docs(cn): 翻译 get-started 和 ai-toolkit 目录 (按规范格式)

src/content/ai-toolkit/chat-client-sample.md

@@ -1,9 +1,13 @@
 ---
 title: Chat client sample
+title: 聊天客户端示例
 description: >
   Learn about the chat client sample included in the AI Toolkit.
+description: >
+  了解 AI Toolkit 中包含的聊天客户端示例。
 prev:
   title: Custom LLM providers
+  title: 自定义 LLM 提供者
   path: /ai-toolkit/custom-llm-providers
 ---
 
@@ -15,27 +19,48 @@ the AI Chat sample shows how to store and manage
 multiple chats at once in your own apps.
 On desktop form-factors, the AI Chat sample looks like the following:
 
+AI Chat 示例是一个功能齐全的聊天应用，
+使用 Flutter AI Toolkit 和 Firebase 的 Vertex AI 构建。
+除了从 AI Toolkit 获得的多轮对话、多媒体、
+流式传输等功能外，
+AI Chat 示例还展示了如何在你的应用中
+同时存储和管理多个聊天会话。
+在桌面设备上，AI Chat 示例如下所示：
+
 ![Desktop app UI](/assets/images/docs/ai-toolkit/desktop-pluto-convo.png)
 
 
 On mobile form-factors, it looks like this:
 
+在移动设备上，它如下所示：
+
 ![Mobile app UI](/assets/images/docs/ai-toolkit/mobile-pluto-convo.png)
 
 The chats are stored in an authenticated
 Cloud Firestore database; any authenticated
 user can have as many chats as they like.
 
+聊天记录存储在经过身份验证的 Cloud Firestore 数据库中；
+任何经过身份验证的用户都可以拥有任意数量的聊天会话。
+
 In addition, for each new chat, while the user can
 manually title it whatever they like,
 the initial prompt and response is used to ask
 the LLM what an appropriate title should be.
 In fact, the titles of the chats in the
 screenshots in this page were set automatically.
 
+此外，对于每个新的聊天会话，虽然用户可以
+手动设置任何喜欢的标题，
+但初始提示和响应会用于询问 LLM 应该使用什么合适的标题。
+实际上，本页截图中的聊天标题都是自动设置的。
+
 To build and run the sample,
 follow the instructions in the [AI Chat README][].
 
+要构建和运行示例，
+请按照 [AI Chat README][] 中的说明进行操作。
+
 {% comment %}
 TODO: If Mit agrees, move this to an official Flutter repo
   Chris didn't want to do it so close to release

src/content/ai-toolkit/custom-llm-providers.md

@@ -1,18 +1,27 @@
 ---
 title: Custom LLM providers
+title: 自定义 LLM provider 
 description: >
   How to integrate with other Flutter features.
+description: >
+  如何与其他 Flutter 功能集成。
 prev:
   title: Feature integration
+  title: 功能集成
   path: /ai-toolkit/feature-integration
 next:
   title: Chat client sample
+  title: 聊天客户端示例
   path: /ai-toolkit/chat-client-sample
 ---
 
 The protocol connecting an LLM and the `LlmChatView`
 is expressed in the [`LlmProvider` interface][]:
 
+连接 LLM 和 `LlmChatView` 的协议在 [`LlmProvider` 接口][] 中定义：
+
+[`LlmProvider` 接口]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/LlmProvider-class.html
+
 ```dart
 abstract class LlmProvider implements Listenable {
   Stream<String> generateStream(String prompt, {Iterable<Attachment> attachments});
@@ -33,33 +42,60 @@ comes with three providers out of the box,
 all of which implement the `LlmProvider` interface
 that is required to plug the provider into the following:
 
+LLM 可以是云端的或本地的，可以托管在 Google Cloud Platform 或其他云服务商上，可以是专有的 LLM 或开源的。任何可以用来实现此接口的 LLM 或类似 LLM 的端点都可以作为 LLM provider 插入到聊天视图中。AI Toolkit 开箱即用地提供了三个提供者，它们都实现了 `LlmProvider` 接口，这是将提供者插入到以下内容所需的：
+
 * The [Gemini provider][],
   which wraps the `google_generative_ai` package
+
+  [Gemini provider][]，它封装了 `google_generative_ai` 包
+
 * The [Vertex provider][],
   which wraps the `firebase_vertexai` package
+
+  [Vertex provider][]，它封装了 `firebase_vertexai` 包
+
 * The [Echo provider][],
   which is useful as a minimal provider example
 
+  [Echo provider][]，作为最小化的 provider 示例很有用
+
 [Echo provider]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/EchoProvider-class.html
 [Gemini provider]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/GeminiProvider-class.html
-[`LlmProvider` interface]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/LlmProvider-class.html
 [Vertex provider]: {{site.pub-api}}/flutter_ai_toolkit/latest/flutter_ai_toolkit/VertexProvider-class.html
 
 ## Implementation
 
+## 实现
+
 To build your own provider, you need to implement
 the `LlmProvider` interface with these things in mind:
 
+要构建你自己的 provider，你需要实现 `LlmProvider` 接口，并牢记以下几点：
+
 1. Providing for full configuration support
+
+   提供完整的配置支持
+
 1. Handling history
+
+   处理历史记录
+
 1. Translating messages and attachments to the underlying LLM
+
+   将消息和附件转换为底层 LLM 的格式
+
 1. Calling the underlying LLM
 
+   调用底层 LLM
+
 1. Configuration
    To support full configurability in your custom provider,
    you should allow the user to create the underlying model
    and pass that in as a parameter, as the Gemini provider does:
 
+   配置
+   为了在自定义提供者中支持完整的可配置性，你应该允许用户创建底层模型并将其作为参数传入，就像 Gemini provider 所做的那样：
+
 ```dart
 class GeminiProvider extends LlmProvider ... {
   @immutable
@@ -79,6 +115,8 @@ to the underlying model in the future,
 the configuration knobs will all be available
 to the user of your custom provider.
 
+通过这种方式，无论底层模型未来发生什么变化，配置选项都将对你的自定义 provider 的用户可用。
+
 2. History
   History is a big part of any provider—not only
   does the provider need to allow history to be
@@ -89,6 +127,11 @@ to the user of your custom provider.
 
   The Gemini provider handles this as shown:
 
+  历史记录
+  历史记录是任何 provider 的重要组成部分—— provider 不仅需要允许直接操作历史记录，还必须在历史记录发生变化时通知监听器。此外，为了支持序列化和更改 provider 参数，它还必须支持在构造过程中保存历史记录。
+
+  Gemini provider 如下所示处理这个问题：
+
 ```dart
 class GeminiProvider extends LlmProvider with ChangeNotifier {
   @immutable
@@ -143,14 +186,31 @@ class GeminiProvider extends LlmProvider with ChangeNotifier {
 ```
 
 You'll notice several things in this code:
+
+你会在这段代码中注意到以下几点：
+
 * The use of `ChangeNotifier` to implement the `Listenable`
   method requirements from the `LlmProvider` interface
+
+  使用 `ChangeNotifier` 来实现 `LlmProvider` 接口中 `Listenable` 的方法要求
+
 * The ability to pass initial history in as a constructor parameter
+
+  可以将初始历史记录作为构造函数参数传入
+
 * Notifying listeners when there's a new user
   prompt/LLM response pair
+
+  在有新的用户提示/LLM 响应对时通知监听器
+
 * Notifying listeners when the history is changed manually
+
+  在手动更改历史记录时通知监听器
+
 * Creating a new chat when the history changes, using the new history
 
+  当历史记录更改时使用新历史记录创建新的聊天
+
 Essentially, a custom provider manages the history
 for a single chat session with the underlying LLM.
 As the history changes, the underlying chat either
@@ -159,8 +219,12 @@ needs to be kept up to date automatically
 the underlying chat-specific methods) or manually recreated
 (as the Gemini provider does whenever the history is set manually).
 
+本质上，自定义 provider 管理与底层 LLM 的单个聊天会话的历史记录。随着历史记录的变化，底层聊天需要自动保持最新（就像当你调用底层聊天特定方法时 Gemini AI SDK for Dart 所做的那样）或手动重新创建（就像每当手动设置历史记录时 Gemini provider 所做的那样）。
+
 3. Messages and attachments
 
+   消息和附件
+
 Attachments must be mapped from the standard
 `ChatMessage` class exposed by the `LlmProvider`
 type to whatever is handled by the underlying LLM.
@@ -169,6 +233,8 @@ For example, the Gemini provider maps from the
 `Content` type provided by the Gemini AI SDK for Dart,
 as shown in the following example:
 
+附件必须从 `LlmProvider` 类型公开的标准 `ChatMessage` 类映射到底层 LLM 处理的任何内容。例如，Gemini provider 将 AI Toolkit 的 `ChatMessage` 类映射到 Gemini AI SDK for Dart 提供的 `Content` 类型，如下例所示：
+
 ```dart
 import 'package:google_generative_ai/google_generative_ai.dart';
 ...
@@ -194,8 +260,12 @@ The `_contentFrom` method is called whenever a user prompt
 needs to be sent to the underlying LLM.
 Every provider needs to provide for its own mapping.
 
+每当需要将用户提示发送到底层 LLM 时，就会调用 `_contentFrom` 方法。每个提供者都需要提供自己的映射。
+
 4. Calling the LLM
 
+   调用 LLM
+
 How you call the underlying LLM to implement
 `generateStream` and `sendMessageStream` methods
 depends on the protocol it exposes.
@@ -204,6 +274,8 @@ handles configuration and history but calls to
 `generateStream` and `sendMessageStream` each
 end up in a call to an API from the Gemini AI SDK for Dart:
 
+如何调用底层 LLM 来实现 `generateStream` 和 `sendMessageStream` 方法取决于它公开的协议。AI Toolkit 中的 Gemini provider 处理配置和历史记录，但对 `generateStream` 和 `sendMessageStream` 的调用最终都会调用 Gemini AI SDK for Dart 的 API：
+
 ```dart
 class GeminiProvider extends LlmProvider with ChangeNotifier {
   ...
@@ -275,6 +347,8 @@ class GeminiProvider extends LlmProvider with ChangeNotifier {
 
 ## Examples
 
+## 示例
+
 The [Gemini provider][] and [Vertex provider][]
 implementations are nearly identical and provide
 a good starting point for your own custom provider.
@@ -284,4 +358,7 @@ check out the [Echo example app][], which simply formats
 the user's prompt and attachments as Markdown
 to send back to the user as its response.
 
+[Gemini provider][] 和 [Vertex provider][] 的实现几乎相同，为你自己的自定义 provider 提供了一个很好的起点。如果你想看一个剥离了所有对底层 LLM 调用的 provider 实现示例，请查看 [Echo 示例应用][Echo example app]，它只是将用户的提示和附件格式化为 Markdown 并将其作为响应发送回用户。
+
 [Echo example app]: {{site.github}}/flutter/ai/blob/main/lib/src/providers/implementations/echo_provider.dart
+[Echo 示例应用]: {{site.github}}/flutter/ai/blob/main/lib/src/providers/implementations/echo_provider.dart