语义下沉会污染数据面
+
+如果把 `direct injection` 或 `restricted mediation` 迁入 `rag_service`：
+
+- 数据面会耦合消费语义
+- MCP / chat_shell / future API 会共享错误边界
+
+因此本轮明确禁止该下沉。
+
+## 后续工作
+
+- 设计知识库 MCP `search`，将其作为 retrieval surface 的新增消费面
+- 将 `summary_vector_index` 接入 `index_family` 执行器体系
+- 为 `tableRAG` 设计独立 family / executor / query policy
+- 在对象存储场景中进一步减少 Backend 对内容拉取链路的参与
+
+## 结论
+
+本轮采用“Backend 作为唯一控制面，`rag_service` 作为独立执行数据面”的路线，并通过 `RagGateway` 保持 local / remote 双实现切换。
+
+首版最重要的设计结论是：
+
+- 不推文件 bytes，统一走 `content_ref`
+- 不将 `direct injection` 与 `restricted mediation` 下沉到 `rag_service`
+- 从首版开始按 `index_family` 设计，为 `summary_vector_index` 预留稳定空间
+
+这样可以在保持灰度和回退能力的同时，把未来 `summary_vector_index`、`tableRAG`、知识库 MCP search 等能力建立在更稳定的服务边界上。

From edda1f27af24cb679d1cdc4e17fbeed3e606b7dd Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Sun, 5 Apr 2026 17:23:39 +0800
Subject: [PATCH 2/9] refactor(knowledge): extract local indexing engine kernel

---
 .../services/rag/local_data_plane/indexing.py |   84 +-
 backend/pyproject.toml                        |    5 +-
 .../tests/services/knowledge/test_indexing.py |   23 +-
 .../rag/test_local_data_plane_indexing.py     |  122 +-
 backend/uv.lock                               |   82 +
 knowledge_engine/knowledge_engine/__init__.py |   11 +
 .../knowledge_engine/embedding/__init__.py    |    5 +
 .../knowledge_engine/index/__init__.py        |    9 +
 .../knowledge_engine/index/indexer.py         |  195 +
 .../knowledge_engine/query/__init__.py        |    5 +
 .../knowledge_engine/services/__init__.py     |    9 +
 .../services/document_service.py              |  196 +
 .../knowledge_engine/splitter/__init__.py     |   33 +
 .../knowledge_engine/splitter/config.py       |   73 +
 .../knowledge_engine/splitter/factory.py      |   47 +
 .../knowledge_engine/splitter/smart.py        |   83 +
 .../knowledge_engine/splitter/splitter.py     |   56 +
 .../knowledge_engine/storage/__init__.py      |   10 +
 .../knowledge_engine/storage/base.py          |   70 +
 .../storage/chunk_metadata.py                 |   34 +
 knowledge_engine/pyproject.toml               |   79 +
 .../tests/test_document_service.py            |   96 +
 knowledge_engine/tests/test_imports.py        |   27 +
 knowledge_engine/uv.lock                      | 3952 +++++++++++++++++
 24 files changed, 5272 insertions(+), 34 deletions(-)
 create mode 100644 knowledge_engine/knowledge_engine/__init__.py
 create mode 100644 knowledge_engine/knowledge_engine/embedding/__init__.py
 create mode 100644 knowledge_engine/knowledge_engine/index/__init__.py
 create mode 100644 knowledge_engine/knowledge_engine/index/indexer.py
 create mode 100644 knowledge_engine/knowledge_engine/query/__init__.py
 create mode 100644 knowledge_engine/knowledge_engine/services/__init__.py
 create mode 100644 knowledge_engine/knowledge_engine/services/document_service.py
 create mode 100644 knowledge_engine/knowledge_engine/splitter/__init__.py
 create mode 100644 knowledge_engine/knowledge_engine/splitter/config.py
 create mode 100644 knowledge_engine/knowledge_engine/splitter/factory.py
 create mode 100644 knowledge_engine/knowledge_engine/splitter/smart.py
 create mode 100644 knowledge_engine/knowledge_engine/splitter/splitter.py
 create mode 100644 knowledge_engine/knowledge_engine/storage/__init__.py
 create mode 100644 knowledge_engine/knowledge_engine/storage/base.py
 create mode 100644 knowledge_engine/knowledge_engine/storage/chunk_metadata.py
 create mode 100644 knowledge_engine/pyproject.toml
 create mode 100644 knowledge_engine/tests/test_document_service.py
 create mode 100644 knowledge_engine/tests/test_imports.py
 create mode 100644 knowledge_engine/uv.lock

diff --git a/backend/app/services/rag/local_data_plane/indexing.py b/backend/app/services/rag/local_data_plane/indexing.py
index a1f3f32fa..42a5a7d8e 100644
--- a/backend/app/services/rag/local_data_plane/indexing.py
+++ b/backend/app/services/rag/local_data_plane/indexing.py
@@ -7,11 +7,13 @@
 from sqlalchemy.orm import Session
 
 from app.models.kind import Kind
+from app.models.subtask_context import ContextStatus, ContextType, SubtaskContext
 from app.services.adapters.retriever_kinds import retriever_kinds_service
-from app.services.rag.document_service import DocumentService
+from app.services.context import context_service
+from app.services.rag.embedding.factory import create_embedding_model_from_crd
 from app.services.rag.runtime_specs import IndexRuntimeSpec
-from app.services.rag.splitter.runtime_config import parse_runtime_splitter_config
 from app.services.rag.storage.factory import create_storage_backend
+from knowledge_engine.services import DocumentService as EngineDocumentService
 from shared.telemetry.decorators import trace_async
 
 logger = logging.getLogger(__name__)
@@ -45,6 +47,37 @@ def _extract_delete_document_attributes(
     }
 
 
+def _get_attachment_binary_source(
+    db: Session,
+    attachment_id: int,
+) -> tuple[bytes, str, str]:
+    context = (
+        db.query(SubtaskContext)
+        .filter(
+            SubtaskContext.id == attachment_id,
+            SubtaskContext.context_type == ContextType.ATTACHMENT.value,
+        )
+        .first()
+    )
+    if not context:
+        raise ValueError(f"Attachment context {attachment_id} not found")
+    if context.status != ContextStatus.READY.value:
+        raise ValueError(
+            f"Attachment context {attachment_id} is not ready (status: {context.status})"
+        )
+
+    binary_data = context_service.get_attachment_binary_data(
+        db=db,
+        context=context,
+    )
+    if binary_data is None:
+        raise ValueError(
+            f"Attachment context {attachment_id} has no binary data available"
+        )
+
+    return binary_data, context.original_filename, context.file_extension
+
+
 @trace_async(
     span_name="rag.index_document_local",
     tracer_name="backend.services.rag",
@@ -78,29 +111,38 @@ async def index_document_local(
         }
 
     storage_backend = create_storage_backend(retriever)
-    service = DocumentService(storage_backend=storage_backend)
+    embed_model = create_embedding_model_from_crd(
+        db=db,
+        user_id=spec.index_owner_user_id,
+        model_name=spec.embedding_model_name,
+        model_namespace=spec.embedding_model_namespace,
+        user_name=spec.user_name,
+    )
+    service = EngineDocumentService(storage_backend=storage_backend)
 
-    splitter_config = spec.splitter_config
-    if splitter_config:
-        splitter_config = parse_runtime_splitter_config(splitter_config)
+    if spec.source.source_type == "attachment":
+        binary_data, source_file, file_extension = _get_attachment_binary_source(
+            db,
+            spec.source.attachment_id,
+        )
+        return await service.index_document_from_binary(
+            knowledge_id=str(spec.knowledge_base_id),
+            binary_data=binary_data,
+            source_file=source_file,
+            file_extension=file_extension,
+            embed_model=embed_model,
+            user_id=spec.index_owner_user_id,
+            splitter_config=spec.splitter_config,
+            document_id=spec.document_id,
+        )
 
-    file_path = (
-        spec.source.file_path if spec.source.source_type == "file_path" else None
-    )
-    attachment_id = (
-        spec.source.attachment_id if spec.source.source_type == "attachment" else None
-    )
-    return await service.index_document(
+    return await service.index_document_from_file(
         knowledge_id=str(spec.knowledge_base_id),
-        embedding_model_name=spec.embedding_model_name,
-        embedding_model_namespace=spec.embedding_model_namespace,
+        file_path=spec.source.file_path,
+        embed_model=embed_model,
         user_id=spec.index_owner_user_id,
-        db=db,
-        file_path=file_path,
-        attachment_id=attachment_id,
-        splitter_config=splitter_config,
+        splitter_config=spec.splitter_config,
         document_id=spec.document_id,
-        user_name=spec.user_name,
     )
 
 
@@ -166,7 +208,7 @@ async def delete_document_index_local(
         }
 
     storage_backend = create_storage_backend(retriever)
-    service = DocumentService(storage_backend=storage_backend)
+    service = EngineDocumentService(storage_backend=storage_backend)
     return await service.delete_document(
         knowledge_id=str(knowledge_base_id),
         doc_ref=document_ref,
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 70840dd4b..74b8063ca 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -123,6 +123,8 @@ dependencies = [
     "docx2txt>=0.8", # Required by llama-index-readers-file for Word document parsing
     # Chat Shell (local workspace dependency for Package mode)
     "wegent-chat-shell",
+    # Knowledge Engine (monorepo workspace dependency)
+    "wegent-knowledge-engine",
     # Shared utilities (monorepo workspace dependency)
     "wegent-shared",
     "minio>=7.2.20",
@@ -130,6 +132,7 @@ dependencies = [
 
 [tool.uv.sources]
 wegent-chat-shell = { path = "../chat_shell", editable = true }
+wegent-knowledge-engine = { path = "../knowledge_engine", editable = true }
 wegent-shared = { path = "../shared", editable = true }
 
 [dependency-groups]
@@ -190,7 +193,7 @@ use_parentheses = true
 ensure_newline_before_comments = true
 skip = ["alembic", ".venv", "venv"]
 skip_gitignore = true
-known_first_party = ["app", "shared", "chat_shell"]
+known_first_party = ["app", "shared", "chat_shell", "knowledge_engine"]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
diff --git a/backend/tests/services/knowledge/test_indexing.py b/backend/tests/services/knowledge/test_indexing.py
index 4325fe093..6dd9dfb5b 100644
--- a/backend/tests/services/knowledge/test_indexing.py
+++ b/backend/tests/services/knowledge/test_indexing.py
@@ -12,6 +12,15 @@ def test_run_document_indexing_propagates_gateway_skip_status() -> None:
     db = MagicMock()
     db.query.return_value.filter.return_value.first.return_value = None
     kb_index_info = SimpleNamespace(index_owner_user_id=3, summary_enabled=False)
+    gateway = MagicMock()
+    gateway.index_document = AsyncMock(
+        return_value={
+            "status": "skipped",
+            "reason": "retriever_not_found",
+            "indexed_count": 0,
+            "index_name": "unknown",
+        }
+    )
 
     with (
         patch(
@@ -23,15 +32,9 @@ def test_run_document_indexing_propagates_gateway_skip_status() -> None:
             return_value=object(),
         ) as mock_build_runtime_spec,
         patch(
-            "app.services.knowledge.indexing.LocalRagGateway.index_document",
-            new_callable=AsyncMock,
-            return_value={
-                "status": "skipped",
-                "reason": "retriever_not_found",
-                "indexed_count": 0,
-                "index_name": "unknown",
-            },
-        ) as mock_index_document,
+            "app.services.knowledge.indexing.get_index_gateway",
+            return_value=gateway,
+        ),
     ):
         result = run_document_indexing(
             knowledge_base_id="1",
@@ -48,7 +51,7 @@ def test_run_document_indexing_propagates_gateway_skip_status() -> None:
             db=db,
         )
 
-    mock_index_document.assert_awaited_once_with(
+    gateway.index_document.assert_awaited_once_with(
         mock_build_runtime_spec.return_value,
         db=db,
     )
diff --git a/backend/tests/services/rag/test_local_data_plane_indexing.py b/backend/tests/services/rag/test_local_data_plane_indexing.py
index daa87c994..efd33ed8e 100644
--- a/backend/tests/services/rag/test_local_data_plane_indexing.py
+++ b/backend/tests/services/rag/test_local_data_plane_indexing.py
@@ -2,11 +2,15 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from app.services.rag.local_data_plane.indexing import index_document_local
+from app.services.rag.local_data_plane.indexing import (
+    delete_document_index_local,
+    index_document_local,
+)
 from app.services.rag.runtime_specs import IndexRuntimeSpec, IndexSource
 
 
@@ -32,7 +36,7 @@ async def test_index_document_local_skips_missing_retriever() -> None:
             "app.services.rag.local_data_plane.indexing.create_storage_backend"
         ) as mock_create_storage_backend,
         patch(
-            "app.services.rag.local_data_plane.indexing.DocumentService.index_document",
+            "app.services.rag.local_data_plane.indexing.EngineDocumentService.index_document_from_binary",
             new_callable=AsyncMock,
         ) as mock_index_document,
     ):
@@ -47,3 +51,117 @@ async def test_index_document_local_skips_missing_retriever() -> None:
     mock_get_retriever.assert_called_once()
     mock_create_storage_backend.assert_not_called()
     mock_index_document.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_index_document_local_delegates_to_engine_document_service() -> None:
+    spec = IndexRuntimeSpec(
+        knowledge_base_id=1,
+        document_id=2,
+        index_owner_user_id=3,
+        retriever_name="retriever-a",
+        retriever_namespace="default",
+        embedding_model_name="embed-a",
+        embedding_model_namespace="default",
+        source=IndexSource(source_type="attachment", attachment_id=9),
+        splitter_config={"type": "sentence", "chunk_size": 256, "chunk_overlap": 32},
+        user_name="tester",
+    )
+    storage_backend = MagicMock()
+    embed_model = object()
+
+    with (
+        patch(
+            "app.services.rag.local_data_plane.indexing.retriever_kinds_service.get_retriever",
+            return_value=MagicMock(),
+        ),
+        patch(
+            "app.services.rag.local_data_plane.indexing.create_storage_backend",
+            return_value=storage_backend,
+        ),
+        patch(
+            "app.services.rag.local_data_plane.indexing.create_embedding_model_from_crd",
+            return_value=embed_model,
+        ),
+        patch(
+            "app.services.rag.local_data_plane.indexing._get_attachment_binary_source",
+            return_value=(b"hello world", "report.pdf", ".pdf"),
+        ),
+        patch(
+            "app.services.rag.local_data_plane.indexing.EngineDocumentService.index_document_from_binary",
+            new_callable=AsyncMock,
+            return_value={
+                "status": "success",
+                "knowledge_id": "1",
+                "doc_ref": "2",
+                "indexed_count": 1,
+                "index_name": "wegent_kb_1",
+                "chunk_count": 1,
+                "source_file": "report.pdf",
+                "created_at": "2026-04-05T00:00:00+00:00",
+                "chunks_data": [{"chunk_index": 0}],
+            },
+        ) as mock_index_document,
+    ):
+        result = await index_document_local(spec, db=MagicMock())
+
+    assert result["status"] == "success"
+    mock_index_document.assert_awaited_once_with(
+        knowledge_id="1",
+        binary_data=b"hello world",
+        source_file="report.pdf",
+        file_extension=".pdf",
+        embed_model=embed_model,
+        user_id=3,
+        splitter_config={"type": "sentence", "chunk_size": 256, "chunk_overlap": 32},
+        document_id=2,
+    )
+
+
+@pytest.mark.asyncio
+async def test_delete_document_index_local_delegates_to_engine_document_service() -> (
+    None
+):
+    db = MagicMock()
+    db.query.return_value.filter.return_value.first.return_value = SimpleNamespace(
+        user_id=11,
+        json={
+            "spec": {
+                "retrievalConfig": {
+                    "retriever_name": "retriever-a",
+                    "retriever_namespace": "default",
+                }
+            }
+        },
+    )
+    storage_backend = MagicMock()
+
+    with (
+        patch(
+            "app.services.rag.local_data_plane.indexing.retriever_kinds_service.get_retriever",
+            return_value=MagicMock(),
+        ) as mock_get_retriever,
+        patch(
+            "app.services.rag.local_data_plane.indexing.create_storage_backend",
+            return_value=storage_backend,
+        ),
+        patch(
+            "app.services.rag.local_data_plane.indexing.EngineDocumentService.delete_document",
+            new_callable=AsyncMock,
+            return_value={"status": "success", "deleted_chunks": 4},
+        ) as mock_delete_document,
+    ):
+        result = await delete_document_index_local(
+            knowledge_base_id=1,
+            document_ref="doc-1",
+            db=db,
+            index_owner_user_id=7,
+        )
+
+    assert result == {"status": "success", "deleted_chunks": 4}
+    mock_get_retriever.assert_called_once()
+    mock_delete_document.assert_awaited_once_with(
+        knowledge_id="1",
+        doc_ref="doc-1",
+        user_id=7,
+    )
diff --git a/backend/uv.lock b/backend/uv.lock
index d24753a87..95a2139fe 100644
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -5868,6 +5868,7 @@ dependencies = [
     { name = "tenacity" },
     { name = "uvicorn", extra = ["standard"] },
     { name = "wegent-chat-shell" },
+    { name = "wegent-knowledge-engine" },
     { name = "wegent-shared" },
 ]
 
@@ -5973,6 +5974,7 @@ requires-dist = [
     { name = "tenacity", specifier = ">=8.2.3" },
     { name = "uvicorn", extras = ["standard"], specifier = ">=0.27.0" },
     { name = "wegent-chat-shell", editable = "../chat_shell" },
+    { name = "wegent-knowledge-engine", editable = "../knowledge_engine" },
     { name = "wegent-shared", editable = "../shared" },
 ]
 
@@ -6116,6 +6118,86 @@ dev = [
     { name = "pytest-xdist", specifier = ">=3.5.0" },
 ]
 
+[[package]]
+name = "wegent-knowledge-engine"
+version = "1.0.0"
+source = { editable = "../knowledge_engine" }
+dependencies = [
+    { name = "beautifulsoup4" },
+    { name = "chardet" },
+    { name = "docx2txt" },
+    { name = "emoji" },
+    { name = "html2text" },
+    { name = "httpx", extra = ["brotli", "socks"] },
+    { name = "langchain-text-splitters" },
+    { name = "llama-index-core" },
+    { name = "llama-index-embeddings-openai" },
+    { name = "llama-index-readers-file" },
+    { name = "llama-index-vector-stores-elasticsearch" },
+    { name = "llama-index-vector-stores-milvus" },
+    { name = "llama-index-vector-stores-qdrant" },
+    { name = "markdown" },
+    { name = "openpyxl" },
+    { name = "orjson" },
+    { name = "pdf2image" },
+    { name = "pillow" },
+    { name = "pydantic" },
+    { name = "pymilvus" },
+    { name = "pypdf2" },
+    { name = "pytesseract" },
+    { name = "python-docx" },
+    { name = "python-dotenv" },
+    { name = "python-magic" },
+    { name = "python-pptx" },
+    { name = "sqlalchemy" },
+    { name = "tenacity" },
+    { name = "wegent-shared" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "beautifulsoup4", specifier = ">=4.12.0" },
+    { name = "chardet", specifier = ">=5.0.0" },
+    { name = "docx2txt", specifier = ">=0.8" },
+    { name = "emoji", specifier = ">=2.9.0" },
+    { name = "html2text", specifier = ">=2024.2.26" },
+    { name = "httpx", extras = ["brotli", "socks"], specifier = ">=0.26.0" },
+    { name = "langchain-text-splitters", specifier = ">=0.3.0" },
+    { name = "llama-index-core", specifier = ">=0.12.0" },
+    { name = "llama-index-embeddings-openai", specifier = ">=0.3.0" },
+    { name = "llama-index-readers-file", specifier = ">=0.4.0" },
+    { name = "llama-index-vector-stores-elasticsearch", specifier = ">=0.4.0" },
+    { name = "llama-index-vector-stores-milvus", specifier = ">=0.4.0" },
+    { name = "llama-index-vector-stores-qdrant", specifier = ">=0.9.0" },
+    { name = "markdown", specifier = ">=3.5.0" },
+    { name = "openpyxl", specifier = ">=3.1.2" },
+    { name = "orjson", specifier = ">=3.9.0" },
+    { name = "pdf2image", specifier = ">=1.16.0" },
+    { name = "pillow", specifier = ">=12.0.0" },
+    { name = "pydantic", specifier = ">=2.5.3" },
+    { name = "pymilvus", specifier = ">=2.4.0,<2.6.4" },
+    { name = "pypdf2", specifier = ">=3.0.0" },
+    { name = "pytesseract", specifier = ">=0.3.10" },
+    { name = "python-docx", specifier = ">=1.1.0" },
+    { name = "python-dotenv", specifier = ">=1.0.0" },
+    { name = "python-magic", specifier = ">=0.4.27" },
+    { name = "python-pptx", specifier = ">=0.6.23" },
+    { name = "sqlalchemy", specifier = ">=2.0.25" },
+    { name = "tenacity", specifier = ">=8.2.3" },
+    { name = "wegent-shared", editable = "../shared" },
+]
+
+[package.metadata.requires-dev]
+dev = [
+    { name = "black", specifier = ">=23.7.0" },
+    { name = "flake8", specifier = ">=6.0.0" },
+    { name = "isort", specifier = ">=5.12.0" },
+    { name = "pytest", specifier = ">=9.0.1" },
+    { name = "pytest-asyncio", specifier = ">=1.3.0" },
+    { name = "pytest-cov", specifier = ">=7.0.0" },
+    { name = "pytest-mock", specifier = ">=3.15.1" },
+]
+
 [[package]]
 name = "wegent-shared"
 version = "1.0.0"
diff --git a/knowledge_engine/knowledge_engine/__init__.py b/knowledge_engine/knowledge_engine/__init__.py
new file mode 100644
index 000000000..c965bdc16
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/__init__.py
@@ -0,0 +1,11 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Backend-agnostic RAG execution kernel shared by Backend and knowledge_runtime."""
+
+__all__ = [
+    "__version__",
+]
+
+__version__ = "1.0.0"
diff --git a/knowledge_engine/knowledge_engine/embedding/__init__.py b/knowledge_engine/knowledge_engine/embedding/__init__.py
new file mode 100644
index 000000000..a093e9923
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/embedding/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Embedding helpers for knowledge_engine."""
diff --git a/knowledge_engine/knowledge_engine/index/__init__.py b/knowledge_engine/knowledge_engine/index/__init__.py
new file mode 100644
index 000000000..c192d3e99
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/index/__init__.py
@@ -0,0 +1,9 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Indexing helpers for knowledge_engine."""
+
+from knowledge_engine.index.indexer import DocumentIndexer, sanitize_metadata
+
+__all__ = ["DocumentIndexer", "sanitize_metadata"]
diff --git a/knowledge_engine/knowledge_engine/index/indexer.py b/knowledge_engine/knowledge_engine/index/indexer.py
new file mode 100644
index 000000000..dfa241d64
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/index/indexer.py
@@ -0,0 +1,195 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+import tempfile
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Dict, List
+
+from llama_index.core import Document, SimpleDirectoryReader
+
+from knowledge_engine.splitter import SmartSplitter, create_splitter
+from knowledge_engine.splitter.config import parse_splitter_config
+from knowledge_engine.splitter.splitter import SentenceSplitter
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+from shared.telemetry.decorators import add_span_event
+
+logger = logging.getLogger(__name__)
+
+SAFE_METADATA_KEYS = {
+    "filename",
+    "file_path",
+    "file_name",
+    "file_type",
+    "file_size",
+    "creation_date",
+    "last_modified_date",
+    "page_label",
+    "page_number",
+}
+
+
+def sanitize_metadata(metadata: Dict[str, Any]) -> Dict[str, Any]:
+    sanitized = {}
+    for key in SAFE_METADATA_KEYS:
+        if key in metadata:
+            value = metadata[key]
+            if value is not None:
+                sanitized[key] = str(value) if not isinstance(value, str) else value
+    return sanitized
+
+
+class DocumentIndexer:
+    def __init__(
+        self,
+        storage_backend: BaseStorageBackend,
+        embed_model,
+        splitter_config: dict | None = None,
+        file_extension: str | None = None,
+    ):
+        self.storage_backend = storage_backend
+        self.embed_model = embed_model
+        self.file_extension = file_extension
+        self.splitter = create_splitter(
+            parse_splitter_config(splitter_config),
+            embed_model,
+            file_extension,
+        )
+
+    def index_document(
+        self,
+        file_path: str,
+        chunk_metadata: ChunkMetadata,
+        **kwargs,
+    ) -> Dict:
+        documents = SimpleDirectoryReader(input_files=[file_path]).load_data()
+        return self._index_documents(
+            documents=documents,
+            chunk_metadata=chunk_metadata,
+            **kwargs,
+        )
+
+    def index_from_binary(
+        self,
+        binary_data: bytes,
+        file_extension: str,
+        chunk_metadata: ChunkMetadata,
+        **kwargs,
+    ) -> Dict:
+        with tempfile.NamedTemporaryFile(
+            suffix=file_extension,
+            delete=False,
+        ) as tmp_file:
+            tmp_file.write(binary_data)
+            tmp_file_path = tmp_file.name
+
+        try:
+            documents = SimpleDirectoryReader(input_files=[tmp_file_path]).load_data()
+            filename_without_ext = Path(chunk_metadata.source_file).stem
+            for doc in documents:
+                doc.metadata["filename"] = filename_without_ext
+
+            return self._index_documents(
+                documents=documents,
+                chunk_metadata=chunk_metadata,
+                **kwargs,
+            )
+        finally:
+            try:
+                Path(tmp_file_path).unlink()
+            except Exception as exc:
+                logger.warning(
+                    "Failed to delete temporary file %s: %s",
+                    tmp_file_path,
+                    exc,
+                )
+
+    def _index_documents(
+        self,
+        documents: List[Document],
+        chunk_metadata: ChunkMetadata,
+        **kwargs,
+    ) -> Dict:
+        add_span_event(
+            "rag.indexer.documents.received",
+            {
+                "knowledge_id": chunk_metadata.knowledge_id,
+                "doc_ref": chunk_metadata.doc_ref,
+                "source_file": chunk_metadata.source_file,
+                "document_count": str(len(documents)),
+            },
+        )
+
+        for doc in documents:
+            doc.metadata = sanitize_metadata(doc.metadata)
+
+        nodes = self.splitter.split_documents(documents)
+        chunk_metadata.apply_to_nodes(nodes)
+
+        add_span_event(
+            "rag.indexer.documents.split",
+            {
+                "knowledge_id": chunk_metadata.knowledge_id,
+                "doc_ref": chunk_metadata.doc_ref,
+                "node_count": str(len(nodes)),
+                "splitter_type": type(self.splitter).__name__,
+            },
+        )
+
+        chunks_data = self._build_chunks_metadata(nodes)
+        result = self.storage_backend.index_with_metadata(
+            nodes=nodes,
+            chunk_metadata=chunk_metadata,
+            embed_model=self.embed_model,
+            **kwargs,
+        )
+
+        result.update(
+            {
+                "doc_ref": chunk_metadata.doc_ref,
+                "knowledge_id": chunk_metadata.knowledge_id,
+                "source_file": chunk_metadata.source_file,
+                "chunk_count": len(nodes),
+                "created_at": chunk_metadata.created_at,
+                "chunks_data": chunks_data,
+            }
+        )
+        return result
+
+    def _build_chunks_metadata(self, nodes: List) -> Dict[str, Any]:
+        items = []
+        current_position = 0
+
+        for idx, node in enumerate(nodes):
+            text = node.text if hasattr(node, "text") else str(node)
+            text_length = len(text)
+            token_count = text_length // 4
+            items.append(
+                {
+                    "index": idx,
+                    "content": text,
+                    "token_count": token_count,
+                    "start_position": current_position,
+                    "end_position": current_position + text_length,
+                }
+            )
+            current_position += text_length
+
+        splitter_type = "semantic"
+        splitter_subtype = None
+        if isinstance(self.splitter, SmartSplitter):
+            splitter_type = "smart"
+            splitter_subtype = self.splitter._get_subtype()
+        elif isinstance(self.splitter, SentenceSplitter):
+            splitter_type = "sentence"
+
+        return {
+            "items": items,
+            "total_count": len(items),
+            "splitter_type": splitter_type,
+            "splitter_subtype": splitter_subtype,
+            "created_at": datetime.now(timezone.utc).isoformat(),
+        }
diff --git a/knowledge_engine/knowledge_engine/query/__init__.py b/knowledge_engine/knowledge_engine/query/__init__.py
new file mode 100644
index 000000000..0262e0ffa
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/query/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Query execution helpers for knowledge_engine."""
diff --git a/knowledge_engine/knowledge_engine/services/__init__.py b/knowledge_engine/knowledge_engine/services/__init__.py
new file mode 100644
index 000000000..b43909e2e
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/services/__init__.py
@@ -0,0 +1,9 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Service-layer execution helpers for knowledge_engine."""
+
+from knowledge_engine.services.document_service import DocumentService
+
+__all__ = ["DocumentService"]
diff --git a/knowledge_engine/knowledge_engine/services/document_service.py b/knowledge_engine/knowledge_engine/services/document_service.py
new file mode 100644
index 000000000..07d8a2d73
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/services/document_service.py
@@ -0,0 +1,196 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict
+
+from knowledge_engine.index.indexer import DocumentIndexer
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+
+
+class DocumentService:
+    def __init__(self, storage_backend: BaseStorageBackend):
+        self.storage_backend = storage_backend
+
+    async def index_document_from_binary(
+        self,
+        *,
+        knowledge_id: str,
+        binary_data: bytes,
+        source_file: str,
+        file_extension: str,
+        embed_model,
+        user_id: int,
+        splitter_config: dict | None = None,
+        document_id: int | None = None,
+    ) -> Dict:
+        return await asyncio.to_thread(
+            self._index_document_from_binary_sync,
+            knowledge_id,
+            binary_data,
+            source_file,
+            file_extension,
+            embed_model,
+            user_id,
+            splitter_config,
+            document_id,
+        )
+
+    async def index_document_from_file(
+        self,
+        *,
+        knowledge_id: str,
+        file_path: str,
+        embed_model,
+        user_id: int,
+        splitter_config: dict | None = None,
+        document_id: int | None = None,
+    ) -> Dict:
+        return await asyncio.to_thread(
+            self._index_document_from_file_sync,
+            knowledge_id,
+            file_path,
+            embed_model,
+            user_id,
+            splitter_config,
+            document_id,
+        )
+
+    async def delete_document(
+        self,
+        *,
+        knowledge_id: str,
+        doc_ref: str,
+        user_id: int | None = None,
+    ) -> Dict:
+        return await asyncio.to_thread(
+            self.storage_backend.delete_document,
+            knowledge_id=knowledge_id,
+            doc_ref=doc_ref,
+            user_id=user_id,
+        )
+
+    async def list_documents(
+        self,
+        *,
+        knowledge_id: str,
+        page: int = 1,
+        page_size: int = 20,
+        user_id: int | None = None,
+    ) -> Dict:
+        return await asyncio.to_thread(
+            self.storage_backend.list_documents,
+            knowledge_id=knowledge_id,
+            page=page,
+            page_size=page_size,
+            user_id=user_id,
+        )
+
+    def _index_document_from_binary_sync(
+        self,
+        knowledge_id: str,
+        binary_data: bytes,
+        source_file: str,
+        file_extension: str,
+        embed_model,
+        user_id: int,
+        splitter_config: dict | None,
+        document_id: int | None,
+    ) -> Dict:
+        chunk_metadata = self._build_chunk_metadata(
+            knowledge_id=knowledge_id,
+            source_file=source_file,
+            document_id=document_id,
+        )
+        indexer = DocumentIndexer(
+            storage_backend=self.storage_backend,
+            embed_model=embed_model,
+            splitter_config=splitter_config,
+            file_extension=file_extension,
+        )
+        result = indexer.index_from_binary(
+            binary_data=binary_data,
+            file_extension=file_extension,
+            chunk_metadata=chunk_metadata,
+            user_id=user_id,
+        )
+        return self._finalize_index_result(result, chunk_metadata)
+
+    def _index_document_from_file_sync(
+        self,
+        knowledge_id: str,
+        file_path: str,
+        embed_model,
+        user_id: int,
+        splitter_config: dict | None,
+        document_id: int | None,
+    ) -> Dict:
+        source_file = Path(file_path).name
+        file_extension = Path(file_path).suffix.lower()
+        chunk_metadata = self._build_chunk_metadata(
+            knowledge_id=knowledge_id,
+            source_file=source_file,
+            document_id=document_id,
+        )
+        indexer = DocumentIndexer(
+            storage_backend=self.storage_backend,
+            embed_model=embed_model,
+            splitter_config=splitter_config,
+            file_extension=file_extension,
+        )
+        result = indexer.index_document(
+            file_path=file_path,
+            chunk_metadata=chunk_metadata,
+            user_id=user_id,
+        )
+        return self._finalize_index_result(result, chunk_metadata)
+
+    def _build_chunk_metadata(
+        self,
+        *,
+        knowledge_id: str,
+        source_file: str,
+        document_id: int | None,
+    ) -> ChunkMetadata:
+        doc_ref = (
+            str(document_id) if document_id is not None else self._generate_doc_ref()
+        )
+        return ChunkMetadata(
+            knowledge_id=knowledge_id,
+            doc_ref=doc_ref,
+            source_file=source_file,
+            created_at=datetime.now(timezone.utc).isoformat(),
+        )
+
+    def _generate_doc_ref(self) -> str:
+        return f"doc_{uuid.uuid4().hex[:12]}"
+
+    def _finalize_index_result(
+        self,
+        result: Dict,
+        chunk_metadata: ChunkMetadata,
+    ) -> Dict:
+        finalized = dict(result)
+        finalized.setdefault("doc_ref", chunk_metadata.doc_ref)
+        finalized.setdefault("knowledge_id", chunk_metadata.knowledge_id)
+        finalized.setdefault("source_file", chunk_metadata.source_file)
+        finalized.setdefault("created_at", chunk_metadata.created_at)
+
+        if "chunk_count" not in finalized:
+            chunks_data = finalized.get("chunks_data")
+            if isinstance(chunks_data, list):
+                finalized["chunk_count"] = len(chunks_data)
+            elif isinstance(chunks_data, dict):
+                finalized["chunk_count"] = chunks_data.get(
+                    "total_count",
+                    finalized.get("indexed_count", 0),
+                )
+            else:
+                finalized["chunk_count"] = finalized.get("indexed_count", 0)
+
+        return finalized
diff --git a/knowledge_engine/knowledge_engine/splitter/__init__.py b/knowledge_engine/knowledge_engine/splitter/__init__.py
new file mode 100644
index 000000000..40f5d5b83
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/splitter/__init__.py
@@ -0,0 +1,33 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Document splitter helpers for knowledge_engine."""
+
+from knowledge_engine.splitter.config import (
+    SemanticSplitterConfig,
+    SentenceSplitterConfig,
+    SmartSplitterConfig,
+    SplitterConfig,
+    parse_splitter_config,
+)
+from knowledge_engine.splitter.factory import create_splitter
+from knowledge_engine.splitter.smart import SmartSplitter
+from knowledge_engine.splitter.splitter import (
+    DocumentSplitter,
+    SemanticSplitter,
+    SentenceSplitter,
+)
+
+__all__ = [
+    "DocumentSplitter",
+    "SemanticSplitter",
+    "SemanticSplitterConfig",
+    "SentenceSplitter",
+    "SentenceSplitterConfig",
+    "SmartSplitter",
+    "SmartSplitterConfig",
+    "SplitterConfig",
+    "create_splitter",
+    "parse_splitter_config",
+]
diff --git a/knowledge_engine/knowledge_engine/splitter/config.py b/knowledge_engine/knowledge_engine/splitter/config.py
new file mode 100644
index 000000000..82e8f7aa5
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/splitter/config.py
@@ -0,0 +1,73 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Literal, Optional, Union
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class SemanticSplitterConfig(BaseModel):
+    type: Literal["semantic"] = "semantic"
+    buffer_size: int = Field(1, ge=1, le=10)
+    breakpoint_percentile_threshold: int = Field(95, ge=50, le=100)
+
+
+class SentenceSplitterConfig(BaseModel):
+    type: Literal["sentence"] = "sentence"
+    chunk_size: int = Field(1024, ge=128, le=8192)
+    chunk_overlap: int = Field(200, ge=0, le=2048)
+    separator: str = "\n\n"
+
+    @field_validator("chunk_overlap")
+    @classmethod
+    def validate_overlap(cls, value: int, info) -> int:
+        chunk_size = info.data.get("chunk_size", 1024)
+        if value >= chunk_size:
+            raise ValueError(
+                f"chunk_overlap ({value}) must be less than chunk_size ({chunk_size})"
+            )
+        return value
+
+
+class SmartSplitterConfig(BaseModel):
+    type: Literal["smart"] = "smart"
+    chunk_size: int = Field(1024, ge=128, le=8192)
+    chunk_overlap: int = Field(50, ge=0, le=2048)
+    file_extension: Optional[str] = None
+    subtype: Optional[str] = None
+
+    @field_validator("chunk_overlap")
+    @classmethod
+    def validate_overlap(cls, value: int, info) -> int:
+        chunk_size = info.data.get("chunk_size", 1024)
+        if value >= chunk_size:
+            raise ValueError(
+                f"chunk_overlap ({value}) must be less than chunk_size ({chunk_size})"
+            )
+        return value
+
+
+SplitterConfig = Union[
+    SemanticSplitterConfig, SentenceSplitterConfig, SmartSplitterConfig
+]
+
+
+def parse_splitter_config(
+    config: dict | SplitterConfig | None,
+) -> SplitterConfig | None:
+    if config is None:
+        return None
+
+    if isinstance(
+        config,
+        (SemanticSplitterConfig, SentenceSplitterConfig, SmartSplitterConfig),
+    ):
+        return config
+
+    splitter_type = config.get("type")
+    if splitter_type == "semantic":
+        return SemanticSplitterConfig(**config)
+    if splitter_type == "smart":
+        return SmartSplitterConfig(**config)
+    return SentenceSplitterConfig(**config)
diff --git a/knowledge_engine/knowledge_engine/splitter/factory.py b/knowledge_engine/knowledge_engine/splitter/factory.py
new file mode 100644
index 000000000..0df7d0e87
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/splitter/factory.py
@@ -0,0 +1,47 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from llama_index.core.base.embeddings.base import BaseEmbedding
+
+from knowledge_engine.splitter.config import (
+    SemanticSplitterConfig,
+    SentenceSplitterConfig,
+    SmartSplitterConfig,
+    SplitterConfig,
+)
+from knowledge_engine.splitter.smart import SmartSplitter
+from knowledge_engine.splitter.splitter import SemanticSplitter, SentenceSplitter
+
+
+def create_splitter(
+    config: SplitterConfig | None,
+    embed_model: BaseEmbedding,
+    file_extension: str | None = None,
+) -> SemanticSplitter | SentenceSplitter | SmartSplitter:
+    if config is None:
+        return SemanticSplitter(embed_model=embed_model)
+
+    if isinstance(config, SemanticSplitterConfig):
+        return SemanticSplitter(
+            embed_model=embed_model,
+            buffer_size=config.buffer_size,
+            breakpoint_percentile_threshold=config.breakpoint_percentile_threshold,
+        )
+
+    if isinstance(config, SentenceSplitterConfig):
+        return SentenceSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            separator=config.separator,
+        )
+
+    if isinstance(config, SmartSplitterConfig):
+        ext = config.file_extension or file_extension or ".txt"
+        return SmartSplitter(
+            file_extension=ext,
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+        )
+
+    raise ValueError(f"Unknown splitter config type: {type(config)}")
diff --git a/knowledge_engine/knowledge_engine/splitter/smart.py b/knowledge_engine/knowledge_engine/splitter/smart.py
new file mode 100644
index 000000000..81d9d65a6
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/splitter/smart.py
@@ -0,0 +1,83 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import List
+
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from llama_index.core import Document
+from llama_index.core.node_parser import (
+    LangchainNodeParser,
+    MarkdownNodeParser,
+    SentenceSplitter,
+)
+from llama_index.core.schema import BaseNode
+
+from shared.telemetry.decorators import set_span_attribute, trace_sync
+
+
+class SmartSplitter:
+    SMART_EXTENSIONS = {".pdf", ".txt", ".doc", ".docx", ".md"}
+    DEFAULT_CHUNK_SIZE = 1024
+    DEFAULT_CHUNK_OVERLAP = 50
+
+    def __init__(
+        self,
+        file_extension: str,
+        chunk_size: int = DEFAULT_CHUNK_SIZE,
+        chunk_overlap: int = DEFAULT_CHUNK_OVERLAP,
+    ):
+        self.file_extension = file_extension.lower()
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+
+    @classmethod
+    def supports_smart_split(cls, file_extension: str) -> bool:
+        return file_extension.lower() in cls.SMART_EXTENSIONS
+
+    @trace_sync("rag.splitter.split_documents")
+    def split_documents(self, documents: List[Document]) -> List[BaseNode]:
+        set_span_attribute("file_extension", self.file_extension)
+        set_span_attribute("chunk_size", self.chunk_size)
+        set_span_attribute("chunk_overlap", self.chunk_overlap)
+
+        if self.file_extension == ".md":
+            return self._split_markdown(documents)
+        if self.file_extension == ".txt":
+            return self._split_text(documents)
+        return self._split_recursive(documents)
+
+    def _split_markdown(self, documents: List[Document]) -> List[BaseNode]:
+        markdown_parser = MarkdownNodeParser()
+        nodes = markdown_parser.get_nodes_from_documents(documents)
+        sentence_splitter = SentenceSplitter(
+            chunk_size=self.chunk_size,
+            chunk_overlap=self.chunk_overlap,
+        )
+        intermediate_docs = [
+            Document(text=node.text, metadata=node.metadata) for node in nodes
+        ]
+        return sentence_splitter.get_nodes_from_documents(intermediate_docs)
+
+    def _split_text(self, documents: List[Document]) -> List[BaseNode]:
+        splitter = SentenceSplitter(
+            chunk_size=self.chunk_size,
+            chunk_overlap=self.chunk_overlap,
+        )
+        return splitter.get_nodes_from_documents(documents)
+
+    def _split_recursive(self, documents: List[Document]) -> List[BaseNode]:
+        lc_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=self.chunk_size,
+            chunk_overlap=self.chunk_overlap,
+            separators=["\n\n", "\n", " ", ""],
+        )
+        parser = LangchainNodeParser(lc_splitter=lc_splitter)
+        return parser.get_nodes_from_documents(documents)
+
+    def _get_subtype(self) -> str:
+        if self.file_extension == ".md":
+            return "markdown_sentence"
+        if self.file_extension == ".txt":
+            return "sentence"
+        return "recursive_character"
diff --git a/knowledge_engine/knowledge_engine/splitter/splitter.py b/knowledge_engine/knowledge_engine/splitter/splitter.py
new file mode 100644
index 000000000..2f20e3ebc
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/splitter/splitter.py
@@ -0,0 +1,56 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import List
+
+from llama_index.core import Document
+from llama_index.core.base.embeddings.base import BaseEmbedding
+from llama_index.core.node_parser import (
+    SemanticSplitterNodeParser,
+)
+from llama_index.core.node_parser import SentenceSplitter as LlamaIndexSentenceSplitter
+from llama_index.core.schema import BaseNode
+
+
+class SemanticSplitter:
+    def __init__(
+        self,
+        embed_model: BaseEmbedding,
+        buffer_size: int = 1,
+        breakpoint_percentile_threshold: int = 95,
+    ):
+        self.embed_model = embed_model
+        self.buffer_size = buffer_size
+        self.breakpoint_percentile_threshold = breakpoint_percentile_threshold
+        self.splitter = SemanticSplitterNodeParser(
+            buffer_size=buffer_size,
+            breakpoint_percentile_threshold=breakpoint_percentile_threshold,
+            embed_model=embed_model,
+        )
+
+    def split_documents(self, documents: List[Document]) -> List[BaseNode]:
+        return self.splitter.get_nodes_from_documents(documents)
+
+
+class SentenceSplitter:
+    def __init__(
+        self,
+        chunk_size: int = 1024,
+        chunk_overlap: int = 200,
+        separator: str = " ",
+    ):
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+        self.separator = separator
+        self.splitter = LlamaIndexSentenceSplitter(
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+            separator=separator,
+        )
+
+    def split_documents(self, documents: List[Document]) -> List[BaseNode]:
+        return self.splitter.get_nodes_from_documents(documents)
+
+
+DocumentSplitter = SemanticSplitter
diff --git a/knowledge_engine/knowledge_engine/storage/__init__.py b/knowledge_engine/knowledge_engine/storage/__init__.py
new file mode 100644
index 000000000..59daed758
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/storage/__init__.py
@@ -0,0 +1,10 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Storage interfaces for knowledge_engine."""
+
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+
+__all__ = ["BaseStorageBackend", "ChunkMetadata"]
diff --git a/knowledge_engine/knowledge_engine/storage/base.py b/knowledge_engine/knowledge_engine/storage/base.py
new file mode 100644
index 000000000..620084029
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/storage/base.py
@@ -0,0 +1,70 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import json
+import logging
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, ClassVar, Dict, List
+
+from llama_index.core.schema import BaseNode
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+
+
+class BaseStorageBackend(ABC):
+    SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = []
+    INDEX_PREFIX: ClassVar[str] = "index"
+
+    def __init__(self, config: Dict):
+        self.config = config
+        self.url = config.get("url")
+        self.username = config.get("username")
+        self.password = config.get("password")
+        self.api_key = config.get("apiKey")
+        self.index_strategy = config.get("indexStrategy", {})
+        self.ext = config.get("ext", {})
+
+    def extract_chunk_text(self, raw_content: Any) -> str:
+        if raw_content is None:
+            return ""
+        if not isinstance(raw_content, str):
+            return str(raw_content)
+
+        stripped = raw_content.strip()
+        if not stripped.startswith("{") or '"text"' not in stripped:
+            return raw_content
+
+        try:
+            data = json.loads(stripped)
+        except Exception:
+            return raw_content
+
+        if isinstance(data, dict):
+            text = data.get("text")
+            if isinstance(text, str):
+                return text
+        return raw_content
+
+    @classmethod
+    def get_supported_retrieval_methods(cls) -> List[str]:
+        return cls.SUPPORTED_RETRIEVAL_METHODS.copy()
+
+    @abstractmethod
+    def create_vector_store(self, index_name: str):
+        pass
+
+    @abstractmethod
+    def index_with_metadata(
+        self,
+        nodes: List[BaseNode],
+        chunk_metadata: ChunkMetadata,
+        embed_model,
+        **kwargs,
+    ) -> Dict:
+        pass
diff --git a/knowledge_engine/knowledge_engine/storage/chunk_metadata.py b/knowledge_engine/knowledge_engine/storage/chunk_metadata.py
new file mode 100644
index 000000000..cdc9707a7
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/storage/chunk_metadata.py
@@ -0,0 +1,34 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import asdict, dataclass, field
+from typing import Any, Dict, List
+
+from llama_index.core.schema import BaseNode
+
+
+@dataclass
+class ChunkMetadata:
+    knowledge_id: str
+    doc_ref: str
+    source_file: str
+    created_at: str
+    chunk_index: int = field(default=0)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)
+
+    def with_chunk_index(self, chunk_index: int) -> "ChunkMetadata":
+        return ChunkMetadata(
+            knowledge_id=self.knowledge_id,
+            doc_ref=self.doc_ref,
+            source_file=self.source_file,
+            created_at=self.created_at,
+            chunk_index=chunk_index,
+        )
+
+    def apply_to_nodes(self, nodes: List[BaseNode]) -> List[BaseNode]:
+        for idx, node in enumerate(nodes):
+            node.metadata.update(self.with_chunk_index(idx).to_dict())
+        return nodes
diff --git a/knowledge_engine/pyproject.toml b/knowledge_engine/pyproject.toml
new file mode 100644
index 000000000..1c1e1913c
--- /dev/null
+++ b/knowledge_engine/pyproject.toml
@@ -0,0 +1,79 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+[project]
+name = "wegent-knowledge-engine"
+version = "1.0.0"
+description = "Wegent Knowledge Engine Execution Kernel"
+requires-python = ">=3.10,<=3.13"
+dependencies = [
+    "pydantic>=2.5.3",
+    "sqlalchemy>=2.0.25",
+    "python-dotenv>=1.0.0",
+    "tenacity>=8.2.3",
+    "llama-index-core>=0.12.0",
+    "llama-index-vector-stores-elasticsearch>=0.4.0",
+    "llama-index-vector-stores-qdrant>=0.9.0",
+    "llama-index-vector-stores-milvus>=0.4.0",
+    "pymilvus>=2.4.0,<2.6.4",
+    "llama-index-embeddings-openai>=0.3.0",
+    "llama-index-readers-file>=0.4.0",
+    "docx2txt>=0.8",
+    "PyPDF2>=3.0.0",
+    "python-docx>=1.1.0",
+    "python-pptx>=0.6.23",
+    "openpyxl>=3.1.2",
+    "chardet>=5.0.0",
+    "pillow>=12.0.0",
+    "python-magic>=0.4.27",
+    "pytesseract>=0.3.10",
+    "pdf2image>=1.16.0",
+    "markdown>=3.5.0",
+    "beautifulsoup4>=4.12.0",
+    "html2text>=2024.2.26",
+    "emoji>=2.9.0",
+    "httpx[brotli,socks]>=0.26.0",
+    "orjson>=3.9.0",
+    "langchain-text-splitters>=0.3.0",
+    "wegent-shared",
+]
+
+[tool.uv.sources]
+wegent-shared = { path = "../shared", editable = true }
+
+[dependency-groups]
+dev = [
+    "pytest>=9.0.1",
+    "pytest-asyncio>=1.3.0",
+    "pytest-cov>=7.0.0",
+    "pytest-mock>=3.15.1",
+    "black>=23.7.0",
+    "isort>=5.12.0",
+    "flake8>=6.0.0",
+]
+
+[tool.black]
+line-length = 88
+target-version = ['py310']
+
+[tool.isort]
+profile = "black"
+line_length = 88
+skip = [".venv", "venv"]
+skip_gitignore = true
+known_first_party = ["knowledge_engine", "shared"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+addopts = "-v --tb=short"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["knowledge_engine"]
diff --git a/knowledge_engine/tests/test_document_service.py b/knowledge_engine/tests/test_document_service.py
new file mode 100644
index 000000000..996824c95
--- /dev/null
+++ b/knowledge_engine/tests/test_document_service.py
@@ -0,0 +1,96 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_index_document_from_binary_delegates_to_indexer() -> None:
+    from knowledge_engine.services.document_service import DocumentService
+
+    storage_backend = MagicMock()
+    embed_model = object()
+    indexer = MagicMock()
+    indexer.index_from_binary.return_value = {
+        "indexed_count": 2,
+        "index_name": "wegent_kb_1",
+        "status": "success",
+        "chunks_data": [{"chunk_index": 0}, {"chunk_index": 1}],
+    }
+
+    service = DocumentService(storage_backend=storage_backend)
+
+    with patch(
+        "knowledge_engine.services.document_service.DocumentIndexer",
+        return_value=indexer,
+    ):
+        result = await service.index_document_from_binary(
+            knowledge_id="1",
+            binary_data=b"hello world",
+            source_file="report.pdf",
+            file_extension=".pdf",
+            embed_model=embed_model,
+            user_id=7,
+            splitter_config={
+                "type": "sentence",
+                "chunk_size": 256,
+                "chunk_overlap": 32,
+            },
+            document_id=9,
+        )
+
+    assert result == {
+        "doc_ref": "9",
+        "knowledge_id": "1",
+        "source_file": "report.pdf",
+        "chunk_count": 2,
+        "index_name": "wegent_kb_1",
+        "status": "success",
+        "created_at": result["created_at"],
+        "chunks_data": [{"chunk_index": 0}, {"chunk_index": 1}],
+        "indexed_count": 2,
+    }
+    indexer.index_from_binary.assert_called_once()
+    call_kwargs = indexer.index_from_binary.call_args.kwargs
+    chunk_metadata = call_kwargs["chunk_metadata"]
+    assert chunk_metadata.knowledge_id == "1"
+    assert chunk_metadata.doc_ref == "9"
+    assert chunk_metadata.source_file == "report.pdf"
+    assert call_kwargs["binary_data"] == b"hello world"
+    assert call_kwargs["file_extension"] == ".pdf"
+    assert call_kwargs["user_id"] == 7
+
+
+@pytest.mark.asyncio
+async def test_delete_document_delegates_to_storage_backend() -> None:
+    from knowledge_engine.services.document_service import DocumentService
+
+    storage_backend = MagicMock()
+    storage_backend.delete_document.return_value = {
+        "doc_ref": "doc-1",
+        "knowledge_id": "1",
+        "deleted_chunks": 3,
+        "status": "success",
+    }
+    service = DocumentService(storage_backend=storage_backend)
+
+    result = await service.delete_document(
+        knowledge_id="1",
+        doc_ref="doc-1",
+        user_id=5,
+    )
+
+    assert result == {
+        "doc_ref": "doc-1",
+        "knowledge_id": "1",
+        "deleted_chunks": 3,
+        "status": "success",
+    }
+    storage_backend.delete_document.assert_called_once_with(
+        knowledge_id="1",
+        doc_ref="doc-1",
+        user_id=5,
+    )
diff --git a/knowledge_engine/tests/test_imports.py b/knowledge_engine/tests/test_imports.py
new file mode 100644
index 000000000..32e0e0d0f
--- /dev/null
+++ b/knowledge_engine/tests/test_imports.py
@@ -0,0 +1,27 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import importlib
+
+
+def test_import_knowledge_engine_root_package() -> None:
+    package = importlib.import_module("knowledge_engine")
+
+    assert package.__version__ == "1.0.0"
+
+
+def test_import_knowledge_engine_subpackages() -> None:
+    module_names = [
+        "knowledge_engine.embedding",
+        "knowledge_engine.index",
+        "knowledge_engine.query",
+        "knowledge_engine.services",
+        "knowledge_engine.splitter",
+        "knowledge_engine.storage",
+    ]
+
+    for module_name in module_names:
+        assert importlib.import_module(module_name) is not None
diff --git a/knowledge_engine/uv.lock b/knowledge_engine/uv.lock
new file mode 100644
index 000000000..8858e9170
--- /dev/null
+++ b/knowledge_engine/uv.lock
@@ -0,0 +1,3952 @@
+version = 1
+revision = 3
+requires-python = ">=3.10, <=3.13"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
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+[[package]]
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+]

From 35de16b9fa855872ff6776a58d6096192548a830 Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Sun, 5 Apr 2026 17:56:07 +0800
Subject: [PATCH 3/9] refactor(knowledge): add remote runtime contracts and
 transport scaffold

---
 backend/app/api/api.py                        |   4 +
 .../app/api/endpoints/internal/__init__.py    |   2 +
 backend/app/api/endpoints/internal/rag.py     |  56 +-
 .../app/api/endpoints/internal/rag_content.py |  85 +++
 backend/app/core/config.py                    |  47 ++
 backend/app/services/auth/__init__.py         |   8 +
 .../app/services/auth/rag_download_token.py   |  66 +++
 .../services/knowledge/knowledge_service.py   |  17 +-
 backend/app/services/rag/content_refs.py      |  63 +++
 backend/app/services/rag/embedding/factory.py |  41 +-
 backend/app/services/rag/gateway.py           |  10 +-
 backend/app/services/rag/gateway_factory.py   |  29 +
 .../services/rag/local_data_plane/indexing.py | 129 ++---
 backend/app/services/rag/local_gateway.py     |  17 +-
 backend/app/services/rag/remote_gateway.py    | 178 +++++++
 backend/app/services/rag/retrieval_service.py |  32 ++
 backend/app/services/rag/runtime_resolver.py  | 288 +++++++++-
 backend/app/services/rag/runtime_specs.py     |  32 ++
 backend/app/services/rag/storage/factory.py   |  27 +
 .../internal/test_rag_content_endpoint.py     |  63 +++
 .../internal/test_rag_retrieve_endpoint.py    | 288 ++++++++++
 backend/tests/core/test_config.py             |  34 ++
 .../tests/integration/test_rag_remote_mode.py | 339 ++++++++++++
 .../tests/services/rag/test_content_refs.py   |  59 +++
 .../rag/test_local_data_plane_indexing.py     |  43 +-
 .../tests/services/rag/test_local_gateway.py  |  21 +-
 .../tests/services/rag/test_remote_gateway.py | 359 +++++++++++++
 .../services/rag/test_retrieval_service.py    |  46 ++
 .../services/rag/test_runtime_resolver.py     | 159 +++++-
 .../tests/services/rag/test_runtime_specs.py  |  74 +++
 docker/knowledge_runtime/Dockerfile           |  28 +
 ...-service-extraction-implementation-plan.md | 347 ++++++++++++
 ...026-04-03-rag-service-extraction-design.md | 497 +++++++++---------
 .../knowledge_runtime/__init__.py             |   5 +
 .../knowledge_runtime/api/health.py           |  12 +
 .../api/internal/__init__.py                  |   3 +
 .../knowledge_runtime/api/internal/rag.py     |  40 ++
 .../knowledge_runtime/api/router.py           |  12 +
 .../knowledge_runtime/core/config.py          |  24 +
 knowledge_runtime/knowledge_runtime/main.py   |  24 +
 .../knowledge_runtime/security.py             |  22 +
 .../knowledge_runtime/services/__init__.py    |   3 +
 .../services/content_fetcher.py               |  25 +
 .../knowledge_runtime/services/handlers.py    |  42 ++
 knowledge_runtime/pyproject.toml              |  59 +++
 knowledge_runtime/start.sh                    |  15 +
 knowledge_runtime/tests/test_health.py        |  27 +
 knowledge_runtime/tests/test_internal_rag.py  | 166 ++++++
 shared/models/__init__.py                     |  28 +
 shared/models/knowledge_runtime_protocol.py   | 165 ++++++
 .../tests/test_knowledge_runtime_protocol.py  | 201 +++++++
 51 files changed, 3948 insertions(+), 413 deletions(-)
 create mode 100644 backend/app/api/endpoints/internal/rag_content.py
 create mode 100644 backend/app/services/auth/rag_download_token.py
 create mode 100644 backend/app/services/rag/content_refs.py
 create mode 100644 backend/app/services/rag/gateway_factory.py
 create mode 100644 backend/app/services/rag/remote_gateway.py
 create mode 100644 backend/tests/api/endpoints/internal/test_rag_content_endpoint.py
 create mode 100644 backend/tests/integration/test_rag_remote_mode.py
 create mode 100644 backend/tests/services/rag/test_content_refs.py
 create mode 100644 backend/tests/services/rag/test_remote_gateway.py
 create mode 100644 docker/knowledge_runtime/Dockerfile
 create mode 100644 docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
 create mode 100644 knowledge_runtime/knowledge_runtime/__init__.py
 create mode 100644 knowledge_runtime/knowledge_runtime/api/health.py
 create mode 100644 knowledge_runtime/knowledge_runtime/api/internal/__init__.py
 create mode 100644 knowledge_runtime/knowledge_runtime/api/internal/rag.py
 create mode 100644 knowledge_runtime/knowledge_runtime/api/router.py
 create mode 100644 knowledge_runtime/knowledge_runtime/core/config.py
 create mode 100644 knowledge_runtime/knowledge_runtime/main.py
 create mode 100644 knowledge_runtime/knowledge_runtime/security.py
 create mode 100644 knowledge_runtime/knowledge_runtime/services/__init__.py
 create mode 100644 knowledge_runtime/knowledge_runtime/services/content_fetcher.py
 create mode 100644 knowledge_runtime/knowledge_runtime/services/handlers.py
 create mode 100644 knowledge_runtime/pyproject.toml
 create mode 100644 knowledge_runtime/start.sh
 create mode 100644 knowledge_runtime/tests/test_health.py
 create mode 100644 knowledge_runtime/tests/test_internal_rag.py
 create mode 100644 shared/models/knowledge_runtime_protocol.py
 create mode 100644 shared/tests/test_knowledge_runtime_protocol.py

diff --git a/backend/app/api/api.py b/backend/app/api/api.py
index 814338e0b..a83223536 100644
--- a/backend/app/api/api.py
+++ b/backend/app/api/api.py
@@ -59,6 +59,7 @@
 from app.api.endpoints.internal import (
     callback_router,
     chat_storage_router,
+    rag_content_router,
     services_router,
     skills_router,
     subscriptions_router,
@@ -195,6 +196,9 @@
 api_router.include_router(
     chat_storage_router, prefix="/internal", tags=["internal-chat"]
 )
+api_router.include_router(
+    rag_content_router, prefix="/internal", tags=["internal-rag-content"]
+)
 
 # RAG internal router is conditionally registered based on STANDALONE_MODE
 if not settings.STANDALONE_MODE:
diff --git a/backend/app/api/endpoints/internal/__init__.py b/backend/app/api/endpoints/internal/__init__.py
index a4cbddd2a..c6f5d8435 100644
--- a/backend/app/api/endpoints/internal/__init__.py
+++ b/backend/app/api/endpoints/internal/__init__.py
@@ -9,6 +9,7 @@
 from .bots import router as bots_router
 from .callback import router as callback_router
 from .chat_storage import router as chat_storage_router
+from .rag_content import router as rag_content_router
 from .services import router as services_router
 from .skills import router as skills_router
 from .subscriptions import router as subscriptions_router
@@ -24,6 +25,7 @@
     "bots_router",
     "callback_router",
     "chat_storage_router",
+    "rag_content_router",
     "services_router",
     "skills_router",
     "subscriptions_router",
diff --git a/backend/app/api/endpoints/internal/rag.py b/backend/app/api/endpoints/internal/rag.py
index 777f6381a..ef9b4bab2 100644
--- a/backend/app/api/endpoints/internal/rag.py
+++ b/backend/app/api/endpoints/internal/rag.py
@@ -24,7 +24,9 @@
 from app.services.knowledge.retrieval_persistence import (
     retrieval_persistence_service,
 )
+from app.services.rag.gateway_factory import get_query_gateway
 from app.services.rag.local_gateway import LocalRagGateway
+from app.services.rag.remote_gateway import RemoteRagGatewayError
 from app.services.rag.retrieval_service import RetrievalService
 from app.services.rag.runtime_resolver import RagRuntimeResolver
 
@@ -36,7 +38,6 @@
 
 router = APIRouter(prefix="/rag", tags=["internal-rag"])
 runtime_resolver = RagRuntimeResolver()
-rag_gateway = LocalRagGateway()
 
 
 class DirectInjectionRuntimeContext(BaseModel):
@@ -166,6 +167,52 @@ class InternalRetrieveResponse(BaseModel):
     total_estimated_tokens: int = 0
 
 
+def _resolve_query_gateway(runtime_spec):
+    route_mode = getattr(runtime_spec, "route_mode", "auto")
+    if route_mode == "rag_retrieval":
+        return get_query_gateway()
+    return LocalRagGateway()
+
+
+def _finalize_query_runtime_spec(runtime_spec, db: Session):
+    if getattr(runtime_spec, "route_mode", "auto") != "auto":
+        return runtime_spec
+    required_attributes = (
+        "query",
+        "knowledge_base_ids",
+        "document_ids",
+        "direct_injection_budget",
+        "model_copy",
+    )
+    if not all(hasattr(runtime_spec, attr) for attr in required_attributes):
+        return runtime_spec
+
+    retrieval_service = RetrievalService()
+    budget = getattr(runtime_spec, "direct_injection_budget", None)
+    resolved_route_mode = retrieval_service.decide_route_mode_for_chat_shell(
+        query=runtime_spec.query,
+        knowledge_base_ids=runtime_spec.knowledge_base_ids,
+        db=db,
+        route_mode=runtime_spec.route_mode,
+        document_ids=runtime_spec.document_ids,
+        context_window=budget.context_window if budget else None,
+    )
+    return runtime_spec.model_copy(update={"route_mode": resolved_route_mode})
+
+
+async def _execute_query_with_remote_fallback(runtime_spec, db: Session):
+    rag_gateway = _resolve_query_gateway(runtime_spec)
+    try:
+        return await rag_gateway.query(runtime_spec, db=db)
+    except RemoteRagGatewayError as exc:
+        logger.warning(
+            "[internal_rag] Remote query failed for KBs %s, falling back to local gateway: %s",
+            getattr(runtime_spec, "knowledge_base_ids", []),
+            exc,
+        )
+        return await LocalRagGateway().query(runtime_spec, db=db)
+
+
 @router.post(
     "/retrieve",
     response_model=InternalRetrieveResponse | ProtectedKnowledgeMediationResponse,
@@ -207,6 +254,7 @@ async def internal_retrieve(
         )
 
         runtime_spec = runtime_resolver.build_query_runtime_spec(
+            db=db,
             knowledge_base_ids=knowledge_base_ids,
             query=request.query,
             max_results=request.max_results,
@@ -229,10 +277,8 @@ async def internal_retrieve(
             ),
             restricted_mode=restricted_mode,
         )
-        result = await rag_gateway.query(
-            runtime_spec,
-            db=db,
-        )
+        runtime_spec = _finalize_query_runtime_spec(runtime_spec, db)
+        result = await _execute_query_with_remote_fallback(runtime_spec, db)
 
         records = result.get("records", [])
 
diff --git a/backend/app/api/endpoints/internal/rag_content.py b/backend/app/api/endpoints/internal/rag_content.py
new file mode 100644
index 000000000..579cc2d59
--- /dev/null
+++ b/backend/app/api/endpoints/internal/rag_content.py
@@ -0,0 +1,85 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Internal attachment streaming endpoint for knowledge_runtime."""
+
+from __future__ import annotations
+
+import logging
+
+from fastapi import APIRouter, Depends, Header, HTTPException, status
+from fastapi.responses import Response
+from sqlalchemy.orm import Session
+
+from app.api.dependencies import get_db
+from app.api.endpoints.adapter.attachments import _build_content_disposition
+from app.models.subtask_context import ContextStatus, ContextType
+from app.services.auth import extract_token_from_header
+from app.services.auth.rag_download_token import verify_rag_download_token
+from app.services.context import context_service
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/rag/content", tags=["internal-rag-content"])
+
+
+def _verify_rag_download_authorization(
+    attachment_id: int,
+    authorization: str = Header(default=""),
+) -> None:
+    """Validate Bearer token for internal RAG attachment download."""
+
+    token = extract_token_from_header(authorization)
+    if not token:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing or invalid Authorization header",
+        )
+
+    token_info = verify_rag_download_token(token)
+    if token_info is None or token_info.attachment_id != attachment_id:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid RAG download token",
+        )
+
+
+@router.get("/{attachment_id}")
+async def stream_rag_attachment_content(
+    attachment_id: int,
+    _: None = Depends(_verify_rag_download_authorization),
+    db: Session = Depends(get_db),
+):
+    """Stream attachment binary content for knowledge_runtime."""
+
+    context = context_service.get_context_optional(
+        db=db,
+        context_id=attachment_id,
+    )
+    if context is None or context.context_type != ContextType.ATTACHMENT.value:
+        raise HTTPException(status_code=404, detail="Attachment not found")
+    if context.status != ContextStatus.READY.value:
+        raise HTTPException(status_code=409, detail="Attachment is not ready")
+
+    binary_data = context_service.get_attachment_binary_data(
+        db=db,
+        context=context,
+    )
+    if binary_data is None:
+        logger.error(
+            "Failed to retrieve binary data for internal RAG attachment %s",
+            attachment_id,
+        )
+        raise HTTPException(
+            status_code=500,
+            detail="Failed to retrieve attachment data",
+        )
+
+    return Response(
+        content=binary_data,
+        media_type=context.mime_type,
+        headers={
+            "Content-Disposition": _build_content_disposition(context.original_filename)
+        },
+    )
diff --git a/backend/app/core/config.py b/backend/app/core/config.py
index 874f7e11c..ad2804a95 100644
--- a/backend/app/core/config.py
+++ b/backend/app/core/config.py
@@ -235,6 +235,36 @@ def parse_mcp_list(cls, v: Any) -> list[str]:
             return [item.strip() for item in raw.split(",") if item.strip()]
         return v
 
+    @field_validator("RAG_RUNTIME_MODE", mode="before")
+    @classmethod
+    def parse_rag_runtime_mode(cls, v: Any) -> str | dict[str, str]:
+        """Parse RAG runtime mode from a global value or JSON operation map."""
+        if v is None:
+            return "local"
+        if isinstance(v, Mapping):
+            return {
+                str(key).strip().lower(): str(value).strip().lower()
+                for key, value in v.items()
+                if str(key).strip() and str(value).strip()
+            }
+        if isinstance(v, str):
+            raw = v.strip()
+            if not raw:
+                return "local"
+            if raw.startswith("{"):
+                try:
+                    parsed = json.loads(raw)
+                except json.JSONDecodeError:
+                    return raw.lower()
+                if isinstance(parsed, Mapping):
+                    return {
+                        str(key).strip().lower(): str(value).strip().lower()
+                        for key, value in parsed.items()
+                        if str(key).strip() and str(value).strip()
+                    }
+            return raw.lower()
+        return v
+
     # Scheduler backend configuration
     # Supported backends: "celery" (default), "apscheduler", "xxljob"
     SCHEDULER_BACKEND: str = "celery"
@@ -406,6 +436,14 @@ def parse_mcp_list(cls, v: Any) -> list[str]:
     # Backend internal URL (for service-to-service communication)
     # Used by chat_shell to download skill binaries
     BACKEND_INTERNAL_URL: str = "http://localhost:8000"
+    # Knowledge runtime service URL for remote RAG execution
+    KNOWLEDGE_RUNTIME_URL: str = "http://localhost:8200"
+    # RAG data-plane execution mode
+    # "local" keeps execution in Backend, "remote" forwards to knowledge_runtime
+    # Accepts either a global mode string or a JSON object with per-operation overrides:
+    # "remote"
+    # {"default":"local","query":"remote"}
+    RAG_RUNTIME_MODE: str | dict[str, str] = "local"
 
     # Streaming architecture mode configuration
     # "legacy" - WebSocketStreamingHandler directly emits to WebSocket (current behavior)
@@ -502,6 +540,15 @@ def parse_mcp_list(cls, v: Any) -> list[str]:
     # Use: from shared.telemetry.config import get_otel_config
     # All OTEL_* environment variables are read from there
 
+    def get_rag_runtime_mode(self, operation: str) -> str:
+        """Resolve the effective RAG runtime mode for an operation."""
+        config = self.RAG_RUNTIME_MODE
+        if isinstance(config, Mapping):
+            normalized_operation = operation.strip().lower()
+            mode = config.get(normalized_operation) or config.get("default", "local")
+            return str(mode).strip().lower() or "local"
+        return str(config).strip().lower() or "local"
+
     @classmethod
     def settings_customise_sources(
         cls,
diff --git a/backend/app/services/auth/__init__.py b/backend/app/services/auth/__init__.py
index d7605dfb5..456c2f796 100644
--- a/backend/app/services/auth/__init__.py
+++ b/backend/app/services/auth/__init__.py
@@ -4,6 +4,11 @@
 
 """Authentication services."""
 
+from app.services.auth.rag_download_token import (
+    RagDownloadTokenInfo,
+    create_rag_download_token,
+    verify_rag_download_token,
+)
 from app.services.auth.skill_identity_token import (
     SkillIdentityTokenInfo,
     create_skill_identity_token,
@@ -25,6 +30,9 @@
     "verify_task_token",
     "get_user_from_task_token",
     "extract_token_from_header",
+    "RagDownloadTokenInfo",
+    "create_rag_download_token",
+    "verify_rag_download_token",
     "SkillIdentityTokenInfo",
     "create_skill_identity_token",
     "verify_skill_identity_token",
diff --git a/backend/app/services/auth/rag_download_token.py b/backend/app/services/auth/rag_download_token.py
new file mode 100644
index 000000000..3151e0b11
--- /dev/null
+++ b/backend/app/services/auth/rag_download_token.py
@@ -0,0 +1,66 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Short-lived token helpers for internal RAG attachment downloads."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from datetime import datetime, timedelta, timezone
+from typing import Optional
+
+import jwt
+
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RagDownloadTokenInfo:
+    """Decoded token data for internal RAG attachment download."""
+
+    attachment_id: int
+
+
+def create_rag_download_token(
+    *,
+    attachment_id: int,
+    expires_delta_seconds: int = 300,
+) -> str:
+    """Create a short-lived token bound to a single attachment ID."""
+
+    expire = datetime.now(timezone.utc) + timedelta(seconds=expires_delta_seconds)
+    payload = {
+        "attachment_id": attachment_id,
+        "exp": expire,
+        "type": "rag_download_token",
+    }
+    return jwt.encode(payload, settings.SECRET_KEY, algorithm=settings.ALGORITHM)
+
+
+def verify_rag_download_token(token: str) -> Optional[RagDownloadTokenInfo]:
+    """Verify a short-lived token for internal RAG attachment download."""
+
+    try:
+        payload = jwt.decode(
+            token, settings.SECRET_KEY, algorithms=[settings.ALGORITHM]
+        )
+        if payload.get("type") != "rag_download_token":
+            logger.warning("Invalid RAG download token type")
+            return None
+
+        attachment_id = payload.get("attachment_id")
+        if not isinstance(attachment_id, int):
+            logger.warning("RAG download token missing attachment_id")
+            return None
+
+        return RagDownloadTokenInfo(attachment_id=attachment_id)
+    except jwt.ExpiredSignatureError:
+        logger.warning("RAG download token has expired")
+        return None
+    except jwt.InvalidTokenError as exc:
+        logger.warning("Invalid RAG download token: %s", exc)
+        return None
diff --git a/backend/app/services/knowledge/knowledge_service.py b/backend/app/services/knowledge/knowledge_service.py
index 06898e58e..9b482371e 100644
--- a/backend/app/services/knowledge/knowledge_service.py
+++ b/backend/app/services/knowledge/knowledge_service.py
@@ -1129,10 +1129,10 @@ def delete_document(
         import logging
 
         from app.services.context import context_service
-        from app.services.rag.local_gateway import LocalRagGateway
+        from app.services.rag.gateway_factory import get_delete_gateway
 
         logger = logging.getLogger(__name__)
-        rag_gateway = LocalRagGateway()
+        rag_gateway = get_delete_gateway()
 
         doc = KnowledgeService.get_document(db, document_id, user_id)
         if not doc:
@@ -1177,14 +1177,21 @@ def delete_document(
                         else:
                             index_owner_user_id = kb.user_id
 
-                        result = asyncio.run(
-                            rag_gateway.delete_document_index(
+                        from app.services.rag.runtime_resolver import RagRuntimeResolver
+
+                        delete_runtime_spec = (
+                            RagRuntimeResolver().build_delete_runtime_spec(
+                                db=db,
                                 knowledge_base_id=kind_id,
                                 document_ref=doc_ref,
-                                db=db,
                                 index_owner_user_id=index_owner_user_id,
                             )
                         )
+                        result = asyncio.run(
+                            rag_gateway.delete_document_index(
+                                delete_runtime_spec, db=db
+                            )
+                        )
                         if result.get("status") == "success":
                             logger.info(
                                 f"Deleted RAG index for doc_ref '{doc_ref}' in knowledge base {kind_id} "
diff --git a/backend/app/services/rag/content_refs.py b/backend/app/services/rag/content_refs.py
new file mode 100644
index 000000000..2e2269733
--- /dev/null
+++ b/backend/app/services/rag/content_refs.py
@@ -0,0 +1,63 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Helpers for generating content references for knowledge_runtime."""
+
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+
+from sqlalchemy.orm import Session
+
+from app.core.config import settings
+from app.models.subtask_context import ContextType
+from app.services.auth.rag_download_token import create_rag_download_token
+from app.services.context import context_service
+from shared.models import (
+    BackendAttachmentStreamContentRef,
+    PresignedUrlContentRef,
+)
+
+DEFAULT_CONTENT_REF_TTL_SECONDS = 300
+
+
+def build_content_ref_for_attachment(
+    *,
+    db: Session,
+    attachment_id: int,
+    expires_delta_seconds: int = DEFAULT_CONTENT_REF_TTL_SECONDS,
+) -> BackendAttachmentStreamContentRef | PresignedUrlContentRef:
+    """Build a content reference for an attachment used by remote indexing."""
+
+    context = context_service.get_context_optional(
+        db=db,
+        context_id=attachment_id,
+    )
+    if context is None or context.context_type != ContextType.ATTACHMENT.value:
+        raise ValueError(f"Attachment {attachment_id} not found")
+
+    expires_at = datetime.now(timezone.utc) + timedelta(seconds=expires_delta_seconds)
+    presigned_url = context_service.get_attachment_url(
+        db=db,
+        context=context,
+        expires=expires_delta_seconds,
+    )
+    if presigned_url:
+        return PresignedUrlContentRef(
+            kind="presigned_url",
+            url=presigned_url,
+            expires_at=expires_at,
+        )
+
+    auth_token = create_rag_download_token(
+        attachment_id=attachment_id,
+        expires_delta_seconds=expires_delta_seconds,
+    )
+    base_url = settings.BACKEND_INTERNAL_URL.rstrip("/")
+    return BackendAttachmentStreamContentRef(
+        kind="backend_attachment_stream",
+        url=f"{base_url}{settings.API_PREFIX}/internal/rag/content/{attachment_id}",
+        auth_token=auth_token,
+        expires_at=expires_at,
+    )
diff --git a/backend/app/services/rag/embedding/factory.py b/backend/app/services/rag/embedding/factory.py
index 21af7c9f5..1dc4803f7 100644
--- a/backend/app/services/rag/embedding/factory.py
+++ b/backend/app/services/rag/embedding/factory.py
@@ -17,6 +17,7 @@
     build_default_headers_with_placeholders,
 )
 from app.services.rag.embedding.custom import CustomEmbedding
+from shared.models import RuntimeEmbeddingModelConfig
 from shared.utils.crypto import decrypt_api_key
 
 logger = logging.getLogger(__name__)
@@ -176,6 +177,43 @@ def create_embedding_model_from_crd(
             f"[EmbeddingFactory] Model '{model_name}' has configured dimensions: {dimensions}"
         )
 
+    return _create_embedding_model_from_resolved_values(
+        protocol=protocol,
+        model_name=model_name,
+        api_key=api_key,
+        base_url=base_url,
+        model_id=model_id,
+        custom_headers=custom_headers if isinstance(custom_headers, dict) else {},
+        dimensions=dimensions,
+    )
+
+
+def create_embedding_model_from_runtime_config(
+    runtime_config: RuntimeEmbeddingModelConfig,
+) -> BaseEmbedding:
+    """Create embedding model directly from resolved runtime config."""
+    resolved_config = runtime_config.resolved_config or {}
+    return _create_embedding_model_from_resolved_values(
+        protocol=resolved_config.get("protocol"),
+        model_name=runtime_config.model_name,
+        api_key=resolved_config.get("api_key"),
+        base_url=resolved_config.get("base_url"),
+        model_id=resolved_config.get("model_id"),
+        custom_headers=resolved_config.get("custom_headers") or {},
+        dimensions=resolved_config.get("dimensions"),
+    )
+
+
+def _create_embedding_model_from_resolved_values(
+    *,
+    protocol: str | None,
+    model_name: str,
+    api_key: str | None,
+    base_url: str | None,
+    model_id: str | None,
+    custom_headers: dict[str, Any],
+    dimensions: int | None,
+) -> BaseEmbedding:
     # Build embedding config based on protocol
     if protocol == "openai":
         # OpenAI protocol (supports custom headers for internal gateways)
@@ -232,5 +270,6 @@ def create_embedding_model_from_crd(
         )
     else:
         raise ValueError(
-            f"Unsupported embedding protocol: {protocol}. Supported: openai, cohere, jina, custom"
+            f"Unsupported embedding protocol for model '{model_name}': {protocol}. "
+            "Supported: openai, cohere, jina, custom"
         )
diff --git a/backend/app/services/rag/gateway.py b/backend/app/services/rag/gateway.py
index 8bd61e786..b43c60f44 100644
--- a/backend/app/services/rag/gateway.py
+++ b/backend/app/services/rag/gateway.py
@@ -2,7 +2,11 @@
 
 from sqlalchemy.orm import Session
 
-from app.services.rag.runtime_specs import IndexRuntimeSpec, QueryRuntimeSpec
+from app.services.rag.runtime_specs import (
+    DeleteRuntimeSpec,
+    IndexRuntimeSpec,
+    QueryRuntimeSpec,
+)
 
 
 class RagGateway(Protocol):
@@ -22,9 +26,7 @@ async def query(
 
     async def delete_document_index(
         self,
-        knowledge_base_id: int,
-        document_ref: str,
+        spec: DeleteRuntimeSpec,
         *,
         db: Session,
-        index_owner_user_id: int | None = None,
     ) -> dict: ...
diff --git a/backend/app/services/rag/gateway_factory.py b/backend/app/services/rag/gateway_factory.py
new file mode 100644
index 000000000..bba341682
--- /dev/null
+++ b/backend/app/services/rag/gateway_factory.py
@@ -0,0 +1,29 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from app.core.config import settings
+from app.services.rag.gateway import RagGateway
+from app.services.rag.local_gateway import LocalRagGateway
+from app.services.rag.remote_gateway import RemoteRagGateway
+
+
+def _build_gateway(mode: str) -> RagGateway:
+    normalized_mode = mode.strip().lower()
+    if normalized_mode == "remote":
+        return RemoteRagGateway()
+    return LocalRagGateway()
+
+
+def get_index_gateway() -> RagGateway:
+    return _build_gateway(settings.get_rag_runtime_mode("index"))
+
+
+def get_query_gateway() -> RagGateway:
+    return _build_gateway(settings.get_rag_runtime_mode("query"))
+
+
+def get_delete_gateway() -> RagGateway:
+    return _build_gateway(settings.get_rag_runtime_mode("delete"))
diff --git a/backend/app/services/rag/local_data_plane/indexing.py b/backend/app/services/rag/local_data_plane/indexing.py
index 42a5a7d8e..cf13292ea 100644
--- a/backend/app/services/rag/local_data_plane/indexing.py
+++ b/backend/app/services/rag/local_data_plane/indexing.py
@@ -6,13 +6,18 @@
 
 from sqlalchemy.orm import Session
 
-from app.models.kind import Kind
 from app.models.subtask_context import ContextStatus, ContextType, SubtaskContext
 from app.services.adapters.retriever_kinds import retriever_kinds_service
 from app.services.context import context_service
-from app.services.rag.embedding.factory import create_embedding_model_from_crd
-from app.services.rag.runtime_specs import IndexRuntimeSpec
-from app.services.rag.storage.factory import create_storage_backend
+from app.services.rag.embedding.factory import (
+    create_embedding_model_from_crd,
+    create_embedding_model_from_runtime_config,
+)
+from app.services.rag.runtime_specs import DeleteRuntimeSpec, IndexRuntimeSpec
+from app.services.rag.storage.factory import (
+    create_storage_backend,
+    create_storage_backend_from_runtime_config,
+)
 from knowledge_engine.services import DocumentService as EngineDocumentService
 from shared.telemetry.decorators import trace_async
 
@@ -34,16 +39,14 @@ def _extract_index_document_attributes(
 
 
 def _extract_delete_document_attributes(
-    knowledge_base_id: int,
-    document_ref: str,
+    spec: DeleteRuntimeSpec,
     *,
     db: Session,
-    index_owner_user_id: int | None = None,
 ) -> dict[str, str | int]:
     return {
-        "rag.knowledge_base_id": knowledge_base_id,
-        "rag.document_ref": document_ref,
-        "rag.index_owner_user_id": index_owner_user_id or 0,
+        "rag.knowledge_base_id": spec.knowledge_base_id,
+        "rag.document_ref": spec.document_ref,
+        "rag.index_owner_user_id": spec.index_owner_user_id,
     }
 
 
@@ -91,13 +94,11 @@ async def index_document_local(
     if db is None:
         raise ValueError("db is required for local indexing execution")
 
-    retriever = retriever_kinds_service.get_retriever(
-        db=db,
-        user_id=spec.index_owner_user_id,
-        name=spec.retriever_name,
-        namespace=spec.retriever_namespace,
-    )
-    if retriever is None:
+    try:
+        storage_backend = _build_index_storage_backend(spec, db=db)
+    except ValueError as exc:
+        if str(exc) != "retriever_not_found":
+            raise
         logger.warning(
             "Retriever %s not found for KB %s during indexing",
             spec.retriever_name,
@@ -110,14 +111,7 @@ async def index_document_local(
             "document_id": spec.document_id,
         }
 
-    storage_backend = create_storage_backend(retriever)
-    embed_model = create_embedding_model_from_crd(
-        db=db,
-        user_id=spec.index_owner_user_id,
-        model_name=spec.embedding_model_name,
-        model_namespace=spec.embedding_model_namespace,
-        user_name=spec.user_name,
-    )
+    embed_model = _build_index_embed_model(spec, db=db)
     service = EngineDocumentService(storage_backend=storage_backend)
 
     if spec.source.source_type == "attachment":
@@ -152,65 +146,52 @@ async def index_document_local(
     extract_attributes=_extract_delete_document_attributes,
 )
 async def delete_document_index_local(
-    knowledge_base_id: int,
-    document_ref: str,
+    spec: DeleteRuntimeSpec,
     *,
     db: Session,
-    index_owner_user_id: int | None = None,
 ) -> dict:
     """Delete document chunks from the local storage backend."""
-    kb = (
-        db.query(Kind)
-        .filter(
-            Kind.id == knowledge_base_id,
-            Kind.kind == "KnowledgeBase",
-            Kind.is_active,
-        )
-        .first()
+    del db
+    storage_backend = create_storage_backend_from_runtime_config(spec.retriever_config)
+    service = EngineDocumentService(storage_backend=storage_backend)
+    return await service.delete_document(
+        knowledge_id=str(spec.knowledge_base_id),
+        doc_ref=spec.document_ref,
+        user_id=spec.index_owner_user_id,
     )
-    if kb is None:
-        return {
-            "status": "skipped",
-            "reason": "knowledge_base_not_found",
-            "knowledge_id": str(knowledge_base_id),
-            "doc_ref": document_ref,
-        }
 
-    retrieval_config = (kb.json or {}).get("spec", {}).get("retrievalConfig") or {}
-    retriever_name = retrieval_config.get("retriever_name")
-    retriever_namespace = retrieval_config.get("retriever_namespace", "default")
-    if not retriever_name:
-        return {
-            "status": "skipped",
-            "reason": "missing_retriever_name",
-            "knowledge_id": str(knowledge_base_id),
-            "doc_ref": document_ref,
-        }
 
-    runtime_user_id = index_owner_user_id or kb.user_id
+def _build_index_storage_backend(
+    spec: IndexRuntimeSpec,
+    *,
+    db: Session,
+):
+    if spec.retriever_config is not None:
+        return create_storage_backend_from_runtime_config(spec.retriever_config)
+
     retriever = retriever_kinds_service.get_retriever(
         db=db,
-        user_id=runtime_user_id,
-        name=retriever_name,
-        namespace=retriever_namespace,
+        user_id=spec.index_owner_user_id,
+        name=spec.retriever_name,
+        namespace=spec.retriever_namespace,
     )
     if retriever is None:
-        logger.warning(
-            "Retriever %s not found for KB %s during delete-index cleanup",
-            retriever_name,
-            knowledge_base_id,
-        )
-        return {
-            "status": "skipped",
-            "reason": "retriever_not_found",
-            "knowledge_id": str(knowledge_base_id),
-            "doc_ref": document_ref,
-        }
+        raise ValueError("retriever_not_found")
+    return create_storage_backend(retriever)
 
-    storage_backend = create_storage_backend(retriever)
-    service = EngineDocumentService(storage_backend=storage_backend)
-    return await service.delete_document(
-        knowledge_id=str(knowledge_base_id),
-        doc_ref=document_ref,
-        user_id=runtime_user_id,
+
+def _build_index_embed_model(
+    spec: IndexRuntimeSpec,
+    *,
+    db: Session,
+):
+    if spec.embedding_model_config is not None:
+        return create_embedding_model_from_runtime_config(spec.embedding_model_config)
+
+    return create_embedding_model_from_crd(
+        db=db,
+        user_id=spec.index_owner_user_id,
+        model_name=spec.embedding_model_name,
+        model_namespace=spec.embedding_model_namespace,
+        user_name=spec.user_name,
     )
diff --git a/backend/app/services/rag/local_gateway.py b/backend/app/services/rag/local_gateway.py
index a10462e18..09bab8f88 100644
--- a/backend/app/services/rag/local_gateway.py
+++ b/backend/app/services/rag/local_gateway.py
@@ -5,7 +5,11 @@
     index_document_local,
 )
 from app.services.rag.local_data_plane.retrieval import query_local
-from app.services.rag.runtime_specs import IndexRuntimeSpec, QueryRuntimeSpec
+from app.services.rag.runtime_specs import (
+    DeleteRuntimeSpec,
+    IndexRuntimeSpec,
+    QueryRuntimeSpec,
+)
 
 
 class LocalRagGateway:
@@ -36,15 +40,8 @@ async def query(
 
     async def delete_document_index(
         self,
-        knowledge_base_id: int,
-        document_ref: str,
+        spec: DeleteRuntimeSpec,
         *,
         db: Session,
-        index_owner_user_id: int | None = None,
     ) -> dict:
-        return await self._delete_executor(
-            knowledge_base_id=knowledge_base_id,
-            document_ref=document_ref,
-            db=db,
-            index_owner_user_id=index_owner_user_id,
-        )
+        return await self._delete_executor(spec, db=db)
diff --git a/backend/app/services/rag/remote_gateway.py b/backend/app/services/rag/remote_gateway.py
new file mode 100644
index 000000000..362462457
--- /dev/null
+++ b/backend/app/services/rag/remote_gateway.py
@@ -0,0 +1,178 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from typing import Any
+
+import httpx
+from pydantic import ValidationError
+from sqlalchemy.orm import Session
+
+from app.core.config import settings
+from app.services.rag.content_refs import build_content_ref_for_attachment
+from app.services.rag.runtime_specs import (
+    DeleteRuntimeSpec,
+    IndexRuntimeSpec,
+    QueryRuntimeSpec,
+)
+from shared.models import (
+    RemoteDeleteDocumentIndexRequest,
+    RemoteIndexRequest,
+    RemoteQueryRequest,
+    RemoteQueryResponse,
+    RemoteRagError,
+)
+
+
+class RemoteRagGatewayError(RuntimeError):
+    """Raised when knowledge_runtime returns an error response."""
+
+    def __init__(
+        self,
+        message: str,
+        *,
+        code: str = "remote_request_failed",
+        retryable: bool = False,
+        status_code: int | None = None,
+        details: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(message)
+        self.code = code
+        self.retryable = retryable
+        self.status_code = status_code
+        self.details = details
+
+
+class RemoteRagGateway:
+    def __init__(
+        self,
+        *,
+        base_url: str | None = None,
+        token: str | None = None,
+        timeout: float = 30.0,
+    ) -> None:
+        self._base_url = (base_url or settings.KNOWLEDGE_RUNTIME_URL).rstrip("/")
+        self._token = token if token is not None else settings.INTERNAL_SERVICE_TOKEN
+        self._timeout = timeout
+
+    def _build_headers(self) -> dict[str, str]:
+        return {"Authorization": f"Bearer {self._token}"}
+
+    async def _post_model(self, path: str, payload: Any) -> dict[str, Any]:
+        async with httpx.AsyncClient(timeout=self._timeout) as client:
+            response = await client.post(
+                f"{self._base_url}{path}",
+                headers=self._build_headers(),
+                json=payload.model_dump(mode="json", exclude_none=True),
+            )
+
+        if response.is_error:
+            self._raise_remote_error(response)
+        return response.json()
+
+    @staticmethod
+    def _raise_remote_error(response: httpx.Response) -> None:
+        payload: dict[str, Any] | None = None
+        try:
+            raw_payload = response.json()
+            if isinstance(raw_payload, dict):
+                payload = raw_payload
+        except ValueError:
+            payload = None
+
+        if payload is not None:
+            try:
+                remote_error = RemoteRagError.model_validate(payload)
+            except ValidationError:
+                remote_error = None
+            else:
+                raise RemoteRagGatewayError(
+                    remote_error.message,
+                    code=remote_error.code,
+                    retryable=remote_error.retryable,
+                    status_code=response.status_code,
+                    details=remote_error.details,
+                )
+
+        raise RemoteRagGatewayError(
+            response.text
+            or f"knowledge_runtime request failed: {response.status_code}",
+            status_code=response.status_code,
+        )
+
+    async def index_document(
+        self,
+        spec: IndexRuntimeSpec,
+        *,
+        db: Session | None = None,
+    ) -> dict[str, Any]:
+        if db is None:
+            raise ValueError("db is required for RemoteRagGateway.index_document")
+        if spec.source.source_type != "attachment" or spec.source.attachment_id is None:
+            raise ValueError("RemoteRagGateway only supports attachment sources")
+
+        payload = RemoteIndexRequest(
+            knowledge_base_id=spec.knowledge_base_id,
+            document_id=spec.document_id,
+            index_owner_user_id=spec.index_owner_user_id,
+            retriever_config=spec.retriever_config
+            or {
+                "name": spec.retriever_name,
+                "namespace": spec.retriever_namespace,
+            },
+            embedding_model_config=spec.embedding_model_config
+            or {
+                "model_name": spec.embedding_model_name,
+                "model_namespace": spec.embedding_model_namespace,
+            },
+            splitter_config=spec.splitter_config,
+            index_families=spec.index_families,
+            content_ref=build_content_ref_for_attachment(
+                db=db,
+                attachment_id=spec.source.attachment_id,
+            ),
+            user_name=spec.user_name,
+        )
+        return await self._post_model("/internal/rag/index", payload)
+
+    async def query(
+        self,
+        spec: QueryRuntimeSpec,
+        *,
+        db: Session | None = None,
+    ) -> dict[str, Any]:
+        del db
+        payload = RemoteQueryRequest(
+            knowledge_base_ids=spec.knowledge_base_ids,
+            query=spec.query,
+            max_results=spec.max_results,
+            document_ids=spec.document_ids,
+            user_name=spec.user_name,
+            knowledge_base_configs=spec.knowledge_base_configs,
+            enabled_index_families=spec.enabled_index_families,
+            retrieval_policy=spec.retrieval_policy,
+        )
+        response_payload = await self._post_model("/internal/rag/query", payload)
+        response = RemoteQueryResponse.model_validate(response_payload)
+        return {
+            "mode": "rag_retrieval",
+            **response.model_dump(),
+        }
+
+    async def delete_document_index(
+        self,
+        spec: DeleteRuntimeSpec,
+        *,
+        db: Session,
+    ) -> dict[str, Any]:
+        del db
+        payload = RemoteDeleteDocumentIndexRequest(
+            knowledge_base_id=spec.knowledge_base_id,
+            document_ref=spec.document_ref,
+            index_owner_user_id=spec.index_owner_user_id,
+            retriever_config=spec.retriever_config,
+            enabled_index_families=spec.enabled_index_families,
+        )
+        return await self._post_model("/internal/rag/delete-document-index", payload)
diff --git a/backend/app/services/rag/retrieval_service.py b/backend/app/services/rag/retrieval_service.py
index 25046fd76..c305b7d49 100644
--- a/backend/app/services/rag/retrieval_service.py
+++ b/backend/app/services/rag/retrieval_service.py
@@ -226,6 +226,38 @@ def _get_direct_injection_rejection_reason(
                 return "runtime_budget_exceeded"
         return None
 
+    def decide_route_mode_for_chat_shell(
+        self,
+        *,
+        query: str,
+        knowledge_base_ids: list[int],
+        db: Session,
+        route_mode: Literal["auto", "direct_injection", "rag_retrieval"] = "auto",
+        document_ids: Optional[list[int]] = None,
+        context_window: Optional[int] = None,
+    ) -> Literal["direct_injection", "rag_retrieval"]:
+        """Resolve the coarse query route while keeping final direct-fit local."""
+        del query
+        if not knowledge_base_ids:
+            return "rag_retrieval"
+
+        total_estimated_tokens = 0
+        if route_mode == "auto":
+            total_estimated_tokens = self._estimate_total_tokens_for_knowledge_bases(
+                db=db,
+                knowledge_base_ids=knowledge_base_ids,
+                document_ids=document_ids,
+            )
+
+        use_direct_injection = self._should_use_direct_injection(
+            context_window=context_window,
+            total_estimated_tokens=total_estimated_tokens,
+            route_mode=route_mode,
+        )
+        if use_direct_injection:
+            return "direct_injection"
+        return "rag_retrieval"
+
     @trace_async(
         span_name="rag.retrieve_for_chat_shell",
         tracer_name="backend.services.rag",
diff --git a/backend/app/services/rag/runtime_resolver.py b/backend/app/services/rag/runtime_resolver.py
index b58d26401..7dde4e765 100644
--- a/backend/app/services/rag/runtime_resolver.py
+++ b/backend/app/services/rag/runtime_resolver.py
@@ -1,17 +1,28 @@
-from typing import Literal
+from __future__ import annotations
+
+from typing import Any, Literal
 
 from sqlalchemy.orm import Session
 
+from app.models.kind import Kind
+from app.services.adapters.retriever_kinds import retriever_kinds_service
 from app.services.knowledge.index_runtime import (
     KnowledgeBaseIndexInfo,
     get_kb_index_info,
 )
+from app.services.rag.embedding.factory import _process_custom_headers_placeholders
 from app.services.rag.runtime_specs import (
+    DeleteRuntimeSpec,
     DirectInjectionBudget,
     IndexRuntimeSpec,
     IndexSource,
+    QueryKnowledgeBaseRuntimeConfig,
     QueryRuntimeSpec,
+    RuntimeEmbeddingModelConfig,
+    RuntimeRetrievalConfig,
+    RuntimeRetrieverConfig,
 )
+from shared.utils.crypto import decrypt_api_key
 
 
 class RagRuntimeResolver:
@@ -50,6 +61,19 @@ def build_index_runtime_spec(
             embedding_model_name=embedding_model_name,
             embedding_model_namespace=embedding_model_namespace,
             source=IndexSource(source_type="attachment", attachment_id=attachment_id),
+            retriever_config=self._build_resolved_retriever_config(
+                db=db,
+                user_id=kb_info.index_owner_user_id,
+                name=retriever_name,
+                namespace=retriever_namespace,
+            ),
+            embedding_model_config=self._build_resolved_embedding_model_config(
+                db=db,
+                user_id=kb_info.index_owner_user_id,
+                model_name=embedding_model_name,
+                model_namespace=embedding_model_namespace,
+                user_name=user_name,
+            ),
             splitter_config=splitter_config_dict,
             user_name=user_name,
         )
@@ -57,6 +81,7 @@ def build_index_runtime_spec(
     def build_query_runtime_spec(
         self,
         *,
+        db: Session | None = None,
         knowledge_base_ids: list[int],
         query: str,
         max_results: int,
@@ -65,6 +90,8 @@ def build_query_runtime_spec(
         restricted_mode: bool = False,
         user_id: int | None = None,
         user_name: str | None = None,
+        enabled_index_families: list[str] | None = None,
+        retrieval_policy: str = "chunk_only",
         context_window: int | None = None,
         used_context_tokens: int = 0,
         reserved_output_tokens: int = 4096,
@@ -90,5 +117,264 @@ def build_query_runtime_spec(
             restricted_mode=restricted_mode,
             user_id=user_id,
             user_name=user_name,
+            knowledge_base_configs=(
+                self._build_query_knowledge_base_configs(
+                    db=db,
+                    knowledge_base_ids=knowledge_base_ids,
+                    user_name=user_name,
+                )
+                if db is not None
+                else []
+            ),
+            enabled_index_families=enabled_index_families or ["chunk_vector"],
+            retrieval_policy=retrieval_policy,
             direct_injection_budget=direct_injection_budget,
         )
+
+    def build_delete_runtime_spec(
+        self,
+        *,
+        db: Session,
+        knowledge_base_id: int,
+        document_ref: str,
+        index_owner_user_id: int | None = None,
+        enabled_index_families: list[str] | None = None,
+    ) -> DeleteRuntimeSpec:
+        kb = self._get_knowledge_base_record(db=db, knowledge_base_id=knowledge_base_id)
+        if kb is None:
+            raise ValueError(f"Knowledge base {knowledge_base_id} not found")
+
+        retrieval_config = (kb.json or {}).get("spec", {}).get("retrievalConfig") or {}
+        retriever_name = retrieval_config.get("retriever_name")
+        retriever_namespace = retrieval_config.get("retriever_namespace", "default")
+        if not retriever_name:
+            raise ValueError(
+                f"Knowledge base {knowledge_base_id} has incomplete retrieval config (missing retriever_name)"
+            )
+
+        runtime_user_id = index_owner_user_id or kb.user_id
+        return DeleteRuntimeSpec(
+            knowledge_base_id=knowledge_base_id,
+            document_ref=document_ref,
+            index_owner_user_id=runtime_user_id,
+            retriever_config=self._build_resolved_retriever_config(
+                db=db,
+                user_id=runtime_user_id,
+                name=retriever_name,
+                namespace=retriever_namespace,
+            ),
+            enabled_index_families=enabled_index_families or ["chunk_vector"],
+        )
+
+    def _build_query_knowledge_base_configs(
+        self,
+        *,
+        db: Session,
+        knowledge_base_ids: list[int],
+        user_name: str | None,
+    ) -> list[QueryKnowledgeBaseRuntimeConfig]:
+        configs: list[QueryKnowledgeBaseRuntimeConfig] = []
+        for knowledge_base_id in knowledge_base_ids:
+            kb = self._get_knowledge_base_record(
+                db=db, knowledge_base_id=knowledge_base_id
+            )
+            if kb is None:
+                raise ValueError(f"Knowledge base {knowledge_base_id} not found")
+
+            retrieval_config = (kb.json or {}).get("spec", {}).get(
+                "retrievalConfig"
+            ) or {}
+            retriever_name = retrieval_config.get("retriever_name")
+            retriever_namespace = retrieval_config.get("retriever_namespace", "default")
+            embedding_config = retrieval_config.get("embedding_config") or {}
+            embedding_model_name = embedding_config.get("model_name")
+            embedding_model_namespace = embedding_config.get(
+                "model_namespace",
+                "default",
+            )
+
+            if not retriever_name:
+                raise ValueError(
+                    f"Knowledge base {knowledge_base_id} has incomplete retrieval config (missing retriever_name)"
+                )
+            if not embedding_model_name:
+                raise ValueError(
+                    f"Knowledge base {knowledge_base_id} has incomplete embedding config"
+                )
+
+            retrieval_mode = retrieval_config.get("retrieval_mode", "vector")
+            hybrid_weights = retrieval_config.get("hybrid_weights") or {}
+            configs.append(
+                QueryKnowledgeBaseRuntimeConfig(
+                    knowledge_base_id=knowledge_base_id,
+                    index_owner_user_id=kb.user_id,
+                    retriever_config=self._build_resolved_retriever_config(
+                        db=db,
+                        user_id=kb.user_id,
+                        name=retriever_name,
+                        namespace=retriever_namespace,
+                    ),
+                    embedding_model_config=self._build_resolved_embedding_model_config(
+                        db=db,
+                        user_id=kb.user_id,
+                        model_name=embedding_model_name,
+                        model_namespace=embedding_model_namespace,
+                        user_name=user_name,
+                    ),
+                    retrieval_config=RuntimeRetrievalConfig(
+                        top_k=retrieval_config.get("top_k", 20),
+                        score_threshold=retrieval_config.get("score_threshold", 0.7),
+                        retrieval_mode=retrieval_mode,
+                        vector_weight=(
+                            hybrid_weights.get("vector_weight")
+                            if retrieval_mode == "hybrid"
+                            else None
+                        ),
+                        keyword_weight=(
+                            hybrid_weights.get("keyword_weight")
+                            if retrieval_mode == "hybrid"
+                            else None
+                        ),
+                    ),
+                )
+            )
+        return configs
+
+    def _get_knowledge_base_record(
+        self,
+        *,
+        db: Session,
+        knowledge_base_id: int,
+    ):
+        return (
+            db.query(Kind)
+            .filter(
+                Kind.id == knowledge_base_id,
+                Kind.kind == "KnowledgeBase",
+                Kind.is_active,
+            )
+            .first()
+        )
+
+    def _build_resolved_retriever_config(
+        self,
+        *,
+        db: Session,
+        user_id: int,
+        name: str,
+        namespace: str,
+    ) -> RuntimeRetrieverConfig:
+        retriever = retriever_kinds_service.get_retriever(
+            db=db,
+            user_id=user_id,
+            name=name,
+            namespace=namespace,
+        )
+        if retriever is None:
+            raise ValueError(f"Retriever {name} (namespace: {namespace}) not found")
+
+        storage_config = retriever.spec.storageConfig
+        return RuntimeRetrieverConfig(
+            name=name,
+            namespace=namespace,
+            storage_config={
+                "type": storage_config.type,
+                "url": storage_config.url,
+                "username": storage_config.username,
+                "password": self._decrypt_optional_secret(storage_config.password),
+                "apiKey": self._decrypt_optional_secret(storage_config.apiKey),
+                "indexStrategy": storage_config.indexStrategy.model_dump(
+                    exclude_none=True
+                ),
+                "ext": storage_config.ext or {},
+            },
+        )
+
+    def _build_resolved_embedding_model_config(
+        self,
+        *,
+        db: Session,
+        user_id: int,
+        model_name: str,
+        model_namespace: str,
+        user_name: str | None,
+    ) -> RuntimeEmbeddingModelConfig:
+        model_kind = self._get_model_kind(
+            db=db,
+            user_id=user_id,
+            model_name=model_name,
+            model_namespace=model_namespace,
+        )
+        if model_kind is None:
+            raise ValueError(
+                f"Embedding model '{model_name}' not found in namespace '{model_namespace}'"
+            )
+
+        spec = (model_kind.json or {}).get("spec", {})
+        model_config = spec.get("modelConfig", {})
+        env = model_config.get("env", {})
+        protocol = spec.get("protocol") or env.get("model")
+        custom_headers = env.get("custom_headers", {})
+        if custom_headers and isinstance(custom_headers, dict):
+            custom_headers = _process_custom_headers_placeholders(
+                custom_headers,
+                user_name,
+            )
+
+        embedding_config = spec.get("embeddingConfig", {})
+        dimensions = embedding_config.get("dimensions") if embedding_config else None
+
+        return RuntimeEmbeddingModelConfig(
+            model_name=model_name,
+            model_namespace=model_namespace,
+            resolved_config={
+                "protocol": protocol,
+                "api_key": self._decrypt_optional_secret(env.get("api_key")),
+                "base_url": env.get("base_url"),
+                "model_id": env.get("model_id"),
+                "custom_headers": (
+                    custom_headers if isinstance(custom_headers, dict) else {}
+                ),
+                "dimensions": dimensions,
+            },
+        )
+
+    def _get_model_kind(
+        self,
+        *,
+        db: Session,
+        user_id: int,
+        model_name: str,
+        model_namespace: str,
+    ):
+        if model_namespace == "default":
+            return (
+                db.query(Kind)
+                .filter(
+                    Kind.kind == "Model",
+                    Kind.name == model_name,
+                    Kind.namespace == model_namespace,
+                    Kind.is_active == True,
+                )
+                .filter((Kind.user_id == user_id) | (Kind.user_id == 0))
+                .order_by(Kind.user_id.desc())
+                .first()
+            )
+        return (
+            db.query(Kind)
+            .filter(
+                Kind.kind == "Model",
+                Kind.name == model_name,
+                Kind.namespace == model_namespace,
+                Kind.is_active == True,
+            )
+            .first()
+        )
+
+    def _decrypt_optional_secret(self, value: Any) -> Any:
+        if not value:
+            return value
+        try:
+            return decrypt_api_key(value)
+        except Exception:
+            return value
diff --git a/backend/app/services/rag/runtime_specs.py b/backend/app/services/rag/runtime_specs.py
index ae49b69e2..5558735b6 100644
--- a/backend/app/services/rag/runtime_specs.py
+++ b/backend/app/services/rag/runtime_specs.py
@@ -2,6 +2,20 @@
 
 from pydantic import BaseModel, ConfigDict, Field, model_validator
 
+from shared.models import (
+    RemoteKnowledgeBaseQueryConfig,
+    RuntimeEmbeddingModelConfig,
+    RuntimeRetrievalConfig,
+    RuntimeRetrieverConfig,
+)
+
+RetrievalPolicy = Literal[
+    "chunk_only",
+    "summary_first",
+    "summary_then_chunk_expand",
+    "hybrid",
+]
+
 
 class RuntimeSpecModel(BaseModel):
     model_config = ConfigDict(extra="forbid")
@@ -44,11 +58,16 @@ class IndexRuntimeSpec(RuntimeSpecModel):
     embedding_model_name: str
     embedding_model_namespace: str
     source: IndexSource
+    retriever_config: RuntimeRetrieverConfig | None = None
+    embedding_model_config: RuntimeEmbeddingModelConfig | None = None
     index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
     splitter_config: Optional[dict] = None
     user_name: Optional[str] = None
 
 
+QueryKnowledgeBaseRuntimeConfig = RemoteKnowledgeBaseQueryConfig
+
+
 class QueryRuntimeSpec(RuntimeSpecModel):
     knowledge_base_ids: list[int]
     query: str
@@ -59,6 +78,19 @@ class QueryRuntimeSpec(RuntimeSpecModel):
     restricted_mode: bool = False
     user_id: Optional[int] = None
     user_name: Optional[str] = None
+    knowledge_base_configs: list[QueryKnowledgeBaseRuntimeConfig] = Field(
+        default_factory=list
+    )
+    enabled_index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
+    retrieval_policy: RetrievalPolicy = "chunk_only"
+
+
+class DeleteRuntimeSpec(RuntimeSpecModel):
+    knowledge_base_id: int
+    document_ref: str
+    index_owner_user_id: int
+    retriever_config: RuntimeRetrieverConfig
+    enabled_index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
 
 
 DEFAULT_DIRECT_INJECTION_BUDGET = DirectInjectionBudget()
diff --git a/backend/app/services/rag/storage/factory.py b/backend/app/services/rag/storage/factory.py
index c1eed62c3..77d57bc57 100644
--- a/backend/app/services/rag/storage/factory.py
+++ b/backend/app/services/rag/storage/factory.py
@@ -13,6 +13,7 @@
 from app.services.rag.storage.elasticsearch_backend import ElasticsearchBackend
 from app.services.rag.storage.milvus_backend import MilvusBackend
 from app.services.rag.storage.qdrant_backend import QdrantBackend
+from shared.models import RuntimeRetrieverConfig
 
 # Registry of storage backend classes by type
 STORAGE_BACKEND_REGISTRY: Dict[str, Type[BaseStorageBackend]] = {
@@ -160,3 +161,29 @@ def create_storage_backend(retriever: Retriever) -> BaseStorageBackend:
     # Create backend instance
     backend_class = STORAGE_BACKEND_REGISTRY[storage_type]
     return backend_class(config)
+
+
+def create_storage_backend_from_runtime_config(
+    retriever_config: RuntimeRetrieverConfig,
+) -> BaseStorageBackend:
+    """Create storage backend from resolved runtime retriever config."""
+    storage_config = retriever_config.storage_config or {}
+    storage_type = storage_config.get("type", "").lower()
+
+    if storage_type not in STORAGE_BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unsupported storage type: {storage_type}. "
+            f"Supported types: {list(STORAGE_BACKEND_REGISTRY.keys())}"
+        )
+
+    config = {
+        "url": storage_config.get("url"),
+        "username": storage_config.get("username"),
+        "password": storage_config.get("password"),
+        "apiKey": storage_config.get("apiKey"),
+        "indexStrategy": storage_config.get("indexStrategy") or {"mode": "per_dataset"},
+        "ext": storage_config.get("ext") or {},
+    }
+
+    backend_class = STORAGE_BACKEND_REGISTRY[storage_type]
+    return backend_class(config)
diff --git a/backend/tests/api/endpoints/internal/test_rag_content_endpoint.py b/backend/tests/api/endpoints/internal/test_rag_content_endpoint.py
new file mode 100644
index 000000000..dbade1dbf
--- /dev/null
+++ b/backend/tests/api/endpoints/internal/test_rag_content_endpoint.py
@@ -0,0 +1,63 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import patch
+
+from app.models.subtask_context import ContextStatus, ContextType, SubtaskContext
+from app.services.auth.rag_download_token import create_rag_download_token
+
+
+def _create_attachment_context(test_db, attachment_id: int = 301) -> SubtaskContext:
+    context = SubtaskContext(
+        id=attachment_id,
+        subtask_id=0,
+        user_id=8,
+        context_type=ContextType.ATTACHMENT.value,
+        name="release-plan.txt",
+        status=ContextStatus.READY.value,
+        type_data={
+            "original_filename": "release-plan.txt",
+            "file_extension": ".txt",
+            "file_size": 12,
+            "mime_type": "text/plain",
+            "storage_backend": "mysql",
+            "storage_key": "attachments/release-plan.txt",
+        },
+    )
+    test_db.add(context)
+    test_db.commit()
+    return context
+
+
+def test_internal_rag_content_rejects_missing_auth(test_client) -> None:
+    response = test_client.get("/api/internal/rag/content/301")
+
+    assert response.status_code == 401
+
+
+def test_internal_rag_content_rejects_invalid_auth(test_client) -> None:
+    response = test_client.get(
+        "/api/internal/rag/content/301",
+        headers={"Authorization": "Bearer invalid-token"},
+    )
+
+    assert response.status_code == 401
+
+
+def test_internal_rag_content_streams_allowed_attachment(test_client, test_db) -> None:
+    context = _create_attachment_context(test_db)
+    token = create_rag_download_token(attachment_id=context.id)
+
+    with patch(
+        "app.api.endpoints.internal.rag_content.context_service.get_attachment_binary_data",
+        return_value=b"release plan",
+    ):
+        response = test_client.get(
+            f"/api/internal/rag/content/{context.id}",
+            headers={"Authorization": f"Bearer {token}"},
+        )
+
+    assert response.status_code == 200
+    assert response.content == b"release plan"
+    assert response.headers["content-type"].startswith("text/plain")
diff --git a/backend/tests/api/endpoints/internal/test_rag_retrieve_endpoint.py b/backend/tests/api/endpoints/internal/test_rag_retrieve_endpoint.py
index 7d51d22a9..4a8bbade7 100644
--- a/backend/tests/api/endpoints/internal/test_rag_retrieve_endpoint.py
+++ b/backend/tests/api/endpoints/internal/test_rag_retrieve_endpoint.py
@@ -4,6 +4,27 @@
 
 from unittest.mock import ANY, AsyncMock, patch
 
+from app.core.config import settings
+from app.services.rag.remote_gateway import RemoteRagGatewayError
+from app.services.rag.runtime_specs import DirectInjectionBudget, QueryRuntimeSpec
+
+
+def _make_runtime_spec(
+    *,
+    route_mode: str = "auto",
+    knowledge_base_ids: list[int] | None = None,
+    query: str = "test",
+    with_budget: bool = False,
+) -> QueryRuntimeSpec:
+    return QueryRuntimeSpec(
+        knowledge_base_ids=knowledge_base_ids or [1],
+        query=query,
+        route_mode=route_mode,
+        direct_injection_budget=(
+            DirectInjectionBudget(context_window=10000) if with_budget else None
+        ),
+    )
+
 
 def test_internal_retrieve_returns_restricted_safe_summary(test_client):
     payload = {
@@ -28,6 +49,14 @@ def test_internal_retrieve_returns_restricted_safe_summary(test_client):
     }
 
     with (
+        patch(
+            "app.api.endpoints.internal.rag.RagRuntimeResolver.build_query_runtime_spec",
+            return_value=_make_runtime_spec(
+                knowledge_base_ids=[1],
+                query=payload["query"],
+                with_budget=True,
+            ),
+        ),
         patch(
             "app.api.endpoints.internal.rag.LocalRagGateway.query",
             new_callable=AsyncMock,
@@ -92,6 +121,13 @@ def test_internal_retrieve_keeps_user_subtask_id_out_of_gateway(test_client):
     }
 
     with (
+        patch(
+            "app.api.endpoints.internal.rag.RagRuntimeResolver.build_query_runtime_spec",
+            return_value=_make_runtime_spec(
+                knowledge_base_ids=[1],
+                query=payload["query"],
+            ),
+        ),
         patch(
             "app.api.endpoints.internal.rag.LocalRagGateway.query",
             new_callable=AsyncMock,
@@ -111,3 +147,255 @@ def test_internal_retrieve_keeps_user_subtask_id_out_of_gateway(test_client):
     assert response.status_code == 200
     mock_query.assert_awaited_once_with(ANY, db=ANY)
     mock_persist.assert_called_once()
+
+
+def test_internal_retrieve_keeps_direct_injection_routing_in_backend(
+    test_client, monkeypatch
+):
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", {"query": "remote"})
+
+    payload = {
+        "query": "How should we proceed?",
+        "knowledge_base_ids": [1],
+        "route_mode": "direct_injection",
+        "persistence_context": {
+            "user_subtask_id": 11,
+            "user_id": 7,
+            "restricted_mode": False,
+        },
+    }
+
+    with (
+        patch(
+            "app.api.endpoints.internal.rag.RagRuntimeResolver.build_query_runtime_spec",
+            return_value=_make_runtime_spec(
+                knowledge_base_ids=[1],
+                query=payload["query"],
+                route_mode="direct_injection",
+            ),
+        ),
+        patch(
+            "app.api.endpoints.internal.rag.get_query_gateway"
+        ) as mock_get_query_gateway,
+        patch(
+            "app.api.endpoints.internal.rag.LocalRagGateway.query",
+            new_callable=AsyncMock,
+            return_value={
+                "mode": "direct_injection",
+                "records": [],
+                "total": 0,
+                "total_estimated_tokens": 0,
+            },
+        ) as mock_query,
+        patch(
+            "app.api.endpoints.internal.rag.retrieval_persistence_service.persist_retrieval_result"
+        ) as mock_persist,
+    ):
+        response = test_client.post("/api/internal/rag/retrieve", json=payload)
+
+    assert response.status_code == 200
+    mock_get_query_gateway.assert_not_called()
+    mock_query.assert_awaited_once_with(ANY, db=ANY)
+    mock_persist.assert_called_once()
+
+
+def test_internal_retrieve_auto_route_uses_remote_gateway_for_rag_retrieval(
+    test_client, monkeypatch
+):
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", {"query": "remote"})
+
+    payload = {
+        "query": "How should we proceed?",
+        "knowledge_base_ids": [1],
+        "route_mode": "auto",
+        "runtime_context": {
+            "context_window": 10000,
+            "used_context_tokens": 100,
+            "reserved_output_tokens": 2048,
+            "context_buffer_ratio": 0.1,
+            "max_direct_chunks": 500,
+        },
+    }
+
+    remote_gateway = AsyncMock()
+    remote_gateway.query.return_value = {
+        "mode": "rag_retrieval",
+        "records": [],
+        "total": 0,
+        "total_estimated_tokens": 0,
+    }
+
+    with (
+        patch(
+            "app.api.endpoints.internal.rag.RagRuntimeResolver.build_query_runtime_spec",
+            return_value=_make_runtime_spec(
+                knowledge_base_ids=[1],
+                query=payload["query"],
+                with_budget=True,
+            ),
+        ),
+        patch(
+            "app.api.endpoints.internal.rag.RetrievalService.decide_route_mode_for_chat_shell",
+            return_value="rag_retrieval",
+        ),
+        patch(
+            "app.api.endpoints.internal.rag.get_query_gateway",
+            return_value=remote_gateway,
+        ) as mock_get_query_gateway,
+        patch(
+            "app.api.endpoints.internal.rag.LocalRagGateway.query",
+            new_callable=AsyncMock,
+        ) as mock_local_query,
+    ):
+        response = test_client.post("/api/internal/rag/retrieve", json=payload)
+
+    assert response.status_code == 200
+    mock_get_query_gateway.assert_called_once()
+    remote_gateway.query.assert_awaited_once_with(ANY, db=ANY)
+    assert remote_gateway.query.await_args.args[0].route_mode == "rag_retrieval"
+    mock_local_query.assert_not_called()
+
+
+def test_internal_retrieve_auto_route_keeps_local_direct_injection(
+    test_client, monkeypatch
+):
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", {"query": "remote"})
+
+    payload = {
+        "query": "How should we proceed?",
+        "knowledge_base_ids": [1],
+        "route_mode": "auto",
+        "runtime_context": {
+            "context_window": 10000,
+            "used_context_tokens": 100,
+            "reserved_output_tokens": 2048,
+            "context_buffer_ratio": 0.1,
+            "max_direct_chunks": 500,
+        },
+    }
+
+    with (
+        patch(
+            "app.api.endpoints.internal.rag.RagRuntimeResolver.build_query_runtime_spec",
+            return_value=_make_runtime_spec(
+                knowledge_base_ids=[1],
+                query=payload["query"],
+                with_budget=True,
+            ),
+        ),
+        patch(
+            "app.api.endpoints.internal.rag.RetrievalService.decide_route_mode_for_chat_shell",
+            return_value="direct_injection",
+        ),
+        patch(
+            "app.api.endpoints.internal.rag.get_query_gateway"
+        ) as mock_get_query_gateway,
+        patch(
+            "app.api.endpoints.internal.rag.LocalRagGateway.query",
+            new_callable=AsyncMock,
+            return_value={
+                "mode": "direct_injection",
+                "records": [],
+                "total": 0,
+                "total_estimated_tokens": 0,
+            },
+        ) as mock_local_query,
+    ):
+        response = test_client.post("/api/internal/rag/retrieve", json=payload)
+
+    assert response.status_code == 200
+    mock_get_query_gateway.assert_not_called()
+    mock_local_query.assert_awaited_once_with(ANY, db=ANY)
+    assert mock_local_query.await_args.args[0].route_mode == "direct_injection"
+
+
+def test_internal_retrieve_falls_back_to_local_when_remote_query_fails(
+    test_client, monkeypatch
+):
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", {"query": "remote"})
+
+    payload = {
+        "query": "How should we proceed?",
+        "knowledge_base_ids": [1],
+        "route_mode": "auto",
+        "runtime_context": {
+            "context_window": 10000,
+            "used_context_tokens": 100,
+            "reserved_output_tokens": 2048,
+            "context_buffer_ratio": 0.1,
+            "max_direct_chunks": 500,
+        },
+        "persistence_context": {
+            "user_subtask_id": 11,
+            "user_id": 7,
+            "restricted_mode": False,
+        },
+    }
+
+    remote_gateway = AsyncMock()
+    remote_gateway.query.side_effect = RemoteRagGatewayError(
+        "knowledge runtime unavailable",
+        code="runtime_unavailable",
+        retryable=True,
+        status_code=503,
+    )
+
+    with (
+        patch(
+            "app.api.endpoints.internal.rag.RagRuntimeResolver.build_query_runtime_spec",
+            return_value=_make_runtime_spec(
+                knowledge_base_ids=[1],
+                query=payload["query"],
+                with_budget=True,
+            ),
+        ),
+        patch(
+            "app.api.endpoints.internal.rag.RetrievalService.decide_route_mode_for_chat_shell",
+            return_value="rag_retrieval",
+        ),
+        patch(
+            "app.api.endpoints.internal.rag.get_query_gateway",
+            return_value=remote_gateway,
+        ) as mock_get_query_gateway,
+        patch(
+            "app.api.endpoints.internal.rag.LocalRagGateway.query",
+            new_callable=AsyncMock,
+            return_value={
+                "mode": "rag_retrieval",
+                "records": [
+                    {
+                        "content": "fallback result",
+                        "title": "Fallback doc",
+                        "knowledge_base_id": 1,
+                    }
+                ],
+                "total": 1,
+                "total_estimated_tokens": 4,
+            },
+        ) as mock_local_query,
+        patch(
+            "app.api.endpoints.internal.rag.retrieval_persistence_service.persist_retrieval_result"
+        ) as mock_persist,
+    ):
+        response = test_client.post("/api/internal/rag/retrieve", json=payload)
+
+    assert response.status_code == 200
+    assert response.json() == {
+        "mode": "rag_retrieval",
+        "records": [
+            {
+                "content": "fallback result",
+                "score": None,
+                "title": "Fallback doc",
+                "metadata": None,
+                "knowledge_base_id": 1,
+            }
+        ],
+        "total": 1,
+        "total_estimated_tokens": 4,
+    }
+    mock_get_query_gateway.assert_called_once()
+    remote_gateway.query.assert_awaited_once_with(ANY, db=ANY)
+    mock_local_query.assert_awaited_once_with(ANY, db=ANY)
+    assert mock_local_query.await_args.args[0].route_mode == "rag_retrieval"
+    mock_persist.assert_called_once()
diff --git a/backend/tests/core/test_config.py b/backend/tests/core/test_config.py
index 3e37b5867..fbb231e39 100644
--- a/backend/tests/core/test_config.py
+++ b/backend/tests/core/test_config.py
@@ -156,3 +156,37 @@ def test_settings_with_custom_values(self):
         assert s.PROJECT_NAME == "Custom Project"
         assert s.ACCESS_TOKEN_EXPIRE_MINUTES == 60
         assert s.MAX_RUNNING_TASKS_PER_USER == 5
+
+    def test_rag_runtime_mode_defaults_to_local_for_all_operations(self):
+        """Test RAG runtime mode defaults to local across operations."""
+        s = Settings()
+
+        assert s.RAG_RUNTIME_MODE == "local"
+        assert s.get_rag_runtime_mode("index") == "local"
+        assert s.get_rag_runtime_mode("query") == "local"
+        assert s.get_rag_runtime_mode("delete") == "local"
+
+    def test_rag_runtime_mode_accepts_global_env_value(self, monkeypatch):
+        """Test RAG runtime mode accepts a single global env value."""
+        monkeypatch.setenv("RAG_RUNTIME_MODE", "remote")
+
+        s = Settings()
+
+        assert s.RAG_RUNTIME_MODE == "remote"
+        assert s.get_rag_runtime_mode("index") == "remote"
+        assert s.get_rag_runtime_mode("query") == "remote"
+        assert s.get_rag_runtime_mode("delete") == "remote"
+
+    def test_rag_runtime_mode_accepts_operation_override_map(self, monkeypatch):
+        """Test RAG runtime mode accepts per-operation overrides."""
+        monkeypatch.setenv(
+            "RAG_RUNTIME_MODE",
+            '{"default":"remote","query":"local"}',
+        )
+
+        s = Settings()
+
+        assert s.RAG_RUNTIME_MODE == {"default": "remote", "query": "local"}
+        assert s.get_rag_runtime_mode("index") == "remote"
+        assert s.get_rag_runtime_mode("query") == "local"
+        assert s.get_rag_runtime_mode("delete") == "remote"
diff --git a/backend/tests/integration/test_rag_remote_mode.py b/backend/tests/integration/test_rag_remote_mode.py
new file mode 100644
index 000000000..bda7e126d
--- /dev/null
+++ b/backend/tests/integration/test_rag_remote_mode.py
@@ -0,0 +1,339 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+from sqlalchemy.orm import Session
+
+from app.core.config import settings
+from app.models.kind import Kind
+from app.models.knowledge import DocumentIndexStatus, DocumentStatus, KnowledgeDocument
+from app.models.user import User
+from app.services.context import context_service
+from app.services.knowledge.indexing import run_document_indexing
+from app.services.knowledge.knowledge_service import KnowledgeService
+from app.services.rag.remote_gateway import RemoteRagGatewayError
+from app.services.rag.runtime_specs import DirectInjectionBudget, QueryRuntimeSpec
+
+COMMON_QUERY_RESULT = {
+    "mode": "rag_retrieval",
+    "records": [
+        {
+            "content": "Release checklist",
+            "title": "Checklist",
+            "knowledge_base_id": 1,
+        }
+    ],
+    "total": 1,
+    "total_estimated_tokens": 12,
+}
+
+EXPECTED_QUERY_RESPONSE = {
+    "mode": "rag_retrieval",
+    "records": [
+        {
+            "content": "Release checklist",
+            "score": None,
+            "title": "Checklist",
+            "metadata": None,
+            "knowledge_base_id": 1,
+        }
+    ],
+    "total": 1,
+    "total_estimated_tokens": 12,
+}
+
+
+def _make_runtime_spec(
+    *,
+    route_mode: str = "rag_retrieval",
+    knowledge_base_ids: list[int] | None = None,
+    query: str = "release checklist",
+    with_budget: bool = False,
+) -> QueryRuntimeSpec:
+    return QueryRuntimeSpec(
+        knowledge_base_ids=knowledge_base_ids or [1],
+        query=query,
+        route_mode=route_mode,
+        direct_injection_budget=(
+            DirectInjectionBudget(context_window=10000) if with_budget else None
+        ),
+    )
+
+
+def _create_delete_test_data(db: Session, user: User) -> tuple[Kind, KnowledgeDocument]:
+    kb = Kind(
+        user_id=user.id,
+        kind="KnowledgeBase",
+        name="kb-rag-remote-mode",
+        namespace="default",
+        json={
+            "spec": {
+                "name": "RAG Remote Mode KB",
+                "retrievalConfig": {
+                    "retriever_name": "retriever-a",
+                    "retriever_namespace": "default",
+                },
+            }
+        },
+        is_active=True,
+    )
+    db.add(kb)
+    db.commit()
+    db.refresh(kb)
+
+    document = KnowledgeDocument(
+        kind_id=kb.id,
+        attachment_id=23,
+        name="release-checklist",
+        file_extension="md",
+        file_size=128,
+        status=DocumentStatus.ENABLED,
+        user_id=user.id,
+        is_active=True,
+        index_status=DocumentIndexStatus.SUCCESS,
+        source_type="file",
+    )
+    db.add(document)
+    db.commit()
+    db.refresh(document)
+    return kb, document
+
+
+@pytest.mark.parametrize(
+    ("runtime_mode", "patch_target"),
+    [
+        ("local", "app.services.rag.local_gateway.LocalRagGateway.query"),
+        (
+            {"default": "local", "query": "remote"},
+            "app.services.rag.remote_gateway.RemoteRagGateway.query",
+        ),
+    ],
+)
+def test_internal_retrieve_preserves_response_shape_in_local_and_remote_modes(
+    test_client: TestClient,
+    monkeypatch,
+    runtime_mode,
+    patch_target: str,
+) -> None:
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", runtime_mode)
+
+    with (
+        patch(
+            "app.api.endpoints.internal.rag.RagRuntimeResolver.build_query_runtime_spec",
+            return_value=_make_runtime_spec(),
+        ),
+        patch(
+            patch_target,
+            new_callable=AsyncMock,
+            return_value=COMMON_QUERY_RESULT,
+        ) as mock_query,
+    ):
+        response = test_client.post(
+            "/api/internal/rag/retrieve",
+            json={
+                "query": "release checklist",
+                "knowledge_base_ids": [1],
+                "route_mode": "rag_retrieval",
+            },
+        )
+
+    assert response.status_code == 200
+    assert response.json() == EXPECTED_QUERY_RESPONSE
+    mock_query.assert_awaited_once()
+
+
+def test_internal_retrieve_falls_back_to_local_when_remote_query_fails_integration(
+    test_client: TestClient,
+    monkeypatch,
+) -> None:
+    monkeypatch.setattr(
+        settings, "RAG_RUNTIME_MODE", {"default": "local", "query": "remote"}
+    )
+
+    with (
+        patch(
+            "app.api.endpoints.internal.rag.RagRuntimeResolver.build_query_runtime_spec",
+            return_value=_make_runtime_spec(),
+        ),
+        patch(
+            "app.services.rag.remote_gateway.RemoteRagGateway.query",
+            new_callable=AsyncMock,
+            side_effect=RemoteRagGatewayError(
+                "knowledge runtime unavailable",
+                code="runtime_unavailable",
+                retryable=True,
+                status_code=503,
+            ),
+        ) as mock_remote_query,
+        patch(
+            "app.services.rag.local_gateway.LocalRagGateway.query",
+            new_callable=AsyncMock,
+            return_value=COMMON_QUERY_RESULT,
+        ) as mock_local_query,
+    ):
+        response = test_client.post(
+            "/api/internal/rag/retrieve",
+            json={
+                "query": "release checklist",
+                "knowledge_base_ids": [1],
+                "route_mode": "rag_retrieval",
+            },
+        )
+
+    assert response.status_code == 200
+    assert response.json() == EXPECTED_QUERY_RESPONSE
+    mock_remote_query.assert_awaited_once()
+    mock_local_query.assert_awaited_once()
+
+
+@pytest.mark.parametrize(
+    ("runtime_mode", "patch_target", "other_patch_target"),
+    [
+        (
+            "local",
+            "app.services.rag.local_gateway.LocalRagGateway.index_document",
+            "app.services.rag.remote_gateway.RemoteRagGateway.index_document",
+        ),
+        (
+            {"default": "local", "index": "remote", "query": "local"},
+            "app.services.rag.remote_gateway.RemoteRagGateway.index_document",
+            "app.services.rag.local_gateway.LocalRagGateway.index_document",
+        ),
+    ],
+)
+def test_run_document_indexing_switches_index_mode_independently(
+    monkeypatch,
+    runtime_mode,
+    patch_target: str,
+    other_patch_target: str,
+) -> None:
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", runtime_mode)
+    db = MagicMock()
+    db.query.return_value.filter.return_value.first.return_value = None
+    kb_index_info = SimpleNamespace(index_owner_user_id=3, summary_enabled=False)
+
+    with (
+        patch(
+            "app.services.knowledge.indexing.resolve_kb_index_info",
+            return_value=kb_index_info,
+        ),
+        patch(
+            "app.services.knowledge.indexing.RagRuntimeResolver.build_index_runtime_spec",
+            return_value=object(),
+        ) as mock_build_runtime_spec,
+        patch(
+            patch_target,
+            new_callable=AsyncMock,
+            return_value={
+                "status": "success",
+                "indexed_count": 2,
+                "index_name": "kb-1",
+            },
+        ) as mock_selected_gateway,
+        patch(other_patch_target, new_callable=AsyncMock) as mock_other_gateway,
+    ):
+        result = run_document_indexing(
+            knowledge_base_id="1",
+            attachment_id=2,
+            retriever_name="retriever-1",
+            retriever_namespace="default",
+            embedding_model_name="embedding-1",
+            embedding_model_namespace="default",
+            user_id=3,
+            user_name="tester",
+            document_id=4,
+            kb_index_info=kb_index_info,
+            trigger_summary=False,
+            db=db,
+        )
+
+    mock_selected_gateway.assert_awaited_once_with(
+        mock_build_runtime_spec.return_value,
+        db=db,
+    )
+    mock_other_gateway.assert_not_called()
+    assert result == {
+        "status": "success",
+        "reason": None,
+        "document_id": 4,
+        "knowledge_base_id": "1",
+        "indexed_count": 2,
+        "index_name": "kb-1",
+        "chunks_data": None,
+    }
+
+
+@pytest.mark.parametrize(
+    ("runtime_mode", "patch_target", "other_patch_target"),
+    [
+        (
+            "local",
+            "app.services.rag.local_gateway.LocalRagGateway.delete_document_index",
+            "app.services.rag.remote_gateway.RemoteRagGateway.delete_document_index",
+        ),
+        (
+            {"default": "local", "delete": "remote", "query": "local"},
+            "app.services.rag.remote_gateway.RemoteRagGateway.delete_document_index",
+            "app.services.rag.local_gateway.LocalRagGateway.delete_document_index",
+        ),
+    ],
+)
+def test_delete_document_switches_delete_mode_independently(
+    test_db: Session,
+    test_user: User,
+    monkeypatch,
+    runtime_mode,
+    patch_target: str,
+    other_patch_target: str,
+) -> None:
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", runtime_mode)
+    kb, document = _create_delete_test_data(test_db, test_user)
+
+    with (
+        patch.object(
+            KnowledgeService,
+            "_assert_can_manage_document",
+            return_value=None,
+        ),
+        patch.object(
+            context_service,
+            "delete_context",
+            return_value=True,
+        ),
+        patch(
+            "app.services.rag.runtime_resolver.RagRuntimeResolver.build_delete_runtime_spec",
+            return_value=object(),
+        ) as mock_build_delete_runtime_spec,
+        patch(
+            patch_target,
+            new_callable=AsyncMock,
+            return_value={"status": "success"},
+        ) as mock_selected_gateway,
+        patch(other_patch_target, new_callable=AsyncMock) as mock_other_gateway,
+    ):
+        result = KnowledgeService.delete_document(
+            db=test_db,
+            document_id=document.id,
+            user_id=test_user.id,
+        )
+
+    assert result.success is True
+    assert result.kb_id == kb.id
+    mock_build_delete_runtime_spec.assert_called_once_with(
+        db=test_db,
+        knowledge_base_id=kb.id,
+        document_ref=str(document.id),
+        index_owner_user_id=test_user.id,
+    )
+    mock_selected_gateway.assert_awaited_once_with(
+        mock_build_delete_runtime_spec.return_value,
+        db=test_db,
+    )
+    mock_other_gateway.assert_not_called()
diff --git a/backend/tests/services/rag/test_content_refs.py b/backend/tests/services/rag/test_content_refs.py
new file mode 100644
index 000000000..8a8a452ff
--- /dev/null
+++ b/backend/tests/services/rag/test_content_refs.py
@@ -0,0 +1,59 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import patch
+
+from app.models.subtask_context import ContextStatus, ContextType, SubtaskContext
+from app.services.rag.content_refs import build_content_ref_for_attachment
+
+
+def _create_attachment_context(test_db, attachment_id: int = 101) -> SubtaskContext:
+    context = SubtaskContext(
+        id=attachment_id,
+        subtask_id=0,
+        user_id=7,
+        context_type=ContextType.ATTACHMENT.value,
+        name="release-notes.md",
+        status=ContextStatus.READY.value,
+        type_data={
+            "original_filename": "release-notes.md",
+            "file_extension": ".md",
+            "file_size": 42,
+            "mime_type": "text/markdown",
+            "storage_backend": "mysql",
+            "storage_key": "attachments/release-notes.md",
+        },
+    )
+    test_db.add(context)
+    test_db.commit()
+    return context
+
+
+def test_build_content_ref_for_attachment_returns_backend_stream_ref(test_db) -> None:
+    context = _create_attachment_context(test_db)
+
+    content_ref = build_content_ref_for_attachment(
+        db=test_db,
+        attachment_id=context.id,
+    )
+
+    assert content_ref.kind == "backend_attachment_stream"
+    assert content_ref.url.endswith(f"/api/internal/rag/content/{context.id}")
+    assert content_ref.auth_token
+
+
+def test_build_content_ref_for_attachment_prefers_presigned_url(test_db) -> None:
+    context = _create_attachment_context(test_db, attachment_id=102)
+
+    with patch(
+        "app.services.rag.content_refs.context_service.get_attachment_url",
+        return_value="https://storage.example.com/presigned/object",
+    ):
+        content_ref = build_content_ref_for_attachment(
+            db=test_db,
+            attachment_id=context.id,
+        )
+
+    assert content_ref.kind == "presigned_url"
+    assert content_ref.url == "https://storage.example.com/presigned/object"
diff --git a/backend/tests/services/rag/test_local_data_plane_indexing.py b/backend/tests/services/rag/test_local_data_plane_indexing.py
index efd33ed8e..33ae098dc 100644
--- a/backend/tests/services/rag/test_local_data_plane_indexing.py
+++ b/backend/tests/services/rag/test_local_data_plane_indexing.py
@@ -2,7 +2,6 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
@@ -11,7 +10,12 @@
     delete_document_index_local,
     index_document_local,
 )
-from app.services.rag.runtime_specs import IndexRuntimeSpec, IndexSource
+from app.services.rag.runtime_specs import (
+    DeleteRuntimeSpec,
+    IndexRuntimeSpec,
+    IndexSource,
+)
+from shared.models import RuntimeRetrieverConfig
 
 
 @pytest.mark.asyncio
@@ -122,28 +126,21 @@ async def test_index_document_local_delegates_to_engine_document_service() -> No
 async def test_delete_document_index_local_delegates_to_engine_document_service() -> (
     None
 ):
-    db = MagicMock()
-    db.query.return_value.filter.return_value.first.return_value = SimpleNamespace(
-        user_id=11,
-        json={
-            "spec": {
-                "retrievalConfig": {
-                    "retriever_name": "retriever-a",
-                    "retriever_namespace": "default",
-                }
-            }
-        },
+    spec = DeleteRuntimeSpec(
+        knowledge_base_id=1,
+        document_ref="doc-1",
+        index_owner_user_id=7,
+        retriever_config=RuntimeRetrieverConfig(
+            name="retriever-a",
+            namespace="default",
+            storage_config={"type": "qdrant"},
+        ),
     )
-    storage_backend = MagicMock()
 
     with (
         patch(
-            "app.services.rag.local_data_plane.indexing.retriever_kinds_service.get_retriever",
+            "app.services.rag.local_data_plane.indexing.create_storage_backend_from_runtime_config",
             return_value=MagicMock(),
-        ) as mock_get_retriever,
-        patch(
-            "app.services.rag.local_data_plane.indexing.create_storage_backend",
-            return_value=storage_backend,
         ),
         patch(
             "app.services.rag.local_data_plane.indexing.EngineDocumentService.delete_document",
@@ -151,15 +148,9 @@ async def test_delete_document_index_local_delegates_to_engine_document_service(
             return_value={"status": "success", "deleted_chunks": 4},
         ) as mock_delete_document,
     ):
-        result = await delete_document_index_local(
-            knowledge_base_id=1,
-            document_ref="doc-1",
-            db=db,
-            index_owner_user_id=7,
-        )
+        result = await delete_document_index_local(spec, db=MagicMock())
 
     assert result == {"status": "success", "deleted_chunks": 4}
-    mock_get_retriever.assert_called_once()
     mock_delete_document.assert_awaited_once_with(
         knowledge_id="1",
         doc_ref="doc-1",
diff --git a/backend/tests/services/rag/test_local_gateway.py b/backend/tests/services/rag/test_local_gateway.py
index cf543dd15..3d62a5bdb 100644
--- a/backend/tests/services/rag/test_local_gateway.py
+++ b/backend/tests/services/rag/test_local_gateway.py
@@ -8,10 +8,12 @@
 
 from app.services.rag.local_gateway import LocalRagGateway
 from app.services.rag.runtime_specs import (
+    DeleteRuntimeSpec,
     IndexRuntimeSpec,
     IndexSource,
     QueryRuntimeSpec,
 )
+from shared.models import RuntimeRetrieverConfig
 
 
 @pytest.mark.asyncio
@@ -58,20 +60,21 @@ async def test_local_gateway_delete_document_index_delegates_to_delete_executor(
     gateway = LocalRagGateway()
     gateway._delete_executor = AsyncMock(return_value={"deleted": True})
     db = MagicMock()
-
-    result = await gateway.delete_document_index(
+    spec = DeleteRuntimeSpec(
         knowledge_base_id=1,
         document_ref="9",
-        db=db,
+        index_owner_user_id=7,
+        retriever_config=RuntimeRetrieverConfig(
+            name="retriever-a",
+            namespace="default",
+            storage_config={"type": "qdrant"},
+        ),
     )
 
+    result = await gateway.delete_document_index(spec, db=db)
+
     assert result == {"deleted": True}
-    gateway._delete_executor.assert_awaited_once_with(
-        knowledge_base_id=1,
-        document_ref="9",
-        db=db,
-        index_owner_user_id=None,
-    )
+    gateway._delete_executor.assert_awaited_once_with(spec, db=db)
 
 
 @pytest.mark.asyncio
diff --git a/backend/tests/services/rag/test_remote_gateway.py b/backend/tests/services/rag/test_remote_gateway.py
new file mode 100644
index 000000000..2cb42bd45
--- /dev/null
+++ b/backend/tests/services/rag/test_remote_gateway.py
@@ -0,0 +1,359 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+import httpx
+import pytest
+
+from app.core.config import settings
+from app.services.rag.gateway_factory import (
+    get_delete_gateway,
+    get_index_gateway,
+    get_query_gateway,
+)
+from app.services.rag.local_gateway import LocalRagGateway
+from app.services.rag.remote_gateway import RemoteRagGateway, RemoteRagGatewayError
+from app.services.rag.runtime_specs import (
+    DeleteRuntimeSpec,
+    IndexRuntimeSpec,
+    IndexSource,
+    QueryKnowledgeBaseRuntimeConfig,
+    QueryRuntimeSpec,
+    RuntimeEmbeddingModelConfig,
+    RuntimeRetrievalConfig,
+    RuntimeRetrieverConfig,
+)
+from shared.models import PresignedUrlContentRef
+
+
+def _build_response(
+    *,
+    url: str,
+    status_code: int,
+    json_body: dict,
+) -> httpx.Response:
+    request = httpx.Request("POST", url)
+    return httpx.Response(status_code, json=json_body, request=request)
+
+
+@pytest.mark.asyncio
+async def test_remote_gateway_index_document_posts_runtime_request(mocker) -> None:
+    db = MagicMock()
+    mocker.patch(
+        "app.services.rag.remote_gateway.build_content_ref_for_attachment",
+        return_value=PresignedUrlContentRef(
+            kind="presigned_url",
+            url="https://storage.example.com/release-notes.md",
+        ),
+    )
+    post_mock = mocker.patch(
+        "httpx.AsyncClient.post",
+        return_value=_build_response(
+            url="http://knowledge-runtime/internal/rag/index",
+            status_code=200,
+            json_body={"status": "accepted", "knowledge_id": "1"},
+        ),
+    )
+    gateway = RemoteRagGateway(
+        base_url="http://knowledge-runtime",
+        token="runtime-token",
+    )
+    spec = IndexRuntimeSpec(
+        knowledge_base_id=1,
+        document_id=2,
+        index_owner_user_id=3,
+        retriever_name="retriever-a",
+        retriever_namespace="default",
+        embedding_model_name="embedding-a",
+        embedding_model_namespace="default",
+        source=IndexSource(source_type="attachment", attachment_id=9),
+        retriever_config=RuntimeRetrieverConfig(
+            name="retriever-a",
+            namespace="default",
+            storage_config={
+                "type": "qdrant",
+                "url": "http://qdrant:6333",
+                "indexStrategy": {"mode": "per_dataset"},
+            },
+        ),
+        embedding_model_config=RuntimeEmbeddingModelConfig(
+            model_name="embedding-a",
+            model_namespace="default",
+            resolved_config={
+                "protocol": "openai",
+                "model_id": "text-embedding-3-small",
+                "base_url": "https://api.openai.com/v1",
+            },
+        ),
+        splitter_config={"type": "sentence"},
+        index_families=["chunk_vector", "summary_vector_index"],
+        user_name="alice",
+    )
+
+    result = await gateway.index_document(spec, db=db)
+
+    assert result == {"status": "accepted", "knowledge_id": "1"}
+    post_mock.assert_awaited_once()
+    args, kwargs = post_mock.await_args
+    assert args[0] == "http://knowledge-runtime/internal/rag/index"
+    assert kwargs["headers"] == {"Authorization": "Bearer runtime-token"}
+    assert kwargs["json"] == {
+        "knowledge_base_id": 1,
+        "document_id": 2,
+        "index_owner_user_id": 3,
+        "retriever_config": {
+            "name": "retriever-a",
+            "namespace": "default",
+            "storage_config": {
+                "type": "qdrant",
+                "url": "http://qdrant:6333",
+                "indexStrategy": {"mode": "per_dataset"},
+            },
+        },
+        "embedding_model_config": {
+            "model_name": "embedding-a",
+            "model_namespace": "default",
+            "resolved_config": {
+                "protocol": "openai",
+                "model_id": "text-embedding-3-small",
+                "base_url": "https://api.openai.com/v1",
+            },
+        },
+        "splitter_config": {"type": "sentence"},
+        "index_families": ["chunk_vector", "summary_vector_index"],
+        "content_ref": {
+            "kind": "presigned_url",
+            "url": "https://storage.example.com/release-notes.md",
+        },
+        "user_name": "alice",
+    }
+
+
+@pytest.mark.asyncio
+async def test_remote_gateway_query_omits_backend_only_route_fields(mocker) -> None:
+    post_mock = mocker.patch(
+        "httpx.AsyncClient.post",
+        return_value=_build_response(
+            url="http://knowledge-runtime/internal/rag/query",
+            status_code=200,
+            json_body={
+                "records": [
+                    {
+                        "content": "Release checklist",
+                        "title": "Checklist",
+                        "knowledge_base_id": 1,
+                        "index_family": "chunk_vector",
+                    }
+                ],
+                "total": 1,
+                "total_estimated_tokens": 12,
+            },
+        ),
+    )
+    gateway = RemoteRagGateway(
+        base_url="http://knowledge-runtime",
+        token="runtime-token",
+    )
+    spec = QueryRuntimeSpec(
+        knowledge_base_ids=[1],
+        query="release checklist",
+        route_mode="direct_injection",
+        restricted_mode=True,
+        user_id=8,
+        knowledge_base_configs=[
+            QueryKnowledgeBaseRuntimeConfig(
+                knowledge_base_id=1,
+                index_owner_user_id=8,
+                retriever_config=RuntimeRetrieverConfig(
+                    name="retriever-a",
+                    namespace="default",
+                    storage_config={
+                        "type": "qdrant",
+                        "url": "http://qdrant:6333",
+                    },
+                ),
+                embedding_model_config=RuntimeEmbeddingModelConfig(
+                    model_name="embedding-a",
+                    model_namespace="default",
+                    resolved_config={
+                        "protocol": "openai",
+                        "model_id": "text-embedding-3-small",
+                    },
+                ),
+                retrieval_config=RuntimeRetrievalConfig(
+                    top_k=20,
+                    score_threshold=0.7,
+                    retrieval_mode="vector",
+                ),
+            )
+        ],
+        enabled_index_families=["chunk_vector", "summary_vector_index"],
+        retrieval_policy="summary_then_chunk_expand",
+    )
+
+    result = await gateway.query(spec)
+
+    assert result == {
+        "mode": "rag_retrieval",
+        "records": [
+            {
+                "content": "Release checklist",
+                "title": "Checklist",
+                "score": None,
+                "metadata": None,
+                "knowledge_base_id": 1,
+                "document_id": None,
+                "index_family": "chunk_vector",
+            }
+        ],
+        "total": 1,
+        "total_estimated_tokens": 12,
+    }
+    args, kwargs = post_mock.await_args
+    assert args[0] == "http://knowledge-runtime/internal/rag/query"
+    assert kwargs["json"] == {
+        "knowledge_base_ids": [1],
+        "query": "release checklist",
+        "max_results": 5,
+        "knowledge_base_configs": [
+            {
+                "knowledge_base_id": 1,
+                "index_owner_user_id": 8,
+                "retriever_config": {
+                    "name": "retriever-a",
+                    "namespace": "default",
+                    "storage_config": {
+                        "type": "qdrant",
+                        "url": "http://qdrant:6333",
+                    },
+                },
+                "embedding_model_config": {
+                    "model_name": "embedding-a",
+                    "model_namespace": "default",
+                    "resolved_config": {
+                        "protocol": "openai",
+                        "model_id": "text-embedding-3-small",
+                    },
+                },
+                "retrieval_config": {
+                    "top_k": 20,
+                    "score_threshold": 0.7,
+                    "retrieval_mode": "vector",
+                },
+            }
+        ],
+        "enabled_index_families": ["chunk_vector", "summary_vector_index"],
+        "retrieval_policy": "summary_then_chunk_expand",
+    }
+
+
+@pytest.mark.asyncio
+async def test_remote_gateway_translates_structured_remote_errors(mocker) -> None:
+    mocker.patch(
+        "httpx.AsyncClient.post",
+        return_value=_build_response(
+            url="http://knowledge-runtime/internal/rag/query",
+            status_code=503,
+            json_body={
+                "code": "runtime_unavailable",
+                "message": "knowledge runtime unavailable",
+                "retryable": True,
+            },
+        ),
+    )
+    gateway = RemoteRagGateway(
+        base_url="http://knowledge-runtime",
+        token="runtime-token",
+    )
+
+    with pytest.raises(
+        RemoteRagGatewayError, match="knowledge runtime unavailable"
+    ) as exc:
+        await gateway.query(QueryRuntimeSpec(knowledge_base_ids=[1], query="release"))
+
+    assert exc.value.code == "runtime_unavailable"
+    assert exc.value.retryable is True
+    assert exc.value.status_code == 503
+
+
+@pytest.mark.asyncio
+async def test_remote_gateway_delete_posts_resolved_retriever_config(mocker) -> None:
+    post_mock = mocker.patch(
+        "httpx.AsyncClient.post",
+        return_value=_build_response(
+            url="http://knowledge-runtime/internal/rag/delete-document-index",
+            status_code=200,
+            json_body={"status": "accepted", "knowledge_id": "1"},
+        ),
+    )
+    gateway = RemoteRagGateway(
+        base_url="http://knowledge-runtime",
+        token="runtime-token",
+    )
+    spec = DeleteRuntimeSpec(
+        knowledge_base_id=1,
+        document_ref="9",
+        index_owner_user_id=7,
+        retriever_config=RuntimeRetrieverConfig(
+            name="retriever-a",
+            namespace="default",
+            storage_config={
+                "type": "elasticsearch",
+                "url": "http://es:9200",
+                "indexStrategy": {"mode": "per_user"},
+            },
+        ),
+    )
+
+    result = await gateway.delete_document_index(spec, db=MagicMock())
+
+    assert result == {"status": "accepted", "knowledge_id": "1"}
+    args, kwargs = post_mock.await_args
+    assert args[0] == "http://knowledge-runtime/internal/rag/delete-document-index"
+    assert kwargs["json"] == {
+        "knowledge_base_id": 1,
+        "document_ref": "9",
+        "index_owner_user_id": 7,
+        "retriever_config": {
+            "name": "retriever-a",
+            "namespace": "default",
+            "storage_config": {
+                "type": "elasticsearch",
+                "url": "http://es:9200",
+                "indexStrategy": {"mode": "per_user"},
+            },
+        },
+        "enabled_index_families": ["chunk_vector"],
+    }
+
+
+def test_gateway_factory_returns_local_gateways_by_default(monkeypatch) -> None:
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", "local")
+
+    assert isinstance(get_index_gateway(), LocalRagGateway)
+    assert isinstance(get_query_gateway(), LocalRagGateway)
+    assert isinstance(get_delete_gateway(), LocalRagGateway)
+
+
+def test_gateway_factory_returns_remote_gateways_when_enabled(monkeypatch) -> None:
+    monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", "remote")
+
+    assert isinstance(get_index_gateway(), RemoteRagGateway)
+    assert isinstance(get_query_gateway(), RemoteRagGateway)
+    assert isinstance(get_delete_gateway(), RemoteRagGateway)
+
+
+def test_gateway_factory_supports_per_operation_overrides(monkeypatch) -> None:
+    monkeypatch.setattr(
+        settings,
+        "RAG_RUNTIME_MODE",
+        {"default": "local", "query": "remote", "delete": "remote"},
+    )
+
+    assert isinstance(get_index_gateway(), LocalRagGateway)
+    assert isinstance(get_query_gateway(), RemoteRagGateway)
+    assert isinstance(get_delete_gateway(), RemoteRagGateway)
diff --git a/backend/tests/services/rag/test_retrieval_service.py b/backend/tests/services/rag/test_retrieval_service.py
index 37e463e5d..93f8aadfb 100644
--- a/backend/tests/services/rag/test_retrieval_service.py
+++ b/backend/tests/services/rag/test_retrieval_service.py
@@ -331,6 +331,52 @@ async def test_auto_route_estimates_only_filtered_documents(self):
         assert result["mode"] == "direct_injection"
         assert result["records"][0]["knowledge_base_id"] == 123
 
+    def test_decide_route_mode_for_chat_shell_returns_rag_retrieval_without_budget(
+        self,
+    ):
+        from app.services.rag.retrieval_service import RetrievalService
+
+        service = RetrievalService()
+        db = MagicMock()
+
+        result = service.decide_route_mode_for_chat_shell(
+            query="test",
+            knowledge_base_ids=[123],
+            db=db,
+            route_mode="auto",
+            context_window=None,
+        )
+
+        assert result == "rag_retrieval"
+
+    def test_decide_route_mode_for_chat_shell_returns_direct_injection_when_auto_fits(
+        self,
+    ):
+        from app.services.rag.retrieval_service import RetrievalService
+
+        service = RetrievalService()
+        db = MagicMock()
+
+        with patch.object(
+            RetrievalService,
+            "_estimate_total_tokens_for_knowledge_bases",
+            return_value=100,
+        ) as mock_estimate:
+            result = service.decide_route_mode_for_chat_shell(
+                query="test",
+                knowledge_base_ids=[123],
+                db=db,
+                route_mode="auto",
+                context_window=10000,
+            )
+
+        mock_estimate.assert_called_once_with(
+            db=db,
+            knowledge_base_ids=[123],
+            document_ids=None,
+        )
+        assert result == "direct_injection"
+
     def test_estimate_total_tokens_supports_decimal_aggregate_result(self):
         """Aggregate text-length queries may return Decimal depending on the driver."""
         from app.services.rag.retrieval_service import RetrievalService
diff --git a/backend/tests/services/rag/test_runtime_resolver.py b/backend/tests/services/rag/test_runtime_resolver.py
index f6bced097..31b7d7c15 100644
--- a/backend/tests/services/rag/test_runtime_resolver.py
+++ b/backend/tests/services/rag/test_runtime_resolver.py
@@ -4,16 +4,42 @@
 import pytest
 
 from app.services.rag.runtime_resolver import RagRuntimeResolver
+from shared.models import (
+    RemoteKnowledgeBaseQueryConfig,
+    RuntimeEmbeddingModelConfig,
+    RuntimeRetrievalConfig,
+    RuntimeRetrieverConfig,
+)
 
 
 def test_build_index_runtime_spec_uses_kb_owner_for_group_kb():
     resolver = RagRuntimeResolver()
     db = MagicMock()
 
-    with patch(
-        "app.services.rag.runtime_resolver.get_kb_index_info",
-        return_value=SimpleNamespace(index_owner_user_id=42, summary_enabled=True),
-    ) as get_kb_index_info_mock:
+    with (
+        patch(
+            "app.services.rag.runtime_resolver.get_kb_index_info",
+            return_value=SimpleNamespace(index_owner_user_id=42, summary_enabled=True),
+        ) as get_kb_index_info_mock,
+        patch.object(
+            resolver,
+            "_build_resolved_retriever_config",
+            return_value=RuntimeRetrieverConfig(
+                name="retriever-a",
+                namespace="default",
+                storage_config={"type": "qdrant"},
+            ),
+        ),
+        patch.object(
+            resolver,
+            "_build_resolved_embedding_model_config",
+            return_value=RuntimeEmbeddingModelConfig(
+                model_name="embed-a",
+                model_namespace="default",
+                resolved_config={"protocol": "openai"},
+            ),
+        ),
+    ):
         spec = resolver.build_index_runtime_spec(
             db=db,
             knowledge_base_id="7",
@@ -36,26 +62,52 @@ def test_build_index_runtime_spec_uses_kb_owner_for_group_kb():
     assert spec.knowledge_base_id == 7
     assert spec.index_owner_user_id == 42
     assert spec.source.attachment_id == 11
+    assert spec.retriever_config.storage_config["type"] == "qdrant"
+    assert spec.embedding_model_config.resolved_config["protocol"] == "openai"
 
 
 def test_build_query_runtime_spec_maps_runtime_budget():
     resolver = RagRuntimeResolver()
 
-    spec = resolver.build_query_runtime_spec(
-        knowledge_base_ids=[1],
-        query="release checklist",
-        max_results=3,
-        route_mode="auto",
-        document_ids=[10],
-        user_id=5,
-        user_name="alice",
-        context_window=200000,
-        used_context_tokens=1200,
-        reserved_output_tokens=4096,
-        context_buffer_ratio=0.1,
-        max_direct_chunks=250,
-        restricted_mode=True,
-    )
+    with patch.object(
+        resolver,
+        "_build_query_knowledge_base_configs",
+        return_value=[
+            RemoteKnowledgeBaseQueryConfig(
+                knowledge_base_id=1,
+                index_owner_user_id=5,
+                retriever_config=RuntimeRetrieverConfig(
+                    name="retriever-a",
+                    namespace="default",
+                    storage_config={"type": "qdrant"},
+                ),
+                embedding_model_config=RuntimeEmbeddingModelConfig(
+                    model_name="embed-a",
+                    model_namespace="default",
+                    resolved_config={"protocol": "openai"},
+                ),
+                retrieval_config=RuntimeRetrievalConfig(top_k=20),
+            )
+        ],
+    ):
+        spec = resolver.build_query_runtime_spec(
+            db=MagicMock(),
+            knowledge_base_ids=[1],
+            query="release checklist",
+            max_results=3,
+            route_mode="auto",
+            document_ids=[10],
+            user_id=5,
+            user_name="alice",
+            context_window=200000,
+            used_context_tokens=1200,
+            reserved_output_tokens=4096,
+            context_buffer_ratio=0.1,
+            max_direct_chunks=250,
+            restricted_mode=True,
+            enabled_index_families=["chunk_vector", "summary_vector"],
+            retrieval_policy="summary_first",
+        )
 
     assert spec.knowledge_base_ids == [1]
     assert spec.query == "release checklist"
@@ -65,6 +117,9 @@ def test_build_query_runtime_spec_maps_runtime_budget():
     assert spec.user_id == 5
     assert spec.user_name == "alice"
     assert spec.restricted_mode is True
+    assert spec.knowledge_base_configs[0].retrieval_config.top_k == 20
+    assert spec.enabled_index_families == ["chunk_vector", "summary_vector"]
+    assert spec.retrieval_policy == "summary_first"
     assert spec.direct_injection_budget.context_window == 200000
     assert spec.direct_injection_budget.used_context_tokens == 1200
     assert spec.direct_injection_budget.reserved_output_tokens == 4096
@@ -75,14 +130,23 @@ def test_build_query_runtime_spec_maps_runtime_budget():
 def test_build_query_runtime_spec_omits_budget_without_context_window():
     resolver = RagRuntimeResolver()
 
-    spec = resolver.build_query_runtime_spec(
-        knowledge_base_ids=[1],
-        query="release checklist",
-        max_results=3,
-        route_mode="auto",
-    )
+    with patch.object(
+        resolver,
+        "_build_query_knowledge_base_configs",
+        return_value=[],
+    ):
+        spec = resolver.build_query_runtime_spec(
+            db=MagicMock(),
+            knowledge_base_ids=[1],
+            query="release checklist",
+            max_results=3,
+            route_mode="auto",
+        )
 
     assert spec.direct_injection_budget is None
+    assert spec.knowledge_base_configs == []
+    assert spec.enabled_index_families == ["chunk_vector"]
+    assert spec.retrieval_policy == "chunk_only"
 
 
 def test_build_query_runtime_spec_rejects_control_plane_only_inputs():
@@ -90,6 +154,7 @@ def test_build_query_runtime_spec_rejects_control_plane_only_inputs():
 
     with pytest.raises(TypeError):
         resolver.build_query_runtime_spec(
+            db=MagicMock(),
             knowledge_base_ids=[1],
             query="release checklist",
             max_results=3,
@@ -130,3 +195,47 @@ def test_build_index_runtime_spec_rejects_non_integer_kb_id():
             )
 
     get_kb_index_info.assert_not_called()
+
+
+def test_build_delete_runtime_spec_resolves_retriever_config():
+    resolver = RagRuntimeResolver()
+    db = MagicMock()
+
+    with (
+        patch.object(
+            resolver,
+            "_get_knowledge_base_record",
+            return_value=SimpleNamespace(
+                user_id=42,
+                json={
+                    "spec": {
+                        "retrievalConfig": {
+                            "retriever_name": "retriever-a",
+                            "retriever_namespace": "default",
+                        }
+                    }
+                },
+            ),
+        ),
+        patch.object(
+            resolver,
+            "_build_resolved_retriever_config",
+            return_value=RuntimeRetrieverConfig(
+                name="retriever-a",
+                namespace="default",
+                storage_config={"type": "qdrant"},
+            ),
+        ),
+    ):
+        spec = resolver.build_delete_runtime_spec(
+            db=db,
+            knowledge_base_id=7,
+            document_ref="doc-8",
+            index_owner_user_id=99,
+            enabled_index_families=["chunk_vector", "summary_vector_index"],
+        )
+
+    assert spec.knowledge_base_id == 7
+    assert spec.document_ref == "doc-8"
+    assert spec.index_owner_user_id == 99
+    assert spec.retriever_config.storage_config["type"] == "qdrant"
diff --git a/backend/tests/services/rag/test_runtime_specs.py b/backend/tests/services/rag/test_runtime_specs.py
index 8ddf70d1f..dd109695b 100644
--- a/backend/tests/services/rag/test_runtime_specs.py
+++ b/backend/tests/services/rag/test_runtime_specs.py
@@ -2,10 +2,15 @@
 from pydantic import ValidationError
 
 from app.services.rag.runtime_specs import (
+    DeleteRuntimeSpec,
     DirectInjectionBudget,
     IndexRuntimeSpec,
     IndexSource,
+    QueryKnowledgeBaseRuntimeConfig,
     QueryRuntimeSpec,
+    RuntimeEmbeddingModelConfig,
+    RuntimeRetrievalConfig,
+    RuntimeRetrieverConfig,
 )
 
 
@@ -19,6 +24,19 @@ def test_index_runtime_spec_keeps_control_plane_free_fields():
         embedding_model_name="embed-a",
         embedding_model_namespace="default",
         source=IndexSource(attachment_id=123, source_type="attachment"),
+        retriever_config=RuntimeRetrieverConfig(
+            name="retriever-a",
+            namespace="default",
+            storage_config={"type": "qdrant", "url": "http://qdrant:6333"},
+        ),
+        embedding_model_config=RuntimeEmbeddingModelConfig(
+            model_name="embed-a",
+            model_namespace="default",
+            resolved_config={
+                "protocol": "openai",
+                "model_id": "text-embedding-3-small",
+            },
+        ),
         index_families=["chunk_vector"],
         splitter_config={"type": "smart"},
         user_name="alice",
@@ -26,6 +44,7 @@ def test_index_runtime_spec_keeps_control_plane_free_fields():
     assert spec.knowledge_base_id == 7
     assert spec.source.attachment_id == 123
     assert spec.index_families == ["chunk_vector"]
+    assert spec.retriever_config.storage_config["type"] == "qdrant"
 
 
 def test_query_runtime_spec_keeps_direct_injection_budget():
@@ -45,10 +64,39 @@ def test_query_runtime_spec_keeps_direct_injection_budget():
         restricted_mode=False,
         user_id=3,
         user_name="alice",
+        knowledge_base_configs=[
+            QueryKnowledgeBaseRuntimeConfig(
+                knowledge_base_id=1,
+                index_owner_user_id=3,
+                retriever_config=RuntimeRetrieverConfig(
+                    name="retriever-a",
+                    namespace="default",
+                    storage_config={"type": "qdrant", "url": "http://qdrant:6333"},
+                ),
+                embedding_model_config=RuntimeEmbeddingModelConfig(
+                    model_name="embed-a",
+                    model_namespace="default",
+                    resolved_config={
+                        "protocol": "openai",
+                        "model_id": "text-embedding-3-small",
+                    },
+                ),
+                retrieval_config=RuntimeRetrievalConfig(
+                    top_k=20,
+                    score_threshold=0.7,
+                    retrieval_mode="vector",
+                ),
+            )
+        ],
+        enabled_index_families=["chunk_vector", "summary_vector"],
+        retrieval_policy="summary_first",
     )
     assert spec.knowledge_base_ids == [1, 2]
     assert spec.document_ids == [11]
     assert spec.direct_injection_budget.max_direct_chunks == 500
+    assert spec.knowledge_base_configs[0].retrieval_config.top_k == 20
+    assert spec.enabled_index_families == ["chunk_vector", "summary_vector"]
+    assert spec.retrieval_policy == "summary_first"
 
 
 def test_query_runtime_spec_forbids_control_plane_only_fields():
@@ -60,6 +108,32 @@ def test_query_runtime_spec_forbids_control_plane_only_fields():
         )
 
 
+def test_query_runtime_spec_defaults_remote_compatible_fields():
+    spec = QueryRuntimeSpec(knowledge_base_ids=[1], query="how to ship")
+
+    assert spec.knowledge_base_configs == []
+    assert spec.enabled_index_families == ["chunk_vector"]
+    assert spec.retrieval_policy == "chunk_only"
+
+
+def test_delete_runtime_spec_keeps_resolved_retriever_config():
+    spec = DeleteRuntimeSpec(
+        knowledge_base_id=7,
+        document_ref="doc-8",
+        index_owner_user_id=9,
+        retriever_config=RuntimeRetrieverConfig(
+            name="retriever-a",
+            namespace="default",
+            storage_config={"type": "qdrant", "url": "http://qdrant:6333"},
+        ),
+        enabled_index_families=["chunk_vector", "summary_vector_index"],
+    )
+
+    assert spec.knowledge_base_id == 7
+    assert spec.retriever_config.storage_config["type"] == "qdrant"
+    assert spec.enabled_index_families == ["chunk_vector", "summary_vector_index"]
+
+
 @pytest.mark.parametrize(
     ("kwargs", "expected_message"),
     [
diff --git a/docker/knowledge_runtime/Dockerfile b/docker/knowledge_runtime/Dockerfile
new file mode 100644
index 000000000..a047d6b97
--- /dev/null
+++ b/docker/knowledge_runtime/Dockerfile
@@ -0,0 +1,28 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+FROM ghcr.io/wecode-ai/wegent-base-python3.12:1.2.1
+
+WORKDIR /app
+
+# Copy local dependencies first so uv can resolve path sources.
+COPY shared /shared
+
+# Copy pyproject.toml and install dependencies.
+COPY knowledge_runtime/pyproject.toml .
+RUN uv pip install --system --no-cache -r pyproject.toml
+
+# Copy application code.
+COPY knowledge_runtime /app
+
+ENV PORT=8200
+ENV HOST=0.0.0.0
+ENV GRACEFUL_SHUTDOWN_TIMEOUT=600
+
+EXPOSE ${PORT}
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:${PORT}/health || exit 1
+
+CMD ["sh", "-c", "exec uvicorn knowledge_runtime.main:app --host ${HOST} --port ${PORT} --workers 1 --timeout-graceful-shutdown ${GRACEFUL_SHUTDOWN_TIMEOUT}"]
diff --git a/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md b/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
new file mode 100644
index 000000000..9509dff7a
--- /dev/null
+++ b/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
@@ -0,0 +1,347 @@
+# Knowledge Runtime Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Extract a reusable `knowledge_engine` execution kernel from Backend, make Backend and `knowledge_runtime` share it, and move real RAG index/query/delete execution behind stable remote contracts without changing Backend's control-plane ownership.
+
+**Architecture:** `shared` continues to hold lightweight service-to-service protocol models only. Backend remains the only control plane and is responsible for permissions, metadata, CRD resolution, orchestration, `direct injection`, and `restricted mediation`. `knowledge_engine` becomes a new top-level Python package for backend-agnostic RAG execution, while `knowledge_runtime` becomes a thin internal service that fetches content, validates internal auth, invokes `knowledge_engine`, and returns protocol responses.
+
+**Tech Stack:** FastAPI, Pydantic v2, SQLAlchemy, httpx, uv, pytest, current RAG storage backends, LlamaIndex-based indexing helpers, Docker Compose.
+
+---
+
+## Current Baseline
+
+The following items already exist and must be preserved while executing this plan:
+
+- `shared` already contains the first version of `knowledge_runtime` protocol models
+- Backend already exposes `content_ref` and internal content streaming
+- Backend already supports unified `RAG_RUNTIME_MODE`
+- Backend retrieve endpoint already keeps `direct injection` decisions in Backend
+- Backend remote query already has local fallback on structured remote failure
+- `knowledge_runtime` service scaffold, auth, Dockerfile, and initial tests already exist
+- Backend parity/integration tests already cover local vs remote route switching at the gateway level
+
+This plan supersedes only the old "independent `knowledge_runtime` reimplementation" direction. It does not discard the already-landed transport and rollout safety work.
+
+## Fixed Decisions
+
+- Service name remains `knowledge_runtime`
+- Execution package name is `knowledge_engine`
+- `knowledge_engine` is a top-level package, not nested under Backend or `knowledge_runtime`
+- Backend remains the only control plane
+- `shared` remains protocol-only and must not contain execution logic
+- `knowledge_runtime` must not read Backend DB directly
+- `direct injection` stays in Backend
+- `restricted mediation` stays in Backend
+- Content transport remains `content_ref`, not raw bytes push
+- Remote execution remains switchable per operation:
+  - index
+  - query
+  - delete
+- Default first index family remains `chunk_vector`
+- `summary_vector_index` remains out of scope, but protocol and engine seams must remain ready for it
+- Do not split the whole Backend `knowledge` module
+- Only extract the RAG execution kernel
+
+## Target File Areas
+
+### Shared protocol
+
+- `shared/models/knowledge_runtime_protocol.py`
+- `shared/models/__init__.py`
+- `shared/tests/test_knowledge_runtime_protocol.py`
+
+### New execution package
+
+- `knowledge_engine/pyproject.toml`
+- `knowledge_engine/knowledge_engine/`
+- `knowledge_engine/tests/`
+
+### Backend
+
+- `backend/pyproject.toml`
+- `backend/app/services/rag/runtime_specs.py`
+- `backend/app/services/rag/runtime_resolver.py`
+- `backend/app/services/rag/gateway.py`
+- `backend/app/services/rag/local_gateway.py`
+- `backend/app/services/rag/remote_gateway.py`
+- `backend/app/services/rag/local_data_plane/`
+- `backend/app/services/rag/retrieval_service.py`
+- `backend/app/services/knowledge/indexing.py`
+- `backend/app/services/knowledge/knowledge_service.py`
+- `backend/app/api/endpoints/internal/rag.py`
+- `backend/tests/services/rag/`
+- `backend/tests/integration/test_rag_remote_mode.py`
+
+### Runtime service
+
+- `knowledge_runtime/pyproject.toml`
+- `knowledge_runtime/knowledge_runtime/services/`
+- `knowledge_runtime/knowledge_runtime/api/internal/`
+- `knowledge_runtime/tests/`
+
+### Deployment
+
+- `docker/knowledge_runtime/Dockerfile`
+- `docker-compose-rag.yml`
+
+---
+
+### Task 1: Realign docs and package boundaries around `knowledge_engine`
+
+**Files:**
+- Modify: [2026-04-03-rag-service-extraction-design.md](/Users/yanhe1/iDev/weibo/wegent/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md)
+- Modify: [2026-04-04-rag-service-extraction-implementation-plan.md](/Users/yanhe1/iDev/weibo/wegent/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md)
+
+- [ ] Update the design and plan to treat `knowledge_engine` as the current execution-kernel extraction target
+- [ ] State explicitly that Backend keeps control-plane logic and does not split the full `knowledge` module
+- [ ] State explicitly that `knowledge_runtime` becomes a thin remote adapter over `knowledge_engine`
+- [ ] Keep the already-accepted boundaries unchanged:
+  - protocol in `shared`
+  - `content_ref` transfer
+  - `direct injection` in Backend
+  - `restricted mediation` in Backend
+
+**Verification**
+
+Run: `rg -n "independent|独立重写|future optional extraction|不拆整个 Backend \`knowledge\` 模块" docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md`
+Expected: old independent-rewrite wording is gone or only appears as historical comparison
+
+### Task 2: Scaffold top-level `knowledge_engine` package
+
+**Files:**
+- Create: `knowledge_engine/pyproject.toml`
+- Create: `knowledge_engine/knowledge_engine/__init__.py`
+- Create: `knowledge_engine/knowledge_engine/storage/`
+- Create: `knowledge_engine/knowledge_engine/embedding/`
+- Create: `knowledge_engine/knowledge_engine/splitter/`
+- Create: `knowledge_engine/knowledge_engine/index/`
+- Create: `knowledge_engine/knowledge_engine/query/`
+- Create: `knowledge_engine/knowledge_engine/services/`
+- Create: `knowledge_engine/tests/test_imports.py`
+- Modify: `backend/pyproject.toml`
+- Modify: `knowledge_runtime/pyproject.toml`
+
+- [ ] Create `knowledge_engine` as a standalone Python package following the same `pyproject + package dir` pattern already used by other Python modules
+- [ ] Add local path dependencies so Backend and `knowledge_runtime` can both import `knowledge_engine`
+- [ ] Keep `knowledge_engine` dependency surface minimal:
+  - storage backend implementations
+  - embedding helpers
+  - splitter helpers
+  - index/query/delete execution services
+- [ ] Do not add Backend-only API, ORM, or CRD modules into `knowledge_engine`
+
+**Verification**
+
+Run: `uv run --project knowledge_engine --group dev pytest knowledge_engine/tests/test_imports.py -v`
+Expected: PASS
+
+### Task 3: Extract backend-agnostic indexing and delete execution into `knowledge_engine`
+
+**Files:**
+- Move or copy into `knowledge_engine` and then switch imports:
+  - `backend/app/services/rag/document_service.py`
+  - `backend/app/services/rag/index/`
+  - `backend/app/services/rag/storage/`
+  - `backend/app/services/rag/embedding/`
+  - `backend/app/services/rag/splitter/`
+- Modify: `backend/app/services/rag/local_data_plane/indexing.py`
+- Modify: `backend/tests/services/rag/test_local_data_plane_indexing.py`
+- Modify: `backend/tests/services/rag/test_local_gateway.py`
+
+- [ ] Move pure execution logic first, starting with index and delete paths that already have clearer backend-agnostic seams
+- [ ] Preserve current storage backend behavior and naming rules
+- [ ] Keep Backend local indexing/delete behavior unchanged from the caller's perspective
+- [ ] Replace Backend imports so local execution flows now call `knowledge_engine`
+- [ ] Do not move retriever lookup or KB metadata lookup into `knowledge_engine`
+
+**Verification**
+
+Run: `uv run --project backend --group dev pytest backend/tests/services/rag/test_local_data_plane_indexing.py backend/tests/services/rag/test_local_gateway.py -v`
+Expected: PASS
+
+### Task 4: Define real remote execution config in `shared`
+
+**Files:**
+- Modify: `shared/models/knowledge_runtime_protocol.py`
+- Modify: `shared/models/__init__.py`
+- Modify: `shared/tests/test_knowledge_runtime_protocol.py`
+- Modify: `backend/app/services/rag/runtime_specs.py`
+- Modify: `backend/tests/services/rag/test_runtime_specs.py`
+
+- [ ] Extend remote protocol so query and delete no longer depend on Backend-side DB lookup after the request leaves Backend
+- [ ] Keep the request model split explicit:
+  - transport-only fields
+  - execution config fields
+  - future extension fields
+- [ ] Add normalized execution config for at least:
+  - retriever identity and storage config
+  - embedding model config where required by index
+  - retrieval mode / top_k / score threshold or equivalent normalized query config
+  - enabled index families
+  - retrieval policy
+- [ ] Keep direct-injection-only fields out of remote protocol
+
+**Verification**
+
+Run: `uv run --project shared --group dev pytest shared/tests/test_knowledge_runtime_protocol.py -v`
+Expected: PASS
+
+### Task 5: Build Backend runtime-config resolvers for local and remote execution
+
+**Files:**
+- Modify: `backend/app/services/rag/runtime_resolver.py`
+- Modify: `backend/app/services/rag/retrieval_service.py`
+- Modify: `backend/app/services/knowledge/indexing.py`
+- Modify: `backend/app/services/knowledge/knowledge_service.py`
+- Modify: `backend/tests/services/rag/test_runtime_resolver.py`
+- Modify: `backend/tests/services/rag/test_retrieval_service.py`
+
+- [ ] Keep Backend responsible for turning CRD + KB metadata into normalized runtime execution config
+- [ ] Add explicit builder paths for:
+  - index execution config
+  - remote rag query execution config
+  - delete execution config
+- [ ] Preserve current Backend-only route decision flow:
+  - auto route resolution
+  - direct injection budgeting
+  - restricted mediation
+- [ ] Ensure Backend local execution and remote execution are fed from the same normalized config wherever possible
+
+**Verification**
+
+Run: `uv run --project backend --group dev pytest backend/tests/services/rag/test_runtime_resolver.py backend/tests/services/rag/test_retrieval_service.py -v`
+Expected: PASS
+
+### Task 6: Implement `knowledge_engine` real query executor
+
+**Files:**
+- Create: `knowledge_engine/knowledge_engine/query/executor.py`
+- Create or modify: `knowledge_engine/knowledge_engine/query/`
+- Modify: `knowledge_engine/tests/`
+- Modify: `backend/app/services/rag/retrieval_service.py`
+
+- [ ] Add a backend-agnostic query executor in `knowledge_engine` for standard RAG retrieval
+- [ ] Reuse existing storage backend abstractions instead of calling Backend retrieval orchestration directly
+- [ ] Limit this executor to true RAG retrieval only
+- [ ] Keep these concerns out of `knowledge_engine.query`:
+  - direct injection
+  - restricted mediation
+  - persistence side effects
+  - chat-specific route decisions
+- [ ] Make Backend `RetrievalService` continue to own chat-specific orchestration while delegating pure retrieval execution where appropriate
+
+**Verification**
+
+Run: `uv run --project knowledge_engine --group dev pytest knowledge_engine/tests -k query -v`
+Expected: PASS
+
+### Task 7: Make `knowledge_runtime` a thin adapter over `knowledge_engine`
+
+**Files:**
+- Modify: `knowledge_runtime/knowledge_runtime/services/handlers.py`
+- Modify: `knowledge_runtime/knowledge_runtime/services/content_fetcher.py`
+- Modify: `knowledge_runtime/knowledge_runtime/api/internal/rag.py`
+- Modify: `knowledge_runtime/tests/test_internal_rag.py`
+
+- [ ] Replace placeholder handler behavior with real `knowledge_engine` execution calls
+- [ ] Keep `knowledge_runtime` responsibilities narrow:
+  - auth validation
+  - `content_ref` fetch
+  - request validation
+  - translating protocol models into `knowledge_engine` inputs
+  - translating results back into protocol responses
+- [ ] Do not let `knowledge_runtime` perform retriever lookup from Backend DB
+- [ ] Query must now return true retrieval results from the configured vector backend
+
+**Verification**
+
+Run: `uv run --project knowledge_runtime --group dev pytest knowledge_runtime/tests/test_internal_rag.py -v`
+Expected: PASS
+
+### Task 8: Update Backend remote gateway to send real execution config
+
+**Files:**
+- Modify: `backend/app/services/rag/remote_gateway.py`
+- Modify: `backend/app/services/rag/gateway_factory.py`
+- Modify: `backend/app/api/endpoints/internal/rag.py`
+- Modify: `backend/tests/services/rag/test_remote_gateway.py`
+- Modify: `backend/tests/api/endpoints/internal/test_rag_retrieve_endpoint.py`
+
+- [ ] Update remote gateway payload mapping to include the new normalized execution config
+- [ ] Preserve `RAG_RUNTIME_MODE` behavior by operation
+- [ ] Preserve Backend ownership of `auto -> direct_injection` finalization
+- [ ] Preserve local fallback when structured remote errors occur
+- [ ] Ensure `rag_retrieval` remote path now hits real `knowledge_engine`-backed execution
+
+**Verification**
+
+Run: `uv run --project backend --group dev pytest backend/tests/services/rag/test_remote_gateway.py backend/tests/api/endpoints/internal/test_rag_retrieve_endpoint.py -v`
+Expected: PASS
+
+### Task 9: Expand parity and end-to-end verification
+
+**Files:**
+- Modify: `backend/tests/integration/test_rag_remote_mode.py`
+- Modify: `knowledge_runtime/tests/`
+- Modify or add focused tests under `backend/tests/services/rag/`
+
+- [ ] Keep the current local/remote route-switch parity coverage
+- [ ] Add a real index -> remote query -> delete round-trip test path against mocked or isolated storage dependencies at the engine boundary
+- [ ] Verify that:
+  - local query still works
+  - remote query now returns real indexed results
+  - remote failure still falls back to local where designed
+  - index/query/delete remain independently switchable
+- [ ] Add regression coverage that protects against reintroducing Backend DB dependency into `knowledge_runtime`
+
+**Verification**
+
+Run: `uv run --project backend --group dev pytest backend/tests/integration/test_rag_remote_mode.py -v`
+Expected: PASS
+
+### Task 10: Align containerization and local runtime wiring
+
+**Files:**
+- Modify: `docker/knowledge_runtime/Dockerfile`
+- Modify: `docker-compose-rag.yml`
+
+- [ ] Ensure `knowledge_runtime` image installs both `wegent-shared` and `knowledge_engine`
+- [ ] Keep base image and startup style aligned with Backend / Chat Shell conventions
+- [ ] Copy top-level package directories needed for local editable installs
+- [ ] Keep healthcheck and service wiring valid after the package split
+
+**Verification**
+
+Run: `docker compose --profile rag -f docker-compose-rag.yml config`
+Expected: valid compose output including `knowledge_runtime` and the new package wiring
+
+## Rollout Guidance
+
+- Keep `RAG_RUNTIME_MODE=local` as the default during extraction
+- First prove that Backend local now runs through `knowledge_engine`
+- Then enable remote index
+- Then enable remote query backed by real `knowledge_engine` execution
+- Keep remote query fallback to local until parity confidence is high
+- Enable remote delete last
+- Remove Backend local entrypoints only after remote paths are proven stable
+
+## Final Verification Checklist
+
+- `uv run --project shared --group dev pytest shared/tests/test_knowledge_runtime_protocol.py -v`
+- `uv run --project knowledge_engine --group dev pytest knowledge_engine/tests -v`
+- `uv run --project backend --group dev pytest backend/tests/services/rag backend/tests/api/endpoints/internal backend/tests/integration/test_rag_remote_mode.py -v`
+- `uv run --project knowledge_runtime --group dev pytest knowledge_runtime/tests -v`
+- `docker compose --profile rag -f docker-compose-rag.yml config`
+
+## Guardrails
+
+- Do not move permissions, namespace rules, or metadata queries into `knowledge_engine`
+- Do not move `direct injection` into `knowledge_runtime` or `knowledge_engine`
+- Do not move `restricted mediation` into `knowledge_runtime` or `knowledge_engine`
+- Do not make `knowledge_runtime` read Backend DB
+- Do not push raw document bytes from Backend to `knowledge_runtime`
+- Do not split the entire Backend `knowledge` module
+- Do ensure remote query/delete requests carry enough runtime config for true independent execution
+- Do ensure `knowledge_engine` remains consumable by both Backend and `knowledge_runtime`
diff --git a/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md b/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
index e8527d367..02c16cd69 100644
--- a/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
+++ b/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
@@ -2,7 +2,7 @@
 sidebar_position: 1
 ---
 
-# RAG Service 独立化设计
+# Knowledge Runtime 独立化设计
 
 ## 背景
 
@@ -15,14 +15,21 @@ sidebar_position: 1
 - `LocalRagGateway`
 - `local_data_plane`
 
-在该基础上，下一步不再是继续做进程内重构，而是将 RAG 数据面执行能力进一步抽为可独立启动的 `rag_service`，同时保留灰度与回退能力。
+在该基础上，最初本轮曾尝试采用“先稳定服务边界，再让 `knowledge_runtime` 独立补齐执行能力”的路线。但随着 remote query 进入真实实现阶段，这条路径暴露出一个更直接的问题：
 
-本设计聚焦：
+- 如果 `knowledge_runtime` 不复用现有底层执行能力，就需要在新服务中重新实现一遍解析、chunking、embedding、storage backend、query / delete 逻辑
+- 一旦进入真实索引与真实检索，这种“独立重写”会快速偏离原本希望保留的 `knowledge_engine` 目标
+- 为了让 `knowledge_runtime` 真正独立执行，又不能访问 Backend DB，remote contract 也必须承载更完整的运行时配置
 
-- 将解析、切分、embedding、索引、检索、删索引迁移到独立 `rag_service`
-- Backend 保持唯一控制面
-- 不将 `direct injection` 与 `restricted mediation` 下沉到 `rag_service`
-- 为 `summary_vector_index` 和后续多索引族检索路线预留稳定扩展点
+因此本文修订后的核心方向是：
+
+- 将跨服务协议放入 `shared`
+- 保持 Backend 继续担任唯一 control plane
+- 从 Backend 中抽离 RAG data-plane execution kernel，作为 `knowledge_engine`
+- Backend local 与 `knowledge_runtime` remote 都依赖同一套 `knowledge_engine`
+- 稳定 remote 后，再删除 Backend 原有 local 入口层
+
+为避免与此前讨论断层，本文中的历史术语 `rag_service`，在目标形态中统一对应 `knowledge_runtime`。
 
 ## 状态快照
 
@@ -50,21 +57,21 @@ sidebar_position: 1
 
 这意味着 Backend 镜像、依赖、启动成本和故障域仍然被重 RAG 能力绑定。
 
-### 2. 首版服务化如果边界错误，返工成本会更高
+### 2. 如果 `knowledge_runtime` 走“独立重写”，真实能力阶段会越来越偏离目标
+
+当 remote 还只是占位 handler 时，“独立重写”看上去更稳；但一旦进入真实 index / query / delete：
 
-如果直接将现有执行逻辑整体通过 HTTP 暴露，而不先明确稳定 contract，会把以下不稳定点固化进服务协议：
+- 需要重新接入现有向量库与 embedding 模型
+- 需要复刻 `DocumentService`、storage backend、retriever 解析相关逻辑
+- 需要再次处理 local / remote 语义对齐
 
-- 文件内容如何获取
-- 执行面是否关心权限与 CRD
-- `direct injection` 是否属于检索服务职责
-- `restricted mediation` 是否属于 RAG 服务职责
-- 未来 `summary_vector_index`、`tableRAG` 如何接入
+这会让实现逐步变成“Backend 有一套真实底层，`knowledge_runtime` 再有一套相似底层”，不仅重复度高，也与“尽快形成可复用的 `knowledge_engine` 执行层”这一目标相冲突。
 
 ### 3. 文件内容传输方式会影响服务职责
 
 索引链路中的解析、切分、embedding 都依赖原始文档内容。
 
-如果 Backend 在每次索引时直接把文件 bytes 推给 `rag_service`：
+如果 Backend 在每次索引时直接把文件 bytes 推给 `knowledge_runtime`：
 
 - Backend 会重新成为大文件中转站
 - 异步重试与 reindex 模型会变差
@@ -72,89 +79,117 @@ sidebar_position: 1
 
 因此，首版必须优先稳定“内容引用 contract”，而不是只追求“是否能传得动文件”。
 
+### 4. “拆整个 knowledge 模块”范围过大，但“抽 execution kernel”是合理边界
+
+当前真正需要迁移的不是整个 Backend `knowledge` 模块，而是其中的 RAG data-plane execution 部分。
+
+如果直接拆整个 `knowledge` 模块：
+
+- 会把权限、元数据、状态机、摘要触发等 control-plane 逻辑一起卷入
+- 会放大数据库、ORM、业务规则迁移范围
+- 会让本轮目标从“拆服务”膨胀成“重构知识模块”
+
+更合理的边界是只抽出 execution kernel，使其：
+
+- 不依赖 Backend DB model
+- 只消费归一化后的 runtime config
+- 同时服务于 Backend local 和 `knowledge_runtime` remote
+
 ## 目标
 
-- 将 RAG 数据面执行迁移为可独立启动的 `rag_service`
+- 将 RAG 数据面执行迁移为可独立启动的 `knowledge_runtime`
 - 保持 Backend 作为唯一 control plane
 - 保持 `chat_shell` 和其他消费者继续只面向 Backend 调用
 - 通过 `RagGateway` 支持 local / remote 双实现切换
-- 建立统一 remote contract，使 `LocalRagGateway` 与 `RemoteRagGateway` 语义一致
-- 以 `index_family` 预留 `summary_vector_index` 扩展能力
+- 将跨服务 transport schema 放入 `shared`
+- 将 Backend 当前 RAG data-plane execution 抽离为可复用的 `knowledge_engine`
+- 让 Backend local 与 `knowledge_runtime` remote 共用同一套底层执行能力
+- 以 `index_family` 与 `retrieval_policy` 预留 `summary_vector_index` 扩展能力
 
 ## 非目标
 
 - 本轮不实现知识库 MCP `search`
-- 本轮不让 `rag_service` 直接访问 Backend 数据库
-- 本轮不把附件存储实现复制到 `rag_service`
-- 本轮不将 `direct injection` 下沉到 `rag_service`
-- 本轮不将 `restricted mediation` 下沉到 `rag_service`
+- 本轮不让 `knowledge_runtime` 直接访问 Backend 数据库
+- 本轮不把附件存储实现复制到 `knowledge_runtime`
+- 本轮不将 `direct injection` 下沉到 `knowledge_runtime`
+- 本轮不将 `restricted mediation` 下沉到 `knowledge_runtime`
+- 本轮不拆整个 Backend `knowledge` 模块
+- 本轮不实现 `summary_vector_index`
 - 本轮不决定 `tableRAG` 的具体底层存储和协议
 
 ## 方案对比
 
-### 方案 1：一次性切换到远程 `rag_service`
+### 方案 1：`shared` 轻协议 + Backend local 基本不动 + `knowledge_runtime` 独立重写
 
 做法：
 
-- Backend 直接移除本地执行主路径
-- 索引、检索、删除全部切到独立 `rag_service`
+- 将 remote contract 抽到 `shared`
+- Backend local 数据面只做必要的协议对齐和 remote 接入改动
+- `knowledge_runtime` 参考现有 Backend 数据面语义独立实现
+- Backend 通过配置决定索引/检索/删除走 local 或 remote
 
 优点：
 
-- 目标形态直接
-- Backend 能最快变轻
+- 服务拆分风险最低
+- local 模式回归面最小
+- 灰度和回滚边界清晰
+- 更容易区分“服务化问题”和“执行逻辑问题”
 
 缺点：
 
-- 首版风险最大
-- 出问题时回退困难
-- 远程 contract、内容获取、鉴权、回写、观测会在同一轮同时收敛
+- 进入真实执行阶段后会持续复制底层实现
+- local / remote 语义漂移风险更高
+- 与 `knowledge_engine` 目标背离
 
-### 方案 2：双实现网关，先落远程能力，再灰度切换
+### 方案 2：先抽 execution kernel，再让 local / remote 共用
 
 做法：
 
-- 保留 `LocalRagGateway`
-- 新增 `RemoteRagGateway`
-- Backend 继续解析 `RuntimeSpec`
-- 通过配置决定索引/检索/删除走 local 或 remote
+- 先从 Backend 中抽出 `knowledge_engine`
+- `knowledge_engine` 只承载解析、chunking、embedding、storage backend、index / query / delete 执行
+- Backend local 和 `knowledge_runtime` 都依赖这套共享实现
+- Backend 继续负责 control-plane 编排与 remote contract 归一化
 
 优点：
 
-- 与现有代码边界最一致
-- 支持渐进灰度
-- 允许快速回退到 local
+- 更符合“尽快形成底层可复用执行层”的目标
+- `knowledge_runtime` 更容易尽快接上真实向量库
+- 可以避免 runtime 为了真实 query 再重写一套底层
 
 缺点：
 
-- 过渡期会同时维护 local / remote 两套执行入口
+- 需要当轮处理 Python 包边界
+- 需要为 remote contract 补充足够的运行时配置
+- 抽离边界如果过宽，容易误伤 control plane
 
-### 方案 3：仅先拆索引，检索继续留在本地
+### 方案 3：一次性切到远程 `knowledge_runtime`
 
 做法：
 
-- 解析、切分、embedding、索引迁到 `rag_service`
-- 检索仍由本地 data-plane 执行
+- Backend 直接移除本地执行主路径
+- 索引、检索、删除全部切到独立 `knowledge_runtime`
 
 优点：
 
-- 优先拆出最重的文件处理链路
+- 目标形态最直接
+- Backend 最快变轻
 
 缺点：
 
-- 会形成“写远程、读本地”的阶段性混合边界
-- 后续还要再做一次检索远程化收敛
+- 首版风险最高
+- 回退困难
+- 远程 contract、内容获取、鉴权、回写、观测会在同一轮同时收敛
 
 ## 选型
 
-采用方案 2。
+采用方案 2，但只抽 execution kernel，不拆整个 `knowledge` 模块。
 
 原因：
 
-- 当前 `RuntimeSpec + RagGateway` 已经形成天然过渡层
-- 可以先稳定远程 contract，再决定何时切换默认路径
-- 能在不打断现有 `chat_shell` 调用面的前提下完成服务化
-- 风险和收益平衡最好
+- 继续走“独立重写”会让真实 query / delete 越做越偏
+- 用户当前更重视尽快复刻底层 `knowledge_engine` 能力，而不是继续维持两套相似实现
+- 真正需要复用的是 RAG execution kernel，而不是整个 `knowledge` 模块
+- 只要抽取边界足够窄，仍然可以把 control-plane 稳定留在 Backend
 
 ## 总体设计
 
@@ -166,12 +201,30 @@ chat_shell / public API / future MCP consumers
      -> RagRuntimeResolver
      -> RagGateway
         -> LocalRagGateway | RemoteRagGateway
-           -> local_data_plane | rag_service
+           -> knowledge_engine | knowledge_runtime -> knowledge_engine
 ```
 
+### `shared` 职责
+
+`shared` 只承载轻协议，建议放置：
+
+- remote request / response schema
+- `content_ref` schema
+- internal auth header / token schema
+- error code / error payload schema
+
+`shared` 不承载：
+
+- 文档解析逻辑
+- embedding / 索引执行逻辑
+- Backend 编排逻辑
+- `direct injection` / `restricted mediation` 语义
+
+换句话说，`shared` 在本轮是 transport contract 层，不是执行层。
+
 ### Backend 职责
 
-Backend 保留：
+Backend 始终保留：
 
 - 权限、多租户、namespace、group / personal 规则
 - `KnowledgeBase` / `KnowledgeDocument` 元数据
@@ -181,25 +234,43 @@ Backend 保留：
 - `restricted mediation`
 - 对 `chat_shell` 和其他消费者暴露统一 API
 
-Backend 不负责：
+迁移期内，Backend 还负责：
 
-- 文档解析实现
-- chunking / embedding / 索引写入执行
-- 远程数据面中的向量检索执行
+- 将 CRD / 元数据解析为归一化 runtime config
+- 为 remote 请求补齐执行所需配置，而不是让 `knowledge_runtime` 反查 Backend DB
+- 保留 local fallback 与回滚能力
 
-### rag_service 职责
+Backend 不再直接承载长期演进的底层 RAG execution 细节；这些能力应沉到 `knowledge_engine`。
 
-`rag_service` 只负责执行面：
+### `knowledge_engine` 职责
+
+`knowledge_engine` 是本轮应落地的 execution kernel，职责包括：
 
-- 文档内容拉取
 - 文档解析
 - splitter
 - embedding
-- index family 写入
+- storage backend 选择与调用
+- index 执行
 - query 执行
-- document index 删除
+- delete document index 执行
+
+`knowledge_engine` 必须满足：
+
+- 不依赖 Backend ORM model
+- 不读取 Backend DB
+- 只消费归一化后的 runtime config 与外部传入资源
+- 可被 Backend local 与 `knowledge_runtime` remote 共用
+
+### `knowledge_runtime` 职责
+
+`knowledge_runtime` 是新的内部执行服务，只负责 remote 数据面执行：
 
-`rag_service` 不负责：
+- 文档内容拉取
+- 将 remote request 转换为 `knowledge_engine` 可执行输入
+- 调用 `knowledge_engine` 执行真实 index / query / delete
+- 返回协议化结果
+
+`knowledge_runtime` 不负责：
 
 - 权限判断
 - CRD 查询与解析
@@ -208,12 +279,31 @@ Backend 不负责：
 - `direct injection` 消费编排
 - `restricted mediation`
 
+### 关于 `knowledge_engine`
+
+`knowledge_engine` 在修订后的方案中不再是“未来可选抽取”，而是当前应落地的窄边界 execution kernel。
+
+它不是：
+
+- 整个 knowledge 模块
+- control plane
+- 轻协议层
+
+它只是：
+
+- Backend local 与 `knowledge_runtime` remote 共同依赖的底层执行库
+
+如果未来 Backend 不再保留 local mode，那么 `knowledge_engine` 可以继续只作为 `knowledge_runtime` 的内部组成部分存在；如果未来需要单独发布，再评估是否独立成 package / repo。
+
 ### 稳定边界原则
 
-真正稳定的边界不是“文件存在哪里”，而是：
+真正稳定的边界不是“服务是否独立重写”，而是：
 
-- Backend 传递 runtime contract 和 content reference
-- `rag_service` 只消费这些 contract，不理解数据库语义
+- Backend 传递 runtime contract 和 `content_ref`
+- `shared` 提供稳定 transport schema
+- `knowledge_engine` 只消费归一化 runtime config，不理解数据库语义
+- `knowledge_runtime` 不理解 Backend DB 与 control-plane 规则
+- Backend local 与 remote 在相同 execution kernel 或相同 contract 下保持语义一致
 
 ## 内容获取设计
 
@@ -228,7 +318,7 @@ Backend 不负责：
 
 因此，首版不以“直推 bytes”作为主路径。
 
-### 采用 content_ref 拉取模式
+### 采用 `content_ref` 拉取模式
 
 索引请求只传内容引用，不直接传大文件内容。
 
@@ -247,12 +337,12 @@ content_ref
 行为：
 
 - Backend 生成内部可鉴权下载 URL
-- `rag_service` 按引用回源拉取内容
+- `knowledge_runtime` 按引用回源拉取内容
 
 意义：
 
 - 保证兼容当前所有附件存储后端
-- 不需要把附件存储实现复制到 `rag_service`
+- 不需要把附件存储实现复制到 `knowledge_runtime`
 
 #### `presigned_url`
 
@@ -261,7 +351,7 @@ content_ref
 行为：
 
 - Backend 生成预签名 URL
-- `rag_service` 直接读取对象存储
+- `knowledge_runtime` 直接读取对象存储
 
 意义：
 
@@ -270,34 +360,46 @@ content_ref
 
 ### 首版解耦目标
 
-首版不追求“`rag_service` 完全不依赖 Backend 提供内容入口”。
+首版不追求“`knowledge_runtime` 完全不依赖 Backend 提供内容入口”。
 
 首版追求的是：
 
-- `rag_service` 不理解附件存储实现
+- `knowledge_runtime` 不理解附件存储实现
 - Backend 不承担长期 bytes push 中转职责
 - 索引重试可围绕统一 `content_ref` 复用
 
-## Runtime Contract 与 Remote Contract
+## Contract 设计
 
-### 本地 contract 保持不变
+### Backend 内部 contract 尽量保持不变
 
-Backend 内部继续使用：
+Backend 内部继续使用现有：
 
 - `IndexRuntimeSpec`
 - `QueryRuntimeSpec`
 
-### 引入远程 payload 映射层
+本轮只做必要扩展，不把 local 路径大改成另一套抽象。
+
+### 远程 contract 放入 `shared`
 
-`RemoteRagGateway` 负责将本地 runtime contract 转换为远程请求：
+`RemoteRagGateway` 与 `knowledge_runtime` 之间共享的 transport schema 放在 `shared`。
+
+建议包括：
 
 - `RemoteIndexRequest`
 - `RemoteQueryRequest`
 - `RemoteDeleteDocumentIndexRequest`
+- `RemoteQueryResult`
+- `ContentRef`
+- internal auth schema
+- remote error schema
+
+这样做的目的不是“共享执行逻辑”，而是：
 
-这样 `local` 与 `remote` 的差异只体现在 transport，而不体现在业务语义。
+- 统一跨服务协议
+- 降低 Backend 接入 remote 的改动面
+- 让 local / remote 行为对齐有共同的 contract 基线
 
-### RemoteIndexRequest
+### `RemoteIndexRequest`
 
 建议包含：
 
@@ -314,9 +416,9 @@ Backend 内部继续使用：
 说明：
 
 - Backend 在发请求前完成 retriever / embedding 配置展开
-- `rag_service` 不再按 name / namespace 回查 CRD
+- `knowledge_runtime` 不再按 name / namespace 回查 CRD
 
-### RemoteQueryRequest
+### `RemoteQueryRequest`
 
 建议尽量贴近 `QueryRuntimeSpec`，包含：
 
@@ -333,9 +435,8 @@ Backend 内部继续使用：
 - `retrieval_policy` 是稳定的远程执行语义，不应直接暴露 chat-specific 的消费策略
 - 当前 `QueryRuntimeSpec` 中已有的 `route_mode` / `direct_injection_budget` 可以在 Backend 内部继续保留，作为 control-plane 路由输入
 - 首版 `RemoteQueryRequest` 不把 `direct injection` 决策语义固化进服务 contract
-- `retrieval_policy` 用于未来扩展多索引族路线
 
-### RemoteDeleteDocumentIndexRequest
+### `RemoteDeleteDocumentIndexRequest`
 
 建议包含：
 
@@ -345,9 +446,9 @@ Backend 内部继续使用：
 - `retriever_config`
 - `enabled_index_families`
 
-## rag_service API 形态
+## `knowledge_runtime` API 形态
 
-首期仅提供三类内部执行接口：
+首期 `knowledge_runtime` 仅提供三类内部执行接口：
 
 - `POST /internal/rag/index`
 - `POST /internal/rag/query`
@@ -357,15 +458,15 @@ Backend 内部继续使用：
 
 - 不接收 DB session、ORM、CRD ref 查找请求
 - 不直接回写 Backend 数据库
-- 返回结构尽量与本地 gateway 一致
+- 返回结构与 `shared` 中的 response schema 对齐
 
 ## 多索引族扩展设计
 
-### 将 index family 作为一等概念
+### 将 `index_family` 作为一等概念
 
 不能将未来扩展继续围绕单一 `chunk_vector` 路径硬编码。
 
-从本轮开始，需将索引执行与检索执行都按 `index_family` 预留。
+从本轮开始，remote contract 与 `knowledge_runtime` 内部执行都要按 `index_family` 预留。
 
 ### 首期默认 family
 
@@ -378,17 +479,7 @@ Backend 内部继续使用：
 - `summary_vector`
 - 其他 family，例如 `table_rag`
 
-### IndexRuntimeSpec 扩展方向
-
-建议保持：
-
-- `index_families: ["chunk_vector"]`
-
-未来可扩：
-
-- `["chunk_vector", "summary_vector"]`
-
-### QueryRuntimeSpec 扩展方向
+### `QueryRuntimeSpec` / RemoteQuery 扩展位
 
 建议新增或保留扩展位：
 
@@ -404,7 +495,7 @@ Backend 内部继续使用：
 
 ### 结果来源标记
 
-`rag_service` 返回结果时应明确记录来源族，例如：
+`knowledge_runtime` 返回结果时应明确记录来源族，例如：
 
 - `index_family = chunk_vector`
 - `index_family = summary_vector`
@@ -415,9 +506,9 @@ Backend 内部继续使用：
 - 调试与观测
 - 不同消费方的结果解释
 
-## direct injection 与 restricted mediation 边界
+## `direct injection` 与 `restricted mediation` 边界
 
-### direct injection 保留在 Backend
+### `direct injection` 保留在 Backend
 
 `direct injection` 不是纯检索执行，而是消费编排决策。
 
@@ -427,188 +518,84 @@ Backend 内部继续使用：
 - 模型窗口与输出保留
 - 消费方的 prompt 注入方式
 
-因此首版不将其作为 `rag_service` 的主职责。
+因此首版不将其作为 `knowledge_runtime` 的主职责。
 
 允许的边界是：
 
-- `rag_service` 提供执行原语，例如常规 query 或 all-chunks 风格执行
+- `knowledge_runtime` 提供常规 query 执行
 - Backend 决定是否走 direct injection
 - Backend 决定如何包装结果供 `chat_shell` 消费
 
-### restricted mediation 保留在 Backend
+### `restricted mediation` 保留在 Backend
 
 `restricted mediation` 明确属于安全与权限策略，而不是检索能力。
 
 因此：
 
-- `rag_service` 返回原始检索候选
+- `knowledge_runtime` 返回原始检索候选
 - Backend 负责决定是否：
   - 直接返回
   - 做 safe-summary / restricted artifact
   - 拒绝输出
 
-不允许 `rag_service` 承担 policy decision。
-
-## 三条主链路
-
-### 索引链路
-
-```text
-Backend
-  -> 权限校验 / 元数据创建 / 任务调度
-  -> RagRuntimeResolver 构造 IndexRuntimeSpec
-  -> RemoteRagGateway 映射 RemoteIndexRequest
-  -> 生成 content_ref
-  -> rag_service 拉取内容并执行解析、切分、embedding、索引
-  -> 返回执行结果
-  -> Backend 回写 document 状态与 chunks，并触发摘要
-```
-
-Backend 回写内容包括：
-
-- `KnowledgeDocument.index_status`
-- `KnowledgeDocument.is_active`
-- `KnowledgeDocument.chunks`
-- 摘要任务触发
-
-### 检索链路
-
-```text
-chat_shell / other consumers
-  -> Backend API
-  -> 权限与目标解析
-  -> QueryRuntimeSpec
-  -> RagGateway(local|remote)
-  -> rag_service 执行 query
-  -> Backend 保持 direct injection / restricted / persistence 编排
-```
-
-首版中，检索远程化不改变以下责任归属：
-
-- direct injection routing：Backend
-- restricted mediation：Backend
-- retrieval persistence：Backend
-
-### 删除索引链路
-
-```text
-Backend
-  -> 基于文档与 KB 元数据决定删索引
-  -> 组装 remote delete request
-  -> rag_service 删除 index family 中的 document 数据
-  -> Backend 回写状态与后续清理
-```
-
-## Gateway 与迁移策略
-
-### 双实现网关
-
-保留：
-
-- `LocalRagGateway`
-
-新增：
-
-- `RemoteRagGateway`
-
-建议引入选择层：
-
-- `ConfigurableRagGateway`
-- 或 factory based gateway selector
-
-### 切换粒度
-
-建议按操作独立切换：
-
-- `index_mode = local | remote`
-- `query_mode = local | remote`
-- `delete_mode = local | remote`
-
-原因：
-
-- 索引与检索风险模型不同
-- 灰度与回退更细粒度
-
-### 推荐迁移顺序
-
-1. 落 `rag_service` 可启动骨架与 remote contract
-2. 实现 `RemoteRagGateway`，默认仍走 local
-3. 先灰度索引链路到 remote
-4. 索引稳定后灰度检索链路
-5. 最后灰度删除链路
-6. 稳定后评估是否降低 local data-plane 主路径权重
-
-### 回退策略
-
-- 任一操作异常时，可通过配置切回 local
-- 对上层 API 不做改动
-- 不引入双写、双查等高复杂度补偿逻辑
-
-## 观测与诊断
+不允许 `knowledge_runtime` 承担 policy decision。
 
-切换期必须显式区分 local / remote：
+## 迁移策略
 
-- `gateway_mode`
-- `operation = index | query | delete`
-- 远程请求耗时
-- 内容拉取耗时
-- 解析耗时
-- embedding 耗时
-- 向量写入耗时
-- 错误类型
-- 回退次数
+### Phase 1：抽出轻协议与 execution kernel 边界
 
-`rag_service` 返回错误时，建议区分：
+- 在 `shared` 中维持 `knowledge_runtime` remote contract
+- 明确 `knowledge_engine` 的输入输出边界
+- 不把 control-plane 规则带入 `knowledge_engine`
 
-- 内容获取失败
-- 解析失败
-- embedding 调用失败
-- 向量存储失败
-- 非法 contract
+### Phase 2：从 Backend 提取 `knowledge_engine`
 
-## 测试策略
+- 将 local data-plane 中可复用的底层执行能力抽入 `knowledge_engine`
+- Backend local 改为通过 `knowledge_engine` 执行
+- 保持 Backend 对外 API 与控制语义不变
 
-### Backend
+### Phase 3：让 `knowledge_runtime` 复用 `knowledge_engine`
 
-- `RagRuntimeResolver` contract 测试
-- `RemoteRagGateway` payload 映射测试
-- gateway 切换配置测试
-- 索引 / 检索 / 删除的回写测试
+- 新服务不独立重写底层 index / query / delete
+- `knowledge_runtime` 只负责协议接入、内容拉取、鉴权和结果序列化
+- remote request 必须携带足够的运行时配置，使 `knowledge_runtime` 无需读取 Backend DB
 
-### rag_service
+### Phase 4：通过 `RemoteRagGateway` 灰度接入
 
-- `content_ref` 解析测试
-- `backend_attachment_stream` 拉取测试
-- `presigned_url` 拉取测试
-- index / query / delete 接口测试
-- 多 index family executor 测试
+- Backend 增加 remote gateway
+- 索引、检索、删除按配置独立切换 local / remote
+- 保留 local fallback 与快速回退能力
 
-### 集成测试
+### Phase 5：稳定后删除 Backend local 入口层
 
-- local 模式回归
-- remote 模式端到端
-- remote 失败后切回 local
+- 当 remote 模式稳定后
+- 删除 Backend 中只用于本地执行的入口层
+- `knowledge_engine` 保留为 `knowledge_runtime` 的核心执行层
 
-## 风险
+## 风险与约束
 
-### 1. MySQL 附件场景仍依赖 Backend 在线
+### 1. `knowledge_engine` 抽取边界过宽会误伤 control plane
 
-这是首版有意识接受的现实约束。
+如果把权限、元数据、状态机、摘要触发也一起抽走：
 
-但该依赖只体现在：
+- 会扩大本轮迁移范围
+- 会引入 DB / ORM / 业务规则耦合
+- 会让服务拆分与业务重构绑定在一起
 
-- Backend 作为内容流入口
+因此必须坚持：
 
-不应扩散为：
+- 只抽 execution kernel
+- 不抽整个 `knowledge` 模块
 
-- `rag_service` 直接依赖 Backend 数据库
-- Backend 重新承担 bytes push 中转职责
+### 2. remote contract 如果不携带足够 runtime config，`knowledge_runtime` 仍无法独立执行
 
-### 2. contract 如果只围绕单一路径设计，未来会返工
+如果 remote query / delete 仍只传 KB id 和 query：
 
-如果远程 query contract 仍默认“只有 chunk_vector”，则 `summary_vector_index` 接入时会再次重做协议。
+- `knowledge_runtime` 仍需要回查 Backend 获取 retriever / storage / embedding 配置
+- 服务边界会重新退化
+- 真实 query 接入现有向量库时会卡住
 
-因此本轮必须将：
+因此本轮 remote contract 必须逐步承载足够的运行时配置，同时保留：
 
 - `index_family`
 - `retrieval_policy`
@@ -618,7 +605,7 @@ Backend
 
 ### 3. chat-specific 语义下沉会污染数据面
 
-如果把 `direct injection` 或 `restricted mediation` 迁入 `rag_service`：
+如果把 `direct injection` 或 `restricted mediation` 迁入 `knowledge_runtime`：
 
 - 数据面会耦合消费语义
 - MCP / chat_shell / future API 会共享错误边界
@@ -628,18 +615,22 @@ Backend
 ## 后续工作
 
 - 设计知识库 MCP `search`，将其作为 retrieval surface 的新增消费面
-- 将 `summary_vector_index` 接入 `index_family` 执行器体系
-- 为 `tableRAG` 设计独立 family / executor / query policy
+- 在 remote 稳定后删除 Backend local 入口层
+- 视独立发布需求评估是否将 `knowledge_engine` 单独 package 化
+- 将 `summary_vector_index` 接入 `index_family` 执行体系
+- 为 `tableRAG` 设计独立 family / query policy
 - 在对象存储场景中进一步减少 Backend 对内容拉取链路的参与
 
 ## 结论
 
-本轮采用“Backend 作为唯一控制面，`rag_service` 作为独立执行数据面”的路线，并通过 `RagGateway` 保持 local / remote 双实现切换。
+本轮修订后采用“`shared` 放轻协议、Backend 保留 control plane、抽离 `knowledge_engine` execution kernel、`knowledge_runtime` 复用该 kernel、通过 `RagGateway` 灰度接入 remote”的路线。
 
 首版最重要的设计结论是：
 
 - 不推文件 bytes，统一走 `content_ref`
-- 不将 `direct injection` 与 `restricted mediation` 下沉到 `rag_service`
-- 从首版开始按 `index_family` 设计，为 `summary_vector_index` 预留稳定空间
+- 不将 `direct injection` 与 `restricted mediation` 下沉到 `knowledge_runtime`
+- 不拆整个 Backend `knowledge` 模块，只抽 RAG execution kernel
+- 让 `knowledge_runtime` 尽快复用真实底层执行能力，而不是继续独立重写
+- 从首版开始按 `index_family` / `retrieval_policy` 设计，为 `summary_vector_index` 预留稳定空间
 
-这样可以在保持灰度和回退能力的同时，把未来 `summary_vector_index`、`tableRAG`、知识库 MCP search 等能力建立在更稳定的服务边界上。
+这样既能保留 Backend control-plane 稳定性，又能尽快让 `knowledge_runtime` 接入真实底层执行能力，避免后续继续维护两套相似的数据面实现。
diff --git a/knowledge_runtime/knowledge_runtime/__init__.py b/knowledge_runtime/knowledge_runtime/__init__.py
new file mode 100644
index 000000000..92bac2df9
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+__version__ = "1.0.0"
diff --git a/knowledge_runtime/knowledge_runtime/api/health.py b/knowledge_runtime/knowledge_runtime/api/health.py
new file mode 100644
index 000000000..9d8935736
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/api/health.py
@@ -0,0 +1,12 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from fastapi import APIRouter
+
+router = APIRouter()
+
+
+@router.get("/health")
+async def health() -> dict[str, str]:
+    return {"status": "ok"}
diff --git a/knowledge_runtime/knowledge_runtime/api/internal/__init__.py b/knowledge_runtime/knowledge_runtime/api/internal/__init__.py
new file mode 100644
index 000000000..defb94adf
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/api/internal/__init__.py
@@ -0,0 +1,3 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
diff --git a/knowledge_runtime/knowledge_runtime/api/internal/rag.py b/knowledge_runtime/knowledge_runtime/api/internal/rag.py
new file mode 100644
index 000000000..984fb9b22
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/api/internal/rag.py
@@ -0,0 +1,40 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from fastapi import APIRouter, Depends
+
+from knowledge_runtime.security import verify_internal_service_token
+from knowledge_runtime.services.handlers import runtime_handlers
+from shared.models.knowledge_runtime_protocol import (
+    RemoteDeleteDocumentIndexRequest,
+    RemoteIndexRequest,
+    RemoteQueryRequest,
+    RemoteQueryResponse,
+)
+
+router = APIRouter(prefix="/internal/rag", tags=["internal-rag"])
+
+
+@router.post("/index")
+async def index_document(
+    request: RemoteIndexRequest,
+    _: None = Depends(verify_internal_service_token),
+) -> dict:
+    return await runtime_handlers.index_document(request)
+
+
+@router.post("/query", response_model=RemoteQueryResponse)
+async def query(
+    request: RemoteQueryRequest,
+    _: None = Depends(verify_internal_service_token),
+) -> RemoteQueryResponse:
+    return await runtime_handlers.query(request)
+
+
+@router.post("/delete-document-index")
+async def delete_document_index(
+    request: RemoteDeleteDocumentIndexRequest,
+    _: None = Depends(verify_internal_service_token),
+) -> dict:
+    return await runtime_handlers.delete_document_index(request)
diff --git a/knowledge_runtime/knowledge_runtime/api/router.py b/knowledge_runtime/knowledge_runtime/api/router.py
new file mode 100644
index 000000000..85d6fda72
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/api/router.py
@@ -0,0 +1,12 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from fastapi import APIRouter
+
+from knowledge_runtime.api.health import router as health_router
+from knowledge_runtime.api.internal.rag import router as rag_router
+
+router = APIRouter()
+router.include_router(health_router)
+router.include_router(rag_router)
diff --git a/knowledge_runtime/knowledge_runtime/core/config.py b/knowledge_runtime/knowledge_runtime/core/config.py
new file mode 100644
index 000000000..5d0787145
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/core/config.py
@@ -0,0 +1,24 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):
+    PROJECT_NAME: str = "Knowledge Runtime"
+    VERSION: str = "1.0.0"
+    ENABLE_API_DOCS: bool = True
+    ENVIRONMENT: str = "development"
+    HOST: str = "0.0.0.0"
+    PORT: int = 8200
+    INTERNAL_SERVICE_TOKEN: str = "knowledge-runtime-token"
+
+    model_config = SettingsConfigDict(
+        env_file=".env",
+        env_file_encoding="utf-8",
+        extra="ignore",
+    )
+
+
+settings = Settings()
diff --git a/knowledge_runtime/knowledge_runtime/main.py b/knowledge_runtime/knowledge_runtime/main.py
new file mode 100644
index 000000000..8780a5d13
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/main.py
@@ -0,0 +1,24 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from fastapi import FastAPI
+
+from knowledge_runtime import __version__
+from knowledge_runtime.api.router import router as api_router
+from knowledge_runtime.core.config import settings
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(
+        title=settings.PROJECT_NAME,
+        version=__version__,
+        docs_url="/docs" if settings.ENABLE_API_DOCS else None,
+        redoc_url="/redoc" if settings.ENABLE_API_DOCS else None,
+        openapi_url="/openapi.json" if settings.ENABLE_API_DOCS else None,
+    )
+    app.include_router(api_router)
+    return app
+
+
+app = create_app()
diff --git a/knowledge_runtime/knowledge_runtime/security.py b/knowledge_runtime/knowledge_runtime/security.py
new file mode 100644
index 000000000..9aef6665b
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/security.py
@@ -0,0 +1,22 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from fastapi import Header, HTTPException, status
+
+from knowledge_runtime.core.config import settings
+
+
+def verify_internal_service_token(authorization: str = Header(default="")) -> None:
+    if not authorization.startswith("Bearer "):
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing or invalid Authorization header",
+        )
+
+    token = authorization[7:].strip()
+    if token != settings.INTERNAL_SERVICE_TOKEN:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid internal service token",
+        )
diff --git a/knowledge_runtime/knowledge_runtime/services/__init__.py b/knowledge_runtime/knowledge_runtime/services/__init__.py
new file mode 100644
index 000000000..defb94adf
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/services/__init__.py
@@ -0,0 +1,3 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
diff --git a/knowledge_runtime/knowledge_runtime/services/content_fetcher.py b/knowledge_runtime/knowledge_runtime/services/content_fetcher.py
new file mode 100644
index 000000000..394a14747
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/services/content_fetcher.py
@@ -0,0 +1,25 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import httpx
+
+from shared.models.knowledge_runtime_protocol import (
+    BackendAttachmentStreamContentRef,
+    PresignedUrlContentRef,
+)
+
+
+async def fetch_content(
+    content_ref: BackendAttachmentStreamContentRef | PresignedUrlContentRef,
+) -> bytes:
+    headers = {}
+    if content_ref.kind == "backend_attachment_stream":
+        headers["Authorization"] = f"Bearer {content_ref.auth_token}"
+
+    async with httpx.AsyncClient() as client:
+        response = await client.get(content_ref.url, headers=headers)
+        response.raise_for_status()
+        return response.content
diff --git a/knowledge_runtime/knowledge_runtime/services/handlers.py b/knowledge_runtime/knowledge_runtime/services/handlers.py
new file mode 100644
index 000000000..8a6b17be6
--- /dev/null
+++ b/knowledge_runtime/knowledge_runtime/services/handlers.py
@@ -0,0 +1,42 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from knowledge_runtime.services import content_fetcher
+from shared.models.knowledge_runtime_protocol import (
+    RemoteDeleteDocumentIndexRequest,
+    RemoteIndexRequest,
+    RemoteQueryRequest,
+    RemoteQueryResponse,
+)
+
+
+class RuntimeHandlers:
+    async def index_document(self, request: RemoteIndexRequest) -> dict:
+        content = await content_fetcher.fetch_content(request.content_ref)
+        return {
+            "status": "accepted",
+            "knowledge_id": str(request.knowledge_base_id),
+            "document_id": request.document_id,
+            "content_bytes": len(content),
+            "content_ref_kind": request.content_ref.kind,
+            "index_families": request.index_families,
+        }
+
+    async def query(self, request: RemoteQueryRequest) -> RemoteQueryResponse:
+        return RemoteQueryResponse(records=[], total=0, total_estimated_tokens=0)
+
+    async def delete_document_index(
+        self, request: RemoteDeleteDocumentIndexRequest
+    ) -> dict:
+        return {
+            "status": "accepted",
+            "knowledge_id": str(request.knowledge_base_id),
+            "doc_ref": request.document_ref,
+            "enabled_index_families": request.enabled_index_families,
+        }
+
+
+runtime_handlers = RuntimeHandlers()
diff --git a/knowledge_runtime/pyproject.toml b/knowledge_runtime/pyproject.toml
new file mode 100644
index 000000000..0dfbaba2e
--- /dev/null
+++ b/knowledge_runtime/pyproject.toml
@@ -0,0 +1,59 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+[project]
+name = "wegent-knowledge-runtime"
+version = "1.0.0"
+description = "Wegent Knowledge Runtime Service"
+requires-python = ">=3.10,<=3.13"
+dependencies = [
+    "fastapi>=0.109.1",
+    "uvicorn[standard]>=0.27.0",
+    "pydantic>=2.5.3",
+    "pydantic-settings>=2.1.0",
+    "python-dotenv>=1.0.0",
+    "httpx>=0.26.0",
+    "wegent-knowledge-engine",
+    "wegent-shared",
+]
+
+[tool.uv.sources]
+wegent-knowledge-engine = { path = "../knowledge_engine", editable = true }
+wegent-shared = { path = "../shared", editable = true }
+
+[dependency-groups]
+dev = [
+    "pytest>=9.0.1",
+    "pytest-asyncio>=1.3.0",
+    "pytest-cov>=7.0.0",
+    "pytest-mock>=3.15.1",
+    "black>=23.7.0",
+    "isort>=5.12.0",
+    "flake8>=6.0.0",
+]
+
+[tool.black]
+line-length = 88
+target-version = ['py310']
+
+[tool.isort]
+profile = "black"
+line_length = 88
+skip = [".venv", "venv"]
+skip_gitignore = true
+known_first_party = ["knowledge_runtime", "knowledge_engine", "shared"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+addopts = "-v --tb=short"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["knowledge_runtime"]
diff --git a/knowledge_runtime/start.sh b/knowledge_runtime/start.sh
new file mode 100644
index 000000000..5d74afd6f
--- /dev/null
+++ b/knowledge_runtime/start.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+set -e
+
+PORT="${PORT:-8200}"
+HOST="${HOST:-0.0.0.0}"
+
+uv run --project knowledge_runtime uvicorn knowledge_runtime.main:app \
+  --host "${HOST}" \
+  --port "${PORT}" \
+  --workers 1
diff --git a/knowledge_runtime/tests/test_health.py b/knowledge_runtime/tests/test_health.py
new file mode 100644
index 000000000..973b9c693
--- /dev/null
+++ b/knowledge_runtime/tests/test_health.py
@@ -0,0 +1,27 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from fastapi.testclient import TestClient
+
+from knowledge_runtime.main import create_app
+
+
+def test_health_endpoint() -> None:
+    client = TestClient(create_app())
+
+    response = client.get("/health")
+
+    assert response.status_code == 200
+    assert response.json()["status"] == "ok"
+
+
+def test_internal_route_rejects_missing_auth() -> None:
+    client = TestClient(create_app())
+
+    response = client.post(
+        "/internal/rag/query",
+        json={"knowledge_base_ids": [1], "query": "release checklist"},
+    )
+
+    assert response.status_code == 401
diff --git a/knowledge_runtime/tests/test_internal_rag.py b/knowledge_runtime/tests/test_internal_rag.py
new file mode 100644
index 000000000..3a66cf0c0
--- /dev/null
+++ b/knowledge_runtime/tests/test_internal_rag.py
@@ -0,0 +1,166 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from unittest.mock import Mock
+
+import pytest
+from fastapi.testclient import TestClient
+
+from knowledge_runtime.main import create_app
+from knowledge_runtime.services.content_fetcher import fetch_content
+from shared.models.knowledge_runtime_protocol import (
+    BackendAttachmentStreamContentRef,
+    PresignedUrlContentRef,
+)
+
+
+def _auth_headers() -> dict[str, str]:
+    return {"Authorization": "Bearer knowledge-runtime-token"}
+
+
+@pytest.mark.asyncio
+async def test_fetch_content_uses_backend_stream_auth_header(mocker) -> None:
+    response = Mock()
+    response.content = b"release plan"
+    response.raise_for_status = Mock()
+    get_mock = mocker.patch("httpx.AsyncClient.get", return_value=response)
+
+    content = await fetch_content(
+        BackendAttachmentStreamContentRef(
+            kind="backend_attachment_stream",
+            url="http://backend/api/internal/rag/content/12",
+            auth_token="download-token",
+        )
+    )
+
+    assert content == b"release plan"
+    get_mock.assert_awaited_once_with(
+        "http://backend/api/internal/rag/content/12",
+        headers={"Authorization": "Bearer download-token"},
+    )
+
+
+@pytest.mark.asyncio
+async def test_fetch_content_uses_presigned_url_without_auth_header(mocker) -> None:
+    response = Mock()
+    response.content = b"knowledge file"
+    response.raise_for_status = Mock()
+    get_mock = mocker.patch("httpx.AsyncClient.get", return_value=response)
+
+    content = await fetch_content(
+        PresignedUrlContentRef(
+            kind="presigned_url",
+            url="https://storage.example.com/object",
+        )
+    )
+
+    assert content == b"knowledge file"
+    get_mock.assert_awaited_once_with(
+        "https://storage.example.com/object",
+        headers={},
+    )
+
+
+def test_index_route_fetches_content_and_returns_execution_metadata(mocker) -> None:
+    client = TestClient(create_app())
+    fetch_mock = mocker.patch(
+        "knowledge_runtime.services.handlers.content_fetcher.fetch_content",
+        return_value=b"indexed text",
+    )
+
+    response = client.post(
+        "/internal/rag/index",
+        headers=_auth_headers(),
+        json={
+            "knowledge_base_id": 101,
+            "document_id": 202,
+            "index_owner_user_id": 303,
+            "retriever_config": {"name": "default-retriever"},
+            "embedding_model_config": {"model_name": "text-embedding-3-small"},
+            "content_ref": {
+                "kind": "presigned_url",
+                "url": "https://storage.example.com/release-notes.md",
+            },
+            "index_families": ["chunk_vector", "summary_vector_index"],
+        },
+    )
+
+    assert response.status_code == 200
+    assert response.json() == {
+        "status": "accepted",
+        "knowledge_id": "101",
+        "document_id": 202,
+        "content_bytes": len(b"indexed text"),
+        "content_ref_kind": "presigned_url",
+        "index_families": ["chunk_vector", "summary_vector_index"],
+    }
+    fetch_mock.assert_awaited_once()
+
+
+def test_delete_route_returns_enabled_index_families() -> None:
+    client = TestClient(create_app())
+
+    response = client.post(
+        "/internal/rag/delete-document-index",
+        headers=_auth_headers(),
+        json={
+            "knowledge_base_id": 101,
+            "document_ref": "202",
+            "retriever_config": {
+                "name": "default-retriever",
+                "namespace": "default",
+                "storage_config": {"type": "qdrant"},
+            },
+            "enabled_index_families": [
+                "chunk_vector",
+                "summary_vector_index",
+            ],
+        },
+    )
+
+    assert response.status_code == 200
+    assert response.json() == {
+        "status": "accepted",
+        "knowledge_id": "101",
+        "doc_ref": "202",
+        "enabled_index_families": [
+            "chunk_vector",
+            "summary_vector_index",
+        ],
+    }
+
+
+def test_query_route_rejects_invalid_auth_token() -> None:
+    client = TestClient(create_app())
+
+    response = client.post(
+        "/internal/rag/query",
+        headers={"Authorization": "Bearer invalid-token"},
+        json={"knowledge_base_ids": [1], "query": "release checklist"},
+    )
+
+    assert response.status_code == 401
+
+
+def test_index_route_validates_content_ref_shape() -> None:
+    client = TestClient(create_app())
+
+    response = client.post(
+        "/internal/rag/index",
+        headers=_auth_headers(),
+        json={
+            "knowledge_base_id": 101,
+            "index_owner_user_id": 303,
+            "retriever_config": {"name": "default-retriever"},
+            "embedding_model_config": {"model_name": "text-embedding-3-small"},
+            "content_ref": {
+                "kind": "backend_attachment_stream",
+                "url": "http://backend/api/internal/rag/content/12",
+            },
+        },
+    )
+
+    assert response.status_code == 422
diff --git a/shared/models/__init__.py b/shared/models/__init__.py
index f1f2b3248..709c71548 100644
--- a/shared/models/__init__.py
+++ b/shared/models/__init__.py
@@ -46,6 +46,21 @@
 
 # Unified execution protocol
 from .execution import EventType, ExecutionEvent, ExecutionRequest
+from .knowledge_runtime_protocol import (
+    BackendAttachmentStreamContentRef,
+    KnowledgeRuntimeAuth,
+    PresignedUrlContentRef,
+    RemoteDeleteDocumentIndexRequest,
+    RemoteIndexRequest,
+    RemoteKnowledgeBaseQueryConfig,
+    RemoteQueryRecord,
+    RemoteQueryRequest,
+    RemoteQueryResponse,
+    RemoteRagError,
+    RuntimeEmbeddingModelConfig,
+    RuntimeRetrievalConfig,
+    RuntimeRetrieverConfig,
+)
 
 # OpenAI Request Converter
 from .openai_converter import (
@@ -86,6 +101,19 @@
     "EventType",
     "ExecutionEvent",
     "ExecutionRequest",
+    "BackendAttachmentStreamContentRef",
+    "PresignedUrlContentRef",
+    "KnowledgeRuntimeAuth",
+    "RemoteRagError",
+    "RuntimeRetrieverConfig",
+    "RuntimeEmbeddingModelConfig",
+    "RuntimeRetrievalConfig",
+    "RemoteKnowledgeBaseQueryConfig",
+    "RemoteIndexRequest",
+    "RemoteDeleteDocumentIndexRequest",
+    "RemoteQueryRequest",
+    "RemoteQueryRecord",
+    "RemoteQueryResponse",
     # OpenAI Responses API
     "ResponsesAPIStreamEvents",
     "ResponsesAPIStreamingResponse",
diff --git a/shared/models/knowledge_runtime_protocol.py b/shared/models/knowledge_runtime_protocol.py
new file mode 100644
index 000000000..29be8a5ab
--- /dev/null
+++ b/shared/models/knowledge_runtime_protocol.py
@@ -0,0 +1,165 @@
+# SPDX-FileCopyrightText: 2025 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Lightweight transport models for Backend <-> knowledge_runtime."""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Annotated, Any, Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class KnowledgeRuntimeProtocolModel(BaseModel):
+    """Base protocol model with strict field validation."""
+
+    model_config = ConfigDict(extra="forbid")
+
+
+class BackendAttachmentStreamContentRef(KnowledgeRuntimeProtocolModel):
+    """Content reference resolved by streaming through Backend."""
+
+    kind: Literal["backend_attachment_stream"]
+    url: str
+    auth_token: str
+    expires_at: datetime | None = None
+
+
+class PresignedUrlContentRef(KnowledgeRuntimeProtocolModel):
+    """Content reference resolved directly from object storage."""
+
+    kind: Literal["presigned_url"]
+    url: str
+    expires_at: datetime | None = None
+
+
+ContentRef = Annotated[
+    BackendAttachmentStreamContentRef | PresignedUrlContentRef,
+    Field(discriminator="kind"),
+]
+
+RetrievalPolicy = Literal[
+    "chunk_only",
+    "summary_first",
+    "summary_then_chunk_expand",
+    "hybrid",
+]
+
+RetrievalMode = Literal["vector", "keyword", "hybrid"]
+
+
+class KnowledgeRuntimeAuth(KnowledgeRuntimeProtocolModel):
+    """Simple internal auth carrier for the runtime service."""
+
+    scheme: Literal["bearer"] = "bearer"
+    token: str
+
+
+class RemoteRagError(KnowledgeRuntimeProtocolModel):
+    """Standardized remote error payload."""
+
+    code: str
+    message: str
+    retryable: bool = False
+    details: dict[str, Any] | None = None
+
+
+class RuntimeRetrieverConfig(KnowledgeRuntimeProtocolModel):
+    """Resolved retriever identity and storage configuration."""
+
+    name: str
+    namespace: str = "default"
+    storage_config: dict[str, Any] = Field(default_factory=dict)
+
+
+class RuntimeEmbeddingModelConfig(KnowledgeRuntimeProtocolModel):
+    """Resolved embedding model configuration."""
+
+    model_name: str
+    model_namespace: str = "default"
+    resolved_config: dict[str, Any] = Field(default_factory=dict)
+
+
+class RuntimeRetrievalConfig(KnowledgeRuntimeProtocolModel):
+    """Normalized retrieval config for a single knowledge base target."""
+
+    top_k: int = 20
+    score_threshold: float = 0.7
+    retrieval_mode: RetrievalMode = "vector"
+    vector_weight: float | None = None
+    keyword_weight: float | None = None
+
+
+class RemoteKnowledgeBaseQueryConfig(KnowledgeRuntimeProtocolModel):
+    """Resolved execution config for one queryable knowledge base."""
+
+    knowledge_base_id: int
+    index_owner_user_id: int
+    retriever_config: RuntimeRetrieverConfig
+    embedding_model_config: RuntimeEmbeddingModelConfig
+    retrieval_config: RuntimeRetrievalConfig
+
+
+class RemoteIndexRequest(KnowledgeRuntimeProtocolModel):
+    """Index request sent from Backend to knowledge_runtime."""
+
+    knowledge_base_id: int
+    document_id: int | None = None
+    index_owner_user_id: int
+    retriever_config: RuntimeRetrieverConfig
+    embedding_model_config: RuntimeEmbeddingModelConfig
+    splitter_config: dict[str, Any] | None = None
+    index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
+    content_ref: ContentRef
+    trace_context: dict[str, Any] | None = None
+    user_name: str | None = None
+    extensions: dict[str, Any] | None = None
+
+
+class RemoteDeleteDocumentIndexRequest(KnowledgeRuntimeProtocolModel):
+    """Delete-document-index request sent from Backend to knowledge_runtime."""
+
+    knowledge_base_id: int
+    document_ref: str
+    index_owner_user_id: int | None = None
+    retriever_config: RuntimeRetrieverConfig
+    enabled_index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
+    extensions: dict[str, Any] | None = None
+
+
+class RemoteQueryRequest(KnowledgeRuntimeProtocolModel):
+    """Query request sent from Backend to knowledge_runtime."""
+
+    knowledge_base_ids: list[int]
+    query: str
+    max_results: int = 5
+    document_ids: list[int] | None = None
+    user_name: str | None = None
+    knowledge_base_configs: list[RemoteKnowledgeBaseQueryConfig] = Field(
+        default_factory=list
+    )
+    enabled_index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
+    retrieval_policy: RetrievalPolicy = "chunk_only"
+    extensions: dict[str, Any] | None = None
+
+
+class RemoteQueryRecord(KnowledgeRuntimeProtocolModel):
+    """Single retrieval record returned by knowledge_runtime."""
+
+    content: str
+    title: str
+    score: float | None = None
+    metadata: dict[str, Any] | None = None
+    knowledge_base_id: int | None = None
+    document_id: int | None = None
+    index_family: str = "chunk_vector"
+
+
+class RemoteQueryResponse(KnowledgeRuntimeProtocolModel):
+    """Query response returned by knowledge_runtime."""
+
+    records: list[RemoteQueryRecord]
+    total: int
+    total_estimated_tokens: int = 0
diff --git a/shared/tests/test_knowledge_runtime_protocol.py b/shared/tests/test_knowledge_runtime_protocol.py
new file mode 100644
index 000000000..bd2e48743
--- /dev/null
+++ b/shared/tests/test_knowledge_runtime_protocol.py
@@ -0,0 +1,201 @@
+# SPDX-FileCopyrightText: 2025 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import pytest
+from pydantic import ValidationError
+
+import shared.models as shared_models
+
+
+def _require_model(name: str):
+    model = getattr(shared_models, name, None)
+    if model is None:
+        pytest.fail(f"shared.models must export {name}")
+    return model
+
+
+def test_shared_models_exports_knowledge_runtime_protocol_types() -> None:
+    exported_names = [
+        "BackendAttachmentStreamContentRef",
+        "PresignedUrlContentRef",
+        "RuntimeEmbeddingModelConfig",
+        "RuntimeRetrievalConfig",
+        "RuntimeRetrieverConfig",
+        "RemoteKnowledgeBaseQueryConfig",
+        "RemoteDeleteDocumentIndexRequest",
+        "RemoteIndexRequest",
+        "RemoteQueryRequest",
+        "RemoteQueryRecord",
+        "RemoteQueryResponse",
+    ]
+
+    for name in exported_names:
+        assert getattr(shared_models, name, None) is not None
+
+
+def test_remote_index_request_accepts_backend_attachment_stream_content_ref() -> None:
+    remote_index_request = _require_model("RemoteIndexRequest")
+
+    request = remote_index_request.model_validate(
+        {
+            "knowledge_base_id": 11,
+            "document_id": 22,
+            "index_owner_user_id": 33,
+            "retriever_config": {
+                "name": "retriever-a",
+                "namespace": "default",
+                "storage_config": {
+                    "type": "milvus",
+                    "url": "http://milvus:19530",
+                },
+            },
+            "embedding_model_config": {
+                "model_name": "text-embedding-3-large",
+                "model_namespace": "default",
+                "resolved_config": {"protocol": "openai"},
+            },
+            "index_families": ["chunk_vector"],
+            "content_ref": {
+                "kind": "backend_attachment_stream",
+                "url": "http://backend:8000/api/internal/rag/content/22",
+                "auth_token": "test-token",
+            },
+        }
+    )
+
+    assert request.content_ref.kind == "backend_attachment_stream"
+    assert request.content_ref.auth_token == "test-token"
+
+
+def test_remote_index_request_rejects_unknown_content_ref_kind() -> None:
+    remote_index_request = _require_model("RemoteIndexRequest")
+
+    with pytest.raises(ValidationError):
+        remote_index_request.model_validate(
+            {
+                "knowledge_base_id": 11,
+                "document_id": 22,
+                "index_owner_user_id": 33,
+                "retriever_config": {"provider": "milvus"},
+                "embedding_model_config": {"model": "text-embedding-3-large"},
+                "index_families": ["chunk_vector"],
+                "content_ref": {
+                    "kind": "unsupported_kind",
+                    "url": "http://backend:8000/api/internal/rag/content/22",
+                },
+            }
+        )
+
+
+def test_remote_query_response_preserves_index_family_per_record() -> None:
+    remote_query_response = _require_model("RemoteQueryResponse")
+
+    response = remote_query_response.model_validate(
+        {
+            "records": [
+                {
+                    "content": "Chunk A",
+                    "title": "Doc A",
+                    "score": 0.91,
+                    "knowledge_base_id": 1001,
+                    "index_family": "chunk_vector",
+                },
+                {
+                    "content": "Summary B",
+                    "title": "Doc B",
+                    "score": 0.88,
+                    "knowledge_base_id": 1002,
+                    "index_family": "summary_vector",
+                },
+            ],
+            "total": 2,
+        }
+    )
+
+    assert [record.index_family for record in response.records] == [
+        "chunk_vector",
+        "summary_vector",
+    ]
+
+
+def test_remote_query_request_accepts_explicit_execution_configs() -> None:
+    remote_query_request = _require_model("RemoteQueryRequest")
+
+    request = remote_query_request.model_validate(
+        {
+            "knowledge_base_ids": [1001],
+            "query": "release checklist",
+            "max_results": 6,
+            "knowledge_base_configs": [
+                {
+                    "knowledge_base_id": 1001,
+                    "index_owner_user_id": 42,
+                    "retriever_config": {
+                        "name": "retriever-a",
+                        "namespace": "default",
+                        "storage_config": {
+                            "type": "qdrant",
+                            "url": "http://qdrant:6333",
+                            "indexStrategy": {"mode": "per_dataset"},
+                        },
+                    },
+                    "embedding_model_config": {
+                        "model_name": "embed-a",
+                        "model_namespace": "default",
+                        "resolved_config": {
+                            "protocol": "openai",
+                            "model_id": "text-embedding-3-small",
+                            "base_url": "https://api.openai.com/v1",
+                        },
+                    },
+                    "retrieval_config": {
+                        "top_k": 8,
+                        "score_threshold": 0.55,
+                        "retrieval_mode": "hybrid",
+                        "vector_weight": 0.8,
+                        "keyword_weight": 0.2,
+                    },
+                }
+            ],
+            "enabled_index_families": ["chunk_vector", "summary_vector_index"],
+            "retrieval_policy": "summary_then_chunk_expand",
+        }
+    )
+
+    assert (
+        request.knowledge_base_configs[0].retriever_config.storage_config["type"]
+        == "qdrant"
+    )
+    assert request.knowledge_base_configs[0].retrieval_config.top_k == 8
+    assert request.enabled_index_families == [
+        "chunk_vector",
+        "summary_vector_index",
+    ]
+
+
+def test_remote_delete_request_requires_resolved_retriever_config() -> None:
+    remote_delete_request = _require_model("RemoteDeleteDocumentIndexRequest")
+
+    request = remote_delete_request.model_validate(
+        {
+            "knowledge_base_id": 101,
+            "document_ref": "202",
+            "index_owner_user_id": 303,
+            "retriever_config": {
+                "name": "retriever-a",
+                "namespace": "default",
+                "storage_config": {
+                    "type": "elasticsearch",
+                    "url": "http://es:9200",
+                    "indexStrategy": {"mode": "per_user", "prefix": "wegent"},
+                },
+            },
+            "enabled_index_families": ["chunk_vector"],
+        }
+    )
+
+    assert request.retriever_config.name == "retriever-a"
+    assert request.retriever_config.storage_config["type"] == "elasticsearch"

From 3807b64daca2d1c1f904877b08e76a232a122f46 Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Sun, 5 Apr 2026 17:57:14 +0800
Subject: [PATCH 4/9] refactor(knowledge): route indexing through runtime
 gateway factory

---
 backend/app/services/knowledge/indexing.py    |  4 +-
 .../tests/services/test_summary_service.py    | 38 +++++++++++++------
 backend/tests/tasks/test_knowledge_tasks.py   | 27 ++++++++-----
 3 files changed, 45 insertions(+), 24 deletions(-)

diff --git a/backend/app/services/knowledge/indexing.py b/backend/app/services/knowledge/indexing.py
index c094dff8e..631ccb35c 100644
--- a/backend/app/services/knowledge/indexing.py
+++ b/backend/app/services/knowledge/indexing.py
@@ -39,14 +39,13 @@
     get_kb_index_info,
     resolve_kb_index_info,
 )
-from app.services.rag.local_gateway import LocalRagGateway
+from app.services.rag.gateway_factory import get_index_gateway
 from app.services.rag.runtime_resolver import RagRuntimeResolver
 from app.services.rag.splitter.runtime_config import parse_runtime_splitter_config
 from shared.telemetry import add_span_event
 
 logger = logging.getLogger(__name__)
 runtime_resolver = RagRuntimeResolver()
-rag_gateway = LocalRagGateway()
 
 # Excel file size limit for RAG indexing (2MB)
 EXCEL_FILE_SIZE_LIMIT = 2 * 1024 * 1024  # 2MB in bytes
@@ -393,6 +392,7 @@ def run_document_indexing(
                     "embedding_model_namespace": embedding_model_namespace,
                 },
             )
+            rag_gateway = get_index_gateway()
             result = loop.run_until_complete(
                 rag_gateway.index_document(runtime_spec, db=db)
             )
diff --git a/backend/tests/services/test_summary_service.py b/backend/tests/services/test_summary_service.py
index b329a57b5..06b185b17 100644
--- a/backend/tests/services/test_summary_service.py
+++ b/backend/tests/services/test_summary_service.py
@@ -405,11 +405,22 @@ def test_delete_document_routes_rag_cleanup_through_gateway(
         }
         flag_modified(test_knowledge_base, "json")
         test_db.commit()
+        delete_runtime_spec = object()
+        mock_gateway = MagicMock()
+        mock_gateway.delete_document_index = AsyncMock(
+            return_value={"status": "success"}
+        )
 
-        with patch(
-            "app.services.rag.local_gateway.LocalRagGateway.delete_document_index",
-            new=AsyncMock(return_value={"status": "success"}),
-        ) as mock_delete_index:
+        with (
+            patch(
+                "app.services.rag.gateway_factory.get_delete_gateway",
+                return_value=mock_gateway,
+            ) as mock_get_delete_gateway,
+            patch(
+                "app.services.rag.runtime_resolver.RagRuntimeResolver.build_delete_runtime_spec",
+                return_value=delete_runtime_spec,
+            ) as mock_build_delete_runtime_spec,
+        ):
             result = KnowledgeService.delete_document(
                 db=test_db,
                 document_id=test_document.id,
@@ -417,16 +428,19 @@ def test_delete_document_routes_rag_cleanup_through_gateway(
             )
 
         assert result.success is True
-        mock_delete_index.assert_awaited_once()
-        assert mock_delete_index.await_args.kwargs["knowledge_base_id"] == (
-            test_knowledge_base.id
+        mock_get_delete_gateway.assert_called_once()
+        mock_build_delete_runtime_spec.assert_called_once_with(
+            db=test_db,
+            knowledge_base_id=test_knowledge_base.id,
+            document_ref=str(test_document.id),
+            index_owner_user_id=test_user.id,
         )
-        assert mock_delete_index.await_args.kwargs["document_ref"] == str(
-            test_document.id
+        mock_gateway.delete_document_index.assert_awaited_once_with(
+            delete_runtime_spec,
+            db=test_db,
         )
-        assert mock_delete_index.await_args.kwargs["db"] is test_db
-        assert mock_delete_index.await_args.kwargs["index_owner_user_id"] == (
-            test_user.id
+        assert mock_build_delete_runtime_spec.call_args.kwargs["knowledge_base_id"] == (
+            test_knowledge_base.id
         )
 
 
diff --git a/backend/tests/tasks/test_knowledge_tasks.py b/backend/tests/tasks/test_knowledge_tasks.py
index c02b2432f..4497930bd 100644
--- a/backend/tests/tasks/test_knowledge_tasks.py
+++ b/backend/tests/tasks/test_knowledge_tasks.py
@@ -193,16 +193,19 @@ def test_index_document_task_routes_indexing_through_gateway():
                 return_value=object(),
             )
         )
-        mock_index = stack.enter_context(
+        mock_gateway = MagicMock()
+        mock_gateway.index_document = AsyncMock(
+            return_value={
+                "status": "success",
+                "document_id": 4,
+                "knowledge_base_id": "1",
+                "chunks_data": {"total_count": 8},
+            }
+        )
+        mock_get_index_gateway = stack.enter_context(
             patch(
-                "app.services.knowledge.indexing.LocalRagGateway.index_document",
-                new_callable=AsyncMock,
-                return_value={
-                    "status": "success",
-                    "document_id": 4,
-                    "knowledge_base_id": "1",
-                    "chunks_data": {"total_count": 8},
-                },
+                "app.services.knowledge.indexing.get_index_gateway",
+                return_value=mock_gateway,
             )
         )
         stack.enter_context(
@@ -222,7 +225,11 @@ def test_index_document_task_routes_indexing_through_gateway():
 
     assert result["status"] == "success"
     mock_resolve.assert_called_once()
-    mock_index.assert_awaited_once_with(mock_resolve.return_value, db=indexing_db)
+    mock_get_index_gateway.assert_called_once()
+    mock_gateway.index_document.assert_awaited_once_with(
+        mock_resolve.return_value,
+        db=indexing_db,
+    )
 
 
 def test_index_document_task_enqueues_summary_after_finalize(

From a96729fdf1777123fbd10f5da85b4bcb8629fd6a Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Mon, 6 Apr 2026 10:14:28 +0800
Subject: [PATCH 5/9] refactor(knowledge): extract rag execution into knowledge
 engine

---
 .../app/api/endpoints/adapter/retrievers.py   |   51 +-
 backend/app/api/endpoints/rag.py              |  544 +--
 backend/app/schemas/rag.py                    |   90 -
 backend/app/services/rag/README.md            |   11 -
 backend/app/services/rag/document_service.py  |  402 --
 backend/app/services/rag/embedding/custom.py  |   97 +-
 backend/app/services/rag/embedding/factory.py |  109 +-
 backend/app/services/rag/gateway.py           |    8 +
 backend/app/services/rag/index/__init__.py    |   13 -
 backend/app/services/rag/index/indexer.py     |  326 --
 .../rag/local_data_plane/administration.py    |   26 +
 .../rag/local_data_plane/retrieval.py         |    2 +
 backend/app/services/rag/local_gateway.py     |   11 +
 backend/app/services/rag/remote_gateway.py    |   13 +
 .../app/services/rag/retrieval/__init__.py    |   11 +-
 backend/app/services/rag/retrieval/filters.py |  142 +-
 .../app/services/rag/retrieval/retriever.py   |   64 -
 backend/app/services/rag/retrieval_service.py |  463 +-
 backend/app/services/rag/runtime_resolver.py  |   77 +-
 backend/app/services/rag/runtime_specs.py     |   26 +-
 backend/app/services/rag/splitter/__init__.py |   18 +-
 backend/app/services/rag/splitter/factory.py  |   68 -
 backend/app/services/rag/splitter/smart.py    |  199 -
 backend/app/services/rag/splitter/splitter.py |  133 -
 backend/app/services/rag/storage/base.py      |  387 +-
 .../services/rag/storage/chunk_metadata.py    |   82 +-
 .../rag/storage/elasticsearch_backend.py      |  613 +--
 backend/app/services/rag/storage/factory.py   |  105 +-
 .../services/rag/storage/milvus_backend.py    |  844 +---
 .../services/rag/storage/qdrant_backend.py    |  570 +--
 backend/tests/api/endpoints/test_rag_api.py   |  112 +
 .../api/endpoints/test_retrievers_api.py      |   75 +
 .../rag/storage/test_elasticsearch_backend.py |    4 +-
 .../rag/storage/test_milvus_backend.py        |   34 +-
 .../rag/test_local_data_plane_retrieval.py    |   30 +-
 .../tests/services/rag/test_local_gateway.py  |   21 +
 .../tests/services/rag/test_remote_gateway.py |   42 +
 .../services/rag/test_retrieval_service.py    |  152 +-
 .../tests/services/rag/test_runtime_specs.py  |   20 +-
 docker-compose.yml                            |   26 +-
 docker/knowledge_runtime/Dockerfile           |    1 +
 .../knowledge/configuring-retrievers.md       |   34 +-
 .../knowledge/configuring-retrievers.md       |   34 +-
 .../knowledge_engine/embedding/__init__.py    |    7 +
 .../knowledge_engine/embedding/custom.py      |   78 +
 .../knowledge_engine/embedding/factory.py     |   80 +
 .../knowledge_engine/query/__init__.py        |    4 +
 .../knowledge_engine/query/executor.py        |   62 +
 .../knowledge_engine/retrieval/__init__.py    |   12 +
 .../knowledge_engine/retrieval/filters.py     |  144 +
 .../knowledge_engine/storage/__init__.py      |   19 +-
 .../knowledge_engine/storage/base.py          |  328 +-
 .../storage/chunk_metadata.py                 |   53 +-
 .../storage/elasticsearch_backend.py          |  615 +++
 .../knowledge_engine/storage/factory.py       |   76 +
 .../storage/milvus_backend.py                 |  829 ++++
 .../storage/qdrant_backend.py                 |  571 +++
 knowledge_engine/tests/test_query_executor.py |   75 +
 .../tests/test_storage_factory.py             |   46 +
 .../knowledge_runtime/api/internal/rag.py     |   11 +-
 .../knowledge_runtime/services/handlers.py    |  172 +-
 knowledge_runtime/tests/test_internal_rag.py  |  110 +-
 .../tests/test_query_execution.py             |  187 +
 knowledge_runtime/uv.lock                     | 4265 +++++++++++++++++
 shared/models/__init__.py                     |    2 +
 shared/models/knowledge_runtime_protocol.py   |    8 +
 .../tests/test_knowledge_runtime_protocol.py  |   34 +
 67 files changed, 8637 insertions(+), 5241 deletions(-)
 delete mode 100644 backend/app/services/rag/document_service.py
 delete mode 100644 backend/app/services/rag/index/__init__.py
 delete mode 100644 backend/app/services/rag/index/indexer.py
 create mode 100644 backend/app/services/rag/local_data_plane/administration.py
 delete mode 100644 backend/app/services/rag/retrieval/retriever.py
 delete mode 100644 backend/app/services/rag/splitter/factory.py
 delete mode 100644 backend/app/services/rag/splitter/smart.py
 delete mode 100644 backend/app/services/rag/splitter/splitter.py
 create mode 100644 backend/tests/api/endpoints/test_rag_api.py
 create mode 100644 backend/tests/api/endpoints/test_retrievers_api.py
 create mode 100644 knowledge_engine/knowledge_engine/embedding/custom.py
 create mode 100644 knowledge_engine/knowledge_engine/embedding/factory.py
 create mode 100644 knowledge_engine/knowledge_engine/query/executor.py
 create mode 100644 knowledge_engine/knowledge_engine/retrieval/__init__.py
 create mode 100644 knowledge_engine/knowledge_engine/retrieval/filters.py
 create mode 100644 knowledge_engine/knowledge_engine/storage/elasticsearch_backend.py
 create mode 100644 knowledge_engine/knowledge_engine/storage/factory.py
 create mode 100644 knowledge_engine/knowledge_engine/storage/milvus_backend.py
 create mode 100644 knowledge_engine/knowledge_engine/storage/qdrant_backend.py
 create mode 100644 knowledge_engine/tests/test_query_executor.py
 create mode 100644 knowledge_engine/tests/test_storage_factory.py
 create mode 100644 knowledge_runtime/tests/test_query_execution.py
 create mode 100644 knowledge_runtime/uv.lock

diff --git a/backend/app/api/endpoints/adapter/retrievers.py b/backend/app/api/endpoints/adapter/retrievers.py
index b8bf33e31..ac5d67895 100644
--- a/backend/app/api/endpoints/adapter/retrievers.py
+++ b/backend/app/api/endpoints/adapter/retrievers.py
@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import logging
-from typing import TYPE_CHECKING, Optional
+from typing import Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Query
 from sqlalchemy.orm import Session
@@ -14,6 +14,11 @@
 from app.models.user import User
 from app.schemas.kind import Retriever
 from app.services.adapters.retriever_kinds import retriever_kinds_service
+from app.services.rag.gateway_factory import get_query_gateway
+from app.services.rag.local_gateway import LocalRagGateway
+from app.services.rag.remote_gateway import RemoteRagGatewayError
+from app.services.rag.runtime_specs import ConnectionTestRuntimeSpec
+from shared.models import RuntimeRetrieverConfig
 
 # RAG storage factory is conditionally imported based on STANDALONE_MODE
 # RAG module is heavy (llama_index, scipy, pandas, grpc) - skip in standalone mode
@@ -213,7 +218,7 @@ def delete_retriever(
 
 
 @router.post("/test-connection")
-def test_retriever_connection(
+async def test_retriever_connection(
     test_data: dict,
     current_user: User = Depends(security.get_current_user),
 ):
@@ -236,6 +241,7 @@ def test_retriever_connection(
     }
     """
     _check_rag_available()
+    del current_user
 
     storage_type = test_data.get("storage_type")
     url = test_data.get("url")
@@ -250,31 +256,28 @@ def test_retriever_connection(
         }
 
     try:
-        # Create storage backend from config
-        backend = storage_factory.create_storage_backend_from_config(
-            storage_type=storage_type,
-            url=url,
-            username=username,
-            password=password,
-            api_key=api_key,
+        runtime_spec = ConnectionTestRuntimeSpec(
+            retriever_config=RuntimeRetrieverConfig(
+                name="connection-test",
+                namespace="default",
+                storage_config={
+                    "type": storage_type,
+                    "url": url,
+                    "username": username,
+                    "password": password,
+                    "apiKey": api_key,
+                    "indexStrategy": {"mode": "per_dataset"},
+                    "ext": {},
+                },
+            )
         )
-
-        # Test connection using backend's test_connection method
-        success = backend.test_connection()
-
-        if success:
-            return {
-                "success": True,
-                "message": f"Successfully connected to {storage_type}",
-            }
-        else:
-            return {
-                "success": False,
-                "message": f"Failed to connect to {storage_type}",
-            }
+        gateway = get_query_gateway()
+        try:
+            return await gateway.test_connection(runtime_spec)
+        except RemoteRagGatewayError:
+            return await LocalRagGateway().test_connection(runtime_spec)
 
     except ValueError as e:
-        # Unsupported storage type
         return {"success": False, "message": str(e)}
 
     except Exception as e:
diff --git a/backend/app/api/endpoints/rag.py b/backend/app/api/endpoints/rag.py
index 2eaa5c205..1bb1c99c1 100644
--- a/backend/app/api/endpoints/rag.py
+++ b/backend/app/api/endpoints/rag.py
@@ -2,258 +2,25 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-RAG API routes.
-Refactored to use Retriever CRD configuration instead of global config.
-"""
+"""Public RAG compatibility routes."""
 
-import json
-import tempfile
-from pathlib import Path
-from typing import Optional
-
-from fastapi import APIRouter, Depends, File, Form, HTTPException, Query, UploadFile
-from pydantic import ValidationError
+from fastapi import APIRouter, Depends, HTTPException
 from sqlalchemy.orm import Session
 
 from app.api.dependencies import get_db
 from app.core.security import get_current_user
-from app.models.kind import Kind
 from app.models.user import User
 from app.schemas.rag import (
-    DocumentDeleteResponse,
-    DocumentDetailResponse,
-    DocumentListResponse,
-    DocumentUploadResponse,
-    IndexNameResponse,
     RetrieveRequest,
     RetrieveResponse,
-    SemanticSplitterConfig,
-    SentenceSplitterConfig,
-    SplitterConfig,
 )
-from app.services.adapters.retriever_kinds import retriever_kinds_service
-from app.services.group_permission import get_effective_role_in_group
-from app.services.knowledge.namespace_utils import is_organization_namespace
-from app.services.rag.document_service import DocumentService
-from app.services.rag.retrieval_service import RetrievalService
-from app.services.rag.storage.factory import create_storage_backend
+from app.services.rag.gateway_factory import get_query_gateway
+from app.services.rag.local_gateway import LocalRagGateway
+from app.services.rag.remote_gateway import RemoteRagGatewayError
+from app.services.rag.runtime_resolver import RagRuntimeResolver
 
 router = APIRouter()
-
-
-def get_retriever_and_backend(
-    retriever_name: str, retriever_namespace: str, user_id: int, db: Session
-):
-    """
-    Get Retriever CRD and create storage backend.
-
-    Args:
-        retriever_name: Retriever name
-        retriever_namespace: Retriever namespace
-        user_id: User ID
-        db: Database session
-
-    Returns:
-        Tuple of (Retriever CRD, storage backend)
-
-    Raises:
-        HTTPException: If retriever not found or access denied
-    """
-    # Get Retriever CRD
-    retriever = retriever_kinds_service.get_retriever(
-        db, user_id=user_id, name=retriever_name, namespace=retriever_namespace
-    )
-
-    # Create storage backend from Retriever config
-    storage_backend = create_storage_backend(retriever)
-
-    return retriever, storage_backend
-
-
-def get_index_owner_user_id(
-    db: Session, knowledge_id: str, current_user_id: int
-) -> int:
-    """
-    Get the user_id that should be used for index naming in per_user strategy.
-
-    For personal knowledge bases (namespace="default"), use the current user's ID.
-    For group knowledge bases (namespace!="default"), use the knowledge base creator's ID.
-
-    This ensures that all group members access the same index created by the KB owner.
-
-    Args:
-        db: Database session
-        knowledge_id: Knowledge base ID (Kind.id as string)
-        current_user_id: Current requesting user's ID
-
-    Returns:
-        User ID to use for index naming
-
-    Raises:
-        HTTPException: If knowledge base not found or access denied
-    """
-    try:
-        kb_id = int(knowledge_id)
-    except ValueError:
-        # If knowledge_id is not a valid integer, return current user's ID
-        # This handles legacy cases where knowledge_id might be a string identifier
-        return current_user_id
-
-    # Get the knowledge base
-    kb = (
-        db.query(Kind)
-        .filter(
-            Kind.id == kb_id,
-            Kind.kind == "KnowledgeBase",
-            Kind.is_active == True,
-        )
-        .first()
-    )
-
-    if not kb:
-        # Knowledge base not found, return current user's ID
-        # The actual access check will happen later in the retrieval process
-        return current_user_id
-
-    # Check access permission
-    if kb.namespace == "default":
-        # Personal knowledge base - must be owned by current user
-        if kb.user_id != current_user_id:
-            raise HTTPException(
-                status_code=403,
-                detail="Access denied to this knowledge base",
-            )
-        return current_user_id
-    elif is_organization_namespace(db, kb.namespace):
-        # Organization knowledge base - accessible to all authenticated users
-        # Return the knowledge base creator's user_id for index naming
-        # This ensures all users access the same index
-        return kb.user_id
-    else:
-        # Group knowledge base - check if user has access to the group
-        role = get_effective_role_in_group(db, current_user_id, kb.namespace)
-        if role is None:
-            raise HTTPException(
-                status_code=403,
-                detail="Access denied to this knowledge base",
-            )
-        # Return the knowledge base creator's user_id for index naming
-        # This ensures all group members access the same index
-        return kb.user_id
-
-
-@router.post("/documents/upload", response_model=DocumentUploadResponse)
-async def upload_document(
-    knowledge_id: str = Form(...),
-    retriever_name: str = Form(...),
-    retriever_namespace: str = Form(default="default"),
-    file: UploadFile = File(...),
-    embedding_model_name: str = Form(...),
-    embedding_model_namespace: str = Form(default="default"),
-    splitter_config: Optional[str] = Form(
-        None,
-        description="JSON string of splitter configuration. Example: "
-        '{"type": "sentence", "chunk_size": 1024, "chunk_overlap": 200, "separator": "\\n\\n"}',
-    ),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """
-    Upload and index a document.
-
-    Args:
-        knowledge_id: Knowledge base ID
-        retriever_name: Retriever name
-        retriever_namespace: Retriever namespace (default: "default")
-        file: Document file (MD/PDF/TXT/DOCX/code)
-        embedding_model_name: Embedding model name
-        embedding_model_namespace: Embedding model namespace (default: "default")
-        splitter_config: Optional JSON string of splitter configuration.
-                        If not provided, defaults to semantic splitter.
-                        Examples:
-                        - Semantic: {"type": "semantic", "buffer_size": 1, "breakpoint_percentile_threshold": 95}
-                        - Sentence: {"type": "sentence", "chunk_size": 1024, "chunk_overlap": 200, "separator": "\\n\\n"}
-        db: Database session
-        current_user: Current user
-
-    Returns:
-        Document upload response
-
-    Raises:
-        HTTPException: If upload or indexing fails
-    """
-    try:
-        # Parse splitter config if provided
-        parsed_splitter_config: Optional[SplitterConfig] = None
-        if splitter_config:
-            try:
-                config_dict = json.loads(splitter_config)
-                config_type = config_dict.get("type", "semantic")
-
-                if config_type == "semantic":
-                    parsed_splitter_config = SemanticSplitterConfig(**config_dict)
-                elif config_type == "sentence":
-                    parsed_splitter_config = SentenceSplitterConfig(**config_dict)
-                else:
-                    raise ValueError(f"Unknown splitter type: {config_type}")
-            except (json.JSONDecodeError, ValidationError) as e:
-                raise HTTPException(
-                    status_code=400,
-                    detail=f"Invalid splitter_config JSON: {str(e)}",
-                ) from e
-
-        # Get the correct user_id for index naming
-        # For group knowledge bases, use the KB creator's user_id
-        # This ensures all group members access the same index
-        index_owner_user_id = get_index_owner_user_id(
-            db=db,
-            knowledge_id=knowledge_id,
-            current_user_id=current_user.id,
-        )
-
-        # Get retriever and create backend
-        _, storage_backend = get_retriever_and_backend(
-            retriever_name=retriever_name,
-            retriever_namespace=retriever_namespace,
-            user_id=current_user.id,
-            db=db,
-        )
-
-        # Create document service
-        doc_service = DocumentService(storage_backend=storage_backend)
-
-        # Save to temp file
-        with tempfile.NamedTemporaryFile(
-            delete=False, suffix=Path(file.filename).suffix
-        ) as tmp:
-            content = await file.read()
-            tmp.write(content)
-            tmp_path = tmp.name
-
-        try:
-            # Index document
-            # Use index_owner_user_id for per_user index strategy to ensure
-            # all group members access the same index created by the KB owner
-            result = await doc_service.index_document(
-                knowledge_id=knowledge_id,
-                file_path=tmp_path,
-                embedding_model_name=embedding_model_name,
-                embedding_model_namespace=embedding_model_namespace,
-                user_id=index_owner_user_id,
-                db=db,
-                splitter_config=parsed_splitter_config,
-                user_name=current_user.user_name,
-            )
-            return result
-        finally:
-            # Cleanup temp file
-            Path(tmp_path).unlink(missing_ok=True)
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+runtime_resolver = RagRuntimeResolver()
 
 
 @router.post("/retrieve", response_model=RetrieveResponse)
@@ -307,278 +74,43 @@ async def retrieve_documents(
         ```
     """
     try:
-        # Get the correct user_id for index naming
-        # For group knowledge bases, use the KB creator's user_id
-        # This ensures all group members access the same index
-        index_owner_user_id = get_index_owner_user_id(
+        knowledge_base_id = int(request.knowledge_id)
+        runtime_spec = runtime_resolver.build_public_query_runtime_spec(
             db=db,
-            knowledge_id=request.knowledge_id,
-            current_user_id=current_user.id,
-        )
-
-        # Get retriever and create backend
-        _, storage_backend = get_retriever_and_backend(
+            knowledge_base_id=knowledge_base_id,
+            query=request.query,
+            max_results=request.top_k,
             retriever_name=request.retriever_ref.name,
             retriever_namespace=request.retriever_ref.namespace,
+            embedding_model_name=request.embedding_model_ref.model_name,
+            embedding_model_namespace=request.embedding_model_ref.model_namespace,
             user_id=current_user.id,
-            db=db,
-        )
-
-        # Create retrieval service
-        retrieval_service = RetrievalService(storage_backend=storage_backend)
-
-        # Prepare retrieval parameters
-        # Use index_owner_user_id for per_user index strategy to ensure
-        # all group members access the same index created by the KB owner
-        retrieval_params = {
-            "query": request.query,
-            "knowledge_id": request.knowledge_id,
-            "embedding_model_name": request.embedding_model_ref.model_name,
-            "embedding_model_namespace": request.embedding_model_ref.model_namespace,
-            "user_id": index_owner_user_id,
-            "db": db,
-            "top_k": request.top_k,
-            "score_threshold": request.score_threshold,
-            "retrieval_mode": request.retrieval_mode.value,
-            "metadata_condition": request.metadata_condition,
-        }
-
-        # Add hybrid weights if in hybrid mode
-        if request.retrieval_mode.value == "hybrid" and request.hybrid_weights:
-            retrieval_params["vector_weight"] = request.hybrid_weights.vector_weight
-            retrieval_params["keyword_weight"] = request.hybrid_weights.keyword_weight
-
-        result = await retrieval_service.retrieve(**retrieval_params)
-        return result
-    except HTTPException:
-        raise
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@router.delete("/documents/{doc_ref}", response_model=DocumentDeleteResponse)
-async def delete_document(
-    doc_ref: str,
-    knowledge_id: str = Query(...),
-    retriever_name: str = Query(...),
-    retriever_namespace: str = Query(default="default"),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """
-    Delete a document.
-
-    Args:
-        doc_ref: Document reference ID
-        knowledge_id: Knowledge base ID
-        retriever_name: Retriever name
-        retriever_namespace: Retriever namespace (default: "default")
-        db: Database session
-        current_user: Current user
-
-    Returns:
-        Deletion result
-
-    Raises:
-        HTTPException: If deletion fails
-    """
-    try:
-        # Get the correct user_id for index naming
-        # For group knowledge bases, use the KB creator's user_id
-        # This ensures all group members access the same index
-        index_owner_user_id = get_index_owner_user_id(
-            db=db,
-            knowledge_id=knowledge_id,
-            current_user_id=current_user.id,
-        )
-
-        # Get retriever and create backend
-        _, storage_backend = get_retriever_and_backend(
-            retriever_name=retriever_name,
-            retriever_namespace=retriever_namespace,
-            user_id=current_user.id,
-            db=db,
-        )
-
-        # Create document service
-        doc_service = DocumentService(storage_backend=storage_backend)
-
-        # Use index_owner_user_id for per_user index strategy
-        result = await doc_service.delete_document(
-            knowledge_id=knowledge_id, doc_ref=doc_ref, user_id=index_owner_user_id
-        )
-        return result
-    except HTTPException:
-        raise
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@router.get("/documents", response_model=DocumentListResponse)
-async def list_documents(
-    knowledge_id: str = Query(...),
-    retriever_name: str = Query(...),
-    retriever_namespace: str = Query(default="default"),
-    page: int = Query(1, ge=1),
-    page_size: int = Query(20, ge=1, le=100),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """
-    List documents in knowledge base with pagination.
-
-    Args:
-        knowledge_id: Knowledge base ID
-        retriever_name: Retriever name
-        retriever_namespace: Retriever namespace (default: "default")
-        page: Page number (1-indexed)
-        page_size: Number of documents per page
-        db: Database session
-        current_user: Current user
-
-    Returns:
-        Document list
-
-    Raises:
-        HTTPException: If listing fails
-    """
-    try:
-        # Get the correct user_id for index naming
-        # For group knowledge bases, use the KB creator's user_id
-        # This ensures all group members access the same index
-        index_owner_user_id = get_index_owner_user_id(
-            db=db,
-            knowledge_id=knowledge_id,
-            current_user_id=current_user.id,
-        )
-
-        # Get retriever and create backend
-        _, storage_backend = get_retriever_and_backend(
-            retriever_name=retriever_name,
-            retriever_namespace=retriever_namespace,
-            user_id=current_user.id,
-            db=db,
-        )
-
-        # Create document service
-        doc_service = DocumentService(storage_backend=storage_backend)
-
-        # Use index_owner_user_id for per_user index strategy
-        result = await doc_service.list_documents(
-            knowledge_id=knowledge_id,
-            page=page,
-            page_size=page_size,
-            user_id=index_owner_user_id,
-        )
-        return result
-    except HTTPException:
-        raise
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@router.post("/test-connection")
-async def test_retriever_connection(
-    retriever_name: str = Query(...),
-    retriever_namespace: str = Query(default="default"),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """
-    Test connection to retriever storage backend.
-
-    Args:
-        retriever_name: Retriever name
-        retriever_namespace: Retriever namespace (default: "default")
-        db: Database session
-        current_user: Current user
-
-    Returns:
-        Connection test result
-
-    Raises:
-        HTTPException: If test fails
-    """
-    try:
-        # Get retriever and create backend
-        _, storage_backend = get_retriever_and_backend(
-            retriever_name=retriever_name,
-            retriever_namespace=retriever_namespace,
-            user_id=current_user.id,
-            db=db,
-        )
-
-        # Test connection
-        success = storage_backend.test_connection()
-
-        return {
-            "success": success,
-            "message": "Connection successful" if success else "Connection failed",
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@router.get("/index-name", response_model=IndexNameResponse)
-async def get_index_name(
-    knowledge_id: str = Query(..., description="Knowledge base ID"),
-    retriever_name: str = Query(..., description="Retriever name"),
-    retriever_namespace: str = Query(
-        default="default", description="Retriever namespace"
-    ),
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """
-    Get the index name for a given knowledge base ID.
-
-    This endpoint computes the index name based on the retriever's index strategy
-    configuration and the provided knowledge_id.
-
-    Args:
-        knowledge_id: Knowledge base ID
-        retriever_name: Retriever name
-        retriever_namespace: Retriever namespace (default: "default")
-        db: Database session
-        current_user: Current user
-
-    Returns:
-        IndexNameResponse with knowledge_id, index_name, and index_strategy
-
-    Raises:
-        HTTPException: If retriever not found or computation fails
-    """
-    try:
-        # Get the correct user_id for index naming (for per_user strategy)
-        index_owner_user_id = get_index_owner_user_id(
-            db=db,
-            knowledge_id=knowledge_id,
-            current_user_id=current_user.id,
-        )
-
-        # Get retriever and create backend
-        retriever, storage_backend = get_retriever_and_backend(
-            retriever_name=retriever_name,
-            retriever_namespace=retriever_namespace,
-            user_id=current_user.id,
-            db=db,
-        )
-
-        # Get index name from storage backend
-        index_name = storage_backend.get_index_name(
-            knowledge_id=knowledge_id,
-            user_id=index_owner_user_id,
-        )
-
-        # Get index strategy mode from retriever config
-        index_strategy = storage_backend.index_strategy.get("mode", "per_dataset")
+            user_name=current_user.user_name,
+            score_threshold=request.score_threshold,
+            retrieval_mode=request.retrieval_mode.value,
+            vector_weight=(
+                request.hybrid_weights.vector_weight
+                if request.hybrid_weights is not None
+                else None
+            ),
+            keyword_weight=(
+                request.hybrid_weights.keyword_weight
+                if request.hybrid_weights is not None
+                else None
+            ),
+            metadata_condition=request.metadata_condition,
+        )
+
+        gateway = get_query_gateway()
+        try:
+            result = await gateway.query(runtime_spec, db=db)
+        except RemoteRagGatewayError:
+            result = await LocalRagGateway().query(runtime_spec, db=db)
 
-        return IndexNameResponse(
-            knowledge_id=knowledge_id,
-            index_name=index_name,
-            index_strategy=index_strategy,
-        )
+        return {"records": result.get("records", [])}
     except HTTPException:
         raise
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
diff --git a/backend/app/schemas/rag.py b/backend/app/schemas/rag.py
index 59090ad1f..296e5cf06 100644
--- a/backend/app/schemas/rag.py
+++ b/backend/app/schemas/rag.py
@@ -175,30 +175,6 @@ def validate_hybrid_config(cls, v, info):
         return v
 
 
-class DocumentUploadRequest(BaseModel):
-    """Document upload request."""
-
-    knowledge_id: str = Field(..., description="Knowledge base ID")
-    retriever_ref: RetrieverRef = Field(
-        ..., description="Reference to Retriever configuration"
-    )
-    embedding_model_ref: EmbeddingModelRef = Field(
-        ..., description="Reference to embedding Model CRD"
-    )
-
-
-class DocumentUploadResponse(BaseModel):
-    """Document upload response."""
-
-    doc_ref: str
-    knowledge_id: str
-    source_file: str
-    chunk_count: int
-    index_name: str
-    status: str
-    created_at: str
-
-
 class RetrievalResult(BaseModel):
     """Single retrieval result (Dify-compatible format)."""
 
@@ -216,69 +192,3 @@ class RetrieveResponse(BaseModel):
     records: List[RetrievalResult] = Field(
         ..., description="List of records from querying the knowledge base"
     )
-
-
-class DocumentDeleteRequest(BaseModel):
-    """Document deletion request."""
-
-    knowledge_id: str = Field(..., description="Knowledge base ID")
-    retriever_ref: RetrieverRef = Field(
-        ..., description="Reference to Retriever configuration"
-    )
-
-
-class DocumentDeleteResponse(BaseModel):
-    """Document deletion response."""
-
-    doc_ref: str
-    knowledge_id: str
-    deleted_chunks: int
-    status: str
-
-
-class DocumentDetailRequest(BaseModel):
-    """Document detail request."""
-
-    knowledge_id: str = Field(..., description="Knowledge base ID")
-    retriever_ref: RetrieverRef = Field(
-        ..., description="Reference to Retriever configuration"
-    )
-
-
-class DocumentDetailResponse(BaseModel):
-    """Document detail response."""
-
-    doc_ref: str
-    knowledge_id: str
-    source_file: str
-    chunk_count: int
-    chunks: List[Dict]
-
-
-class DocumentListRequest(BaseModel):
-    """Document list request."""
-
-    knowledge_id: str = Field(..., description="Knowledge base ID")
-    retriever_ref: RetrieverRef = Field(
-        ..., description="Reference to Retriever configuration"
-    )
-    page: int = Field(1, ge=1)
-    page_size: int = Field(20, ge=1, le=100)
-
-
-class DocumentListResponse(BaseModel):
-    """Document list response."""
-
-    documents: List[Dict]
-    total: int
-    page: int
-    page_size: int
-    knowledge_id: str
-
-
-class IndexNameResponse(BaseModel):
-    """Index name query response."""
-
-    knowledge_id: str = Field(..., description="Knowledge base ID")
-    index_name: str = Field(..., description="Computed index name")
-    index_strategy: str = Field(..., description="Index strategy mode used")
diff --git a/backend/app/services/rag/README.md b/backend/app/services/rag/README.md
index 2d2912489..3c4af4fbe 100644
--- a/backend/app/services/rag/README.md
+++ b/backend/app/services/rag/README.md
@@ -347,17 +347,6 @@ POST   /api/v1/namespaces/{namespace}/retrievers/{name}/test # Test connection
 ### RAG Operations (Updated)
 
 ```
-POST /api/rag/documents/upload
-{
-  "knowledge_id": "kb_123",
-  "retriever_ref": {
-    "name": "my-es-retriever",
-    "namespace": "default"
-  },
-  "file": <binary>,
-  "embedding_config": {...}
-}
-
 POST /api/rag/retrieve
 {
   "knowledge_id": "kb_123",
diff --git a/backend/app/services/rag/document_service.py b/backend/app/services/rag/document_service.py
deleted file mode 100644
index 5f3ccefdc..000000000
--- a/backend/app/services/rag/document_service.py
+++ /dev/null
@@ -1,402 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Document service for RAG functionality.
-Refactored to use modular architecture with pluggable storage backends.
-"""
-import asyncio
-import logging
-import uuid
-from datetime import datetime, timezone
-from typing import Dict, Optional, Tuple
-
-from sqlalchemy.orm import Session
-
-from app.models.subtask_context import ContextStatus, ContextType, SubtaskContext
-from app.schemas.rag import SplitterConfig
-from app.services.context import context_service
-from app.services.rag.embedding.factory import create_embedding_model_from_crd
-from app.services.rag.index import DocumentIndexer
-from app.services.rag.storage.base import BaseStorageBackend
-from app.services.rag.storage.chunk_metadata import ChunkMetadata
-from shared.telemetry.decorators import (
-    add_span_event,
-    capture_trace_context,
-    trace_background,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class DocumentService:
-    """
-    High-level document management service.
-    Uses modular architecture with pluggable storage backends.
-    """
-
-    def __init__(self, storage_backend: BaseStorageBackend):
-        """
-        Initialize document service.
-
-        Args:
-            storage_backend: Storage backend instance (Elasticsearch, Qdrant, etc.)
-        """
-        self.storage_backend = storage_backend
-
-    def _get_attachment_binary(
-        self, db: Session, attachment_id: int
-    ) -> Tuple[bytes, str, str]:
-        """
-        Get original binary data and metadata from context (attachment type).
-
-        This method retrieves the original binary data from storage (MySQL or
-        external storage like S3/MinIO) for RAG indexing.
-
-        Args:
-            db: Database session
-            attachment_id: Context ID (for attachment type contexts)
-
-        Returns:
-            Tuple of (binary_data, filename, file_extension)
-
-        Raises:
-            ValueError: If context not found, not ready, or binary data unavailable
-        """
-        # Query context (attachment type)
-        context = (
-            db.query(SubtaskContext)
-            .filter(
-                SubtaskContext.id == attachment_id,
-                SubtaskContext.context_type == ContextType.ATTACHMENT.value,
-            )
-            .first()
-        )
-
-        if not context:
-            raise ValueError(f"Attachment context {attachment_id} not found")
-
-        # Check context status
-        if context.status != ContextStatus.READY.value:
-            raise ValueError(
-                f"Attachment context {attachment_id} is not ready (status: {context.status})"
-            )
-
-        # Get original binary data from storage (supports MySQL and external storage)
-        binary_data = context_service.get_attachment_binary_data(
-            db=db,
-            context=context,
-        )
-
-        if binary_data is None:
-            logger.error(
-                f"Failed to retrieve binary data for attachment context {attachment_id}, "
-                f"storage_backend={context.storage_backend}, "
-                f"storage_key={context.storage_key}"
-            )
-            raise ValueError(
-                f"Attachment context {attachment_id} has no binary data available"
-            )
-
-        logger.info(
-            f"Retrieved binary data for attachment context {attachment_id}: "
-            f"filename={context.original_filename}, "
-            f"size={len(binary_data)} bytes, "
-            f"extension={context.file_extension}"
-        )
-
-        return binary_data, context.original_filename, context.file_extension
-
-    @trace_background("document_service.index_document_sync", "rag.document_service")
-    def _index_document_sync(
-        self,
-        knowledge_id: str,
-        file_path: Optional[str],
-        attachment_id: Optional[int],
-        embedding_model_name: str,
-        embedding_model_namespace: str,
-        user_id: int,
-        db: Session,
-        splitter_config: Optional[SplitterConfig] = None,
-        document_id: Optional[int] = None,
-        trace_context: Optional[dict] = None,
-        user_name: Optional[str] = None,
-    ) -> Dict:
-        """
-        Synchronous document indexing implementation.
-        Runs in thread pool to avoid event loop conflicts.
-
-        The trace_context parameter is used by @trace_background decorator to restore
-        the parent trace context, enabling distributed tracing across thread boundaries.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            file_path: Path to document file (optional, mutually exclusive with attachment_id)
-            attachment_id: Attachment ID (optional, mutually exclusive with file_path)
-            embedding_model_name: Embedding model name
-            embedding_model_namespace: Embedding model namespace
-            user_id: User ID
-            db: Database session
-            splitter_config: Optional splitter configuration
-            document_id: Optional document ID to use as doc_ref
-            trace_context: Trace context for distributed tracing (captured via capture_trace_context())
-            user_name: User name for placeholder replacement in embedding headers (optional)
-
-        Returns:
-            Indexing result dict
-        """
-        if file_path is None and attachment_id is None:
-            raise ValueError("Either file_path or attachment_id must be provided")
-
-        # Use document_id as doc_ref if provided, otherwise generate a random one
-        if document_id is not None:
-            doc_ref = str(document_id)
-        else:
-            doc_ref = f"doc_{uuid.uuid4().hex[:12]}"
-
-        add_span_event(
-            "rag.document_service.indexing.started",
-            {
-                "knowledge_id": str(knowledge_id),
-                "doc_ref": doc_ref,
-                "attachment_id": str(attachment_id) if attachment_id else "None",
-                "embedding_model": embedding_model_name,
-            },
-        )
-
-        # Create embedding model from CRD
-        embed_model = create_embedding_model_from_crd(
-            db=db,
-            user_id=user_id,
-            model_name=embedding_model_name,
-            model_namespace=embedding_model_namespace,
-            user_name=user_name,
-        )
-        add_span_event(
-            "rag.document_service.embedding_model.created",
-            {
-                "model_name": embedding_model_name,
-                "model_namespace": embedding_model_namespace,
-            },
-        )
-
-        if attachment_id is not None:
-            # Get original binary data from attachment (supports MySQL and external storage)
-            binary_data, filename, file_extension = self._get_attachment_binary(
-                db, attachment_id
-            )
-            add_span_event(
-                "rag.document_service.attachment.loaded",
-                {
-                    "attachment_id": str(attachment_id),
-                    "filename": filename,
-                    "file_extension": file_extension,
-                    "binary_size": str(len(binary_data)),
-                },
-            )
-
-            # Create ChunkMetadata at the service layer
-            chunk_metadata = ChunkMetadata(
-                knowledge_id=knowledge_id,
-                doc_ref=doc_ref,
-                source_file=filename,
-                created_at=datetime.now(timezone.utc).isoformat(),
-            )
-
-            # Create indexer with file_extension for SmartSplitter to use correct strategy
-            # This is critical for .md files to use MarkdownNodeParser
-            indexer = DocumentIndexer(
-                storage_backend=self.storage_backend,
-                embed_model=embed_model,
-                splitter_config=splitter_config,
-                file_extension=file_extension,
-            )
-
-            # Index from binary data directly (indexer handles parsing)
-            result = indexer.index_from_binary(
-                binary_data=binary_data,
-                file_extension=file_extension,
-                chunk_metadata=chunk_metadata,
-                user_id=user_id,
-            )
-        else:
-            # For file path, extract extension from path
-            from pathlib import Path
-
-            file_extension = Path(file_path).suffix.lower() if file_path else None
-            source_file = Path(file_path).name if file_path else ""
-
-            # Create ChunkMetadata at the service layer
-            chunk_metadata = ChunkMetadata(
-                knowledge_id=knowledge_id,
-                doc_ref=doc_ref,
-                source_file=source_file,
-                created_at=datetime.now(timezone.utc).isoformat(),
-            )
-
-            # Create indexer with file_extension for SmartSplitter
-            indexer = DocumentIndexer(
-                storage_backend=self.storage_backend,
-                embed_model=embed_model,
-                splitter_config=splitter_config,
-                file_extension=file_extension,
-            )
-
-            # Index from file path
-            result = indexer.index_document(
-                file_path=file_path,
-                chunk_metadata=chunk_metadata,
-                user_id=user_id,
-            )
-
-        add_span_event(
-            "rag.document_service.indexing.completed",
-            {
-                "knowledge_id": str(knowledge_id),
-                "doc_ref": doc_ref,
-                "chunk_count": str(result.get("chunk_count", 0)),
-                "index_name": result.get("index_name", "unknown"),
-                "status": result.get("status", "unknown"),
-            },
-        )
-
-        return result
-
-    async def index_document(
-        self,
-        knowledge_id: str,
-        embedding_model_name: str,
-        embedding_model_namespace: str,
-        user_id: int,
-        db: Session,
-        file_path: Optional[str] = None,
-        attachment_id: Optional[int] = None,
-        splitter_config: Optional[SplitterConfig] = None,
-        document_id: Optional[int] = None,
-        trace_context: Optional[dict] = None,
-        user_name: Optional[str] = None,
-    ) -> Dict:
-        """
-        Index a document into storage backend.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            embedding_model_name: Embedding model name
-            embedding_model_namespace: Embedding model namespace
-            user_id: User ID
-            db: Database session
-            file_path: Path to document file (optional, mutually exclusive with attachment_id)
-            attachment_id: Attachment ID (optional, mutually exclusive with file_path)
-            splitter_config: Optional splitter configuration. If None, defaults to SemanticSplitter
-            document_id: Optional document ID to use as doc_ref
-            trace_context: Optional trace context for distributed tracing (captured via capture_trace_context()).
-                          If provided, this context will be propagated to the background thread.
-                          If None, the current trace context will be captured automatically.
-            user_name: User name for placeholder replacement in embedding headers (optional)
-
-        Returns:
-            Indexing result dict with:
-                - doc_ref: Document reference ID (document_id if provided, otherwise generated)
-                - knowledge_id: Knowledge base ID
-                - source_file: Source filename
-                - chunk_count: Number of chunks created
-                - index_name: Index/collection name
-                - status: Indexing status
-                - created_at: Creation timestamp
-
-        Raises:
-            Exception: If indexing fails
-        """
-        # Use provided trace context or capture current context before switching to thread pool
-        # This allows the background thread to restore the trace context
-        trace_ctx = (
-            trace_context if trace_context is not None else capture_trace_context()
-        )
-
-        # Run synchronous indexing in thread pool to avoid uvloop conflicts
-        # IMPORTANT: trace_context MUST be passed as a keyword argument for @trace_background
-        # decorator to properly extract it from kwargs (see decorators.py line 388)
-        return await asyncio.to_thread(
-            self._index_document_sync,
-            knowledge_id,
-            file_path,
-            attachment_id,
-            embedding_model_name,
-            embedding_model_namespace,
-            user_id,
-            db,
-            splitter_config,
-            document_id,
-            trace_context=trace_ctx,
-            user_name=user_name,
-        )
-
-    async def delete_document(
-        self,
-        knowledge_id: str,
-        doc_ref: str,
-        user_id: Optional[int] = None,
-    ) -> Dict:
-        """
-        Delete a document from storage.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID to delete
-
-        Returns:
-            Deletion result dict with:
-                - doc_ref: Deleted document reference ID
-                - knowledge_id: Knowledge base ID
-                - deleted_chunks: Number of chunks deleted
-                - status: Deletion status
-        """
-        # Run in thread pool to avoid uvloop conflicts
-        return await asyncio.to_thread(
-            self.storage_backend.delete_document,
-            knowledge_id=knowledge_id,
-            doc_ref=doc_ref,
-            user_id=user_id,
-        )
-
-    async def list_documents(
-        self,
-        knowledge_id: str,
-        page: int = 1,
-        page_size: int = 20,
-        user_id: Optional[int] = None,
-    ) -> Dict:
-        """
-        List documents in knowledge base with pagination.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            page: Page number (1-indexed)
-            page_size: Number of documents per page
-
-        Returns:
-            Document list dict with:
-                - documents: List of document summary dicts
-                - total: Total number of documents
-                - page: Current page number
-                - page_size: Page size
-                - knowledge_id: Knowledge base ID
-        """
-        # Run in thread pool to avoid uvloop conflicts
-        return await asyncio.to_thread(
-            self.storage_backend.list_documents,
-            knowledge_id=knowledge_id,
-            page=page,
-            page_size=page_size,
-            user_id=user_id,
-        )
-
-    def test_connection(self) -> bool:
-        """
-        Test connection to storage backend.
-
-        Returns:
-            True if connection successful, False otherwise
-        """
-        return self.storage_backend.test_connection()
diff --git a/backend/app/services/rag/embedding/custom.py b/backend/app/services/rag/embedding/custom.py
index ee3a8e0f0..86f5d57a3 100644
--- a/backend/app/services/rag/embedding/custom.py
+++ b/backend/app/services/rag/embedding/custom.py
@@ -2,99 +2,8 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Custom embedding implementation for external APIs.
-"""
+"""Backward-compatible re-export of the shared engine custom embedding."""
 
-import asyncio
-from typing import Dict, List, Optional
+from knowledge_engine.embedding.custom import CustomEmbedding
 
-import requests
-from llama_index.core.base.embeddings.base import BaseEmbedding
-from tenacity import retry, stop_after_attempt, wait_exponential
-
-
-class CustomEmbedding(BaseEmbedding):
-    """
-    Custom embedding wrapper for external APIs.
-    Implements LlamaIndex's BaseEmbedding interface with retry mechanism.
-    """
-
-    # Declare fields as class attributes for Pydantic compatibility
-    api_url: str
-    model: str
-    headers: Dict[str, str]
-    api_key: Optional[str] = None
-    _dimension: Optional[int] = None
-
-    def __init__(
-        self,
-        api_url: str,
-        model: str,
-        headers: Optional[Dict[str, str]] = None,
-        api_key: Optional[str] = None,
-        embed_batch_size: int = 10,
-        dimensions: Optional[int] = None,
-        **kwargs,
-    ):
-        # Merge api_key into headers if provided
-        final_headers = headers.copy() if headers else {}
-        if api_key and "Authorization" not in final_headers:
-            final_headers["Authorization"] = f"Bearer {api_key}"
-
-        super().__init__(
-            model_name=model,
-            embed_batch_size=embed_batch_size,
-            api_url=api_url,
-            model=model,
-            headers=final_headers,
-            api_key=api_key,
-            **kwargs,
-        )
-
-        # Set dimension from Model CRD config if provided
-        # This allows Milvus to create collections with the correct dimension
-        if dimensions is not None:
-            self._dimension = dimensions
-
-    def _get_query_embedding(self, query: str) -> List[float]:
-        """Get embedding for a query string."""
-        return self._call_api(query)
-
-    def _get_text_embedding(self, text: str) -> List[float]:
-        """Get embedding for a text string."""
-        return self._call_api(text)
-
-    async def _aget_query_embedding(self, query: str) -> List[float]:
-        """Async version - runs sync call in thread pool to avoid blocking."""
-        return await asyncio.to_thread(self._get_query_embedding, query)
-
-    @retry(
-        stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=10)
-    )
-    def _call_api(self, text: str) -> List[float]:
-        """
-        Call external embedding API with retry mechanism.
-
-        Args:
-            text: Text to embed
-
-        Returns:
-            Embedding vector
-
-        Raises:
-            requests.HTTPError: If API call fails after retries
-        """
-        response = requests.post(
-            self.api_url,
-            json={"model": self.model, "input": text},
-            headers=self.headers,
-            timeout=30,
-        )
-        response.raise_for_status()
-        embedding = response.json()["data"][0]["embedding"]
-
-        if self._dimension is None:
-            self._dimension = len(embedding)
-
-        return embedding
+__all__ = ["CustomEmbedding"]
diff --git a/backend/app/services/rag/embedding/factory.py b/backend/app/services/rag/embedding/factory.py
index 1dc4803f7..f79f76ad4 100644
--- a/backend/app/services/rag/embedding/factory.py
+++ b/backend/app/services/rag/embedding/factory.py
@@ -16,7 +16,9 @@
 from app.services.chat.config.model_resolver import (
     build_default_headers_with_placeholders,
 )
-from app.services.rag.embedding.custom import CustomEmbedding
+from knowledge_engine.embedding.factory import (
+    create_embedding_model_from_runtime_config as engine_create_embedding_model_from_runtime_config,
+)
 from shared.models import RuntimeEmbeddingModelConfig
 from shared.utils.crypto import decrypt_api_key
 
@@ -177,14 +179,21 @@ def create_embedding_model_from_crd(
             f"[EmbeddingFactory] Model '{model_name}' has configured dimensions: {dimensions}"
         )
 
-    return _create_embedding_model_from_resolved_values(
-        protocol=protocol,
-        model_name=model_name,
-        api_key=api_key,
-        base_url=base_url,
-        model_id=model_id,
-        custom_headers=custom_headers if isinstance(custom_headers, dict) else {},
-        dimensions=dimensions,
+    return engine_create_embedding_model_from_runtime_config(
+        RuntimeEmbeddingModelConfig(
+            model_name=model_name,
+            model_namespace=model_namespace,
+            resolved_config={
+                "protocol": protocol,
+                "api_key": api_key,
+                "base_url": base_url,
+                "model_id": model_id,
+                "custom_headers": (
+                    custom_headers if isinstance(custom_headers, dict) else {}
+                ),
+                "dimensions": dimensions,
+            },
+        )
     )
 
 
@@ -192,84 +201,4 @@ def create_embedding_model_from_runtime_config(
     runtime_config: RuntimeEmbeddingModelConfig,
 ) -> BaseEmbedding:
     """Create embedding model directly from resolved runtime config."""
-    resolved_config = runtime_config.resolved_config or {}
-    return _create_embedding_model_from_resolved_values(
-        protocol=resolved_config.get("protocol"),
-        model_name=runtime_config.model_name,
-        api_key=resolved_config.get("api_key"),
-        base_url=resolved_config.get("base_url"),
-        model_id=resolved_config.get("model_id"),
-        custom_headers=resolved_config.get("custom_headers") or {},
-        dimensions=resolved_config.get("dimensions"),
-    )
-
-
-def _create_embedding_model_from_resolved_values(
-    *,
-    protocol: str | None,
-    model_name: str,
-    api_key: str | None,
-    base_url: str | None,
-    model_id: str | None,
-    custom_headers: dict[str, Any],
-    dimensions: int | None,
-) -> BaseEmbedding:
-    # Build embedding config based on protocol
-    if protocol == "openai":
-        # OpenAI protocol (supports custom headers for internal gateways)
-        # If custom headers are provided, use CustomEmbedding for flexibility
-        if (
-            custom_headers
-            and isinstance(custom_headers, dict)
-            and len(custom_headers) > 0
-        ):
-            # Construct OpenAI-compatible endpoint
-            api_url = (
-                f"{base_url.rstrip('/')}/embeddings"
-                if base_url
-                else "https://api.openai.com/v1/embeddings"
-            )
-
-            return CustomEmbedding(
-                api_url=api_url,
-                model=model_id or "text-embedding-3-small",
-                headers=custom_headers,
-                api_key=api_key,
-                dimensions=dimensions,
-            )
-        else:
-            # Standard OpenAI embedding
-            from llama_index.embeddings.openai import OpenAIEmbedding
-
-            # Note: OpenAIEmbedding doesn't support dimensions parameter
-            # The dimension is determined by the model itself
-            return OpenAIEmbedding(
-                model=model_id or "text-embedding-3-small",
-                api_key=api_key,
-                api_base=base_url if base_url else None,
-            )
-    elif protocol in ["cohere", "jina", "custom"]:
-        # Custom API endpoint for Cohere, Jina, or other custom providers
-        if not base_url:
-            raise ValueError(
-                f"Model '{model_name}' with protocol '{protocol}' requires base_url"
-            )
-
-        # Ensure base_url ends with /embeddings or appropriate endpoint
-        if not base_url.endswith("/embeddings"):
-            api_url = f"{base_url.rstrip('/')}/embeddings"
-        else:
-            api_url = base_url
-
-        return CustomEmbedding(
-            api_url=api_url,
-            model=model_id,
-            headers=custom_headers if isinstance(custom_headers, dict) else {},
-            api_key=api_key,
-            dimensions=dimensions,
-        )
-    else:
-        raise ValueError(
-            f"Unsupported embedding protocol for model '{model_name}': {protocol}. "
-            "Supported: openai, cohere, jina, custom"
-        )
+    return engine_create_embedding_model_from_runtime_config(runtime_config)
diff --git a/backend/app/services/rag/gateway.py b/backend/app/services/rag/gateway.py
index b43c60f44..2746a9b79 100644
--- a/backend/app/services/rag/gateway.py
+++ b/backend/app/services/rag/gateway.py
@@ -3,6 +3,7 @@
 from sqlalchemy.orm import Session
 
 from app.services.rag.runtime_specs import (
+    ConnectionTestRuntimeSpec,
     DeleteRuntimeSpec,
     IndexRuntimeSpec,
     QueryRuntimeSpec,
@@ -30,3 +31,10 @@ async def delete_document_index(
         *,
         db: Session,
     ) -> dict: ...
+
+    async def test_connection(
+        self,
+        spec: ConnectionTestRuntimeSpec,
+        *,
+        db: Session | None = None,
+    ) -> dict: ...
diff --git a/backend/app/services/rag/index/__init__.py b/backend/app/services/rag/index/__init__.py
deleted file mode 100644
index 2be430a19..000000000
--- a/backend/app/services/rag/index/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Index module for RAG functionality.
-"""
-
-from app.services.rag.index.indexer import DocumentIndexer
-
-__all__ = [
-    "DocumentIndexer",
-]
diff --git a/backend/app/services/rag/index/indexer.py b/backend/app/services/rag/index/indexer.py
deleted file mode 100644
index 8c30884dd..000000000
--- a/backend/app/services/rag/index/indexer.py
+++ /dev/null
@@ -1,326 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Document indexing orchestration.
-"""
-
-import logging
-import tempfile
-import uuid
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-from llama_index.core import Document, SimpleDirectoryReader
-
-from app.schemas.rag import SplitterConfig
-from app.services.rag.splitter import SemanticSplitter, SentenceSplitter, SmartSplitter
-from app.services.rag.splitter.factory import create_splitter
-from app.services.rag.storage.base import BaseStorageBackend
-from app.services.rag.storage.chunk_metadata import ChunkMetadata
-from shared.telemetry.decorators import add_span_event
-
-logger = logging.getLogger(__name__)
-
-# Metadata keys to preserve during indexing
-# These are simple string/number fields that won't cause ES mapping conflicts
-SAFE_METADATA_KEYS = {
-    "filename",
-    "file_path",
-    "file_name",
-    "file_type",
-    "file_size",
-    "creation_date",
-    "last_modified_date",
-    "page_label",
-    "page_number",
-}
-
-
-def sanitize_metadata(metadata: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Sanitize document metadata to prevent Elasticsearch mapping conflicts.
-
-    LlamaIndex's SimpleDirectoryReader extracts complex nested metadata from
-    documents (especially PPTX, DOCX, etc.) that can cause ES dynamic mapping
-    conflicts when field values have inconsistent types across documents.
-
-    This function:
-    1. Keeps only safe, simple metadata fields
-    2. Converts all values to strings to prevent type inference issues
-    3. Removes nested structures that cause mapping conflicts
-
-    Args:
-        metadata: Original metadata dict from LlamaIndex document
-
-    Returns:
-        Sanitized metadata dict with only safe fields
-    """
-    sanitized = {}
-    for key in SAFE_METADATA_KEYS:
-        if key in metadata:
-            value = metadata[key]
-            # Convert to string to prevent type inference issues
-            if value is not None:
-                sanitized[key] = str(value) if not isinstance(value, str) else value
-    return sanitized
-
-
-class DocumentIndexer:
-    """Orchestrates document indexing process."""
-
-    def __init__(
-        self,
-        storage_backend: BaseStorageBackend,
-        embed_model,
-        splitter_config: Optional[SplitterConfig] = None,
-        file_extension: Optional[str] = None,
-    ):
-        """
-        Initialize document indexer.
-
-        Args:
-            storage_backend: Storage backend instance
-            embed_model: Embedding model
-            splitter_config: Optional splitter configuration. If None, defaults to SemanticSplitter
-            file_extension: Optional file extension for smart splitter
-        """
-        self.storage_backend = storage_backend
-        self.embed_model = embed_model
-        self.file_extension = file_extension
-        self.splitter = create_splitter(splitter_config, embed_model, file_extension)
-
-    def index_document(
-        self, file_path: str, chunk_metadata: ChunkMetadata, **kwargs
-    ) -> Dict:
-        """
-        Index a document from file path (synchronous).
-
-        This method is synchronous because it's called from asyncio.to_thread()
-        in DocumentService to avoid event loop conflicts with LlamaIndex.
-
-        Args:
-            file_path: Path to document file
-            chunk_metadata: Metadata for the document chunks (created at service layer)
-            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
-
-        Returns:
-            Indexing result dict
-
-        Raises:
-            Exception: If indexing fails
-        """
-        # Load document from file
-        documents = SimpleDirectoryReader(input_files=[file_path]).load_data()
-
-        return self._index_documents(
-            documents=documents,
-            chunk_metadata=chunk_metadata,
-            **kwargs,
-        )
-
-    def index_from_binary(
-        self,
-        binary_data: bytes,
-        file_extension: str,
-        chunk_metadata: ChunkMetadata,
-        **kwargs,
-    ) -> Dict:
-        """
-        Index a document from binary data (synchronous).
-
-        This method writes binary data to a temporary file and uses
-        SimpleDirectoryReader to parse it, leveraging LlamaIndex's
-        built-in document parsing capabilities for various file formats.
-
-        Supports MySQL and external storage (S3/MinIO) binary data.
-
-        Args:
-            binary_data: Original file binary data from attachment storage
-            file_extension: File extension (e.g., '.pdf', '.docx')
-            chunk_metadata: Metadata for the document chunks (created at service layer)
-            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
-
-        Returns:
-            Indexing result dict
-
-        Raises:
-            Exception: If indexing fails
-        """
-        # Write binary data to a temporary file for SimpleDirectoryReader
-        # This allows LlamaIndex to use its built-in parsers for various formats
-        with tempfile.NamedTemporaryFile(
-            suffix=file_extension, delete=False
-        ) as tmp_file:
-            tmp_file.write(binary_data)
-            tmp_file_path = tmp_file.name
-
-        try:
-            logger.info(
-                f"Indexing document from binary: source_file={chunk_metadata.source_file}, "
-                f"extension={file_extension}, size={len(binary_data)} bytes"
-            )
-
-            # Load document using SimpleDirectoryReader
-            documents = SimpleDirectoryReader(input_files=[tmp_file_path]).load_data()
-
-            # Update metadata with original filename (without extension)
-            filename_without_ext = Path(chunk_metadata.source_file).stem
-            for doc in documents:
-                doc.metadata["filename"] = filename_without_ext
-
-            return self._index_documents(
-                documents=documents,
-                chunk_metadata=chunk_metadata,
-                **kwargs,
-            )
-        finally:
-            # Clean up temporary file
-            try:
-                Path(tmp_file_path).unlink()
-            except Exception as e:
-                logger.warning(f"Failed to delete temporary file {tmp_file_path}: {e}")
-
-    def _index_documents(
-        self,
-        documents: List[Document],
-        chunk_metadata: ChunkMetadata,
-        **kwargs,
-    ) -> Dict:
-        """
-        Internal method to index documents.
-
-        Args:
-            documents: List of LlamaIndex Document objects
-            chunk_metadata: Metadata for the document chunks (passed from service layer)
-            **kwargs: Additional parameters
-
-        Returns:
-            Indexing result dict including chunk metadata for storage
-        """
-        add_span_event(
-            "rag.indexer.documents.received",
-            {
-                "knowledge_id": chunk_metadata.knowledge_id,
-                "doc_ref": chunk_metadata.doc_ref,
-                "source_file": chunk_metadata.source_file,
-                "document_count": str(len(documents)),
-            },
-        )
-
-        # Sanitize document metadata to prevent ES mapping conflicts
-        # This removes complex nested structures from PPTX/DOCX metadata
-        for doc in documents:
-            doc.metadata = sanitize_metadata(doc.metadata)
-
-        # Split documents into nodes
-        nodes = self.splitter.split_documents(documents)
-
-        # Apply chunk metadata to all nodes immediately after splitting
-        chunk_metadata.apply_to_nodes(nodes)
-
-        add_span_event(
-            "rag.indexer.documents.split",
-            {
-                "knowledge_id": chunk_metadata.knowledge_id,
-                "doc_ref": chunk_metadata.doc_ref,
-                "node_count": str(len(nodes)),
-                "splitter_type": type(self.splitter).__name__,
-            },
-        )
-
-        # Build chunk metadata for storage in database
-        chunks_data = self._build_chunks_metadata(nodes)
-
-        # Delegate to storage backend for indexing (metadata already applied to nodes)
-        add_span_event(
-            "rag.indexer.vector_store.indexing_started",
-            {
-                "knowledge_id": chunk_metadata.knowledge_id,
-                "doc_ref": chunk_metadata.doc_ref,
-                "node_count": str(len(nodes)),
-                "storage_backend": type(self.storage_backend).__name__,
-            },
-        )
-        result = self.storage_backend.index_with_metadata(
-            nodes=nodes,
-            chunk_metadata=chunk_metadata,
-            embed_model=self.embed_model,
-            **kwargs,
-        )
-        add_span_event(
-            "rag.indexer.vector_store.indexing_completed",
-            {
-                "knowledge_id": chunk_metadata.knowledge_id,
-                "doc_ref": chunk_metadata.doc_ref,
-                "indexed_count": str(result.get("indexed_count", 0)),
-                "index_name": result.get("index_name", "unknown"),
-                "status": result.get("status", "unknown"),
-            },
-        )
-
-        # Add document info to result
-        result.update(
-            {
-                "doc_ref": chunk_metadata.doc_ref,
-                "knowledge_id": chunk_metadata.knowledge_id,
-                "source_file": chunk_metadata.source_file,
-                "chunk_count": len(nodes),
-                "created_at": chunk_metadata.created_at,
-                "chunks_data": chunks_data,  # Include chunk metadata for DB storage
-            }
-        )
-
-        return result
-
-    def _build_chunks_metadata(self, nodes: List) -> Dict[str, Any]:
-        """
-        Build chunk metadata from nodes for storage in database.
-
-        Args:
-            nodes: List of nodes from splitter
-
-        Returns:
-            Dictionary with chunk metadata suitable for JSON storage
-        """
-        items = []
-        current_position = 0
-
-        for idx, node in enumerate(nodes):
-            text = node.text if hasattr(node, "text") else str(node)
-            text_length = len(text)
-
-            # Estimate token count (roughly 4 characters per token)
-            token_count = text_length // 4
-
-            items.append(
-                {
-                    "index": idx,
-                    "content": text,
-                    "token_count": token_count,
-                    "start_position": current_position,
-                    "end_position": current_position + text_length,
-                }
-            )
-
-            current_position += text_length
-
-        # Get splitter type info
-        splitter_type = "semantic"  # default
-        splitter_subtype = None
-
-        if isinstance(self.splitter, SmartSplitter):
-            splitter_type = "smart"
-            splitter_subtype = self.splitter._get_subtype()
-        elif isinstance(self.splitter, SentenceSplitter):
-            splitter_type = "sentence"
-
-        return {
-            "items": items,
-            "total_count": len(items),
-            "splitter_type": splitter_type,
-            "splitter_subtype": splitter_subtype,
-            "created_at": datetime.now(timezone.utc).isoformat(),
-        }
diff --git a/backend/app/services/rag/local_data_plane/administration.py b/backend/app/services/rag/local_data_plane/administration.py
new file mode 100644
index 000000000..396f7522c
--- /dev/null
+++ b/backend/app/services/rag/local_data_plane/administration.py
@@ -0,0 +1,26 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import asyncio
+
+from sqlalchemy.orm import Session
+
+from app.services.rag.runtime_specs import ConnectionTestRuntimeSpec
+from app.services.rag.storage.factory import create_storage_backend_from_runtime_config
+
+
+async def test_connection_local(
+    spec: ConnectionTestRuntimeSpec,
+    *,
+    db: Session | None = None,
+) -> dict:
+    del db
+    storage_backend = create_storage_backend_from_runtime_config(spec.retriever_config)
+    success = await asyncio.to_thread(storage_backend.test_connection)
+    return {
+        "success": success,
+        "message": "Connection successful" if success else "Connection failed",
+    }
diff --git a/backend/app/services/rag/local_data_plane/retrieval.py b/backend/app/services/rag/local_data_plane/retrieval.py
index 0f6a5b472..328b9cd81 100644
--- a/backend/app/services/rag/local_data_plane/retrieval.py
+++ b/backend/app/services/rag/local_data_plane/retrieval.py
@@ -45,6 +45,8 @@ async def query_local(
         db=db,
         max_results=spec.max_results,
         document_ids=spec.document_ids,
+        metadata_condition=spec.metadata_condition,
+        knowledge_base_configs=spec.knowledge_base_configs,
         user_name=spec.user_name,
         route_mode=spec.route_mode,
         user_id=spec.user_id,
diff --git a/backend/app/services/rag/local_gateway.py b/backend/app/services/rag/local_gateway.py
index 09bab8f88..f188db269 100644
--- a/backend/app/services/rag/local_gateway.py
+++ b/backend/app/services/rag/local_gateway.py
@@ -1,11 +1,13 @@
 from sqlalchemy.orm import Session
 
+from app.services.rag.local_data_plane.administration import test_connection_local
 from app.services.rag.local_data_plane.indexing import (
     delete_document_index_local,
     index_document_local,
 )
 from app.services.rag.local_data_plane.retrieval import query_local
 from app.services.rag.runtime_specs import (
+    ConnectionTestRuntimeSpec,
     DeleteRuntimeSpec,
     IndexRuntimeSpec,
     QueryRuntimeSpec,
@@ -17,6 +19,7 @@ def __init__(self) -> None:
         self._index_executor = index_document_local
         self._delete_executor = delete_document_index_local
         self._retrieval_executor = query_local
+        self._connection_test_executor = test_connection_local
 
     async def index_document(
         self,
@@ -45,3 +48,11 @@ async def delete_document_index(
         db: Session,
     ) -> dict:
         return await self._delete_executor(spec, db=db)
+
+    async def test_connection(
+        self,
+        spec: ConnectionTestRuntimeSpec,
+        *,
+        db: Session | None = None,
+    ) -> dict:
+        return await self._connection_test_executor(spec, db=db)
diff --git a/backend/app/services/rag/remote_gateway.py b/backend/app/services/rag/remote_gateway.py
index 362462457..2fbb1046b 100644
--- a/backend/app/services/rag/remote_gateway.py
+++ b/backend/app/services/rag/remote_gateway.py
@@ -13,6 +13,7 @@
 from app.core.config import settings
 from app.services.rag.content_refs import build_content_ref_for_attachment
 from app.services.rag.runtime_specs import (
+    ConnectionTestRuntimeSpec,
     DeleteRuntimeSpec,
     IndexRuntimeSpec,
     QueryRuntimeSpec,
@@ -23,6 +24,7 @@
     RemoteQueryRequest,
     RemoteQueryResponse,
     RemoteRagError,
+    RemoteTestConnectionRequest,
 )
 
 
@@ -149,6 +151,7 @@ async def query(
             query=spec.query,
             max_results=spec.max_results,
             document_ids=spec.document_ids,
+            metadata_condition=spec.metadata_condition,
             user_name=spec.user_name,
             knowledge_base_configs=spec.knowledge_base_configs,
             enabled_index_families=spec.enabled_index_families,
@@ -176,3 +179,13 @@ async def delete_document_index(
             enabled_index_families=spec.enabled_index_families,
         )
         return await self._post_model("/internal/rag/delete-document-index", payload)
+
+    async def test_connection(
+        self,
+        spec: ConnectionTestRuntimeSpec,
+        *,
+        db: Session | None = None,
+    ) -> dict[str, Any]:
+        del db
+        payload = RemoteTestConnectionRequest(retriever_config=spec.retriever_config)
+        return await self._post_model("/internal/rag/test-connection", payload)
diff --git a/backend/app/services/rag/retrieval/__init__.py b/backend/app/services/rag/retrieval/__init__.py
index af534c5fb..4f7eae57b 100644
--- a/backend/app/services/rag/retrieval/__init__.py
+++ b/backend/app/services/rag/retrieval/__init__.py
@@ -2,18 +2,11 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Retrieval module for RAG functionality.
-"""
+"""Backward-compatible retrieval helper exports."""
 
 from app.services.rag.retrieval.filters import (
     build_elasticsearch_filters,
     parse_metadata_filters,
 )
-from app.services.rag.retrieval.retriever import DocumentRetriever
 
-__all__ = [
-    "DocumentRetriever",
-    "parse_metadata_filters",
-    "build_elasticsearch_filters",
-]
+__all__ = ["parse_metadata_filters", "build_elasticsearch_filters"]
diff --git a/backend/app/services/rag/retrieval/filters.py b/backend/app/services/rag/retrieval/filters.py
index e97fb45ef..4f45737a3 100644
--- a/backend/app/services/rag/retrieval/filters.py
+++ b/backend/app/services/rag/retrieval/filters.py
@@ -2,143 +2,11 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Metadata filtering parser for RAG retrieval.
-Converts Dify-style metadata conditions to LlamaIndex MetadataFilters.
-"""
+"""Backward-compatible re-exports for retrieval filter helpers."""
 
-from typing import Any, Dict, List, Optional
-
-from llama_index.core.vector_stores import (
-    ExactMatchFilter,
-    FilterOperator,
-    MetadataFilter,
-    MetadataFilters,
+from knowledge_engine.retrieval.filters import (
+    build_elasticsearch_filters,
+    parse_metadata_filters,
 )
 
-
-def parse_metadata_filters(
-    knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
-) -> MetadataFilters:
-    """
-    Parse Dify-style metadata condition into LlamaIndex MetadataFilters.
-
-    Args:
-        knowledge_id: Knowledge base ID (always filtered)
-        metadata_condition: Optional metadata conditions in Dify-style format:
-            {
-                "operator": "and" | "or",
-                "conditions": [
-                    {"key": "field_name", "operator": "eq", "value": "value"},
-                    {"key": "year", "operator": "gte", "value": 2020},
-                    {"key": "status", "operator": "in", "value": ["active", "pending"]}
-                ]
-            }
-
-    Returns:
-        MetadataFilters object compatible with LlamaIndex
-
-    Examples:
-        >>> # Simple equality filter
-        >>> parse_metadata_filters("kb_123", {
-        ...     "operator": "and",
-        ...     "conditions": [
-        ...         {"key": "category", "operator": "eq", "value": "tech"}
-        ...     ]
-        ... })
-
-        >>> # Multiple conditions
-        >>> parse_metadata_filters("kb_123", {
-        ...     "operator": "and",
-        ...     "conditions": [
-        ...         {"key": "year", "operator": "gte", "value": 2020},
-        ...         {"key": "status", "operator": "in", "value": ["active", "pending"]}
-        ...     ]
-        ... })
-    """
-    # Operator mapping from Dify-style to LlamaIndex FilterOperator
-    OPERATOR_MAP = {
-        "eq": FilterOperator.EQ,
-        "ne": FilterOperator.NE,
-        "gt": FilterOperator.GT,
-        "gte": FilterOperator.GTE,
-        "lt": FilterOperator.LT,
-        "lte": FilterOperator.LTE,
-        "in": FilterOperator.IN,
-        "nin": FilterOperator.NIN,
-        "contains": FilterOperator.CONTAINS,
-        "text_match": FilterOperator.TEXT_MATCH,
-    }
-
-    # Always filter by knowledge_id
-    filters = [ExactMatchFilter(key="knowledge_id", value=knowledge_id)]
-
-    # Parse additional metadata conditions
-    if metadata_condition and "conditions" in metadata_condition:
-        for cond in metadata_condition["conditions"]:
-            key = cond.get("key")
-            operator = cond.get("operator", "eq")
-            value = cond.get("value")
-
-            if not key or value is None:
-                continue
-
-            # Map operator string to FilterOperator enum
-            filter_op = OPERATOR_MAP.get(operator.lower(), FilterOperator.EQ)
-
-            # Create MetadataFilter using LlamaIndex's built-in class
-            filters.append(MetadataFilter(key=key, value=value, operator=filter_op))
-
-    # Determine condition type (AND/OR)
-    condition = "and"
-    if metadata_condition:
-        condition = metadata_condition.get("operator", "and").lower()
-
-    return MetadataFilters(filters=filters, condition=condition)
-
-
-def build_elasticsearch_filters(
-    knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
-) -> List[Dict[str, Any]]:
-    """
-    Build Elasticsearch filter clauses for hybrid search (direct ES queries).
-
-    Args:
-        knowledge_id: Knowledge base ID
-        metadata_condition: Optional metadata conditions in Dify-style format
-
-    Returns:
-        List of Elasticsearch filter clauses for use in ES query DSL
-    """
-    filters = [{"term": {"metadata.knowledge_id.keyword": knowledge_id}}]
-
-    if not metadata_condition or "conditions" not in metadata_condition:
-        return filters
-
-    for cond in metadata_condition["conditions"]:
-        key = cond.get("key")
-        operator = cond.get("operator", "eq")
-        value = cond.get("value")
-
-        if not key or value is None:
-            continue
-
-        field_name = f"metadata.{key}.keyword"
-
-        # Build Elasticsearch filter based on operator
-        if operator == "eq":
-            filters.append({"term": {field_name: value}})
-        elif operator == "ne":
-            filters.append({"bool": {"must_not": {"term": {field_name: value}}}})
-        elif operator == "in":
-            filters.append({"terms": {field_name: value}})
-        elif operator == "nin":
-            filters.append({"bool": {"must_not": {"terms": {field_name: value}}}})
-        elif operator in ["gt", "gte", "lt", "lte"]:
-            filters.append({"range": {field_name: {operator: value}}})
-        elif operator == "contains":
-            filters.append({"wildcard": {field_name: f"*{value}*"}})
-        elif operator == "text_match":
-            filters.append({"match": {f"metadata.{key}": value}})
-
-    return filters
+__all__ = ["parse_metadata_filters", "build_elasticsearch_filters"]
diff --git a/backend/app/services/rag/retrieval/retriever.py b/backend/app/services/rag/retrieval/retriever.py
deleted file mode 100644
index ca6dc30fc..000000000
--- a/backend/app/services/rag/retrieval/retriever.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Document retrieval orchestration.
-"""
-
-from typing import Any, Dict, Optional
-
-from app.services.rag.storage.base import BaseStorageBackend
-
-
-class DocumentRetriever:
-    """Orchestrates document retrieval process."""
-
-    def __init__(self, storage_backend: BaseStorageBackend, embed_model):
-        """
-        Initialize document retriever.
-
-        Args:
-            storage_backend: Storage backend instance
-            embed_model: Embedding model
-        """
-        self.storage_backend = storage_backend
-        self.embed_model = embed_model
-
-    def retrieve(
-        self,
-        knowledge_id: str,
-        query: str,
-        retrieval_setting: Dict[str, Any],
-        metadata_condition: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ) -> Dict:
-        """
-        Retrieve relevant documents (synchronous).
-
-        This method is synchronous because it's called from asyncio.to_thread()
-        in RetrievalService to avoid event loop conflicts with LlamaIndex.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            query: Search query
-            retrieval_setting: Retrieval settings dict with:
-                - top_k: Maximum number of results
-                - score_threshold: Minimum similarity score
-                - retrieval_mode: Optional 'vector'/'keyword'/'hybrid'
-                - vector_weight: Optional weight for vector search
-                - keyword_weight: Optional weight for keyword search
-            metadata_condition: Optional metadata filtering conditions
-            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
-
-        Returns:
-            Retrieval result dict
-        """
-        return self.storage_backend.retrieve(
-            knowledge_id=knowledge_id,
-            query=query,
-            embed_model=self.embed_model,
-            retrieval_setting=retrieval_setting,
-            metadata_condition=metadata_condition,
-            **kwargs,
-        )
diff --git a/backend/app/services/rag/retrieval_service.py b/backend/app/services/rag/retrieval_service.py
index c305b7d49..add9a6db2 100644
--- a/backend/app/services/rag/retrieval_service.py
+++ b/backend/app/services/rag/retrieval_service.py
@@ -13,12 +13,11 @@
 
 from sqlalchemy.orm import Session
 
-from app.services.adapters.retriever_kinds import retriever_kinds_service
-from app.services.knowledge import KnowledgeService
-from app.services.rag.embedding.factory import create_embedding_model_from_crd
-from app.services.rag.retrieval.retriever import DocumentRetriever
-from app.services.rag.storage.base import BaseStorageBackend
-from app.services.rag.storage.factory import create_storage_backend
+from app.services.rag.runtime_resolver import RagRuntimeResolver
+from knowledge_engine.embedding import create_embedding_model_from_runtime_config
+from knowledge_engine.query import QueryExecutor
+from knowledge_engine.storage.factory import create_storage_backend_from_runtime_config
+from shared.models import RemoteKnowledgeBaseQueryConfig
 from shared.telemetry.decorators import add_span_event, set_span_attribute, trace_async
 
 logger = logging.getLogger(__name__)
@@ -33,18 +32,12 @@
 class RetrievalService:
     """
     High-level retrieval service.
-    Uses modular architecture with pluggable storage backends.
+    Owns Backend-side routing and permission-aware retrieval orchestration.
     """
 
-    def __init__(self, storage_backend: Optional[BaseStorageBackend] = None):
-        """
-        Initialize retrieval service.
-
-        Args:
-            storage_backend: Optional storage backend instance (Elasticsearch, Qdrant, etc.)
-                           If not provided, must be created from retriever configuration
-        """
-        self.storage_backend = storage_backend
+    def __init__(self):
+        """Initialize retrieval service."""
+        self.runtime_resolver = RagRuntimeResolver()
 
     @staticmethod
     def _build_document_filter(document_ids: Optional[list[int]]) -> Optional[Dict]:
@@ -64,6 +57,20 @@ def _build_document_filter(document_ids: Optional[list[int]]) -> Optional[Dict]:
             ],
         }
 
+    @staticmethod
+    def _combine_metadata_conditions(
+        *conditions: Optional[Dict[str, Any]],
+    ) -> Optional[Dict[str, Any]]:
+        normalized_conditions = [condition for condition in conditions if condition]
+        if not normalized_conditions:
+            return None
+        if len(normalized_conditions) == 1:
+            return normalized_conditions[0]
+        return {
+            "operator": "and",
+            "conditions": normalized_conditions,
+        }
+
     @staticmethod
     def _estimate_total_tokens_for_knowledge_bases(
         db: Session,
@@ -269,6 +276,8 @@ async def retrieve_for_chat_shell(
         db: Session,
         max_results: int = 5,
         document_ids: Optional[list[int]] = None,
+        metadata_condition: Optional[Dict[str, Any]] = None,
+        knowledge_base_configs: Optional[list[RemoteKnowledgeBaseQueryConfig]] = None,
         user_name: Optional[str] = None,
         context_window: Optional[int] = None,
         route_mode: Literal["auto", "direct_injection", "rag_retrieval"] = "auto",
@@ -299,7 +308,10 @@ async def retrieve_for_chat_shell(
                 "total_estimated_tokens": 0,
             }
 
-        metadata_condition = self._build_document_filter(document_ids)
+        metadata_condition = self._combine_metadata_conditions(
+            self._build_document_filter(document_ids),
+            metadata_condition,
+        )
         total_estimated_tokens = 0
         if route_mode == "auto":
             total_estimated_tokens = self._estimate_total_tokens_for_knowledge_bases(
@@ -348,6 +360,9 @@ async def retrieve_for_chat_shell(
         )
 
         records: list[Dict[str, Any]] = []
+        runtime_config_by_kb_id = {
+            config.knowledge_base_id: config for config in knowledge_base_configs or []
+        }
 
         if use_direct_injection:
             direct_records: list[Dict[str, Any]] = []
@@ -439,6 +454,7 @@ async def retrieve_for_chat_shell(
                     db=db,
                     metadata_condition=metadata_condition,
                     user_name=user_name,
+                    knowledge_base_config=runtime_config_by_kb_id.get(kb_id),
                 )
                 kb_records = result.get("records", [])[:max_results]
                 for record in kb_records:
@@ -463,215 +479,6 @@ async def retrieve_for_chat_shell(
             "total_estimated_tokens": total_estimated_tokens,
         }
 
-    def _retrieve_sync(
-        self,
-        knowledge_id: str,
-        query: str,
-        embedding_model_name: str,
-        embedding_model_namespace: str,
-        user_id: int,
-        db: Session,
-        retrieval_setting: Dict[str, Any],
-        metadata_condition: Optional[Dict[str, Any]] = None,
-        user_name: Optional[str] = None,
-    ) -> Dict:
-        """
-        Synchronous retrieval implementation.
-        Runs in thread pool to avoid event loop conflicts.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            query: Search query
-            embedding_model_name: Embedding model name
-            embedding_model_namespace: Embedding model namespace
-            user_id: User ID
-            db: Database session
-            retrieval_setting: Retrieval settings (Dify-style)
-            metadata_condition: Optional metadata filtering
-            user_name: User name for placeholder replacement in embedding headers (optional)
-
-        Returns:
-            Retrieval result dict
-        """
-        # Create embedding model from CRD
-        embed_model = create_embedding_model_from_crd(
-            db=db,
-            user_id=user_id,
-            model_name=embedding_model_name,
-            model_namespace=embedding_model_namespace,
-            user_name=user_name,
-        )
-
-        # Create retriever with storage backend
-        retriever = DocumentRetriever(
-            storage_backend=self.storage_backend, embed_model=embed_model
-        )
-
-        # Retrieve documents (pass user_id)
-        result = retriever.retrieve(
-            knowledge_id=knowledge_id,
-            query=query,
-            retrieval_setting=retrieval_setting,
-            metadata_condition=metadata_condition,
-            user_id=user_id,
-        )
-
-        return result
-
-    async def retrieve(
-        self,
-        query: str,
-        knowledge_id: str,
-        embedding_model_name: str,
-        embedding_model_namespace: str,
-        user_id: int,
-        db: Session,
-        top_k: int = 5,
-        score_threshold: float = 0.7,
-        retrieval_mode: str = "vector",
-        vector_weight: Optional[float] = None,
-        keyword_weight: Optional[float] = None,
-        metadata_condition: Optional[Dict[str, Any]] = None,
-        user_name: Optional[str] = None,
-    ) -> Dict:
-        """
-        Retrieve relevant document chunks (Dify-compatible API).
-
-        Args:
-            query: Search query
-            knowledge_id: Knowledge base ID
-            embedding_model_name: Embedding model name
-            embedding_model_namespace: Embedding model namespace
-            user_id: User ID
-            db: Database session
-            top_k: Maximum number of results
-            score_threshold: Minimum similarity score (0-1)
-            retrieval_mode: 'vector' or 'hybrid'
-            vector_weight: Weight for vector search (hybrid mode only)
-            keyword_weight: Weight for BM25 search (hybrid mode only)
-            metadata_condition: Optional metadata filtering conditions
-            user_name: User name for placeholder replacement in embedding headers (optional)
-
-        Returns:
-            Dict with Dify-compatible format:
-                {
-                    "records": [
-                        {
-                            "content": str,      # Chunk text content
-                            "score": float,      # Relevance score (0-1)
-                            "title": str,        # Document title/source file
-                            "metadata": dict     # Additional metadata
-                        }
-                    ]
-                }
-        """
-        # Build retrieval_setting dict (Dify-style API)
-        retrieval_setting = {
-            "top_k": top_k,
-            "score_threshold": score_threshold,
-            "retrieval_mode": retrieval_mode,
-        }
-
-        # Add hybrid search weights if provided
-        if retrieval_mode == "hybrid":
-            retrieval_setting["vector_weight"] = (
-                vector_weight if vector_weight is not None else 0.7
-            )
-            retrieval_setting["keyword_weight"] = (
-                keyword_weight if keyword_weight is not None else 0.3
-            )
-
-        # Run retrieval in thread pool to avoid uvloop conflicts
-        return await asyncio.to_thread(
-            self._retrieve_sync,
-            knowledge_id,
-            query,
-            embedding_model_name,
-            embedding_model_namespace,
-            user_id,
-            db,
-            retrieval_setting,
-            metadata_condition,
-            user_name,
-        )
-
-    async def retrieve_from_knowledge_base(
-        self,
-        query: str,
-        knowledge_base_id: int,
-        user_id: int,
-        db: Session,
-        metadata_condition: Optional[Dict[str, Any]] = None,
-    ) -> Dict:
-        """
-        Retrieve relevant chunks from a knowledge base using its configuration.
-
-        This method encapsulates the logic of:
-        1. Fetching knowledge base configuration
-        2. Getting retriever CRD
-        3. Creating storage backend
-        4. Performing retrieval with configured parameters
-
-        Args:
-            query: Search query
-            knowledge_base_id: Knowledge base ID
-            user_id: User ID
-            db: Database session
-            metadata_condition: Optional metadata filtering conditions
-
-        Returns:
-            Dict with retrieval results in Dify-compatible format:
-                {
-                    "records": [
-                        {
-                            "content": str,
-                            "score": float,
-                            "title": str,
-                            "metadata": dict
-                        }
-                    ]
-                }
-
-        Raises:
-            ValueError: If knowledge base not found, access denied, or configuration invalid
-        """
-        # Get knowledge base configuration with permission check
-        kb, has_access = KnowledgeService.get_knowledge_base(
-            db=db,
-            knowledge_base_id=knowledge_base_id,
-            user_id=user_id,
-        )
-
-        if not kb:
-            raise ValueError(f"Knowledge base {knowledge_base_id} not found")
-
-        if not has_access:
-            raise ValueError(
-                f"Access denied to knowledge base {knowledge_base_id} for user {user_id}"
-            )
-
-        # Check if user is a Restricted Analyst in the knowledge base's group
-        # Restricted Analysts cannot access knowledge base content
-        if kb.namespace != "default":
-            from app.services.group_permission import is_restricted_analyst
-
-            if is_restricted_analyst(db, user_id, kb.namespace):
-                logger.warning(
-                    f"[RetrievalService] User {user_id} is Restricted Analyst in group "
-                    f"'{kb.namespace}', blocking RAG retrieval from KB {knowledge_base_id}"
-                )
-                raise ValueError(
-                    f"Access denied: You have Restricted Analyst permissions in group "
-                    f"'{kb.namespace}'. You cannot retrieve content from this knowledge base."
-                )
-
-        return await self._retrieve_from_kb_internal(
-            query=query,
-            kb=kb,
-            db=db,
-            metadata_condition=metadata_condition,
-        )
-
     async def retrieve_from_knowledge_base_internal(
         self,
         query: str,
@@ -679,6 +486,7 @@ async def retrieve_from_knowledge_base_internal(
         db: Session,
         metadata_condition: Optional[Dict[str, Any]] = None,
         user_name: Optional[str] = None,
+        knowledge_base_config: Optional[RemoteKnowledgeBaseQueryConfig] = None,
     ) -> Dict:
         """
         Internal method to retrieve from knowledge base without user permission check.
@@ -725,6 +533,7 @@ async def retrieve_from_knowledge_base_internal(
             db=db,
             metadata_condition=metadata_condition,
             user_name=user_name,
+            knowledge_base_config=knowledge_base_config,
         )
 
     async def _retrieve_from_kb_internal(
@@ -734,6 +543,7 @@ async def _retrieve_from_kb_internal(
         db: Session,
         metadata_condition: Optional[Dict[str, Any]] = None,
         user_name: Optional[str] = None,
+        knowledge_base_config: Optional[RemoteKnowledgeBaseQueryConfig] = None,
     ) -> Dict:
         """
         Internal helper method to perform retrieval from a knowledge base.
@@ -751,126 +561,15 @@ async def _retrieve_from_kb_internal(
         Raises:
             ValueError: If configuration is invalid
         """
-        # Extract retrieval configuration from knowledge base spec
-        kb_json = kb.json or {}
-        spec = kb_json.get("spec", {})
-        retrieval_config = spec.get("retrievalConfig")
-
-        if not retrieval_config:
-            raise ValueError(f"Knowledge base {kb.id} has no retrieval configuration")
-
-        # Extract retriever reference
-        retriever_name = retrieval_config.get("retriever_name")
-        retriever_namespace = retrieval_config.get("retriever_namespace", "default")
-
-        if not retriever_name:
-            raise ValueError(
-                f"Knowledge base {kb.id} has incomplete retrieval config (missing retriever_name)"
-            )
-
-        logger.info(
-            f"[RAG] Using retriever: {retriever_name} (namespace: {retriever_namespace})"
-        )
-
-        # Determine the correct user_id for resource lookup
-        # For personal resources (namespace='default'), use the KB creator's user_id
-        # because the resources belong to the KB creator, not the current user
-        # For group resources (namespace!='default'), use KB creator's user_id as well
-        resource_owner_user_id = kb.user_id
-
-        # Get retriever CRD
-        retriever = retriever_kinds_service.get_retriever(
-            db=db,
-            user_id=resource_owner_user_id,
-            name=retriever_name,
-            namespace=retriever_namespace,
-        )
-
-        if not retriever:
-            raise ValueError(
-                f"Retriever {retriever_name} (namespace: {retriever_namespace}) not found"
-            )
-
-        # Create storage backend from retriever
-        storage_backend = create_storage_backend(retriever)
-        logger.info(
-            f"[RAG] Storage backend created: {storage_backend.__class__.__name__}"
-        )
-
-        # Extract embedding model configuration
-        embedding_config = retrieval_config.get("embedding_config", {})
-        embedding_model_name = embedding_config.get("model_name")
-        embedding_model_namespace = embedding_config.get("model_namespace", "default")
-
-        if not embedding_model_name:
-            raise ValueError(f"Knowledge base {kb.id} has incomplete embedding config")
-
-        # Determine the correct user_id for embedding model lookup
-        # Use KB creator's user_id for consistent resource access
-        embedding_owner_user_id = kb.user_id
-
-        # Extract retrieval parameters
-        # Increased default top_k from 5 to 20 for better RAG coverage
-        top_k = retrieval_config.get("top_k", 20)
-        score_threshold = retrieval_config.get("score_threshold", 0.7)
-        retrieval_mode = retrieval_config.get("retrieval_mode", "vector")
-
-        # Extract hybrid weights if in hybrid mode
-        vector_weight = None
-        keyword_weight = None
-        if retrieval_mode == "hybrid":
-            hybrid_weights = retrieval_config.get("hybrid_weights", {})
-            vector_weight = hybrid_weights.get("vector_weight", 0.7)
-            keyword_weight = hybrid_weights.get("keyword_weight", 0.3)
-
-        # Use knowledge base ID as knowledge_id for retrieval
-        knowledge_id = str(kb.id)
-
-        logger.info(
-            f"[RAG] Retrieving chunks: knowledge_id={knowledge_id}, "
-            f"embedding_model={embedding_model_name}, "
-            f"top_k={top_k}, score_threshold={score_threshold}, "
-            f"retrieval_mode={retrieval_mode}"
-        )
-
-        # Build retrieval_setting dict
-        retrieval_setting = {
-            "top_k": top_k,
-            "score_threshold": score_threshold,
-            "retrieval_mode": retrieval_mode,
-        }
-
-        # Add hybrid search weights if provided
-        if retrieval_mode == "hybrid":
-            retrieval_setting["vector_weight"] = vector_weight
-            retrieval_setting["keyword_weight"] = keyword_weight
-
-        # Create embedding model from CRD
-        # Use embedding_owner_user_id for correct resource lookup
-        # For group KBs, the embedding model may be created by other users in the same group
-        embed_model = create_embedding_model_from_crd(
+        resolved_config = knowledge_base_config or self._build_runtime_query_config(
+            kb=kb,
             db=db,
-            user_id=embedding_owner_user_id,
-            model_name=embedding_model_name,
-            model_namespace=embedding_model_namespace,
             user_name=user_name,
         )
-
-        # Create retriever with storage backend
-        retriever_instance = DocumentRetriever(
-            storage_backend=storage_backend, embed_model=embed_model
-        )
-
-        # Retrieve documents (run in thread pool to avoid event loop conflicts)
-        # Use KB creator's user_id for index naming (required for per_user strategy)
-        # This ensures consistent index access for all users accessing this KB
-        result = await asyncio.to_thread(
-            retriever_instance.retrieve,
-            knowledge_id=knowledge_id,
+        result = await self._execute_runtime_query(
             query=query,
-            retrieval_setting=retrieval_setting,
+            knowledge_base_config=resolved_config,
             metadata_condition=metadata_condition,
-            user_id=kb.user_id,
         )
 
         # Log detailed retrieval results for debugging
@@ -897,6 +596,45 @@ async def _retrieve_from_kb_internal(
 
         return result
 
+    def _build_runtime_query_config(
+        self,
+        *,
+        kb,
+        db: Session,
+        user_name: Optional[str] = None,
+    ) -> RemoteKnowledgeBaseQueryConfig:
+        configs = self.runtime_resolver.build_query_knowledge_base_configs(
+            db=db,
+            knowledge_base_ids=[kb.id],
+            user_name=user_name,
+        )
+        return configs[0]
+
+    async def _execute_runtime_query(
+        self,
+        *,
+        query: str,
+        knowledge_base_config: RemoteKnowledgeBaseQueryConfig,
+        metadata_condition: Optional[Dict[str, Any]] = None,
+    ) -> Dict[str, Any]:
+        storage_backend = create_storage_backend_from_runtime_config(
+            knowledge_base_config.retriever_config
+        )
+        embed_model = create_embedding_model_from_runtime_config(
+            knowledge_base_config.embedding_model_config
+        )
+        executor = QueryExecutor(
+            storage_backend=storage_backend,
+            embed_model=embed_model,
+        )
+        return await executor.execute(
+            knowledge_id=str(knowledge_base_config.knowledge_base_id),
+            query=query,
+            retrieval_config=knowledge_base_config.retrieval_config,
+            metadata_condition=metadata_condition,
+            user_id=knowledge_base_config.index_owner_user_id,
+        )
+
     async def get_all_chunks_from_knowledge_base(
         self,
         knowledge_base_id: int,
@@ -938,38 +676,13 @@ async def get_all_chunks_from_knowledge_base(
         if not kb:
             raise ValueError(f"Knowledge base {knowledge_base_id} not found")
 
-        # Extract retrieval configuration from knowledge base spec
-        kb_json = kb.json or {}
-        spec = kb_json.get("spec", {})
-        retrieval_config = spec.get("retrievalConfig")
-
-        if not retrieval_config:
-            raise ValueError(f"Knowledge base {kb.id} has no retrieval configuration")
-
-        # Extract retriever reference
-        retriever_name = retrieval_config.get("retriever_name")
-        retriever_namespace = retrieval_config.get("retriever_namespace", "default")
-
-        if not retriever_name:
-            raise ValueError(
-                f"Knowledge base {kb.id} has incomplete retrieval config (missing retriever_name)"
-            )
-
-        # Get retriever CRD
-        retriever = retriever_kinds_service.get_retriever(
+        resolved_config = self._build_runtime_query_config(
+            kb=kb,
             db=db,
-            user_id=kb.user_id,
-            name=retriever_name,
-            namespace=retriever_namespace,
         )
-
-        if not retriever:
-            raise ValueError(
-                f"Retriever {retriever_name} (namespace: {retriever_namespace}) not found"
-            )
-
-        # Create storage backend from retriever
-        storage_backend = create_storage_backend(retriever)
+        storage_backend = create_storage_backend_from_runtime_config(
+            resolved_config.retriever_config
+        )
 
         # Use knowledge base ID as knowledge_id
         knowledge_id = str(kb.id)
@@ -999,8 +712,8 @@ async def get_all_chunks_from_knowledge_base(
             knowledge_id,
             kb.namespace,
             backend_name,
-            retriever_namespace,
-            retriever_name,
+            resolved_config.retriever_config.namespace,
+            resolved_config.retriever_config.name,
             index_name or "<unknown>",
             max_chunks,
             kb.user_id,
@@ -1035,8 +748,8 @@ async def get_all_chunks_from_knowledge_base(
                 knowledge_id,
                 backend_name,
                 index_name or "<unknown>",
-                retriever_namespace,
-                retriever_name,
+                resolved_config.retriever_config.namespace,
+                resolved_config.retriever_config.name,
                 query_log,
             )
 
diff --git a/backend/app/services/rag/runtime_resolver.py b/backend/app/services/rag/runtime_resolver.py
index 7dde4e765..93764f56d 100644
--- a/backend/app/services/rag/runtime_resolver.py
+++ b/backend/app/services/rag/runtime_resolver.py
@@ -12,6 +12,7 @@
 )
 from app.services.rag.embedding.factory import _process_custom_headers_placeholders
 from app.services.rag.runtime_specs import (
+    ConnectionTestRuntimeSpec,
     DeleteRuntimeSpec,
     DirectInjectionBudget,
     IndexRuntimeSpec,
@@ -87,6 +88,7 @@ def build_query_runtime_spec(
         max_results: int,
         route_mode: Literal["auto", "direct_injection", "rag_retrieval"],
         document_ids: list[int] | None = None,
+        metadata_condition: dict | None = None,
         restricted_mode: bool = False,
         user_id: int | None = None,
         user_name: str | None = None,
@@ -114,11 +116,12 @@ def build_query_runtime_spec(
             max_results=max_results,
             route_mode=route_mode,
             document_ids=document_ids,
+            metadata_condition=metadata_condition,
             restricted_mode=restricted_mode,
             user_id=user_id,
             user_name=user_name,
             knowledge_base_configs=(
-                self._build_query_knowledge_base_configs(
+                self.build_query_knowledge_base_configs(
                     db=db,
                     knowledge_base_ids=knowledge_base_ids,
                     user_name=user_name,
@@ -131,6 +134,78 @@ def build_query_runtime_spec(
             direct_injection_budget=direct_injection_budget,
         )
 
+    def build_public_query_runtime_spec(
+        self,
+        *,
+        db: Session,
+        knowledge_base_id: int,
+        query: str,
+        max_results: int,
+        retriever_name: str,
+        retriever_namespace: str,
+        embedding_model_name: str,
+        embedding_model_namespace: str,
+        user_id: int,
+        user_name: str | None,
+        score_threshold: float,
+        retrieval_mode: str,
+        vector_weight: float | None = None,
+        keyword_weight: float | None = None,
+        metadata_condition: dict | None = None,
+    ) -> QueryRuntimeSpec:
+        kb = self._get_knowledge_base_record(db=db, knowledge_base_id=knowledge_base_id)
+        if kb is None:
+            raise ValueError(f"Knowledge base {knowledge_base_id} not found")
+
+        return QueryRuntimeSpec(
+            knowledge_base_ids=[knowledge_base_id],
+            query=query,
+            max_results=max_results,
+            route_mode="rag_retrieval",
+            metadata_condition=metadata_condition,
+            user_id=user_id,
+            user_name=user_name,
+            knowledge_base_configs=[
+                QueryKnowledgeBaseRuntimeConfig(
+                    knowledge_base_id=knowledge_base_id,
+                    index_owner_user_id=kb.user_id,
+                    retriever_config=self._build_resolved_retriever_config(
+                        db=db,
+                        user_id=user_id,
+                        name=retriever_name,
+                        namespace=retriever_namespace,
+                    ),
+                    embedding_model_config=self._build_resolved_embedding_model_config(
+                        db=db,
+                        user_id=user_id,
+                        model_name=embedding_model_name,
+                        model_namespace=embedding_model_namespace,
+                        user_name=user_name,
+                    ),
+                    retrieval_config=RuntimeRetrievalConfig(
+                        top_k=max_results,
+                        score_threshold=score_threshold,
+                        retrieval_mode=retrieval_mode,
+                        vector_weight=vector_weight,
+                        keyword_weight=keyword_weight,
+                    ),
+                )
+            ],
+        )
+
+    def build_query_knowledge_base_configs(
+        self,
+        *,
+        db: Session,
+        knowledge_base_ids: list[int],
+        user_name: str | None,
+    ) -> list[QueryKnowledgeBaseRuntimeConfig]:
+        return self._build_query_knowledge_base_configs(
+            db=db,
+            knowledge_base_ids=knowledge_base_ids,
+            user_name=user_name,
+        )
+
     def build_delete_runtime_spec(
         self,
         *,
diff --git a/backend/app/services/rag/runtime_specs.py b/backend/app/services/rag/runtime_specs.py
index 5558735b6..2ad8ac5e8 100644
--- a/backend/app/services/rag/runtime_specs.py
+++ b/backend/app/services/rag/runtime_specs.py
@@ -1,6 +1,6 @@
 from typing import Literal, Optional
 
-from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pydantic import BaseModel, ConfigDict, Field
 
 from shared.models import (
     RemoteKnowledgeBaseQueryConfig,
@@ -22,23 +22,8 @@ class RuntimeSpecModel(BaseModel):
 
 
 class IndexSource(RuntimeSpecModel):
-    source_type: Literal["attachment", "file_path"]
-    attachment_id: Optional[int] = None
-    file_path: Optional[str] = None
-
-    @model_validator(mode="after")
-    def validate_source_shape(self) -> "IndexSource":
-        if self.source_type == "attachment":
-            if self.attachment_id is None:
-                raise ValueError("attachment_id is required")
-            if self.file_path is not None:
-                raise ValueError("file_path must not be provided")
-        if self.source_type == "file_path":
-            if self.file_path is None:
-                raise ValueError("file_path is required")
-            if self.attachment_id is not None:
-                raise ValueError("attachment_id must not be provided")
-        return self
+    source_type: Literal["attachment"]
+    attachment_id: int
 
 
 class DirectInjectionBudget(RuntimeSpecModel):
@@ -75,6 +60,7 @@ class QueryRuntimeSpec(RuntimeSpecModel):
     route_mode: Literal["auto", "direct_injection", "rag_retrieval"] = "auto"
     direct_injection_budget: Optional[DirectInjectionBudget] = None
     document_ids: Optional[list[int]] = None
+    metadata_condition: Optional[dict] = None
     restricted_mode: bool = False
     user_id: Optional[int] = None
     user_name: Optional[str] = None
@@ -93,4 +79,8 @@ class DeleteRuntimeSpec(RuntimeSpecModel):
     enabled_index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
 
 
+class ConnectionTestRuntimeSpec(RuntimeSpecModel):
+    retriever_config: RuntimeRetrieverConfig
+
+
 DEFAULT_DIRECT_INJECTION_BUDGET = DirectInjectionBudget()
diff --git a/backend/app/services/rag/splitter/__init__.py b/backend/app/services/rag/splitter/__init__.py
index 74368c068..ff7c52731 100644
--- a/backend/app/services/rag/splitter/__init__.py
+++ b/backend/app/services/rag/splitter/__init__.py
@@ -2,20 +2,8 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Splitter module for document chunking.
-"""
+"""Runtime splitter helpers kept in backend control plane."""
 
-from app.services.rag.splitter.smart import SmartSplitter
-from app.services.rag.splitter.splitter import (
-    DocumentSplitter,
-    SemanticSplitter,
-    SentenceSplitter,
-)
+from app.services.rag.splitter.runtime_config import parse_runtime_splitter_config
 
-__all__ = [
-    "DocumentSplitter",
-    "SemanticSplitter",
-    "SentenceSplitter",
-    "SmartSplitter",
-]
+__all__ = ["parse_runtime_splitter_config"]
diff --git a/backend/app/services/rag/splitter/factory.py b/backend/app/services/rag/splitter/factory.py
deleted file mode 100644
index 6c0815e5d..000000000
--- a/backend/app/services/rag/splitter/factory.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Factory for creating document splitters based on configuration.
-"""
-
-from typing import Union
-
-from llama_index.core.base.embeddings.base import BaseEmbedding
-
-from app.schemas.rag import (
-    SemanticSplitterConfig,
-    SentenceSplitterConfig,
-    SmartSplitterConfig,
-    SplitterConfig,
-)
-from app.services.rag.splitter.smart import SmartSplitter
-from app.services.rag.splitter.splitter import SemanticSplitter, SentenceSplitter
-
-
-def create_splitter(
-    config: Union[SplitterConfig, None],
-    embed_model: BaseEmbedding,
-    file_extension: str = None,
-) -> Union[SemanticSplitter, SentenceSplitter, SmartSplitter]:
-    """
-    Create a document splitter based on configuration.
-
-    Args:
-        config: Splitter configuration. If None, defaults to SemanticSplitter
-        embed_model: Embedding model (required for SemanticSplitter)
-        file_extension: File extension for smart splitter (optional, used when
-                       config.file_extension is not set)
-
-    Returns:
-        Configured splitter instance
-
-    Raises:
-        ValueError: If config type is unknown
-    """
-    # Default to semantic splitter if no config provided
-    if config is None:
-        return SemanticSplitter(embed_model=embed_model)
-
-    if isinstance(config, SemanticSplitterConfig):
-        return SemanticSplitter(
-            embed_model=embed_model,
-            buffer_size=config.buffer_size,
-            breakpoint_percentile_threshold=config.breakpoint_percentile_threshold,
-        )
-    elif isinstance(config, SentenceSplitterConfig):
-        return SentenceSplitter(
-            chunk_size=config.chunk_size,
-            chunk_overlap=config.chunk_overlap,
-            separator=config.separator,
-        )
-    elif isinstance(config, SmartSplitterConfig):
-        # Use config.file_extension if available, otherwise use parameter
-        ext = config.file_extension or file_extension or ".txt"
-        return SmartSplitter(
-            file_extension=ext,
-            chunk_size=config.chunk_size,
-            chunk_overlap=config.chunk_overlap,
-        )
-    else:
-        raise ValueError(f"Unknown splitter config type: {type(config)}")
diff --git a/backend/app/services/rag/splitter/smart.py b/backend/app/services/rag/splitter/smart.py
deleted file mode 100644
index cdb0daa04..000000000
--- a/backend/app/services/rag/splitter/smart.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Smart document splitter that automatically selects the best splitting strategy
-based on file type.
-
-Supported file types and their strategies:
-- .md: Markdown structure splitting + sentence splitting
-- .txt: Sentence-based splitting
-- .pdf, .doc, .docx: Recursive character splitting (via LangChain)
-"""
-
-from typing import List
-
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from llama_index.core import Document
-from llama_index.core.node_parser import (
-    LangchainNodeParser,
-    MarkdownNodeParser,
-    SentenceSplitter,
-)
-from llama_index.core.schema import BaseNode
-
-from shared.telemetry.decorators import set_span_attribute, trace_sync
-
-
-class SmartSplitter:
-    """Smart splitter that selects splitting strategy based on file type.
-
-    Uses different splitting strategies for different file types:
-    - Markdown (.md): First splits by markdown structure (headers), then
-      applies sentence splitting for large sections
-    - Text (.txt): Uses sentence-based splitting
-    - PDF/DOC/DOCX: Uses recursive character splitting via LangChain
-
-    All strategies use unified chunk_size=1024 and chunk_overlap=50.
-    """
-
-    # File extensions that support smart splitting
-    SMART_EXTENSIONS = {".pdf", ".txt", ".doc", ".docx", ".md"}
-
-    # Default configuration
-    DEFAULT_CHUNK_SIZE = 1024
-    DEFAULT_CHUNK_OVERLAP = 50
-
-    def __init__(
-        self,
-        file_extension: str,
-        chunk_size: int = DEFAULT_CHUNK_SIZE,
-        chunk_overlap: int = DEFAULT_CHUNK_OVERLAP,
-    ):
-        """Initialize smart splitter.
-
-        Args:
-            file_extension: File extension (e.g., '.pdf', '.md')
-            chunk_size: Maximum chunk size in characters (default: 1024)
-            chunk_overlap: Number of characters to overlap between chunks (default: 50)
-        """
-        self.file_extension = file_extension.lower()
-        self.chunk_size = chunk_size
-        self.chunk_overlap = chunk_overlap
-
-    @classmethod
-    def supports_smart_split(cls, file_extension: str) -> bool:
-        """Check if file type supports smart splitting.
-
-        Args:
-            file_extension: File extension to check
-
-        Returns:
-            True if smart splitting is supported for this file type
-        """
-        return file_extension.lower() in cls.SMART_EXTENSIONS
-
-    @trace_sync("rag.splitter.split_documents")
-    def split_documents(self, documents: List[Document]) -> List[BaseNode]:
-        """Split documents using the appropriate strategy for the file type.
-
-        Args:
-            documents: List of LlamaIndex Document objects to split
-
-        Returns:
-            List of BaseNode objects (chunks)
-        """
-        # Add span attributes for observability
-        set_span_attribute("file_extension", self.file_extension)
-        set_span_attribute("chunk_size", self.chunk_size)
-        set_span_attribute("chunk_overlap", self.chunk_overlap)
-
-        if self.file_extension == ".md":
-            return self._split_markdown(documents)
-        elif self.file_extension == ".txt":
-            return self._split_text(documents)
-        else:
-            # PDF, DOC, DOCX use recursive character splitting
-            return self._split_recursive(documents)
-
-    def _split_markdown(self, documents: List[Document]) -> List[BaseNode]:
-        """Split markdown documents.
-
-        Uses MarkdownNodeParser to split by structure (headers), then
-        applies SentenceSplitter to break down large sections.
-
-        Args:
-            documents: List of Document objects
-
-        Returns:
-            List of BaseNode objects
-        """
-        # First pass: Split by markdown structure (headers)
-        markdown_parser = MarkdownNodeParser()
-        nodes = markdown_parser.get_nodes_from_documents(documents)
-
-        # Second pass: Apply sentence splitting to large nodes
-        sentence_splitter = SentenceSplitter(
-            chunk_size=self.chunk_size,
-            chunk_overlap=self.chunk_overlap,
-        )
-
-        # Convert nodes back to documents for second pass
-        intermediate_docs = [
-            Document(text=node.text, metadata=node.metadata) for node in nodes
-        ]
-
-        return sentence_splitter.get_nodes_from_documents(intermediate_docs)
-
-    def _split_text(self, documents: List[Document]) -> List[BaseNode]:
-        """Split text documents using sentence-based splitting.
-
-        Args:
-            documents: List of Document objects
-
-        Returns:
-            List of BaseNode objects
-        """
-        splitter = SentenceSplitter(
-            chunk_size=self.chunk_size,
-            chunk_overlap=self.chunk_overlap,
-        )
-        return splitter.get_nodes_from_documents(documents)
-
-    def _split_recursive(self, documents: List[Document]) -> List[BaseNode]:
-        """Split documents using recursive character splitting.
-
-        Uses LangChain's RecursiveCharacterTextSplitter via LangchainNodeParser.
-        This is suitable for PDF and DOC/DOCX files that have been converted
-        to plain text.
-
-        Args:
-            documents: List of Document objects
-
-        Returns:
-            List of BaseNode objects
-        """
-        # Create LangChain splitter with hierarchical separators
-        lc_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=self.chunk_size,
-            chunk_overlap=self.chunk_overlap,
-            separators=["\n\n", "\n", " ", ""],
-        )
-
-        # Wrap in LlamaIndex parser
-        parser = LangchainNodeParser(lc_splitter=lc_splitter)
-        return parser.get_nodes_from_documents(documents)
-
-    @trace_sync("rag.splitter.get_config")
-    def get_config(self) -> dict:
-        """Get splitter configuration for storage.
-
-        Returns:
-            Configuration dict with type and parameters
-        """
-        # Add span attributes for observability
-        set_span_attribute("file_extension", self.file_extension)
-        set_span_attribute("chunk_size", self.chunk_size)
-        set_span_attribute("chunk_overlap", self.chunk_overlap)
-
-        return {
-            "type": "smart",
-            "subtype": self._get_subtype(),
-            "chunk_size": self.chunk_size,
-            "chunk_overlap": self.chunk_overlap,
-            "file_extension": self.file_extension,
-        }
-
-    def _get_subtype(self) -> str:
-        """Get the subtype identifier for this splitter.
-
-        Returns:
-            Subtype string identifying the splitting strategy
-        """
-        if self.file_extension == ".md":
-            return "markdown_sentence"
-        elif self.file_extension == ".txt":
-            return "sentence"
-        else:
-            return "recursive_character"
diff --git a/backend/app/services/rag/splitter/splitter.py b/backend/app/services/rag/splitter/splitter.py
deleted file mode 100644
index b1e89e12c..000000000
--- a/backend/app/services/rag/splitter/splitter.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Document splitting for RAG indexing.
-Supports multiple splitting strategies: semantic and sentence-based.
-"""
-
-from typing import List
-
-from llama_index.core import Document
-from llama_index.core.base.embeddings.base import BaseEmbedding
-from llama_index.core.node_parser import (
-    SemanticSplitterNodeParser,
-)
-from llama_index.core.node_parser import SentenceSplitter as LlamaIndexSentenceSplitter
-from llama_index.core.schema import BaseNode
-
-
-class SemanticSplitter:
-    """
-    Semantic-based document splitter.
-
-    Uses embedding similarity to determine natural breakpoints in documents.
-    """
-
-    def __init__(
-        self,
-        embed_model: BaseEmbedding,
-        buffer_size: int = 1,
-        breakpoint_percentile_threshold: int = 95,
-    ):
-        """
-        Initialize semantic splitter.
-
-        Args:
-            embed_model: Embedding model for semantic splitting
-            buffer_size: Buffer size for semantic splitter
-            breakpoint_percentile_threshold: Percentile threshold for breakpoints
-        """
-        self.embed_model = embed_model
-        self.buffer_size = buffer_size
-        self.breakpoint_percentile_threshold = breakpoint_percentile_threshold
-        self.splitter = SemanticSplitterNodeParser(
-            buffer_size=buffer_size,
-            breakpoint_percentile_threshold=breakpoint_percentile_threshold,
-            embed_model=embed_model,
-        )
-
-    def split_documents(self, documents: List[Document]) -> List[BaseNode]:
-        """
-        Split documents into nodes using semantic splitting.
-
-        Args:
-            documents: List of documents to split
-
-        Returns:
-            List of nodes
-        """
-        return self.splitter.get_nodes_from_documents(documents)
-
-    def get_config(self) -> dict:
-        """Get splitter configuration."""
-        return {
-            "type": "semantic",
-            "buffer_size": self.buffer_size,
-            "breakpoint_percentile_threshold": self.breakpoint_percentile_threshold,
-        }
-
-
-class SentenceSplitter:
-    """
-    Sentence-based document splitter.
-
-    Splits documents based on separators (e.g., sentences, paragraphs, newlines)
-    with configurable chunk size and overlap.
-    """
-
-    def __init__(
-        self,
-        chunk_size: int = 1024,
-        chunk_overlap: int = 200,
-        separator: str = " ",
-    ):
-        """
-        Initialize sentence splitter.
-
-        Args:
-            chunk_size: Maximum chunk size in characters (default: 1024)
-            chunk_overlap: Number of characters to overlap between chunks (default: 200)
-            separator: Separator to use for splitting (default: " " for word-level)
-                      Common options:
-                      - " " (space): word-level splitting
-                      - "\n" (newline): line-level splitting
-                      - "\n\n" (double newline): paragraph-level splitting
-                      - "." (period): sentence-level splitting
-        """
-        self.chunk_size = chunk_size
-        self.chunk_overlap = chunk_overlap
-        self.separator = separator
-
-        # Use LlamaIndex's SentenceSplitter
-        self.splitter = LlamaIndexSentenceSplitter(
-            chunk_size=chunk_size,
-            chunk_overlap=chunk_overlap,
-            separator=separator,
-        )
-
-    def split_documents(self, documents: List[Document]) -> List[BaseNode]:
-        """
-        Split documents into nodes using sentence-based splitting.
-
-        Args:
-            documents: List of documents to split
-
-        Returns:
-            List of nodes
-        """
-        return self.splitter.get_nodes_from_documents(documents)
-
-    def get_config(self) -> dict:
-        """Get splitter configuration."""
-        return {
-            "type": "sentence",
-            "chunk_size": self.chunk_size,
-            "chunk_overlap": self.chunk_overlap,
-            "separator": self.separator,
-        }
-
-
-# Backward compatibility alias
-DocumentSplitter = SemanticSplitter
diff --git a/backend/app/services/rag/storage/base.py b/backend/app/services/rag/storage/base.py
index 3de57b418..18884d17e 100644
--- a/backend/app/services/rag/storage/base.py
+++ b/backend/app/services/rag/storage/base.py
@@ -2,389 +2,8 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Base storage backend interface for RAG functionality.
-"""
+"""Backward-compatible re-export of the engine storage base abstraction."""
 
-from __future__ import annotations
+from knowledge_engine.storage.base import BaseStorageBackend
 
-import json
-import logging
-from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional
-
-logger = logging.getLogger(__name__)
-
-from llama_index.core.schema import BaseNode
-
-if TYPE_CHECKING:
-    from app.services.rag.storage.chunk_metadata import ChunkMetadata
-
-
-class BaseStorageBackend(ABC):
-    """Abstract base class for storage backends."""
-
-    # Subclasses should override this with their supported methods
-    SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = []
-
-    # Index name prefix for different storage types (can be overridden)
-    INDEX_PREFIX: ClassVar[str] = "index"
-
-    def __init__(self, config: Dict):
-        """
-        Initialize storage backend.
-
-        Args:
-            config: Storage configuration dict containing:
-                - url: Connection URL
-                - username: Optional username
-                - password: Optional password
-                - apiKey: Optional API key
-                - indexStrategy: Index naming strategy config
-                - ext: Additional provider-specific config
-        """
-        self.config = config
-        self.url = config.get("url")
-        self.username = config.get("username")
-        self.password = config.get("password")
-        self.api_key = config.get("apiKey")
-        self.index_strategy = config.get("indexStrategy", {})
-        self.ext = config.get("ext", {})
-
-    def extract_chunk_text(self, raw_content: Any) -> str:
-        """Extract normalized plain text from raw chunk content.
-
-        Some storage providers (e.g., LlamaIndex-based backends) may store
-        serialized node objects (such as TextNode JSON) in the content field
-        when using get_all_chunks. For direct injection we only want the
-        human-readable `text` field and should drop internal fields
-        (id_, embedding, relationships, etc.).
-
-        This helper attempts to parse such JSON structures and extract the
-        `text` value. If parsing fails or the expected structure is missing,
-        it falls back to the original content.
-        """
-        if raw_content is None:
-            return ""
-
-        # Most backends already return plain text
-        if not isinstance(raw_content, str):
-            return str(raw_content)
-
-        stripped = raw_content.strip()
-        # Fast path: not a JSON object or does not contain a `text` key
-        if not stripped.startswith("{") or '"text"' not in stripped:
-            return raw_content
-
-        try:
-            data = json.loads(stripped)
-        except Exception:
-            # If parsing fails, fall back to original content
-            return raw_content
-
-        if isinstance(data, dict):
-            text = data.get("text")
-            if isinstance(text, str):
-                return text
-
-        # Fallback: return original content
-        return raw_content
-
-    def _validate_prefix(self, mode: str) -> str:
-        """
-        Validate and return prefix for index naming.
-
-        Args:
-            mode: Index strategy mode
-
-        Returns:
-            Validated prefix string
-
-        Raises:
-            ValueError: If prefix is empty or None
-        """
-        prefix = self.index_strategy.get("prefix", "wegent")
-        if not prefix:
-            raise ValueError(f"prefix cannot be empty for '{mode}' index strategy mode")
-        return prefix
-
-    def _validate_knowledge_id(self, knowledge_id: str, mode: str) -> None:
-        """
-        Validate knowledge_id is not None or empty.
-
-        Args:
-            knowledge_id: Knowledge base ID to validate
-            mode: Index strategy mode (for error message)
-
-        Raises:
-            ValueError: If knowledge_id is None or empty
-        """
-        if not knowledge_id:
-            raise ValueError(
-                f"knowledge_id is required for '{mode}' index strategy mode"
-            )
-
-    def get_index_name(self, knowledge_id: str, **kwargs) -> str:
-        """
-        Get index/collection name based on strategy.
-
-        Strategies:
-        - fixed: Use a single fixed index name (requires fixedName)
-        - rolling: Use rolling indices based on numeric knowledge_id (uses prefix)
-                   Groups N knowledge bases per index, where N = rollingStep (default 10)
-                   e.g., step=10: kb_id 1-10 -> index_0, kb_id 11-20 -> index_10, etc.
-        - per_dataset: Use separate index per knowledge base (default)
-        - per_user: Use separate index per user (requires user_id)
-
-        Args:
-            knowledge_id: Knowledge base ID (must be numeric string for rolling mode)
-            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
-
-        Returns:
-            Index/collection name
-        """
-        mode = self.index_strategy.get("mode", "per_dataset")
-
-        # Debug logging for index strategy
-        logger.debug(
-            f"get_index_name called: knowledge_id={knowledge_id}, "
-            f"index_strategy={self.index_strategy}, mode={mode}"
-        )
-
-        if mode == "fixed":
-            fixed_name = self.index_strategy.get("fixedName")
-            if not fixed_name:
-                raise ValueError(
-                    "fixedName is required for 'fixed' index strategy mode"
-                )
-            return fixed_name
-        elif mode == "rolling":
-            # Validate knowledge_id and prefix
-            self._validate_knowledge_id(knowledge_id, mode)
-            prefix = self._validate_prefix(mode)
-
-            # Validate rollingStep (number of knowledge_ids per index bucket)
-            step = self.index_strategy.get("rollingStep", 10)
-            if not isinstance(step, int) or step <= 0:
-                raise ValueError(f"rollingStep must be a positive integer, got: {step}")
-
-            # Convert knowledge_id to integer for bucket calculation
-            # knowledge_id is expected to be a numeric string (e.g., "1", "2", "123")
-            try:
-                kb_id_num = int(knowledge_id)
-            except ValueError:
-                raise ValueError(
-                    f"knowledge_id must be a numeric value for 'rolling' mode, got: {knowledge_id}"
-                )
-
-            # Calculate index base using floor division
-            # e.g., step=10: kb_id 1-10 -> index_0, kb_id 11-20 -> index_10, etc.
-            # Using (kb_id_num - 1) to make it 0-indexed:
-            #   kb_id 1-10 -> bucket 0 -> index_0
-            #   kb_id 11-20 -> bucket 1 -> index_10
-            bucket_num = (kb_id_num - 1) // step if kb_id_num > 0 else 0
-            index_base = bucket_num * step
-            index_name = f"{prefix}_{self.INDEX_PREFIX}_{index_base}"
-
-            logger.debug(
-                f"Rolling index: step={step}, kb_id_num={kb_id_num}, "
-                f"bucket_num={bucket_num}, index_base={index_base}, index_name={index_name}"
-            )
-            return index_name
-        elif mode == "per_dataset":
-            # Validate knowledge_id and prefix
-            self._validate_knowledge_id(knowledge_id, mode)
-            prefix = self._validate_prefix(mode)
-            return f"{prefix}_kb_{knowledge_id}"
-        elif mode == "per_user":
-            # Per-user index strategy: separate index for each user
-            user_id = kwargs.get("user_id")
-            if not user_id:
-                raise ValueError(
-                    "user_id is required for 'per_user' index strategy mode"
-                )
-            prefix = self._validate_prefix(mode)
-            return f"{prefix}_user_{user_id}"
-        else:
-            raise ValueError(f"Unknown index strategy mode: {mode}")
-
-    @classmethod
-    def get_supported_retrieval_methods(cls) -> List[str]:
-        """
-        Return list of supported retrieval methods.
-
-        Returns:
-            List of method names supported by this backend
-        """
-        return cls.SUPPORTED_RETRIEVAL_METHODS.copy()
-
-    @abstractmethod
-    def create_vector_store(self, index_name: str):
-        """
-        Create vector store instance.
-
-        Args:
-            index_name: Index/collection name
-
-        Returns:
-            Vector store instance compatible with LlamaIndex
-        """
-        pass
-
-    @abstractmethod
-    def index_with_metadata(
-        self,
-        nodes: List[BaseNode],
-        chunk_metadata: ChunkMetadata,
-        embed_model,
-        **kwargs,
-    ) -> Dict:
-        """
-        Index nodes into storage.
-
-        Note: Metadata is already applied to nodes by the indexer layer via
-        chunk_metadata.apply_to_nodes() before calling this method. Storage
-        backends should NOT apply metadata to nodes again.
-
-        Args:
-            nodes: List of nodes to index (metadata already applied)
-            chunk_metadata: ChunkMetadata instance containing:
-                - knowledge_id: Knowledge base ID
-                - doc_ref: Document reference ID (doc_xxx format)
-                - source_file: Source file name
-                - created_at: Creation timestamp (ISO 8601)
-                - chunk_index: Set per node during apply_to_nodes()
-            embed_model: Embedding model
-            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
-
-        Returns:
-            Indexing result dict with:
-                - indexed_count: Number of nodes indexed
-                - index_name: Index/collection name
-                - status: Indexing status
-        """
-        pass
-
-    @abstractmethod
-    def retrieve(
-        self,
-        knowledge_id: str,
-        query: str,
-        embed_model,
-        retrieval_setting: Dict[str, Any],
-        metadata_condition: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ) -> Dict:
-        """
-        Retrieve nodes from storage (Dify-compatible API).
-
-        Args:
-            knowledge_id: Knowledge base ID
-            query: Search query
-            embed_model: Embedding model
-            retrieval_setting: Dict with keys:
-                - top_k: Maximum number of results
-                - score_threshold: Minimum similarity score (0-1)
-                - retrieval_mode: Optional, 'vector'/'keyword'/'hybrid'
-                - vector_weight: Optional, weight for vector search
-                - keyword_weight: Optional, weight for keyword search
-            metadata_condition: Optional metadata filtering conditions
-            **kwargs: Additional parameters
-
-        Returns:
-            Dict with Dify-compatible format:
-                {
-                    "records": [
-                        {
-                            "content": str,      # Chunk text content
-                            "score": float,      # Relevance score (0-1)
-                            "title": str,        # Document title/source file
-                            "metadata": dict     # Additional metadata
-                        }
-                    ]
-                }
-        """
-        pass
-
-    @abstractmethod
-    def delete_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
-        """
-        Delete document from storage.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID
-            **kwargs: Additional parameters
-
-        Returns:
-            Deletion result dict
-        """
-        pass
-
-    @abstractmethod
-    def get_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
-        """
-        Get document details.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID
-            **kwargs: Additional parameters
-
-        Returns:
-            Document details dict
-        """
-        pass
-
-    @abstractmethod
-    def list_documents(
-        self, knowledge_id: str, page: int = 1, page_size: int = 20, **kwargs
-    ) -> Dict:
-        """
-        List documents in knowledge base.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            page: Page number
-            page_size: Page size
-            **kwargs: Additional parameters
-
-        Returns:
-            Document list dict
-        """
-        pass
-
-    @abstractmethod
-    def test_connection(self) -> bool:
-        """
-        Test connection to storage backend.
-
-        Returns:
-            True if connection successful, False otherwise
-        """
-        pass
-
-    @abstractmethod
-    def get_all_chunks(
-        self, knowledge_id: str, max_chunks: int = 10000, **kwargs
-    ) -> List[Dict[str, Any]]:
-        """
-        Get all chunks from a knowledge base.
-
-        This method retrieves all chunks stored for a specific knowledge base,
-        used for direct context injection when content fits within context window.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            max_chunks: Maximum number of chunks to retrieve (safety limit)
-            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
-
-        Returns:
-            List of chunk dicts with:
-                - content: str, chunk text content
-                - title: str, source document name
-                - chunk_id: int, chunk index within document
-                - doc_ref: str, document reference ID
-                - metadata: dict, additional metadata
-        """
-        pass
+__all__ = ["BaseStorageBackend"]
diff --git a/backend/app/services/rag/storage/chunk_metadata.py b/backend/app/services/rag/storage/chunk_metadata.py
index 1f78b1dbc..dcce4af87 100644
--- a/backend/app/services/rag/storage/chunk_metadata.py
+++ b/backend/app/services/rag/storage/chunk_metadata.py
@@ -2,84 +2,8 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Data models for RAG storage backends.
-"""
+"""Backward-compatible re-export of engine chunk metadata."""
 
-from dataclasses import asdict, dataclass, field
-from typing import Any, Dict, List
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
 
-from llama_index.core.schema import BaseNode
-
-
-@dataclass
-class ChunkMetadata:
-    """
-    Standard metadata structure for document chunks in vector stores.
-
-    This dataclass defines the common metadata fields used across all storage
-    backends (Milvus, Elasticsearch, Qdrant) for indexing document chunks.
-
-    The chunk_index field is optional and typically set during node iteration.
-
-    Attributes:
-        knowledge_id: Knowledge base ID that this chunk belongs to
-        doc_ref: Document reference ID (doc_xxx format)
-        source_file: Original source file name
-        created_at: ISO 8601 timestamp when the chunk was created
-        chunk_index: Index of this chunk within the document (0-based),
-                     set during indexing iteration
-    """
-
-    knowledge_id: str
-    doc_ref: str
-    source_file: str
-    created_at: str
-    chunk_index: int = field(default=0)
-
-    def to_dict(self) -> Dict[str, Any]:
-        """
-        Convert metadata to dictionary for node.metadata.update().
-
-        Returns:
-            Dictionary representation of the metadata
-        """
-        return asdict(self)
-
-    def with_chunk_index(self, chunk_index: int) -> "ChunkMetadata":
-        """
-        Create a new ChunkMetadata with the specified chunk_index.
-
-        This method is used during node iteration to set the chunk index
-        for each node while keeping other metadata fields unchanged.
-
-        Args:
-            chunk_index: The index of the chunk within the document
-
-        Returns:
-            New ChunkMetadata instance with updated chunk_index
-        """
-        return ChunkMetadata(
-            knowledge_id=self.knowledge_id,
-            doc_ref=self.doc_ref,
-            source_file=self.source_file,
-            created_at=self.created_at,
-            chunk_index=chunk_index,
-        )
-
-    def apply_to_nodes(self, nodes: List[BaseNode]) -> List[BaseNode]:
-        """
-        Apply metadata to all nodes with appropriate chunk_index.
-
-        This method iterates through the nodes and updates each node's metadata
-        with the chunk metadata, setting the correct chunk_index for each node.
-
-        Args:
-            nodes: List of nodes to apply metadata to
-
-        Returns:
-            The same list of nodes with updated metadata (modified in place)
-        """
-        for idx, node in enumerate(nodes):
-            node.metadata.update(self.with_chunk_index(idx).to_dict())
-        return nodes
+__all__ = ["ChunkMetadata"]
diff --git a/backend/app/services/rag/storage/elasticsearch_backend.py b/backend/app/services/rag/storage/elasticsearch_backend.py
index 94e9d5bec..c99277897 100644
--- a/backend/app/services/rag/storage/elasticsearch_backend.py
+++ b/backend/app/services/rag/storage/elasticsearch_backend.py
@@ -2,614 +2,11 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Elasticsearch storage backend implementation.
+"""Backward-compatible re-export of the engine Elasticsearch backend."""
 
-Supported retrieval modes:
-- vector: Pure vector similarity search using embeddings
-- keyword: Pure BM25 keyword search (full-text search)
-- hybrid: Combined vector + BM25 search with configurable weights
-"""
-
-import logging
-from typing import Any, ClassVar, Dict, List, Optional
-
-from elasticsearch import Elasticsearch
-from elasticsearch.helpers.vectorstore._async.strategies import (
-    AsyncBM25Strategy,
-    AsyncDenseVectorStrategy,
-)
-from llama_index.core import StorageContext, VectorStoreIndex
-from llama_index.core.schema import BaseNode
-from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
-from llama_index.core.vector_stores.types import (
-    VectorStoreQuery,
-    VectorStoreQueryMode,
+from knowledge_engine.storage.elasticsearch_backend import (
+    Elasticsearch,
+    ElasticsearchBackend,
 )
-from llama_index.vector_stores.elasticsearch import ElasticsearchStore
-
-from app.services.rag.retrieval.filters import parse_metadata_filters
-from app.services.rag.storage.base import BaseStorageBackend
-from app.services.rag.storage.chunk_metadata import ChunkMetadata
-from shared.telemetry.decorators import add_span_event
-
-logger = logging.getLogger(__name__)
-
-
-class ElasticsearchBackend(BaseStorageBackend):
-    """
-    Elasticsearch storage backend implementation.
-
-    Supported retrieval modes:
-    - vector: Pure vector similarity search (default)
-    - keyword: Pure BM25 keyword search
-    - hybrid: Combined vector + BM25 search
-
-    Class Attributes:
-        SUPPORTED_RETRIEVAL_METHODS: List of supported retrieval method names
-    """
-
-    # Class-level constant defining supported retrieval methods
-    SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = ["vector", "keyword", "hybrid"]
-
-    # Uses default INDEX_PREFIX = "index" from base class
-
-    def __init__(self, config: Dict):
-        """Initialize Elasticsearch backend."""
-        super().__init__(config)
-
-        # Build connection kwargs for native Elasticsearch client
-        # (used in list_documents and test_connection)
-        self.es_kwargs = {}
-        if self.username and self.password:
-            self.es_kwargs["basic_auth"] = (self.username, self.password)
-        elif self.api_key:
-            self.es_kwargs["api_key"] = self.api_key
-
-        # Build connection kwargs for LlamaIndex ElasticsearchStore
-        # Note: ElasticsearchStore uses different parameter names (es_user, es_password, es_api_key)
-        self.llama_es_kwargs = {}
-        if self.username and self.password:
-            self.llama_es_kwargs["es_user"] = self.username
-            self.llama_es_kwargs["es_password"] = self.password
-        elif self.api_key:
-            self.llama_es_kwargs["es_api_key"] = self.api_key
-
-    def create_vector_store(
-        self, index_name: str, retrieval_mode: str = "vector"
-    ) -> ElasticsearchStore:
-        """
-        Create Elasticsearch vector store with appropriate retrieval strategy.
-
-        Args:
-            index_name: Index name
-            retrieval_mode: Retrieval mode - 'vector', 'keyword', or 'hybrid'
-                - 'vector': Pure dense vector search (default)
-                - 'keyword': Pure BM25 text search
-                - 'hybrid': Combined vector + BM25 search
-
-        Returns:
-            ElasticsearchStore instance configured for the specified mode
-        """
-        # Select retrieval strategy based on mode
-        if retrieval_mode == "keyword":
-            # Pure BM25 keyword search
-            retrieval_strategy = AsyncBM25Strategy()
-        elif retrieval_mode == "hybrid":
-            # Hybrid search: vector + BM25 with linear combination
-            # Note: rrf=False uses linear combination instead of RRF fusion
-            # RRF requires Elasticsearch paid license (Platinum/Enterprise)
-            retrieval_strategy = AsyncDenseVectorStrategy(
-                hybrid=True, rrf=False, text_field="content"
-            )
-        else:
-            # Default: Pure vector search
-            retrieval_strategy = AsyncDenseVectorStrategy(hybrid=False)
-
-        return ElasticsearchStore(
-            index_name=index_name,
-            es_url=self.url,
-            retrieval_strategy=retrieval_strategy,
-            **self.llama_es_kwargs,
-        )
-
-    def index_with_metadata(
-        self,
-        nodes: List[BaseNode],
-        chunk_metadata: ChunkMetadata,
-        embed_model,
-        **kwargs,
-    ) -> Dict:
-        """
-        Index nodes into Elasticsearch.
-
-        Note: Metadata is already applied to nodes by the indexer layer via
-        chunk_metadata.apply_to_nodes() before calling this method.
-
-        Args:
-            nodes: List of nodes to index (metadata already applied)
-            chunk_metadata: ChunkMetadata instance containing document metadata
-            embed_model: Embedding model
-            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
-
-        Returns:
-            Indexing result dict
-
-        Note:
-            We use 'doc_ref' in metadata to store our custom doc_xxx ID.
-            LlamaIndex has its own internal 'document_id' field (ref_doc_id UUID).
-        """
-        add_span_event(
-            "elasticsearch.index_with_metadata.started",
-            {
-                "knowledge_id": chunk_metadata.knowledge_id,
-                "doc_ref": chunk_metadata.doc_ref,
-                "source_file": chunk_metadata.source_file,
-                "node_count": len(nodes),
-            },
-        )
-
-        # Get index name
-        index_name = self.get_index_name(chunk_metadata.knowledge_id, **kwargs)
-
-        add_span_event(
-            "elasticsearch.index_name_resolved",
-            {
-                "index_name": index_name,
-                "knowledge_id": chunk_metadata.knowledge_id,
-            },
-        )
-
-        # Index nodes
-        vector_store = self.create_vector_store(index_name)
-        storage_context = StorageContext.from_defaults(vector_store=vector_store)
-
-        add_span_event(
-            "elasticsearch.vector_store_created",
-            {
-                "index_name": index_name,
-            },
-        )
-
-        VectorStoreIndex(
-            nodes,
-            storage_context=storage_context,
-            embed_model=embed_model,
-            show_progress=True,
-        )
-
-        add_span_event(
-            "elasticsearch.index_with_metadata.completed",
-            {
-                "indexed_count": len(nodes),
-                "index_name": index_name,
-                "doc_ref": chunk_metadata.doc_ref,
-                "status": "success",
-            },
-        )
-
-        return {
-            "indexed_count": len(nodes),
-            "index_name": index_name,
-            "status": "success",
-        }
-
-    def retrieve(
-        self,
-        knowledge_id: str,
-        query: str,
-        embed_model,
-        retrieval_setting: Dict[str, Any],
-        metadata_condition: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ) -> Dict:
-        """
-        Retrieve nodes from Elasticsearch (Dify-style API).
-
-        Uses LlamaIndex's VectorStoreQuery with different modes:
-        - DEFAULT: Pure vector similarity search
-        - TEXT_SEARCH: Pure BM25 keyword search
-        - HYBRID: Combined vector + BM25 search
-
-        Args:
-            knowledge_id: Knowledge base ID
-            query: Search query
-            embed_model: Embedding model
-            retrieval_setting: Dict with:
-                - top_k: Maximum number of results
-                - score_threshold: Minimum similarity score (0-1)
-                - retrieval_mode: Optional 'vector'/'keyword'/'hybrid' (default: 'vector')
-                - alpha: Optional weight for hybrid search (0=keyword only, 1=vector only, default: 0.7)
-            metadata_condition: Optional metadata filtering
-            **kwargs: Additional parameters
-
-        Returns:
-            Retrieval result dict
-        """
-        index_name = self.get_index_name(knowledge_id, **kwargs)
-        # Increased default top_k from 5 to 20 for better RAG coverage
-        top_k = retrieval_setting.get("top_k", 20)
-        score_threshold = retrieval_setting.get("score_threshold", 0.7)
-        retrieval_mode = retrieval_setting.get("retrieval_mode", "vector")
-
-        # Create vector store with appropriate retrieval strategy
-        vector_store = self.create_vector_store(index_name, retrieval_mode)
-
-        # Build metadata filters
-        filters = self._build_metadata_filters(knowledge_id, metadata_condition)
-
-        # Determine query mode and parameters
-        if retrieval_mode == "keyword":
-            # Pure BM25 keyword search - no embedding needed
-            query_mode = VectorStoreQueryMode.TEXT_SEARCH
-            query_embedding = None
-            alpha = None
-        elif retrieval_mode == "hybrid":
-            # Hybrid search - needs embedding
-            # alpha: 0 = pure keyword, 1 = pure vector, default 0.7 (70% vector)
-            query_mode = VectorStoreQueryMode.HYBRID
-            query_embedding = embed_model.get_query_embedding(query)
-            # Convert vector_weight to alpha (they have the same meaning)
-            alpha = retrieval_setting.get(
-                "alpha", retrieval_setting.get("vector_weight", 0.7)
-            )
-        else:
-            # Default: Pure vector search
-            query_mode = VectorStoreQueryMode.DEFAULT
-            query_embedding = embed_model.get_query_embedding(query)
-            alpha = None
-
-        # Create VectorStoreQuery
-        vs_query = VectorStoreQuery(
-            query_str=query,
-            query_embedding=query_embedding,
-            similarity_top_k=top_k,
-            mode=query_mode,
-            filters=filters,
-            alpha=alpha,
-        )
-
-        # Execute query
-        result = vector_store.query(vs_query)
-
-        # Process results
-        return self._process_query_results(result, score_threshold)
-
-    def _build_metadata_filters(
-        self, knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
-    ):
-        """
-        Build metadata filters from condition dict.
-
-        Args:
-            knowledge_id: Knowledge base ID (always filtered)
-            metadata_condition: Optional additional metadata conditions
-
-        Returns:
-            MetadataFilters object
-        """
-        return parse_metadata_filters(knowledge_id, metadata_condition)
-
-    def _process_query_results(
-        self,
-        result,
-        score_threshold: float,
-    ) -> Dict:
-        """
-        Process VectorStoreQueryResult into Dify-compatible format.
-
-        Args:
-            result: VectorStoreQueryResult from LlamaIndex
-            score_threshold: Minimum relevance score (0-1)
-
-        Returns:
-            Dict with 'records' list in Dify-compatible format
-        """
-        # Handle empty results
-        if not result.nodes:
-            return {"records": []}
-
-        # Get max score for normalization (for keyword search scores may not be normalized)
-        similarities = result.similarities or []
-        if similarities:
-            max_score = max((s for s in similarities if s is not None), default=1.0)
-            if max_score == 0:
-                max_score = 1.0
-        else:
-            max_score = 1.0
-
-        # Process results (Dify-compatible format)
-        results = []
-        for i, node in enumerate(result.nodes):
-            score = (
-                similarities[i]
-                if i < len(similarities) and similarities[i] is not None
-                else 0.0
-            )
-
-            # Normalize score to 0-1 range if needed
-            # For vector search, scores are already normalized (cosine similarity)
-            # For keyword search, scores may need normalization
-            if max_score > 1.0:
-                normalized_score = score / max_score
-            else:
-                normalized_score = score
-
-            if normalized_score >= score_threshold:
-                results.append(
-                    {
-                        "content": node.text,
-                        "score": float(normalized_score),
-                        "title": node.metadata.get("source_file", ""),
-                        "metadata": node.metadata,
-                    }
-                )
-
-        return {"records": results}
-
-    def delete_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
-        """
-        Delete document from Elasticsearch using LlamaIndex API.
-
-        Uses delete_nodes with metadata filters to remove all chunks
-        with matching doc_ref (doc_xxx format).
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
-
-        Returns:
-            Deletion result dict
-        """
-        index_name = self.get_index_name(knowledge_id, **kwargs)
-        vector_store = self.create_vector_store(index_name)
-
-        # Build filters to match the document
-        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
-
-        # Get nodes first to count them
-        nodes = vector_store.get_nodes(filters=filters)
-        deleted_count = len(nodes)
-
-        # Delete nodes using LlamaIndex API
-        vector_store.delete_nodes(filters=filters)
-
-        return {
-            "doc_ref": doc_ref,
-            "knowledge_id": knowledge_id,
-            "deleted_chunks": deleted_count,
-            "status": "deleted",
-        }
-
-    def get_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
-        """
-        Get document details from Elasticsearch using LlamaIndex API.
-
-        Uses get_nodes with metadata filters to retrieve all chunks
-        with matching doc_ref (doc_xxx format).
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
-
-        Returns:
-            Document details dict with chunks
-        """
-        index_name = self.get_index_name(knowledge_id, **kwargs)
-        vector_store = self.create_vector_store(index_name)
-
-        # Build filters to match the document
-        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
-
-        # Get nodes using LlamaIndex API
-        nodes = vector_store.get_nodes(filters=filters)
-
-        if not nodes:
-            raise ValueError(f"Document {doc_ref} not found")
-
-        # Extract chunks and sort by chunk_index
-        chunks = []
-        source_file = None
-        for node in nodes:
-            metadata = node.metadata
-
-            if source_file is None:
-                source_file = metadata.get("source_file")
-
-            chunks.append(
-                {
-                    "chunk_index": metadata.get("chunk_index"),
-                    "content": node.text,
-                    "metadata": metadata,
-                }
-            )
-
-        # Sort by chunk_index
-        chunks.sort(key=lambda x: x.get("chunk_index", 0))
-
-        return {
-            "doc_ref": doc_ref,
-            "knowledge_id": knowledge_id,
-            "source_file": source_file,
-            "chunk_count": len(chunks),
-            "chunks": chunks,
-        }
-
-    def _build_doc_ref_filters(self, knowledge_id: str, doc_ref: str):
-        """
-        Build metadata filters for document reference lookup.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-
-        Returns:
-            MetadataFilters object for filtering by knowledge_id and doc_ref
-        """
-        return MetadataFilters(
-            filters=[
-                ExactMatchFilter(key="knowledge_id", value=knowledge_id),
-                ExactMatchFilter(key="doc_ref", value=doc_ref),
-            ],
-            condition="and",
-        )
-
-    def list_documents(
-        self, knowledge_id: str, page: int = 1, page_size: int = 20, **kwargs
-    ) -> Dict:
-        """
-        List documents in knowledge base.
-
-        Uses metadata.doc_ref (our custom doc_xxx format) for aggregation
-        to match the doc_ref returned in retrieve API metadata.
-        """
-        index_name = self.get_index_name(knowledge_id, **kwargs)
-        es_client = Elasticsearch(self.url, **self.es_kwargs)
-
-        # Aggregate by doc_ref (our custom document ID), filtered by knowledge_id
-        search_body = {
-            "size": 0,
-            "query": {"term": {"metadata.knowledge_id.keyword": knowledge_id}},
-            "aggs": {
-                "documents": {
-                    "terms": {
-                        "field": "metadata.doc_ref.keyword",  # Our custom doc_xxx ID
-                        "size": 10000,
-                    },
-                    "aggs": {
-                        "source_file": {
-                            "terms": {
-                                "field": "metadata.source_file.keyword",
-                                "size": 1,
-                            }
-                        },
-                        "created_at": {
-                            "min": {
-                                "field": "metadata.created_at"  # date field, no .keyword
-                            }
-                        },
-                    },
-                }
-            },
-        }
-
-        response = es_client.search(index=index_name, body=search_body)
-
-        # Process results
-        all_docs = []
-        for bucket in response["aggregations"]["documents"]["buckets"]:
-            doc_id = bucket["key"]
-            chunk_count = bucket["doc_count"]
-            source_file = (
-                bucket["source_file"]["buckets"][0]["key"]
-                if bucket["source_file"]["buckets"]
-                else None
-            )
-            created_at = bucket.get("created_at", {}).get("value_as_string")
-
-            all_docs.append(
-                {
-                    "doc_ref": doc_id,
-                    "source_file": source_file,
-                    "chunk_count": chunk_count,
-                    "created_at": created_at,
-                }
-            )
-
-        # Pagination
-        total = len(all_docs)
-        start = (page - 1) * page_size
-        end = start + page_size
-        documents = all_docs[start:end]
-
-        return {
-            "documents": documents,
-            "total": total,
-            "page": page,
-            "page_size": page_size,
-            "knowledge_id": knowledge_id,
-        }
-
-    def test_connection(self) -> bool:
-        """Test connection to Elasticsearch."""
-        try:
-            es_client = Elasticsearch(self.url, **self.es_kwargs)
-            return es_client.ping()
-        except Exception:
-            return False
-
-    def get_all_chunks(
-        self, knowledge_id: str, max_chunks: int = 10000, **kwargs
-    ) -> List[Dict[str, Any]]:
-        """
-        Get all chunks from a knowledge base in Elasticsearch.
-
-        Retrieves all chunks for a knowledge base with a single search request.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            max_chunks: Maximum number of chunks to retrieve (safety limit)
-            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
-
-        Returns:
-            List of chunk dicts with content, title, chunk_id, doc_ref, metadata
-        """
-        index_name = self.get_index_name(knowledge_id, **kwargs)
-        es_client = Elasticsearch(self.url, **self.es_kwargs)
-        user_id = kwargs.get("user_id")
-
-        # Query all chunks for this knowledge base
-        search_body = {
-            "size": min(max_chunks, 10000),  # ES limit per request
-            "query": {"term": {"metadata.knowledge_id.keyword": knowledge_id}},
-            "sort": [
-                {"metadata.doc_ref.keyword": "asc"},
-                {"metadata.chunk_index": "asc"},
-            ],
-        }
-
-        try:
-            # Check if index exists
-            if not es_client.indices.exists(index=index_name):
-                return []
-
-            response = es_client.search(index=index_name, body=search_body)
-
-            chunks = []
-            for hit in response["hits"]["hits"]:
-                source = hit.get("_source", {})
-                metadata = source.get("metadata", {})
-
-                # Normalize content to plain text. In most cases Elasticsearch
-                # stores human-readable text in the "content" field. However,
-                # for robustness we still pass it through extract_chunk_text
-                # to handle potential serialized node payloads.
-                raw_content = source.get("content", "")
-
-                chunks.append(
-                    {
-                        "content": self.extract_chunk_text(raw_content),
-                        "title": metadata.get("source_file", ""),
-                        "chunk_id": metadata.get("chunk_index", 0),
-                        "doc_ref": metadata.get("doc_ref", ""),
-                        "metadata": metadata,
-                    }
-                )
-
-                if len(chunks) >= max_chunks:
-                    break
-
-            return chunks
 
-        except Exception as e:
-            logger.warning(
-                "[Elasticsearch] Failed to get all chunks: knowledge_id=%s, "
-                "index_name=%s, search_body=%s, error=%s",
-                knowledge_id,
-                index_name,
-                search_body,
-                e,
-            )
-            return []
+__all__ = ["ElasticsearchBackend", "Elasticsearch"]
diff --git a/backend/app/services/rag/storage/factory.py b/backend/app/services/rag/storage/factory.py
index 77d57bc57..43f41fdb9 100644
--- a/backend/app/services/rag/storage/factory.py
+++ b/backend/app/services/rag/storage/factory.py
@@ -2,25 +2,27 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Storage backend factory for creating storage backends from Retriever CRD.
-"""
+"""Storage backend factory for backend control-plane resolution."""
 
-from typing import Dict, List, Optional, Type
+from typing import Dict, List, Optional
 
 from app.schemas.kind import Retriever
-from app.services.rag.storage.base import BaseStorageBackend
-from app.services.rag.storage.elasticsearch_backend import ElasticsearchBackend
-from app.services.rag.storage.milvus_backend import MilvusBackend
-from app.services.rag.storage.qdrant_backend import QdrantBackend
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.factory import (
+    STORAGE_BACKEND_REGISTRY as ENGINE_STORAGE_BACKEND_REGISTRY,
+)
+from knowledge_engine.storage.factory import (
+    create_storage_backend_from_config as engine_create_storage_backend_from_config,
+)
+from knowledge_engine.storage.factory import (
+    create_storage_backend_from_runtime_config as engine_create_storage_backend_from_runtime_config,
+)
+from knowledge_engine.storage.factory import (
+    get_supported_storage_types as engine_get_supported_storage_types,
+)
 from shared.models import RuntimeRetrieverConfig
 
-# Registry of storage backend classes by type
-STORAGE_BACKEND_REGISTRY: Dict[str, Type[BaseStorageBackend]] = {
-    "elasticsearch": ElasticsearchBackend,
-    "qdrant": QdrantBackend,
-    "milvus": MilvusBackend,
-}
+STORAGE_BACKEND_REGISTRY = ENGINE_STORAGE_BACKEND_REGISTRY
 
 
 def get_supported_storage_types() -> List[str]:
@@ -30,7 +32,7 @@ def get_supported_storage_types() -> List[str]:
     Returns:
         List of storage type names
     """
-    return list(STORAGE_BACKEND_REGISTRY.keys())
+    return engine_get_supported_storage_types()
 
 
 def get_supported_retrieval_methods(storage_type: str) -> List[str]:
@@ -111,19 +113,15 @@ def create_storage_backend_from_config(
             f"Supported types: {list(STORAGE_BACKEND_REGISTRY.keys())}"
         )
 
-    # Build config dict for backend
-    config = {
-        "url": url,
-        "username": username,
-        "password": password,
-        "apiKey": api_key,
-        "indexStrategy": index_strategy or {"mode": "per_dataset"},
-        "ext": ext or {},
-    }
-
-    # Create backend instance
-    backend_class = STORAGE_BACKEND_REGISTRY[storage_type]
-    return backend_class(config)
+    return engine_create_storage_backend_from_config(
+        storage_type=storage_type,
+        url=url,
+        username=username,
+        password=password,
+        api_key=api_key,
+        index_strategy=index_strategy,
+        ext=ext,
+    )
 
 
 def create_storage_backend(retriever: Retriever) -> BaseStorageBackend:
@@ -148,42 +146,27 @@ def create_storage_backend(retriever: Retriever) -> BaseStorageBackend:
             f"Supported types: {list(STORAGE_BACKEND_REGISTRY.keys())}"
         )
 
-    # Build config dict for backend
-    config = {
-        "url": storage_config.url,
-        "username": storage_config.username,
-        "password": storage_config.password,
-        "apiKey": storage_config.apiKey,
-        "indexStrategy": storage_config.indexStrategy.model_dump(exclude_none=True),
-        "ext": storage_config.ext or {},
-    }
-
-    # Create backend instance
-    backend_class = STORAGE_BACKEND_REGISTRY[storage_type]
-    return backend_class(config)
+    return engine_create_storage_backend_from_runtime_config(
+        RuntimeRetrieverConfig(
+            name=retriever.metadata.name,
+            namespace=retriever.metadata.namespace,
+            storage_config={
+                "type": storage_type,
+                "url": storage_config.url,
+                "username": storage_config.username,
+                "password": storage_config.password,
+                "apiKey": storage_config.apiKey,
+                "indexStrategy": storage_config.indexStrategy.model_dump(
+                    exclude_none=True
+                ),
+                "ext": storage_config.ext or {},
+            },
+        )
+    )
 
 
 def create_storage_backend_from_runtime_config(
     retriever_config: RuntimeRetrieverConfig,
 ) -> BaseStorageBackend:
     """Create storage backend from resolved runtime retriever config."""
-    storage_config = retriever_config.storage_config or {}
-    storage_type = storage_config.get("type", "").lower()
-
-    if storage_type not in STORAGE_BACKEND_REGISTRY:
-        raise ValueError(
-            f"Unsupported storage type: {storage_type}. "
-            f"Supported types: {list(STORAGE_BACKEND_REGISTRY.keys())}"
-        )
-
-    config = {
-        "url": storage_config.get("url"),
-        "username": storage_config.get("username"),
-        "password": storage_config.get("password"),
-        "apiKey": storage_config.get("apiKey"),
-        "indexStrategy": storage_config.get("indexStrategy") or {"mode": "per_dataset"},
-        "ext": storage_config.get("ext") or {},
-    }
-
-    backend_class = STORAGE_BACKEND_REGISTRY[storage_type]
-    return backend_class(config)
+    return engine_create_storage_backend_from_runtime_config(retriever_config)
diff --git a/backend/app/services/rag/storage/milvus_backend.py b/backend/app/services/rag/storage/milvus_backend.py
index 627645d5c..49425af22 100644
--- a/backend/app/services/rag/storage/milvus_backend.py
+++ b/backend/app/services/rag/storage/milvus_backend.py
@@ -2,828 +2,26 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Milvus storage backend implementation.
-
-Supported retrieval modes:
-- vector: Pure vector similarity search using embeddings (VectorStoreQueryMode.DEFAULT)
-- keyword: BM25 keyword search (VectorStoreQueryMode.TEXT_SEARCH)
-- hybrid: Combined vector + BM25 search with RRF ranking (VectorStoreQueryMode.HYBRID)
-
-Note: Requires Milvus 2.5+ for keyword and hybrid search support.
-"""
-
-import logging
-from typing import Any, ClassVar, Dict, List, Optional
-
-from llama_index.core import StorageContext, VectorStoreIndex
-from llama_index.core.base.embeddings.base import BaseEmbedding
-from llama_index.core.schema import BaseNode
-from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
-from llama_index.core.vector_stores.types import (
-    FilterOperator,
-    VectorStoreQuery,
-    VectorStoreQueryMode,
+"""Backward-compatible re-export of the engine Milvus backend."""
+
+from knowledge_engine.storage.milvus_backend import (
+    DEFAULT_EMBEDDING_DIM,
+    DEFAULT_TOP_K,
+    MAX_QUERY_LIMIT,
+    LazyAsyncMilvusVectorStore,
+    MilvusBackend,
+    MilvusClient,
+    StorageContext,
+    VectorStoreIndex,
 )
-from llama_index.vector_stores.milvus import MilvusVectorStore
-from llama_index.vector_stores.milvus.base import IndexManagement
-from pymilvus import AsyncMilvusClient, MilvusClient
-
-from app.services.rag.retrieval.filters import parse_metadata_filters
-from app.services.rag.storage.base import BaseStorageBackend
-from app.services.rag.storage.chunk_metadata import ChunkMetadata
-
-logger = logging.getLogger(__name__)
-
-# Named constants for magic numbers
-DEFAULT_EMBEDDING_DIM = 1024  # Default vector dimension (OpenAI text-embedding-ada-002)
-MAX_QUERY_LIMIT = 10000  # Maximum records to fetch for aggregation queries
-DEFAULT_TOP_K = 20  # Default top_k for retrieval
-
-
-class LazyAsyncMilvusVectorStore(MilvusVectorStore):
-    """
-    MilvusVectorStore subclass with lazy AsyncMilvusClient initialization.
-
-    The original MilvusVectorStore creates AsyncMilvusClient in __init__,
-    which requires an event loop. This causes issues when running in
-    thread pools (e.g., Celery tasks via asyncio.to_thread).
-
-    This subclass defers AsyncMilvusClient creation to first access,
-    allowing synchronous operations to work without an event loop.
-
-    See: https://github.com/run-llama/llama_index/issues/20313
-    See: https://github.com/run-llama/llama_index/pull/20695
-    """
-
-    # Store config for lazy async client creation
-    _milvusclient_config: Dict[str, Any] = {}
-
-    def __init__(self, **kwargs: Any) -> None:
-        """
-        Initialize without creating AsyncMilvusClient.
-
-        Stores connection params for lazy initialization and patches
-        the parent class to skip AsyncMilvusClient creation.
-        """
-        import llama_index.vector_stores.milvus.base as milvus_base
-
-        # Store the original AsyncMilvusClient class
-        original_async_client = milvus_base.AsyncMilvusClient
-
-        # Replace AsyncMilvusClient with a dummy that does nothing
-        # This prevents the parent __init__ from creating the async client
-        milvus_base.AsyncMilvusClient = lambda **kw: None  # type: ignore
-
-        try:
-            # Call parent __init__ - it will use our dummy AsyncMilvusClient
-            super().__init__(**kwargs)
-        finally:
-            # Restore the original AsyncMilvusClient
-            milvus_base.AsyncMilvusClient = original_async_client
-
-        # Store connection params for lazy async client creation
-        # Following the pattern from PR #20695
-        uri = kwargs.get("uri", "./milvus_llamaindex.db")
-        token = kwargs.get("token", "")
-        # Filter out 'alias' as pymilvus sets it internally
-        filtered_kwargs = {k: v for k, v in kwargs.items() if k != "alias"}
-
-        self._milvusclient_config = {
-            "uri": uri,
-            "token": token,
-            "kwargs": filtered_kwargs,
-        }
-
-        # Set _async_milvusclient to None for lazy initialization
-        self._async_milvusclient = None  # type: ignore
-
-    @property
-    def aclient(self) -> AsyncMilvusClient:
-        """
-        Get async client (lazily created on first access).
-
-        This property creates the AsyncMilvusClient only when needed,
-        allowing synchronous operations to work without an event loop.
-        """
-        if self._async_milvusclient is None:
-            self._async_milvusclient = AsyncMilvusClient(
-                uri=self._milvusclient_config["uri"],
-                token=self._milvusclient_config["token"],
-                **self._milvusclient_config["kwargs"],
-            )
-        return self._async_milvusclient
-
-
-class MilvusBackend(BaseStorageBackend):
-    """
-    Milvus storage backend implementation.
-
-    Supported retrieval modes:
-    - vector: Pure vector similarity search (default)
-    - keyword: Pure BM25 keyword search
-    - hybrid: Combined vector + BM25 search with RRF ranking
-
-    Class Attributes:
-        SUPPORTED_RETRIEVAL_METHODS: List of supported retrieval method names
-        INDEX_PREFIX: Prefix for collection names
-    """
-
-    # Milvus supports vector, keyword (BM25), and hybrid search
-    SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = ["vector", "keyword", "hybrid"]
-
-    # Override INDEX_PREFIX for Milvus collections
-    INDEX_PREFIX: ClassVar[str] = "collection"
-
-    def __init__(self, config: Dict):
-        """
-        Initialize Milvus backend.
-
-        Args:
-            config: Storage configuration dict containing:
-                - url: Milvus connection URL with optional db_name path
-                  (e.g., "http://localhost:19530/mydb" or "http://localhost:19530")
-                - username: Optional username for authentication
-                - password: Optional password for authentication
-                - indexStrategy: Index/collection naming strategy
-                - ext: Additional config (e.g., dim for vector dimension, db_name)
-
-        Authentication:
-            If both username and password are provided, they are concatenated
-            as "{username}:{password}" to form the token.
-
-        Database Name:
-            The db_name can be specified in three ways (in order of priority):
-            1. ext.db_name - explicit db_name in ext config
-            2. URL path - e.g., "http://localhost:19530/mydb"
-            3. Default - "default" if not specified
-        """
-        super().__init__(config)
-
-        # Get vector dimension from ext (default: 1536 for OpenAI embeddings)
-        self.dim = self.ext.get("dim", DEFAULT_EMBEDDING_DIM)
-
-        # Build token for authentication: username:password
-        if self.username and self.password:
-            self.token = f"{self.username}:{self.password}"
-        else:
-            self.token = ""
-
-        # Parse db_name from URL or ext config
-        # pymilvus requires db_name as a separate parameter, not in URL path
-        self.db_name, self.base_url = self._parse_db_name_from_url(self.url)
-
-    def _parse_db_name_from_url(self, url: str) -> tuple:
-        """
-        Parse db_name from URL path and return base URL without db_name.
-
-        Milvus requires db_name as a separate parameter, not in the URL path.
-        This method extracts db_name from URL like "http://host:port/dbname"
-        and returns the base URL "http://host:port".
-
-        Priority for db_name:
-        1. ext.db_name - explicit config takes highest priority
-        2. URL path - extracted from URL if present
-        3. Default - "default" if not specified
-
-        Args:
-            url: Milvus connection URL (e.g., "http://localhost:19530/mydb")
-
-        Returns:
-            Tuple of (db_name, base_url)
-        """
-        from urllib.parse import urlparse, urlunparse
-
-        # Priority 1: Check ext.db_name first
-        if self.ext.get("db_name"):
-            return self.ext["db_name"], url
-
-        # Priority 2: Parse from URL path
-        if not url:
-            return "default", url
-
-        parsed = urlparse(url)
-
-        # Extract db_name from path (e.g., "/mydb" -> "mydb")
-        path = parsed.path.strip("/")
-
-        if path:
-            # Has path component - use it as db_name
-            db_name = path
-            # Rebuild URL without the path
-            base_url = urlunparse(
-                (parsed.scheme, parsed.netloc, "", parsed.params, parsed.query, "")
-            )
-            return db_name, base_url
-        else:
-            # No path - use default db_name
-            return "default", url
-
-    @staticmethod
-    def _sanitize_filter_value(value: str) -> str:
-        """
-        Sanitize a string value for use in Milvus filter expressions.
-
-        Escapes backslashes and double quotes to prevent expression injection.
-
-        Args:
-            value: The string value to sanitize
-
-        Returns:
-            Sanitized string safe for use in filter expressions
-        """
-        return value.replace("\\", "\\\\").replace('"', '\\"')
-
-    def _get_client(self) -> MilvusClient:
-        """
-        Create a MilvusClient instance.
-
-        Uses base_url (without db_name path) and passes db_name as separate parameter.
-
-        Returns:
-            MilvusClient instance for direct Milvus operations
-        """
-        return MilvusClient(uri=self.base_url, token=self.token, db_name=self.db_name)
-
-    def create_vector_store(
-        self,
-        collection_name: str,
-        retrieval_mode: str = "vector",
-        dim: Optional[int] = None,
-    ) -> MilvusVectorStore:
-        """
-        Create Milvus vector store instance.
-
-        Uses base_url (without db_name path) and passes db_name as separate parameter.
-
-        Args:
-            collection_name: Name of the collection
-            retrieval_mode: Retrieval mode - 'vector', 'keyword', or 'hybrid'
-            dim: Optional embedding dimension. If provided, overrides self.dim.
-                 This allows dynamic dimension based on the actual embedding model.
-
-        Returns:
-            MilvusVectorStore instance
-        """
-        # Use provided dim if available, otherwise fall back to configured dim
-        effective_dim = dim if dim is not None else self.dim
-
-        logger.info(
-            f"[Milvus] create_vector_store: collection={collection_name}, "
-            f"dim_param={dim}, self.dim={self.dim}, effective_dim={effective_dim}"
-        )
-
-        return LazyAsyncMilvusVectorStore(
-            uri=self.base_url,
-            token=self.token,
-            db_name=self.db_name,
-            collection_name=collection_name,
-            dim=effective_dim,
-            upsert_mode=True,
-            overwrite=False,  # Do not overwrite existing collection
-            enable_sparse=True,  # Enable sparse vector for keyword/hybrid search
-            hybrid_ranker="RRFRanker",  # Use RRF for hybrid search ranking
-        )
-
-    def index_with_metadata(
-        self,
-        nodes: List[BaseNode],
-        chunk_metadata: ChunkMetadata,
-        embed_model,
-        **kwargs,
-    ) -> Dict:
-        """
-        Index nodes into Milvus.
-
-        Note: Metadata is already applied to nodes by the indexer layer via
-        chunk_metadata.apply_to_nodes() before calling this method.
-
-        This method automatically uses the embedding dimension from the embed_model
-        if available (via _dimension attribute set from Model CRD's embeddingConfig).
-        This ensures the Milvus collection schema matches the actual embedding vectors.
-
-        Args:
-            nodes: List of nodes to index (metadata already applied)
-            chunk_metadata: ChunkMetadata instance containing document metadata
-            embed_model: Embedding model (may have _dimension attribute from Model CRD)
-            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
-
-        Returns:
-            Indexing result dict
-        """
-        # Get collection name
-        collection_name = self.get_index_name(chunk_metadata.knowledge_id, **kwargs)
-
-        # Get embedding dimension from embed_model if available
-        # CustomEmbedding stores dimension in _dimension attribute (set from Model CRD)
-        embed_dim = getattr(embed_model, "_dimension", None)
-        if embed_dim:
-            logger.info(f"[Milvus] Using embedding dimension from model: {embed_dim}")
-
-        # Create vector store with detected dimension (or fall back to configured dim)
-        vector_store = self.create_vector_store(collection_name, dim=embed_dim)
-
-        # Index nodes using LlamaIndex
-        storage_context = StorageContext.from_defaults(vector_store=vector_store)
-
-        VectorStoreIndex(
-            nodes,
-            storage_context=storage_context,
-            embed_model=embed_model,
-            show_progress=True,
-        )
-
-        return {
-            "indexed_count": len(nodes),
-            "index_name": collection_name,
-            "status": "success",
-        }
-
-    def retrieve(
-        self,
-        knowledge_id: str,
-        query: str,
-        embed_model,
-        retrieval_setting: Dict[str, Any],
-        metadata_condition: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ) -> Dict:
-        """
-        Retrieve nodes from Milvus (Dify-style API).
-
-        Uses LlamaIndex's VectorStoreQuery with different modes:
-        - DEFAULT: Pure vector similarity search
-        - TEXT_SEARCH: Pure BM25 keyword search
-        - HYBRID: Combined vector + BM25 search with RRF ranking
-
-        Note on score_threshold:
-        - For vector search: score_threshold is applied (cosine similarity 0-1)
-        - For keyword search: score_threshold is applied (BM25 scores vary)
-        - For hybrid search: score_threshold is IGNORED because RRF scores are
-          in a different range (typically 0.01-0.05) and ranking is already
-          optimized by the fusion algorithm
-
-        Args:
-            knowledge_id: Knowledge base ID
-            query: Search query
-            embed_model: Embedding model
-            retrieval_setting: Dict with:
-                - top_k: Maximum number of results
-                - score_threshold: Minimum similarity score (0-1, ignored for hybrid)
-                - retrieval_mode: Optional 'vector'/'keyword'/'hybrid' (default: 'vector')
-            metadata_condition: Optional metadata filtering
-            **kwargs: Additional parameters
-
-        Returns:
-            Retrieval result dict
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-        # Increased default top_k from 5 to 20 for better RAG coverage
-        top_k = retrieval_setting.get("top_k", DEFAULT_TOP_K)
-        score_threshold = retrieval_setting.get("score_threshold", 0.7)
-        retrieval_mode = retrieval_setting.get("retrieval_mode", "vector")
-
-        # Validate retrieval mode
-        if retrieval_mode not in self.SUPPORTED_RETRIEVAL_METHODS:
-            raise ValueError(
-                f"Milvus does not support '{retrieval_mode}' retrieval mode. "
-                f"Supported modes: {self.SUPPORTED_RETRIEVAL_METHODS}."
-            )
-
-        # Create vector store
-        vector_store = self.create_vector_store(collection_name, retrieval_mode)
-
-        # Build metadata filters
-        filters = self._build_metadata_filters(knowledge_id, metadata_condition)
-
-        # Determine query mode and parameters
-        if retrieval_mode == "keyword":
-            # Pure BM25 keyword search - no embedding needed
-            query_mode = VectorStoreQueryMode.TEXT_SEARCH
-            query_embedding = None
-        elif retrieval_mode == "hybrid":
-            # Hybrid search - needs embedding
-            query_mode = VectorStoreQueryMode.HYBRID
-            query_embedding = embed_model.get_query_embedding(query)
-        else:
-            # Default: Pure vector search
-            query_mode = VectorStoreQueryMode.DEFAULT
-            query_embedding = embed_model.get_query_embedding(query)
-
-        # Create VectorStoreQuery
-        vs_query = VectorStoreQuery(
-            query_str=query,
-            query_embedding=query_embedding,
-            similarity_top_k=top_k,
-            mode=query_mode,
-            filters=filters,
-        )
-
-        # Debug logging for hybrid search troubleshooting
-        logger.debug(
-            f"[Milvus] retrieve: mode={retrieval_mode}, query_mode={query_mode}, "
-            f"sparse_embedding_function={type(vector_store.sparse_embedding_function)}, "
-            f"enable_sparse={vector_store.enable_sparse}"
-        )
-
-        # Execute query
-        result = vector_store.query(vs_query)
-
-        # Debug logging for query results
-        logger.debug(
-            f"[Milvus] query result: nodes_count={len(result.nodes) if result.nodes else 0}, "
-            f"similarities={result.similarities[:5] if result.similarities else None}"
-        )
-
-        # Process results
-        # For hybrid search, skip score_threshold because RRF scores are in a different
-        # range (0.01-0.05) and the ranking is already optimized by the fusion algorithm
-        effective_threshold = 0.0 if retrieval_mode == "hybrid" else score_threshold
-        return self._process_query_results(result, effective_threshold)
-
-    def _build_metadata_filters(
-        self, knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
-    ):
-        """
-        Build metadata filters from condition dict.
-
-        Args:
-            knowledge_id: Knowledge base ID (always filtered)
-            metadata_condition: Optional additional metadata conditions
-
-        Returns:
-            MetadataFilters object
-        """
-        return parse_metadata_filters(knowledge_id, metadata_condition)
-
-    def _process_query_results(
-        self,
-        result,
-        score_threshold: float,
-    ) -> Dict:
-        """
-        Process VectorStoreQueryResult into Dify-compatible format.
-
-        Args:
-            result: VectorStoreQueryResult from LlamaIndex
-            score_threshold: Minimum relevance score (0-1).
-                            For hybrid search, this should be set to 0.0 because
-                            RRF scores are in a different range (0.01-0.05).
-
-        Returns:
-            Dict with 'records' list in Dify-compatible format
-        """
-        # Handle empty results
-        if not result.nodes:
-            return {"records": []}
-
-        # Process results (Dify-compatible format)
-        results = []
-        similarities = result.similarities or []
-
-        for i, node in enumerate(result.nodes):
-            score = (
-                similarities[i]
-                if i < len(similarities) and similarities[i] is not None
-                else 0.0
-            )
-
-            # Apply score threshold filter
-            if score >= score_threshold:
-                results.append(
-                    {
-                        "content": node.text,
-                        "score": float(score),
-                        "title": node.metadata.get("source_file", ""),
-                        "metadata": node.metadata,
-                    }
-                )
-
-        return {"records": results}
-
-    def delete_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
-        """
-        Delete document from Milvus using LlamaIndex API.
-
-        Uses delete_nodes with metadata filters to remove all chunks
-        with matching doc_ref.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-            **kwargs: Additional parameters
-
-        Returns:
-            Deletion result dict
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-        vector_store = self.create_vector_store(collection_name)
-
-        # Build filters to match the document
-        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
-
-        # Get nodes first to count them
-        try:
-            nodes = vector_store.get_nodes(filters=filters)
-            deleted_count = len(nodes)
-        except Exception:
-            deleted_count = 0
-
-        # Delete nodes using LlamaIndex API
-        vector_store.delete_nodes(filters=filters)
-
-        return {
-            "doc_ref": doc_ref,
-            "knowledge_id": knowledge_id,
-            "deleted_chunks": deleted_count,
-            "status": "deleted",
-        }
-
-    def get_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
-        """
-        Get document details from Milvus using LlamaIndex API.
-
-        Uses get_nodes with metadata filters to retrieve all chunks
-        with matching doc_ref.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-            **kwargs: Additional parameters
-
-        Returns:
-            Document details dict with chunks
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-        vector_store = self.create_vector_store(collection_name)
-
-        # Build filters to match the document
-        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
-
-        # Get nodes using LlamaIndex API
-        nodes = vector_store.get_nodes(filters=filters)
-
-        if not nodes:
-            raise ValueError(f"Document {doc_ref} not found")
-
-        # Extract chunks and sort by chunk_index
-        chunks = []
-        source_file = None
-        for node in nodes:
-            metadata = node.metadata
-
-            if source_file is None:
-                source_file = metadata.get("source_file")
-
-            chunks.append(
-                {
-                    "chunk_index": metadata.get("chunk_index"),
-                    "content": node.text,
-                    "metadata": metadata,
-                }
-            )
-
-        # Sort by chunk_index
-        chunks.sort(key=lambda x: x.get("chunk_index", 0))
-
-        return {
-            "doc_ref": doc_ref,
-            "knowledge_id": knowledge_id,
-            "source_file": source_file,
-            "chunk_count": len(chunks),
-            "chunks": chunks,
-        }
-
-    def _build_doc_ref_filters(self, knowledge_id: str, doc_ref: str):
-        """
-        Build metadata filters for document reference lookup.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-
-        Returns:
-            MetadataFilters object for filtering by knowledge_id and doc_ref
-        """
-        return MetadataFilters(
-            filters=[
-                MetadataFilter(
-                    key="knowledge_id", value=knowledge_id, operator=FilterOperator.EQ
-                ),
-                MetadataFilter(
-                    key="doc_ref", value=doc_ref, operator=FilterOperator.EQ
-                ),
-            ],
-            condition="and",
-        )
-
-    def list_documents(
-        self, knowledge_id: str, page: int = 1, page_size: int = 20, **kwargs
-    ) -> Dict:
-        """
-        List documents in Milvus collection.
-
-        Uses MilvusClient directly for aggregation functionality.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            page: Page number
-            page_size: Page size
-            **kwargs: Additional parameters
-
-        Returns:
-            Document list dict
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-        client = None
-
-        try:
-            # Create MilvusClient for direct query
-            client = self._get_client()
-
-            # Check if collection exists
-            collections = client.list_collections()
-            if collection_name not in collections:
-                return {
-                    "documents": [],
-                    "total": 0,
-                    "page": page,
-                    "page_size": page_size,
-                    "knowledge_id": knowledge_id,
-                }
-
-            # Sanitize knowledge_id to prevent expression injection
-            safe_knowledge_id = self._sanitize_filter_value(knowledge_id)
-            filter_expr = f'knowledge_id == "{safe_knowledge_id}"'
-
-            # Query all records with matching knowledge_id
-            # Note: Milvus requires specifying output fields
-            results = client.query(
-                collection_name=collection_name,
-                filter=filter_expr,
-                output_fields=["doc_ref", "source_file", "created_at", "chunk_index"],
-                limit=MAX_QUERY_LIMIT,
-            )
-
-            # Warn if results may be truncated
-            if len(results) >= MAX_QUERY_LIMIT:
-                logger.warning(
-                    f"[Milvus] Knowledge base {knowledge_id} has >= {MAX_QUERY_LIMIT} "
-                    "chunks; document listing may be incomplete."
-                )
-
-            # Aggregate by doc_ref
-            doc_map: Dict[str, Dict] = {}
-            for record in results:
-                doc_ref = record.get("doc_ref")
-                if not doc_ref:
-                    continue
-
-                if doc_ref not in doc_map:
-                    doc_map[doc_ref] = {
-                        "doc_ref": doc_ref,
-                        "source_file": record.get("source_file"),
-                        "chunk_count": 0,
-                        "created_at": record.get("created_at"),
-                    }
-                doc_map[doc_ref]["chunk_count"] += 1
-
-            # Convert to list and sort by created_at
-            all_docs = list(doc_map.values())
-            all_docs.sort(key=lambda x: x.get("created_at") or "", reverse=True)
-
-            # Pagination
-            total = len(all_docs)
-            start = (page - 1) * page_size
-            end = start + page_size
-            documents = all_docs[start:end]
-
-            return {
-                "documents": documents,
-                "total": total,
-                "page": page,
-                "page_size": page_size,
-                "knowledge_id": knowledge_id,
-            }
-
-        except Exception as e:
-            logger.warning(
-                f"[Milvus] Failed to list documents for KB {knowledge_id}: {e}"
-            )
-            return {
-                "documents": [],
-                "total": 0,
-                "page": page,
-                "page_size": page_size,
-                "knowledge_id": knowledge_id,
-            }
-        finally:
-            # Ensure client is closed to avoid connection leaks
-            if client:
-                try:
-                    client.close()
-                except Exception:
-                    pass
-
-    def test_connection(self) -> bool:
-        """
-        Test connection to Milvus.
-
-        Returns:
-            True if connection successful, False otherwise
-        """
-        client = None
-        try:
-            client = self._get_client()
-            # Try to list collections as a connection test
-            client.list_collections()
-            return True
-        except Exception:
-            return False
-        finally:
-            # Ensure client is closed to avoid connection leaks
-            if client:
-                try:
-                    client.close()
-                except Exception:
-                    pass
-
-    def get_all_chunks(
-        self, knowledge_id: str, max_chunks: int = MAX_QUERY_LIMIT, **kwargs
-    ) -> List[Dict[str, Any]]:
-        """
-        Get all chunks from a knowledge base in Milvus.
-
-        Uses MilvusClient directly for efficient batch retrieval.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            max_chunks: Maximum number of chunks to retrieve (safety limit)
-            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
-
-        Returns:
-            List of chunk dicts with content, title, chunk_id, doc_ref, metadata
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-        client = None
-
-        try:
-            # Create MilvusClient for direct query
-            client = self._get_client()
-
-            # Check if collection exists
-            collections = client.list_collections()
-            if collection_name not in collections:
-                return []
-
-            # Sanitize knowledge_id to prevent expression injection
-            safe_knowledge_id = self._sanitize_filter_value(knowledge_id)
-            filter_expr = f'knowledge_id == "{safe_knowledge_id}"'
-
-            # Query all records with matching knowledge_id
-            results = client.query(
-                collection_name=collection_name,
-                filter=filter_expr,
-                output_fields=[
-                    "doc_ref",
-                    "source_file",
-                    "created_at",
-                    "chunk_index",
-                    "text",
-                ],
-                limit=max_chunks,
-            )
-
-            # Convert to chunk format
-            chunks = []
-            for record in results:
-                # Get text content - try 'text' field first, then fallback
-                raw_content = record.get("text", "")
-
-                chunks.append(
-                    {
-                        "content": self.extract_chunk_text(raw_content),
-                        "title": record.get("source_file", ""),
-                        "chunk_id": record.get("chunk_index", 0),
-                        "doc_ref": record.get("doc_ref", ""),
-                        "metadata": record,
-                    }
-                )
-
-            # Sort by doc_ref and chunk_index
-            chunks.sort(key=lambda x: (x.get("doc_ref", ""), x.get("chunk_id", 0)))
-
-            return chunks[:max_chunks]
 
-        except Exception as e:
-            logger.warning(
-                f"[Milvus] Failed to get all chunks for KB {knowledge_id}: {e}"
-            )
-            return []
-        finally:
-            # Ensure client is closed to avoid connection leaks
-            if client:
-                try:
-                    client.close()
-                except Exception:
-                    pass
+__all__ = [
+    "DEFAULT_EMBEDDING_DIM",
+    "DEFAULT_TOP_K",
+    "MAX_QUERY_LIMIT",
+    "LazyAsyncMilvusVectorStore",
+    "MilvusBackend",
+    "MilvusClient",
+    "StorageContext",
+    "VectorStoreIndex",
+]
diff --git a/backend/app/services/rag/storage/qdrant_backend.py b/backend/app/services/rag/storage/qdrant_backend.py
index 21d7555ac..52aebe0ba 100644
--- a/backend/app/services/rag/storage/qdrant_backend.py
+++ b/backend/app/services/rag/storage/qdrant_backend.py
@@ -2,570 +2,12 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Qdrant storage backend implementation.
+"""Backward-compatible re-export of the engine Qdrant backend."""
 
-Supported retrieval modes:
-- vector: Pure vector similarity search using embeddings
-
-Note on non-vector retrieval support:
-- Current implementation only supports vector search
-- Qdrant's newer versions (1.7+) do support full-text search via BM42 algorithm
-  and hybrid search capabilities, but this implementation does not yet utilize them
-- For keyword or hybrid search needs, use Elasticsearch backend instead
-- Future enhancement: Add BM42/hybrid search support when upgrading Qdrant integration
-"""
-
-from typing import Any, ClassVar, Dict, List, Optional
-
-from llama_index.core import StorageContext, VectorStoreIndex
-from llama_index.core.schema import BaseNode
-from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
-from llama_index.core.vector_stores.types import (
-    VectorStoreQuery,
-    VectorStoreQueryMode,
+from knowledge_engine.storage.qdrant_backend import (
+    QdrantBackend,
+    QdrantClient,
+    QdrantVectorStore,
 )
-from llama_index.vector_stores.qdrant import QdrantVectorStore
-from qdrant_client import QdrantClient
-from qdrant_client.http import models as qdrant_models
-
-from app.services.rag.retrieval.filters import parse_metadata_filters
-from app.services.rag.storage.base import BaseStorageBackend
-from app.services.rag.storage.chunk_metadata import ChunkMetadata
-
-
-class QdrantBackend(BaseStorageBackend):
-    """
-    Qdrant storage backend implementation.
-
-    Supported retrieval modes:
-    - vector: Pure vector similarity search (default and only mode)
-
-    Note on non-vector retrieval support:
-    - Current implementation only supports vector search
-    - Qdrant's newer versions (1.7+) do support full-text search via BM42 algorithm
-      and hybrid search capabilities, but this implementation does not yet utilize them
-    - For keyword or hybrid search needs, use Elasticsearch backend instead
-    - Future enhancement: Add BM42/hybrid search support when upgrading Qdrant integration
-
-    Class Attributes:
-        SUPPORTED_RETRIEVAL_METHODS: List of supported retrieval method names
-        INDEX_PREFIX: Prefix for rolling collection names
-    """
-
-    # Qdrant only supports vector search
-    SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = ["vector"]
-
-    # Override INDEX_PREFIX for Qdrant collections
-    INDEX_PREFIX: ClassVar[str] = "collection"
-
-    def __init__(self, config: Dict):
-        """
-        Initialize Qdrant backend.
-
-        Args:
-            config: Storage configuration dict containing:
-                - url: Qdrant server URL (e.g., "http://localhost:6333")
-                - apiKey: Optional API key for Qdrant Cloud
-                - indexStrategy: Index/collection naming strategy
-                - ext: Additional config (e.g., vector_size, distance)
-        """
-        super().__init__(config)
-
-        # Get vector configuration from ext (used by QdrantVectorStore for auto-creation)
-        self.vector_size = self.ext.get("vector_size", 1536)  # Default for OpenAI
-        self.distance = self.ext.get("distance", "Cosine")  # Cosine, Euclid, Dot
-
-        # Initialize Qdrant client
-        if self.api_key:
-            # Qdrant Cloud connection
-            self.client = QdrantClient(
-                url=self.url,
-                api_key=self.api_key,
-            )
-        else:
-            # Local Qdrant connection
-            self.client = QdrantClient(url=self.url)
-
-    def create_vector_store(self, collection_name: str):
-        """
-        Create Qdrant vector store.
-
-        LlamaIndex's QdrantVectorStore automatically creates the collection
-        if it doesn't exist when adding nodes.
-
-        Args:
-            collection_name: Name of the collection
-
-        Returns:
-            QdrantVectorStore instance
-        """
-        return QdrantVectorStore(
-            client=self.client,
-            collection_name=collection_name,
-        )
-
-    def index_with_metadata(
-        self,
-        nodes: List[BaseNode],
-        chunk_metadata: ChunkMetadata,
-        embed_model,
-        **kwargs,
-    ) -> Dict:
-        """
-        Index nodes into Qdrant.
-
-        Note: Metadata is already applied to nodes by the indexer layer via
-        chunk_metadata.apply_to_nodes() before calling this method.
-
-        Args:
-            nodes: List of nodes to index (metadata already applied)
-            chunk_metadata: ChunkMetadata instance containing document metadata
-            embed_model: Embedding model
-            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
-
-        Returns:
-            Indexing result dict
-        """
-        # Get collection name
-        collection_name = self.get_index_name(chunk_metadata.knowledge_id, **kwargs)
-
-        # Index nodes (LlamaIndex auto-creates collection if needed)
-        vector_store = self.create_vector_store(collection_name)
-        storage_context = StorageContext.from_defaults(vector_store=vector_store)
-
-        VectorStoreIndex(
-            nodes,
-            storage_context=storage_context,
-            embed_model=embed_model,
-            show_progress=True,
-        )
-
-        return {
-            "indexed_count": len(nodes),
-            "index_name": collection_name,
-            "status": "success",
-        }
-
-    def retrieve(
-        self,
-        knowledge_id: str,
-        query: str,
-        embed_model,
-        retrieval_setting: Dict[str, Any],
-        metadata_condition: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ) -> Dict:
-        """
-        Retrieve nodes from Qdrant (Dify-style API).
-
-        Uses LlamaIndex's VectorStoreQuery with DEFAULT mode (vector search only).
-
-        Args:
-            knowledge_id: Knowledge base ID
-            query: Search query
-            embed_model: Embedding model
-            retrieval_setting: Dict with:
-                - top_k: Maximum number of results
-                - score_threshold: Minimum similarity score (0-1)
-                - retrieval_mode: Only 'vector' is supported
-            metadata_condition: Optional metadata filtering
-            **kwargs: Additional parameters
-
-        Returns:
-            Retrieval result dict
-
-        Raises:
-            ValueError: If retrieval_mode is not 'vector'
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-        # Increased default top_k from 5 to 20 for better RAG coverage
-        top_k = retrieval_setting.get("top_k", 20)
-        score_threshold = retrieval_setting.get("score_threshold", 0.7)
-        retrieval_mode = retrieval_setting.get("retrieval_mode", "vector")
-
-        # Validate retrieval mode - Qdrant only supports vector search
-        if retrieval_mode not in self.SUPPORTED_RETRIEVAL_METHODS:
-            raise ValueError(
-                f"Qdrant does not support '{retrieval_mode}' retrieval mode. "
-                f"Supported modes: {self.SUPPORTED_RETRIEVAL_METHODS}. "
-                f"For keyword or hybrid search, use Elasticsearch backend."
-            )
-
-        # Create vector store
-        vector_store = self.create_vector_store(collection_name)
-
-        # Build metadata filters
-        filters = self._build_metadata_filters(knowledge_id, metadata_condition)
-
-        # Generate query embedding
-        query_embedding = embed_model.get_query_embedding(query)
-
-        # Create VectorStoreQuery (vector mode only)
-        vs_query = VectorStoreQuery(
-            query_str=query,
-            query_embedding=query_embedding,
-            similarity_top_k=top_k,
-            mode=VectorStoreQueryMode.DEFAULT,
-            filters=filters,
-        )
-
-        # Execute query
-        result = vector_store.query(vs_query)
-
-        # Process results
-        return self._process_query_results(result, score_threshold)
-
-    def _build_metadata_filters(
-        self, knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
-    ):
-        """
-        Build metadata filters from condition dict.
-
-        Args:
-            knowledge_id: Knowledge base ID (always filtered)
-            metadata_condition: Optional additional metadata conditions
-
-        Returns:
-            MetadataFilters object
-        """
-        return parse_metadata_filters(knowledge_id, metadata_condition)
-
-    def _process_query_results(
-        self,
-        result,
-        score_threshold: float,
-    ) -> Dict:
-        """
-        Process VectorStoreQueryResult into Dify-compatible format.
-
-        Args:
-            result: VectorStoreQueryResult from LlamaIndex
-            score_threshold: Minimum relevance score (0-1)
-
-        Returns:
-            Dict with 'records' list in Dify-compatible format
-        """
-        # Handle empty results
-        if not result.nodes:
-            return {"records": []}
-
-        # Process results (Dify-compatible format)
-        results = []
-        similarities = result.similarities or []
-
-        for i, node in enumerate(result.nodes):
-            score = (
-                similarities[i]
-                if i < len(similarities) and similarities[i] is not None
-                else 0.0
-            )
-
-            # Qdrant with cosine similarity returns scores in 0-1 range
-            if score >= score_threshold:
-                results.append(
-                    {
-                        "content": node.text,
-                        "score": float(score),
-                        "title": node.metadata.get("source_file", ""),
-                        "metadata": node.metadata,
-                    }
-                )
-
-        return {"records": results}
-
-    def delete_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
-        """
-        Delete document from Qdrant using LlamaIndex API.
-
-        Uses delete_nodes with metadata filters to remove all chunks
-        with matching doc_ref.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-            **kwargs: Additional parameters
-
-        Returns:
-            Deletion result dict
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-        vector_store = self.create_vector_store(collection_name)
-
-        # Build filters to match the document
-        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
-
-        # Get nodes first to count them
-        nodes = vector_store.get_nodes(filters=filters)
-        deleted_count = len(nodes)
-
-        # Delete nodes using LlamaIndex API
-        vector_store.delete_nodes(filters=filters)
-
-        return {
-            "doc_ref": doc_ref,
-            "knowledge_id": knowledge_id,
-            "deleted_chunks": deleted_count,
-            "status": "deleted",
-        }
-
-    def get_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
-        """
-        Get document details from Qdrant using LlamaIndex API.
-
-        Uses get_nodes with metadata filters to retrieve all chunks
-        with matching doc_ref.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-            **kwargs: Additional parameters
-
-        Returns:
-            Document details dict with chunks
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-        vector_store = self.create_vector_store(collection_name)
-
-        # Build filters to match the document
-        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
-
-        # Get nodes using LlamaIndex API
-        nodes = vector_store.get_nodes(filters=filters)
-
-        if not nodes:
-            raise ValueError(f"Document {doc_ref} not found")
-
-        # Extract chunks and sort by chunk_index
-        chunks = []
-        source_file = None
-        for node in nodes:
-            metadata = node.metadata
-
-            if source_file is None:
-                source_file = metadata.get("source_file")
-
-            chunks.append(
-                {
-                    "chunk_index": metadata.get("chunk_index"),
-                    "content": node.text,
-                    "metadata": metadata,
-                }
-            )
-
-        # Sort by chunk_index
-        chunks.sort(key=lambda x: x.get("chunk_index", 0))
-
-        return {
-            "doc_ref": doc_ref,
-            "knowledge_id": knowledge_id,
-            "source_file": source_file,
-            "chunk_count": len(chunks),
-            "chunks": chunks,
-        }
-
-    def _build_doc_ref_filters(self, knowledge_id: str, doc_ref: str):
-        """
-        Build metadata filters for document reference lookup.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            doc_ref: Document reference ID (doc_xxx format)
-
-        Returns:
-            MetadataFilters object for filtering by knowledge_id and doc_ref
-        """
-        return MetadataFilters(
-            filters=[
-                ExactMatchFilter(key="knowledge_id", value=knowledge_id),
-                ExactMatchFilter(key="doc_ref", value=doc_ref),
-            ],
-            condition="and",
-        )
-
-    def list_documents(
-        self, knowledge_id: str, page: int = 1, page_size: int = 20, **kwargs
-    ) -> Dict:
-        """
-        List documents in Qdrant collection.
-
-        Uses Qdrant's scroll API to aggregate documents by doc_ref.
-        Note: This uses native Qdrant API as LlamaIndex doesn't provide
-        aggregation functionality.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            page: Page number
-            page_size: Page size
-            **kwargs: Additional parameters
-
-        Returns:
-            Document list dict
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-
-        # Use Qdrant scroll to get all points with matching knowledge_id
-        # Then aggregate by doc_ref in Python
-        scroll_filter = qdrant_models.Filter(
-            must=[
-                qdrant_models.FieldCondition(
-                    key="knowledge_id",
-                    match=qdrant_models.MatchValue(value=knowledge_id),
-                )
-            ]
-        )
-
-        # Scroll through all matching points
-        all_points = []
-        offset = None
-        while True:
-            results, offset = self.client.scroll(
-                collection_name=collection_name,
-                scroll_filter=scroll_filter,
-                limit=1000,
-                offset=offset,
-                with_payload=True,
-                with_vectors=False,
-            )
-            all_points.extend(results)
-            if offset is None:
-                break
-
-        # Aggregate by doc_ref
-        doc_map: Dict[str, Dict] = {}
-        for point in all_points:
-            payload = point.payload or {}
-            doc_ref = payload.get("doc_ref")
-            if not doc_ref:
-                continue
-
-            if doc_ref not in doc_map:
-                doc_map[doc_ref] = {
-                    "doc_ref": doc_ref,
-                    "source_file": payload.get("source_file"),
-                    "chunk_count": 0,
-                    "created_at": payload.get("created_at"),
-                }
-            doc_map[doc_ref]["chunk_count"] += 1
-
-        # Convert to list and sort by created_at
-        all_docs = list(doc_map.values())
-        all_docs.sort(key=lambda x: x.get("created_at") or "", reverse=True)
-
-        # Pagination
-        total = len(all_docs)
-        start = (page - 1) * page_size
-        end = start + page_size
-        documents = all_docs[start:end]
-
-        return {
-            "documents": documents,
-            "total": total,
-            "page": page,
-            "page_size": page_size,
-            "knowledge_id": knowledge_id,
-        }
-
-    def test_connection(self) -> bool:
-        """
-        Test connection to Qdrant.
-
-        Returns:
-            True if connection successful, False otherwise
-        """
-        try:
-            # Try to get collections list as a connection test
-            self.client.get_collections()
-            return True
-        except Exception:
-            return False
-
-    def get_all_chunks(
-        self, knowledge_id: str, max_chunks: int = 10000, **kwargs
-    ) -> List[Dict[str, Any]]:
-        """
-        Get all chunks from a knowledge base in Qdrant.
-
-        Uses scroll API to efficiently retrieve all chunks for a knowledge base.
-
-        Args:
-            knowledge_id: Knowledge base ID
-            max_chunks: Maximum number of chunks to retrieve (safety limit)
-            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
-
-        Returns:
-            List of chunk dicts with content, title, chunk_id, doc_ref, metadata
-        """
-        collection_name = self.get_index_name(knowledge_id, **kwargs)
-
-        # Build filter for knowledge_id
-        scroll_filter = qdrant_models.Filter(
-            must=[
-                qdrant_models.FieldCondition(
-                    key="knowledge_id",
-                    match=qdrant_models.MatchValue(value=knowledge_id),
-                )
-            ]
-        )
-
-        try:
-            # Check if collection exists
-            try:
-                self.client.get_collection(collection_name)
-            except Exception:
-                return []
-
-            # Scroll through all matching points
-            all_points = []
-            offset = None
-            batch_size = min(1000, max_chunks)
-
-            while len(all_points) < max_chunks:
-                results, offset = self.client.scroll(
-                    collection_name=collection_name,
-                    scroll_filter=scroll_filter,
-                    limit=batch_size,
-                    offset=offset,
-                    with_payload=True,
-                    with_vectors=False,
-                )
-                all_points.extend(results)
-                if offset is None or len(results) == 0:
-                    break
-
-            # Convert to chunk format
-            chunks = []
-            for point in all_points[:max_chunks]:
-                payload = point.payload or {}
-
-                # Normalize content to plain text. Qdrant stores the original
-                # LlamaIndex node payload in `_node_content`, which may be a
-                # serialized TextNode JSON. We use extract_chunk_text to
-                # extract the human-readable `text` field and drop internal
-                # fields (id_, relationships, embeddings, etc.).
-                raw_content = payload.get("_node_content", "")
-
-                chunks.append(
-                    {
-                        "content": self.extract_chunk_text(raw_content),
-                        "title": payload.get("source_file", ""),
-                        "chunk_id": payload.get("chunk_index", 0),
-                        "doc_ref": payload.get("doc_ref", ""),
-                        "metadata": payload,
-                    }
-                )
-
-            # Sort by doc_ref and chunk_index
-            chunks.sort(key=lambda x: (x.get("doc_ref", ""), x.get("chunk_id", 0)))
-
-            return chunks
-
-        except Exception as e:
-            # Log error but return empty list to allow fallback to RAG
-            import logging
 
-            logger = logging.getLogger(__name__)
-            logger.warning(
-                f"[Qdrant] Failed to get all chunks for KB {knowledge_id}: {e}"
-            )
-            return []
+__all__ = ["QdrantBackend", "QdrantClient", "QdrantVectorStore"]
diff --git a/backend/tests/api/endpoints/test_rag_api.py b/backend/tests/api/endpoints/test_rag_api.py
new file mode 100644
index 000000000..eed7da1f6
--- /dev/null
+++ b/backend/tests/api/endpoints/test_rag_api.py
@@ -0,0 +1,112 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import ANY, AsyncMock, patch
+
+from app.services.rag.runtime_specs import QueryRuntimeSpec
+
+
+def _auth_header(token: str) -> dict[str, str]:
+    return {"Authorization": f"Bearer {token}"}
+
+
+def test_public_rag_retrieve_uses_gateway_runtime_spec(
+    test_client,
+    test_token: str,
+):
+    payload = {
+        "query": "release checklist",
+        "knowledge_id": "7",
+        "retriever_ref": {
+            "name": "retriever-a",
+            "namespace": "default",
+        },
+        "embedding_model_ref": {
+            "model_name": "embed-a",
+            "model_namespace": "default",
+        },
+        "top_k": 6,
+        "score_threshold": 0.45,
+        "retrieval_mode": "hybrid",
+        "hybrid_weights": {
+            "vector_weight": 0.8,
+            "keyword_weight": 0.2,
+        },
+        "metadata_condition": {
+            "operator": "or",
+            "conditions": [
+                {"key": "source", "operator": "==", "value": "kb"},
+                {"key": "lang", "operator": "==", "value": "zh"},
+            ],
+        },
+    }
+    runtime_spec = QueryRuntimeSpec(
+        knowledge_base_ids=[7],
+        query="release checklist",
+        max_results=6,
+        route_mode="rag_retrieval",
+        metadata_condition=payload["metadata_condition"],
+    )
+    gateway = AsyncMock()
+    gateway.query.return_value = {
+        "mode": "rag_retrieval",
+        "records": [
+            {
+                "content": "release checklist",
+                "score": 0.91,
+                "title": "Checklist",
+                "metadata": {"source": "kb"},
+            }
+        ],
+        "total": 1,
+        "total_estimated_tokens": 0,
+    }
+
+    with (
+        patch(
+            "app.api.endpoints.rag.runtime_resolver.build_public_query_runtime_spec",
+            create=True,
+            return_value=runtime_spec,
+        ) as mock_build_spec,
+        patch(
+            "app.api.endpoints.rag.get_query_gateway",
+            return_value=gateway,
+        ) as mock_get_gateway,
+    ):
+        response = test_client.post(
+            "/api/rag/retrieve",
+            headers=_auth_header(test_token),
+            json=payload,
+        )
+
+    assert response.status_code == 200
+    assert response.json() == {
+        "records": [
+            {
+                "content": "release checklist",
+                "score": 0.91,
+                "title": "Checklist",
+                "metadata": {"source": "kb"},
+            }
+        ]
+    }
+    mock_build_spec.assert_called_once_with(
+        db=ANY,
+        knowledge_base_id=7,
+        query="release checklist",
+        max_results=6,
+        retriever_name="retriever-a",
+        retriever_namespace="default",
+        embedding_model_name="embed-a",
+        embedding_model_namespace="default",
+        user_id=ANY,
+        user_name=ANY,
+        score_threshold=0.45,
+        retrieval_mode="hybrid",
+        vector_weight=0.8,
+        keyword_weight=0.2,
+        metadata_condition=payload["metadata_condition"],
+    )
+    mock_get_gateway.assert_called_once()
+    gateway.query.assert_awaited_once_with(runtime_spec, db=ANY)
diff --git a/backend/tests/api/endpoints/test_retrievers_api.py b/backend/tests/api/endpoints/test_retrievers_api.py
new file mode 100644
index 000000000..090637252
--- /dev/null
+++ b/backend/tests/api/endpoints/test_retrievers_api.py
@@ -0,0 +1,75 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import AsyncMock, patch
+
+from app.services.rag.runtime_specs import ConnectionTestRuntimeSpec
+
+
+def _auth_header(token: str) -> dict[str, str]:
+    return {"Authorization": f"Bearer {token}"}
+
+
+def test_retriever_test_connection_uses_gateway_runtime_spec(
+    test_client,
+    test_token: str,
+):
+    gateway = AsyncMock()
+    gateway.test_connection.return_value = {
+        "success": True,
+        "message": "Connection successful",
+    }
+
+    with patch(
+        "app.api.endpoints.adapter.retrievers.get_query_gateway",
+        return_value=gateway,
+    ) as mock_get_gateway:
+        response = test_client.post(
+            "/api/retrievers/test-connection",
+            headers=_auth_header(test_token),
+            json={
+                "storage_type": "qdrant",
+                "url": "http://qdrant:6333",
+                "username": "alice",
+                "password": "secret",
+                "api_key": "api-token",
+            },
+        )
+
+    assert response.status_code == 200
+    assert response.json() == {
+        "success": True,
+        "message": "Connection successful",
+    }
+    mock_get_gateway.assert_called_once()
+    gateway.test_connection.assert_awaited_once()
+
+    runtime_spec = gateway.test_connection.await_args.args[0]
+    assert isinstance(runtime_spec, ConnectionTestRuntimeSpec)
+    assert runtime_spec.retriever_config.storage_config == {
+        "type": "qdrant",
+        "url": "http://qdrant:6333",
+        "username": "alice",
+        "password": "secret",
+        "apiKey": "api-token",
+        "indexStrategy": {"mode": "per_dataset"},
+        "ext": {},
+    }
+
+
+def test_retriever_test_connection_validates_required_fields(
+    test_client,
+    test_token: str,
+):
+    response = test_client.post(
+        "/api/retrievers/test-connection",
+        headers=_auth_header(test_token),
+        json={"storage_type": "qdrant"},
+    )
+
+    assert response.status_code == 200
+    assert response.json() == {
+        "success": False,
+        "message": "Missing required fields: storage_type, url",
+    }
diff --git a/backend/tests/services/rag/storage/test_elasticsearch_backend.py b/backend/tests/services/rag/storage/test_elasticsearch_backend.py
index 64680e065..4832967c8 100644
--- a/backend/tests/services/rag/storage/test_elasticsearch_backend.py
+++ b/backend/tests/services/rag/storage/test_elasticsearch_backend.py
@@ -10,7 +10,7 @@
 class TestGetAllChunks:
     """Tests for ElasticsearchBackend.get_all_chunks."""
 
-    @patch("app.services.rag.storage.elasticsearch_backend.Elasticsearch")
+    @patch("knowledge_engine.storage.elasticsearch_backend.Elasticsearch")
     def test_get_all_chunks_returns_parsed_chunks(self, mock_client_class):
         """Should parse hits into normalized chunk payloads."""
         from app.services.rag.storage.elasticsearch_backend import ElasticsearchBackend
@@ -49,7 +49,7 @@ def test_get_all_chunks_returns_parsed_chunks(self, mock_client_class):
         assert result[0]["doc_ref"] == "doc_1"
         assert result[0]["chunk_id"] == 3
 
-    @patch("app.services.rag.storage.elasticsearch_backend.Elasticsearch")
+    @patch("knowledge_engine.storage.elasticsearch_backend.Elasticsearch")
     def test_get_all_chunks_returns_empty_when_no_hits(self, mock_client_class):
         """Should return an empty list when the knowledge_id term query has no hits."""
         from app.services.rag.storage.elasticsearch_backend import ElasticsearchBackend
diff --git a/backend/tests/services/rag/storage/test_milvus_backend.py b/backend/tests/services/rag/storage/test_milvus_backend.py
index 908bfd60a..d7b8ee43a 100644
--- a/backend/tests/services/rag/storage/test_milvus_backend.py
+++ b/backend/tests/services/rag/storage/test_milvus_backend.py
@@ -129,7 +129,7 @@ def test_get_supported_retrieval_methods(self):
 class TestCreateVectorStore:
     """Tests for create_vector_store method."""
 
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_create_vector_store_basic(self, mock_milvus_vs):
         """Test creating a vector store with basic parameters.
 
@@ -162,7 +162,7 @@ def test_create_vector_store_basic(self, mock_milvus_vs):
 class TestRetrieve:
     """Tests for retrieve method."""
 
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_retrieve_vector_mode(self, mock_milvus_vs):
         """Test retrieval in vector mode."""
         # Setup mock
@@ -202,7 +202,7 @@ def test_retrieve_vector_mode(self, mock_milvus_vs):
         assert result["records"][0]["content"] == "test content"
         assert result["records"][0]["score"] == 0.9
 
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_retrieve_keyword_mode(self, mock_milvus_vs):
         """Test retrieval in keyword mode."""
         mock_store = MagicMock()
@@ -239,7 +239,7 @@ def test_retrieve_keyword_mode(self, mock_milvus_vs):
         # Keyword mode should not call get_query_embedding
         mock_embed_model.get_query_embedding.assert_not_called()
 
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_retrieve_hybrid_mode(self, mock_milvus_vs):
         """Test retrieval in hybrid mode."""
         mock_store = MagicMock()
@@ -297,7 +297,7 @@ def test_retrieve_invalid_mode(self):
                 retrieval_setting={"retrieval_mode": "invalid"},
             )
 
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_retrieve_score_threshold_filtering(self, mock_milvus_vs):
         """Test that results below score threshold are filtered out."""
         mock_store = MagicMock()
@@ -342,7 +342,7 @@ def test_retrieve_score_threshold_filtering(self, mock_milvus_vs):
 class TestDeleteDocument:
     """Tests for delete_document method."""
 
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_delete_document(self, mock_milvus_vs):
         """Test deleting a document."""
         mock_store = MagicMock()
@@ -369,7 +369,7 @@ def test_delete_document(self, mock_milvus_vs):
 class TestGetDocument:
     """Tests for get_document method."""
 
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_get_document(self, mock_milvus_vs):
         """Test getting document details."""
         mock_store = MagicMock()
@@ -399,7 +399,7 @@ def test_get_document(self, mock_milvus_vs):
         assert result["chunks"][0]["chunk_index"] == 0
         assert result["chunks"][1]["chunk_index"] == 1
 
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_get_document_not_found(self, mock_milvus_vs):
         """Test getting a document that doesn't exist."""
         mock_store = MagicMock()
@@ -419,7 +419,7 @@ def test_get_document_not_found(self, mock_milvus_vs):
 class TestListDocuments:
     """Tests for list_documents method."""
 
-    @patch("app.services.rag.storage.milvus_backend.MilvusClient")
+    @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
     def test_list_documents(self, mock_client_class):
         """Test listing documents with pagination."""
         mock_client = MagicMock()
@@ -465,7 +465,7 @@ def test_list_documents(self, mock_client_class):
         # Verify client.close() is called to prevent resource leaks
         mock_client.close.assert_called_once()
 
-    @patch("app.services.rag.storage.milvus_backend.MilvusClient")
+    @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
     def test_list_documents_empty_collection(self, mock_client_class):
         """Test listing documents when collection doesn't exist."""
         mock_client = MagicMock()
@@ -489,7 +489,7 @@ def test_list_documents_empty_collection(self, mock_client_class):
 class TestTestConnection:
     """Tests for test_connection method."""
 
-    @patch("app.services.rag.storage.milvus_backend.MilvusClient")
+    @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
     def test_connection_success(self, mock_client_class):
         """Test successful connection."""
         mock_client = MagicMock()
@@ -506,7 +506,7 @@ def test_connection_success(self, mock_client_class):
         # Verify client.close() is called to prevent resource leaks
         mock_client.close.assert_called_once()
 
-    @patch("app.services.rag.storage.milvus_backend.MilvusClient")
+    @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
     def test_connection_failure(self, mock_client_class):
         """Test failed connection."""
         mock_client = MagicMock()
@@ -527,7 +527,7 @@ def test_connection_failure(self, mock_client_class):
 class TestGetAllChunks:
     """Tests for get_all_chunks method."""
 
-    @patch("app.services.rag.storage.milvus_backend.MilvusClient")
+    @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
     def test_get_all_chunks(self, mock_client_class):
         """Test getting all chunks."""
         mock_client = MagicMock()
@@ -564,7 +564,7 @@ def test_get_all_chunks(self, mock_client_class):
         # Verify client.close() is called to prevent resource leaks
         mock_client.close.assert_called_once()
 
-    @patch("app.services.rag.storage.milvus_backend.MilvusClient")
+    @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
     def test_get_all_chunks_collection_not_exists(self, mock_client_class):
         """Test getting chunks when collection doesn't exist."""
         mock_client = MagicMock()
@@ -592,9 +592,9 @@ class TestIndexWithMetadata:
     to nodes - it only uses ChunkMetadata for index name generation.
     """
 
-    @patch("app.services.rag.storage.milvus_backend.VectorStoreIndex")
-    @patch("app.services.rag.storage.milvus_backend.StorageContext")
-    @patch("app.services.rag.storage.milvus_backend.LazyAsyncMilvusVectorStore")
+    @patch("knowledge_engine.storage.milvus_backend.VectorStoreIndex")
+    @patch("knowledge_engine.storage.milvus_backend.StorageContext")
+    @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_index_with_metadata(self, mock_milvus_vs, mock_storage_ctx, mock_vs_index):
         """Test indexing nodes with metadata.
 
diff --git a/backend/tests/services/rag/test_local_data_plane_retrieval.py b/backend/tests/services/rag/test_local_data_plane_retrieval.py
index 46526ec2c..18d0118ac 100644
--- a/backend/tests/services/rag/test_local_data_plane_retrieval.py
+++ b/backend/tests/services/rag/test_local_data_plane_retrieval.py
@@ -11,11 +11,37 @@
     DEFAULT_DIRECT_INJECTION_BUDGET,
     QueryRuntimeSpec,
 )
+from shared.models import (
+    RemoteKnowledgeBaseQueryConfig,
+    RuntimeEmbeddingModelConfig,
+    RuntimeRetrievalConfig,
+    RuntimeRetrieverConfig,
+)
 
 
 @pytest.mark.asyncio
 async def test_query_local_uses_shared_default_budget() -> None:
-    spec = QueryRuntimeSpec(knowledge_base_ids=[1], query="release checklist")
+    spec = QueryRuntimeSpec(
+        knowledge_base_ids=[1],
+        query="release checklist",
+        knowledge_base_configs=[
+            RemoteKnowledgeBaseQueryConfig(
+                knowledge_base_id=1,
+                index_owner_user_id=8,
+                retriever_config=RuntimeRetrieverConfig(
+                    name="retriever-a",
+                    namespace="default",
+                    storage_config={"type": "qdrant"},
+                ),
+                embedding_model_config=RuntimeEmbeddingModelConfig(
+                    model_name="embed-a",
+                    model_namespace="default",
+                    resolved_config={"protocol": "openai"},
+                ),
+                retrieval_config=RuntimeRetrievalConfig(top_k=20),
+            )
+        ],
+    )
     db = MagicMock()
 
     with patch(
@@ -32,6 +58,8 @@ async def test_query_local_uses_shared_default_budget() -> None:
         db=db,
         max_results=5,
         document_ids=None,
+        metadata_condition=None,
+        knowledge_base_configs=spec.knowledge_base_configs,
         user_name=None,
         route_mode="auto",
         user_id=None,
diff --git a/backend/tests/services/rag/test_local_gateway.py b/backend/tests/services/rag/test_local_gateway.py
index 3d62a5bdb..c2df324f7 100644
--- a/backend/tests/services/rag/test_local_gateway.py
+++ b/backend/tests/services/rag/test_local_gateway.py
@@ -8,6 +8,7 @@
 
 from app.services.rag.local_gateway import LocalRagGateway
 from app.services.rag.runtime_specs import (
+    ConnectionTestRuntimeSpec,
     DeleteRuntimeSpec,
     IndexRuntimeSpec,
     IndexSource,
@@ -77,6 +78,26 @@ async def test_local_gateway_delete_document_index_delegates_to_delete_executor(
     gateway._delete_executor.assert_awaited_once_with(spec, db=db)
 
 
+@pytest.mark.asyncio
+async def test_local_gateway_test_connection_delegates_to_connection_executor():
+    gateway = LocalRagGateway()
+    gateway._connection_test_executor = AsyncMock(
+        return_value={"success": True, "message": "Connection successful"}
+    )
+    spec = ConnectionTestRuntimeSpec(
+        retriever_config=RuntimeRetrieverConfig(
+            name="retriever-a",
+            namespace="default",
+            storage_config={"type": "qdrant"},
+        )
+    )
+
+    result = await gateway.test_connection(spec)
+
+    assert result == {"success": True, "message": "Connection successful"}
+    gateway._connection_test_executor.assert_awaited_once_with(spec, db=None)
+
+
 @pytest.mark.asyncio
 async def test_local_gateway_query_requires_db():
     gateway = LocalRagGateway()
diff --git a/backend/tests/services/rag/test_remote_gateway.py b/backend/tests/services/rag/test_remote_gateway.py
index 2cb42bd45..b478de018 100644
--- a/backend/tests/services/rag/test_remote_gateway.py
+++ b/backend/tests/services/rag/test_remote_gateway.py
@@ -18,6 +18,7 @@
 from app.services.rag.local_gateway import LocalRagGateway
 from app.services.rag.remote_gateway import RemoteRagGateway, RemoteRagGatewayError
 from app.services.rag.runtime_specs import (
+    ConnectionTestRuntimeSpec,
     DeleteRuntimeSpec,
     IndexRuntimeSpec,
     IndexSource,
@@ -331,6 +332,47 @@ async def test_remote_gateway_delete_posts_resolved_retriever_config(mocker) ->
     }
 
 
+@pytest.mark.asyncio
+async def test_remote_gateway_test_connection_posts_resolved_retriever_config(
+    mocker,
+) -> None:
+    post_mock = mocker.patch(
+        "httpx.AsyncClient.post",
+        return_value=_build_response(
+            url="http://knowledge-runtime/internal/rag/test-connection",
+            status_code=200,
+            json_body={"success": True, "message": "Connection successful"},
+        ),
+    )
+    gateway = RemoteRagGateway(
+        base_url="http://knowledge-runtime",
+        token="runtime-token",
+    )
+    spec = ConnectionTestRuntimeSpec(
+        retriever_config=RuntimeRetrieverConfig(
+            name="retriever-a",
+            namespace="default",
+            storage_config={"type": "elasticsearch", "url": "http://es:9200"},
+        )
+    )
+
+    result = await gateway.test_connection(spec, db=MagicMock())
+
+    assert result == {"success": True, "message": "Connection successful"}
+    args, kwargs = post_mock.await_args
+    assert args[0] == "http://knowledge-runtime/internal/rag/test-connection"
+    assert kwargs["json"] == {
+        "retriever_config": {
+            "name": "retriever-a",
+            "namespace": "default",
+            "storage_config": {
+                "type": "elasticsearch",
+                "url": "http://es:9200",
+            },
+        }
+    }
+
+
 def test_gateway_factory_returns_local_gateways_by_default(monkeypatch) -> None:
     monkeypatch.setattr(settings, "RAG_RUNTIME_MODE", "local")
 
diff --git a/backend/tests/services/rag/test_retrieval_service.py b/backend/tests/services/rag/test_retrieval_service.py
index 93f8aadfb..b991b7ea7 100644
--- a/backend/tests/services/rag/test_retrieval_service.py
+++ b/backend/tests/services/rag/test_retrieval_service.py
@@ -58,6 +58,12 @@ class TestGetAllChunksFromKnowledgeBase:
     async def test_get_all_chunks_without_user_auth_check(self):
         """Internal all-chunks should work without passing a request user."""
         from app.services.rag.retrieval_service import RetrievalService
+        from shared.models import (
+            RemoteKnowledgeBaseQueryConfig,
+            RuntimeEmbeddingModelConfig,
+            RuntimeRetrievalConfig,
+            RuntimeRetrieverConfig,
+        )
 
         kb = MagicMock()
         kb.id = 123
@@ -81,23 +87,48 @@ async def test_get_all_chunks_without_user_auth_check(self):
         mock_backend.get_all_chunks.return_value = [
             {"content": "chunk", "title": "doc-1", "doc_ref": "1"}
         ]
+        runtime_config = RemoteKnowledgeBaseQueryConfig(
+            knowledge_base_id=123,
+            index_owner_user_id=42,
+            retriever_config=RuntimeRetrieverConfig(
+                name="retriever-a",
+                namespace="default",
+                storage_config={"type": "qdrant", "url": "http://qdrant:6333"},
+            ),
+            embedding_model_config=RuntimeEmbeddingModelConfig(
+                model_name="embed-a",
+                model_namespace="default",
+                resolved_config={"protocol": "openai"},
+            ),
+            retrieval_config=RuntimeRetrievalConfig(top_k=20),
+        )
 
-        with patch(
-            "app.services.rag.retrieval_service.retriever_kinds_service.get_retriever",
-            return_value=MagicMock(),
-        ):
-            with patch(
-                "app.services.rag.retrieval_service.create_storage_backend",
+        with (
+            patch(
+                "app.services.rag.retrieval_service.RagRuntimeResolver.build_query_knowledge_base_configs",
+                return_value=[runtime_config],
+            ) as mock_build_runtime_configs,
+            patch(
+                "app.services.rag.retrieval_service.create_storage_backend_from_runtime_config",
                 return_value=mock_backend,
-            ):
-                result = await RetrievalService().get_all_chunks_from_knowledge_base(
-                    knowledge_base_id=123,
-                    db=db,
-                    max_chunks=50,
-                    query="debug query",
-                )
+            ) as mock_create_storage_backend,
+        ):
+            result = await RetrievalService().get_all_chunks_from_knowledge_base(
+                knowledge_base_id=123,
+                db=db,
+                max_chunks=50,
+                query="debug query",
+            )
 
         assert result == [{"content": "chunk", "title": "doc-1", "doc_ref": "1"}]
+        mock_build_runtime_configs.assert_called_once_with(
+            db=db,
+            knowledge_base_ids=[123],
+            user_name=None,
+        )
+        mock_create_storage_backend.assert_called_once_with(
+            runtime_config.retriever_config
+        )
         mock_backend.get_index_name.assert_called_once_with("123", user_id=42)
         mock_backend.get_all_chunks.assert_called_once_with(
             knowledge_id="123",
@@ -482,3 +513,98 @@ async def test_force_rag_route_sorts_and_limits_results_globally(self):
             456,
             123,
         ]
+
+    @pytest.mark.asyncio
+    async def test_force_rag_route_uses_knowledge_engine_query_executor_when_runtime_configs_are_available(
+        self,
+    ):
+        """Resolved runtime configs should drive the engine query seam in local mode."""
+        from app.services.rag.retrieval_service import RetrievalService
+        from shared.models import (
+            RemoteKnowledgeBaseQueryConfig,
+            RuntimeEmbeddingModelConfig,
+            RuntimeRetrievalConfig,
+            RuntimeRetrieverConfig,
+        )
+
+        db = MagicMock()
+        storage_backend = MagicMock()
+        embed_model = object()
+        kb_config = RemoteKnowledgeBaseQueryConfig(
+            knowledge_base_id=123,
+            index_owner_user_id=7,
+            retriever_config=RuntimeRetrieverConfig(
+                name="retriever-a",
+                namespace="default",
+                storage_config={"type": "qdrant", "url": "http://qdrant:6333"},
+            ),
+            embedding_model_config=RuntimeEmbeddingModelConfig(
+                model_name="embed-a",
+                model_namespace="default",
+                resolved_config={"protocol": "openai"},
+            ),
+            retrieval_config=RuntimeRetrievalConfig(
+                top_k=8,
+                score_threshold=0.45,
+                retrieval_mode="hybrid",
+                vector_weight=0.8,
+                keyword_weight=0.2,
+            ),
+        )
+
+        with (
+            patch(
+                "app.services.rag.retrieval_service.create_storage_backend_from_runtime_config",
+                return_value=storage_backend,
+            ) as mock_storage,
+            patch(
+                "app.services.rag.retrieval_service.create_embedding_model_from_runtime_config",
+                return_value=embed_model,
+            ) as mock_embedding,
+            patch(
+                "app.services.rag.retrieval_service.QueryExecutor.execute",
+                new_callable=AsyncMock,
+                return_value={
+                    "records": [
+                        {
+                            "content": "release checklist",
+                            "score": 0.91,
+                            "title": "Checklist",
+                            "metadata": {"doc_ref": "9"},
+                        }
+                    ]
+                },
+            ) as mock_execute,
+        ):
+            result = await RetrievalService().retrieve_for_chat_shell(
+                query="release checklist",
+                knowledge_base_ids=[123],
+                db=db,
+                max_results=5,
+                route_mode="rag_retrieval",
+                document_ids=[9],
+                knowledge_base_configs=[kb_config],
+            )
+
+        assert result["mode"] == "rag_retrieval"
+        assert result["records"] == [
+            {
+                "content": "release checklist",
+                "score": 0.91,
+                "title": "Checklist",
+                "metadata": {"doc_ref": "9"},
+                "knowledge_base_id": 123,
+            }
+        ]
+        mock_storage.assert_called_once_with(kb_config.retriever_config)
+        mock_embedding.assert_called_once_with(kb_config.embedding_model_config)
+        mock_execute.assert_awaited_once_with(
+            knowledge_id="123",
+            query="release checklist",
+            retrieval_config=kb_config.retrieval_config,
+            metadata_condition={
+                "operator": "and",
+                "conditions": [{"key": "doc_ref", "operator": "in", "value": ["9"]}],
+            },
+            user_id=7,
+        )
diff --git a/backend/tests/services/rag/test_runtime_specs.py b/backend/tests/services/rag/test_runtime_specs.py
index dd109695b..27a0d887e 100644
--- a/backend/tests/services/rag/test_runtime_specs.py
+++ b/backend/tests/services/rag/test_runtime_specs.py
@@ -61,6 +61,7 @@ def test_query_runtime_spec_keeps_direct_injection_budget():
             max_direct_chunks=500,
         ),
         document_ids=[11],
+        metadata_condition={"key": "source", "operator": "==", "value": "kb"},
         restricted_mode=False,
         user_id=3,
         user_name="alice",
@@ -93,6 +94,11 @@ def test_query_runtime_spec_keeps_direct_injection_budget():
     )
     assert spec.knowledge_base_ids == [1, 2]
     assert spec.document_ids == [11]
+    assert spec.metadata_condition == {
+        "key": "source",
+        "operator": "==",
+        "value": "kb",
+    }
     assert spec.direct_injection_budget.max_direct_chunks == 500
     assert spec.knowledge_base_configs[0].retrieval_config.top_k == 20
     assert spec.enabled_index_families == ["chunk_vector", "summary_vector"]
@@ -139,19 +145,7 @@ def test_delete_runtime_spec_keeps_resolved_retriever_config():
     [
         (
             {"source_type": "attachment"},
-            "attachment_id is required",
-        ),
-        (
-            {"source_type": "attachment", "attachment_id": 123, "file_path": "/tmp/a"},
-            "file_path must not be provided",
-        ),
-        (
-            {"source_type": "file_path"},
-            "file_path is required",
-        ),
-        (
-            {"source_type": "file_path", "file_path": "/tmp/a", "attachment_id": 123},
-            "attachment_id must not be provided",
+            "Field required",
         ),
     ],
 )
diff --git a/docker-compose.yml b/docker-compose.yml
index e66081c59..b21fc0475 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -67,6 +67,29 @@ services:
       retries: 5
       start_period: 40s
 
+  knowledge_runtime:
+    build:
+      context: .
+      dockerfile: docker/knowledge_runtime/Dockerfile
+    container_name: wegent-knowledge-runtime
+    restart: always
+    profiles:
+      - rag
+    ports:
+      - "${KNOWLEDGE_RUNTIME_PORT:-8200}:8200"
+    environment:
+      PORT: 8200
+      HOST: 0.0.0.0
+      INTERNAL_SERVICE_TOKEN: ${INTERNAL_SERVICE_TOKEN:-knowledge-runtime-token}
+    networks:
+      - wegent-network
+    healthcheck:
+      test: ["CMD-SHELL", "curl -f http://localhost:8200/health || exit 1"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 10s
+
   backend:
     # build:
       # dockerfile: docker/backend/Dockerfile
@@ -107,7 +130,8 @@ services:
       CHAT_SHELL_MODE: ${CHAT_SHELL_MODE:-http}
       CHAT_SHELL_URL: ${CHAT_SHELL_URL:-http://chat_shell:8100}
       # CHAT_SHELL_TOKEN: ${INTERNAL_SERVICE_TOKEN:-chat-shell-token}
-      # INTERNAL_SERVICE_TOKEN: ${INTERNAL_SERVICE_TOKEN:-chat-shell-token}
+      INTERNAL_SERVICE_TOKEN: ${INTERNAL_SERVICE_TOKEN:-knowledge-runtime-token}
+      KNOWLEDGE_RUNTIME_URL: ${KNOWLEDGE_RUNTIME_URL:-http://knowledge_runtime:8200}
       # Task Dispatch Configuration
       # Mode: 'push' (default) or 'pull' (deprecated, executor_manager polls)
       TASK_DISPATCH_MODE: ${TASK_DISPATCH_MODE:-push}
diff --git a/docker/knowledge_runtime/Dockerfile b/docker/knowledge_runtime/Dockerfile
index a047d6b97..381fcb9fd 100644
--- a/docker/knowledge_runtime/Dockerfile
+++ b/docker/knowledge_runtime/Dockerfile
@@ -8,6 +8,7 @@ WORKDIR /app
 
 # Copy local dependencies first so uv can resolve path sources.
 COPY shared /shared
+COPY knowledge_engine /knowledge_engine
 
 # Copy pyproject.toml and install dependencies.
 COPY knowledge_runtime/pyproject.toml .
diff --git a/docs/en/user-guide/knowledge/configuring-retrievers.md b/docs/en/user-guide/knowledge/configuring-retrievers.md
index 1f555587c..bc285fc83 100644
--- a/docs/en/user-guide/knowledge/configuring-retrievers.md
+++ b/docs/en/user-guide/knowledge/configuring-retrievers.md
@@ -178,26 +178,7 @@ Retrieval test helps you optimize retrieval configuration before actual use.
 
 ## Using Retrievers with RAG
 
-### 1. Upload and Index Documents
-
-```bash
-POST /api/rag/documents/upload
-Content-Type: multipart/form-data
-
-- knowledge_id: "kb_001"
-- retriever_name: "my-es-retriever"
-- retriever_namespace: "default"
-- file: <document.pdf>
-- embedding_config: {
-    "provider": "openai",
-    "model": "text-embedding-3-small",
-    "api_key": "sk-..."
-  }
-```
-
-**Supported file types**: MD, PDF, TXT, DOCX, and code files
-
-### 2. Retrieve Relevant Chunks
+### 1. Retrieve Relevant Chunks
 
 ```bash
 POST /api/rag/retrieve
@@ -225,19 +206,6 @@ Content-Type: application/json
 }
 ```
 
-### 3. Manage Documents
-
-```bash
-# List documents
-GET /api/rag/documents?knowledge_id=kb_001&retriever_name=my-es-retriever&page=1&page_size=20
-
-# Get document details
-GET /api/rag/documents/{doc_ref}?knowledge_id=kb_001&retriever_name=my-es-retriever
-
-# Delete document
-DELETE /api/rag/documents/{doc_ref}?knowledge_id=kb_001&retriever_name=my-es-retriever
-```
-
 ## Embedding Providers
 
 ### OpenAI
diff --git a/docs/zh/user-guide/knowledge/configuring-retrievers.md b/docs/zh/user-guide/knowledge/configuring-retrievers.md
index 96ede6bf2..c858be9e3 100644
--- a/docs/zh/user-guide/knowledge/configuring-retrievers.md
+++ b/docs/zh/user-guide/knowledge/configuring-retrievers.md
@@ -178,26 +178,7 @@ Content-Type: application/json
 
 ## 使用检索器进行 RAG
 
-### 1. 上传和索引文档
-
-```bash
-POST /api/rag/documents/upload
-Content-Type: multipart/form-data
-
-- knowledge_id: "kb_001"
-- retriever_name: "my-es-retriever"
-- retriever_namespace: "default"
-- file: <document.pdf>
-- embedding_config: {
-    "provider": "openai",
-    "model": "text-embedding-3-small",
-    "api_key": "sk-..."
-  }
-```
-
-**支持的文件类型**：MD、PDF、TXT、DOCX 和代码文件
-
-### 2. 检索相关片段
+### 1. 检索相关片段
 
 ```bash
 POST /api/rag/retrieve
@@ -225,19 +206,6 @@ Content-Type: application/json
 }
 ```
 
-### 3. 管理文档
-
-```bash
-# 列出文档
-GET /api/rag/documents?knowledge_id=kb_001&retriever_name=my-es-retriever&page=1&page_size=20
-
-# 获取文档详情
-GET /api/rag/documents/{doc_ref}?knowledge_id=kb_001&retriever_name=my-es-retriever
-
-# 删除文档
-DELETE /api/rag/documents/{doc_ref}?knowledge_id=kb_001&retriever_name=my-es-retriever
-```
-
 ## 嵌入提供商
 
 ### OpenAI
diff --git a/knowledge_engine/knowledge_engine/embedding/__init__.py b/knowledge_engine/knowledge_engine/embedding/__init__.py
index a093e9923..029300ea1 100644
--- a/knowledge_engine/knowledge_engine/embedding/__init__.py
+++ b/knowledge_engine/knowledge_engine/embedding/__init__.py
@@ -3,3 +3,10 @@
 # SPDX-License-Identifier: Apache-2.0
 
 """Embedding helpers for knowledge_engine."""
+
+from knowledge_engine.embedding.custom import CustomEmbedding
+from knowledge_engine.embedding.factory import (
+    create_embedding_model_from_runtime_config,
+)
+
+__all__ = ["CustomEmbedding", "create_embedding_model_from_runtime_config"]
diff --git a/knowledge_engine/knowledge_engine/embedding/custom.py b/knowledge_engine/knowledge_engine/embedding/custom.py
new file mode 100644
index 000000000..707d8320d
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/embedding/custom.py
@@ -0,0 +1,78 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Custom embedding implementation for external APIs."""
+
+import asyncio
+from typing import Optional
+
+import requests
+from llama_index.core.base.embeddings.base import BaseEmbedding
+from tenacity import retry, stop_after_attempt, wait_exponential
+
+
+class CustomEmbedding(BaseEmbedding):
+    """Custom embedding wrapper for OpenAI-compatible endpoints."""
+
+    api_url: str
+    model: str
+    headers: dict[str, str]
+    api_key: Optional[str] = None
+    _dimension: Optional[int] = None
+
+    def __init__(
+        self,
+        *,
+        api_url: str,
+        model: str,
+        headers: dict[str, str] | None = None,
+        api_key: str | None = None,
+        embed_batch_size: int = 10,
+        dimensions: int | None = None,
+        **kwargs,
+    ) -> None:
+        final_headers = headers.copy() if headers else {}
+        if api_key and "Authorization" not in final_headers:
+            final_headers["Authorization"] = f"Bearer {api_key}"
+
+        super().__init__(
+            model_name=model,
+            embed_batch_size=embed_batch_size,
+            api_url=api_url,
+            model=model,
+            headers=final_headers,
+            api_key=api_key,
+            **kwargs,
+        )
+
+        if dimensions is not None:
+            self._dimension = dimensions
+
+    def _get_query_embedding(self, query: str) -> list[float]:
+        return self._call_api(query)
+
+    def _get_text_embedding(self, text: str) -> list[float]:
+        return self._call_api(text)
+
+    async def _aget_query_embedding(self, query: str) -> list[float]:
+        return await asyncio.to_thread(self._get_query_embedding, query)
+
+    @retry(
+        stop=stop_after_attempt(3),
+        wait=wait_exponential(multiplier=1, min=2, max=10),
+    )
+    def _call_api(self, text: str) -> list[float]:
+        response = requests.post(
+            self.api_url,
+            json={"model": self.model, "input": text},
+            headers=self.headers,
+            timeout=30,
+        )
+        response.raise_for_status()
+        embedding = response.json()["data"][0]["embedding"]
+
+        if self._dimension is None:
+            self._dimension = len(embedding)
+
+        return embedding
diff --git a/knowledge_engine/knowledge_engine/embedding/factory.py b/knowledge_engine/knowledge_engine/embedding/factory.py
new file mode 100644
index 000000000..6608a86d4
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/embedding/factory.py
@@ -0,0 +1,80 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Embedding model factory for resolved runtime config."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from knowledge_engine.embedding.custom import CustomEmbedding
+from shared.models import RuntimeEmbeddingModelConfig
+
+
+def create_embedding_model_from_runtime_config(
+    runtime_config: RuntimeEmbeddingModelConfig,
+):
+    resolved_config = runtime_config.resolved_config or {}
+    return _create_embedding_model_from_resolved_values(
+        protocol=resolved_config.get("protocol"),
+        model_name=runtime_config.model_name,
+        api_key=resolved_config.get("api_key"),
+        base_url=resolved_config.get("base_url"),
+        model_id=resolved_config.get("model_id"),
+        custom_headers=resolved_config.get("custom_headers") or {},
+        dimensions=resolved_config.get("dimensions"),
+    )
+
+
+def _create_embedding_model_from_resolved_values(
+    *,
+    protocol: str | None,
+    model_name: str,
+    api_key: str | None,
+    base_url: str | None,
+    model_id: str | None,
+    custom_headers: dict[str, Any],
+    dimensions: int | None,
+):
+    if protocol == "openai":
+        if custom_headers:
+            api_url = (
+                f"{base_url.rstrip('/')}/embeddings"
+                if base_url
+                else "https://api.openai.com/v1/embeddings"
+            )
+            return CustomEmbedding(
+                api_url=api_url,
+                model=model_id or "text-embedding-3-small",
+                headers=custom_headers,
+                api_key=api_key,
+                dimensions=dimensions,
+            )
+
+        from llama_index.embeddings.openai import OpenAIEmbedding
+
+        return OpenAIEmbedding(
+            model=model_id or "text-embedding-3-small",
+            api_key=api_key,
+            api_base=base_url,
+            dimensions=dimensions,
+        )
+
+    if protocol in {"cohere", "jina", "custom"}:
+        if not base_url:
+            raise ValueError(
+                f"Embedding model '{model_name}' with protocol '{protocol}' "
+                "requires base_url"
+            )
+        return CustomEmbedding(
+            api_url=base_url,
+            model=model_id or model_name,
+            headers=custom_headers if isinstance(custom_headers, dict) else {},
+            api_key=api_key,
+            dimensions=dimensions,
+        )
+
+    raise ValueError(
+        f"Unsupported embedding protocol for model '{model_name}': {protocol}"
+    )
diff --git a/knowledge_engine/knowledge_engine/query/__init__.py b/knowledge_engine/knowledge_engine/query/__init__.py
index 0262e0ffa..3f4873e7d 100644
--- a/knowledge_engine/knowledge_engine/query/__init__.py
+++ b/knowledge_engine/knowledge_engine/query/__init__.py
@@ -3,3 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 """Query execution helpers for knowledge_engine."""
+
+from knowledge_engine.query.executor import QueryExecutor
+
+__all__ = ["QueryExecutor"]
diff --git a/knowledge_engine/knowledge_engine/query/executor.py b/knowledge_engine/knowledge_engine/query/executor.py
new file mode 100644
index 000000000..4c606bbb1
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/query/executor.py
@@ -0,0 +1,62 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import asyncio
+from typing import Any
+
+from shared.models import RuntimeRetrievalConfig
+
+
+class QueryExecutor:
+    """Backend-agnostic RAG query executor."""
+
+    def __init__(self, *, storage_backend, embed_model) -> None:
+        self.storage_backend = storage_backend
+        self.embed_model = embed_model
+
+    async def execute(
+        self,
+        *,
+        knowledge_id: str,
+        query: str,
+        retrieval_config: RuntimeRetrievalConfig | dict[str, Any],
+        metadata_condition: dict[str, Any] | None = None,
+        user_id: int | None = None,
+    ) -> dict[str, Any]:
+        retrieval_setting = self._normalize_retrieval_setting(retrieval_config)
+        kwargs: dict[str, Any] = {}
+        if user_id is not None:
+            kwargs["user_id"] = user_id
+
+        return await asyncio.to_thread(
+            self.storage_backend.retrieve,
+            knowledge_id=knowledge_id,
+            query=query,
+            embed_model=self.embed_model,
+            retrieval_setting=retrieval_setting,
+            metadata_condition=metadata_condition,
+            **kwargs,
+        )
+
+    @staticmethod
+    def _normalize_retrieval_setting(
+        retrieval_config: RuntimeRetrievalConfig | dict[str, Any],
+    ) -> dict[str, Any]:
+        if isinstance(retrieval_config, RuntimeRetrievalConfig):
+            config = retrieval_config.model_dump(exclude_none=True)
+        else:
+            config = dict(retrieval_config)
+
+        retrieval_setting = {
+            "top_k": config.get("top_k", 20),
+            "score_threshold": config.get("score_threshold", 0.7),
+            "retrieval_mode": config.get("retrieval_mode", "vector"),
+        }
+        if "vector_weight" in config:
+            retrieval_setting["vector_weight"] = config["vector_weight"]
+        if "keyword_weight" in config:
+            retrieval_setting["keyword_weight"] = config["keyword_weight"]
+        return retrieval_setting
diff --git a/knowledge_engine/knowledge_engine/retrieval/__init__.py b/knowledge_engine/knowledge_engine/retrieval/__init__.py
new file mode 100644
index 000000000..f7caf2d7b
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/retrieval/__init__.py
@@ -0,0 +1,12 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Retrieval helpers for knowledge_engine."""
+
+from knowledge_engine.retrieval.filters import (
+    build_elasticsearch_filters,
+    parse_metadata_filters,
+)
+
+__all__ = ["build_elasticsearch_filters", "parse_metadata_filters"]
diff --git a/knowledge_engine/knowledge_engine/retrieval/filters.py b/knowledge_engine/knowledge_engine/retrieval/filters.py
new file mode 100644
index 000000000..e97fb45ef
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/retrieval/filters.py
@@ -0,0 +1,144 @@
+# SPDX-FileCopyrightText: 2025 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Metadata filtering parser for RAG retrieval.
+Converts Dify-style metadata conditions to LlamaIndex MetadataFilters.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from llama_index.core.vector_stores import (
+    ExactMatchFilter,
+    FilterOperator,
+    MetadataFilter,
+    MetadataFilters,
+)
+
+
+def parse_metadata_filters(
+    knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
+) -> MetadataFilters:
+    """
+    Parse Dify-style metadata condition into LlamaIndex MetadataFilters.
+
+    Args:
+        knowledge_id: Knowledge base ID (always filtered)
+        metadata_condition: Optional metadata conditions in Dify-style format:
+            {
+                "operator": "and" | "or",
+                "conditions": [
+                    {"key": "field_name", "operator": "eq", "value": "value"},
+                    {"key": "year", "operator": "gte", "value": 2020},
+                    {"key": "status", "operator": "in", "value": ["active", "pending"]}
+                ]
+            }
+
+    Returns:
+        MetadataFilters object compatible with LlamaIndex
+
+    Examples:
+        >>> # Simple equality filter
+        >>> parse_metadata_filters("kb_123", {
+        ...     "operator": "and",
+        ...     "conditions": [
+        ...         {"key": "category", "operator": "eq", "value": "tech"}
+        ...     ]
+        ... })
+
+        >>> # Multiple conditions
+        >>> parse_metadata_filters("kb_123", {
+        ...     "operator": "and",
+        ...     "conditions": [
+        ...         {"key": "year", "operator": "gte", "value": 2020},
+        ...         {"key": "status", "operator": "in", "value": ["active", "pending"]}
+        ...     ]
+        ... })
+    """
+    # Operator mapping from Dify-style to LlamaIndex FilterOperator
+    OPERATOR_MAP = {
+        "eq": FilterOperator.EQ,
+        "ne": FilterOperator.NE,
+        "gt": FilterOperator.GT,
+        "gte": FilterOperator.GTE,
+        "lt": FilterOperator.LT,
+        "lte": FilterOperator.LTE,
+        "in": FilterOperator.IN,
+        "nin": FilterOperator.NIN,
+        "contains": FilterOperator.CONTAINS,
+        "text_match": FilterOperator.TEXT_MATCH,
+    }
+
+    # Always filter by knowledge_id
+    filters = [ExactMatchFilter(key="knowledge_id", value=knowledge_id)]
+
+    # Parse additional metadata conditions
+    if metadata_condition and "conditions" in metadata_condition:
+        for cond in metadata_condition["conditions"]:
+            key = cond.get("key")
+            operator = cond.get("operator", "eq")
+            value = cond.get("value")
+
+            if not key or value is None:
+                continue
+
+            # Map operator string to FilterOperator enum
+            filter_op = OPERATOR_MAP.get(operator.lower(), FilterOperator.EQ)
+
+            # Create MetadataFilter using LlamaIndex's built-in class
+            filters.append(MetadataFilter(key=key, value=value, operator=filter_op))
+
+    # Determine condition type (AND/OR)
+    condition = "and"
+    if metadata_condition:
+        condition = metadata_condition.get("operator", "and").lower()
+
+    return MetadataFilters(filters=filters, condition=condition)
+
+
+def build_elasticsearch_filters(
+    knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
+) -> List[Dict[str, Any]]:
+    """
+    Build Elasticsearch filter clauses for hybrid search (direct ES queries).
+
+    Args:
+        knowledge_id: Knowledge base ID
+        metadata_condition: Optional metadata conditions in Dify-style format
+
+    Returns:
+        List of Elasticsearch filter clauses for use in ES query DSL
+    """
+    filters = [{"term": {"metadata.knowledge_id.keyword": knowledge_id}}]
+
+    if not metadata_condition or "conditions" not in metadata_condition:
+        return filters
+
+    for cond in metadata_condition["conditions"]:
+        key = cond.get("key")
+        operator = cond.get("operator", "eq")
+        value = cond.get("value")
+
+        if not key or value is None:
+            continue
+
+        field_name = f"metadata.{key}.keyword"
+
+        # Build Elasticsearch filter based on operator
+        if operator == "eq":
+            filters.append({"term": {field_name: value}})
+        elif operator == "ne":
+            filters.append({"bool": {"must_not": {"term": {field_name: value}}}})
+        elif operator == "in":
+            filters.append({"terms": {field_name: value}})
+        elif operator == "nin":
+            filters.append({"bool": {"must_not": {"terms": {field_name: value}}}})
+        elif operator in ["gt", "gte", "lt", "lte"]:
+            filters.append({"range": {field_name: {operator: value}}})
+        elif operator == "contains":
+            filters.append({"wildcard": {field_name: f"*{value}*"}})
+        elif operator == "text_match":
+            filters.append({"match": {f"metadata.{key}": value}})
+
+    return filters
diff --git a/knowledge_engine/knowledge_engine/storage/__init__.py b/knowledge_engine/knowledge_engine/storage/__init__.py
index 59daed758..4197a82f3 100644
--- a/knowledge_engine/knowledge_engine/storage/__init__.py
+++ b/knowledge_engine/knowledge_engine/storage/__init__.py
@@ -6,5 +6,22 @@
 
 from knowledge_engine.storage.base import BaseStorageBackend
 from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+from knowledge_engine.storage.elasticsearch_backend import ElasticsearchBackend
+from knowledge_engine.storage.factory import (
+    create_storage_backend_from_config,
+    create_storage_backend_from_runtime_config,
+    get_supported_storage_types,
+)
+from knowledge_engine.storage.milvus_backend import MilvusBackend
+from knowledge_engine.storage.qdrant_backend import QdrantBackend
 
-__all__ = ["BaseStorageBackend", "ChunkMetadata"]
+__all__ = [
+    "BaseStorageBackend",
+    "ChunkMetadata",
+    "ElasticsearchBackend",
+    "MilvusBackend",
+    "QdrantBackend",
+    "create_storage_backend_from_config",
+    "create_storage_backend_from_runtime_config",
+    "get_supported_storage_types",
+]
diff --git a/knowledge_engine/knowledge_engine/storage/base.py b/knowledge_engine/knowledge_engine/storage/base.py
index 620084029..e75a229c6 100644
--- a/knowledge_engine/knowledge_engine/storage/base.py
+++ b/knowledge_engine/knowledge_engine/storage/base.py
@@ -1,27 +1,48 @@
-# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+# SPDX-FileCopyrightText: 2025 Weibo, Inc.
 #
 # SPDX-License-Identifier: Apache-2.0
 
+"""
+Base storage backend interface for RAG functionality.
+"""
+
 from __future__ import annotations
 
 import json
 import logging
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, ClassVar, Dict, List
-
-from llama_index.core.schema import BaseNode
+from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional
 
 logger = logging.getLogger(__name__)
 
+from llama_index.core.schema import BaseNode
+
 if TYPE_CHECKING:
     from knowledge_engine.storage.chunk_metadata import ChunkMetadata
 
 
 class BaseStorageBackend(ABC):
+    """Abstract base class for storage backends."""
+
+    # Subclasses should override this with their supported methods
     SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = []
+
+    # Index name prefix for different storage types (can be overridden)
     INDEX_PREFIX: ClassVar[str] = "index"
 
     def __init__(self, config: Dict):
+        """
+        Initialize storage backend.
+
+        Args:
+            config: Storage configuration dict containing:
+                - url: Connection URL
+                - username: Optional username
+                - password: Optional password
+                - apiKey: Optional API key
+                - indexStrategy: Index naming strategy config
+                - ext: Additional provider-specific config
+        """
         self.config = config
         self.url = config.get("url")
         self.username = config.get("username")
@@ -31,32 +52,183 @@ def __init__(self, config: Dict):
         self.ext = config.get("ext", {})
 
     def extract_chunk_text(self, raw_content: Any) -> str:
+        """Extract normalized plain text from raw chunk content.
+
+        Some storage providers (e.g., LlamaIndex-based backends) may store
+        serialized node objects (such as TextNode JSON) in the content field
+        when using get_all_chunks. For direct injection we only want the
+        human-readable `text` field and should drop internal fields
+        (id_, embedding, relationships, etc.).
+
+        This helper attempts to parse such JSON structures and extract the
+        `text` value. If parsing fails or the expected structure is missing,
+        it falls back to the original content.
+        """
         if raw_content is None:
             return ""
+
+        # Most backends already return plain text
         if not isinstance(raw_content, str):
             return str(raw_content)
 
         stripped = raw_content.strip()
+        # Fast path: not a JSON object or does not contain a `text` key
         if not stripped.startswith("{") or '"text"' not in stripped:
             return raw_content
 
         try:
             data = json.loads(stripped)
         except Exception:
+            # If parsing fails, fall back to original content
             return raw_content
 
         if isinstance(data, dict):
             text = data.get("text")
             if isinstance(text, str):
                 return text
+
+        # Fallback: return original content
         return raw_content
 
+    def _validate_prefix(self, mode: str) -> str:
+        """
+        Validate and return prefix for index naming.
+
+        Args:
+            mode: Index strategy mode
+
+        Returns:
+            Validated prefix string
+
+        Raises:
+            ValueError: If prefix is empty or None
+        """
+        prefix = self.index_strategy.get("prefix", "wegent")
+        if not prefix:
+            raise ValueError(f"prefix cannot be empty for '{mode}' index strategy mode")
+        return prefix
+
+    def _validate_knowledge_id(self, knowledge_id: str, mode: str) -> None:
+        """
+        Validate knowledge_id is not None or empty.
+
+        Args:
+            knowledge_id: Knowledge base ID to validate
+            mode: Index strategy mode (for error message)
+
+        Raises:
+            ValueError: If knowledge_id is None or empty
+        """
+        if not knowledge_id:
+            raise ValueError(
+                f"knowledge_id is required for '{mode}' index strategy mode"
+            )
+
+    def get_index_name(self, knowledge_id: str, **kwargs) -> str:
+        """
+        Get index/collection name based on strategy.
+
+        Strategies:
+        - fixed: Use a single fixed index name (requires fixedName)
+        - rolling: Use rolling indices based on numeric knowledge_id (uses prefix)
+                   Groups N knowledge bases per index, where N = rollingStep (default 10)
+                   e.g., step=10: kb_id 1-10 -> index_0, kb_id 11-20 -> index_10, etc.
+        - per_dataset: Use separate index per knowledge base (default)
+        - per_user: Use separate index per user (requires user_id)
+
+        Args:
+            knowledge_id: Knowledge base ID (must be numeric string for rolling mode)
+            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
+
+        Returns:
+            Index/collection name
+        """
+        mode = self.index_strategy.get("mode", "per_dataset")
+
+        # Debug logging for index strategy
+        logger.debug(
+            f"get_index_name called: knowledge_id={knowledge_id}, "
+            f"index_strategy={self.index_strategy}, mode={mode}"
+        )
+
+        if mode == "fixed":
+            fixed_name = self.index_strategy.get("fixedName")
+            if not fixed_name:
+                raise ValueError(
+                    "fixedName is required for 'fixed' index strategy mode"
+                )
+            return fixed_name
+        elif mode == "rolling":
+            # Validate knowledge_id and prefix
+            self._validate_knowledge_id(knowledge_id, mode)
+            prefix = self._validate_prefix(mode)
+
+            # Validate rollingStep (number of knowledge_ids per index bucket)
+            step = self.index_strategy.get("rollingStep", 10)
+            if not isinstance(step, int) or step <= 0:
+                raise ValueError(f"rollingStep must be a positive integer, got: {step}")
+
+            # Convert knowledge_id to integer for bucket calculation
+            # knowledge_id is expected to be a numeric string (e.g., "1", "2", "123")
+            try:
+                kb_id_num = int(knowledge_id)
+            except ValueError:
+                raise ValueError(
+                    f"knowledge_id must be a numeric value for 'rolling' mode, got: {knowledge_id}"
+                )
+
+            # Calculate index base using floor division
+            # e.g., step=10: kb_id 1-10 -> index_0, kb_id 11-20 -> index_10, etc.
+            # Using (kb_id_num - 1) to make it 0-indexed:
+            #   kb_id 1-10 -> bucket 0 -> index_0
+            #   kb_id 11-20 -> bucket 1 -> index_10
+            bucket_num = (kb_id_num - 1) // step if kb_id_num > 0 else 0
+            index_base = bucket_num * step
+            index_name = f"{prefix}_{self.INDEX_PREFIX}_{index_base}"
+
+            logger.debug(
+                f"Rolling index: step={step}, kb_id_num={kb_id_num}, "
+                f"bucket_num={bucket_num}, index_base={index_base}, index_name={index_name}"
+            )
+            return index_name
+        elif mode == "per_dataset":
+            # Validate knowledge_id and prefix
+            self._validate_knowledge_id(knowledge_id, mode)
+            prefix = self._validate_prefix(mode)
+            return f"{prefix}_kb_{knowledge_id}"
+        elif mode == "per_user":
+            # Per-user index strategy: separate index for each user
+            user_id = kwargs.get("user_id")
+            if not user_id:
+                raise ValueError(
+                    "user_id is required for 'per_user' index strategy mode"
+                )
+            prefix = self._validate_prefix(mode)
+            return f"{prefix}_user_{user_id}"
+        else:
+            raise ValueError(f"Unknown index strategy mode: {mode}")
+
     @classmethod
     def get_supported_retrieval_methods(cls) -> List[str]:
+        """
+        Return list of supported retrieval methods.
+
+        Returns:
+            List of method names supported by this backend
+        """
         return cls.SUPPORTED_RETRIEVAL_METHODS.copy()
 
     @abstractmethod
     def create_vector_store(self, index_name: str):
+        """
+        Create vector store instance.
+
+        Args:
+            index_name: Index/collection name
+
+        Returns:
+            Vector store instance compatible with LlamaIndex
+        """
         pass
 
     @abstractmethod
@@ -67,4 +239,152 @@ def index_with_metadata(
         embed_model,
         **kwargs,
     ) -> Dict:
+        """
+        Index nodes into storage.
+
+        Note: Metadata is already applied to nodes by the indexer layer via
+        chunk_metadata.apply_to_nodes() before calling this method. Storage
+        backends should NOT apply metadata to nodes again.
+
+        Args:
+            nodes: List of nodes to index (metadata already applied)
+            chunk_metadata: ChunkMetadata instance containing:
+                - knowledge_id: Knowledge base ID
+                - doc_ref: Document reference ID (doc_xxx format)
+                - source_file: Source file name
+                - created_at: Creation timestamp (ISO 8601)
+                - chunk_index: Set per node during apply_to_nodes()
+            embed_model: Embedding model
+            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
+
+        Returns:
+            Indexing result dict with:
+                - indexed_count: Number of nodes indexed
+                - index_name: Index/collection name
+                - status: Indexing status
+        """
+        pass
+
+    @abstractmethod
+    def retrieve(
+        self,
+        knowledge_id: str,
+        query: str,
+        embed_model,
+        retrieval_setting: Dict[str, Any],
+        metadata_condition: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> Dict:
+        """
+        Retrieve nodes from storage (Dify-compatible API).
+
+        Args:
+            knowledge_id: Knowledge base ID
+            query: Search query
+            embed_model: Embedding model
+            retrieval_setting: Dict with keys:
+                - top_k: Maximum number of results
+                - score_threshold: Minimum similarity score (0-1)
+                - retrieval_mode: Optional, 'vector'/'keyword'/'hybrid'
+                - vector_weight: Optional, weight for vector search
+                - keyword_weight: Optional, weight for keyword search
+            metadata_condition: Optional metadata filtering conditions
+            **kwargs: Additional parameters
+
+        Returns:
+            Dict with Dify-compatible format:
+                {
+                    "records": [
+                        {
+                            "content": str,      # Chunk text content
+                            "score": float,      # Relevance score (0-1)
+                            "title": str,        # Document title/source file
+                            "metadata": dict     # Additional metadata
+                        }
+                    ]
+                }
+        """
+        pass
+
+    @abstractmethod
+    def delete_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
+        """
+        Delete document from storage.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID
+            **kwargs: Additional parameters
+
+        Returns:
+            Deletion result dict
+        """
+        pass
+
+    @abstractmethod
+    def get_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
+        """
+        Get document details.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID
+            **kwargs: Additional parameters
+
+        Returns:
+            Document details dict
+        """
+        pass
+
+    @abstractmethod
+    def list_documents(
+        self, knowledge_id: str, page: int = 1, page_size: int = 20, **kwargs
+    ) -> Dict:
+        """
+        List documents in knowledge base.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            page: Page number
+            page_size: Page size
+            **kwargs: Additional parameters
+
+        Returns:
+            Document list dict
+        """
+        pass
+
+    @abstractmethod
+    def test_connection(self) -> bool:
+        """
+        Test connection to storage backend.
+
+        Returns:
+            True if connection successful, False otherwise
+        """
+        pass
+
+    @abstractmethod
+    def get_all_chunks(
+        self, knowledge_id: str, max_chunks: int = 10000, **kwargs
+    ) -> List[Dict[str, Any]]:
+        """
+        Get all chunks from a knowledge base.
+
+        This method retrieves all chunks stored for a specific knowledge base,
+        used for direct context injection when content fits within context window.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            max_chunks: Maximum number of chunks to retrieve (safety limit)
+            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
+
+        Returns:
+            List of chunk dicts with:
+                - content: str, chunk text content
+                - title: str, source document name
+                - chunk_id: int, chunk index within document
+                - doc_ref: str, document reference ID
+                - metadata: dict, additional metadata
+        """
         pass
diff --git a/knowledge_engine/knowledge_engine/storage/chunk_metadata.py b/knowledge_engine/knowledge_engine/storage/chunk_metadata.py
index cdc9707a7..1f78b1dbc 100644
--- a/knowledge_engine/knowledge_engine/storage/chunk_metadata.py
+++ b/knowledge_engine/knowledge_engine/storage/chunk_metadata.py
@@ -1,7 +1,11 @@
-# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+# SPDX-FileCopyrightText: 2025 Weibo, Inc.
 #
 # SPDX-License-Identifier: Apache-2.0
 
+"""
+Data models for RAG storage backends.
+"""
+
 from dataclasses import asdict, dataclass, field
 from typing import Any, Dict, List
 
@@ -10,6 +14,23 @@
 
 @dataclass
 class ChunkMetadata:
+    """
+    Standard metadata structure for document chunks in vector stores.
+
+    This dataclass defines the common metadata fields used across all storage
+    backends (Milvus, Elasticsearch, Qdrant) for indexing document chunks.
+
+    The chunk_index field is optional and typically set during node iteration.
+
+    Attributes:
+        knowledge_id: Knowledge base ID that this chunk belongs to
+        doc_ref: Document reference ID (doc_xxx format)
+        source_file: Original source file name
+        created_at: ISO 8601 timestamp when the chunk was created
+        chunk_index: Index of this chunk within the document (0-based),
+                     set during indexing iteration
+    """
+
     knowledge_id: str
     doc_ref: str
     source_file: str
@@ -17,9 +38,27 @@ class ChunkMetadata:
     chunk_index: int = field(default=0)
 
     def to_dict(self) -> Dict[str, Any]:
+        """
+        Convert metadata to dictionary for node.metadata.update().
+
+        Returns:
+            Dictionary representation of the metadata
+        """
         return asdict(self)
 
     def with_chunk_index(self, chunk_index: int) -> "ChunkMetadata":
+        """
+        Create a new ChunkMetadata with the specified chunk_index.
+
+        This method is used during node iteration to set the chunk index
+        for each node while keeping other metadata fields unchanged.
+
+        Args:
+            chunk_index: The index of the chunk within the document
+
+        Returns:
+            New ChunkMetadata instance with updated chunk_index
+        """
         return ChunkMetadata(
             knowledge_id=self.knowledge_id,
             doc_ref=self.doc_ref,
@@ -29,6 +68,18 @@ def with_chunk_index(self, chunk_index: int) -> "ChunkMetadata":
         )
 
     def apply_to_nodes(self, nodes: List[BaseNode]) -> List[BaseNode]:
+        """
+        Apply metadata to all nodes with appropriate chunk_index.
+
+        This method iterates through the nodes and updates each node's metadata
+        with the chunk metadata, setting the correct chunk_index for each node.
+
+        Args:
+            nodes: List of nodes to apply metadata to
+
+        Returns:
+            The same list of nodes with updated metadata (modified in place)
+        """
         for idx, node in enumerate(nodes):
             node.metadata.update(self.with_chunk_index(idx).to_dict())
         return nodes
diff --git a/knowledge_engine/knowledge_engine/storage/elasticsearch_backend.py b/knowledge_engine/knowledge_engine/storage/elasticsearch_backend.py
new file mode 100644
index 000000000..0541c0d9f
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/storage/elasticsearch_backend.py
@@ -0,0 +1,615 @@
+# SPDX-FileCopyrightText: 2025 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Elasticsearch storage backend implementation.
+
+Supported retrieval modes:
+- vector: Pure vector similarity search using embeddings
+- keyword: Pure BM25 keyword search (full-text search)
+- hybrid: Combined vector + BM25 search with configurable weights
+"""
+
+import logging
+from typing import Any, ClassVar, Dict, List, Optional
+
+from elasticsearch import Elasticsearch
+from elasticsearch.helpers.vectorstore._async.strategies import (
+    AsyncBM25Strategy,
+    AsyncDenseVectorStrategy,
+)
+from llama_index.core import StorageContext, VectorStoreIndex
+from llama_index.core.schema import BaseNode
+from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
+from llama_index.core.vector_stores.types import (
+    VectorStoreQuery,
+    VectorStoreQueryMode,
+)
+from llama_index.vector_stores.elasticsearch import ElasticsearchStore
+
+from knowledge_engine.retrieval.filters import parse_metadata_filters
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+from shared.telemetry.decorators import add_span_event
+
+logger = logging.getLogger(__name__)
+
+
+class ElasticsearchBackend(BaseStorageBackend):
+    """
+    Elasticsearch storage backend implementation.
+
+    Supported retrieval modes:
+    - vector: Pure vector similarity search (default)
+    - keyword: Pure BM25 keyword search
+    - hybrid: Combined vector + BM25 search
+
+    Class Attributes:
+        SUPPORTED_RETRIEVAL_METHODS: List of supported retrieval method names
+    """
+
+    # Class-level constant defining supported retrieval methods
+    SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = ["vector", "keyword", "hybrid"]
+
+    # Uses default INDEX_PREFIX = "index" from base class
+
+    def __init__(self, config: Dict):
+        """Initialize Elasticsearch backend."""
+        super().__init__(config)
+
+        # Build connection kwargs for native Elasticsearch client
+        # (used in list_documents and test_connection)
+        self.es_kwargs = {}
+        if self.username and self.password:
+            self.es_kwargs["basic_auth"] = (self.username, self.password)
+        elif self.api_key:
+            self.es_kwargs["api_key"] = self.api_key
+
+        # Build connection kwargs for LlamaIndex ElasticsearchStore
+        # Note: ElasticsearchStore uses different parameter names (es_user, es_password, es_api_key)
+        self.llama_es_kwargs = {}
+        if self.username and self.password:
+            self.llama_es_kwargs["es_user"] = self.username
+            self.llama_es_kwargs["es_password"] = self.password
+        elif self.api_key:
+            self.llama_es_kwargs["es_api_key"] = self.api_key
+
+    def create_vector_store(
+        self, index_name: str, retrieval_mode: str = "vector"
+    ) -> ElasticsearchStore:
+        """
+        Create Elasticsearch vector store with appropriate retrieval strategy.
+
+        Args:
+            index_name: Index name
+            retrieval_mode: Retrieval mode - 'vector', 'keyword', or 'hybrid'
+                - 'vector': Pure dense vector search (default)
+                - 'keyword': Pure BM25 text search
+                - 'hybrid': Combined vector + BM25 search
+
+        Returns:
+            ElasticsearchStore instance configured for the specified mode
+        """
+        # Select retrieval strategy based on mode
+        if retrieval_mode == "keyword":
+            # Pure BM25 keyword search
+            retrieval_strategy = AsyncBM25Strategy()
+        elif retrieval_mode == "hybrid":
+            # Hybrid search: vector + BM25 with linear combination
+            # Note: rrf=False uses linear combination instead of RRF fusion
+            # RRF requires Elasticsearch paid license (Platinum/Enterprise)
+            retrieval_strategy = AsyncDenseVectorStrategy(
+                hybrid=True, rrf=False, text_field="content"
+            )
+        else:
+            # Default: Pure vector search
+            retrieval_strategy = AsyncDenseVectorStrategy(hybrid=False)
+
+        return ElasticsearchStore(
+            index_name=index_name,
+            es_url=self.url,
+            retrieval_strategy=retrieval_strategy,
+            **self.llama_es_kwargs,
+        )
+
+    def index_with_metadata(
+        self,
+        nodes: List[BaseNode],
+        chunk_metadata: ChunkMetadata,
+        embed_model,
+        **kwargs,
+    ) -> Dict:
+        """
+        Index nodes into Elasticsearch.
+
+        Note: Metadata is already applied to nodes by the indexer layer via
+        chunk_metadata.apply_to_nodes() before calling this method.
+
+        Args:
+            nodes: List of nodes to index (metadata already applied)
+            chunk_metadata: ChunkMetadata instance containing document metadata
+            embed_model: Embedding model
+            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
+
+        Returns:
+            Indexing result dict
+
+        Note:
+            We use 'doc_ref' in metadata to store our custom doc_xxx ID.
+            LlamaIndex has its own internal 'document_id' field (ref_doc_id UUID).
+        """
+        add_span_event(
+            "elasticsearch.index_with_metadata.started",
+            {
+                "knowledge_id": chunk_metadata.knowledge_id,
+                "doc_ref": chunk_metadata.doc_ref,
+                "source_file": chunk_metadata.source_file,
+                "node_count": len(nodes),
+            },
+        )
+
+        # Get index name
+        index_name = self.get_index_name(chunk_metadata.knowledge_id, **kwargs)
+
+        add_span_event(
+            "elasticsearch.index_name_resolved",
+            {
+                "index_name": index_name,
+                "knowledge_id": chunk_metadata.knowledge_id,
+            },
+        )
+
+        # Index nodes
+        vector_store = self.create_vector_store(index_name)
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+        add_span_event(
+            "elasticsearch.vector_store_created",
+            {
+                "index_name": index_name,
+            },
+        )
+
+        VectorStoreIndex(
+            nodes,
+            storage_context=storage_context,
+            embed_model=embed_model,
+            show_progress=True,
+        )
+
+        add_span_event(
+            "elasticsearch.index_with_metadata.completed",
+            {
+                "indexed_count": len(nodes),
+                "index_name": index_name,
+                "doc_ref": chunk_metadata.doc_ref,
+                "status": "success",
+            },
+        )
+
+        return {
+            "indexed_count": len(nodes),
+            "index_name": index_name,
+            "status": "success",
+        }
+
+    def retrieve(
+        self,
+        knowledge_id: str,
+        query: str,
+        embed_model,
+        retrieval_setting: Dict[str, Any],
+        metadata_condition: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> Dict:
+        """
+        Retrieve nodes from Elasticsearch (Dify-style API).
+
+        Uses LlamaIndex's VectorStoreQuery with different modes:
+        - DEFAULT: Pure vector similarity search
+        - TEXT_SEARCH: Pure BM25 keyword search
+        - HYBRID: Combined vector + BM25 search
+
+        Args:
+            knowledge_id: Knowledge base ID
+            query: Search query
+            embed_model: Embedding model
+            retrieval_setting: Dict with:
+                - top_k: Maximum number of results
+                - score_threshold: Minimum similarity score (0-1)
+                - retrieval_mode: Optional 'vector'/'keyword'/'hybrid' (default: 'vector')
+                - alpha: Optional weight for hybrid search (0=keyword only, 1=vector only, default: 0.7)
+            metadata_condition: Optional metadata filtering
+            **kwargs: Additional parameters
+
+        Returns:
+            Retrieval result dict
+        """
+        index_name = self.get_index_name(knowledge_id, **kwargs)
+        # Increased default top_k from 5 to 20 for better RAG coverage
+        top_k = retrieval_setting.get("top_k", 20)
+        score_threshold = retrieval_setting.get("score_threshold", 0.7)
+        retrieval_mode = retrieval_setting.get("retrieval_mode", "vector")
+
+        # Create vector store with appropriate retrieval strategy
+        vector_store = self.create_vector_store(index_name, retrieval_mode)
+
+        # Build metadata filters
+        filters = self._build_metadata_filters(knowledge_id, metadata_condition)
+
+        # Determine query mode and parameters
+        if retrieval_mode == "keyword":
+            # Pure BM25 keyword search - no embedding needed
+            query_mode = VectorStoreQueryMode.TEXT_SEARCH
+            query_embedding = None
+            alpha = None
+        elif retrieval_mode == "hybrid":
+            # Hybrid search - needs embedding
+            # alpha: 0 = pure keyword, 1 = pure vector, default 0.7 (70% vector)
+            query_mode = VectorStoreQueryMode.HYBRID
+            query_embedding = embed_model.get_query_embedding(query)
+            # Convert vector_weight to alpha (they have the same meaning)
+            alpha = retrieval_setting.get(
+                "alpha", retrieval_setting.get("vector_weight", 0.7)
+            )
+        else:
+            # Default: Pure vector search
+            query_mode = VectorStoreQueryMode.DEFAULT
+            query_embedding = embed_model.get_query_embedding(query)
+            alpha = None
+
+        # Create VectorStoreQuery
+        vs_query = VectorStoreQuery(
+            query_str=query,
+            query_embedding=query_embedding,
+            similarity_top_k=top_k,
+            mode=query_mode,
+            filters=filters,
+            alpha=alpha,
+        )
+
+        # Execute query
+        result = vector_store.query(vs_query)
+
+        # Process results
+        return self._process_query_results(result, score_threshold)
+
+    def _build_metadata_filters(
+        self, knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
+    ):
+        """
+        Build metadata filters from condition dict.
+
+        Args:
+            knowledge_id: Knowledge base ID (always filtered)
+            metadata_condition: Optional additional metadata conditions
+
+        Returns:
+            MetadataFilters object
+        """
+        return parse_metadata_filters(knowledge_id, metadata_condition)
+
+    def _process_query_results(
+        self,
+        result,
+        score_threshold: float,
+    ) -> Dict:
+        """
+        Process VectorStoreQueryResult into Dify-compatible format.
+
+        Args:
+            result: VectorStoreQueryResult from LlamaIndex
+            score_threshold: Minimum relevance score (0-1)
+
+        Returns:
+            Dict with 'records' list in Dify-compatible format
+        """
+        # Handle empty results
+        if not result.nodes:
+            return {"records": []}
+
+        # Get max score for normalization (for keyword search scores may not be normalized)
+        similarities = result.similarities or []
+        if similarities:
+            max_score = max((s for s in similarities if s is not None), default=1.0)
+            if max_score == 0:
+                max_score = 1.0
+        else:
+            max_score = 1.0
+
+        # Process results (Dify-compatible format)
+        results = []
+        for i, node in enumerate(result.nodes):
+            score = (
+                similarities[i]
+                if i < len(similarities) and similarities[i] is not None
+                else 0.0
+            )
+
+            # Normalize score to 0-1 range if needed
+            # For vector search, scores are already normalized (cosine similarity)
+            # For keyword search, scores may need normalization
+            if max_score > 1.0:
+                normalized_score = score / max_score
+            else:
+                normalized_score = score
+
+            if normalized_score >= score_threshold:
+                results.append(
+                    {
+                        "content": node.text,
+                        "score": float(normalized_score),
+                        "title": node.metadata.get("source_file", ""),
+                        "metadata": node.metadata,
+                    }
+                )
+
+        return {"records": results}
+
+    def delete_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
+        """
+        Delete document from Elasticsearch using LlamaIndex API.
+
+        Uses delete_nodes with metadata filters to remove all chunks
+        with matching doc_ref (doc_xxx format).
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
+
+        Returns:
+            Deletion result dict
+        """
+        index_name = self.get_index_name(knowledge_id, **kwargs)
+        vector_store = self.create_vector_store(index_name)
+
+        # Build filters to match the document
+        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
+
+        # Get nodes first to count them
+        nodes = vector_store.get_nodes(filters=filters)
+        deleted_count = len(nodes)
+
+        # Delete nodes using LlamaIndex API
+        vector_store.delete_nodes(filters=filters)
+
+        return {
+            "doc_ref": doc_ref,
+            "knowledge_id": knowledge_id,
+            "deleted_chunks": deleted_count,
+            "status": "deleted",
+        }
+
+    def get_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
+        """
+        Get document details from Elasticsearch using LlamaIndex API.
+
+        Uses get_nodes with metadata filters to retrieve all chunks
+        with matching doc_ref (doc_xxx format).
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+            **kwargs: Additional parameters (e.g., user_id for per_user index strategy)
+
+        Returns:
+            Document details dict with chunks
+        """
+        index_name = self.get_index_name(knowledge_id, **kwargs)
+        vector_store = self.create_vector_store(index_name)
+
+        # Build filters to match the document
+        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
+
+        # Get nodes using LlamaIndex API
+        nodes = vector_store.get_nodes(filters=filters)
+
+        if not nodes:
+            raise ValueError(f"Document {doc_ref} not found")
+
+        # Extract chunks and sort by chunk_index
+        chunks = []
+        source_file = None
+        for node in nodes:
+            metadata = node.metadata
+
+            if source_file is None:
+                source_file = metadata.get("source_file")
+
+            chunks.append(
+                {
+                    "chunk_index": metadata.get("chunk_index"),
+                    "content": node.text,
+                    "metadata": metadata,
+                }
+            )
+
+        # Sort by chunk_index
+        chunks.sort(key=lambda x: x.get("chunk_index", 0))
+
+        return {
+            "doc_ref": doc_ref,
+            "knowledge_id": knowledge_id,
+            "source_file": source_file,
+            "chunk_count": len(chunks),
+            "chunks": chunks,
+        }
+
+    def _build_doc_ref_filters(self, knowledge_id: str, doc_ref: str):
+        """
+        Build metadata filters for document reference lookup.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+
+        Returns:
+            MetadataFilters object for filtering by knowledge_id and doc_ref
+        """
+        return MetadataFilters(
+            filters=[
+                ExactMatchFilter(key="knowledge_id", value=knowledge_id),
+                ExactMatchFilter(key="doc_ref", value=doc_ref),
+            ],
+            condition="and",
+        )
+
+    def list_documents(
+        self, knowledge_id: str, page: int = 1, page_size: int = 20, **kwargs
+    ) -> Dict:
+        """
+        List documents in knowledge base.
+
+        Uses metadata.doc_ref (our custom doc_xxx format) for aggregation
+        to match the doc_ref returned in retrieve API metadata.
+        """
+        index_name = self.get_index_name(knowledge_id, **kwargs)
+        es_client = Elasticsearch(self.url, **self.es_kwargs)
+
+        # Aggregate by doc_ref (our custom document ID), filtered by knowledge_id
+        search_body = {
+            "size": 0,
+            "query": {"term": {"metadata.knowledge_id.keyword": knowledge_id}},
+            "aggs": {
+                "documents": {
+                    "terms": {
+                        "field": "metadata.doc_ref.keyword",  # Our custom doc_xxx ID
+                        "size": 10000,
+                    },
+                    "aggs": {
+                        "source_file": {
+                            "terms": {
+                                "field": "metadata.source_file.keyword",
+                                "size": 1,
+                            }
+                        },
+                        "created_at": {
+                            "min": {
+                                "field": "metadata.created_at"  # date field, no .keyword
+                            }
+                        },
+                    },
+                }
+            },
+        }
+
+        response = es_client.search(index=index_name, body=search_body)
+
+        # Process results
+        all_docs = []
+        for bucket in response["aggregations"]["documents"]["buckets"]:
+            doc_id = bucket["key"]
+            chunk_count = bucket["doc_count"]
+            source_file = (
+                bucket["source_file"]["buckets"][0]["key"]
+                if bucket["source_file"]["buckets"]
+                else None
+            )
+            created_at = bucket.get("created_at", {}).get("value_as_string")
+
+            all_docs.append(
+                {
+                    "doc_ref": doc_id,
+                    "source_file": source_file,
+                    "chunk_count": chunk_count,
+                    "created_at": created_at,
+                }
+            )
+
+        # Pagination
+        total = len(all_docs)
+        start = (page - 1) * page_size
+        end = start + page_size
+        documents = all_docs[start:end]
+
+        return {
+            "documents": documents,
+            "total": total,
+            "page": page,
+            "page_size": page_size,
+            "knowledge_id": knowledge_id,
+        }
+
+    def test_connection(self) -> bool:
+        """Test connection to Elasticsearch."""
+        try:
+            es_client = Elasticsearch(self.url, **self.es_kwargs)
+            return es_client.ping()
+        except Exception:
+            return False
+
+    def get_all_chunks(
+        self, knowledge_id: str, max_chunks: int = 10000, **kwargs
+    ) -> List[Dict[str, Any]]:
+        """
+        Get all chunks from a knowledge base in Elasticsearch.
+
+        Retrieves all chunks for a knowledge base with a single search request.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            max_chunks: Maximum number of chunks to retrieve (safety limit)
+            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
+
+        Returns:
+            List of chunk dicts with content, title, chunk_id, doc_ref, metadata
+        """
+        index_name = self.get_index_name(knowledge_id, **kwargs)
+        es_client = Elasticsearch(self.url, **self.es_kwargs)
+        user_id = kwargs.get("user_id")
+
+        # Query all chunks for this knowledge base
+        search_body = {
+            "size": min(max_chunks, 10000),  # ES limit per request
+            "query": {"term": {"metadata.knowledge_id.keyword": knowledge_id}},
+            "sort": [
+                {"metadata.doc_ref.keyword": "asc"},
+                {"metadata.chunk_index": "asc"},
+            ],
+        }
+
+        try:
+            # Check if index exists
+            if not es_client.indices.exists(index=index_name):
+                return []
+
+            response = es_client.search(index=index_name, body=search_body)
+
+            chunks = []
+            for hit in response["hits"]["hits"]:
+                source = hit.get("_source", {})
+                metadata = source.get("metadata", {})
+
+                # Normalize content to plain text. In most cases Elasticsearch
+                # stores human-readable text in the "content" field. However,
+                # for robustness we still pass it through extract_chunk_text
+                # to handle potential serialized node payloads.
+                raw_content = source.get("content", "")
+
+                chunks.append(
+                    {
+                        "content": self.extract_chunk_text(raw_content),
+                        "title": metadata.get("source_file", ""),
+                        "chunk_id": metadata.get("chunk_index", 0),
+                        "doc_ref": metadata.get("doc_ref", ""),
+                        "metadata": metadata,
+                    }
+                )
+
+                if len(chunks) >= max_chunks:
+                    break
+
+            return chunks
+
+        except Exception as e:
+            logger.warning(
+                "[Elasticsearch] Failed to get all chunks: knowledge_id=%s, "
+                "index_name=%s, search_body=%s, error=%s",
+                knowledge_id,
+                index_name,
+                search_body,
+                e,
+            )
+            return []
diff --git a/knowledge_engine/knowledge_engine/storage/factory.py b/knowledge_engine/knowledge_engine/storage/factory.py
new file mode 100644
index 000000000..a40ab5a95
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/storage/factory.py
@@ -0,0 +1,76 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Storage backend factory for resolved runtime config."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Type
+
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.elasticsearch_backend import ElasticsearchBackend
+from knowledge_engine.storage.milvus_backend import MilvusBackend
+from knowledge_engine.storage.qdrant_backend import QdrantBackend
+from shared.models import RuntimeRetrieverConfig
+
+STORAGE_BACKEND_REGISTRY: Dict[str, Type[BaseStorageBackend]] = {
+    "elasticsearch": ElasticsearchBackend,
+    "qdrant": QdrantBackend,
+    "milvus": MilvusBackend,
+}
+
+
+def get_supported_storage_types() -> List[str]:
+    return list(STORAGE_BACKEND_REGISTRY.keys())
+
+
+def create_storage_backend_from_config(
+    storage_type: str,
+    url: str,
+    username: Optional[str] = None,
+    password: Optional[str] = None,
+    api_key: Optional[str] = None,
+    index_strategy: Optional[Dict[str, Any]] = None,
+    ext: Optional[Dict[str, Any]] = None,
+) -> BaseStorageBackend:
+    normalized_type = storage_type.lower()
+    if normalized_type not in STORAGE_BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unsupported storage type: {normalized_type}. "
+            f"Supported types: {list(STORAGE_BACKEND_REGISTRY.keys())}"
+        )
+
+    config = {
+        "url": url,
+        "username": username,
+        "password": password,
+        "apiKey": api_key,
+        "indexStrategy": index_strategy or {"mode": "per_dataset"},
+        "ext": ext or {},
+    }
+    backend_class = STORAGE_BACKEND_REGISTRY[normalized_type]
+    return backend_class(config)
+
+
+def create_storage_backend_from_runtime_config(
+    retriever_config: RuntimeRetrieverConfig,
+):
+    storage_config = retriever_config.storage_config or {}
+    storage_type = (storage_config.get("type") or "").lower()
+    if storage_type not in STORAGE_BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unsupported storage type: {storage_type or '<missing>'}. "
+            f"Supported types: {list(STORAGE_BACKEND_REGISTRY.keys())}"
+        )
+
+    config = {
+        "url": storage_config.get("url"),
+        "username": storage_config.get("username"),
+        "password": storage_config.get("password"),
+        "apiKey": storage_config.get("apiKey"),
+        "indexStrategy": storage_config.get("indexStrategy") or {"mode": "per_dataset"},
+        "ext": storage_config.get("ext") or {},
+    }
+    backend_class = STORAGE_BACKEND_REGISTRY[storage_type]
+    return backend_class(config)
diff --git a/knowledge_engine/knowledge_engine/storage/milvus_backend.py b/knowledge_engine/knowledge_engine/storage/milvus_backend.py
new file mode 100644
index 000000000..8ec41d4ab
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/storage/milvus_backend.py
@@ -0,0 +1,829 @@
+# SPDX-FileCopyrightText: 2025 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Milvus storage backend implementation.
+
+Supported retrieval modes:
+- vector: Pure vector similarity search using embeddings (VectorStoreQueryMode.DEFAULT)
+- keyword: BM25 keyword search (VectorStoreQueryMode.TEXT_SEARCH)
+- hybrid: Combined vector + BM25 search with RRF ranking (VectorStoreQueryMode.HYBRID)
+
+Note: Requires Milvus 2.5+ for keyword and hybrid search support.
+"""
+
+import logging
+from typing import Any, ClassVar, Dict, List, Optional
+
+from llama_index.core import StorageContext, VectorStoreIndex
+from llama_index.core.base.embeddings.base import BaseEmbedding
+from llama_index.core.schema import BaseNode
+from llama_index.core.vector_stores import MetadataFilter, MetadataFilters
+from llama_index.core.vector_stores.types import (
+    FilterOperator,
+    VectorStoreQuery,
+    VectorStoreQueryMode,
+)
+from llama_index.vector_stores.milvus import MilvusVectorStore
+from llama_index.vector_stores.milvus.base import IndexManagement
+from pymilvus import AsyncMilvusClient, MilvusClient
+
+from knowledge_engine.retrieval.filters import parse_metadata_filters
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+
+logger = logging.getLogger(__name__)
+
+# Named constants for magic numbers
+DEFAULT_EMBEDDING_DIM = 1024  # Default vector dimension (OpenAI text-embedding-ada-002)
+MAX_QUERY_LIMIT = 10000  # Maximum records to fetch for aggregation queries
+DEFAULT_TOP_K = 20  # Default top_k for retrieval
+
+
+class LazyAsyncMilvusVectorStore(MilvusVectorStore):
+    """
+    MilvusVectorStore subclass with lazy AsyncMilvusClient initialization.
+
+    The original MilvusVectorStore creates AsyncMilvusClient in __init__,
+    which requires an event loop. This causes issues when running in
+    thread pools (e.g., Celery tasks via asyncio.to_thread).
+
+    This subclass defers AsyncMilvusClient creation to first access,
+    allowing synchronous operations to work without an event loop.
+
+    See: https://github.com/run-llama/llama_index/issues/20313
+    See: https://github.com/run-llama/llama_index/pull/20695
+    """
+
+    # Store config for lazy async client creation
+    _milvusclient_config: Dict[str, Any] = {}
+
+    def __init__(self, **kwargs: Any) -> None:
+        """
+        Initialize without creating AsyncMilvusClient.
+
+        Stores connection params for lazy initialization and patches
+        the parent class to skip AsyncMilvusClient creation.
+        """
+        import llama_index.vector_stores.milvus.base as milvus_base
+
+        # Store the original AsyncMilvusClient class
+        original_async_client = milvus_base.AsyncMilvusClient
+
+        # Replace AsyncMilvusClient with a dummy that does nothing
+        # This prevents the parent __init__ from creating the async client
+        milvus_base.AsyncMilvusClient = lambda **kw: None  # type: ignore
+
+        try:
+            # Call parent __init__ - it will use our dummy AsyncMilvusClient
+            super().__init__(**kwargs)
+        finally:
+            # Restore the original AsyncMilvusClient
+            milvus_base.AsyncMilvusClient = original_async_client
+
+        # Store connection params for lazy async client creation
+        # Following the pattern from PR #20695
+        uri = kwargs.get("uri", "./milvus_llamaindex.db")
+        token = kwargs.get("token", "")
+        # Filter out 'alias' as pymilvus sets it internally
+        filtered_kwargs = {k: v for k, v in kwargs.items() if k != "alias"}
+
+        self._milvusclient_config = {
+            "uri": uri,
+            "token": token,
+            "kwargs": filtered_kwargs,
+        }
+
+        # Set _async_milvusclient to None for lazy initialization
+        self._async_milvusclient = None  # type: ignore
+
+    @property
+    def aclient(self) -> AsyncMilvusClient:
+        """
+        Get async client (lazily created on first access).
+
+        This property creates the AsyncMilvusClient only when needed,
+        allowing synchronous operations to work without an event loop.
+        """
+        if self._async_milvusclient is None:
+            self._async_milvusclient = AsyncMilvusClient(
+                uri=self._milvusclient_config["uri"],
+                token=self._milvusclient_config["token"],
+                **self._milvusclient_config["kwargs"],
+            )
+        return self._async_milvusclient
+
+
+class MilvusBackend(BaseStorageBackend):
+    """
+    Milvus storage backend implementation.
+
+    Supported retrieval modes:
+    - vector: Pure vector similarity search (default)
+    - keyword: Pure BM25 keyword search
+    - hybrid: Combined vector + BM25 search with RRF ranking
+
+    Class Attributes:
+        SUPPORTED_RETRIEVAL_METHODS: List of supported retrieval method names
+        INDEX_PREFIX: Prefix for collection names
+    """
+
+    # Milvus supports vector, keyword (BM25), and hybrid search
+    SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = ["vector", "keyword", "hybrid"]
+
+    # Override INDEX_PREFIX for Milvus collections
+    INDEX_PREFIX: ClassVar[str] = "collection"
+
+    def __init__(self, config: Dict):
+        """
+        Initialize Milvus backend.
+
+        Args:
+            config: Storage configuration dict containing:
+                - url: Milvus connection URL with optional db_name path
+                  (e.g., "http://localhost:19530/mydb" or "http://localhost:19530")
+                - username: Optional username for authentication
+                - password: Optional password for authentication
+                - indexStrategy: Index/collection naming strategy
+                - ext: Additional config (e.g., dim for vector dimension, db_name)
+
+        Authentication:
+            If both username and password are provided, they are concatenated
+            as "{username}:{password}" to form the token.
+
+        Database Name:
+            The db_name can be specified in three ways (in order of priority):
+            1. ext.db_name - explicit db_name in ext config
+            2. URL path - e.g., "http://localhost:19530/mydb"
+            3. Default - "default" if not specified
+        """
+        super().__init__(config)
+
+        # Get vector dimension from ext (default: 1536 for OpenAI embeddings)
+        self.dim = self.ext.get("dim", DEFAULT_EMBEDDING_DIM)
+
+        # Build token for authentication: username:password
+        if self.username and self.password:
+            self.token = f"{self.username}:{self.password}"
+        else:
+            self.token = ""
+
+        # Parse db_name from URL or ext config
+        # pymilvus requires db_name as a separate parameter, not in URL path
+        self.db_name, self.base_url = self._parse_db_name_from_url(self.url)
+
+    def _parse_db_name_from_url(self, url: str) -> tuple:
+        """
+        Parse db_name from URL path and return base URL without db_name.
+
+        Milvus requires db_name as a separate parameter, not in the URL path.
+        This method extracts db_name from URL like "http://host:port/dbname"
+        and returns the base URL "http://host:port".
+
+        Priority for db_name:
+        1. ext.db_name - explicit config takes highest priority
+        2. URL path - extracted from URL if present
+        3. Default - "default" if not specified
+
+        Args:
+            url: Milvus connection URL (e.g., "http://localhost:19530/mydb")
+
+        Returns:
+            Tuple of (db_name, base_url)
+        """
+        from urllib.parse import urlparse, urlunparse
+
+        # Priority 1: Check ext.db_name first
+        if self.ext.get("db_name"):
+            return self.ext["db_name"], url
+
+        # Priority 2: Parse from URL path
+        if not url:
+            return "default", url
+
+        parsed = urlparse(url)
+
+        # Extract db_name from path (e.g., "/mydb" -> "mydb")
+        path = parsed.path.strip("/")
+
+        if path:
+            # Has path component - use it as db_name
+            db_name = path
+            # Rebuild URL without the path
+            base_url = urlunparse(
+                (parsed.scheme, parsed.netloc, "", parsed.params, parsed.query, "")
+            )
+            return db_name, base_url
+        else:
+            # No path - use default db_name
+            return "default", url
+
+    @staticmethod
+    def _sanitize_filter_value(value: str) -> str:
+        """
+        Sanitize a string value for use in Milvus filter expressions.
+
+        Escapes backslashes and double quotes to prevent expression injection.
+
+        Args:
+            value: The string value to sanitize
+
+        Returns:
+            Sanitized string safe for use in filter expressions
+        """
+        return value.replace("\\", "\\\\").replace('"', '\\"')
+
+    def _get_client(self) -> MilvusClient:
+        """
+        Create a MilvusClient instance.
+
+        Uses base_url (without db_name path) and passes db_name as separate parameter.
+
+        Returns:
+            MilvusClient instance for direct Milvus operations
+        """
+        return MilvusClient(uri=self.base_url, token=self.token, db_name=self.db_name)
+
+    def create_vector_store(
+        self,
+        collection_name: str,
+        retrieval_mode: str = "vector",
+        dim: Optional[int] = None,
+    ) -> MilvusVectorStore:
+        """
+        Create Milvus vector store instance.
+
+        Uses base_url (without db_name path) and passes db_name as separate parameter.
+
+        Args:
+            collection_name: Name of the collection
+            retrieval_mode: Retrieval mode - 'vector', 'keyword', or 'hybrid'
+            dim: Optional embedding dimension. If provided, overrides self.dim.
+                 This allows dynamic dimension based on the actual embedding model.
+
+        Returns:
+            MilvusVectorStore instance
+        """
+        # Use provided dim if available, otherwise fall back to configured dim
+        effective_dim = dim if dim is not None else self.dim
+
+        logger.info(
+            f"[Milvus] create_vector_store: collection={collection_name}, "
+            f"dim_param={dim}, self.dim={self.dim}, effective_dim={effective_dim}"
+        )
+
+        return LazyAsyncMilvusVectorStore(
+            uri=self.base_url,
+            token=self.token,
+            db_name=self.db_name,
+            collection_name=collection_name,
+            dim=effective_dim,
+            upsert_mode=True,
+            overwrite=False,  # Do not overwrite existing collection
+            enable_sparse=True,  # Enable sparse vector for keyword/hybrid search
+            hybrid_ranker="RRFRanker",  # Use RRF for hybrid search ranking
+        )
+
+    def index_with_metadata(
+        self,
+        nodes: List[BaseNode],
+        chunk_metadata: ChunkMetadata,
+        embed_model,
+        **kwargs,
+    ) -> Dict:
+        """
+        Index nodes into Milvus.
+
+        Note: Metadata is already applied to nodes by the indexer layer via
+        chunk_metadata.apply_to_nodes() before calling this method.
+
+        This method automatically uses the embedding dimension from the embed_model
+        if available (via _dimension attribute set from Model CRD's embeddingConfig).
+        This ensures the Milvus collection schema matches the actual embedding vectors.
+
+        Args:
+            nodes: List of nodes to index (metadata already applied)
+            chunk_metadata: ChunkMetadata instance containing document metadata
+            embed_model: Embedding model (may have _dimension attribute from Model CRD)
+            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
+
+        Returns:
+            Indexing result dict
+        """
+        # Get collection name
+        collection_name = self.get_index_name(chunk_metadata.knowledge_id, **kwargs)
+
+        # Get embedding dimension from embed_model if available
+        # CustomEmbedding stores dimension in _dimension attribute (set from Model CRD)
+        embed_dim = getattr(embed_model, "_dimension", None)
+        if embed_dim:
+            logger.info(f"[Milvus] Using embedding dimension from model: {embed_dim}")
+
+        # Create vector store with detected dimension (or fall back to configured dim)
+        vector_store = self.create_vector_store(collection_name, dim=embed_dim)
+
+        # Index nodes using LlamaIndex
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+        VectorStoreIndex(
+            nodes,
+            storage_context=storage_context,
+            embed_model=embed_model,
+            show_progress=True,
+        )
+
+        return {
+            "indexed_count": len(nodes),
+            "index_name": collection_name,
+            "status": "success",
+        }
+
+    def retrieve(
+        self,
+        knowledge_id: str,
+        query: str,
+        embed_model,
+        retrieval_setting: Dict[str, Any],
+        metadata_condition: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> Dict:
+        """
+        Retrieve nodes from Milvus (Dify-style API).
+
+        Uses LlamaIndex's VectorStoreQuery with different modes:
+        - DEFAULT: Pure vector similarity search
+        - TEXT_SEARCH: Pure BM25 keyword search
+        - HYBRID: Combined vector + BM25 search with RRF ranking
+
+        Note on score_threshold:
+        - For vector search: score_threshold is applied (cosine similarity 0-1)
+        - For keyword search: score_threshold is applied (BM25 scores vary)
+        - For hybrid search: score_threshold is IGNORED because RRF scores are
+          in a different range (typically 0.01-0.05) and ranking is already
+          optimized by the fusion algorithm
+
+        Args:
+            knowledge_id: Knowledge base ID
+            query: Search query
+            embed_model: Embedding model
+            retrieval_setting: Dict with:
+                - top_k: Maximum number of results
+                - score_threshold: Minimum similarity score (0-1, ignored for hybrid)
+                - retrieval_mode: Optional 'vector'/'keyword'/'hybrid' (default: 'vector')
+            metadata_condition: Optional metadata filtering
+            **kwargs: Additional parameters
+
+        Returns:
+            Retrieval result dict
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+        # Increased default top_k from 5 to 20 for better RAG coverage
+        top_k = retrieval_setting.get("top_k", DEFAULT_TOP_K)
+        score_threshold = retrieval_setting.get("score_threshold", 0.7)
+        retrieval_mode = retrieval_setting.get("retrieval_mode", "vector")
+
+        # Validate retrieval mode
+        if retrieval_mode not in self.SUPPORTED_RETRIEVAL_METHODS:
+            raise ValueError(
+                f"Milvus does not support '{retrieval_mode}' retrieval mode. "
+                f"Supported modes: {self.SUPPORTED_RETRIEVAL_METHODS}."
+            )
+
+        # Create vector store
+        vector_store = self.create_vector_store(collection_name, retrieval_mode)
+
+        # Build metadata filters
+        filters = self._build_metadata_filters(knowledge_id, metadata_condition)
+
+        # Determine query mode and parameters
+        if retrieval_mode == "keyword":
+            # Pure BM25 keyword search - no embedding needed
+            query_mode = VectorStoreQueryMode.TEXT_SEARCH
+            query_embedding = None
+        elif retrieval_mode == "hybrid":
+            # Hybrid search - needs embedding
+            query_mode = VectorStoreQueryMode.HYBRID
+            query_embedding = embed_model.get_query_embedding(query)
+        else:
+            # Default: Pure vector search
+            query_mode = VectorStoreQueryMode.DEFAULT
+            query_embedding = embed_model.get_query_embedding(query)
+
+        # Create VectorStoreQuery
+        vs_query = VectorStoreQuery(
+            query_str=query,
+            query_embedding=query_embedding,
+            similarity_top_k=top_k,
+            mode=query_mode,
+            filters=filters,
+        )
+
+        # Debug logging for hybrid search troubleshooting
+        logger.debug(
+            f"[Milvus] retrieve: mode={retrieval_mode}, query_mode={query_mode}, "
+            f"sparse_embedding_function={type(vector_store.sparse_embedding_function)}, "
+            f"enable_sparse={vector_store.enable_sparse}"
+        )
+
+        # Execute query
+        result = vector_store.query(vs_query)
+
+        # Debug logging for query results
+        logger.debug(
+            f"[Milvus] query result: nodes_count={len(result.nodes) if result.nodes else 0}, "
+            f"similarities={result.similarities[:5] if result.similarities else None}"
+        )
+
+        # Process results
+        # For hybrid search, skip score_threshold because RRF scores are in a different
+        # range (0.01-0.05) and the ranking is already optimized by the fusion algorithm
+        effective_threshold = 0.0 if retrieval_mode == "hybrid" else score_threshold
+        return self._process_query_results(result, effective_threshold)
+
+    def _build_metadata_filters(
+        self, knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
+    ):
+        """
+        Build metadata filters from condition dict.
+
+        Args:
+            knowledge_id: Knowledge base ID (always filtered)
+            metadata_condition: Optional additional metadata conditions
+
+        Returns:
+            MetadataFilters object
+        """
+        return parse_metadata_filters(knowledge_id, metadata_condition)
+
+    def _process_query_results(
+        self,
+        result,
+        score_threshold: float,
+    ) -> Dict:
+        """
+        Process VectorStoreQueryResult into Dify-compatible format.
+
+        Args:
+            result: VectorStoreQueryResult from LlamaIndex
+            score_threshold: Minimum relevance score (0-1).
+                            For hybrid search, this should be set to 0.0 because
+                            RRF scores are in a different range (0.01-0.05).
+
+        Returns:
+            Dict with 'records' list in Dify-compatible format
+        """
+        # Handle empty results
+        if not result.nodes:
+            return {"records": []}
+
+        # Process results (Dify-compatible format)
+        results = []
+        similarities = result.similarities or []
+
+        for i, node in enumerate(result.nodes):
+            score = (
+                similarities[i]
+                if i < len(similarities) and similarities[i] is not None
+                else 0.0
+            )
+
+            # Apply score threshold filter
+            if score >= score_threshold:
+                results.append(
+                    {
+                        "content": node.text,
+                        "score": float(score),
+                        "title": node.metadata.get("source_file", ""),
+                        "metadata": node.metadata,
+                    }
+                )
+
+        return {"records": results}
+
+    def delete_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
+        """
+        Delete document from Milvus using LlamaIndex API.
+
+        Uses delete_nodes with metadata filters to remove all chunks
+        with matching doc_ref.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+            **kwargs: Additional parameters
+
+        Returns:
+            Deletion result dict
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+        vector_store = self.create_vector_store(collection_name)
+
+        # Build filters to match the document
+        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
+
+        # Get nodes first to count them
+        try:
+            nodes = vector_store.get_nodes(filters=filters)
+            deleted_count = len(nodes)
+        except Exception:
+            deleted_count = 0
+
+        # Delete nodes using LlamaIndex API
+        vector_store.delete_nodes(filters=filters)
+
+        return {
+            "doc_ref": doc_ref,
+            "knowledge_id": knowledge_id,
+            "deleted_chunks": deleted_count,
+            "status": "deleted",
+        }
+
+    def get_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
+        """
+        Get document details from Milvus using LlamaIndex API.
+
+        Uses get_nodes with metadata filters to retrieve all chunks
+        with matching doc_ref.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+            **kwargs: Additional parameters
+
+        Returns:
+            Document details dict with chunks
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+        vector_store = self.create_vector_store(collection_name)
+
+        # Build filters to match the document
+        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
+
+        # Get nodes using LlamaIndex API
+        nodes = vector_store.get_nodes(filters=filters)
+
+        if not nodes:
+            raise ValueError(f"Document {doc_ref} not found")
+
+        # Extract chunks and sort by chunk_index
+        chunks = []
+        source_file = None
+        for node in nodes:
+            metadata = node.metadata
+
+            if source_file is None:
+                source_file = metadata.get("source_file")
+
+            chunks.append(
+                {
+                    "chunk_index": metadata.get("chunk_index"),
+                    "content": node.text,
+                    "metadata": metadata,
+                }
+            )
+
+        # Sort by chunk_index
+        chunks.sort(key=lambda x: x.get("chunk_index", 0))
+
+        return {
+            "doc_ref": doc_ref,
+            "knowledge_id": knowledge_id,
+            "source_file": source_file,
+            "chunk_count": len(chunks),
+            "chunks": chunks,
+        }
+
+    def _build_doc_ref_filters(self, knowledge_id: str, doc_ref: str):
+        """
+        Build metadata filters for document reference lookup.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+
+        Returns:
+            MetadataFilters object for filtering by knowledge_id and doc_ref
+        """
+        return MetadataFilters(
+            filters=[
+                MetadataFilter(
+                    key="knowledge_id", value=knowledge_id, operator=FilterOperator.EQ
+                ),
+                MetadataFilter(
+                    key="doc_ref", value=doc_ref, operator=FilterOperator.EQ
+                ),
+            ],
+            condition="and",
+        )
+
+    def list_documents(
+        self, knowledge_id: str, page: int = 1, page_size: int = 20, **kwargs
+    ) -> Dict:
+        """
+        List documents in Milvus collection.
+
+        Uses MilvusClient directly for aggregation functionality.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            page: Page number
+            page_size: Page size
+            **kwargs: Additional parameters
+
+        Returns:
+            Document list dict
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+        client = None
+
+        try:
+            # Create MilvusClient for direct query
+            client = self._get_client()
+
+            # Check if collection exists
+            collections = client.list_collections()
+            if collection_name not in collections:
+                return {
+                    "documents": [],
+                    "total": 0,
+                    "page": page,
+                    "page_size": page_size,
+                    "knowledge_id": knowledge_id,
+                }
+
+            # Sanitize knowledge_id to prevent expression injection
+            safe_knowledge_id = self._sanitize_filter_value(knowledge_id)
+            filter_expr = f'knowledge_id == "{safe_knowledge_id}"'
+
+            # Query all records with matching knowledge_id
+            # Note: Milvus requires specifying output fields
+            results = client.query(
+                collection_name=collection_name,
+                filter=filter_expr,
+                output_fields=["doc_ref", "source_file", "created_at", "chunk_index"],
+                limit=MAX_QUERY_LIMIT,
+            )
+
+            # Warn if results may be truncated
+            if len(results) >= MAX_QUERY_LIMIT:
+                logger.warning(
+                    f"[Milvus] Knowledge base {knowledge_id} has >= {MAX_QUERY_LIMIT} "
+                    "chunks; document listing may be incomplete."
+                )
+
+            # Aggregate by doc_ref
+            doc_map: Dict[str, Dict] = {}
+            for record in results:
+                doc_ref = record.get("doc_ref")
+                if not doc_ref:
+                    continue
+
+                if doc_ref not in doc_map:
+                    doc_map[doc_ref] = {
+                        "doc_ref": doc_ref,
+                        "source_file": record.get("source_file"),
+                        "chunk_count": 0,
+                        "created_at": record.get("created_at"),
+                    }
+                doc_map[doc_ref]["chunk_count"] += 1
+
+            # Convert to list and sort by created_at
+            all_docs = list(doc_map.values())
+            all_docs.sort(key=lambda x: x.get("created_at") or "", reverse=True)
+
+            # Pagination
+            total = len(all_docs)
+            start = (page - 1) * page_size
+            end = start + page_size
+            documents = all_docs[start:end]
+
+            return {
+                "documents": documents,
+                "total": total,
+                "page": page,
+                "page_size": page_size,
+                "knowledge_id": knowledge_id,
+            }
+
+        except Exception as e:
+            logger.warning(
+                f"[Milvus] Failed to list documents for KB {knowledge_id}: {e}"
+            )
+            return {
+                "documents": [],
+                "total": 0,
+                "page": page,
+                "page_size": page_size,
+                "knowledge_id": knowledge_id,
+            }
+        finally:
+            # Ensure client is closed to avoid connection leaks
+            if client:
+                try:
+                    client.close()
+                except Exception:
+                    pass
+
+    def test_connection(self) -> bool:
+        """
+        Test connection to Milvus.
+
+        Returns:
+            True if connection successful, False otherwise
+        """
+        client = None
+        try:
+            client = self._get_client()
+            # Try to list collections as a connection test
+            client.list_collections()
+            return True
+        except Exception:
+            return False
+        finally:
+            # Ensure client is closed to avoid connection leaks
+            if client:
+                try:
+                    client.close()
+                except Exception:
+                    pass
+
+    def get_all_chunks(
+        self, knowledge_id: str, max_chunks: int = MAX_QUERY_LIMIT, **kwargs
+    ) -> List[Dict[str, Any]]:
+        """
+        Get all chunks from a knowledge base in Milvus.
+
+        Uses MilvusClient directly for efficient batch retrieval.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            max_chunks: Maximum number of chunks to retrieve (safety limit)
+            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
+
+        Returns:
+            List of chunk dicts with content, title, chunk_id, doc_ref, metadata
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+        client = None
+
+        try:
+            # Create MilvusClient for direct query
+            client = self._get_client()
+
+            # Check if collection exists
+            collections = client.list_collections()
+            if collection_name not in collections:
+                return []
+
+            # Sanitize knowledge_id to prevent expression injection
+            safe_knowledge_id = self._sanitize_filter_value(knowledge_id)
+            filter_expr = f'knowledge_id == "{safe_knowledge_id}"'
+
+            # Query all records with matching knowledge_id
+            results = client.query(
+                collection_name=collection_name,
+                filter=filter_expr,
+                output_fields=[
+                    "doc_ref",
+                    "source_file",
+                    "created_at",
+                    "chunk_index",
+                    "text",
+                ],
+                limit=max_chunks,
+            )
+
+            # Convert to chunk format
+            chunks = []
+            for record in results:
+                # Get text content - try 'text' field first, then fallback
+                raw_content = record.get("text", "")
+
+                chunks.append(
+                    {
+                        "content": self.extract_chunk_text(raw_content),
+                        "title": record.get("source_file", ""),
+                        "chunk_id": record.get("chunk_index", 0),
+                        "doc_ref": record.get("doc_ref", ""),
+                        "metadata": record,
+                    }
+                )
+
+            # Sort by doc_ref and chunk_index
+            chunks.sort(key=lambda x: (x.get("doc_ref", ""), x.get("chunk_id", 0)))
+
+            return chunks[:max_chunks]
+
+        except Exception as e:
+            logger.warning(
+                f"[Milvus] Failed to get all chunks for KB {knowledge_id}: {e}"
+            )
+            return []
+        finally:
+            # Ensure client is closed to avoid connection leaks
+            if client:
+                try:
+                    client.close()
+                except Exception:
+                    pass
diff --git a/knowledge_engine/knowledge_engine/storage/qdrant_backend.py b/knowledge_engine/knowledge_engine/storage/qdrant_backend.py
new file mode 100644
index 000000000..6c7587874
--- /dev/null
+++ b/knowledge_engine/knowledge_engine/storage/qdrant_backend.py
@@ -0,0 +1,571 @@
+# SPDX-FileCopyrightText: 2025 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Qdrant storage backend implementation.
+
+Supported retrieval modes:
+- vector: Pure vector similarity search using embeddings
+
+Note on non-vector retrieval support:
+- Current implementation only supports vector search
+- Qdrant's newer versions (1.7+) do support full-text search via BM42 algorithm
+  and hybrid search capabilities, but this implementation does not yet utilize them
+- For keyword or hybrid search needs, use Elasticsearch backend instead
+- Future enhancement: Add BM42/hybrid search support when upgrading Qdrant integration
+"""
+
+from typing import Any, ClassVar, Dict, List, Optional
+
+from llama_index.core import StorageContext, VectorStoreIndex
+from llama_index.core.schema import BaseNode
+from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
+from llama_index.core.vector_stores.types import (
+    VectorStoreQuery,
+    VectorStoreQueryMode,
+)
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+from qdrant_client import QdrantClient
+from qdrant_client.http import models as qdrant_models
+
+from knowledge_engine.retrieval.filters import parse_metadata_filters
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+
+
+class QdrantBackend(BaseStorageBackend):
+    """
+    Qdrant storage backend implementation.
+
+    Supported retrieval modes:
+    - vector: Pure vector similarity search (default and only mode)
+
+    Note on non-vector retrieval support:
+    - Current implementation only supports vector search
+    - Qdrant's newer versions (1.7+) do support full-text search via BM42 algorithm
+      and hybrid search capabilities, but this implementation does not yet utilize them
+    - For keyword or hybrid search needs, use Elasticsearch backend instead
+    - Future enhancement: Add BM42/hybrid search support when upgrading Qdrant integration
+
+    Class Attributes:
+        SUPPORTED_RETRIEVAL_METHODS: List of supported retrieval method names
+        INDEX_PREFIX: Prefix for rolling collection names
+    """
+
+    # Qdrant only supports vector search
+    SUPPORTED_RETRIEVAL_METHODS: ClassVar[List[str]] = ["vector"]
+
+    # Override INDEX_PREFIX for Qdrant collections
+    INDEX_PREFIX: ClassVar[str] = "collection"
+
+    def __init__(self, config: Dict):
+        """
+        Initialize Qdrant backend.
+
+        Args:
+            config: Storage configuration dict containing:
+                - url: Qdrant server URL (e.g., "http://localhost:6333")
+                - apiKey: Optional API key for Qdrant Cloud
+                - indexStrategy: Index/collection naming strategy
+                - ext: Additional config (e.g., vector_size, distance)
+        """
+        super().__init__(config)
+
+        # Get vector configuration from ext (used by QdrantVectorStore for auto-creation)
+        self.vector_size = self.ext.get("vector_size", 1536)  # Default for OpenAI
+        self.distance = self.ext.get("distance", "Cosine")  # Cosine, Euclid, Dot
+
+        # Initialize Qdrant client
+        if self.api_key:
+            # Qdrant Cloud connection
+            self.client = QdrantClient(
+                url=self.url,
+                api_key=self.api_key,
+            )
+        else:
+            # Local Qdrant connection
+            self.client = QdrantClient(url=self.url)
+
+    def create_vector_store(self, collection_name: str):
+        """
+        Create Qdrant vector store.
+
+        LlamaIndex's QdrantVectorStore automatically creates the collection
+        if it doesn't exist when adding nodes.
+
+        Args:
+            collection_name: Name of the collection
+
+        Returns:
+            QdrantVectorStore instance
+        """
+        return QdrantVectorStore(
+            client=self.client,
+            collection_name=collection_name,
+        )
+
+    def index_with_metadata(
+        self,
+        nodes: List[BaseNode],
+        chunk_metadata: ChunkMetadata,
+        embed_model,
+        **kwargs,
+    ) -> Dict:
+        """
+        Index nodes into Qdrant.
+
+        Note: Metadata is already applied to nodes by the indexer layer via
+        chunk_metadata.apply_to_nodes() before calling this method.
+
+        Args:
+            nodes: List of nodes to index (metadata already applied)
+            chunk_metadata: ChunkMetadata instance containing document metadata
+            embed_model: Embedding model
+            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
+
+        Returns:
+            Indexing result dict
+        """
+        # Get collection name
+        collection_name = self.get_index_name(chunk_metadata.knowledge_id, **kwargs)
+
+        # Index nodes (LlamaIndex auto-creates collection if needed)
+        vector_store = self.create_vector_store(collection_name)
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+        VectorStoreIndex(
+            nodes,
+            storage_context=storage_context,
+            embed_model=embed_model,
+            show_progress=True,
+        )
+
+        return {
+            "indexed_count": len(nodes),
+            "index_name": collection_name,
+            "status": "success",
+        }
+
+    def retrieve(
+        self,
+        knowledge_id: str,
+        query: str,
+        embed_model,
+        retrieval_setting: Dict[str, Any],
+        metadata_condition: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> Dict:
+        """
+        Retrieve nodes from Qdrant (Dify-style API).
+
+        Uses LlamaIndex's VectorStoreQuery with DEFAULT mode (vector search only).
+
+        Args:
+            knowledge_id: Knowledge base ID
+            query: Search query
+            embed_model: Embedding model
+            retrieval_setting: Dict with:
+                - top_k: Maximum number of results
+                - score_threshold: Minimum similarity score (0-1)
+                - retrieval_mode: Only 'vector' is supported
+            metadata_condition: Optional metadata filtering
+            **kwargs: Additional parameters
+
+        Returns:
+            Retrieval result dict
+
+        Raises:
+            ValueError: If retrieval_mode is not 'vector'
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+        # Increased default top_k from 5 to 20 for better RAG coverage
+        top_k = retrieval_setting.get("top_k", 20)
+        score_threshold = retrieval_setting.get("score_threshold", 0.7)
+        retrieval_mode = retrieval_setting.get("retrieval_mode", "vector")
+
+        # Validate retrieval mode - Qdrant only supports vector search
+        if retrieval_mode not in self.SUPPORTED_RETRIEVAL_METHODS:
+            raise ValueError(
+                f"Qdrant does not support '{retrieval_mode}' retrieval mode. "
+                f"Supported modes: {self.SUPPORTED_RETRIEVAL_METHODS}. "
+                f"For keyword or hybrid search, use Elasticsearch backend."
+            )
+
+        # Create vector store
+        vector_store = self.create_vector_store(collection_name)
+
+        # Build metadata filters
+        filters = self._build_metadata_filters(knowledge_id, metadata_condition)
+
+        # Generate query embedding
+        query_embedding = embed_model.get_query_embedding(query)
+
+        # Create VectorStoreQuery (vector mode only)
+        vs_query = VectorStoreQuery(
+            query_str=query,
+            query_embedding=query_embedding,
+            similarity_top_k=top_k,
+            mode=VectorStoreQueryMode.DEFAULT,
+            filters=filters,
+        )
+
+        # Execute query
+        result = vector_store.query(vs_query)
+
+        # Process results
+        return self._process_query_results(result, score_threshold)
+
+    def _build_metadata_filters(
+        self, knowledge_id: str, metadata_condition: Optional[Dict[str, Any]] = None
+    ):
+        """
+        Build metadata filters from condition dict.
+
+        Args:
+            knowledge_id: Knowledge base ID (always filtered)
+            metadata_condition: Optional additional metadata conditions
+
+        Returns:
+            MetadataFilters object
+        """
+        return parse_metadata_filters(knowledge_id, metadata_condition)
+
+    def _process_query_results(
+        self,
+        result,
+        score_threshold: float,
+    ) -> Dict:
+        """
+        Process VectorStoreQueryResult into Dify-compatible format.
+
+        Args:
+            result: VectorStoreQueryResult from LlamaIndex
+            score_threshold: Minimum relevance score (0-1)
+
+        Returns:
+            Dict with 'records' list in Dify-compatible format
+        """
+        # Handle empty results
+        if not result.nodes:
+            return {"records": []}
+
+        # Process results (Dify-compatible format)
+        results = []
+        similarities = result.similarities or []
+
+        for i, node in enumerate(result.nodes):
+            score = (
+                similarities[i]
+                if i < len(similarities) and similarities[i] is not None
+                else 0.0
+            )
+
+            # Qdrant with cosine similarity returns scores in 0-1 range
+            if score >= score_threshold:
+                results.append(
+                    {
+                        "content": node.text,
+                        "score": float(score),
+                        "title": node.metadata.get("source_file", ""),
+                        "metadata": node.metadata,
+                    }
+                )
+
+        return {"records": results}
+
+    def delete_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
+        """
+        Delete document from Qdrant using LlamaIndex API.
+
+        Uses delete_nodes with metadata filters to remove all chunks
+        with matching doc_ref.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+            **kwargs: Additional parameters
+
+        Returns:
+            Deletion result dict
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+        vector_store = self.create_vector_store(collection_name)
+
+        # Build filters to match the document
+        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
+
+        # Get nodes first to count them
+        nodes = vector_store.get_nodes(filters=filters)
+        deleted_count = len(nodes)
+
+        # Delete nodes using LlamaIndex API
+        vector_store.delete_nodes(filters=filters)
+
+        return {
+            "doc_ref": doc_ref,
+            "knowledge_id": knowledge_id,
+            "deleted_chunks": deleted_count,
+            "status": "deleted",
+        }
+
+    def get_document(self, knowledge_id: str, doc_ref: str, **kwargs) -> Dict:
+        """
+        Get document details from Qdrant using LlamaIndex API.
+
+        Uses get_nodes with metadata filters to retrieve all chunks
+        with matching doc_ref.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+            **kwargs: Additional parameters
+
+        Returns:
+            Document details dict with chunks
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+        vector_store = self.create_vector_store(collection_name)
+
+        # Build filters to match the document
+        filters = self._build_doc_ref_filters(knowledge_id, doc_ref)
+
+        # Get nodes using LlamaIndex API
+        nodes = vector_store.get_nodes(filters=filters)
+
+        if not nodes:
+            raise ValueError(f"Document {doc_ref} not found")
+
+        # Extract chunks and sort by chunk_index
+        chunks = []
+        source_file = None
+        for node in nodes:
+            metadata = node.metadata
+
+            if source_file is None:
+                source_file = metadata.get("source_file")
+
+            chunks.append(
+                {
+                    "chunk_index": metadata.get("chunk_index"),
+                    "content": node.text,
+                    "metadata": metadata,
+                }
+            )
+
+        # Sort by chunk_index
+        chunks.sort(key=lambda x: x.get("chunk_index", 0))
+
+        return {
+            "doc_ref": doc_ref,
+            "knowledge_id": knowledge_id,
+            "source_file": source_file,
+            "chunk_count": len(chunks),
+            "chunks": chunks,
+        }
+
+    def _build_doc_ref_filters(self, knowledge_id: str, doc_ref: str):
+        """
+        Build metadata filters for document reference lookup.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            doc_ref: Document reference ID (doc_xxx format)
+
+        Returns:
+            MetadataFilters object for filtering by knowledge_id and doc_ref
+        """
+        return MetadataFilters(
+            filters=[
+                ExactMatchFilter(key="knowledge_id", value=knowledge_id),
+                ExactMatchFilter(key="doc_ref", value=doc_ref),
+            ],
+            condition="and",
+        )
+
+    def list_documents(
+        self, knowledge_id: str, page: int = 1, page_size: int = 20, **kwargs
+    ) -> Dict:
+        """
+        List documents in Qdrant collection.
+
+        Uses Qdrant's scroll API to aggregate documents by doc_ref.
+        Note: This uses native Qdrant API as LlamaIndex doesn't provide
+        aggregation functionality.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            page: Page number
+            page_size: Page size
+            **kwargs: Additional parameters
+
+        Returns:
+            Document list dict
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+
+        # Use Qdrant scroll to get all points with matching knowledge_id
+        # Then aggregate by doc_ref in Python
+        scroll_filter = qdrant_models.Filter(
+            must=[
+                qdrant_models.FieldCondition(
+                    key="knowledge_id",
+                    match=qdrant_models.MatchValue(value=knowledge_id),
+                )
+            ]
+        )
+
+        # Scroll through all matching points
+        all_points = []
+        offset = None
+        while True:
+            results, offset = self.client.scroll(
+                collection_name=collection_name,
+                scroll_filter=scroll_filter,
+                limit=1000,
+                offset=offset,
+                with_payload=True,
+                with_vectors=False,
+            )
+            all_points.extend(results)
+            if offset is None:
+                break
+
+        # Aggregate by doc_ref
+        doc_map: Dict[str, Dict] = {}
+        for point in all_points:
+            payload = point.payload or {}
+            doc_ref = payload.get("doc_ref")
+            if not doc_ref:
+                continue
+
+            if doc_ref not in doc_map:
+                doc_map[doc_ref] = {
+                    "doc_ref": doc_ref,
+                    "source_file": payload.get("source_file"),
+                    "chunk_count": 0,
+                    "created_at": payload.get("created_at"),
+                }
+            doc_map[doc_ref]["chunk_count"] += 1
+
+        # Convert to list and sort by created_at
+        all_docs = list(doc_map.values())
+        all_docs.sort(key=lambda x: x.get("created_at") or "", reverse=True)
+
+        # Pagination
+        total = len(all_docs)
+        start = (page - 1) * page_size
+        end = start + page_size
+        documents = all_docs[start:end]
+
+        return {
+            "documents": documents,
+            "total": total,
+            "page": page,
+            "page_size": page_size,
+            "knowledge_id": knowledge_id,
+        }
+
+    def test_connection(self) -> bool:
+        """
+        Test connection to Qdrant.
+
+        Returns:
+            True if connection successful, False otherwise
+        """
+        try:
+            # Try to get collections list as a connection test
+            self.client.get_collections()
+            return True
+        except Exception:
+            return False
+
+    def get_all_chunks(
+        self, knowledge_id: str, max_chunks: int = 10000, **kwargs
+    ) -> List[Dict[str, Any]]:
+        """
+        Get all chunks from a knowledge base in Qdrant.
+
+        Uses scroll API to efficiently retrieve all chunks for a knowledge base.
+
+        Args:
+            knowledge_id: Knowledge base ID
+            max_chunks: Maximum number of chunks to retrieve (safety limit)
+            **kwargs: Additional parameters (e.g., user_id for per_user strategy)
+
+        Returns:
+            List of chunk dicts with content, title, chunk_id, doc_ref, metadata
+        """
+        collection_name = self.get_index_name(knowledge_id, **kwargs)
+
+        # Build filter for knowledge_id
+        scroll_filter = qdrant_models.Filter(
+            must=[
+                qdrant_models.FieldCondition(
+                    key="knowledge_id",
+                    match=qdrant_models.MatchValue(value=knowledge_id),
+                )
+            ]
+        )
+
+        try:
+            # Check if collection exists
+            try:
+                self.client.get_collection(collection_name)
+            except Exception:
+                return []
+
+            # Scroll through all matching points
+            all_points = []
+            offset = None
+            batch_size = min(1000, max_chunks)
+
+            while len(all_points) < max_chunks:
+                results, offset = self.client.scroll(
+                    collection_name=collection_name,
+                    scroll_filter=scroll_filter,
+                    limit=batch_size,
+                    offset=offset,
+                    with_payload=True,
+                    with_vectors=False,
+                )
+                all_points.extend(results)
+                if offset is None or len(results) == 0:
+                    break
+
+            # Convert to chunk format
+            chunks = []
+            for point in all_points[:max_chunks]:
+                payload = point.payload or {}
+
+                # Normalize content to plain text. Qdrant stores the original
+                # LlamaIndex node payload in `_node_content`, which may be a
+                # serialized TextNode JSON. We use extract_chunk_text to
+                # extract the human-readable `text` field and drop internal
+                # fields (id_, relationships, embeddings, etc.).
+                raw_content = payload.get("_node_content", "")
+
+                chunks.append(
+                    {
+                        "content": self.extract_chunk_text(raw_content),
+                        "title": payload.get("source_file", ""),
+                        "chunk_id": payload.get("chunk_index", 0),
+                        "doc_ref": payload.get("doc_ref", ""),
+                        "metadata": payload,
+                    }
+                )
+
+            # Sort by doc_ref and chunk_index
+            chunks.sort(key=lambda x: (x.get("doc_ref", ""), x.get("chunk_id", 0)))
+
+            return chunks
+
+        except Exception as e:
+            # Log error but return empty list to allow fallback to RAG
+            import logging
+
+            logger = logging.getLogger(__name__)
+            logger.warning(
+                f"[Qdrant] Failed to get all chunks for KB {knowledge_id}: {e}"
+            )
+            return []
diff --git a/knowledge_engine/tests/test_query_executor.py b/knowledge_engine/tests/test_query_executor.py
new file mode 100644
index 000000000..1cc8c3a24
--- /dev/null
+++ b/knowledge_engine/tests/test_query_executor.py
@@ -0,0 +1,75 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from shared.models import RuntimeRetrievalConfig
+
+
+@pytest.mark.asyncio
+async def test_query_executor_delegates_to_storage_backend_with_normalized_config() -> (
+    None
+):
+    from knowledge_engine.query import QueryExecutor
+
+    storage_backend = MagicMock()
+    storage_backend.retrieve.return_value = {
+        "records": [
+            {
+                "content": "Release checklist",
+                "score": 0.91,
+                "title": "Checklist",
+                "metadata": {"doc_ref": "12"},
+            }
+        ]
+    }
+    embed_model = object()
+    executor = QueryExecutor(storage_backend=storage_backend, embed_model=embed_model)
+
+    result = await executor.execute(
+        knowledge_id="1",
+        query="release checklist",
+        retrieval_config=RuntimeRetrievalConfig(
+            top_k=8,
+            score_threshold=0.45,
+            retrieval_mode="hybrid",
+            vector_weight=0.8,
+            keyword_weight=0.2,
+        ),
+        metadata_condition={
+            "operator": "and",
+            "conditions": [{"key": "doc_ref", "operator": "in", "value": ["12"]}],
+        },
+        user_id=7,
+    )
+
+    assert result == {
+        "records": [
+            {
+                "content": "Release checklist",
+                "score": 0.91,
+                "title": "Checklist",
+                "metadata": {"doc_ref": "12"},
+            }
+        ]
+    }
+    storage_backend.retrieve.assert_called_once_with(
+        knowledge_id="1",
+        query="release checklist",
+        embed_model=embed_model,
+        retrieval_setting={
+            "top_k": 8,
+            "score_threshold": 0.45,
+            "retrieval_mode": "hybrid",
+            "vector_weight": 0.8,
+            "keyword_weight": 0.2,
+        },
+        metadata_condition={
+            "operator": "and",
+            "conditions": [{"key": "doc_ref", "operator": "in", "value": ["12"]}],
+        },
+        user_id=7,
+    )
diff --git a/knowledge_engine/tests/test_storage_factory.py b/knowledge_engine/tests/test_storage_factory.py
new file mode 100644
index 000000000..efd40e6c6
--- /dev/null
+++ b/knowledge_engine/tests/test_storage_factory.py
@@ -0,0 +1,46 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from shared.models import RuntimeRetrieverConfig
+
+
+def test_create_storage_backend_from_runtime_config_uses_registered_backend(
+    monkeypatch,
+) -> None:
+    from knowledge_engine.storage.factory import (
+        STORAGE_BACKEND_REGISTRY,
+        create_storage_backend_from_runtime_config,
+    )
+
+    captured: dict[str, object] = {}
+
+    class FakeBackend:
+        def __init__(self, config):
+            captured["config"] = config
+
+    monkeypatch.setitem(STORAGE_BACKEND_REGISTRY, "fake", FakeBackend)
+
+    backend = create_storage_backend_from_runtime_config(
+        RuntimeRetrieverConfig(
+            name="retriever-a",
+            storage_config={
+                "type": "fake",
+                "url": "http://vector-store:1234",
+                "username": "tester",
+                "apiKey": "secret",
+                "indexStrategy": {"mode": "per_dataset"},
+                "ext": {"vector_size": 1536},
+            },
+        )
+    )
+
+    assert isinstance(backend, FakeBackend)
+    assert captured["config"] == {
+        "url": "http://vector-store:1234",
+        "username": "tester",
+        "password": None,
+        "apiKey": "secret",
+        "indexStrategy": {"mode": "per_dataset"},
+        "ext": {"vector_size": 1536},
+    }
diff --git a/knowledge_runtime/knowledge_runtime/api/internal/rag.py b/knowledge_runtime/knowledge_runtime/api/internal/rag.py
index 984fb9b22..94caf035a 100644
--- a/knowledge_runtime/knowledge_runtime/api/internal/rag.py
+++ b/knowledge_runtime/knowledge_runtime/api/internal/rag.py
@@ -3,14 +3,15 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from fastapi import APIRouter, Depends
-
 from knowledge_runtime.security import verify_internal_service_token
 from knowledge_runtime.services.handlers import runtime_handlers
+
 from shared.models.knowledge_runtime_protocol import (
     RemoteDeleteDocumentIndexRequest,
     RemoteIndexRequest,
     RemoteQueryRequest,
     RemoteQueryResponse,
+    RemoteTestConnectionRequest,
 )
 
 router = APIRouter(prefix="/internal/rag", tags=["internal-rag"])
@@ -38,3 +39,11 @@ async def delete_document_index(
     _: None = Depends(verify_internal_service_token),
 ) -> dict:
     return await runtime_handlers.delete_document_index(request)
+
+
+@router.post("/test-connection")
+async def test_connection(
+    request: RemoteTestConnectionRequest,
+    _: None = Depends(verify_internal_service_token),
+) -> dict:
+    return await runtime_handlers.test_connection(request)
diff --git a/knowledge_runtime/knowledge_runtime/services/handlers.py b/knowledge_runtime/knowledge_runtime/services/handlers.py
index 8a6b17be6..36eb5096f 100644
--- a/knowledge_runtime/knowledge_runtime/services/handlers.py
+++ b/knowledge_runtime/knowledge_runtime/services/handlers.py
@@ -4,39 +4,185 @@
 
 from __future__ import annotations
 
+import asyncio
+from pathlib import PurePosixPath
+from urllib.parse import urlparse
+
 from knowledge_runtime.services import content_fetcher
+
+from knowledge_engine.embedding import create_embedding_model_from_runtime_config
+from knowledge_engine.query import QueryExecutor
+from knowledge_engine.services import DocumentService
+from knowledge_engine.storage.factory import create_storage_backend_from_runtime_config
 from shared.models.knowledge_runtime_protocol import (
     RemoteDeleteDocumentIndexRequest,
     RemoteIndexRequest,
+    RemoteQueryRecord,
     RemoteQueryRequest,
     RemoteQueryResponse,
+    RemoteTestConnectionRequest,
 )
 
 
 class RuntimeHandlers:
     async def index_document(self, request: RemoteIndexRequest) -> dict:
         content = await content_fetcher.fetch_content(request.content_ref)
-        return {
-            "status": "accepted",
-            "knowledge_id": str(request.knowledge_base_id),
-            "document_id": request.document_id,
-            "content_bytes": len(content),
-            "content_ref_kind": request.content_ref.kind,
-            "index_families": request.index_families,
-        }
+        storage_backend = create_storage_backend_from_runtime_config(
+            request.retriever_config
+        )
+        embed_model = create_embedding_model_from_runtime_config(
+            request.embedding_model_config
+        )
+        service = DocumentService(storage_backend=storage_backend)
+        source_file, file_extension = self._resolve_source_file_metadata(request)
+        return await service.index_document_from_binary(
+            knowledge_id=str(request.knowledge_base_id),
+            binary_data=content,
+            source_file=source_file,
+            file_extension=file_extension,
+            embed_model=embed_model,
+            user_id=request.index_owner_user_id,
+            splitter_config=request.splitter_config,
+            document_id=request.document_id,
+        )
 
     async def query(self, request: RemoteQueryRequest) -> RemoteQueryResponse:
-        return RemoteQueryResponse(records=[], total=0, total_estimated_tokens=0)
+        metadata_condition = self._combine_metadata_conditions(
+            self._build_document_filter(request.document_ids),
+            request.metadata_condition,
+        )
+        records: list[RemoteQueryRecord] = []
+
+        for kb_config in request.knowledge_base_configs:
+            storage_backend = create_storage_backend_from_runtime_config(
+                kb_config.retriever_config
+            )
+            embed_model = create_embedding_model_from_runtime_config(
+                kb_config.embedding_model_config
+            )
+            executor = QueryExecutor(
+                storage_backend=storage_backend,
+                embed_model=embed_model,
+            )
+            result = await executor.execute(
+                knowledge_id=str(kb_config.knowledge_base_id),
+                query=request.query,
+                retrieval_config=kb_config.retrieval_config,
+                metadata_condition=metadata_condition,
+                user_id=kb_config.index_owner_user_id,
+            )
+            records.extend(
+                self._build_remote_query_records(
+                    knowledge_base_id=kb_config.knowledge_base_id,
+                    records=result.get("records", []),
+                )
+            )
+
+        records.sort(key=lambda item: item.score or 0.0, reverse=True)
+        limited_records = records[: request.max_results]
+        return RemoteQueryResponse(
+            records=limited_records,
+            total=len(limited_records),
+            total_estimated_tokens=0,
+        )
 
     async def delete_document_index(
         self, request: RemoteDeleteDocumentIndexRequest
     ) -> dict:
+        storage_backend = create_storage_backend_from_runtime_config(
+            request.retriever_config
+        )
+        service = DocumentService(storage_backend=storage_backend)
+        return await service.delete_document(
+            knowledge_id=str(request.knowledge_base_id),
+            doc_ref=request.document_ref,
+            user_id=request.index_owner_user_id,
+        )
+
+    async def test_connection(self, request: RemoteTestConnectionRequest) -> dict:
+        storage_backend = create_storage_backend_from_runtime_config(
+            request.retriever_config
+        )
+        success = await asyncio.to_thread(storage_backend.test_connection)
+        return {
+            "success": success,
+            "message": "Connection successful" if success else "Connection failed",
+        }
+
+    @staticmethod
+    def _build_document_filter(document_ids: list[int] | None) -> dict | None:
+        if not document_ids:
+            return None
+
+        return {
+            "operator": "and",
+            "conditions": [
+                {
+                    "key": "doc_ref",
+                    "operator": "in",
+                    "value": [str(document_id) for document_id in document_ids],
+                }
+            ],
+        }
+
+    @staticmethod
+    def _combine_metadata_conditions(*conditions: dict | None) -> dict | None:
+        normalized_conditions = [condition for condition in conditions if condition]
+        if not normalized_conditions:
+            return None
+        if len(normalized_conditions) == 1:
+            return normalized_conditions[0]
+
         return {
-            "status": "accepted",
-            "knowledge_id": str(request.knowledge_base_id),
-            "doc_ref": request.document_ref,
-            "enabled_index_families": request.enabled_index_families,
+            "operator": "and",
+            "conditions": normalized_conditions,
         }
 
+    def _build_remote_query_records(
+        self,
+        *,
+        knowledge_base_id: int,
+        records: list[dict],
+    ) -> list[RemoteQueryRecord]:
+        return [
+            RemoteQueryRecord(
+                content=record.get("content", ""),
+                title=record.get("title", "Unknown"),
+                score=record.get("score"),
+                metadata=record.get("metadata"),
+                knowledge_base_id=knowledge_base_id,
+                document_id=self._extract_document_id(record.get("metadata")),
+                index_family="chunk_vector",
+            )
+            for record in records
+        ]
+
+    @staticmethod
+    def _extract_document_id(metadata: dict | None) -> int | None:
+        if not isinstance(metadata, dict):
+            return None
+
+        doc_ref = metadata.get("doc_ref")
+        if isinstance(doc_ref, str) and doc_ref.isdigit():
+            return int(doc_ref)
+        if isinstance(doc_ref, int):
+            return doc_ref
+        return None
+
+    @staticmethod
+    def _resolve_source_file_metadata(
+        request: RemoteIndexRequest,
+    ) -> tuple[str, str]:
+        parsed_url = urlparse(request.content_ref.url)
+        file_name = PurePosixPath(parsed_url.path).name
+        if file_name:
+            suffix = PurePosixPath(file_name).suffix.lower()
+            return file_name, suffix
+
+        fallback_name = f"knowledge-{request.knowledge_base_id}"
+        if request.document_id is not None:
+            fallback_name = f"document-{request.document_id}"
+        return fallback_name, ""
+
 
 runtime_handlers = RuntimeHandlers()
diff --git a/knowledge_runtime/tests/test_internal_rag.py b/knowledge_runtime/tests/test_internal_rag.py
index 3a66cf0c0..219434829 100644
--- a/knowledge_runtime/tests/test_internal_rag.py
+++ b/knowledge_runtime/tests/test_internal_rag.py
@@ -4,13 +4,13 @@
 
 from __future__ import annotations
 
-from unittest.mock import Mock
+from unittest.mock import AsyncMock, Mock
 
 import pytest
 from fastapi.testclient import TestClient
-
 from knowledge_runtime.main import create_app
 from knowledge_runtime.services.content_fetcher import fetch_content
+
 from shared.models.knowledge_runtime_protocol import (
     BackendAttachmentStreamContentRef,
     PresignedUrlContentRef,
@@ -64,12 +64,35 @@ async def test_fetch_content_uses_presigned_url_without_auth_header(mocker) -> N
     )
 
 
-def test_index_route_fetches_content_and_returns_execution_metadata(mocker) -> None:
+def test_index_route_fetches_content_and_delegates_to_document_service(mocker) -> None:
     client = TestClient(create_app())
     fetch_mock = mocker.patch(
         "knowledge_runtime.services.handlers.content_fetcher.fetch_content",
         return_value=b"indexed text",
     )
+    storage_backend = object()
+    embed_model = object()
+    mocker.patch(
+        "knowledge_runtime.services.handlers.create_storage_backend_from_runtime_config",
+        return_value=storage_backend,
+    )
+    mocker.patch(
+        "knowledge_runtime.services.handlers.create_embedding_model_from_runtime_config",
+        return_value=embed_model,
+    )
+    document_service = Mock()
+    document_service.index_document_from_binary = AsyncMock(
+        return_value={
+            "status": "success",
+            "knowledge_id": "101",
+            "doc_ref": "202",
+            "chunk_count": 3,
+        }
+    )
+    document_service_cls = mocker.patch(
+        "knowledge_runtime.services.handlers.DocumentService",
+        return_value=document_service,
+    )
 
     response = client.post(
         "/internal/rag/index",
@@ -90,18 +113,45 @@ def test_index_route_fetches_content_and_returns_execution_metadata(mocker) -> N
 
     assert response.status_code == 200
     assert response.json() == {
-        "status": "accepted",
+        "status": "success",
         "knowledge_id": "101",
-        "document_id": 202,
-        "content_bytes": len(b"indexed text"),
-        "content_ref_kind": "presigned_url",
-        "index_families": ["chunk_vector", "summary_vector_index"],
+        "doc_ref": "202",
+        "chunk_count": 3,
     }
     fetch_mock.assert_awaited_once()
+    document_service_cls.assert_called_once_with(storage_backend=storage_backend)
+    document_service.index_document_from_binary.assert_awaited_once_with(
+        knowledge_id="101",
+        binary_data=b"indexed text",
+        source_file="release-notes.md",
+        file_extension=".md",
+        embed_model=embed_model,
+        user_id=303,
+        splitter_config=None,
+        document_id=202,
+    )
 
 
-def test_delete_route_returns_enabled_index_families() -> None:
+def test_delete_route_delegates_to_document_service(mocker) -> None:
     client = TestClient(create_app())
+    storage_backend = object()
+    document_service = Mock()
+    document_service.delete_document = AsyncMock(
+        return_value={
+            "status": "success",
+            "knowledge_id": "101",
+            "doc_ref": "202",
+            "deleted_chunks": 3,
+        }
+    )
+    mocker.patch(
+        "knowledge_runtime.services.handlers.create_storage_backend_from_runtime_config",
+        return_value=storage_backend,
+    )
+    document_service_cls = mocker.patch(
+        "knowledge_runtime.services.handlers.DocumentService",
+        return_value=document_service,
+    )
 
     response = client.post(
         "/internal/rag/delete-document-index",
@@ -123,14 +173,46 @@ def test_delete_route_returns_enabled_index_families() -> None:
 
     assert response.status_code == 200
     assert response.json() == {
-        "status": "accepted",
+        "status": "success",
         "knowledge_id": "101",
         "doc_ref": "202",
-        "enabled_index_families": [
-            "chunk_vector",
-            "summary_vector_index",
-        ],
+        "deleted_chunks": 3,
+    }
+    document_service_cls.assert_called_once_with(storage_backend=storage_backend)
+    document_service.delete_document.assert_awaited_once_with(
+        knowledge_id="101",
+        doc_ref="202",
+        user_id=None,
+    )
+
+
+def test_test_connection_route_delegates_to_storage_backend(mocker) -> None:
+    client = TestClient(create_app())
+    storage_backend = Mock()
+    storage_backend.test_connection.return_value = True
+    mocker.patch(
+        "knowledge_runtime.services.handlers.create_storage_backend_from_runtime_config",
+        return_value=storage_backend,
+    )
+
+    response = client.post(
+        "/internal/rag/test-connection",
+        headers=_auth_headers(),
+        json={
+            "retriever_config": {
+                "name": "default-retriever",
+                "namespace": "default",
+                "storage_config": {"type": "qdrant"},
+            }
+        },
+    )
+
+    assert response.status_code == 200
+    assert response.json() == {
+        "success": True,
+        "message": "Connection successful",
     }
+    storage_backend.test_connection.assert_called_once_with()
 
 
 def test_query_route_rejects_invalid_auth_token() -> None:
diff --git a/knowledge_runtime/tests/test_query_execution.py b/knowledge_runtime/tests/test_query_execution.py
new file mode 100644
index 000000000..b8f55f297
--- /dev/null
+++ b/knowledge_runtime/tests/test_query_execution.py
@@ -0,0 +1,187 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+from knowledge_runtime.services.handlers import RuntimeHandlers
+
+from shared.models import (
+    RemoteKnowledgeBaseQueryConfig,
+    RemoteQueryRequest,
+    RuntimeEmbeddingModelConfig,
+    RuntimeRetrievalConfig,
+    RuntimeRetrieverConfig,
+)
+
+
+@pytest.mark.asyncio
+async def test_query_handler_executes_engine_queries_and_merges_results(mocker) -> None:
+    first_backend = object()
+    second_backend = object()
+    first_embed_model = object()
+    second_embed_model = object()
+    first_executor = MagicMock()
+    first_executor.execute = AsyncMock(
+        return_value={
+            "records": [
+                {
+                    "content": "Lower score",
+                    "score": 0.5,
+                    "title": "Doc B",
+                    "metadata": {"doc_ref": "22"},
+                }
+            ]
+        }
+    )
+    second_executor = MagicMock()
+    second_executor.execute = AsyncMock(
+        return_value={
+            "records": [
+                {
+                    "content": "Higher score",
+                    "score": 0.9,
+                    "title": "Doc A",
+                    "metadata": {"doc_ref": "11"},
+                }
+            ]
+        }
+    )
+
+    mocker.patch(
+        "knowledge_runtime.services.handlers.create_storage_backend_from_runtime_config",
+        side_effect=[first_backend, second_backend],
+    )
+    mocker.patch(
+        "knowledge_runtime.services.handlers.create_embedding_model_from_runtime_config",
+        side_effect=[first_embed_model, second_embed_model],
+    )
+    query_executor_cls = mocker.patch(
+        "knowledge_runtime.services.handlers.QueryExecutor",
+        side_effect=[first_executor, second_executor],
+    )
+
+    handlers = RuntimeHandlers()
+    request = RemoteQueryRequest(
+        knowledge_base_ids=[1, 2],
+        query="release checklist",
+        max_results=1,
+        document_ids=[11, 22],
+        metadata_condition={
+            "operator": "or",
+            "conditions": [
+                {"key": "source", "operator": "==", "value": "kb"},
+                {"key": "lang", "operator": "==", "value": "zh"},
+            ],
+        },
+        knowledge_base_configs=[
+            RemoteKnowledgeBaseQueryConfig(
+                knowledge_base_id=1,
+                index_owner_user_id=101,
+                retriever_config=RuntimeRetrieverConfig(
+                    name="retriever-a",
+                    storage_config={"type": "qdrant", "url": "http://qdrant:6333"},
+                ),
+                embedding_model_config=RuntimeEmbeddingModelConfig(
+                    model_name="embedding-a",
+                    resolved_config={
+                        "protocol": "openai",
+                        "model_id": "text-embedding-3-small",
+                    },
+                ),
+                retrieval_config=RuntimeRetrievalConfig(
+                    top_k=5,
+                    score_threshold=0.4,
+                    retrieval_mode="vector",
+                ),
+            ),
+            RemoteKnowledgeBaseQueryConfig(
+                knowledge_base_id=2,
+                index_owner_user_id=202,
+                retriever_config=RuntimeRetrieverConfig(
+                    name="retriever-b",
+                    storage_config={"type": "qdrant", "url": "http://qdrant:6333"},
+                ),
+                embedding_model_config=RuntimeEmbeddingModelConfig(
+                    model_name="embedding-b",
+                    resolved_config={
+                        "protocol": "openai",
+                        "model_id": "text-embedding-3-small",
+                    },
+                ),
+                retrieval_config=RuntimeRetrievalConfig(
+                    top_k=5,
+                    score_threshold=0.4,
+                    retrieval_mode="vector",
+                ),
+            ),
+        ],
+    )
+
+    response = await handlers.query(request)
+
+    assert response.model_dump() == {
+        "records": [
+            {
+                "content": "Higher score",
+                "title": "Doc A",
+                "score": 0.9,
+                "metadata": {"doc_ref": "11"},
+                "knowledge_base_id": 2,
+                "document_id": 11,
+                "index_family": "chunk_vector",
+            }
+        ],
+        "total": 1,
+        "total_estimated_tokens": 0,
+    }
+    assert query_executor_cls.call_count == 2
+    first_executor.execute.assert_awaited_once_with(
+        knowledge_id="1",
+        query="release checklist",
+        retrieval_config=request.knowledge_base_configs[0].retrieval_config,
+        metadata_condition={
+            "operator": "and",
+            "conditions": [
+                {
+                    "operator": "and",
+                    "conditions": [
+                        {"key": "doc_ref", "operator": "in", "value": ["11", "22"]}
+                    ],
+                },
+                {
+                    "operator": "or",
+                    "conditions": [
+                        {"key": "source", "operator": "==", "value": "kb"},
+                        {"key": "lang", "operator": "==", "value": "zh"},
+                    ],
+                },
+            ],
+        },
+        user_id=101,
+    )
+    second_executor.execute.assert_awaited_once_with(
+        knowledge_id="2",
+        query="release checklist",
+        retrieval_config=request.knowledge_base_configs[1].retrieval_config,
+        metadata_condition={
+            "operator": "and",
+            "conditions": [
+                {
+                    "operator": "and",
+                    "conditions": [
+                        {"key": "doc_ref", "operator": "in", "value": ["11", "22"]}
+                    ],
+                },
+                {
+                    "operator": "or",
+                    "conditions": [
+                        {"key": "source", "operator": "==", "value": "kb"},
+                        {"key": "lang", "operator": "==", "value": "zh"},
+                    ],
+                },
+            ],
+        },
+        user_id=202,
+    )
diff --git a/knowledge_runtime/uv.lock b/knowledge_runtime/uv.lock
new file mode 100644
index 000000000..c9d35369a
--- /dev/null
+++ b/knowledge_runtime/uv.lock
@@ -0,0 +1,4265 @@
+version = 1
+revision = 3
+requires-python = ">=3.10, <=3.13"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
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+
+[[package]]
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+    { name = "attrs" },
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+]
diff --git a/shared/models/__init__.py b/shared/models/__init__.py
index 709c71548..1aae81fe6 100644
--- a/shared/models/__init__.py
+++ b/shared/models/__init__.py
@@ -57,6 +57,7 @@
     RemoteQueryRequest,
     RemoteQueryResponse,
     RemoteRagError,
+    RemoteTestConnectionRequest,
     RuntimeEmbeddingModelConfig,
     RuntimeRetrievalConfig,
     RuntimeRetrieverConfig,
@@ -111,6 +112,7 @@
     "RemoteKnowledgeBaseQueryConfig",
     "RemoteIndexRequest",
     "RemoteDeleteDocumentIndexRequest",
+    "RemoteTestConnectionRequest",
     "RemoteQueryRequest",
     "RemoteQueryRecord",
     "RemoteQueryResponse",
diff --git a/shared/models/knowledge_runtime_protocol.py b/shared/models/knowledge_runtime_protocol.py
index 29be8a5ab..4b2715ea2 100644
--- a/shared/models/knowledge_runtime_protocol.py
+++ b/shared/models/knowledge_runtime_protocol.py
@@ -129,6 +129,13 @@ class RemoteDeleteDocumentIndexRequest(KnowledgeRuntimeProtocolModel):
     extensions: dict[str, Any] | None = None
 
 
+class RemoteTestConnectionRequest(KnowledgeRuntimeProtocolModel):
+    """Test-connection request sent from Backend to knowledge_runtime."""
+
+    retriever_config: RuntimeRetrieverConfig
+    extensions: dict[str, Any] | None = None
+
+
 class RemoteQueryRequest(KnowledgeRuntimeProtocolModel):
     """Query request sent from Backend to knowledge_runtime."""
 
@@ -136,6 +143,7 @@ class RemoteQueryRequest(KnowledgeRuntimeProtocolModel):
     query: str
     max_results: int = 5
     document_ids: list[int] | None = None
+    metadata_condition: dict[str, Any] | None = None
     user_name: str | None = None
     knowledge_base_configs: list[RemoteKnowledgeBaseQueryConfig] = Field(
         default_factory=list
diff --git a/shared/tests/test_knowledge_runtime_protocol.py b/shared/tests/test_knowledge_runtime_protocol.py
index bd2e48743..82b8637d0 100644
--- a/shared/tests/test_knowledge_runtime_protocol.py
+++ b/shared/tests/test_knowledge_runtime_protocol.py
@@ -30,6 +30,7 @@ def test_shared_models_exports_knowledge_runtime_protocol_types() -> None:
         "RemoteQueryRequest",
         "RemoteQueryRecord",
         "RemoteQueryResponse",
+        "RemoteTestConnectionRequest",
     ]
 
     for name in exported_names:
@@ -129,6 +130,13 @@ def test_remote_query_request_accepts_explicit_execution_configs() -> None:
             "knowledge_base_ids": [1001],
             "query": "release checklist",
             "max_results": 6,
+            "metadata_condition": {
+                "operator": "or",
+                "conditions": [
+                    {"key": "source", "operator": "==", "value": "kb"},
+                    {"key": "lang", "operator": "==", "value": "zh"},
+                ],
+            },
             "knowledge_base_configs": [
                 {
                     "knowledge_base_id": 1001,
@@ -169,6 +177,13 @@ def test_remote_query_request_accepts_explicit_execution_configs() -> None:
         request.knowledge_base_configs[0].retriever_config.storage_config["type"]
         == "qdrant"
     )
+    assert request.metadata_condition == {
+        "operator": "or",
+        "conditions": [
+            {"key": "source", "operator": "==", "value": "kb"},
+            {"key": "lang", "operator": "==", "value": "zh"},
+        ],
+    }
     assert request.knowledge_base_configs[0].retrieval_config.top_k == 8
     assert request.enabled_index_families == [
         "chunk_vector",
@@ -199,3 +214,22 @@ def test_remote_delete_request_requires_resolved_retriever_config() -> None:
 
     assert request.retriever_config.name == "retriever-a"
     assert request.retriever_config.storage_config["type"] == "elasticsearch"
+
+
+def test_remote_test_connection_request_requires_retriever_config() -> None:
+    remote_test_connection_request = _require_model("RemoteTestConnectionRequest")
+
+    request = remote_test_connection_request.model_validate(
+        {
+            "retriever_config": {
+                "name": "retriever-a",
+                "namespace": "default",
+                "storage_config": {
+                    "type": "qdrant",
+                    "url": "http://qdrant:6333",
+                },
+            }
+        }
+    )
+
+    assert request.retriever_config.storage_config["type"] == "qdrant"

From d4b97eccd39716501e820ce187408c333d62a36b Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Mon, 6 Apr 2026 10:23:29 +0800
Subject: [PATCH 6/9] test(knowledge): move engine storage tests out of backend

---
 .../tests/services/rag/storage/__init__.py    |  7 ----
 .../storage/test_elasticsearch_backend.py     |  6 +--
 .../tests}/storage/test_milvus_backend.py     | 39 ++++---------------
 3 files changed, 10 insertions(+), 42 deletions(-)
 delete mode 100644 backend/tests/services/rag/storage/__init__.py
 rename {backend/tests/services/rag => knowledge_engine/tests}/storage/test_elasticsearch_backend.py (93%)
 rename {backend/tests/services/rag => knowledge_engine/tests}/storage/test_milvus_backend.py (93%)

diff --git a/backend/tests/services/rag/storage/__init__.py b/backend/tests/services/rag/storage/__init__.py
deleted file mode 100644
index a41f569e4..000000000
--- a/backend/tests/services/rag/storage/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Tests for RAG storage backends.
-"""
diff --git a/backend/tests/services/rag/storage/test_elasticsearch_backend.py b/knowledge_engine/tests/storage/test_elasticsearch_backend.py
similarity index 93%
rename from backend/tests/services/rag/storage/test_elasticsearch_backend.py
rename to knowledge_engine/tests/storage/test_elasticsearch_backend.py
index 4832967c8..5c38b23ba 100644
--- a/backend/tests/services/rag/storage/test_elasticsearch_backend.py
+++ b/knowledge_engine/tests/storage/test_elasticsearch_backend.py
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
 #
 # SPDX-License-Identifier: Apache-2.0
 
@@ -13,7 +13,7 @@ class TestGetAllChunks:
     @patch("knowledge_engine.storage.elasticsearch_backend.Elasticsearch")
     def test_get_all_chunks_returns_parsed_chunks(self, mock_client_class):
         """Should parse hits into normalized chunk payloads."""
-        from app.services.rag.storage.elasticsearch_backend import ElasticsearchBackend
+        from knowledge_engine.storage.elasticsearch_backend import ElasticsearchBackend
 
         mock_client = MagicMock()
         mock_client_class.return_value = mock_client
@@ -52,7 +52,7 @@ def test_get_all_chunks_returns_parsed_chunks(self, mock_client_class):
     @patch("knowledge_engine.storage.elasticsearch_backend.Elasticsearch")
     def test_get_all_chunks_returns_empty_when_no_hits(self, mock_client_class):
         """Should return an empty list when the knowledge_id term query has no hits."""
-        from app.services.rag.storage.elasticsearch_backend import ElasticsearchBackend
+        from knowledge_engine.storage.elasticsearch_backend import ElasticsearchBackend
 
         mock_client = MagicMock()
         mock_client_class.return_value = mock_client
diff --git a/backend/tests/services/rag/storage/test_milvus_backend.py b/knowledge_engine/tests/storage/test_milvus_backend.py
similarity index 93%
rename from backend/tests/services/rag/storage/test_milvus_backend.py
rename to knowledge_engine/tests/storage/test_milvus_backend.py
index d7b8ee43a..0464b67d7 100644
--- a/backend/tests/services/rag/storage/test_milvus_backend.py
+++ b/knowledge_engine/tests/storage/test_milvus_backend.py
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
 #
 # SPDX-License-Identifier: Apache-2.0
 
@@ -10,8 +10,8 @@
 
 import pytest
 
-from app.services.rag.storage.chunk_metadata import ChunkMetadata
-from app.services.rag.storage.milvus_backend import MilvusBackend
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+from knowledge_engine.storage.milvus_backend import MilvusBackend
 
 
 class TestMilvusBackendInit:
@@ -165,7 +165,6 @@ class TestRetrieve:
     @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_retrieve_vector_mode(self, mock_milvus_vs):
         """Test retrieval in vector mode."""
-        # Setup mock
         mock_store = MagicMock()
         mock_milvus_vs.return_value = mock_store
 
@@ -236,7 +235,6 @@ def test_retrieve_keyword_mode(self, mock_milvus_vs):
         )
 
         assert "records" in result
-        # Keyword mode should not call get_query_embedding
         mock_embed_model.get_query_embedding.assert_not_called()
 
     @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
@@ -274,7 +272,6 @@ def test_retrieve_hybrid_mode(self, mock_milvus_vs):
         )
 
         assert "records" in result
-        # Hybrid mode should call get_query_embedding
         mock_embed_model.get_query_embedding.assert_called_once()
 
     def test_retrieve_invalid_mode(self):
@@ -334,7 +331,6 @@ def test_retrieve_score_threshold_filtering(self, mock_milvus_vs):
             },
         )
 
-        # Only high score result should be returned
         assert len(result["records"]) == 1
         assert result["records"][0]["content"] == "high score"
 
@@ -381,7 +377,7 @@ def test_get_document(self, mock_milvus_vs):
         mock_node2 = MagicMock()
         mock_node2.text = "chunk 2"
         mock_node2.metadata = {"source_file": "test.txt", "chunk_index": 1}
-        mock_store.get_nodes.return_value = [mock_node2, mock_node1]  # Out of order
+        mock_store.get_nodes.return_value = [mock_node2, mock_node1]
 
         config = {
             "url": "http://localhost:19530/default",
@@ -395,7 +391,6 @@ def test_get_document(self, mock_milvus_vs):
         assert result["knowledge_id"] == "kb_1"
         assert result["source_file"] == "test.txt"
         assert result["chunk_count"] == 2
-        # Verify chunks are sorted by chunk_index
         assert result["chunks"][0]["chunk_index"] == 0
         assert result["chunks"][1]["chunk_index"] == 1
 
@@ -426,7 +421,6 @@ def test_list_documents(self, mock_client_class):
         mock_client_class.return_value = mock_client
         mock_client.list_collections.return_value = ["test_kb_kb_1"]
 
-        # Simulate 3 chunks from 2 documents
         mock_client.query.return_value = [
             {
                 "doc_ref": "doc_1",
@@ -457,12 +451,10 @@ def test_list_documents(self, mock_client_class):
         assert len(result["documents"]) == 2
         assert result["page"] == 1
         assert result["page_size"] == 10
-        # doc_2 should be first (newer created_at)
         assert result["documents"][0]["doc_ref"] == "doc_2"
         assert result["documents"][0]["chunk_count"] == 1
         assert result["documents"][1]["doc_ref"] == "doc_1"
         assert result["documents"][1]["chunk_count"] == 2
-        # Verify client.close() is called to prevent resource leaks
         mock_client.close.assert_called_once()
 
     @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
@@ -470,7 +462,7 @@ def test_list_documents_empty_collection(self, mock_client_class):
         """Test listing documents when collection doesn't exist."""
         mock_client = MagicMock()
         mock_client_class.return_value = mock_client
-        mock_client.list_collections.return_value = []  # Collection doesn't exist
+        mock_client.list_collections.return_value = []
 
         config = {
             "url": "http://localhost:19530/default",
@@ -482,7 +474,6 @@ def test_list_documents_empty_collection(self, mock_client_class):
 
         assert result["total"] == 0
         assert result["documents"] == []
-        # Verify client.close() is called to prevent resource leaks
         mock_client.close.assert_called_once()
 
 
@@ -503,7 +494,6 @@ def test_connection_success(self, mock_client_class):
         backend = MilvusBackend(config)
 
         assert backend.test_connection() is True
-        # Verify client.close() is called to prevent resource leaks
         mock_client.close.assert_called_once()
 
     @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
@@ -520,7 +510,6 @@ def test_connection_failure(self, mock_client_class):
         backend = MilvusBackend(config)
 
         assert backend.test_connection() is False
-        # Verify client.close() is called even on failure to prevent resource leaks
         mock_client.close.assert_called_once()
 
 
@@ -558,10 +547,8 @@ def test_get_all_chunks(self, mock_client_class):
         result = backend.get_all_chunks(knowledge_id="kb_1", max_chunks=100)
 
         assert len(result) == 2
-        # Verify sorted by doc_ref and chunk_index
         assert result[0]["chunk_id"] == 0
         assert result[1]["chunk_id"] == 1
-        # Verify client.close() is called to prevent resource leaks
         mock_client.close.assert_called_once()
 
     @patch("knowledge_engine.storage.milvus_backend.MilvusClient")
@@ -580,7 +567,6 @@ def test_get_all_chunks_collection_not_exists(self, mock_client_class):
         result = backend.get_all_chunks(knowledge_id="kb_1", max_chunks=100)
 
         assert result == []
-        # Verify client.close() is called to prevent resource leaks
         mock_client.close.assert_called_once()
 
 
@@ -596,20 +582,13 @@ class TestIndexWithMetadata:
     @patch("knowledge_engine.storage.milvus_backend.StorageContext")
     @patch("knowledge_engine.storage.milvus_backend.LazyAsyncMilvusVectorStore")
     def test_index_with_metadata(self, mock_milvus_vs, mock_storage_ctx, mock_vs_index):
-        """Test indexing nodes with metadata.
-
-        Verifies that:
-        1. The correct index name is generated from ChunkMetadata
-        2. VectorStoreIndex is called with the correct parameters
-        3. The result contains expected fields
-        """
+        """Test indexing nodes with metadata."""
         mock_store = MagicMock()
         mock_milvus_vs.return_value = mock_store
 
         mock_ctx = MagicMock()
         mock_storage_ctx.from_defaults.return_value = mock_ctx
 
-        # Create mock nodes with metadata already applied (simulating indexer layer)
         mock_node1 = MagicMock()
         mock_node1.metadata = {
             "knowledge_id": "kb_1",
@@ -648,15 +627,13 @@ def test_index_with_metadata(self, mock_milvus_vs, mock_storage_ctx, mock_vs_ind
             embed_model=mock_embed_model,
         )
 
-        # Verify result structure
         assert result["indexed_count"] == 2
         assert result["status"] == "success"
         assert result["index_name"] == "test_kb_kb_1"
 
-        # Verify VectorStoreIndex was called with correct parameters
         mock_vs_index.assert_called_once()
         call_args = mock_vs_index.call_args
-        assert call_args[0][0] == [mock_node1, mock_node2]  # nodes
+        assert call_args[0][0] == [mock_node1, mock_node2]
         assert call_args[1]["storage_context"] == mock_ctx
         assert call_args[1]["embed_model"] == mock_embed_model
         assert call_args[1]["show_progress"] is True
@@ -710,8 +687,6 @@ def test_rolling_index_name(self):
         }
         backend = MilvusBackend(config)
 
-        # kb_id 1-100 should go to bucket 0
         assert backend.get_index_name("1") == "milvus_collection_0"
         assert backend.get_index_name("100") == "milvus_collection_0"
-        # kb_id 101-200 should go to bucket 100
         assert backend.get_index_name("101") == "milvus_collection_100"

From 4b1dcc637943f13e70c600c381f81d0f145f370a Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Mon, 6 Apr 2026 10:36:52 +0800
Subject: [PATCH 7/9] docs(knowledge): converge runtime split documentation

---
 backend/app/services/rag/README.md            | 542 +++---------------
 .../retrieval-and-direct-injection.md         |  45 +-
 .../2026-03-24-rag-service-split-plan.md      |  80 +--
 ...-service-extraction-implementation-plan.md |  30 +-
 ...026-03-31-rag-modular-data-plane-design.md |  30 +-
 ...026-04-03-rag-service-extraction-design.md |  21 +-
 .../retrieval-and-direct-injection.md         |  45 +-
 7 files changed, 240 insertions(+), 553 deletions(-)

diff --git a/backend/app/services/rag/README.md b/backend/app/services/rag/README.md
index 3c4af4fbe..3c79c7e61 100644
--- a/backend/app/services/rag/README.md
+++ b/backend/app/services/rag/README.md
@@ -1,502 +1,134 @@
-# RAG Retriever Architecture Design
+# RAG Runtime Architecture
 
 ## Overview
 
-This document describes the architecture design of the Retriever abstraction for the RAG (Retrieval-Augmented Generation) system. The Retriever is implemented as an independent CRD (Custom Resource Definition) Kind, parallel to Model and Shell, providing flexible storage backend configuration capabilities.
+This directory now holds the Backend-side runtime boundary for knowledge retrieval and indexing. It is no longer the home of the full execution implementation.
 
-## Design Goals
+Current architecture:
 
-1. **Storage Backend Abstraction**: Support multiple vector databases (Elasticsearch, Qdrant, etc.)
-2. **Flexible Index Strategies**: Support fixed index, rolling index, and per-knowledge-base index
-3. **Multiple Retrieval Methods**: Support vector search, keyword search, and hybrid search
-4. **Dify-style API**: Reference Dify External Knowledge API design for standardized interface
-5. **Metadata Filtering**: Support advanced filtering based on metadata conditions
+- Backend remains the only control plane.
+- `shared` holds transport-only protocol models for Backend <-> `knowledge_runtime`.
+- `knowledge_engine` holds the backend-agnostic execution kernel.
+- `knowledge_runtime` is a thin remote adapter over `knowledge_engine`.
+- `backend/app/services/rag/` owns runtime specs, gateway routing, and Backend-facing adapters.
 
-## Modular Data Plane Foundation
+## Responsibility Split
 
-The current Backend-side RAG foundation is now split into control-plane contracts and local data-plane execution seams:
+### Backend control plane
 
-- `runtime_specs.py`: stable `IndexRuntimeSpec` and `QueryRuntimeSpec` contracts used below control-plane orchestration.
-- `runtime_resolver.py`: converts Backend KB / retriever / embedding / owner context into runtime specs.
-- `gateway.py`: `RagGateway` protocol for control-plane-to-data-plane dispatch.
-- `local_gateway.py`: `LocalRagGateway` implementation that delegates to `local_data_plane`.
-- `local_data_plane/`: local execution adapters for retrieval and indexing while Backend remains the only control plane.
+Backend continues to own:
 
-The boundary is intentional:
+- permissions and multi-tenant namespace rules
+- `KnowledgeBase` / `KnowledgeDocument` metadata
+- retriever / embedding / runtime config resolution
+- task orchestration, retries, and state write-back
+- `direct injection` route decisions
+- `restricted mediation`
+- public and internal API surfaces
 
-- Backend keeps persistence metadata, task state, permission checks, and CRD resolution.
-- Runtime specs stay free of ORM objects, DB sessions, and task-only fields such as `user_subtask_id`.
-- Control-plane persistence, document-read tracking, and restricted mediation now live under `backend/app/services/knowledge/`.
+Control-plane logic lives primarily under `backend/app/services/knowledge/`.
 
-## Current Boundary Status
+### Backend runtime boundary
 
-### Phase 2.5 Status
+`backend/app/services/rag/` owns the seam between control plane and execution:
 
-Phase 2.5 is complete for the current local module split:
+- `runtime_specs.py`: normalized runtime contracts
+- `runtime_resolver.py`: resolves CRD + KB metadata into runtime specs
+- `gateway.py`: common gateway protocol
+- `local_gateway.py`: local execution path
+- `remote_gateway.py`: remote execution path through `knowledge_runtime`
+- `gateway_factory.py`: chooses local / remote by `RAG_RUNTIME_MODE`
 
-- `services/rag/` is the execution-side module boundary only.
-- Backend control-plane orchestration owns `SubtaskContext` persistence, `kb_head` usage recording, and restricted safe-summary mediation.
-- `/api/internal/rag/retrieve` is the unified internal retrieval surface for both normal and restricted flows.
-- `/api/internal/rag/all-chunks` remains only as a legacy internal endpoint for transitional compatibility.
+### Execution kernel
 
-### Roadmap Status
+`knowledge_engine` owns backend-agnostic execution details:
 
-At this release node, the first four roadmap steps are complete:
+- document parsing and splitting
+- embedding model construction
+- storage backend creation and operations
+- index / query / delete execution
+- retrieval filter helpers
 
-- runtime contract extraction
-- modular local data-plane foundation
-- Phase 2.5 control/data boundary cleanup
-- Backend-owned restricted mediation
+Several modules under `backend/app/services/rag/embedding/`, `storage/`, and `retrieval/` now exist mainly as compatibility import surfaces over `knowledge_engine`.
 
-### Deferred Follow-up Work
+### Remote runtime service
 
-Follow-up work is intentionally not implemented in this foundation layer:
+`knowledge_runtime` is intentionally narrow:
 
-- `summary_vector_index`
-- `tableRAG`
-- remote `rag_service` extraction
-
-## Architecture Components
-
-### 1. Retriever CRD Schema
-
-**Location**: `backend/app/schemas/kind.py`
-
-```python
-class Retriever(BaseModel):
-    """Retriever CRD"""
-    apiVersion: str = "agent.wecode.io/v1"
-    kind: str = "Retriever"
-    metadata: ObjectMeta
-    spec: RetrieverSpec
-```
-
-**Core Configuration**:
-
-- **IndexStrategy**: Index naming strategy
-  - `mode`: 'fixed' | 'rolling' | 'per_dataset'
-  - `fixedName`: Fixed index name (for fixed mode)
-  - `rollingStep`: Rolling step size (for rolling mode)
-  - `prefix`: Index prefix (for per_dataset mode)
-
-- **StorageConfig**: Storage backend configuration
-  - `type`: 'elasticsearch' | 'qdrant'
-  - `url`: Connection URL
-  - `username/password`: Authentication credentials
-  - `apiKey`: API key (optional)
-  - `indexStrategy`: Index strategy configuration
-  - `ext`: Extended provider-specific configuration
-
-- **RetrievalMethods**: Retrieval method configuration
-  - `vector`: Vector search (default weight 0.7)
-  - `keyword`: Keyword search (default weight 0.3)
-  - `hybrid`: Hybrid search
-
-### 2. Storage Backend Abstraction Layer
-
-**Location**: `backend/app/services/rag/storage/`
-
-#### Base Class (base.py)
-
-```python
-class BaseStorageBackend(ABC):
-    @abstractmethod
-    def get_index_name(self, knowledge_id: str, **kwargs) -> str:
-        """Get index/collection name"""
-        
-    @abstractmethod
-    def index(self, nodes: List[BaseNode], index_name: str, embed_model) -> Dict:
-        """Index documents"""
-        
-    @abstractmethod
-    def retrieve(
-        self,
-        knowledge_id: str,
-        query: str,
-        embed_model,
-        retrieval_setting: Dict[str, Any],
-        metadata_condition: Optional[Dict[str, Any]] = None,
-        **kwargs
-    ) -> Dict:
-        """Retrieve documents (Dify-style API)"""
-```
-
-#### Elasticsearch Implementation (elasticsearch_backend.py)
-
-- Supports three index strategies
-- Implements vector and hybrid search
-- Supports metadata filtering (to be enhanced)
-- Provides document management (CRUD)
-
-#### Qdrant Implementation (qdrant_backend.py)
-
-- Placeholder implementation, to be completed
-
-#### Factory Function (factory.py)
-
-```python
-def create_storage_backend(config: Dict) -> BaseStorageBackend:
-    """Create storage backend instance based on configuration"""
-```
-
-### 3. Index Strategy Details
-
-#### Fixed Mode
-- All knowledge bases share a single index
-- Use case: Small-scale deployment with limited data
-- Configuration example:
-```json
-{
-  "mode": "fixed",
-  "fixedName": "rag_documents"
-}
-```
-
-#### Rolling Mode
-- Shard based on knowledge_id hash
-- Use case: Medium-scale deployment requiring load balancing
-- Configuration example:
-```json
-{
-  "mode": "rolling",
-  "rollingStep": 5000
-}
-```
-
-#### Per Dataset Mode
-- Each knowledge base has its own index
-- Use case: Large-scale deployment requiring isolation
-- Configuration example:
-```json
-{
-  "mode": "per_dataset",
-  "prefix": "rag"
-}
-```
-Generated index name: `rag_kb_{knowledge_id}`
-
-### 4. Dify-style API Design
-
-#### Retrieval Endpoint
-
-**Request Parameters**:
-```json
-{
-  "knowledge_id": "AAA-BBB-CCC",
-  "query": "What is Dify?",
-  "retrieval_setting": {
-    "top_k": 5,
-    "score_threshold": 0.5,
-    "retrieval_mode": "hybrid",
-    "vector_weight": 0.7,
-    "keyword_weight": 0.3
-  },
-  "metadata_condition": {
-    "operator": "and",
-    "conditions": [
-      {"key": "category", "operator": "eq", "value": "tech"},
-      {"key": "year", "operator": "gte", "value": 2020}
-    ]
-  }
-}
-```
-
-**Response Format**:
-```json
-{
-  "records": [
-    {
-      "doc_ref": "doc_123",
-      "chunk_index": 0,
-      "source_file": "example.md",
-      "content": "...",
-      "score": 0.85,
-      "metadata": {...}
-    }
-  ],
-  "query": "What is Dify?",
-  "knowledge_id": "AAA-BBB-CCC",
-  "total": 5,
-  "retrieval_mode": "hybrid"
-}
-```
+- validates internal auth
+- fetches document content through `content_ref`
+- translates transport requests into `knowledge_engine` inputs
+- returns protocol responses
 
-### 5. Metadata Filtering (To Be Implemented)
+It does not read Backend DB or own control-plane policy.
 
-Supported operators:
-- `eq`: Equal
-- `ne`: Not equal
-- `in`: In array
-- `nin`: Not in array
-- `gt`: Greater than
-- `gte`: Greater than or equal
-- `lt`: Less than
-- `lte`: Less than or equal
+## Current Request Flow
 
-Logical operators:
-- `and`: Logical AND
-- `or`: Logical OR
-
-## Data Flow
-
-### Indexing Flow
+### Retrieval
 
-```
-Document Upload
-    ↓
-Document Service
-    ↓
-Retriever CRD (loaded from kinds table)
-    ↓
-Storage Backend Factory
-    ↓
-Elasticsearch/Qdrant Backend
-    ↓
-Index Strategy → Index Name
-    ↓
-Vector Store → Index Nodes
+```text
+chat_shell / internal callers
+  -> /api/internal/rag/retrieve
+  -> RagRuntimeResolver
+  -> Backend route decision
+     -> direct_injection stays in Backend
+     -> rag_retrieval goes through RagGateway
+  -> LocalRagGateway or RemoteRagGateway
+  -> knowledge_engine (directly or via knowledge_runtime)
 ```
 
-### Retrieval Flow
+Notes:
 
-```
-Retrieval Request
-    ↓
-Retrieval Service
-    ↓
-Retriever CRD (loaded from kinds table)
-    ↓
-Storage Backend Factory
-    ↓
-Elasticsearch/Qdrant Backend
-    ↓
-Index Strategy → Index Name
-    ↓
-Metadata Filters → Build Query
-    ↓
-Execute Search (Vector/Hybrid)
-    ↓
-Filter by Score Threshold
-    ↓
-Return Results
-```
+- `/api/internal/rag/retrieve` is the primary internal retrieval surface.
+- `/api/internal/rag/all-chunks` remains a legacy internal endpoint only.
+- restricted flows are mediated in Backend after raw retrieval returns.
 
-## Database Design
-
-Retriever is stored as a Kind in the `kinds` table:
-
-```sql
--- kinds table structure (existing)
-CREATE TABLE kinds (
-    id INT PRIMARY KEY AUTO_INCREMENT,
-    user_id INT NOT NULL,
-    kind VARCHAR(50) NOT NULL,  -- 'Retriever'
-    name VARCHAR(100) NOT NULL,
-    namespace VARCHAR(100) NOT NULL DEFAULT 'default',
-    json JSON NOT NULL,  -- Retriever CRD JSON
-    is_active BOOLEAN DEFAULT TRUE,
-    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
-    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
-);
-```
+### Index / Delete / Connection Test
 
-**Retriever JSON Example**:
-```json
-{
-  "apiVersion": "agent.wecode.io/v1",
-  "kind": "Retriever",
-  "metadata": {
-    "name": "my-es-retriever",
-    "namespace": "default",
-    "displayName": "My ES Retriever"
-  },
-  "spec": {
-    "storageConfig": {
-      "type": "elasticsearch",
-      "url": "http://localhost:9200",
-      "username": "elastic",
-      "password": "changeme",
-      "indexStrategy": {
-        "mode": "per_dataset",
-        "prefix": "rag"
-      }
-    },
-    "retrievalMethods": {
-      "vector": {"enabled": true, "defaultWeight": 0.7},
-      "keyword": {"enabled": true, "defaultWeight": 0.3},
-      "hybrid": {"enabled": true}
-    },
-    "description": "Elasticsearch retriever for production"
-  }
-}
+```text
+Backend task or API
+  -> RagRuntimeResolver
+  -> RagGateway
+  -> LocalRagGateway or RemoteRagGateway
+  -> knowledge_engine (directly or via knowledge_runtime)
 ```
 
-## API Endpoints Design (To Be Implemented)
+`/api/retrievers/test-connection` also routes through this gateway boundary.
 
-### Retriever Management
+## Runtime Modes
 
-```
-GET    /api/v1/namespaces/{namespace}/retrievers       # List
-POST   /api/v1/namespaces/{namespace}/retrievers       # Create
-GET    /api/v1/namespaces/{namespace}/retrievers/{name} # Get
-PUT    /api/v1/namespaces/{namespace}/retrievers/{name} # Update
-DELETE /api/v1/namespaces/{namespace}/retrievers/{name} # Delete
-POST   /api/v1/namespaces/{namespace}/retrievers/{name}/test # Test connection
-```
-
-### RAG Operations (Updated)
+`RAG_RUNTIME_MODE` controls whether each operation executes locally or remotely.
 
-```
-POST /api/rag/retrieve
-{
-  "knowledge_id": "kb_123",
-  "retriever_ref": {
-    "name": "my-es-retriever",
-    "namespace": "default"
-  },
-  "query": "...",
-  "retrieval_setting": {...},
-  "metadata_condition": {...}
-}
-```
+Supported shapes:
 
-## Migration Plan
-
-### Phase 1: Core Architecture (Completed)
-- [x] Retriever CRD schema definition
-- [x] Storage backend abstract base class
-- [x] Elasticsearch backend implementation
-- [x] Qdrant backend placeholder
-- [x] Factory function
-
-### Phase 2: Service Layer Integration (Pending)
-- [ ] Retriever service adapter (reference public_model.py)
-- [ ] Update DocumentService to use Retriever
-- [ ] Update RetrievalService to use Retriever
-- [ ] Retriever API endpoints
-
-### Phase 3: Data Migration (Pending)
-- [ ] Create default Retriever (based on existing RAGSettings)
-- [ ] Migrate existing RAG data to new architecture
-- [ ] Deprecate RAGSettings global configuration
-
-### Phase 4: Advanced Features (Pending)
-- [ ] Complete metadata filtering parser
-- [ ] Full Qdrant backend implementation
-- [ ] Retrieval performance optimization
-- [ ] Monitoring and logging
-
-## Usage Examples
-
-### Create a Retriever
-
-```python
-from app.schemas.kind import Retriever, RetrieverSpec, StorageConfig, IndexStrategy
-
-retriever = Retriever(
-    metadata=ObjectMeta(
-        name="my-retriever",
-        namespace="default",
-        displayName="My Retriever"
-    ),
-    spec=RetrieverSpec(
-        storageConfig=StorageConfig(
-            type="elasticsearch",
-            url="http://localhost:9200",
-            indexStrategy=IndexStrategy(
-                mode="per_dataset",
-                prefix="rag"
-            )
-        ),
-        description="Production retriever"
-    )
-)
-```
+- `"local"`
+- `"remote"`
+- per-operation map such as `{"default": "local", "query": "remote"}`
 
-### Use Retriever for Retrieval
+The current rollout target keeps local mode as the default while remote parity continues to harden.
 
-```python
-from app.services.rag.storage import create_storage_backend
+## Content Transport
 
-# Load Retriever CRD from kinds table
-retriever_crd = load_retriever("my-retriever", "default")
+Remote indexing uses `content_ref`, not raw file bytes push.
 
-# Create storage backend
-backend = create_storage_backend(retriever_crd.spec.storageConfig.dict())
+Current supported content references:
 
-# Execute retrieval
-results = backend.retrieve(
-    knowledge_id="kb_123",
-    query="What is RAG?",
-    embed_model=embed_model,
-    retrieval_setting={
-        "top_k": 5,
-        "score_threshold": 0.7,
-        "retrieval_mode": "hybrid"
-    }
-)
-```
+- Backend attachment streaming
+- presigned URL
 
-## Extensibility
+This keeps Backend as the control plane without forcing `knowledge_runtime` to understand attachment storage internals.
 
-### Adding a New Storage Backend
+## Deferred Work
 
-1. Inherit from `BaseStorageBackend`
-2. Implement all abstract methods
-3. Register in `factory.py`
-4. Update documentation
+The following areas are intentionally not part of this boundary yet:
 
-### Adding a New Index Strategy
-
-1. Add new mode to `IndexStrategy` schema
-2. Implement in `get_index_name` for each backend
-3. Update documentation
-
-### Adding a New Retrieval Method
-
-1. Define in `RetrievalMethod`
-2. Implement in storage backends
-3. Update API documentation
-
-## Important Notes
-
-1. **Backward Compatibility**: Existing RAG APIs must remain compatible during gradual migration
-2. **Performance Considerations**: Index strategy choice affects query performance
-3. **Security**: Sensitive information (password, API keys) must be encrypted in storage
-4. **Monitoring**: Need to add retrieval performance and error monitoring
-5. **Testing**: Each storage backend requires comprehensive unit and integration tests
-
-## References
-
-- [Dify External Knowledge API](https://docs.dify.ai/guides/knowledge-base/external-knowledge-api-documentation)
-- [LlamaIndex Vector Stores](https://docs.llamaindex.ai/en/stable/module_guides/storing/vector_stores/)
-- [Elasticsearch Vector Search](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html)
-- [Qdrant Documentation](https://qdrant.tech/documentation/)
-
-## File Structure
+- `summary_vector_index`
+- `tableRAG`
+- MCP `search`
+- full remote-primary rollout
 
-```
-backend/
-├── app/
-│   ├── schemas/
-│   │   └── kind.py                    # Retriever CRD schema
-│   └── services/
-│       └── rag/
-│           └── storage/
-│               ├── __init__.py        # Package exports
-│               ├── base.py            # Abstract base class
-│               ├── factory.py         # Backend factory
-│               ├── elasticsearch_backend.py  # ES implementation
-│               └── qdrant_backend.py  # Qdrant implementation
-└── docs/
-    └── RAG_RETRIEVER_ARCHITECTURE.md  # This document
-```
+## Practical Rule
 
-## Next Steps
+If a change needs DB lookups, permissions, KB metadata, or chat-specific policy, it belongs in Backend control-plane code.
 
-1. **Create Retriever Service Adapter**: Similar to `public_model.py`, create CRUD operations for Retriever Kind
-2. **Update RAG Services**: Refactor `DocumentService` and `RetrievalService` to use Retriever
-3. **Implement API Endpoints**: Create RESTful endpoints for Retriever management
-4. **Add Tests**: Write unit tests for storage backends and integration tests for the full flow
-5. **Create Migration Script**: Script to create default Retriever from existing RAGSettings
-6. **Update Frontend**: Add UI for Retriever management in settings page
+If a change needs parsing, embedding, vector storage, or pure retrieval execution, it belongs in `knowledge_engine`.
diff --git a/docs/en/user-guide/knowledge/retrieval-and-direct-injection.md b/docs/en/user-guide/knowledge/retrieval-and-direct-injection.md
index 69f45ab75..036cfd0f7 100644
--- a/docs/en/user-guide/knowledge/retrieval-and-direct-injection.md
+++ b/docs/en/user-guide/knowledge/retrieval-and-direct-injection.md
@@ -4,13 +4,13 @@ sidebar_position: 11
 
 # Retrieval And Direct Injection
 
-Knowledge Base conversations do not rely on a single access pattern. In addition to standard RAG retrieval, Wegent also keeps a "small KB full direct injection" path that can load all chunks from a knowledge base and pass them to the model when needed.
+Knowledge Base conversations do not rely on a single access pattern. In addition to standard RAG retrieval, Wegent also keeps a "small KB full direct injection" path that can pass as much KB context as possible to the model when needed.
 
 ---
 
 ## Why direct injection exists
 
-The main reason `all-chunks` was introduced is that vector retrieval quality was not stable enough in some scenarios.
+The main reason direct injection was introduced is that vector retrieval quality was not stable enough in some scenarios.
 
 For smaller knowledge bases with limited total content, but where the question depends on broader context, pure vector retrieval can fail in a few common ways:
 
@@ -24,19 +24,20 @@ In those cases, loading all chunks and injecting them into the model is often mo
 
 ## The two paths
 
-| Path | Main API | Best for | Characteristics |
-|------|----------|----------|-----------------|
+| Path | Main entry | Best for | Characteristics |
+|------|------------|----------|-----------------|
 | **Retrieval mode** | `/api/internal/rag/retrieve` | Medium to large KBs, targeted queries, normal RAG Q&A | Lower cost, more focused context |
-| **Direct injection mode** | `/api/internal/rag/all-chunks` | Small KBs, full-context reasoning, unstable vector recall | More complete recall, higher context cost |
+| **Direct injection mode** | `/api/internal/rag/retrieve` | Small KBs, full-context reasoning, unstable vector recall | More complete recall, higher context cost |
 
 In practice:
 
-- `retrieve` is the normal search path
-- `all-chunks` is a complementary path for small knowledge bases, not a replacement for retrievers
+- `/api/internal/rag/retrieve` is the unified entrypoint
+- Backend decides internally whether the request should stay in normal retrieval or switch to direct injection
+- the old `/api/internal/rag/all-chunks` endpoint remains only as a legacy internal compatibility surface
 
 ---
 
-## When all-chunks is useful
+## When direct injection is useful
 
 This path is typically useful when:
 
@@ -55,31 +56,31 @@ These prompts depend on global context more than a few top-k chunks.
 
 ## Permission model
 
-`all-chunks` should follow the same permission model as `/api/internal/rag/retrieve`.
+Direct injection should follow the same permission model as `/api/internal/rag/retrieve`.
 
 That means:
 
 - It is part of the **internal RAG** service-to-service API
 - Access control should be validated earlier, during task context and knowledge base selection
-- `all-chunks` should not introduce an extra user-level blocking rule that behaves differently from `retrieve`
+- direct injection should not introduce an extra user-level blocking rule that behaves differently from normal retrieval
 
 This keeps the chain consistent:
 
 - Both APIs serve the same knowledge access flow
-- If `retrieve` is allowed but `all-chunks` is blocked separately, behavior becomes inconsistent
+- If normal retrieval is allowed but direct injection is blocked separately, behavior becomes inconsistent
 - Small-KB direct injection should not be blocked by an endpoint-specific rule
 
 ---
 
 ## Safe Summaries For Restricted Analyst
 
-When the KB user permission is `Restricted Analyst`, the system no longer passes raw retrieved chunks directly to the final answering model. Instead, `knowledge_base_search` first produces a **safe summary** internally.
+When the KB user permission is `Restricted Analyst`, the system no longer passes raw retrieved chunks directly to the final answering model. Instead, Backend internal retrieval first produces a **safe summary**.
 
 The goals of this path are:
 
 - allow the model to use the KB for high-level analysis, diagnosis, risk judgment, and recommendations
 - avoid exposing original wording, exact definitions, KPI values, titles, filenames, or document structure
-- move the “can this be answered safely?” decision into the KB tool itself
+- move the “can this be answered safely?” decision into Backend-side KB orchestration
 
 In practice:
 
@@ -129,16 +130,16 @@ In these cases the system should either:
 
 ---
 
-## Impact On all-chunks
+## Impact On direct injection
 
-`all-chunks` still exists to support "small KB full direct injection", but in restricted mode its role changes:
+Direct injection still exists to support "small KB full direct injection", but in restricted mode its role changes:
 
 - it can still provide broader context when vector retrieval is unstable
 - the raw full-KB chunks are then converted into a safe summary before reaching the main model
 
 That means:
 
-- `all-chunks` still improves stability for small KBs
+- direct injection still improves stability for small KBs
 - but in restricted mode the final model sees a safe summary, not raw chunk content
 
 This preserves the recall benefit of full-context access while reducing disclosure risk.
@@ -149,16 +150,16 @@ This preserves the recall benefit of full-context access while reducing disclosu
 
 | Recommendation | Explanation |
 |----------------|-------------|
-| Keep retrievers configured | `all-chunks` is a supplement, not a substitute for normal indexing and retrieval |
+| Keep retrievers configured | Direct injection is a supplement, not a substitute for normal indexing and retrieval |
 | Keep direct injection for small KBs | It often improves stability when KB size is manageable |
 | Use retrieval for large KBs | Full injection becomes expensive in context and cost |
-| Use logs for debugging | If `all-chunks` returns empty, check indexing, document state, and backend logs |
+| Use logs for debugging | If direct injection returns empty, check indexing, document state, and backend logs |
 
 ---
 
-## Troubleshooting empty all-chunks results
+## Troubleshooting empty direct injection results
 
-If `all-chunks` returns no content, check:
+If direct injection returns no content, check:
 
 | Check | Description |
 |-------|-------------|
@@ -170,8 +171,8 @@ If `all-chunks` returns no content, check:
 
 If logs show:
 
-- the request reached `/api/internal/rag/all-chunks`
-- `get_all_chunks start` was logged
+- the request reached `/api/internal/rag/retrieve`
+- Backend resolved the route to `direct_injection`
 - but the final hit count is 0
 
 then the problem is usually in indexing or data layout, not request authentication.
diff --git a/docs/plans/2026-03-24-rag-service-split-plan.md b/docs/plans/2026-03-24-rag-service-split-plan.md
index 70fd9a846..1547ec57d 100644
--- a/docs/plans/2026-03-24-rag-service-split-plan.md
+++ b/docs/plans/2026-03-24-rag-service-split-plan.md
@@ -10,19 +10,26 @@ sidebar_position: 1
 
 本次方案聚焦“拆 RAG 数据面”，不拆整个知识库域。
 
-### 状态更新（2026-04-01）
+### 状态更新（2026-04-06）
 
 - 本文中的 `Phase 0` 已经作为历史阶段完成：`chat_shell` 不再负责 `/all-chunks` 与 `/retrieve` 的主路由决策，Backend internal RAG API 已接管该选择。
-- 当前一线落地工作不再是“先把路由下沉到 Backend”，而是“先稳定模块边界”，即 `RuntimeSpec + RagGateway + local data-plane modules`。
+- 当前已完成的核心收敛不是“先把路由下沉到 Backend”，而是“先稳定模块边界”，即 `RuntimeSpec + RagGateway + knowledge_engine`。
+- `knowledge_engine` 已经作为顶层执行库落地，承接解析、切分、embedding、storage backend、index / query / delete 等 backend-agnostic 执行逻辑。
+- `knowledge_runtime` 已经作为薄 remote adapter 落地，负责 `content_ref` 拉取、协议校验、鉴权和调用 `knowledge_engine`；默认 rollout 目标仍以 Backend local mode 为主。
+- Backend 继续保留唯一 control plane：权限、多租户、KB / Document 元数据、CRD 解析、任务编排、`direct injection` 路由决策、`restricted mediation`。
 - 当前生效的主设计文档以以下内容为准：
   - `docs/specs/knowledge/2026-03-31-rag-modular-data-plane-design.md`
+  - `docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md`
   - `backend/app/services/rag/README.md`
-- `2026-04-01` 的两份 implementation plan 已随代码落地完成，作为执行清单的价值已经结束，因此不再继续保留。
 
 ### 定位说明
 
 - 本文保留为“为什么最终没有立刻拆独立 `rag_service`”的历史背景
-- 当前如果要继续推进后续阶段，应优先更新 `2026-03-31` 的 spec，而不是回到本文继续追加任务清单
+- 当前如果要继续推进后续阶段，应优先更新 `2026-03-31` / `2026-04-03` 两份 spec，而不是回到本文继续追加任务清单
+- 文中后续出现的历史术语：
+  - `rag_service`：当前对应 `knowledge_runtime`
+  - `rag data plane module`：当前已经具体化为 `knowledge_engine`
+  - `/all-chunks`：当前仅保留为 legacy internal surface，主入口已经收敛到 `/api/internal/rag/retrieve`
 
 目标：
 - 将解析、切分、向量化、索引、检索等重依赖能力从 Backend 主进程中解耦。
@@ -32,24 +39,20 @@ sidebar_position: 1
 
 ## 现状
 
-以下“现状”描述包含 2026-03-24 制定方案时的历史上下文，其中部分检索路由职责已经在后续实现中下沉到 Backend。
+以下“现状”描述保留 2026-03-24 制定方案时的历史背景，但已按当前实现补充修正。
 
 当前实现具备以下特征：
 
-- Backend 已提供面向 `chat_shell` 的内部 RAG API，包括：
+- Backend 已提供面向 `chat_shell` 的统一 internal RAG 主入口：
   - `/api/internal/rag/retrieve`
   - `/api/internal/rag/kb-size`
-  - `/api/internal/rag/all-chunks`
   - `/api/internal/rag/list-docs`
   - `/api/internal/rag/read-doc`
-- `chat_shell` 当前会先获取 KB 信息，判断是否 direct injection，然后自行决定调用 `/all-chunks` 还是 `/retrieve`。
-- Backend 已将 RAG 视为重模块，通过 `STANDALONE_MODE` 条件导入部分 RAG 路由和存储能力。
-- 索引主流程目前仍深度耦合在 Backend 中：
-  - 从附件存储取二进制
-  - 用 `llama_index` 解析
-  - 调 embedding / vector store
-  - 回写 `KnowledgeDocument.is_active/status/chunks`
-  - 触发摘要任务
+  - `/api/internal/rag/all-chunks` 仅保留为 legacy internal endpoint
+- `chat_shell` 不再自行决定 `/all-chunks` 与 `/retrieve` 的主路由，只消费 Backend internal RAG 结果，并保留自身的 direct-injection / restricted 格式化处理。
+- Backend 已将 RAG 视为重模块，通过 `RAG_RUNTIME_MODE` 统一控制 local / remote 路由。
+- `knowledge_engine` 已承接底层索引与检索执行；Backend local data-plane 与 `knowledge_runtime` remote 都围绕该执行库收敛。
+- Backend 仍负责回写 `KnowledgeDocument.is_active/status/chunks`、摘要触发和所有 control-plane 状态机。
 
 ## 现状问题
 
@@ -101,21 +104,26 @@ Backend 继续负责：
 - 摘要触发
 - 对 `chat_shell` 暴露统一内部 API
 
-### 数据面迁移到 rag_service
+### 数据面迁移到 `knowledge_runtime` + `knowledge_engine`
 
-`rag_service` 负责：
+`knowledge_engine` 负责：
 - 文档解析
 - splitter
 - embedding 调用
 - vector store 读写
 - document index/delete
 - chunk retrieval
-- all-chunks retrieval
 - 后续层级检索能力
 
+`knowledge_runtime` 负责：
+- `content_ref` 拉取
+- internal auth
+- 远程 request / response 协议适配
+- 调用 `knowledge_engine` 执行真实 index / query / delete
+
 ### chat_shell 不直接感知底层 RAG 演进
 
-`chat_shell` 继续面向 Backend 的知识库 API 编程，不直接连 `rag_service`。
+`chat_shell` 继续面向 Backend 的知识库 API 编程，不直接连 `knowledge_runtime`。
 
 理想调用链：
 
@@ -124,7 +132,7 @@ chat_shell
    -> Backend internal RAG API
       -> RagGateway
          -> LocalRagGateway | RemoteRagGateway
-            -> local implementation | rag_service
+            -> knowledge_engine | knowledge_runtime -> knowledge_engine
 ```
 
 ## 核心设计
@@ -149,9 +157,9 @@ Backend 中新增统一抽象：
 - `get_all_chunks(...)`
 - `get_kb_runtime_info(...)`
 
-### 2. 运行时配置由 Backend 解析后传给 rag_service
+### 2. 运行时配置由 Backend 解析后传给 `knowledge_runtime`
 
-`rag_service` 不直接访问 Backend 数据库。
+`knowledge_runtime` 不直接访问 Backend 数据库。
 
 Backend 负责将以下内容解析为 runtime payload：
 - storage backend 配置
@@ -246,7 +254,7 @@ Backend 负责将以下内容解析为 runtime payload：
 - knowledge delete index 流程
 
 交付结果：
-- `RAG_EXECUTION_MODE=local`
+- `RAG_RUNTIME_MODE=local`
 - 所有行为保持不变
 
 ### Phase 2：抽 runtime config resolver
@@ -263,16 +271,16 @@ Backend 负责将以下内容解析为 runtime payload：
 交付结果：
 - 数据面执行不再需要直接查数据库
 
-### Phase 3：创建 rag_service
+### Phase 3：创建 `knowledge_runtime`
 
 目标：
-- 搭建独立部署服务，只承载数据面逻辑
+- 搭建独立部署服务，只承载 remote 数据面逻辑
 
 首版接口建议：
-- `POST /v1/index/document`
-- `POST /v1/retrieve`
-- `POST /v1/chunks/all`
-- `POST /v1/index/delete-document`
+- `POST /internal/rag/index`
+- `POST /internal/rag/query`
+- `POST /internal/rag/delete-document-index`
+- `POST /internal/rag/test-connection`
 - `GET /healthz`
 
 首版非目标：
@@ -280,14 +288,14 @@ Backend 负责将以下内容解析为 runtime payload：
 - 不做 CRD 解析
 - 不做摘要任务
 
-### Phase 4：Backend 接入 RemoteRagGateway
+### Phase 4：Backend 接入 `RemoteRagGateway`
 
 目标：
-- Backend 通过 HTTP 调用 `rag_service`
+- Backend 通过 HTTP 调用 `knowledge_runtime`
 
 新增环境变量建议：
-- `RAG_EXECUTION_MODE=local|remote`
-- `RAG_SERVICE_URL=http://rag_service:8002`
+- `RAG_RUNTIME_MODE=local|remote|{default,index,query,delete}`
+- `KNOWLEDGE_RUNTIME_URL=http://knowledge_runtime:8200`
 
 交付结果：
 - local/remote 可切换
@@ -387,9 +395,9 @@ internal retrieve 合并后必须保证返回结构兼容，避免 tool 层大
 
 ### 服务拆分验收
 
-- `RAG_EXECUTION_MODE=local|remote` 可切换
+- `RAG_RUNTIME_MODE=local|remote|{default,index,query,delete}` 可切换
 - Frontend 无需改动
-- `chat_shell` 无需直接对接 `rag_service`
+- `chat_shell` 无需直接对接 `knowledge_runtime`
 - 文档上传、索引、检索、删除链路行为一致
 - 摘要链路保持在 Backend 触发
 
@@ -400,7 +408,7 @@ internal retrieve 合并后必须保证返回结构兼容，避免 tool 层大
 1. 先下沉 `/all-chunks` 与 `/retrieve` 路由决策到 Backend internal RAG 层
 2. 再抽 `RagGateway`
 3. 再抽 runtime config resolver
-4. 再建立 `rag_service`
+4. 再建立 `knowledge_runtime`
 5. 最后切 remote mode 并清理依赖
 
 这是风险最低、收益最直接的路径。
diff --git a/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md b/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
index 9509dff7a..26293af64 100644
--- a/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
+++ b/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
@@ -1,3 +1,7 @@
+---
+sidebar_position: 1
+---
+
 # Knowledge Runtime Implementation Plan
 
 > **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
@@ -24,6 +28,24 @@ The following items already exist and must be preserved while executing this pla
 
 This plan supersedes only the old "independent `knowledge_runtime` reimplementation" direction. It does not discard the already-landed transport and rollout safety work.
 
+## Implementation Status (2026-04-06)
+
+This plan has been substantially executed for the extraction phase. The current authoritative status is:
+
+- Completed:
+  - `shared` protocol models for `knowledge_runtime`
+  - top-level `knowledge_engine` package extraction
+  - Backend local indexing / retrieval / delete execution routed through `knowledge_engine`
+  - `knowledge_runtime` thin adapter over `knowledge_engine`
+  - Backend remote gateway payload wiring for index / query / delete / test-connection
+  - storage and gateway-focused test migration into `knowledge_engine` / Backend
+- Still follow-up work:
+  - broader remote rollout / parity verification beyond focused regression coverage
+  - release and operational hardening for `knowledge_runtime`
+  - future `summary_vector_index`, `tableRAG`, and MCP `search`
+
+The checkbox states below are preserved as the original execution breakdown and should be read as historical plan detail, not as the authoritative current rollout status.
+
 ## Fixed Decisions
 
 - Service name remains `knowledge_runtime`
@@ -84,7 +106,7 @@ This plan supersedes only the old "independent `knowledge_runtime` reimplementat
 ### Deployment
 
 - `docker/knowledge_runtime/Dockerfile`
-- `docker-compose-rag.yml`
+- `docker-compose.yml`
 
 ---
 
@@ -305,7 +327,7 @@ Expected: PASS
 
 **Files:**
 - Modify: `docker/knowledge_runtime/Dockerfile`
-- Modify: `docker-compose-rag.yml`
+- Modify: `docker-compose.yml`
 
 - [ ] Ensure `knowledge_runtime` image installs both `wegent-shared` and `knowledge_engine`
 - [ ] Keep base image and startup style aligned with Backend / Chat Shell conventions
@@ -314,7 +336,7 @@ Expected: PASS
 
 **Verification**
 
-Run: `docker compose --profile rag -f docker-compose-rag.yml config`
+Run: `docker compose -f docker-compose.yml config`
 Expected: valid compose output including `knowledge_runtime` and the new package wiring
 
 ## Rollout Guidance
@@ -333,7 +355,7 @@ Expected: valid compose output including `knowledge_runtime` and the new package
 - `uv run --project knowledge_engine --group dev pytest knowledge_engine/tests -v`
 - `uv run --project backend --group dev pytest backend/tests/services/rag backend/tests/api/endpoints/internal backend/tests/integration/test_rag_remote_mode.py -v`
 - `uv run --project knowledge_runtime --group dev pytest knowledge_runtime/tests -v`
-- `docker compose --profile rag -f docker-compose-rag.yml config`
+- `docker compose -f docker-compose.yml config`
 
 ## Guardrails
 
diff --git a/docs/specs/knowledge/2026-03-31-rag-modular-data-plane-design.md b/docs/specs/knowledge/2026-03-31-rag-modular-data-plane-design.md
index 1dc51c1dc..5bb007065 100644
--- a/docs/specs/knowledge/2026-03-31-rag-modular-data-plane-design.md
+++ b/docs/specs/knowledge/2026-03-31-rag-modular-data-plane-design.md
@@ -19,7 +19,7 @@ sidebar_position: 1
 - 应该先拆模块边界还是先拆部署边界
 - 未来的 `summary index`、`tableRAG`、受限知识压缩、第三方 RAG engine 应该分别放在哪一层
 
-## 状态快照（2026-04-02）
+## 状态快照（2026-04-06）
 
 当前这一轮拆分已经完成推荐路线中的前四步：
 
@@ -27,18 +27,32 @@ sidebar_position: 1
 - 模块化 `rag data plane` 已落地
 - `Phase 2.5` 已完成，control/data 边界已收敛
 - restricted safe-summary 已迁入 Backend internal retrieval
+- `knowledge_engine` 已落地为顶层 execution kernel，并承接 backend-agnostic 的 index / query / delete / storage execution
+- Backend local data-plane 已通过 `knowledge_engine` 执行核心索引与检索逻辑
+- `knowledge_runtime` 已作为薄 remote adapter 落地，负责 `content_ref` 拉取、协议校验与调用 `knowledge_engine`
 
 当前稳定边界如下：
 
-- `backend/app/services/rag/` 负责执行侧 runtime contract、gateway、local data-plane adapter
+- `shared` 只负责 lightweight transport protocol
+- `backend/app/services/rag/` 负责 runtime contract、gateway、runtime resolver、local/remote adapter
 - `backend/app/services/knowledge/` 负责 control-plane persistence、`kb_head`、protected mediation
+- `knowledge_engine` 负责真正的执行内核
 - `chat_shell` 只消费 Backend internal RAG 结果，不再承担 restricted 二级模型压缩编排
 
 当前仍属于后续路线的问题只剩：
 
+- remote rollout / parity 继续收敛
 - `summary_vector_index`
 - `tableRAG`
-- 是否独立 `rag_service`
+- MCP `search`
+
+### 术语映射
+
+由于本文写于模块化优先阶段，文中的部分术语需要按当前实现理解：
+
+- `rag data plane module`：当前已经具体化为 `knowledge_engine`
+- “独立 `rag_service`”：当前命名下对应 `knowledge_runtime`
+- `/api/internal/rag/all-chunks`：当前仅是 legacy internal surface，主入口已统一收敛到 `/api/internal/rag/retrieve`
 
 ## 问题
 
@@ -99,7 +113,7 @@ sidebar_position: 1
 
 本次不做以下内容：
 
-- 不直接实现独立 `rag_service`
+- 不要求立即将主路径切换到 remote `knowledge_runtime`
 - 不直接重写现有索引与检索实现
 - 不在本次设计中决定具体使用哪一种 `tableRAG` 存储方案
 - 不要求 restricted 模式在首期就从 `chat_shell` 迁出
@@ -611,7 +625,7 @@ class ProtectedKnowledgeMediator(Protocol):
   - 最后继续 `Phase 3` 和 `Phase 4`
 - `Phase 3`、`Phase 4`、`Phase 5` 的编号保留为概念路线图编号，不强制要求按数字顺序实施
 
-### Phase 6：评估独立 `rag_service`
+### Phase 6：评估独立 `knowledge_runtime`
 
 前置条件：
 
@@ -619,7 +633,7 @@ class ProtectedKnowledgeMediator(Protocol):
 - 多索引族与多检索路径已在进程内验证
 - 数据面模块内部边界清晰
 
-满足以上条件后，再将 `rag data plane module` 迁移为独立 `rag_service`，风险最低。
+满足以上条件后，再将 `knowledge_engine` 所承载的数据面能力以 `knowledge_runtime` 形式继续外部化 rollout，风险最低。
 
 ## 兼容性要求
 
@@ -651,7 +665,7 @@ class ProtectedKnowledgeMediator(Protocol):
 - Backend 控制面、RuntimeResolver、ProtectedKnowledgeMediator、RAG 数据面模块的职责边界清晰
 - runtime contract 可以承载 future `summary index`、`tableRAG`、第三方 engine
 - `chat_shell` 不新增底层 RAG 策略分支
-- 后续独立 `rag_service` 时，无需重新设计核心 contract
+- 后续扩大 `knowledge_runtime` rollout 时，无需重新设计核心 contract
 
 ## 最终结论
 
@@ -667,6 +681,6 @@ Wegent 应继续推进 RAG 拆分，但本阶段应优先做“模块边界拆
 4. 优先迁移 restricted 安全压缩到 Backend internal search
 5. 再落地 `summary_vector_index`
 6. 再落地 `tableRAG`
-7. 最后视 contract 稳定度决定是否独立 `rag_service`
+7. 最后视 contract 稳定度决定是否扩大 `knowledge_runtime` 的 remote 主路径
 
 这样可以在不提前冻结错误服务边界的前提下，为 Backend 变轻、索引与检索能力扩展、存储多形态共存、第三方 RAG engine 接入同时铺平道路。
diff --git a/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md b/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
index 02c16cd69..5ec512bee 100644
--- a/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
+++ b/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
@@ -31,18 +31,27 @@ sidebar_position: 1
 
 为避免与此前讨论断层，本文中的历史术语 `rag_service`，在目标形态中统一对应 `knowledge_runtime`。
 
-## 状态快照
+## 状态快照（2026-04-06）
 
 当前代码中已经具备以下基础：
 
 - Backend internal retrieval 已收敛到 `backend/app/api/endpoints/internal/rag.py`
 - `chat_shell` 中 `knowledge_base_search` 通过 Backend internal RAG API 完成检索
-- `backend/app/services/rag/runtime_specs.py` 已提供 `IndexRuntimeSpec` 与 `QueryRuntimeSpec`
+- `backend/app/services/rag/runtime_specs.py` 已提供 `IndexRuntimeSpec`、`QueryRuntimeSpec`、`DeleteRuntimeSpec` 与 `ConnectionTestRuntimeSpec`
 - `backend/app/services/rag/gateway.py` 已定义 `RagGateway`
-- `backend/app/services/rag/local_gateway.py` 已将本地执行路径封装到 `LocalRagGateway`
+- `backend/app/services/rag/local_gateway.py` 与 `backend/app/services/rag/remote_gateway.py` 已完成 local / remote gateway 封装
+- `knowledge_engine` 已经作为顶层执行库落地，承接 storage backend、embedding、query executor、document index / delete 等执行逻辑
+- `knowledge_runtime` 已经作为薄服务落地，并通过 `knowledge_engine` 执行真实 index / query / delete
+- Backend `RAG_RUNTIME_MODE` 已统一 local / remote 路由开关，并支持按 operation 切换
 
 当前知识库 MCP 尚未提供 `search` 能力，但本轮不将其纳入拆分主线，只在后续工作中说明。
 
+### 当前落地结论
+
+- 本轮已经完成 `knowledge_engine` 抽离，Backend local mode 继续作为主 rollout 目标。
+- `knowledge_runtime` 已具备真实执行能力，但 remote mode 仍属于后续 rollout / parity 收敛工作，不是本轮主切换目标。
+- `shared` 继续只承载 transport protocol；`direct injection` 与 `restricted mediation` 仍明确保留在 Backend。
+
 ## 问题
 
 ### 1. Backend 仍承载重数据面执行
@@ -615,15 +624,15 @@ Backend 内部继续使用现有：
 ## 后续工作
 
 - 设计知识库 MCP `search`，将其作为 retrieval surface 的新增消费面
-- 在 remote 稳定后删除 Backend local 入口层
-- 视独立发布需求评估是否将 `knowledge_engine` 单独 package 化
+- 在 remote 稳定后，再评估是否继续删除 Backend 仅用于 local mode 的入口层
+- 视独立发布需求评估是否进一步独立发布 `knowledge_engine`
 - 将 `summary_vector_index` 接入 `index_family` 执行体系
 - 为 `tableRAG` 设计独立 family / query policy
 - 在对象存储场景中进一步减少 Backend 对内容拉取链路的参与
 
 ## 结论
 
-本轮修订后采用“`shared` 放轻协议、Backend 保留 control plane、抽离 `knowledge_engine` execution kernel、`knowledge_runtime` 复用该 kernel、通过 `RagGateway` 灰度接入 remote”的路线。
+本轮修订后采用“`shared` 放轻协议、Backend 保留 control plane、抽离 `knowledge_engine` execution kernel、`knowledge_runtime` 复用该 kernel、通过 `RagGateway` 灰度接入 remote”的路线；其中 `knowledge_engine` 抽离已经完成，后续重点转为 remote rollout、parity 验证与后续索引族扩展。
 
 首版最重要的设计结论是：
 
diff --git a/docs/zh/user-guide/knowledge/retrieval-and-direct-injection.md b/docs/zh/user-guide/knowledge/retrieval-and-direct-injection.md
index b98e4da1f..1f8d0ba1a 100644
--- a/docs/zh/user-guide/knowledge/retrieval-and-direct-injection.md
+++ b/docs/zh/user-guide/knowledge/retrieval-and-direct-injection.md
@@ -4,13 +4,13 @@ sidebar_position: 11
 
 # 检索与直注入
 
-知识库在对话中并不只有一种使用方式。除了常见的 RAG 检索，Wegent 还保留了一个“小库整库直注入”路径，用于在特定场景下直接读取知识库的全部 chunks，再交给模型使用。
+知识库在对话中并不只有一种使用方式。除了常见的 RAG 检索，Wegent 还保留了一个“小库整库直注入”路径，用于在特定场景下把知识库内容尽量完整地交给模型使用。
 
 ---
 
 ## 为什么会有直注入
 
-`all-chunks` 这条路径最初引入的主要原因，是向量搜索在部分场景下的召回效果不够稳定。
+直注入路径最初引入的主要原因，是向量搜索在部分场景下的召回效果不够稳定。
 
 对于文档数量少、总体内容不大、但问题又需要较完整上下文的小型知识库，单纯依赖向量检索可能会出现这些问题：
 
@@ -24,19 +24,20 @@ sidebar_position: 11
 
 ## 两种路径的区别
 
-| 路径 | 主要接口 | 适用场景 | 特点 |
-|------|----------|----------|------|
+| 路径 | 主入口 | 适用场景 | 特点 |
+|------|--------|----------|------|
 | **检索模式** | `/api/internal/rag/retrieve` | 中大型知识库、明确查询、RAG 常规问答 | 成本更低，返回更聚焦 |
-| **直注入模式** | `/api/internal/rag/all-chunks` | 小型知识库、需要更完整上下文、向量召回不稳定 | 召回更完整，但上下文开销更高 |
+| **直注入模式** | `/api/internal/rag/retrieve` | 小型知识库、需要更完整上下文、向量召回不稳定 | 召回更完整，但上下文开销更高 |
 
 可以把它理解为：
 
-- `retrieve` 是常规搜索路径
-- `all-chunks` 是小型知识库的补充路径，而不是替代检索器
+- `/api/internal/rag/retrieve` 是统一入口
+- Backend 会在内部决定当前请求走常规检索还是直注入
+- 旧的 `/api/internal/rag/all-chunks` 只保留为兼容性 internal endpoint，不再是推荐主入口
 
 ---
 
-## 什么时候会走 all-chunks
+## 什么时候会走直注入
 
 通常会在下面这类条件中启用：
 
@@ -55,31 +56,31 @@ sidebar_position: 11
 
 ## 权限原则
 
-`all-chunks` 的权限设计应与 `/api/internal/rag/retrieve` 保持一致。
+直注入的权限设计应与 `/api/internal/rag/retrieve` 保持一致。
 
 也就是说：
 
 - 它属于 **internal RAG** 服务间调用接口
 - 是否允许访问某个知识库，应在更上游的任务上下文和知识库选择阶段完成校验
-- `all-chunks` 本身不应额外再引入一套与 `retrieve` 不一致的用户级拦截逻辑
+- 直注入本身不应额外再引入一套与常规检索不一致的用户级拦截逻辑
 
 这样做的原因很简单：
 
 - 两个接口服务的是同一条知识库使用链路
-- 如果 `retrieve` 可以访问而 `all-chunks` 被单独拦截，就会造成行为不一致
+- 如果常规检索可以访问而直注入被单独拦截，就会造成行为不一致
 - 对小型知识库启用直注入时，接口层不应成为额外障碍
 
 ---
 
 ## Restricted Analyst 下的安全摘要
 
-当知识库使用者权限是 `Restricted Analyst` 时，系统不会把检索出的原始 chunks 直接交给最终回答模型，而是先在 `knowledge_base_search` 内部完成一次**安全摘要**。
+当知识库使用者权限是 `Restricted Analyst` 时，系统不会把检索出的原始 chunks 直接交给最终回答模型，而是先在 Backend internal retrieve 链路内完成一次**安全摘要**。
 
 这条链路的目标是：
 
 - 允许模型利用知识库做高层分析、诊断、风险判断和建议
 - 避免把原文、原始定义、具体指标、标题、文件名、文档结构等内容直接暴露出来
-- 把“能否回答”与“如何安全回答”的控制，前置到知识库工具内部
+- 把“能否回答”与“如何安全回答”的控制，前置到 Backend 的知识库检索编排层
 
 你可以把它理解为：
 
@@ -129,16 +130,16 @@ sidebar_position: 11
 
 ---
 
-## 对 all-chunks 的影响
+## 对直注入的影响
 
-`all-chunks` 仍然保留“小库整库直注入”的用途，但在 Restricted 模式下，它的作用会变成：
+直注入仍然保留“小库整库注入”的用途，但在 Restricted 模式下，它的作用会变成：
 
 - 先为安全摘要链路提供更完整上下文
 - 再由二级模型把原始内容压缩成安全可用的高层结果
 
 也就是说：
 
-- `all-chunks` 仍然是为了补足向量检索效果不稳定的问题
+- 直注入仍然是为了补足向量检索效果不稳定的问题
 - 但 Restricted 模式下，最终交给主模型的不是原始整库 chunks，而是安全摘要结果
 
 这样既保留了小库场景下的稳定性，也降低了原文泄露风险。
@@ -149,16 +150,16 @@ sidebar_position: 11
 
 | 建议 | 说明 |
 |------|------|
-| 优先配置好检索器 | `all-chunks` 是补充能力，不应替代正常索引与检索配置 |
+| 优先配置好检索器 | 直注入是补充能力，不应替代正常索引与检索配置 |
 | 小库保留直注入 | 当知识库规模可控时，直注入通常能提升稳定性 |
 | 大库仍以检索为主 | 大型知识库整库注入会带来明显的上下文和成本压力 |
-| 结合日志排查问题 | `all-chunks` 返回空结果时，优先检查索引、文档状态和后端日志 |
+| 结合日志排查问题 | 直注入结果为空时，优先检查索引、文档状态和后端日志 |
 
 ---
 
-## 排查 all-chunks 结果为空
+## 排查直注入结果为空
 
-如果 `all-chunks` 没有返回内容，建议重点检查：
+如果直注入没有返回内容，建议重点检查：
 
 | 检查项 | 说明 |
 |--------|------|
@@ -170,8 +171,8 @@ sidebar_position: 11
 
 如果日志里能看到：
 
-- 请求已经进入 `/api/internal/rag/all-chunks`
-- `get_all_chunks start`
+- 请求已经进入 `/api/internal/rag/retrieve`
+- 当前 route mode 被 Backend 判定为 `direct_injection`
 - 但最终命中数为 0
 
 那通常说明问题已经从“接口没通”进入到“索引或数据不匹配”的层面了。

From 5014e28d3fddf5620cbf4c0b11d3176bf1fe6145 Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Mon, 6 Apr 2026 13:47:15 +0800
Subject: [PATCH 8/9] refactor(knowledge): remove backend rag shim modules

---
 .../app/services/rag/embedding/__init__.py    | 12 ++++----
 backend/app/services/rag/embedding/custom.py  |  9 ------
 .../app/services/rag/retrieval/__init__.py    |  4 +--
 backend/app/services/rag/retrieval/filters.py | 12 --------
 backend/app/services/rag/storage/__init__.py  | 28 +++++++++++++------
 backend/app/services/rag/storage/base.py      |  9 ------
 .../services/rag/storage/chunk_metadata.py    |  9 ------
 .../rag/storage/elasticsearch_backend.py      | 12 --------
 .../services/rag/storage/milvus_backend.py    | 27 ------------------
 .../services/rag/storage/qdrant_backend.py    | 13 ---------
 10 files changed, 28 insertions(+), 107 deletions(-)
 delete mode 100644 backend/app/services/rag/embedding/custom.py
 delete mode 100644 backend/app/services/rag/retrieval/filters.py
 delete mode 100644 backend/app/services/rag/storage/base.py
 delete mode 100644 backend/app/services/rag/storage/chunk_metadata.py
 delete mode 100644 backend/app/services/rag/storage/elasticsearch_backend.py
 delete mode 100644 backend/app/services/rag/storage/milvus_backend.py
 delete mode 100644 backend/app/services/rag/storage/qdrant_backend.py

diff --git a/backend/app/services/rag/embedding/__init__.py b/backend/app/services/rag/embedding/__init__.py
index 0fbdc21ee..6c079df5f 100644
--- a/backend/app/services/rag/embedding/__init__.py
+++ b/backend/app/services/rag/embedding/__init__.py
@@ -2,14 +2,16 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Embedding module for RAG functionality.
-"""
+"""Embedding helpers exposed at the Backend runtime boundary."""
 
-from app.services.rag.embedding.custom import CustomEmbedding
-from app.services.rag.embedding.factory import create_embedding_model_from_crd
+from app.services.rag.embedding.factory import (
+    create_embedding_model_from_crd,
+    create_embedding_model_from_runtime_config,
+)
+from knowledge_engine.embedding.custom import CustomEmbedding
 
 __all__ = [
     "create_embedding_model_from_crd",
+    "create_embedding_model_from_runtime_config",
     "CustomEmbedding",
 ]
diff --git a/backend/app/services/rag/embedding/custom.py b/backend/app/services/rag/embedding/custom.py
deleted file mode 100644
index 86f5d57a3..000000000
--- a/backend/app/services/rag/embedding/custom.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""Backward-compatible re-export of the shared engine custom embedding."""
-
-from knowledge_engine.embedding.custom import CustomEmbedding
-
-__all__ = ["CustomEmbedding"]
diff --git a/backend/app/services/rag/retrieval/__init__.py b/backend/app/services/rag/retrieval/__init__.py
index 4f7eae57b..216d80a3a 100644
--- a/backend/app/services/rag/retrieval/__init__.py
+++ b/backend/app/services/rag/retrieval/__init__.py
@@ -2,9 +2,9 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""Backward-compatible retrieval helper exports."""
+"""Retrieval helper exports from the shared execution kernel."""
 
-from app.services.rag.retrieval.filters import (
+from knowledge_engine.retrieval.filters import (
     build_elasticsearch_filters,
     parse_metadata_filters,
 )
diff --git a/backend/app/services/rag/retrieval/filters.py b/backend/app/services/rag/retrieval/filters.py
deleted file mode 100644
index 4f45737a3..000000000
--- a/backend/app/services/rag/retrieval/filters.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""Backward-compatible re-exports for retrieval filter helpers."""
-
-from knowledge_engine.retrieval.filters import (
-    build_elasticsearch_filters,
-    parse_metadata_filters,
-)
-
-__all__ = ["parse_metadata_filters", "build_elasticsearch_filters"]
diff --git a/backend/app/services/rag/storage/__init__.py b/backend/app/services/rag/storage/__init__.py
index 2a38c0061..8d872bd58 100644
--- a/backend/app/services/rag/storage/__init__.py
+++ b/backend/app/services/rag/storage/__init__.py
@@ -2,16 +2,21 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-"""
-Storage backend abstraction for RAG functionality.
-"""
+"""Storage helpers exposed at the Backend runtime boundary."""
 
-from app.services.rag.storage.base import BaseStorageBackend
-from app.services.rag.storage.chunk_metadata import ChunkMetadata
-from app.services.rag.storage.elasticsearch_backend import ElasticsearchBackend
-from app.services.rag.storage.factory import create_storage_backend
-from app.services.rag.storage.milvus_backend import MilvusBackend
-from app.services.rag.storage.qdrant_backend import QdrantBackend
+from app.services.rag.storage.factory import (
+    create_storage_backend,
+    create_storage_backend_from_config,
+    create_storage_backend_from_runtime_config,
+    get_all_storage_retrieval_methods,
+    get_supported_retrieval_methods,
+    get_supported_storage_types,
+)
+from knowledge_engine.storage.base import BaseStorageBackend
+from knowledge_engine.storage.chunk_metadata import ChunkMetadata
+from knowledge_engine.storage.elasticsearch_backend import ElasticsearchBackend
+from knowledge_engine.storage.milvus_backend import MilvusBackend
+from knowledge_engine.storage.qdrant_backend import QdrantBackend
 
 __all__ = [
     "BaseStorageBackend",
@@ -20,4 +25,9 @@
     "MilvusBackend",
     "QdrantBackend",
     "create_storage_backend",
+    "create_storage_backend_from_config",
+    "create_storage_backend_from_runtime_config",
+    "get_supported_storage_types",
+    "get_supported_retrieval_methods",
+    "get_all_storage_retrieval_methods",
 ]
diff --git a/backend/app/services/rag/storage/base.py b/backend/app/services/rag/storage/base.py
deleted file mode 100644
index 18884d17e..000000000
--- a/backend/app/services/rag/storage/base.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""Backward-compatible re-export of the engine storage base abstraction."""
-
-from knowledge_engine.storage.base import BaseStorageBackend
-
-__all__ = ["BaseStorageBackend"]
diff --git a/backend/app/services/rag/storage/chunk_metadata.py b/backend/app/services/rag/storage/chunk_metadata.py
deleted file mode 100644
index dcce4af87..000000000
--- a/backend/app/services/rag/storage/chunk_metadata.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""Backward-compatible re-export of engine chunk metadata."""
-
-from knowledge_engine.storage.chunk_metadata import ChunkMetadata
-
-__all__ = ["ChunkMetadata"]
diff --git a/backend/app/services/rag/storage/elasticsearch_backend.py b/backend/app/services/rag/storage/elasticsearch_backend.py
deleted file mode 100644
index c99277897..000000000
--- a/backend/app/services/rag/storage/elasticsearch_backend.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""Backward-compatible re-export of the engine Elasticsearch backend."""
-
-from knowledge_engine.storage.elasticsearch_backend import (
-    Elasticsearch,
-    ElasticsearchBackend,
-)
-
-__all__ = ["ElasticsearchBackend", "Elasticsearch"]
diff --git a/backend/app/services/rag/storage/milvus_backend.py b/backend/app/services/rag/storage/milvus_backend.py
deleted file mode 100644
index 49425af22..000000000
--- a/backend/app/services/rag/storage/milvus_backend.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""Backward-compatible re-export of the engine Milvus backend."""
-
-from knowledge_engine.storage.milvus_backend import (
-    DEFAULT_EMBEDDING_DIM,
-    DEFAULT_TOP_K,
-    MAX_QUERY_LIMIT,
-    LazyAsyncMilvusVectorStore,
-    MilvusBackend,
-    MilvusClient,
-    StorageContext,
-    VectorStoreIndex,
-)
-
-__all__ = [
-    "DEFAULT_EMBEDDING_DIM",
-    "DEFAULT_TOP_K",
-    "MAX_QUERY_LIMIT",
-    "LazyAsyncMilvusVectorStore",
-    "MilvusBackend",
-    "MilvusClient",
-    "StorageContext",
-    "VectorStoreIndex",
-]
diff --git a/backend/app/services/rag/storage/qdrant_backend.py b/backend/app/services/rag/storage/qdrant_backend.py
deleted file mode 100644
index 52aebe0ba..000000000
--- a/backend/app/services/rag/storage/qdrant_backend.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# SPDX-FileCopyrightText: 2025 Weibo, Inc.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""Backward-compatible re-export of the engine Qdrant backend."""
-
-from knowledge_engine.storage.qdrant_backend import (
-    QdrantBackend,
-    QdrantClient,
-    QdrantVectorStore,
-)
-
-__all__ = ["QdrantBackend", "QdrantClient", "QdrantVectorStore"]

From 743ee7e93656cafd80e17ae6d2bfff778c9bb366 Mon Sep 17 00:00:00 2001
From: yanhe1 <yanhe1@staff.weibo.com>
Date: Mon, 6 Apr 2026 19:52:27 +0800
Subject: [PATCH 9/9] fix(knowledge): address runtime extraction review
 feedback

---
 .../app/api/endpoints/internal/rag_content.py | 11 ++-
 backend/app/services/rag/remote_gateway.py    | 23 +++++++
 backend/app/services/rag/runtime_resolver.py  | 14 +++-
 .../internal/test_rag_content_endpoint.py     | 27 ++++++++
 .../tests/services/rag/test_remote_gateway.py | 41 +++++++++++-
 .../services/rag/test_runtime_resolver.py     | 27 ++++++++
 ...-service-extraction-implementation-plan.md |  4 +-
 ...026-04-03-rag-service-extraction-design.md |  4 +-
 .../knowledge_engine/embedding/custom.py      |  3 +
 .../knowledge_engine/embedding/factory.py     |  4 +-
 .../knowledge_engine/query/executor.py        | 10 ++-
 .../knowledge_engine/retrieval/filters.py     |  2 +-
 .../storage/elasticsearch_backend.py          |  9 +++
 .../storage/test_elasticsearch_backend.py     | 30 +++++++++
 .../tests/test_embedding_factory.py           | 54 +++++++++++++++
 knowledge_engine/tests/test_query_executor.py | 31 +++++++++
 .../tests/test_retrieval_filters.py           | 24 +++++++
 .../knowledge_runtime/core/config.py          |  3 +-
 .../knowledge_runtime/services/handlers.py    |  4 ++
 knowledge_runtime/tests/conftest.py           |  7 ++
 knowledge_runtime/tests/test_config.py        | 14 ++++
 .../tests/test_query_execution.py             | 35 ++++++++++
 shared/models/knowledge_runtime_protocol.py   | 25 +++++--
 .../tests/test_knowledge_runtime_protocol.py  | 67 ++++++++++++++++++-
 24 files changed, 449 insertions(+), 24 deletions(-)
 create mode 100644 knowledge_engine/tests/test_embedding_factory.py
 create mode 100644 knowledge_engine/tests/test_retrieval_filters.py
 create mode 100644 knowledge_runtime/tests/conftest.py
 create mode 100644 knowledge_runtime/tests/test_config.py

diff --git a/backend/app/api/endpoints/internal/rag_content.py b/backend/app/api/endpoints/internal/rag_content.py
index 579cc2d59..32ae14575 100644
--- a/backend/app/api/endpoints/internal/rag_content.py
+++ b/backend/app/api/endpoints/internal/rag_content.py
@@ -7,9 +7,10 @@
 from __future__ import annotations
 
 import logging
+from collections.abc import Iterator
 
 from fastapi import APIRouter, Depends, Header, HTTPException, status
-from fastapi.responses import Response
+from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
 
 from app.api.dependencies import get_db
@@ -24,6 +25,10 @@
 router = APIRouter(prefix="/rag/content", tags=["internal-rag-content"])
 
 
+def _binary_stream(binary_data: bytes) -> Iterator[bytes]:
+    yield binary_data
+
+
 def _verify_rag_download_authorization(
     attachment_id: int,
     authorization: str = Header(default=""),
@@ -76,8 +81,8 @@ async def stream_rag_attachment_content(
             detail="Failed to retrieve attachment data",
         )
 
-    return Response(
-        content=binary_data,
+    return StreamingResponse(
+        _binary_stream(binary_data),
         media_type=context.mime_type,
         headers={
             "Content-Disposition": _build_content_disposition(context.original_filename)
diff --git a/backend/app/services/rag/remote_gateway.py b/backend/app/services/rag/remote_gateway.py
index 2fbb1046b..969c57988 100644
--- a/backend/app/services/rag/remote_gateway.py
+++ b/backend/app/services/rag/remote_gateway.py
@@ -11,6 +11,8 @@
 from sqlalchemy.orm import Session
 
 from app.core.config import settings
+from app.models.subtask_context import ContextType
+from app.services.context import context_service
 from app.services.rag.content_refs import build_content_ref_for_attachment
 from app.services.rag.runtime_specs import (
     ConnectionTestRuntimeSpec,
@@ -115,6 +117,10 @@ async def index_document(
         if spec.source.source_type != "attachment" or spec.source.attachment_id is None:
             raise ValueError("RemoteRagGateway only supports attachment sources")
 
+        source_file, file_extension = _get_attachment_source_metadata(
+            db=db,
+            attachment_id=spec.source.attachment_id,
+        )
         payload = RemoteIndexRequest(
             knowledge_base_id=spec.knowledge_base_id,
             document_id=spec.document_id,
@@ -135,6 +141,8 @@ async def index_document(
                 db=db,
                 attachment_id=spec.source.attachment_id,
             ),
+            source_file=source_file,
+            file_extension=file_extension,
             user_name=spec.user_name,
         )
         return await self._post_model("/internal/rag/index", payload)
@@ -189,3 +197,18 @@ async def test_connection(
         del db
         payload = RemoteTestConnectionRequest(retriever_config=spec.retriever_config)
         return await self._post_model("/internal/rag/test-connection", payload)
+
+
+def _get_attachment_source_metadata(
+    *,
+    db: Session,
+    attachment_id: int,
+) -> tuple[str | None, str | None]:
+    context = context_service.get_context_optional(
+        db=db,
+        context_id=attachment_id,
+    )
+    if context is None or context.context_type != ContextType.ATTACHMENT.value:
+        return None, None
+
+    return context.original_filename or None, context.file_extension or None
diff --git a/backend/app/services/rag/runtime_resolver.py b/backend/app/services/rag/runtime_resolver.py
index 93764f56d..7ca235f88 100644
--- a/backend/app/services/rag/runtime_resolver.py
+++ b/backend/app/services/rag/runtime_resolver.py
@@ -153,9 +153,17 @@ def build_public_query_runtime_spec(
         keyword_weight: float | None = None,
         metadata_condition: dict | None = None,
     ) -> QueryRuntimeSpec:
-        kb = self._get_knowledge_base_record(db=db, knowledge_base_id=knowledge_base_id)
-        if kb is None:
-            raise ValueError(f"Knowledge base {knowledge_base_id} not found")
+        from app.services.knowledge.knowledge_service import KnowledgeService
+
+        kb, has_access = KnowledgeService.get_knowledge_base(
+            db=db,
+            knowledge_base_id=knowledge_base_id,
+            user_id=user_id,
+        )
+        if kb is None or not has_access:
+            raise ValueError(
+                f"Knowledge base {knowledge_base_id} not found or access denied"
+            )
 
         return QueryRuntimeSpec(
             knowledge_base_ids=[knowledge_base_id],
diff --git a/backend/tests/api/endpoints/internal/test_rag_content_endpoint.py b/backend/tests/api/endpoints/internal/test_rag_content_endpoint.py
index dbade1dbf..e7b7ac628 100644
--- a/backend/tests/api/endpoints/internal/test_rag_content_endpoint.py
+++ b/backend/tests/api/endpoints/internal/test_rag_content_endpoint.py
@@ -4,6 +4,10 @@
 
 from unittest.mock import patch
 
+import pytest
+from fastapi.responses import StreamingResponse
+
+from app.api.endpoints.internal.rag_content import stream_rag_attachment_content
 from app.models.subtask_context import ContextStatus, ContextType, SubtaskContext
 from app.services.auth.rag_download_token import create_rag_download_token
 
@@ -61,3 +65,26 @@ def test_internal_rag_content_streams_allowed_attachment(test_client, test_db) -
     assert response.status_code == 200
     assert response.content == b"release plan"
     assert response.headers["content-type"].startswith("text/plain")
+
+
+@pytest.mark.asyncio
+async def test_internal_rag_content_returns_streaming_response(test_db) -> None:
+    context = _create_attachment_context(test_db)
+
+    with (
+        patch(
+            "app.api.endpoints.internal.rag_content.context_service.get_context_optional",
+            return_value=context,
+        ),
+        patch(
+            "app.api.endpoints.internal.rag_content.context_service.get_attachment_binary_data",
+            return_value=b"release plan",
+        ),
+    ):
+        response = await stream_rag_attachment_content(
+            attachment_id=context.id,
+            _=None,
+            db=test_db,
+        )
+
+    assert isinstance(response, StreamingResponse)
diff --git a/backend/tests/services/rag/test_remote_gateway.py b/backend/tests/services/rag/test_remote_gateway.py
index b478de018..33f0db47f 100644
--- a/backend/tests/services/rag/test_remote_gateway.py
+++ b/backend/tests/services/rag/test_remote_gateway.py
@@ -51,6 +51,11 @@ async def test_remote_gateway_index_document_posts_runtime_request(mocker) -> No
             url="https://storage.example.com/release-notes.md",
         ),
     )
+    mocker.patch(
+        "app.services.rag.remote_gateway._get_attachment_source_metadata",
+        return_value=("release-notes.md", ".md"),
+        create=True,
+    )
     post_mock = mocker.patch(
         "httpx.AsyncClient.post",
         return_value=_build_response(
@@ -130,6 +135,8 @@ async def test_remote_gateway_index_document_posts_runtime_request(mocker) -> No
             "kind": "presigned_url",
             "url": "https://storage.example.com/release-notes.md",
         },
+        "source_file": "release-notes.md",
+        "file_extension": ".md",
         "user_name": "alice",
     }
 
@@ -274,7 +281,39 @@ async def test_remote_gateway_translates_structured_remote_errors(mocker) -> Non
     with pytest.raises(
         RemoteRagGatewayError, match="knowledge runtime unavailable"
     ) as exc:
-        await gateway.query(QueryRuntimeSpec(knowledge_base_ids=[1], query="release"))
+        await gateway.query(
+            QueryRuntimeSpec(
+                knowledge_base_ids=[1],
+                query="release",
+                knowledge_base_configs=[
+                    QueryKnowledgeBaseRuntimeConfig(
+                        knowledge_base_id=1,
+                        index_owner_user_id=8,
+                        retriever_config=RuntimeRetrieverConfig(
+                            name="retriever-a",
+                            namespace="default",
+                            storage_config={
+                                "type": "qdrant",
+                                "url": "http://qdrant:6333",
+                            },
+                        ),
+                        embedding_model_config=RuntimeEmbeddingModelConfig(
+                            model_name="embedding-a",
+                            model_namespace="default",
+                            resolved_config={
+                                "protocol": "openai",
+                                "model_id": "text-embedding-3-small",
+                            },
+                        ),
+                        retrieval_config=RuntimeRetrievalConfig(
+                            top_k=20,
+                            score_threshold=0.7,
+                            retrieval_mode="vector",
+                        ),
+                    )
+                ],
+            )
+        )
 
     assert exc.value.code == "runtime_unavailable"
     assert exc.value.retryable is True
diff --git a/backend/tests/services/rag/test_runtime_resolver.py b/backend/tests/services/rag/test_runtime_resolver.py
index 31b7d7c15..0267de550 100644
--- a/backend/tests/services/rag/test_runtime_resolver.py
+++ b/backend/tests/services/rag/test_runtime_resolver.py
@@ -239,3 +239,30 @@ def test_build_delete_runtime_spec_resolves_retriever_config():
     assert spec.document_ref == "doc-8"
     assert spec.index_owner_user_id == 99
     assert spec.retriever_config.storage_config["type"] == "qdrant"
+
+
+def test_build_public_query_runtime_spec_requires_kb_access():
+    resolver = RagRuntimeResolver()
+    db = MagicMock()
+
+    with patch(
+        "app.services.knowledge.knowledge_service.KnowledgeService.get_knowledge_base",
+        return_value=(None, False),
+    ):
+        with pytest.raises(
+            ValueError, match="Knowledge base 7 not found or access denied"
+        ):
+            resolver.build_public_query_runtime_spec(
+                db=db,
+                knowledge_base_id=7,
+                query="release checklist",
+                max_results=5,
+                retriever_name="retriever-a",
+                retriever_namespace="default",
+                embedding_model_name="embed-a",
+                embedding_model_namespace="default",
+                user_id=9,
+                user_name="alice",
+                score_threshold=0.7,
+                retrieval_mode="vector",
+            )
diff --git a/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md b/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
index 26293af64..ec7bfab1a 100644
--- a/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
+++ b/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md
@@ -113,8 +113,8 @@ The checkbox states below are preserved as the original execution breakdown and
 ### Task 1: Realign docs and package boundaries around `knowledge_engine`
 
 **Files:**
-- Modify: [2026-04-03-rag-service-extraction-design.md](/Users/yanhe1/iDev/weibo/wegent/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md)
-- Modify: [2026-04-04-rag-service-extraction-implementation-plan.md](/Users/yanhe1/iDev/weibo/wegent/docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md)
+- Modify: `docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md`
+- Modify: `docs/plans/2026-04-04-rag-service-extraction-implementation-plan.md`
 
 - [ ] Update the design and plan to treat `knowledge_engine` as the current execution-kernel extraction target
 - [ ] State explicitly that Backend keeps control-plane logic and does not split the full `knowledge` module
diff --git a/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md b/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
index 5ec512bee..2a6eee7ff 100644
--- a/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
+++ b/docs/specs/knowledge/2026-04-03-rag-service-extraction-design.md
@@ -339,7 +339,7 @@ content_ref
   -> presigned_url
 ```
 
-#### `backend_attachment_stream`
+### `backend_attachment_stream`
 
 适用于当前 MySQL 或仅 Backend 可读的附件存储。
 
@@ -353,7 +353,7 @@ content_ref
 - 保证兼容当前所有附件存储后端
 - 不需要把附件存储实现复制到 `knowledge_runtime`
 
-#### `presigned_url`
+### `presigned_url`
 
 适用于 S3 / MinIO / 其他对象存储。
 
diff --git a/knowledge_engine/knowledge_engine/embedding/custom.py b/knowledge_engine/knowledge_engine/embedding/custom.py
index 707d8320d..a49e6f105 100644
--- a/knowledge_engine/knowledge_engine/embedding/custom.py
+++ b/knowledge_engine/knowledge_engine/embedding/custom.py
@@ -58,6 +58,9 @@ def _get_text_embedding(self, text: str) -> list[float]:
     async def _aget_query_embedding(self, query: str) -> list[float]:
         return await asyncio.to_thread(self._get_query_embedding, query)
 
+    async def _aget_text_embedding(self, text: str) -> list[float]:
+        return await asyncio.to_thread(self._get_text_embedding, text)
+
     @retry(
         stop=stop_after_attempt(3),
         wait=wait_exponential(multiplier=1, min=2, max=10),
diff --git a/knowledge_engine/knowledge_engine/embedding/factory.py b/knowledge_engine/knowledge_engine/embedding/factory.py
index 6608a86d4..86b5827ef 100644
--- a/knowledge_engine/knowledge_engine/embedding/factory.py
+++ b/knowledge_engine/knowledge_engine/embedding/factory.py
@@ -46,7 +46,7 @@ def _create_embedding_model_from_resolved_values(
             )
             return CustomEmbedding(
                 api_url=api_url,
-                model=model_id or "text-embedding-3-small",
+                model=model_id or model_name or "text-embedding-3-small",
                 headers=custom_headers,
                 api_key=api_key,
                 dimensions=dimensions,
@@ -55,7 +55,7 @@ def _create_embedding_model_from_resolved_values(
         from llama_index.embeddings.openai import OpenAIEmbedding
 
         return OpenAIEmbedding(
-            model=model_id or "text-embedding-3-small",
+            model=model_id or model_name or "text-embedding-3-small",
             api_key=api_key,
             api_base=base_url,
             dimensions=dimensions,
diff --git a/knowledge_engine/knowledge_engine/query/executor.py b/knowledge_engine/knowledge_engine/query/executor.py
index 4c606bbb1..ada3cbe78 100644
--- a/knowledge_engine/knowledge_engine/query/executor.py
+++ b/knowledge_engine/knowledge_engine/query/executor.py
@@ -51,9 +51,13 @@ def _normalize_retrieval_setting(
             config = dict(retrieval_config)
 
         retrieval_setting = {
-            "top_k": config.get("top_k", 20),
-            "score_threshold": config.get("score_threshold", 0.7),
-            "retrieval_mode": config.get("retrieval_mode", "vector"),
+            "top_k": config.get("top_k") or 20,
+            "score_threshold": (
+                config.get("score_threshold")
+                if config.get("score_threshold") is not None
+                else 0.7
+            ),
+            "retrieval_mode": config.get("retrieval_mode") or "vector",
         }
         if "vector_weight" in config:
             retrieval_setting["vector_weight"] = config["vector_weight"]
diff --git a/knowledge_engine/knowledge_engine/retrieval/filters.py b/knowledge_engine/knowledge_engine/retrieval/filters.py
index e97fb45ef..300190b4c 100644
--- a/knowledge_engine/knowledge_engine/retrieval/filters.py
+++ b/knowledge_engine/knowledge_engine/retrieval/filters.py
@@ -117,7 +117,7 @@ def build_elasticsearch_filters(
 
     for cond in metadata_condition["conditions"]:
         key = cond.get("key")
-        operator = cond.get("operator", "eq")
+        operator = str(cond.get("operator", "eq")).lower()
         value = cond.get("value")
 
         if not key or value is None:
diff --git a/knowledge_engine/knowledge_engine/storage/elasticsearch_backend.py b/knowledge_engine/knowledge_engine/storage/elasticsearch_backend.py
index 0541c0d9f..54ab70d9c 100644
--- a/knowledge_engine/knowledge_engine/storage/elasticsearch_backend.py
+++ b/knowledge_engine/knowledge_engine/storage/elasticsearch_backend.py
@@ -468,6 +468,15 @@ def list_documents(
         index_name = self.get_index_name(knowledge_id, **kwargs)
         es_client = Elasticsearch(self.url, **self.es_kwargs)
 
+        if not es_client.indices.exists(index=index_name):
+            return {
+                "documents": [],
+                "total": 0,
+                "page": page,
+                "page_size": page_size,
+                "knowledge_id": knowledge_id,
+            }
+
         # Aggregate by doc_ref (our custom document ID), filtered by knowledge_id
         search_body = {
             "size": 0,
diff --git a/knowledge_engine/tests/storage/test_elasticsearch_backend.py b/knowledge_engine/tests/storage/test_elasticsearch_backend.py
index 5c38b23ba..8d9a3972c 100644
--- a/knowledge_engine/tests/storage/test_elasticsearch_backend.py
+++ b/knowledge_engine/tests/storage/test_elasticsearch_backend.py
@@ -70,3 +70,33 @@ def test_get_all_chunks_returns_empty_when_no_hits(self, mock_client_class):
 
         result = backend.get_all_chunks(knowledge_id="kb_1", max_chunks=100)
         assert result == []
+
+
+class TestListDocuments:
+    @patch("knowledge_engine.storage.elasticsearch_backend.Elasticsearch")
+    def test_list_documents_returns_empty_page_when_index_missing(
+        self, mock_client_class
+    ):
+        from knowledge_engine.storage.elasticsearch_backend import ElasticsearchBackend
+
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.indices.exists.return_value = False
+
+        backend = ElasticsearchBackend(
+            {
+                "url": "http://localhost:9200",
+                "indexStrategy": {"mode": "per_dataset", "prefix": "test"},
+            }
+        )
+
+        result = backend.list_documents(knowledge_id="kb_1", page=2, page_size=10)
+
+        assert result == {
+            "documents": [],
+            "total": 0,
+            "page": 2,
+            "page_size": 10,
+            "knowledge_id": "kb_1",
+        }
+        mock_client.search.assert_not_called()
diff --git a/knowledge_engine/tests/test_embedding_factory.py b/knowledge_engine/tests/test_embedding_factory.py
new file mode 100644
index 000000000..9c158712c
--- /dev/null
+++ b/knowledge_engine/tests/test_embedding_factory.py
@@ -0,0 +1,54 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import AsyncMock
+
+import pytest
+
+from knowledge_engine.embedding.custom import CustomEmbedding
+from knowledge_engine.embedding.factory import (
+    create_embedding_model_from_runtime_config,
+)
+from shared.models import RuntimeEmbeddingModelConfig
+
+
+def test_create_embedding_model_uses_runtime_model_name_when_model_id_missing(
+    mocker,
+) -> None:
+    custom_embedding_cls = mocker.patch(
+        "knowledge_engine.embedding.factory.CustomEmbedding",
+        return_value=object(),
+    )
+
+    create_embedding_model_from_runtime_config(
+        RuntimeEmbeddingModelConfig(
+            model_name="custom-embedding-model",
+            resolved_config={
+                "protocol": "openai",
+                "base_url": "https://api.openai.com/v1",
+                "custom_headers": {"X-Test": "1"},
+            },
+        )
+    )
+
+    custom_embedding_cls.assert_called_once_with(
+        api_url="https://api.openai.com/v1/embeddings",
+        model="custom-embedding-model",
+        headers={"X-Test": "1"},
+        api_key=None,
+        dimensions=None,
+    )
+
+
+@pytest.mark.asyncio
+async def test_custom_embedding_supports_async_text_embedding(mocker) -> None:
+    embedding = CustomEmbedding(
+        api_url="https://api.openai.com/v1/embeddings",
+        model="text-embedding-3-small",
+    )
+    mocker.patch.object(embedding, "_call_api", return_value=[0.1, 0.2, 0.3])
+
+    result = await embedding._aget_text_embedding("release plan")
+
+    assert result == [0.1, 0.2, 0.3]
diff --git a/knowledge_engine/tests/test_query_executor.py b/knowledge_engine/tests/test_query_executor.py
index 1cc8c3a24..a9fd5550d 100644
--- a/knowledge_engine/tests/test_query_executor.py
+++ b/knowledge_engine/tests/test_query_executor.py
@@ -73,3 +73,34 @@ async def test_query_executor_delegates_to_storage_backend_with_normalized_confi
         },
         user_id=7,
     )
+
+
+@pytest.mark.asyncio
+async def test_query_executor_normalizes_explicit_none_values_to_defaults() -> None:
+    from knowledge_engine.query import QueryExecutor
+
+    storage_backend = MagicMock()
+    storage_backend.retrieve.return_value = {"records": []}
+    executor = QueryExecutor(storage_backend=storage_backend, embed_model=object())
+
+    await executor.execute(
+        knowledge_id="1",
+        query="release checklist",
+        retrieval_config={
+            "top_k": None,
+            "score_threshold": None,
+            "retrieval_mode": None,
+        },
+    )
+
+    storage_backend.retrieve.assert_called_once_with(
+        knowledge_id="1",
+        query="release checklist",
+        embed_model=executor.embed_model,
+        retrieval_setting={
+            "top_k": 20,
+            "score_threshold": 0.7,
+            "retrieval_mode": "vector",
+        },
+        metadata_condition=None,
+    )
diff --git a/knowledge_engine/tests/test_retrieval_filters.py b/knowledge_engine/tests/test_retrieval_filters.py
new file mode 100644
index 000000000..a985805d4
--- /dev/null
+++ b/knowledge_engine/tests/test_retrieval_filters.py
@@ -0,0 +1,24 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from knowledge_engine.retrieval.filters import build_elasticsearch_filters
+
+
+def test_build_elasticsearch_filters_normalizes_mixed_case_operators() -> None:
+    filters = build_elasticsearch_filters(
+        "kb_1",
+        {
+            "operator": "and",
+            "conditions": [
+                {"key": "priority", "operator": "GTE", "value": 3},
+                {"key": "tag", "operator": "CONTAINS", "value": "release"},
+            ],
+        },
+    )
+
+    assert filters == [
+        {"term": {"metadata.knowledge_id.keyword": "kb_1"}},
+        {"range": {"metadata.priority.keyword": {"gte": 3}}},
+        {"wildcard": {"metadata.tag.keyword": "*release*"}},
+    ]
diff --git a/knowledge_runtime/knowledge_runtime/core/config.py b/knowledge_runtime/knowledge_runtime/core/config.py
index 5d0787145..6326c18f9 100644
--- a/knowledge_runtime/knowledge_runtime/core/config.py
+++ b/knowledge_runtime/knowledge_runtime/core/config.py
@@ -2,6 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+from pydantic import Field
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
@@ -12,7 +13,7 @@ class Settings(BaseSettings):
     ENVIRONMENT: str = "development"
     HOST: str = "0.0.0.0"
     PORT: int = 8200
-    INTERNAL_SERVICE_TOKEN: str = "knowledge-runtime-token"
+    INTERNAL_SERVICE_TOKEN: str = Field(...)
 
     model_config = SettingsConfigDict(
         env_file=".env",
diff --git a/knowledge_runtime/knowledge_runtime/services/handlers.py b/knowledge_runtime/knowledge_runtime/services/handlers.py
index 36eb5096f..2bb9d0085 100644
--- a/knowledge_runtime/knowledge_runtime/services/handlers.py
+++ b/knowledge_runtime/knowledge_runtime/services/handlers.py
@@ -173,6 +173,10 @@ def _extract_document_id(metadata: dict | None) -> int | None:
     def _resolve_source_file_metadata(
         request: RemoteIndexRequest,
     ) -> tuple[str, str]:
+        if request.source_file:
+            suffix = request.file_extension or PurePosixPath(request.source_file).suffix
+            return request.source_file, suffix.lower()
+
         parsed_url = urlparse(request.content_ref.url)
         file_name = PurePosixPath(parsed_url.path).name
         if file_name:
diff --git a/knowledge_runtime/tests/conftest.py b/knowledge_runtime/tests/conftest.py
new file mode 100644
index 000000000..fff512592
--- /dev/null
+++ b/knowledge_runtime/tests/conftest.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+os.environ.setdefault("INTERNAL_SERVICE_TOKEN", "knowledge-runtime-token")
diff --git a/knowledge_runtime/tests/test_config.py b/knowledge_runtime/tests/test_config.py
new file mode 100644
index 000000000..068838858
--- /dev/null
+++ b/knowledge_runtime/tests/test_config.py
@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: 2026 Weibo, Inc.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+from knowledge_runtime.core.config import Settings
+from pydantic import ValidationError
+
+
+def test_settings_require_internal_service_token(monkeypatch) -> None:
+    monkeypatch.delenv("INTERNAL_SERVICE_TOKEN", raising=False)
+
+    with pytest.raises(ValidationError):
+        Settings(_env_file=None)
diff --git a/knowledge_runtime/tests/test_query_execution.py b/knowledge_runtime/tests/test_query_execution.py
index b8f55f297..b178c1ced 100644
--- a/knowledge_runtime/tests/test_query_execution.py
+++ b/knowledge_runtime/tests/test_query_execution.py
@@ -8,6 +8,7 @@
 from knowledge_runtime.services.handlers import RuntimeHandlers
 
 from shared.models import (
+    RemoteIndexRequest,
     RemoteKnowledgeBaseQueryConfig,
     RemoteQueryRequest,
     RuntimeEmbeddingModelConfig,
@@ -185,3 +186,37 @@ async def test_query_handler_executes_engine_queries_and_merges_results(mocker)
         },
         user_id=202,
     )
+
+
+def test_resolve_source_file_metadata_prefers_explicit_request_metadata() -> None:
+    handlers = RuntimeHandlers()
+
+    remote_index_request = RemoteIndexRequest.model_validate(
+        {
+            "knowledge_base_id": 101,
+            "document_id": 202,
+            "index_owner_user_id": 303,
+            "retriever_config": {
+                "name": "default-retriever",
+                "namespace": "default",
+                "storage_config": {"type": "qdrant"},
+            },
+            "embedding_model_config": {
+                "model_name": "text-embedding-3-small",
+                "model_namespace": "default",
+                "resolved_config": {"protocol": "openai"},
+            },
+            "content_ref": {
+                "kind": "backend_attachment_stream",
+                "url": "http://backend/api/internal/rag/content/12",
+                "auth_token": "download-token",
+            },
+            "source_file": "release-notes.md",
+            "file_extension": ".md",
+        }
+    )
+
+    assert handlers._resolve_source_file_metadata(remote_index_request) == (
+        "release-notes.md",
+        ".md",
+    )
diff --git a/shared/models/knowledge_runtime_protocol.py b/shared/models/knowledge_runtime_protocol.py
index 4b2715ea2..b6777d0cf 100644
--- a/shared/models/knowledge_runtime_protocol.py
+++ b/shared/models/knowledge_runtime_protocol.py
@@ -9,7 +9,7 @@
 from datetime import datetime
 from typing import Annotated, Any, Literal
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 
 class KnowledgeRuntimeProtocolModel(BaseModel):
@@ -111,6 +111,8 @@ class RemoteIndexRequest(KnowledgeRuntimeProtocolModel):
     retriever_config: RuntimeRetrieverConfig
     embedding_model_config: RuntimeEmbeddingModelConfig
     splitter_config: dict[str, Any] | None = None
+    source_file: str | None = None
+    file_extension: str | None = None
     index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
     content_ref: ContentRef
     trace_context: dict[str, Any] | None = None
@@ -145,13 +147,28 @@ class RemoteQueryRequest(KnowledgeRuntimeProtocolModel):
     document_ids: list[int] | None = None
     metadata_condition: dict[str, Any] | None = None
     user_name: str | None = None
-    knowledge_base_configs: list[RemoteKnowledgeBaseQueryConfig] = Field(
-        default_factory=list
-    )
+    knowledge_base_configs: list[RemoteKnowledgeBaseQueryConfig]
     enabled_index_families: list[str] = Field(default_factory=lambda: ["chunk_vector"])
     retrieval_policy: RetrievalPolicy = "chunk_only"
     extensions: dict[str, Any] | None = None
 
+    @model_validator(mode="after")
+    def validate_knowledge_base_configs(self) -> "RemoteQueryRequest":
+        if not self.knowledge_base_configs:
+            raise ValueError("knowledge_base_configs must not be empty")
+
+        requested_ids = list(self.knowledge_base_ids)
+        configured_ids = [
+            config.knowledge_base_id for config in self.knowledge_base_configs
+        ]
+        if len(requested_ids) != len(configured_ids) or set(requested_ids) != set(
+            configured_ids
+        ):
+            raise ValueError(
+                "knowledge_base_configs must align with knowledge_base_ids"
+            )
+        return self
+
 
 class RemoteQueryRecord(KnowledgeRuntimeProtocolModel):
     """Single retrieval record returned by knowledge_runtime."""
diff --git a/shared/tests/test_knowledge_runtime_protocol.py b/shared/tests/test_knowledge_runtime_protocol.py
index 82b8637d0..d15d6e0d3 100644
--- a/shared/tests/test_knowledge_runtime_protocol.py
+++ b/shared/tests/test_knowledge_runtime_protocol.py
@@ -80,8 +80,19 @@ def test_remote_index_request_rejects_unknown_content_ref_kind() -> None:
                 "knowledge_base_id": 11,
                 "document_id": 22,
                 "index_owner_user_id": 33,
-                "retriever_config": {"provider": "milvus"},
-                "embedding_model_config": {"model": "text-embedding-3-large"},
+                "retriever_config": {
+                    "name": "retriever-a",
+                    "namespace": "default",
+                    "storage_config": {
+                        "type": "milvus",
+                        "url": "http://milvus:19530",
+                    },
+                },
+                "embedding_model_config": {
+                    "model_name": "text-embedding-3-large",
+                    "model_namespace": "default",
+                    "resolved_config": {"protocol": "openai"},
+                },
                 "index_families": ["chunk_vector"],
                 "content_ref": {
                     "kind": "unsupported_kind",
@@ -191,6 +202,58 @@ def test_remote_query_request_accepts_explicit_execution_configs() -> None:
     ]
 
 
+def test_remote_query_request_rejects_empty_knowledge_base_configs() -> None:
+    remote_query_request = _require_model("RemoteQueryRequest")
+
+    with pytest.raises(ValidationError):
+        remote_query_request.model_validate(
+            {
+                "knowledge_base_ids": [1001],
+                "query": "release checklist",
+                "knowledge_base_configs": [],
+            }
+        )
+
+
+def test_remote_query_request_rejects_misaligned_knowledge_base_configs() -> None:
+    remote_query_request = _require_model("RemoteQueryRequest")
+
+    with pytest.raises(ValidationError):
+        remote_query_request.model_validate(
+            {
+                "knowledge_base_ids": [1001, 1002],
+                "query": "release checklist",
+                "knowledge_base_configs": [
+                    {
+                        "knowledge_base_id": 1001,
+                        "index_owner_user_id": 42,
+                        "retriever_config": {
+                            "name": "retriever-a",
+                            "namespace": "default",
+                            "storage_config": {
+                                "type": "qdrant",
+                                "url": "http://qdrant:6333",
+                            },
+                        },
+                        "embedding_model_config": {
+                            "model_name": "embed-a",
+                            "model_namespace": "default",
+                            "resolved_config": {
+                                "protocol": "openai",
+                                "model_id": "text-embedding-3-small",
+                            },
+                        },
+                        "retrieval_config": {
+                            "top_k": 8,
+                            "score_threshold": 0.55,
+                            "retrieval_mode": "hybrid",
+                        },
+                    }
+                ],
+            }
+        )
+
+
 def test_remote_delete_request_requires_resolved_retriever_config() -> None:
     remote_delete_request = _require_model("RemoteDeleteDocumentIndexRequest")