feat: Add VectorXDB Vector Search Tool

crewai_tools/__init__.py

@@ -83,10 +83,13 @@
     SnowflakeConfig,
     SnowflakeSearchTool,
     SpiderTool,
+    SpladeSparseEmbedder,
     StagehandTool,
     TavilyExtractorTool,
     TavilySearchTool,
     TXTSearchTool,
+    VectorXVectorSearchTool,
+    VectorXSearchArgs,
     VisionTool,
     WeaviateVectorSearchTool,
     WebsiteSearchTool,

crewai_tools/tools/__init__.py

@@ -122,6 +122,11 @@
 )
 from .youtube_video_search_tool.youtube_video_search_tool import YoutubeVideoSearchTool
 from .zapier_action_tool.zapier_action_tool import ZapierActionTools
+from .vectorx_vector_search_tool.vectorx_search_tool import (
+    VectorXVectorSearchTool,
+    VectorXSearchArgs,
+    SpladeSparseEmbedder,
+)
 from .parallel_tools import (
     ParallelSearchTool,
 )

crewai_tools/tools/vectorx_vector_search_tool/README.md

@@ -0,0 +1,163 @@
+# VectorX Search Tool for CrewAI
+
+This repository provides a **CrewAI tool integration** for [VectorX](https://vectorxdb.ai), enabling **semantic search** and optional **hybrid (dense + sparse) retrieval** inside CrewAI workflows.
+It uses **Google Gemini embeddings** by default for dense vectors and supports **SPLADE** for sparse vectors.
+
+---
+
+## Features
+
+* 🔹 **Dense search** using Gemini (`gemini-embedding-001` by default, user-configurable)
+* 🔹 **Hybrid search** (dense + sparse) with support for custom sparse models by default, [prithivida/Splade\_PP\_en\_v1](https://huggingface.co/prithivida/Splade_PP_en_v1)
+* 🔹 Seamless integration with **CrewAI Agents**
+* 🔹 Document upsert and query
+* 🔹 Custom encryption and collection support
+
+---
+
+## Installation
+
+You can install the required packages in one of two ways:
+
+### Option 1: Install manually via pip
+
+```bash
+pip install crewai vecx google-genai
+````
+
+> ⚠️ If you want to enable **sparse embeddings (SPLADE)**, also install:
+
+```bash
+pip install transformers torch
+```
+
+---
+
+### Option 2: Install everything from `requirements.txt`
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+### `requirements.txt` contents:
+
+```txt
+crewai==0.175.0
+vecx==0.33.1b5
+google-genai==1.32.0
+torch==2.8.0
+transformers==4.45.2
+tokenizers==0.20.3
+numpy==2.2.4
+```
+---
+
+
+## Usage
+
+### 1. Import & Configure
+
+```python
+import os
+from crewai import Agent, Crew, LLM, Task, Process
+from crewai_tools import VectorXVectorSearchTool
+
+# Initialize the tool
+tool = VectorXVectorSearchTool(
+    api_token=os.getenv("VECTORX_TOKEN"),
+    collection_name="my_vectorx_collection",
+    encryption_key=os.getenv("ENCRYPTION_KEY"),
+    use_sparse=False,   # set True to enable hybrid SPLADE search
+    top_k=3,
+)
+```
+
+---
+
+### 2. Store Documents (Example Usage)
+
+```python
+tool.store_documents(
+    [
+        "Python is a versatile programming language.",
+        "JavaScript is widely used in web development.",
+        "Rust is known for safety and performance.",
+    ],
+    [
+        {"category": "language", "name": "Python"},
+        {"category": "language", "name": "JavaScript"},
+        {"category": "language", "name": "Rust"},
+    ],
+)
+```
+
+---
+
+### 3. Setup CrewAI Agent
+
+```python
+llm = LLM(
+    model="gemini/gemini-1.5-flash",
+    api_key=os.getenv("GEMINI_API_KEY"),
+)
+
+agent = Agent(
+    role="Vector Search Agent",
+    goal="Answer user questions using VectorX search",
+    backstory="You're a helpful assistant that uses VectorX for semantic retrieval.",
+    llm=llm,
+    tools=[tool],
+)
+
+task = Task(
+    description="Answer the user's question using VectorX search. The user asked: {query}",
+    agent=agent,
+    expected_output="A concise, relevant answer based on documents.",
+)
+
+crew = Crew(agents=[agent], tasks=[task], process=Process.sequential)
+```
+
+---
+
+### 4. Run a Query (Example Usage)
+
+```python
+if __name__ == "__main__":
+    question = "Tell me about Python language features."
+    print(f"\nQuery: {question}")
+    result = crew.kickoff({"query": question})
+    print("\nAnswer:\n", result)
+```
+
+---
+
+## Hybrid Search with SPLADE
+
+Enable hybrid mode:
+
+```python
+tool = VectorXVectorSearchTool(
+    api_token=os.getenv("VECTORX_TOKEN"),
+    collection_name="my_vectorx_collection",
+    use_sparse=True,   # 🔹 enable SPLADE hybrid retrieval
+)
+```
+
+This will combine **dense Gemini embeddings** with **sparse lexical signals** from SPLADE, improving recall on keyword-heavy queries.
+
+---
+
+## Environment Variables (.env)
+
+| Variable         | Description                                  |
+| ---------------- | -------------------------------------------- |
+| `VECTORX_TOKEN`  | API token for your VectorX instance          |
+| `GEMINI_API_KEY` | Google Gemini API key for embeddings & LLM   |
+| `ENCRYPTION_KEY` | (Optional) Encryption key for secure storage |
+| `GEMINI_MODEL` | (Optional) Gemini embedding model ID. Defaults to models/embedding-001   |
+| `SPLADE_MODEL` | (Optional) SPLADE model name from HuggingFace. Defaults to prithivida/Splade_PP_en_v1 |
+
+---

crewai_tools/tools/vectorx_vector_search_tool/__init__.py

@@ -0,0 +1,11 @@
+from .vectorx_search_tool import (
+    VectorXVectorSearchTool,
+    VectorXSearchArgs,
+    SpladeSparseEmbedder,
+)
+
+__all__ = [
+    "VectorXVectorSearchTool",
+    "VectorXSearchArgs",
+    "SpladeSparseEmbedder",
+]