chore: release v4.1.2

* Update package.json

* Update PluginMarketComponent.tsx

* Update PluginMarketComponent.tsx

* Update package.json

* Update PluginCardComponent.tsx

* perf: no display github button when plugin has no github url

---------

Co-authored-by: Junyan Qin <rockchinq@gmail.com>

.gitignore

@@ -42,4 +42,5 @@ botpy.log*
 test.py
 /web_ui
 .venv/
-uv.lock
+uv.lock
+/test

README.md

@@ -6,14 +6,14 @@
 
 <div align="center">
 
-简体中文 / [English](README_EN.md) / [日本語](README_JP.md) / (PR for your language)
+[English](README_EN.md) / 简体中文 / [繁體中文](README_TW.md) / [日本語](README_JP.md) / (PR for your language)
 
 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-966235608-blue)](https://qm.qq.com/q/JLi38whHum)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
-[![star](https://gitcode.com/langbot-app/LangBot/star/badge.svg)](https://gitcode.com/langbot-app/LangBot)
+[![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
 
 <a href="https://langbot.app">项目主页</a> ｜
 <a href="https://docs.langbot.app/zh/insight/guide.html">部署文档</a> ｜
@@ -25,12 +25,7 @@
 
 </p>
 
-## ✨ 特性
-
-- 💬 大模型对话、Agent：支持多种大模型，适配群聊和私聊；具有多轮对话、工具调用、多模态能力，并深度适配 [Dify](https://dify.ai)。目前支持 QQ、QQ频道、企业微信、个人微信、飞书、Discord、Telegram 等平台。
-- 🛠️ 高稳定性、功能完备：原生支持访问控制、限速、敏感词过滤等机制；配置简单，支持多种部署方式。支持多流水线配置，不同机器人用于不同应用场景。
-- 🧩 插件扩展、活跃社区：支持事件驱动、组件扩展等插件机制；适配 Anthropic [MCP 协议](https://modelcontextprotocol.io/)；目前已有数百个插件。
-- 😻 Web 管理面板：支持通过浏览器管理 LangBot 实例，不再需要手动编写配置文件。
+LangBot 是一个开源的大语言模型原生即时通信机器人开发平台，旨在提供开箱即用的 IM 机器人开发体验，具有 Agent、RAG、MCP 等多种 LLM 应用功能，适配全球主流即时通信平台，并提供丰富的 API 接口，支持自定义开发。
 
 ## 📦 开始使用
 
@@ -64,23 +59,25 @@ docker compose up -d
 
 直接使用发行版运行，查看文档[手动部署](https://docs.langbot.app/zh/deploy/langbot/manual.html)。
 
-## 📸 效果展示
+## 😎 保持更新
 
-<img alt="bots" src="https://docs.langbot.app/webui/bot-page.png" width="450px"/>
+点击仓库右上角 Star 和 Watch 按钮，获取最新动态。
 
-<img alt="bots" src="https://docs.langbot.app/webui/create-model.png" width="450px"/>
+![star gif](https://docs.langbot.app/star.gif)
 
-<img alt="bots" src="https://docs.langbot.app/webui/edit-pipeline.png" width="450px"/>
+## ✨ 特性
 
-<img alt="bots" src="https://docs.langbot.app/webui/plugin-market.png" width="450px"/>
+- 💬 大模型对话、Agent：支持多种大模型，适配群聊和私聊；具有多轮对话、工具调用、多模态能力，自带 RAG（知识库）实现，并深度适配 [Dify](https://dify.ai)。
+- 🤖 多平台支持：目前支持 QQ、QQ频道、企业微信、个人微信、飞书、Discord、Telegram 等平台。
+- 🛠️ 高稳定性、功能完备：原生支持访问控制、限速、敏感词过滤等机制；配置简单，支持多种部署方式。支持多流水线配置，不同机器人用于不同应用场景。
+- 🧩 插件扩展、活跃社区：支持事件驱动、组件扩展等插件机制；适配 Anthropic [MCP 协议](https://modelcontextprotocol.io/)；目前已有数百个插件。
+- 😻 Web 管理面板：支持通过浏览器管理 LangBot 实例，不再需要手动编写配置文件。
 
-<img alt="回复效果（带有联网插件）" src="https://docs.langbot.app/QChatGPT-0516.png" width="500px"/>
+详细规格特性请访问[文档](https://docs.langbot.app/zh/insight/features.html)。
 
-- WebUI Demo: https://demo.langbot.dev/
-    - 登录信息：邮箱：`demo@langbot.app` 密码：`langbot123456`
-    - 注意：仅展示webui效果，公开环境，请不要在其中填入您的任何敏感信息。
-
-## 🔌 组件兼容性
+或访问 demo 环境：https://demo.langbot.dev/  
+  - 登录信息：邮箱：`demo@langbot.app` 密码：`langbot123456`
+  - 注意：仅展示 WebUI 效果，公开环境，请不要在其中填入您的任何敏感信息。
 
 ### 消息平台
 
@@ -88,19 +85,14 @@ docker compose up -d
 | --- | --- | --- |
 | QQ 个人号 | ✅ | QQ 个人号私聊、群聊 |
 | QQ 官方机器人 | ✅ | QQ 官方机器人，支持频道、私聊、群聊 |
-| 企业微信 | ✅ |  |
+| 微信 | ✅ |  |
 | 企微对外客服 | ✅ |  |
-| 个人微信 | ✅ |  |
 | 微信公众号 | ✅ |  |
 | 飞书 | ✅ |  |
 | 钉钉 | ✅ |  |
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
-| LINE | 🚧 |  |
-| WhatsApp | 🚧 |  |
-
-🚧: 正在开发中
 
 ### 大模型能力
 
@@ -147,9 +139,3 @@ docker compose up -d
 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
 </a>
-
-## 😎 保持更新
-
-点击仓库右上角 Star 和 Watch 按钮，获取最新动态。
-
-![star gif](https://docs.langbot.app/star.gif)

README_EN.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-[简体中文](README.md) / English / [日本語](README_JP.md) / (PR for your language)
+English / [简体中文](README.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / (PR for your language)
 
 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
@@ -21,12 +21,7 @@
 
 </p>
 
-## ✨ Features
-
-- 💬 Chat with LLM / Agent: Supports multiple LLMs, adapt to group chats and private chats; Supports multi-round conversations, tool calls, and multi-modal capabilities. Deeply integrates with [Dify](https://dify.ai). Currently supports QQ, QQ Channel, WeCom, personal WeChat, Lark, DingTalk, Discord, Telegram, etc.
-- 🛠️ High Stability, Feature-rich: Native access control, rate limiting, sensitive word filtering, etc. mechanisms; Easy to use, supports multiple deployment methods. Supports multiple pipeline configurations, different bots can be used for different scenarios.
-- 🧩 Plugin Extension, Active Community: Support event-driven, component extension, etc. plugin mechanisms; Integrate Anthropic [MCP protocol](https://modelcontextprotocol.io/); Currently has hundreds of plugins.
-- 😻 [New] Web UI: Support management LangBot instance through the browser. No need to manually write configuration files.
+LangBot is an open-source LLM native instant messaging robot development platform, aiming to provide out-of-the-box IM robot development experience, with Agent, RAG, MCP and other LLM application functions, adapting to global instant messaging platforms, and providing rich API interfaces, supporting custom development.
 
 ## 📦 Getting Started
 
@@ -60,23 +55,25 @@ Community contributed Zeabur template.
 
 Directly use the released version to run, see the [Manual Deployment](https://docs.langbot.app/en/deploy/langbot/manual.html) documentation.
 
-## 📸 Demo
+## 😎 Stay Ahead
 
-<img alt="bots" src="https://docs.langbot.app/webui/bot-page.png" width="400px"/>
+Click the Star and Watch button in the upper right corner of the repository to get the latest updates.
 
-<img alt="bots" src="https://docs.langbot.app/webui/create-model.png" width="400px"/>
+![star gif](https://docs.langbot.app/star.gif)
 
-<img alt="bots" src="https://docs.langbot.app/webui/edit-pipeline.png" width="400px"/>
+## ✨ Features
 
-<img alt="bots" src="https://docs.langbot.app/webui/plugin-market.png" width="400px"/>
+- 💬 Chat with LLM / Agent: Supports multiple LLMs, adapt to group chats and private chats; Supports multi-round conversations, tool calls, and multi-modal capabilities. Built-in RAG (knowledge base) implementation, and deeply integrates with [Dify](https://dify.ai).
+- 🤖 Multi-platform Support: Currently supports QQ, QQ Channel, WeCom, personal WeChat, Lark, DingTalk, Discord, Telegram, etc.
+- 🛠️ High Stability, Feature-rich: Native access control, rate limiting, sensitive word filtering, etc. mechanisms; Easy to use, supports multiple deployment methods. Supports multiple pipeline configurations, different bots can be used for different scenarios.
+- 🧩 Plugin Extension, Active Community: Support event-driven, component extension, etc. plugin mechanisms; Integrate Anthropic [MCP protocol](https://modelcontextprotocol.io/); Currently has hundreds of plugins.
+- 😻 Web UI: Support management LangBot instance through the browser. No need to manually write configuration files.
 
-<img alt="Reply Effect (with Internet Plugin)" src="https://docs.langbot.app/QChatGPT-0516.png" width="500px"/>
+For more detailed specifications, please refer to the [documentation](https://docs.langbot.app/en/insight/features.html).
 
-- WebUI Demo: https://demo.langbot.dev/
-    - Login information: Email: `demo@langbot.app` Password: `langbot123456`
-    - Note: Only the WebUI effect is shown, please do not fill in any sensitive information in the public environment.
-
-## 🔌 Component Compatibility
+Or visit the demo environment: https://demo.langbot.dev/
+  - Login information: Email: `demo@langbot.app` Password: `langbot123456`
+  - Note: For WebUI demo only, please do not fill in any sensitive information in the public environment.
 
 ### Message Platform
 
@@ -92,10 +89,6 @@ Directly use the released version to run, see the [Manual Deployment](https://do
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
-| LINE | 🚧 |  |
-| WhatsApp | 🚧 |  |
-
-🚧: In development
 
 ### LLMs
 
@@ -128,9 +121,3 @@ Thank you for the following [code contributors](https://github.com/langbot-app/L
 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
 </a>
-
-## 😎 Stay Ahead
-
-Click the Star and Watch button in the upper right corner of the repository to get the latest updates.
-
-![star gif](https://docs.langbot.app/star.gif)

README_JP.md

@@ -5,7 +5,7 @@
 
 <div align="center">
 
-[简体中文](README.md) / [English](README_EN.md) / 日本語 / (PR for your language)
+[English](README_EN.md) / [简体中文](README.md) / [繁體中文](README_TW.md) / 日本語 / (PR for your language)
 
 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
@@ -21,12 +21,7 @@
 
 </p>
 
-## ✨ 機能
-
-- 💬 LLM / エージェントとのチャット: 複数のLLMをサポートし、グループチャットとプライベートチャットに対応。マルチラウンドの会話、ツールの呼び出し、マルチモーダル機能をサポート。 [Dify](https://dify.ai) と深く統合。現在、QQ、QQ チャンネル、WeChat、個人 WeChat、Lark、DingTalk、Discord、Telegram など、複数のプラットフォームをサポートしています。
-- 🛠️ 高い安定性、豊富な機能: ネイティブのアクセス制御、レート制限、敏感な単語のフィルタリングなどのメカニズムをサポート。使いやすく、複数のデプロイ方法をサポート。複数のパイプライン設定をサポートし、異なるボットを異なる用途に使用できます。
-- 🧩 プラグイン拡張、活発なコミュニティ: イベント駆動、コンポーネント拡張などのプラグインメカニズムをサポート。適配 Anthropic [MCP プロトコル](https://modelcontextprotocol.io/)；豊富なエコシステム、現在数百のプラグインが存在。
-- 😻 Web UI: ブラウザを通じてLangBotインスタンスを管理することをサポート。
+LangBot は、エージェント、RAG、MCP などの LLM アプリケーション機能を備えた、オープンソースの LLM ネイティブのインスタントメッセージングロボット開発プラットフォームです。世界中のインスタントメッセージングプラットフォームに適応し、豊富な API インターフェースを提供し、カスタム開発をサポートします。
 
 ## 📦 始め方
 
@@ -42,7 +37,7 @@ http://localhost:5300 にアクセスして使用を開始します。
 
 詳細なドキュメントは[Dockerデプロイ](https://docs.langbot.app/en/deploy/langbot/docker.html)を参照してください。
 
-#### BTPanelでのワンクリックデプロイ
+#### Panelでのワンクリックデプロイ
 
 LangBotはBTPanelにリストされています。BTPanelをインストールしている場合は、[ドキュメント](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html)を使用して使用できます。
 
@@ -60,23 +55,25 @@ LangBotはBTPanelにリストされています。BTPanelをインストール
 
 リリースバージョンを直接使用して実行します。[手動デプロイ](https://docs.langbot.app/en/deploy/langbot/manual.html)のドキュメントを参照してください。
 
-## 📸 デモ
+## 😎 最新情報を入手
 
-<img alt="bots" src="https://docs.langbot.app/webui/bot-page.png" width="400px"/>
+リポジトリの右上にある Star と Watch ボタンをクリックして、最新の更新を取得してください。
 
-<img alt="bots" src="https://docs.langbot.app/webui/create-model.png" width="400px"/>
+![star gif](https://docs.langbot.app/star.gif)
 
-<img alt="bots" src="https://docs.langbot.app/webui/edit-pipeline.png" width="400px"/>
+## ✨ 機能
 
-<img alt="bots" src="https://docs.langbot.app/webui/plugin-market.png" width="400px"/>
+- 💬 LLM / エージェントとのチャット: 複数のLLMをサポートし、グループチャットとプライベートチャットに対応。マルチラウンドの会話、ツールの呼び出し、マルチモーダル機能をサポート、RAG（知識ベース）を組み込み、[Dify](https://dify.ai) と深く統合。
+- 🤖 多プラットフォーム対応: 現在、QQ、QQ チャンネル、WeChat、個人 WeChat、Lark、DingTalk、Discord、Telegram など、複数のプラットフォームをサポートしています。
+- 🛠️ 高い安定性、豊富な機能: ネイティブのアクセス制御、レート制限、敏感な単語のフィルタリングなどのメカニズムをサポート。使いやすく、複数のデプロイ方法をサポート。複数のパイプライン設定をサポートし、異なるボットを異なる用途に使用できます。
+- 🧩 プラグイン拡張、活発なコミュニティ: イベント駆動、コンポーネント拡張などのプラグインメカニズムをサポート。適配 Anthropic [MCP プロトコル](https://modelcontextprotocol.io/)；豊富なエコシステム、現在数百のプラグインが存在。
+- 😻 Web UI: ブラウザを通じてLangBotインスタンスを管理することをサポート。
 
-<img alt="返信効果（インターネットプラグイン付き）" src="https://docs.langbot.app/QChatGPT-0516.png" width="500px"/>
+詳細な仕様については、[ドキュメント](https://docs.langbot.app/en/insight/features.html)を参照してください。
 
-- WebUIデモ: https://demo.langbot.dev/
-    - ログイン情報: メール: `demo@langbot.app` パスワード: `langbot123456`
-    - 注意: WebUIの効果のみを示しています。公開環境では、機密情報を入力しないでください。
-
-## 🔌 コンポーネントの互換性
+または、デモ環境にアクセスしてください: https://demo.langbot.dev/
+  - ログイン情報: メール: `demo@langbot.app` パスワード: `langbot123456`
+  - 注意: WebUI のデモンストレーションのみの場合、公開環境では機密情報を入力しないでください。
 
 ### メッセージプラットフォーム
 
@@ -92,10 +89,6 @@ LangBotはBTPanelにリストされています。BTPanelをインストール
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
-| LINE | 🚧 |  |
-| WhatsApp | 🚧 |  |
-
-🚧: 開発中
 
 ### LLMs
 
@@ -128,9 +121,3 @@ LangBot への貢献に対して、以下の [コード貢献者](https://github
 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
 </a>
-
-## 😎 最新情報を入手
-
-リポジトリの右上にある Star と Watch ボタンをクリックして、最新の更新を取得してください。
-
-![star gif](https://docs.langbot.app/star.gif)

README_TW.md

@@ -0,0 +1,139 @@
+<p align="center">
+<a href="https://langbot.app">
+<img src="https://docs.langbot.app/social_zh.png" alt="LangBot"/>
+</a>
+
+<div align="center">
+
+[English](README_EN.md) / [简体中文](README.md) / 繁體中文 / [日本語](README_JP.md) / (PR for your language)
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
+[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-966235608-blue)](https://qm.qq.com/q/JLi38whHum)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
+[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
+<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
+
+<a href="https://langbot.app">主頁</a> ｜
+<a href="https://docs.langbot.app/zh/insight/guide.html">部署文件</a> ｜
+<a href="https://docs.langbot.app/zh/plugin/plugin-intro.html">外掛介紹</a> ｜
+<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">提交外掛</a>
+
+</div>
+
+</p>
+
+LangBot 是一個開源的大語言模型原生即時通訊機器人開發平台，旨在提供開箱即用的 IM 機器人開發體驗，具有 Agent、RAG、MCP 等多種 LLM 應用功能，適配全球主流即時通訊平台，並提供豐富的 API 介面，支援自定義開發。
+
+## 📦 開始使用
+
+#### Docker Compose 部署
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot
+docker compose up -d
+```
+
+訪問 http://localhost:5300 即可開始使用。
+
+詳細文件[Docker 部署](https://docs.langbot.app/zh/deploy/langbot/docker.html)。
+
+#### 寶塔面板部署
+
+已上架寶塔面板，若您已安裝寶塔面板，可以根據[文件](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html)使用。
+
+#### Zeabur 雲端部署
+
+社群貢獻的 Zeabur 模板。
+
+[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
+
+#### Railway 雲端部署
+
+[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
+
+#### 手動部署
+
+直接使用發行版運行，查看文件[手動部署](https://docs.langbot.app/zh/deploy/langbot/manual.html)。
+
+## 😎 保持更新
+
+點擊倉庫右上角 Star 和 Watch 按鈕，獲取最新動態。
+
+![star gif](https://docs.langbot.app/star.gif)
+
+## ✨ 特性
+
+- 💬 大模型對話、Agent：支援多種大模型，適配群聊和私聊；具有多輪對話、工具調用、多模態能力，自帶 RAG（知識庫）實現，並深度適配 [Dify](https://dify.ai)。
+- 🤖 多平台支援：目前支援 QQ、QQ頻道、企業微信、個人微信、飛書、Discord、Telegram 等平台。
+- 🛠️ 高穩定性、功能完備：原生支援訪問控制、限速、敏感詞過濾等機制；配置簡單，支援多種部署方式。支援多流水線配置，不同機器人用於不同應用場景。
+- 🧩 外掛擴展、活躍社群：支援事件驅動、組件擴展等外掛機制；適配 Anthropic [MCP 協議](https://modelcontextprotocol.io/)；目前已有數百個外掛。
+- 😻 Web 管理面板：支援通過瀏覽器管理 LangBot 實例，不再需要手動編寫配置文件。
+
+詳細規格特性請訪問[文件](https://docs.langbot.app/zh/insight/features.html)。
+
+或訪問 demo 環境：https://demo.langbot.dev/  
+  - 登入資訊：郵箱：`demo@langbot.app` 密碼：`langbot123456`
+  - 注意：僅展示 WebUI 效果，公開環境，請不要在其中填入您的任何敏感資訊。
+
+### 訊息平台
+
+| 平台 | 狀態 | 備註 |
+| --- | --- | --- |
+| QQ 個人號 | ✅ | QQ 個人號私聊、群聊 |
+| QQ 官方機器人 | ✅ | QQ 官方機器人，支援頻道、私聊、群聊 |
+| 微信 | ✅ |  |
+| 企微對外客服 | ✅ |  |
+| 微信公眾號 | ✅ |  |
+| Lark | ✅ |  |
+| DingTalk | ✅ |  |
+| Discord | ✅ |  |
+| Telegram | ✅ |  |
+| Slack | ✅ |  |
+
+### 大模型能力
+
+| 模型 | 狀態 | 備註 |
+| --- | --- | --- |
+| [OpenAI](https://platform.openai.com/) | ✅ | 可接入任何 OpenAI 介面格式模型 |
+| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
+| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
+| [Anthropic](https://www.anthropic.com/) | ✅ |  |
+| [xAI](https://x.ai/) | ✅ |  |
+| [智譜AI](https://open.bigmodel.cn/) | ✅ |  |
+| [優雲智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | 大模型和 GPU 資源平台 |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | 大模型和 GPU 資源平台 |
+| [302.AI](https://share.302.ai/SuTG99) | ✅ | 大模型聚合平台 |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
+| [Dify](https://dify.ai) | ✅ | LLMOps 平台 |
+| [Ollama](https://ollama.com/) | ✅ | 本地大模型運行平台 |
+| [LMStudio](https://lmstudio.ai/) | ✅ | 本地大模型運行平台 |
+| [GiteeAI](https://ai.gitee.com/) | ✅ | 大模型介面聚合平台 |
+| [SiliconFlow](https://siliconflow.cn/) | ✅ | 大模型聚合平台 |
+| [阿里雲百煉](https://bailian.console.aliyun.com/) | ✅ | 大模型聚合平台, LLMOps 平台 |
+| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | 大模型聚合平台, LLMOps 平台 |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | 大模型聚合平台 |
+| [MCP](https://modelcontextprotocol.io/) | ✅ | 支援通過 MCP 協議獲取工具 |
+
+### TTS
+
+| 平台/模型 | 備註 |
+| --- | --- |
+| [FishAudio](https://fish.audio/zh-CN/discovery/) | [外掛](https://github.com/the-lazy-me/NewChatVoice) |
+| [海豚 AI](https://www.ttson.cn/?source=thelazy) | [外掛](https://github.com/the-lazy-me/NewChatVoice) |
+| [AzureTTS](https://portal.azure.com/) | [外掛](https://github.com/Ingnaryk/LangBot_AzureTTS) |
+
+### 文生圖
+
+| 平台/模型 | 備註 |
+| --- | --- |
+| 阿里雲百煉 | [外掛](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin)
+
+## 😘 社群貢獻
+
+感謝以下[程式碼貢獻者](https://github.com/langbot-app/LangBot/graphs/contributors)和社群裡其他成員對 LangBot 的貢獻：
+
+<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
+</a> 

libs/wechatpad_api/__init__.py

@@ -1 +1 @@
-from .client import WeChatPadClient
+from .client import WeChatPadClient as WeChatPadClient

libs/wechatpad_api/api/chatroom.py

@@ -1,4 +1,4 @@
-from libs.wechatpad_api.util.http_util import async_request, post_json
+from libs.wechatpad_api.util.http_util import post_json
 
 
 class ChatRoomApi:
@@ -7,8 +7,6 @@ class ChatRoomApi:
         self.token = token
 
     def get_chatroom_member_detail(self, chatroom_name):
-        params = {
-            "ChatRoomName": chatroom_name
-        }
+        params = {'ChatRoomName': chatroom_name}
         url = self.base_url + '/group/GetChatroomMemberDetail'
         return post_json(url, token=self.token, data=params)

libs/wechatpad_api/api/downloadpai.py

@@ -1,32 +1,23 @@
-from libs.wechatpad_api.util.http_util import async_request, post_json
+from libs.wechatpad_api.util.http_util import post_json
 import httpx
 import base64
 
+
 class DownloadApi:
     def __init__(self, base_url, token):
         self.base_url = base_url
         self.token = token
 
     def send_download(self, aeskey, file_type, file_url):
-        json_data = {
-                  "AesKey": aeskey,
-                  "FileType": file_type,
-                  "FileURL": file_url
-                }
-        url = self.base_url + "/message/SendCdnDownload"
+        json_data = {'AesKey': aeskey, 'FileType': file_type, 'FileURL': file_url}
+        url = self.base_url + '/message/SendCdnDownload'
         return post_json(url, token=self.token, data=json_data)
 
-    def get_msg_voice(self,buf_id, length, new_msgid):
-        json_data = {
-            "Bufid": buf_id,
-            "Length": length,
-            "NewMsgId": new_msgid,
-            "ToUserName": ""
-        }
-        url = self.base_url + "/message/GetMsgVoice"
+    def get_msg_voice(self, buf_id, length, new_msgid):
+        json_data = {'Bufid': buf_id, 'Length': length, 'NewMsgId': new_msgid, 'ToUserName': ''}
+        url = self.base_url + '/message/GetMsgVoice'
         return post_json(url, token=self.token, data=json_data)
 
-
     async def download_url_to_base64(self, download_url):
         async with httpx.AsyncClient() as client:
             response = await client.get(download_url)
@@ -36,4 +27,4 @@ class DownloadApi:
                 base64_str = base64.b64encode(file_bytes).decode('utf-8')  # 返回字符串格式
                 return base64_str
             else:
-                raise Exception('获取文件失败')
+                raise Exception('获取文件失败')

libs/wechatpad_api/api/friend.py

@@ -1,11 +1,6 @@
-from libs.wechatpad_api.util.http_util import post_json,async_request
-from typing import List, Dict, Any, Optional
-
-
 class FriendApi:
     """联系人API类，处理所有与联系人相关的操作"""
 
     def __init__(self, base_url: str, token: str):
         self.base_url = base_url
         self.token = token
-

libs/wechatpad_api/api/login.py

@@ -1,37 +1,34 @@
-from libs.wechatpad_api.util.http_util import async_request,post_json,get_json
+from libs.wechatpad_api.util.http_util import post_json, get_json
 
 
 class LoginApi:
     def __init__(self, base_url: str, token: str = None, admin_key: str = None):
-        '''
+        """
 
         Args:
             base_url: 原始路径
             token: token
             admin_key: 管理员key
-        '''
+        """
         self.base_url = base_url
         self.token = token
         # self.admin_key = admin_key
 
-    def get_token(self, admin_key, day: int=365):
+    def get_token(self, admin_key, day: int = 365):
         # 获取普通token
-        url = f"{self.base_url}/admin/GenAuthKey1"
-        json_data = {
-                "Count": 1,
-                "Days": day
-            }
+        url = f'{self.base_url}/admin/GenAuthKey1'
+        json_data = {'Count': 1, 'Days': day}
         return post_json(base_url=url, token=admin_key, data=json_data)
 
-    def get_login_qr(self, Proxy: str = ""):
-        '''
+    def get_login_qr(self, Proxy: str = ''):
+        """
 
         Args:
             Proxy:异地使用时代理
 
         Returns:json数据
 
-        '''
+        """
         """
         
         {
@@ -49,54 +46,37 @@ class LoginApi:
 }
         
         """
-        #获取登录二维码
-        url = f"{self.base_url}/login/GetLoginQrCodeNew"
+        # 获取登录二维码
+        url = f'{self.base_url}/login/GetLoginQrCodeNew'
         check = False
-        if Proxy != "":
+        if Proxy != '':
             check = True
-        json_data = {
-              "Check": check,
-              "Proxy": Proxy
-            }
+        json_data = {'Check': check, 'Proxy': Proxy}
         return post_json(base_url=url, token=self.token, data=json_data)
 
-
     def get_login_status(self):
         # 获取登录状态
         url = f'{self.base_url}/login/GetLoginStatus'
         return get_json(base_url=url, token=self.token)
 
-
-
     def logout(self):
         # 退出登录
         url = f'{self.base_url}/login/LogOut'
         return post_json(base_url=url, token=self.token)
 
-
-
-
-    def wake_up_login(self, Proxy: str = ""):
+    def wake_up_login(self, Proxy: str = ''):
         # 唤醒登录
         url = f'{self.base_url}/login/WakeUpLogin'
         check = False
-        if Proxy != "":
+        if Proxy != '':
             check = True
-        json_data = {
-            "Check": check,
-            "Proxy": ""
-        }
+        json_data = {'Check': check, 'Proxy': ''}
 
         return post_json(base_url=url, token=self.token, data=json_data)
 
-
-
-    def login(self,admin_key):
+    def login(self, admin_key):
         login_status = self.get_login_status()
-        if login_status["Code"] == 300 and login_status["Text"] == "你已退出微信":
-            print("token已经失效，重新获取")
+        if login_status['Code'] == 300 and login_status['Text'] == '你已退出微信':
+            print('token已经失效，重新获取')
             token_data = self.get_token(admin_key)
-            self.token = token_data["Data"][0]
-
-
-
+            self.token = token_data['Data'][0]

libs/wechatpad_api/api/message.py

@@ -1,5 +1,4 @@
-
-from libs.wechatpad_api.util.http_util import async_request, post_json
+from libs.wechatpad_api.util.http_util import post_json
 
 
 class MessageApi:
@@ -7,8 +6,8 @@ class MessageApi:
         self.base_url = base_url
         self.token = token
 
-    def post_text(self, to_wxid, content, ats: list= []):
-        '''
+    def post_text(self, to_wxid, content, ats: list = []):
+        """
 
         Args:
             app_id: 微信id
@@ -18,106 +17,64 @@ class MessageApi:
 
         Returns:
 
-        '''
-        url = self.base_url + "/message/SendTextMessage"
+        """
+        url = self.base_url + '/message/SendTextMessage'
         """发送文字消息"""
         json_data = {
-                      "MsgItem": [
-                        {
-                          "AtWxIDList": ats,
-                          "ImageContent": "",
-                          "MsgType": 0,
-                          "TextContent": content,
-                          "ToUserName": to_wxid
-                        }
-                      ]
-                    }
-        return  post_json(base_url=url, token=self.token, data=json_data)
+            'MsgItem': [
+                {'AtWxIDList': ats, 'ImageContent': '', 'MsgType': 0, 'TextContent': content, 'ToUserName': to_wxid}
+            ]
+        }
+        return post_json(base_url=url, token=self.token, data=json_data)
 
-
-
-
-    def post_image(self, to_wxid, img_url, ats: list= []):
+    def post_image(self, to_wxid, img_url, ats: list = []):
         """发送图片消息"""
         # 这里好像可以尝试发送多个暂时未测试
         json_data = {
-            "MsgItem": [
-                {
-                    "AtWxIDList": ats,
-                    "ImageContent": img_url,
-                    "MsgType": 0,
-                    "TextContent": '',
-                    "ToUserName": to_wxid
-                }
+            'MsgItem': [
+                {'AtWxIDList': ats, 'ImageContent': img_url, 'MsgType': 0, 'TextContent': '', 'ToUserName': to_wxid}
             ]
         }
-        url = self.base_url + "/message/SendImageMessage"
+        url = self.base_url + '/message/SendImageMessage'
         return post_json(base_url=url, token=self.token, data=json_data)
 
     def post_voice(self, to_wxid, voice_data, voice_forma, voice_duration):
         """发送语音消息"""
         json_data = {
-            "ToUserName": to_wxid,
-            "VoiceData": voice_data,
-            "VoiceFormat": voice_forma,
-            "VoiceSecond": voice_duration
+            'ToUserName': to_wxid,
+            'VoiceData': voice_data,
+            'VoiceFormat': voice_forma,
+            'VoiceSecond': voice_duration,
         }
-        url = self.base_url + "/message/SendVoice"
+        url = self.base_url + '/message/SendVoice'
         return post_json(base_url=url, token=self.token, data=json_data)
 
-
-
-
-
     def post_name_card(self, alias, to_wxid, nick_name, name_card_wxid, flag):
         """发送名片消息"""
         param = {
-                "CardAlias": alias,
-                "CardFlag": flag,
-                "CardNickName": nick_name,
-                "CardWxId": name_card_wxid,
-                "ToUserName": to_wxid
+            'CardAlias': alias,
+            'CardFlag': flag,
+            'CardNickName': nick_name,
+            'CardWxId': name_card_wxid,
+            'ToUserName': to_wxid,
         }
-        url = f"{self.base_url}/message/ShareCardMessage"
+        url = f'{self.base_url}/message/ShareCardMessage'
         return post_json(base_url=url, token=self.token, data=param)
 
-    def post_emoji(self, to_wxid, emoji_md5, emoji_size:int=0):
+    def post_emoji(self, to_wxid, emoji_md5, emoji_size: int = 0):
         """发送emoji消息"""
-        json_data = {
-            "EmojiList": [
-                {
-                    "EmojiMd5": emoji_md5,
-                    "EmojiSize": emoji_size,
-                    "ToUserName": to_wxid
-                }
-            ]
-        }
-        url = f"{self.base_url}/message/SendEmojiMessage"
+        json_data = {'EmojiList': [{'EmojiMd5': emoji_md5, 'EmojiSize': emoji_size, 'ToUserName': to_wxid}]}
+        url = f'{self.base_url}/message/SendEmojiMessage'
         return post_json(base_url=url, token=self.token, data=json_data)
 
-    def post_app_msg(self, to_wxid,xml_data, contenttype:int=0):
+    def post_app_msg(self, to_wxid, xml_data, contenttype: int = 0):
         """发送appmsg消息"""
-        json_data = {
-            "AppList": [
-                {
-                    "ContentType": contenttype,
-                    "ContentXML": xml_data,
-                    "ToUserName": to_wxid
-                }
-            ]
-        }
-        url = f"{self.base_url}/message/SendAppMessage"
+        json_data = {'AppList': [{'ContentType': contenttype, 'ContentXML': xml_data, 'ToUserName': to_wxid}]}
+        url = f'{self.base_url}/message/SendAppMessage'
         return post_json(base_url=url, token=self.token, data=json_data)
 
-
-
     def revoke_msg(self, to_wxid, msg_id, new_msg_id, create_time):
         """撤回消息"""
-        param = {
-              "ClientMsgId": msg_id,
-              "CreateTime": create_time,
-              "NewMsgId": new_msg_id,
-              "ToUserName": to_wxid
-            }
-        url = f"{self.base_url}/message/RevokeMsg"
-        return post_json(base_url=url, token=self.token, data=param)
+        param = {'ClientMsgId': msg_id, 'CreateTime': create_time, 'NewMsgId': new_msg_id, 'ToUserName': to_wxid}
+        url = f'{self.base_url}/message/RevokeMsg'
+        return post_json(base_url=url, token=self.token, data=param)

libs/wechatpad_api/util/http_util.py

@@ -1,10 +1,9 @@
 import requests
+import aiohttp
+
 
 def post_json(base_url, token, data=None):
-    headers = {
-        'Content-Type': 'application/json'
-    }
-
+    headers = {'Content-Type': 'application/json'}
 
     url = base_url + f'?key={token}'
 
@@ -18,14 +17,12 @@ def post_json(base_url, token, data=None):
         else:
             raise RuntimeError(response.text)
     except Exception as e:
-        print(f"http请求失败, url={url}, exception={e}")
+        print(f'http请求失败, url={url}, exception={e}')
         raise RuntimeError(str(e))
 
-def get_json(base_url, token):
-    headers = {
-        'Content-Type': 'application/json'
-    }
 
+def get_json(base_url, token):
+    headers = {'Content-Type': 'application/json'}
 
     url = base_url + f'?key={token}'
 
@@ -39,21 +36,18 @@ def get_json(base_url, token):
         else:
             raise RuntimeError(response.text)
     except Exception as e:
-        print(f"http请求失败, url={url}, exception={e}")
+        print(f'http请求失败, url={url}, exception={e}')
         raise RuntimeError(str(e))
 
-import aiohttp
-import asyncio
-
 
 async def async_request(
-        base_url: str,
-        token_key: str,
-        method: str = 'POST',
-        params: dict = None,
-        # headers: dict = None,
-        data: dict = None,
-        json: dict = None
+    base_url: str,
+    token_key: str,
+    method: str = 'POST',
+    params: dict = None,
+    # headers: dict = None,
+    data: dict = None,
+    json: dict = None,
 ):
     """
     通用异步请求函数
@@ -67,18 +61,11 @@ async def async_request(
     :param json: JSON数据
     :return: 响应文本
     """
-    headers = {
-        'Content-Type': 'application/json'
-    }
-    url = f"{base_url}?key={token_key}"
+    headers = {'Content-Type': 'application/json'}
+    url = f'{base_url}?key={token_key}'
     async with aiohttp.ClientSession() as session:
         async with session.request(
-                method=method,
-                url=url,
-                params=params,
-                headers=headers,
-                data=data,
-                json=json
+            method=method, url=url, params=params, headers=headers, data=data, json=json
         ) as response:
             response.raise_for_status()  # 如果状态码不是200，抛出异常
             result = await response.json()
@@ -89,4 +76,3 @@ async def async_request(
             #     return await result
             # else:
             #     raise RuntimeError("请求失败",response.text)
-

pkg/api/http/controller/group.py

@@ -14,8 +14,8 @@ preregistered_groups: list[type[RouterGroup]] = []
 """Pre-registered list of RouterGroup"""
 
 
-def group_class(name: str, path: str) -> None:
-    """Register a RouterGroup"""
+def group_class(name: str, path: str) -> typing.Callable[[typing.Type[RouterGroup]], typing.Type[RouterGroup]]:
+    """注册一个 RouterGroup"""
 
     def decorator(cls: typing.Type[RouterGroup]) -> typing.Type[RouterGroup]:
         cls.name = name
@@ -86,10 +86,11 @@ class RouterGroup(abc.ABC):
 
                 try:
                     return await f(*args, **kwargs)
-                except Exception:  # auto 500
+
+                except Exception as e:  # 自动 500
                     traceback.print_exc()
                     # return self.http_status(500, -2, str(e))
-                    return self.http_status(500, -2, 'internal server error')
+                    return self.http_status(500, -2, str(e))
 
             new_f = handler_error
             new_f.__name__ = (self.name + rule).replace('/', '__')
@@ -120,6 +121,6 @@ class RouterGroup(abc.ABC):
             }
         )
 
-    def http_status(self, status: int, code: int, msg: str) -> quart.Response:
-        """Return a response with a specified status code"""
-        return self.fail(code, msg), status
+    def http_status(self, status: int, code: int, msg: str) -> typing.Tuple[quart.Response, int]:
+        """返回一个指定状态码的响应"""
+        return (self.fail(code, msg), status)

pkg/api/http/controller/groups/files.py

@@ -2,6 +2,10 @@ from __future__ import annotations
 
 import quart
 import mimetypes
+import uuid
+import asyncio
+
+import quart.datastructures
 
 from .. import group
 
@@ -20,3 +24,23 @@ class FilesRouterGroup(group.RouterGroup):
                 mime_type = 'image/jpeg'
 
             return quart.Response(image_bytes, mimetype=mime_type)
+
+        @self.route('/documents', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
+        async def _() -> quart.Response:
+            request = quart.request
+            # get file bytes from 'file'
+            file = (await request.files)['file']
+            assert isinstance(file, quart.datastructures.FileStorage)
+
+            file_bytes = await asyncio.to_thread(file.stream.read)
+            extension = file.filename.split('.')[-1]
+            file_name = file.filename.split('.')[0]
+
+            file_key = file_name + '_' + str(uuid.uuid4())[:8] + '.' + extension
+            # save file to storage
+            await self.ap.storage_mgr.storage_provider.save(file_key, file_bytes)
+            return self.success(
+                data={
+                    'file_id': file_key,
+                }
+            )

pkg/api/http/controller/groups/knowledge/base.py

@@ -0,0 +1,90 @@
+import quart
+from ... import group
+
+
+@group.group_class('knowledge_base', '/api/v1/knowledge/bases')
+class KnowledgeBaseRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.route('', methods=['POST', 'GET'])
+        async def handle_knowledge_bases() -> quart.Response:
+            if quart.request.method == 'GET':
+                knowledge_bases = await self.ap.knowledge_service.get_knowledge_bases()
+                return self.success(data={'bases': knowledge_bases})
+
+            elif quart.request.method == 'POST':
+                json_data = await quart.request.json
+                knowledge_base_uuid = await self.ap.knowledge_service.create_knowledge_base(json_data)
+                return self.success(data={'uuid': knowledge_base_uuid})
+
+            return self.http_status(405, -1, 'Method not allowed')
+
+        @self.route(
+            '/<knowledge_base_uuid>',
+            methods=['GET', 'DELETE', 'PUT'],
+        )
+        async def handle_specific_knowledge_base(knowledge_base_uuid: str) -> quart.Response:
+            if quart.request.method == 'GET':
+                knowledge_base = await self.ap.knowledge_service.get_knowledge_base(knowledge_base_uuid)
+
+                if knowledge_base is None:
+                    return self.http_status(404, -1, 'knowledge base not found')
+
+                return self.success(
+                    data={
+                        'base': knowledge_base,
+                    }
+                )
+
+            elif quart.request.method == 'PUT':
+                json_data = await quart.request.json
+                await self.ap.knowledge_service.update_knowledge_base(knowledge_base_uuid, json_data)
+                return self.success({})
+
+            elif quart.request.method == 'DELETE':
+                await self.ap.knowledge_service.delete_knowledge_base(knowledge_base_uuid)
+                return self.success({})
+
+        @self.route(
+            '/<knowledge_base_uuid>/files',
+            methods=['GET', 'POST'],
+        )
+        async def get_knowledge_base_files(knowledge_base_uuid: str) -> str:
+            if quart.request.method == 'GET':
+                files = await self.ap.knowledge_service.get_files_by_knowledge_base(knowledge_base_uuid)
+                return self.success(
+                    data={
+                        'files': files,
+                    }
+                )
+
+            elif quart.request.method == 'POST':
+                json_data = await quart.request.json
+                file_id = json_data.get('file_id')
+                if not file_id:
+                    return self.http_status(400, -1, 'File ID is required')
+
+                # 调用服务层方法将文件与知识库关联
+                task_id = await self.ap.knowledge_service.store_file(knowledge_base_uuid, file_id)
+                return self.success(
+                    {
+                        'task_id': task_id,
+                    }
+                )
+
+        @self.route(
+            '/<knowledge_base_uuid>/files/<file_id>',
+            methods=['DELETE'],
+        )
+        async def delete_specific_file_in_kb(file_id: str, knowledge_base_uuid: str) -> str:
+            await self.ap.knowledge_service.delete_file(knowledge_base_uuid, file_id)
+            return self.success({})
+
+        @self.route(
+            '/<knowledge_base_uuid>/retrieve',
+            methods=['POST'],
+        )
+        async def retrieve_knowledge_base(knowledge_base_uuid: str) -> str:
+            json_data = await quart.request.json
+            query = json_data.get('query')
+            results = await self.ap.knowledge_service.retrieve_knowledge_base(knowledge_base_uuid, query)
+            return self.success(data={'results': results})

pkg/api/http/controller/groups/pipelines/pipelines.py

@@ -11,7 +11,9 @@ class PipelinesRouterGroup(group.RouterGroup):
         @self.route('', methods=['GET', 'POST'])
         async def _() -> str:
             if quart.request.method == 'GET':
-                return self.success(data={'pipelines': await self.ap.pipeline_service.get_pipelines()})
+                sort_by = quart.request.args.get('sort_by', 'created_at')
+                sort_order = quart.request.args.get('sort_order', 'DESC')
+                return self.success(data={'pipelines': await self.ap.pipeline_service.get_pipelines(sort_by, sort_order)})
             elif quart.request.method == 'POST':
                 json_data = await quart.request.json
 

pkg/api/http/controller/groups/provider/models.py

@@ -9,18 +9,18 @@ class LLMModelsRouterGroup(group.RouterGroup):
         @self.route('', methods=['GET', 'POST'])
         async def _() -> str:
             if quart.request.method == 'GET':
-                return self.success(data={'models': await self.ap.model_service.get_llm_models()})
+                return self.success(data={'models': await self.ap.llm_model_service.get_llm_models()})
             elif quart.request.method == 'POST':
                 json_data = await quart.request.json
 
-                model_uuid = await self.ap.model_service.create_llm_model(json_data)
+                model_uuid = await self.ap.llm_model_service.create_llm_model(json_data)
 
                 return self.success(data={'uuid': model_uuid})
 
         @self.route('/<model_uuid>', methods=['GET', 'PUT', 'DELETE'])
         async def _(model_uuid: str) -> str:
             if quart.request.method == 'GET':
-                model = await self.ap.model_service.get_llm_model(model_uuid)
+                model = await self.ap.llm_model_service.get_llm_model(model_uuid)
 
                 if model is None:
                     return self.http_status(404, -1, 'model not found')
@@ -29,11 +29,11 @@ class LLMModelsRouterGroup(group.RouterGroup):
             elif quart.request.method == 'PUT':
                 json_data = await quart.request.json
 
-                await self.ap.model_service.update_llm_model(model_uuid, json_data)
+                await self.ap.llm_model_service.update_llm_model(model_uuid, json_data)
 
                 return self.success()
             elif quart.request.method == 'DELETE':
-                await self.ap.model_service.delete_llm_model(model_uuid)
+                await self.ap.llm_model_service.delete_llm_model(model_uuid)
 
                 return self.success()
 
@@ -41,6 +41,49 @@ class LLMModelsRouterGroup(group.RouterGroup):
         async def _(model_uuid: str) -> str:
             json_data = await quart.request.json
 
-            await self.ap.model_service.test_llm_model(model_uuid, json_data)
+            await self.ap.llm_model_service.test_llm_model(model_uuid, json_data)
+
+            return self.success()
+
+
+@group.group_class('models/embedding', '/api/v1/provider/models/embedding')
+class EmbeddingModelsRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.route('', methods=['GET', 'POST'])
+        async def _() -> str:
+            if quart.request.method == 'GET':
+                return self.success(data={'models': await self.ap.embedding_models_service.get_embedding_models()})
+            elif quart.request.method == 'POST':
+                json_data = await quart.request.json
+
+                model_uuid = await self.ap.embedding_models_service.create_embedding_model(json_data)
+
+                return self.success(data={'uuid': model_uuid})
+
+        @self.route('/<model_uuid>', methods=['GET', 'PUT', 'DELETE'])
+        async def _(model_uuid: str) -> str:
+            if quart.request.method == 'GET':
+                model = await self.ap.embedding_models_service.get_embedding_model(model_uuid)
+
+                if model is None:
+                    return self.http_status(404, -1, 'model not found')
+
+                return self.success(data={'model': model})
+            elif quart.request.method == 'PUT':
+                json_data = await quart.request.json
+
+                await self.ap.embedding_models_service.update_embedding_model(model_uuid, json_data)
+
+                return self.success()
+            elif quart.request.method == 'DELETE':
+                await self.ap.embedding_models_service.delete_embedding_model(model_uuid)
+
+                return self.success()
+
+        @self.route('/<model_uuid>/test', methods=['POST'])
+        async def _(model_uuid: str) -> str:
+            json_data = await quart.request.json
+
+            await self.ap.embedding_models_service.test_embedding_model(model_uuid, json_data)
 
             return self.success()

pkg/api/http/controller/groups/provider/requesters.py

@@ -8,7 +8,8 @@ class RequestersRouterGroup(group.RouterGroup):
     async def initialize(self) -> None:
         @self.route('', methods=['GET'])
         async def _() -> quart.Response:
-            return self.success(data={'requesters': self.ap.model_mgr.get_available_requesters_info()})
+            model_type = quart.request.args.get('type', '')
+            return self.success(data={'requesters': self.ap.model_mgr.get_available_requesters_info(model_type)})
 
         @self.route('/<requester_name>', methods=['GET'])
         async def _(requester_name: str) -> quart.Response:

pkg/api/http/controller/main.py

@@ -14,11 +14,13 @@ from . import group
 from .groups import provider as groups_provider
 from .groups import platform as groups_platform
 from .groups import pipelines as groups_pipelines
+from .groups import knowledge as groups_knowledge
 
 importutil.import_modules_in_pkg(groups)
 importutil.import_modules_in_pkg(groups_provider)
 importutil.import_modules_in_pkg(groups_platform)
 importutil.import_modules_in_pkg(groups_pipelines)
+importutil.import_modules_in_pkg(groups_knowledge)
 
 
 class HTTPController:

pkg/api/http/service/knowledge.py

@@ -0,0 +1,118 @@
+from __future__ import annotations
+
+import uuid
+import sqlalchemy
+
+from ....core import app
+from ....entity.persistence import rag as persistence_rag
+
+
+class KnowledgeService:
+    """知识库服务"""
+
+    ap: app.Application
+
+    def __init__(self, ap: app.Application) -> None:
+        self.ap = ap
+
+    async def get_knowledge_bases(self) -> list[dict]:
+        """获取所有知识库"""
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
+        knowledge_bases = result.all()
+        return [
+            self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
+            for knowledge_base in knowledge_bases
+        ]
+
+    async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
+        """获取知识库"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
+        )
+        knowledge_base = result.first()
+        if knowledge_base is None:
+            return None
+        return self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
+
+    async def create_knowledge_base(self, kb_data: dict) -> str:
+        """创建知识库"""
+        kb_data['uuid'] = str(uuid.uuid4())
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
+
+        kb = await self.get_knowledge_base(kb_data['uuid'])
+
+        await self.ap.rag_mgr.load_knowledge_base(kb)
+
+        return kb_data['uuid']
+
+    async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
+        """更新知识库"""
+        if 'uuid' in kb_data:
+            del kb_data['uuid']
+
+        if 'embedding_model_uuid' in kb_data:
+            del kb_data['embedding_model_uuid']
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(persistence_rag.KnowledgeBase)
+            .values(kb_data)
+            .where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
+        )
+        await self.ap.rag_mgr.remove_knowledge_base_from_runtime(kb_uuid)
+
+        kb = await self.get_knowledge_base(kb_uuid)
+
+        await self.ap.rag_mgr.load_knowledge_base(kb)
+
+    async def store_file(self, kb_uuid: str, file_id: str) -> int:
+        """存储文件"""
+        # await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(kb_id=kb_uuid, file_id=file_id))
+        # await self.ap.rag_mgr.store_file(file_id)
+        runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
+        if runtime_kb is None:
+            raise Exception('Knowledge base not found')
+        return await runtime_kb.store_file(file_id)
+
+    async def retrieve_knowledge_base(self, kb_uuid: str, query: str) -> list[dict]:
+        """检索知识库"""
+        runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
+        if runtime_kb is None:
+            raise Exception('Knowledge base not found')
+        return [result.model_dump() for result in await runtime_kb.retrieve(query)]
+
+    async def get_files_by_knowledge_base(self, kb_uuid: str) -> list[dict]:
+        """获取知识库文件"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_rag.File).where(persistence_rag.File.kb_id == kb_uuid)
+        )
+        files = result.all()
+        return [self.ap.persistence_mgr.serialize_model(persistence_rag.File, file) for file in files]
+
+    async def delete_file(self, kb_uuid: str, file_id: str) -> None:
+        """删除文件"""
+        runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
+        if runtime_kb is None:
+            raise Exception('Knowledge base not found')
+        await runtime_kb.delete_file(file_id)
+
+    async def delete_knowledge_base(self, kb_uuid: str) -> None:
+        """删除知识库"""
+        await self.ap.rag_mgr.delete_knowledge_base(kb_uuid)
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
+        )
+
+        # delete files
+        files = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_rag.File).where(persistence_rag.File.kb_id == kb_uuid)
+        )
+        for file in files:
+            # delete chunks
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.delete(persistence_rag.Chunk).where(persistence_rag.Chunk.file_id == file.uuid)
+            )
+            # delete file
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file.uuid)
+            )

pkg/api/http/service/model.py

@@ -10,7 +10,7 @@ from ....provider.modelmgr import requester as model_requester
 from ....provider import entities as llm_entities
 
 
-class ModelsService:
+class LLMModelsService:
     ap: app.Application
 
     def __init__(self, ap: app.Application) -> None:
@@ -103,3 +103,89 @@ class ModelsService:
             funcs=[],
             extra_args={},
         )
+
+
+class EmbeddingModelsService:
+    ap: app.Application
+
+    def __init__(self, ap: app.Application) -> None:
+        self.ap = ap
+
+    async def get_embedding_models(self) -> list[dict]:
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.EmbeddingModel))
+
+        models = result.all()
+        return [self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model) for model in models]
+
+    async def create_embedding_model(self, model_data: dict) -> str:
+        model_data['uuid'] = str(uuid.uuid4())
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.insert(persistence_model.EmbeddingModel).values(**model_data)
+        )
+
+        embedding_model = await self.get_embedding_model(model_data['uuid'])
+
+        await self.ap.model_mgr.load_embedding_model(embedding_model)
+
+        return model_data['uuid']
+
+    async def get_embedding_model(self, model_uuid: str) -> dict | None:
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.EmbeddingModel).where(
+                persistence_model.EmbeddingModel.uuid == model_uuid
+            )
+        )
+
+        model = result.first()
+
+        if model is None:
+            return None
+
+        return self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model)
+
+    async def update_embedding_model(self, model_uuid: str, model_data: dict) -> None:
+        if 'uuid' in model_data:
+            del model_data['uuid']
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(persistence_model.EmbeddingModel)
+            .where(persistence_model.EmbeddingModel.uuid == model_uuid)
+            .values(**model_data)
+        )
+
+        await self.ap.model_mgr.remove_embedding_model(model_uuid)
+
+        embedding_model = await self.get_embedding_model(model_uuid)
+
+        await self.ap.model_mgr.load_embedding_model(embedding_model)
+
+    async def delete_embedding_model(self, model_uuid: str) -> None:
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_model.EmbeddingModel).where(
+                persistence_model.EmbeddingModel.uuid == model_uuid
+            )
+        )
+
+        await self.ap.model_mgr.remove_embedding_model(model_uuid)
+
+    async def test_embedding_model(self, model_uuid: str, model_data: dict) -> None:
+        runtime_embedding_model: model_requester.RuntimeEmbeddingModel | None = None
+
+        if model_uuid != '_':
+            for model in self.ap.model_mgr.embedding_models:
+                if model.model_entity.uuid == model_uuid:
+                    runtime_embedding_model = model
+                    break
+
+            if runtime_embedding_model is None:
+                raise Exception('model not found')
+
+        else:
+            runtime_embedding_model = await self.ap.model_mgr.init_runtime_embedding_model(model_data)
+
+        await runtime_embedding_model.requester.invoke_embedding(
+            model=runtime_embedding_model,
+            input_text=['Hello, world!'],
+            extra_args={},
+        )

pkg/api/http/service/pipeline.py

@@ -38,9 +38,21 @@ class PipelineService:
             self.ap.pipeline_config_meta_output.data,
         ]
 
-    async def get_pipelines(self) -> list[dict]:
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
-
+    async def get_pipelines(self, sort_by: str = 'created_at', sort_order: str = 'DESC') -> list[dict]:
+        query = sqlalchemy.select(persistence_pipeline.LegacyPipeline)
+        
+        if sort_by == 'created_at':
+            if sort_order == 'DESC':
+                query = query.order_by(persistence_pipeline.LegacyPipeline.created_at.desc())
+            else:
+                query = query.order_by(persistence_pipeline.LegacyPipeline.created_at.asc())
+        elif sort_by == 'updated_at':
+            if sort_order == 'DESC':
+                query = query.order_by(persistence_pipeline.LegacyPipeline.updated_at.desc())
+            else:
+                query = query.order_by(persistence_pipeline.LegacyPipeline.updated_at.asc())
+        
+        result = await self.ap.persistence_mgr.execute_async(query)
         pipelines = result.all()
         return [
             self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)

pkg/core/app.py

@@ -22,11 +22,14 @@ from ..api.http.service import user as user_service
 from ..api.http.service import model as model_service
 from ..api.http.service import pipeline as pipeline_service
 from ..api.http.service import bot as bot_service
+from ..api.http.service import knowledge as knowledge_service
 from ..discover import engine as discover_engine
 from ..storage import mgr as storagemgr
 from ..utils import logcache
 from . import taskmgr
 from . import entities as core_entities
+from ..rag.knowledge import kbmgr as rag_mgr
+from ..vector import mgr as vectordb_mgr
 
 
 class Application:
@@ -47,6 +50,8 @@ class Application:
 
     model_mgr: llm_model_mgr.ModelManager = None
 
+    rag_mgr: rag_mgr.RAGManager = None
+
     # TODO move to pipeline
     tool_mgr: llm_tool_mgr.ToolManager = None
 
@@ -93,6 +98,8 @@ class Application:
 
     persistence_mgr: persistencemgr.PersistenceManager = None
 
+    vector_db_mgr: vectordb_mgr.VectorDBManager = None
+
     http_ctrl: http_controller.HTTPController = None
 
     log_cache: logcache.LogCache = None
@@ -103,12 +110,16 @@ class Application:
 
     user_service: user_service.UserService = None
 
-    model_service: model_service.ModelsService = None
+    llm_model_service: model_service.LLMModelsService = None
+
+    embedding_models_service: model_service.EmbeddingModelsService = None
 
     pipeline_service: pipeline_service.PipelineService = None
 
     bot_service: bot_service.BotService = None
 
+    knowledge_service: knowledge_service.KnowledgeService = None
+
     def __init__(self):
         pass
 
@@ -143,6 +154,7 @@ class Application:
                 name='http-api-controller',
                 scopes=[core_entities.LifecycleControlScope.APPLICATION],
             )
+
             self.task_mgr.create_task(
                 never_ending(),
                 name='never-ending-task',

pkg/core/entities.py

@@ -19,7 +19,7 @@ class LifecycleControlScope(enum.Enum):
     APPLICATION = 'application'
     PLATFORM = 'platform'
     PLUGIN = 'plugin'
-    PROVIDER = 'provider'
+    PROVIDER = 'provider'  
 
 
 class LauncherTypes(enum.Enum):

pkg/core/stages/build_app.py

@@ -9,6 +9,7 @@ from ...command import cmdmgr
 from ...provider.session import sessionmgr as llm_session_mgr
 from ...provider.modelmgr import modelmgr as llm_model_mgr
 from ...provider.tools import toolmgr as llm_tool_mgr
+from ...rag.knowledge import kbmgr as rag_mgr
 from ...platform import botmgr as im_mgr
 from ...persistence import mgr as persistencemgr
 from ...api.http.controller import main as http_controller
@@ -16,9 +17,11 @@ from ...api.http.service import user as user_service
 from ...api.http.service import model as model_service
 from ...api.http.service import pipeline as pipeline_service
 from ...api.http.service import bot as bot_service
+from ...api.http.service import knowledge as knowledge_service
 from ...discover import engine as discover_engine
 from ...storage import mgr as storagemgr
 from ...utils import logcache
+from ...vector import mgr as vectordb_mgr
 from .. import taskmgr
 
 
@@ -88,6 +91,15 @@ class BuildAppStage(stage.BootingStage):
         await pipeline_mgr.initialize()
         ap.pipeline_mgr = pipeline_mgr
 
+        rag_mgr_inst = rag_mgr.RAGManager(ap)
+        await rag_mgr_inst.initialize()
+        ap.rag_mgr = rag_mgr_inst
+
+        # 初始化向量数据库管理器
+        vectordb_mgr_inst = vectordb_mgr.VectorDBManager(ap)
+        await vectordb_mgr_inst.initialize()
+        ap.vector_db_mgr = vectordb_mgr_inst
+
         http_ctrl = http_controller.HTTPController(ap)
         await http_ctrl.initialize()
         ap.http_ctrl = http_ctrl
@@ -95,8 +107,11 @@ class BuildAppStage(stage.BootingStage):
         user_service_inst = user_service.UserService(ap)
         ap.user_service = user_service_inst
 
-        model_service_inst = model_service.ModelsService(ap)
-        ap.model_service = model_service_inst
+        llm_model_service_inst = model_service.LLMModelsService(ap)
+        ap.llm_model_service = llm_model_service_inst
+
+        embedding_models_service_inst = model_service.EmbeddingModelsService(ap)
+        ap.embedding_models_service = embedding_models_service_inst
 
         pipeline_service_inst = pipeline_service.PipelineService(ap)
         ap.pipeline_service = pipeline_service_inst
@@ -104,5 +119,8 @@ class BuildAppStage(stage.BootingStage):
         bot_service_inst = bot_service.BotService(ap)
         ap.bot_service = bot_service_inst
 
+        knowledge_service_inst = knowledge_service.KnowledgeService(ap)
+        ap.knowledge_service = knowledge_service_inst
+
         ctrl = controller.Controller(ap)
         ap.ctrl = ctrl

pkg/entity/persistence/model.py

@@ -23,3 +23,24 @@ class LLMModel(Base):
         server_default=sqlalchemy.func.now(),
         onupdate=sqlalchemy.func.now(),
     )
+
+
+class EmbeddingModel(Base):
+    """Embedding 模型"""
+
+    __tablename__ = 'embedding_models'
+
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    description = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    requester = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    requester_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+    api_keys = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
+    extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
+    updated_at = sqlalchemy.Column(
+        sqlalchemy.DateTime,
+        nullable=False,
+        server_default=sqlalchemy.func.now(),
+        onupdate=sqlalchemy.func.now(),
+    )

pkg/entity/persistence/pipeline.py

@@ -20,7 +20,6 @@ class LegacyPipeline(Base):
     )
     for_version = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
     is_default = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=False)
-
     stages = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
     config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
 
@@ -43,3 +42,4 @@ class PipelineRunRecord(Base):
     started_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False)
     finished_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False)
     result = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
+    knowledge_base_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)

pkg/entity/persistence/rag.py

@@ -0,0 +1,50 @@
+import sqlalchemy
+from .base import Base
+
+# Base = declarative_base()
+# DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///./rag_knowledge.db')
+# print("Using database URL:", DATABASE_URL)
+
+
+# engine = create_engine(DATABASE_URL, connect_args={'check_same_thread': False})
+
+# SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+# def create_db_and_tables():
+#     """Creates all database tables defined in the Base."""
+#     Base.metadata.create_all(bind=engine)
+#     print('Database tables created or already exist.')
+
+
+class KnowledgeBase(Base):
+    __tablename__ = 'knowledge_bases'
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    name = sqlalchemy.Column(sqlalchemy.String, index=True)
+    description = sqlalchemy.Column(sqlalchemy.Text)
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())
+    embedding_model_uuid = sqlalchemy.Column(sqlalchemy.String, default='')
+    top_k = sqlalchemy.Column(sqlalchemy.Integer, default=5)
+
+
+class File(Base):
+    __tablename__ = 'knowledge_base_files'
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    kb_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    file_name = sqlalchemy.Column(sqlalchemy.String)
+    extension = sqlalchemy.Column(sqlalchemy.String)
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())
+    status = sqlalchemy.Column(sqlalchemy.String, default='pending')  # pending, processing, completed, failed
+
+
+class Chunk(Base):
+    __tablename__ = 'knowledge_base_chunks'
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    file_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    text = sqlalchemy.Column(sqlalchemy.Text)
+
+
+# class Vector(Base):
+#     __tablename__ = 'knowledge_base_vectors'
+#     uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+#     chunk_id = sqlalchemy.Column(sqlalchemy.String, nullable=True)
+#     embedding = sqlalchemy.Column(sqlalchemy.LargeBinary)

pkg/entity/persistence/vector.py

@@ -0,0 +1,13 @@
+from sqlalchemy import Column, Integer, ForeignKey, LargeBinary
+from sqlalchemy.orm import declarative_base, relationship
+
+Base = declarative_base()
+
+
+class Vector(Base):
+    __tablename__ = 'vectors'
+    id = Column(Integer, primary_key=True, index=True)
+    chunk_id = Column(Integer, ForeignKey('chunks.id'), unique=True)
+    embedding = Column(LargeBinary)  # Store embeddings as binary
+
+    chunk = relationship('Chunk', back_populates='vector')

pkg/entity/rag/retriever.py

@@ -0,0 +1,13 @@
+from __future__ import annotations
+
+import pydantic
+
+from typing import Any
+
+
+class RetrieveResultEntry(pydantic.BaseModel):
+    id: str
+
+    metadata: dict[str, Any]
+
+    distance: float

pkg/persistence/mgr.py

@@ -79,7 +79,7 @@ class PersistenceManager:
                 'stages': pipeline_service.default_stage_order,
                 'is_default': True,
                 'name': 'ChatPipeline',
-                'description': 'Default pipeline provided, your new bots will be automatically bound to this pipeline | 默认提供的流水线，您配置的机器人将自动绑定到此流水线',
+                'description': 'Default pipeline, new bots will be bound to this pipeline | 默认提供的流水线，您配置的机器人将自动绑定到此流水线',
                 'config': pipeline_config,
             }
 

pkg/persistence/migrations/dbm004_rag_kb_uuid.py

@@ -0,0 +1,38 @@
+from .. import migration
+
+import sqlalchemy
+
+from ...entity.persistence import pipeline as persistence_pipeline
+
+
+@migration.migration_class(4)
+class DBMigrateRAGKBUUID(migration.DBMigration):
+    """RAG知识库UUID"""
+
+    async def upgrade(self):
+        """升级"""
+        # read all pipelines
+        pipelines = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_pipeline.LegacyPipeline))
+
+        for pipeline in pipelines:
+            serialized_pipeline = self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
+
+            config = serialized_pipeline['config']
+
+            if 'knowledge-base' not in config['ai']['local-agent']:
+                config['ai']['local-agent']['knowledge-base'] = ''
+
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_pipeline.LegacyPipeline)
+                .where(persistence_pipeline.LegacyPipeline.uuid == serialized_pipeline['uuid'])
+                .values(
+                    {
+                        'config': config,
+                        'for_version': self.ap.ver_mgr.get_current_version(),
+                    }
+                )
+            )
+
+    async def downgrade(self):
+        """降级"""
+        pass

pkg/pipeline/pipelinemgr.py

@@ -144,23 +144,27 @@ class RuntimePipeline:
                 result = await result
 
             if isinstance(result, pipeline_entities.StageProcessResult):  # 直接返回结果
-                self.ap.logger.debug(f'Stage {stage_container.inst_name} processed query {query} res {result}')
+                self.ap.logger.debug(
+                    f'Stage {stage_container.inst_name} processed query {query.query_id} res {result.result_type}'
+                )
                 await self._check_output(query, result)
 
                 if result.result_type == pipeline_entities.ResultType.INTERRUPT:
-                    self.ap.logger.debug(f'Stage {stage_container.inst_name} interrupted query {query}')
+                    self.ap.logger.debug(f'Stage {stage_container.inst_name} interrupted query {query.query_id}')
                     break
                 elif result.result_type == pipeline_entities.ResultType.CONTINUE:
                     query = result.new_query
             elif isinstance(result, typing.AsyncGenerator):  # 生成器
-                self.ap.logger.debug(f'Stage {stage_container.inst_name} processed query {query} gen')
+                self.ap.logger.debug(f'Stage {stage_container.inst_name} processed query {query.query_id} gen')
 
                 async for sub_result in result:
-                    self.ap.logger.debug(f'Stage {stage_container.inst_name} processed query {query} res {sub_result}')
+                    self.ap.logger.debug(
+                        f'Stage {stage_container.inst_name} processed query {query.query_id} res {sub_result.result_type}'
+                    )
                     await self._check_output(query, sub_result)
 
                     if sub_result.result_type == pipeline_entities.ResultType.INTERRUPT:
-                        self.ap.logger.debug(f'Stage {stage_container.inst_name} interrupted query {query}')
+                        self.ap.logger.debug(f'Stage {stage_container.inst_name} interrupted query {query.query_id}')
                         break
                     elif sub_result.result_type == pipeline_entities.ResultType.CONTINUE:
                         query = sub_result.new_query
@@ -192,7 +196,7 @@ class RuntimePipeline:
             if event_ctx.is_prevented_default():
                 return
 
-            self.ap.logger.debug(f'Processing query {query}')
+            self.ap.logger.debug(f'Processing query {query.query_id}')
 
             await self._execute_from_stage(0, query)
         except Exception as e:
@@ -200,7 +204,7 @@ class RuntimePipeline:
             self.ap.logger.error(f'处理请求时出错 query_id={query.query_id} stage={inst_name} : {e}')
             self.ap.logger.error(f'Traceback: {traceback.format_exc()}')
         finally:
-            self.ap.logger.debug(f'Query {query} processed')
+            self.ap.logger.debug(f'Query {query.query_id} processed')
 
 
 class PipelineManager:

pkg/pipeline/preproc/preproc.py

@@ -80,14 +80,15 @@ class PreProcessor(stage.PipelineStage):
                         if me.type == 'image_url':
                             msg.content.remove(me)
 
-        content_list = []
+        content_list: list[llm_entities.ContentElement] = []
 
         plain_text = ''
         qoute_msg = query.pipeline_config['trigger'].get('misc', '').get('combine-quote-message')
 
+        # tidy the content_list
+        # combine all text content into one, and put it in the first position
         for me in query.message_chain:
             if isinstance(me, platform_message.Plain):
-                content_list.append(llm_entities.ContentElement.from_text(me.text))
                 plain_text += me.text
             elif isinstance(me, platform_message.Image):
                 if selected_runner != 'local-agent' or query.use_llm_model.model_entity.abilities.__contains__(
@@ -106,6 +107,8 @@ class PreProcessor(stage.PipelineStage):
                             if msg.base64 is not None:
                                 content_list.append(llm_entities.ContentElement.from_image_base64(msg.base64))
 
+        content_list.insert(0, llm_entities.ContentElement.from_text(plain_text))
+
         query.variables['user_message_text'] = plain_text
 
         query.user_message = llm_entities.Message(role='user', content=content_list)

pkg/platform/logger.py

@@ -119,7 +119,7 @@ class EventLogger:
     async def _truncate_logs(self):
         if len(self.logs) > MAX_LOG_COUNT:
             for i in range(DELETE_COUNT_PER_TIME):
-                for image_key in self.logs[i].images:
+                for image_key in self.logs[i].images:  # type: ignore
                     await self.ap.storage_mgr.storage_provider.delete(image_key)
             self.logs = self.logs[DELETE_COUNT_PER_TIME:]
 

pkg/platform/sources/discord.py

@@ -654,10 +654,10 @@ class DiscordMessageConverter(adapter.MessageConverter):
                     # 确保路径没有空字节
                     clean_path = ele.path.replace('\x00', '')
                     clean_path = os.path.abspath(clean_path)
-                    
+
                     if not os.path.exists(clean_path):
                         continue  # 跳过不存在的文件
-                    
+
                     try:
                         with open(clean_path, 'rb') as f:
                             image_bytes = f.read()
@@ -677,12 +677,13 @@ class DiscordMessageConverter(adapter.MessageConverter):
                                 filename = f'{uuid.uuid4()}.webp'
                             # 默认保持PNG
                     except Exception as e:
-                        print(f"Error reading image file {clean_path}: {e}")
+                        print(f'Error reading image file {clean_path}: {e}')
                         continue  # 跳过读取失败的文件
 
                 if image_bytes:
                     # 使用BytesIO创建文件对象，避免路径问题
                     import io
+
                     image_files.append(discord.File(fp=io.BytesIO(image_bytes), filename=filename))
             elif isinstance(ele, platform_message.Plain):
                 text_string += ele.text
@@ -1003,25 +1004,25 @@ class DiscordAdapter(adapter.MessagePlatformAdapter):
 
     async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
         msg_to_send, image_files = await self.message_converter.yiri2target(message)
-        
+
         try:
             # 获取频道对象
             channel = self.bot.get_channel(int(target_id))
             if channel is None:
                 # 如果本地缓存中没有，尝试从API获取
                 channel = await self.bot.fetch_channel(int(target_id))
-            
+
             args = {
                 'content': msg_to_send,
             }
-            
+
             if len(image_files) > 0:
                 args['files'] = image_files
-            
+
             await channel.send(**args)
-            
+
         except Exception as e:
-            await self.logger.error(f"Discord send_message failed: {e}")
+            await self.logger.error(f'Discord send_message failed: {e}')
             raise e
 
     async def reply_message(

pkg/platform/sources/lark.py

@@ -378,15 +378,15 @@ class LarkAdapter(adapter.MessagePlatformAdapter):
                 if 'im.message.receive_v1' == type:
                     try:
                         event = await self.event_converter.target2yiri(p2v1, self.api_client)
-                    except Exception as e:
-                        await self.logger.error(f"Error in lark callback: {traceback.format_exc()}")
+                    except Exception:
+                        await self.logger.error(f'Error in lark callback: {traceback.format_exc()}')
 
                     if event.__class__ in self.listeners:
                         await self.listeners[event.__class__](event, self)
 
                 return {'code': 200, 'message': 'ok'}
-            except Exception as e:
-                await self.logger.error(f"Error in lark callback: {traceback.format_exc()}")
+            except Exception:
+                await self.logger.error(f'Error in lark callback: {traceback.format_exc()}')
                 return {'code': 500, 'message': 'error'}
 
         async def on_message(event: lark_oapi.im.v1.P2ImMessageReceiveV1):

pkg/platform/sources/nakuru.py

@@ -72,8 +72,9 @@ class NakuruProjectMessageConverter(adapter_model.MessageConverter):
                             content=content_list,
                         )
                         nakuru_forward_node_list.append(nakuru_forward_node)
-                    except Exception as e:
+                    except Exception:
                         import traceback
+
                         traceback.print_exc()
 
                 nakuru_msg_list.append(nakuru_forward_node_list)
@@ -276,7 +277,7 @@ class NakuruAdapter(adapter_model.MessagePlatformAdapter):
             # 注册监听器
             self.bot.receiver(source_cls.__name__)(listener_wrapper)
         except Exception as e:
-            self.logger.error(f"Error in nakuru register_listener: {traceback.format_exc()}")
+            self.logger.error(f'Error in nakuru register_listener: {traceback.format_exc()}')
             raise e
 
     def unregister_listener(

pkg/platform/sources/officialaccount.py

@@ -125,8 +125,8 @@ class OfficialAccountAdapter(adapter.MessagePlatformAdapter):
             self.bot_account_id = event.receiver_id
             try:
                 return await callback(await self.event_converter.target2yiri(event), self)
-            except Exception as e:
-                await self.logger.error(f"Error in officialaccount callback: {traceback.format_exc()}")
+            except Exception:
+                await self.logger.error(f'Error in officialaccount callback: {traceback.format_exc()}')
 
         if event_type == platform_events.FriendMessage:
             self.bot.on_message('text')(on_message)

pkg/platform/sources/qqofficial.py

@@ -154,10 +154,7 @@ class QQOfficialAdapter(adapter.MessagePlatformAdapter):
             raise ParamNotEnoughError('QQ官方机器人缺少相关配置项，请查看文档或联系管理员')
 
         self.bot = QQOfficialClient(
-            app_id=config['appid'],
-            secret=config['secret'],
-            token=config['token'],
-            logger=self.logger
+            app_id=config['appid'], secret=config['secret'], token=config['token'], logger=self.logger
         )
 
     async def reply_message(
@@ -224,8 +221,8 @@ class QQOfficialAdapter(adapter.MessagePlatformAdapter):
             self.bot_account_id = 'justbot'
             try:
                 return await callback(await self.event_converter.target2yiri(event), self)
-            except Exception as e:
-                await self.logger.error(f"Error in qqofficial callback: {traceback.format_exc()}")
+            except Exception:
+                await self.logger.error(f'Error in qqofficial callback: {traceback.format_exc()}')
 
         if event_type == platform_events.FriendMessage:
             self.bot.on_message('DIRECT_MESSAGE_CREATE')(on_message)

pkg/platform/sources/slack.py

@@ -104,7 +104,9 @@ class SlackAdapter(adapter.MessagePlatformAdapter):
         if missing_keys:
             raise ParamNotEnoughError('Slack机器人缺少相关配置项，请查看文档或联系管理员')
 
-        self.bot = SlackClient(bot_token=self.config['bot_token'], signing_secret=self.config['signing_secret'], logger=self.logger)
+        self.bot = SlackClient(
+            bot_token=self.config['bot_token'], signing_secret=self.config['signing_secret'], logger=self.logger
+        )
 
     async def reply_message(
         self,
@@ -139,8 +141,8 @@ class SlackAdapter(adapter.MessagePlatformAdapter):
             self.bot_account_id = 'SlackBot'
             try:
                 return await callback(await self.event_converter.target2yiri(event, self.bot), self)
-            except Exception as e:
-                await self.logger.error(f"Error in slack callback: {traceback.format_exc()}")
+            except Exception:
+                await self.logger.error(f'Error in slack callback: {traceback.format_exc()}')
 
         if event_type == platform_events.FriendMessage:
             self.bot.on_message('im')(on_message)

pkg/platform/sources/telegram.py

@@ -160,8 +160,8 @@ class TelegramAdapter(adapter.MessagePlatformAdapter):
             try:
                 lb_event = await self.event_converter.target2yiri(update, self.bot, self.bot_account_id)
                 await self.listeners[type(lb_event)](lb_event, self)
-            except Exception as e:
-                await self.logger.error(f"Error in telegram callback: {traceback.format_exc()}")
+            except Exception:
+                await self.logger.error(f'Error in telegram callback: {traceback.format_exc()}')
 
         self.application = ApplicationBuilder().token(self.config['token']).build()
         self.bot = self.application.bot

pkg/platform/sources/wechatpad.py

@@ -1,5 +1,4 @@
 import requests
-import websockets
 import websocket
 import json
 import time
@@ -10,32 +9,25 @@ from libs.wechatpad_api.client import WeChatPadClient
 import typing
 import asyncio
 import traceback
-import time
 import re
 import base64
-import uuid
-import json
-import os
 import copy
-import datetime
 import threading
 
 import quart
-import aiohttp
 
 from .. import adapter
-from ...pipeline.longtext.strategies import forward
 from ...core import app
 from ..types import message as platform_message
 from ..types import events as platform_events
 from ..types import entities as platform_entities
-from ...utils import image
 from ..logger import EventLogger
 import xml.etree.ElementTree as ET
-from typing import Optional, List, Tuple
+from typing import Optional, Tuple
 from functools import partial
 import logging
 
+
 class WeChatPadMessageConverter(adapter.MessageConverter):
 
     def __init__(self, config: dict, logger: logging.Logger):
@@ -44,19 +36,16 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
         self.logger = logger
 
     @staticmethod
-    async def yiri2target(
-            message_chain: platform_message.MessageChain
-    ) -> list[dict]:
+    async def yiri2target(message_chain: platform_message.MessageChain) -> list[dict]:
         content_list = []
-        current_file_path = os.path.abspath(__file__)
-
-
 
         for component in message_chain:
-            if isinstance(component, platform_message.At):
+            if isinstance(component, platform_message.AtAll):
+                content_list.append({"type": "at", "target": "all"})
+            elif isinstance(component, platform_message.At):
                 content_list.append({"type": "at", "target": component.target})
             elif isinstance(component, platform_message.Plain):
-                content_list.append({"type": "text", "content": component.text})
+                content_list.append({'type': 'text', 'content': component.text})
             elif isinstance(component, platform_message.Image):
                 if component.url:
                     async with httpx.AsyncClient() as client:
@@ -68,15 +57,16 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
                         else:
                             raise Exception('获取文件失败')
                     # pass
-                    content_list.append({"type": "image", "image": base64_str})
+                    content_list.append({'type': 'image', 'image': base64_str})
                 elif component.base64:
-                    content_list.append({"type": "image", "image": component.base64})
+                    content_list.append({'type': 'image', 'image': component.base64})
 
             elif isinstance(component, platform_message.WeChatEmoji):
                 content_list.append(
-                    {'type': 'WeChatEmoji', 'emoji_md5': component.emoji_md5, 'emoji_size': component.emoji_size})
+                    {'type': 'WeChatEmoji', 'emoji_md5': component.emoji_md5, 'emoji_size': component.emoji_size}
+                )
             elif isinstance(component, platform_message.Voice):
-                content_list.append({"type": "voice", "data": component.url, "duration": component.length, "forma": 0})
+                content_list.append({'type': 'voice', 'data': component.url, 'duration': component.length, 'forma': 0})
             elif isinstance(component, platform_message.WeChatAppMsg):
                 content_list.append({'type': 'WeChatAppMsg', 'app_msg': component.app_msg})
             elif isinstance(component, platform_message.Forward):
@@ -86,7 +76,6 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
 
         return content_list
 
-
     async def target2yiri(
             self,
             message: dict,
@@ -97,15 +86,16 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
         message_list = []
         bot_wxid = self.config['wxid']
         ats_bot = False  # 是否被@
-        content = message["content"]["str"]
+        content = message['content']['str']
         content_no_preifx = content  # 群消息则去掉前缀
         is_group_message = self._is_group_message(message)
         if is_group_message:
             ats_bot = self._ats_bot(message, bot_account_id)
+
             self.logger.info(f"ats_bot: {ats_bot}; bot_account_id: {bot_account_id}; bot_wxid: {bot_wxid}")
             if "@所有人" in content:
                 message_list.append(platform_message.AtAll())
-            elif ats_bot:
+            if ats_bot:
                 message_list.append(platform_message.At(target=bot_account_id))
             
             # 解析@信息并生成At组件
@@ -116,7 +106,7 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
             
             content_no_preifx, _ = self._extract_content_and_sender(content)
 
-        msg_type = message["msg_type"]
+        msg_type = message['msg_type']
 
         # 映射消息类型到处理器方法
         handler_map = {
@@ -138,11 +128,7 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
 
         return platform_message.MessageChain(message_list)
 
-    async def _handler_text(
-            self,
-            message: Optional[dict],
-            content_no_preifx: str
-    ) -> platform_message.MessageChain:
+    async def _handler_text(self, message: Optional[dict], content_no_preifx: str) -> platform_message.MessageChain:
         """处理文本消息 (msg_type=1)"""
         if message and self._is_group_message(message):
             pattern = r'@\S{1,20}'
@@ -150,16 +136,12 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
 
         return platform_message.MessageChain([platform_message.Plain(content_no_preifx)])
 
-    async def _handler_image(
-            self,
-            message: Optional[dict],
-            content_no_preifx: str
-    ) -> platform_message.MessageChain:
+    async def _handler_image(self, message: Optional[dict], content_no_preifx: str) -> platform_message.MessageChain:
         """处理图像消息 (msg_type=3)"""
         try:
             image_xml = content_no_preifx
             if not image_xml:
-                return platform_message.MessageChain([platform_message.Unknown("[图片内容为空]")])
+                return platform_message.MessageChain([platform_message.Unknown('[图片内容为空]')])
             root = ET.fromstring(image_xml)
 
             # 提取img标签的属性
@@ -169,28 +151,22 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
                 cdnthumburl = img_tag.get('cdnthumburl')
                 # cdnmidimgurl = img_tag.get('cdnmidimgurl')
 
-
             image_data = self.bot.cdn_download(aeskey=aeskey, file_type=1, file_url=cdnthumburl)
-            if image_data["Data"]['FileData'] == '':
+            if image_data['Data']['FileData'] == '':
                 image_data = self.bot.cdn_download(aeskey=aeskey, file_type=2, file_url=cdnthumburl)
-            base64_str = image_data["Data"]['FileData']
+            base64_str = image_data['Data']['FileData']
             # self.logger.info(f"data:image/png;base64,{base64_str}")
 
-
             elements = [
-                platform_message.Image(base64=f"data:image/png;base64,{base64_str}"),
+                platform_message.Image(base64=f'data:image/png;base64,{base64_str}'),
                 # platform_message.WeChatForwardImage(xml_data=image_xml)  # 微信消息转发
             ]
             return platform_message.MessageChain(elements)
         except Exception as e:
-            self.logger.error(f"处理图片失败: {str(e)}")
-            return platform_message.MessageChain([platform_message.Unknown("[图片处理失败]")])
+            self.logger.error(f'处理图片失败: {str(e)}')
+            return platform_message.MessageChain([platform_message.Unknown('[图片处理失败]')])
 
-    async def _handler_voice(
-            self,
-            message: Optional[dict],
-            content_no_preifx: str
-    ) -> platform_message.MessageChain:
+    async def _handler_voice(self, message: Optional[dict], content_no_preifx: str) -> platform_message.MessageChain:
         """处理语音消息 (msg_type=34)"""
         message_List = []
         try:
@@ -206,39 +182,33 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
                 bufid = voicemsg.get('bufid')
                 length = voicemsg.get('voicelength')
             voice_data = self.bot.get_msg_voice(buf_id=str(bufid), length=int(length), msgid=str(new_msg_id))
-            audio_base64 = voice_data["Data"]['Base64']
+            audio_base64 = voice_data['Data']['Base64']
 
             # 验证语音数据有效性
             if not audio_base64:
-                message_List.append(platform_message.Unknown(text="[语音内容为空]"))
+                message_List.append(platform_message.Unknown(text='[语音内容为空]'))
                 return platform_message.MessageChain(message_List)
 
             # 转换为平台支持的语音格式（如 Silk 格式）
-            voice_element = platform_message.Voice(
-                base64=f"data:audio/silk;base64,{audio_base64}"
-            )
+            voice_element = platform_message.Voice(base64=f'data:audio/silk;base64,{audio_base64}')
             message_List.append(voice_element)
 
         except KeyError as e:
-            self.logger.error(f"语音数据字段缺失: {str(e)}")
-            message_List.append(platform_message.Unknown(text="[语音数据解析失败]"))
+            self.logger.error(f'语音数据字段缺失: {str(e)}')
+            message_List.append(platform_message.Unknown(text='[语音数据解析失败]'))
         except Exception as e:
-            self.logger.error(f"处理语音消息异常: {str(e)}")
-            message_List.append(platform_message.Unknown(text="[语音处理失败]"))
+            self.logger.error(f'处理语音消息异常: {str(e)}')
+            message_List.append(platform_message.Unknown(text='[语音处理失败]'))
 
         return platform_message.MessageChain(message_List)
 
-    async def _handler_compound(
-            self,
-            message: Optional[dict],
-            content_no_preifx: str
-    ) -> platform_message.MessageChain:
+    async def _handler_compound(self, message: Optional[dict], content_no_preifx: str) -> platform_message.MessageChain:
         """处理复合消息 (msg_type=49)，根据子类型分派"""
         try:
             xml_data = ET.fromstring(content_no_preifx)
             appmsg_data = xml_data.find('.//appmsg')
             if appmsg_data:
-                data_type = appmsg_data.findtext('.//type', "")
+                data_type = appmsg_data.findtext('.//type', '')
                 # 二次分派处理器
                 sub_handler_map = {
                     '57': self._handler_compound_quote,
@@ -247,9 +217,9 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
                     '74': self._handler_compound_file,
                     '33': self._handler_compound_mini_program,
                     '36': self._handler_compound_mini_program,
-                    '2000': partial(self._handler_compound_unsupported, text="[转账消息]"),
-                    '2001': partial(self._handler_compound_unsupported, text="[红包消息]"),
-                    '51': partial(self._handler_compound_unsupported, text="[视频号消息]"),
+                    '2000': partial(self._handler_compound_unsupported, text='[转账消息]'),
+                    '2001': partial(self._handler_compound_unsupported, text='[红包消息]'),
+                    '51': partial(self._handler_compound_unsupported, text='[视频号消息]'),
                 }
 
                 handler = sub_handler_map.get(data_type, self._handler_compound_unsupported)
@@ -260,56 +230,54 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
             else:
                 return platform_message.MessageChain([platform_message.Unknown(text=content_no_preifx)])
         except Exception as e:
-            self.logger.error(f"解析复合消息失败: {str(e)}")
+            self.logger.error(f'解析复合消息失败: {str(e)}')
             return platform_message.MessageChain([platform_message.Unknown(text=content_no_preifx)])
 
     async def _handler_compound_quote(
-            self,
-            message: Optional[dict],
-            xml_data: ET.Element
+        self, message: Optional[dict], xml_data: ET.Element
     ) -> platform_message.MessageChain:
         """处理引用消息 (data_type=57)"""
         message_list = []
-#         self.logger.info("_handler_compound_quote", ET.tostring(xml_data, encoding='unicode'))
+        #         self.logger.info("_handler_compound_quote", ET.tostring(xml_data, encoding='unicode'))
         appmsg_data = xml_data.find('.//appmsg')
-        quote_data = ""  # 引用原文
+        quote_data = ''  # 引用原文
         quote_id = None  # 引用消息的原发送者
         tousername = None  # 接收方: 所属微信的wxid
-        user_data = ""  # 用户消息
+        user_data = ''  # 用户消息
         sender_id = xml_data.findtext('.//fromusername')  # 发送方：单聊用户/群member
 
         # 引用消息转发
         if appmsg_data:
-            user_data = appmsg_data.findtext('.//title') or ""
+            user_data = appmsg_data.findtext('.//title') or ''
             quote_data = appmsg_data.find('.//refermsg').findtext('.//content')
             quote_id = appmsg_data.find('.//refermsg').findtext('.//chatusr')
-            message_list.append(
-                platform_message.WeChatAppMsg(
-                    app_msg=ET.tostring(appmsg_data, encoding='unicode'))
-            )
+            message_list.append(platform_message.WeChatAppMsg(app_msg=ET.tostring(appmsg_data, encoding='unicode')))
         if message:
-            tousername = message['to_user_name']["str"]
-        
+            tousername = message['to_user_name']['str']
+
+        _ = quote_id
+        _ = tousername
+
         if quote_data:
             quote_data_message_list = platform_message.MessageChain()
             # 文本消息
             try:
-                if "<msg>" not in quote_data:
+                if '<msg>' not in quote_data:
                     quote_data_message_list.append(platform_message.Plain(quote_data))
                 else:
                     # 引用消息展开
                     quote_data_xml = ET.fromstring(quote_data)
-                    if quote_data_xml.find("img"):
+                    if quote_data_xml.find('img'):
                         quote_data_message_list.extend(await self._handler_image(None, quote_data))
-                    elif quote_data_xml.find("voicemsg"):
+                    elif quote_data_xml.find('voicemsg'):
                         quote_data_message_list.extend(await self._handler_voice(None, quote_data))
-                    elif quote_data_xml.find("videomsg"):
+                    elif quote_data_xml.find('videomsg'):
                         quote_data_message_list.extend(await self._handler_default(None, quote_data))  # 先不处理
                     else:
                         # appmsg
                         quote_data_message_list.extend(await self._handler_compound(None, quote_data))
             except Exception as e:
-                self.logger.error(f"处理引用消息异常 expcetion:{e}")
+                self.logger.error(f'处理引用消息异常 expcetion:{e}')
                 quote_data_message_list.append(platform_message.Plain(quote_data))
             message_list.append(
                 platform_message.Quote(
@@ -324,15 +292,11 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
 
         return platform_message.MessageChain(message_list)
 
-    async def _handler_compound_file(
-            self,
-            message: dict,
-            xml_data: ET.Element
-    ) -> platform_message.MessageChain:
+    async def _handler_compound_file(self, message: dict, xml_data: ET.Element) -> platform_message.MessageChain:
         """处理文件消息 (data_type=6)"""
         file_data = xml_data.find('.//appmsg')
 
-        if file_data.findtext('.//type', "") == "74":
+        if file_data.findtext('.//type', '') == '74':
             return None
 
         else:
@@ -355,22 +319,21 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
 
             file_data = self.bot.cdn_download(aeskey=aeskey, file_type=5, file_url=cdnthumburl)
 
-            file_base64 = file_data["Data"]['FileData']
+            file_base64 = file_data['Data']['FileData']
             # print(file_data)
-            file_size = file_data["Data"]['TotalSize']
+            file_size = file_data['Data']['TotalSize']
 
             # print(file_base64)
-            return platform_message.MessageChain([
-                platform_message.WeChatFile(file_id=file_id, file_name=file_name, file_size=file_size,
-                                            file_base64=file_base64),
-                platform_message.WeChatForwardFile(xml_data=xml_data_str)
-            ])
+            return platform_message.MessageChain(
+                [
+                    platform_message.WeChatFile(
+                        file_id=file_id, file_name=file_name, file_size=file_size, file_base64=file_base64
+                    ),
+                    platform_message.WeChatForwardFile(xml_data=xml_data_str),
+                ]
+            )
 
-    async def _handler_compound_link(
-            self,
-            message: dict,
-            xml_data: ET.Element
-    ) -> platform_message.MessageChain:
+    async def _handler_compound_link(self, message: dict, xml_data: ET.Element) -> platform_message.MessageChain:
         """处理链接消息（如公众号文章、外部网页）"""
         message_list = []
         try:
@@ -383,56 +346,38 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
                     link_title=appmsg.findtext('title', ''),
                     link_desc=appmsg.findtext('des', ''),
                     link_url=appmsg.findtext('url', ''),
-                    link_thumb_url=appmsg.findtext("thumburl", '')  # 这个字段拿不到
+                    link_thumb_url=appmsg.findtext('thumburl', ''),  # 这个字段拿不到
                 )
             )
             # 还没有发链接的接口, 暂时还需要自己构造appmsg, 先用WeChatAppMsg。
-            message_list.append(
-                platform_message.WeChatAppMsg(
-                    app_msg=ET.tostring(appmsg, encoding='unicode')
-                )
-            )
+            message_list.append(platform_message.WeChatAppMsg(app_msg=ET.tostring(appmsg, encoding='unicode')))
         except Exception as e:
-            self.logger.error(f"解析链接消息失败: {str(e)}")
+            self.logger.error(f'解析链接消息失败: {str(e)}')
         return platform_message.MessageChain(message_list)
 
     async def _handler_compound_mini_program(
-            self,
-            message: dict,
-            xml_data: ET.Element
+        self, message: dict, xml_data: ET.Element
     ) -> platform_message.MessageChain:
         """处理小程序消息（如小程序卡片、服务通知）"""
         xml_data_str = ET.tostring(xml_data, encoding='unicode')
-        return platform_message.MessageChain([
-            platform_message.WeChatForwardMiniPrograms(xml_data=xml_data_str)
-        ])
+        return platform_message.MessageChain([platform_message.WeChatForwardMiniPrograms(xml_data=xml_data_str)])
 
-    async def _handler_default(
-            self,
-            message: Optional[dict],
-            content_no_preifx: str
-    ) -> platform_message.MessageChain:
+    async def _handler_default(self, message: Optional[dict], content_no_preifx: str) -> platform_message.MessageChain:
         """处理未知消息类型"""
         if message:
-            msg_type = message["msg_type"]
+            msg_type = message['msg_type']
         else:
-            msg_type = ""
-        return platform_message.MessageChain([
-            platform_message.Unknown(text=f"[未知消息类型 msg_type:{msg_type}]")
-        ])
+            msg_type = ''
+        return platform_message.MessageChain([platform_message.Unknown(text=f'[未知消息类型 msg_type:{msg_type}]')])
 
     def _handler_compound_unsupported(
-            self,
-            message: dict,
-            xml_data: str,
-            text: Optional[str] = None
+        self, message: dict, xml_data: str, text: Optional[str] = None
     ) -> platform_message.MessageChain:
         """处理未支持复合消息类型(msg_type=49)子类型"""
         if not text:
-            text = f"[xml_data={xml_data}]"
+            text = f'[xml_data={xml_data}]'
         content_list = []
-        content_list.append(
-            platform_message.Unknown(text=f"[处理未支持复合消息类型[msg_type=49]|{text}"))
+        content_list.append(platform_message.Unknown(text=f'[处理未支持复合消息类型[msg_type=49]|{text}'))
 
         return platform_message.MessageChain(content_list)
 
@@ -441,7 +386,7 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
         ats_bot = False
         try:
             to_user_name = message['to_user_name']['str']  # 接收方: 所属微信的wxid
-            raw_content = message["content"]["str"]  # 原始消息内容
+            raw_content = message['content']['str']  # 原始消息内容
             content_no_prefix, _ = self._extract_content_and_sender(raw_content)
             # 直接艾特机器人（这个有bug，当被引用的消息里面有@bot,会套娃
             # ats_bot =  ats_bot or (f"@{bot_account_id}" in content_no_prefix)
@@ -452,7 +397,7 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
             msg_source = message.get('msg_source', '') or ''
             if len(msg_source) > 0:
                 msg_source_data = ET.fromstring(msg_source)
-                at_user_list = msg_source_data.findtext("atuserlist") or ""
+                at_user_list = msg_source_data.findtext('atuserlist') or ''
                 ats_bot = ats_bot or (to_user_name in at_user_list)
             # 引用bot
             if message.get('msg_type', 0) == 49:
@@ -463,7 +408,7 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
                     quote_id = appmsg_data.find('.//refermsg').findtext('.//chatusr')  # 引用消息的原发送者
                     ats_bot = ats_bot or (quote_id == tousername)
         except Exception as e:
-            self.logger.error(f"_ats_bot got except: {e}")
+            self.logger.error(f'_ats_bot got except: {e}')
         finally:
             return ats_bot
 
@@ -489,21 +434,21 @@ class WeChatPadMessageConverter(adapter.MessageConverter):
         try:
             # 检查消息开头，如果有 wxid_sbitaz0mt65n22:\n 则删掉
             # add: 有些用户的wxid不是上述格式。换成user_name:
-            regex = re.compile(r"^[a-zA-Z0-9_\-]{5,20}:")
-            line_split = raw_content.split("\n")
+            regex = re.compile(r'^[a-zA-Z0-9_\-]{5,20}:')
+            line_split = raw_content.split('\n')
             if len(line_split) > 0 and regex.match(line_split[0]):
-                raw_content = "\n".join(line_split[1:])
-                sender_id = line_split[0].strip(":")
+                raw_content = '\n'.join(line_split[1:])
+                sender_id = line_split[0].strip(':')
                 return raw_content, sender_id
         except Exception as e:
-            self.logger.error(f"_extract_content_and_sender got except: {e}")
+            self.logger.error(f'_extract_content_and_sender got except: {e}')
         finally:
             return raw_content, None
 
     # 是否是群消息
     def _is_group_message(self, message: dict) -> bool:
         from_user_name = message['from_user_name']['str']
-        return from_user_name.endswith("@chatroom")
+        return from_user_name.endswith('@chatroom')
 
 
 class WeChatPadEventConverter(adapter.EventConverter):
@@ -514,9 +459,7 @@ class WeChatPadEventConverter(adapter.EventConverter):
         self.logger = logger
         
     @staticmethod
-    async def yiri2target(
-            event: platform_events.MessageEvent
-    ) -> dict:
+    async def yiri2target(event: platform_events.MessageEvent) -> dict:
         pass
 
     async def target2yiri(
@@ -526,10 +469,12 @@ class WeChatPadEventConverter(adapter.EventConverter):
     ) -> platform_events.MessageEvent:
 
         # 排除公众号以及微信团队消息
-        if event['from_user_name']['str'].startswith('gh_') \
-                or event['from_user_name']['str']=='weixin'\
-                or event['from_user_name']['str'] == "newsapp"\
-                or event['from_user_name']['str'] == self.config["wxid"]:
+        if (
+            event['from_user_name']['str'].startswith('gh_')
+            or event['from_user_name']['str'] == 'weixin'
+            or event['from_user_name']['str'] == 'newsapp'
+            or event['from_user_name']['str'] == self.config['wxid']
+        ):
             return None
         message_chain = await self.message_converter.target2yiri(copy.deepcopy(event), bot_account_id)
 
@@ -538,7 +483,7 @@ class WeChatPadEventConverter(adapter.EventConverter):
 
         if '@chatroom' in event['from_user_name']['str']:
             # 找出开头的 wxid_ 字符串，以:结尾
-            sender_wxid = event['content']['str'].split(":")[0]
+            sender_wxid = event['content']['str'].split(':')[0]
 
             return platform_events.GroupMessage(
                 sender=platform_entities.GroupMember(
@@ -550,13 +495,13 @@ class WeChatPadEventConverter(adapter.EventConverter):
                         name=event['from_user_name']['str'],
                         permission=platform_entities.Permission.Member,
                     ),
-                    special_title="",
+                    special_title='',
                     join_timestamp=0,
                     last_speak_timestamp=0,
                     mute_time_remaining=0,
                 ),
                 message_chain=message_chain,
-                time=event["create_time"],
+                time=event['create_time'],
                 source_platform_object=event,
             )
         else:
@@ -567,13 +512,13 @@ class WeChatPadEventConverter(adapter.EventConverter):
                     remark='',
                 ),
                 message_chain=message_chain,
-                time=event["create_time"],
+                time=event['create_time'],
                 source_platform_object=event,
             )
 
 
 class WeChatPadAdapter(adapter.MessagePlatformAdapter):
-    name: str = "WeChatPad"  # 定义适配器名称
+    name: str = 'WeChatPad'  # 定义适配器名称
 
     bot: WeChatPadClient
     quart_app: quart.Quart
@@ -606,27 +551,21 @@ class WeChatPadAdapter(adapter.MessagePlatformAdapter):
         # self.ap.logger.debug(f"Gewechat callback event: {data}")
         # print(data)
 
-
         try:
             event = await self.event_converter.target2yiri(data.copy(), self.bot_account_id)
-        except Exception as e:
-            await self.logger.error(f"Error in wechatpad callback: {traceback.format_exc()}")
+        except Exception:
+            await self.logger.error(f'Error in wechatpad callback: {traceback.format_exc()}')
 
         if event.__class__ in self.listeners:
             await self.listeners[event.__class__](event, self)
 
         return 'ok'
 
-
-    async def _handle_message(
-            self,
-            message: platform_message.MessageChain,
-            target_id: str
-    ):
+    async def _handle_message(self, message: platform_message.MessageChain, target_id: str):
         """统一消息处理核心逻辑"""
         content_list = await self.message_converter.yiri2target(message)
         # print(content_list)
-        at_targets = [item["target"] for item in content_list if item["type"] == "at"]
+        at_targets = [item['target'] for item in content_list if item['type'] == 'at']
         # print(at_targets)
         # 处理@逻辑
         at_targets = at_targets or []
@@ -634,71 +573,66 @@ class WeChatPadAdapter(adapter.MessagePlatformAdapter):
         if at_targets:
             member_info = self.bot.get_chatroom_member_detail(
                 target_id,
-            )["Data"]["member_data"]["chatroom_member_list"]
+            )['Data']['member_data']['chatroom_member_list']
 
         # 处理消息组件
         for msg in content_list:
             # 文本消息处理@
             if msg['type'] == 'text' and at_targets:
-                at_nick_name_list = []
-                for member in member_info:
-                    if member["user_name"] in at_targets:
-                        at_nick_name_list.append(f'@{member["nick_name"]}')
-                msg['content'] = f'{" ".join(at_nick_name_list)} {msg["content"]}'
+                if "all" in at_targets:
+                    msg['content'] = f'@所有人 {msg["content"]}'
+                else:
+                    at_nick_name_list = []
+                    for member in member_info:
+                        if member["user_name"] in at_targets:
+                            at_nick_name_list.append(f'@{member["nick_name"]}')
+                    msg['content'] = f'{" ".join(at_nick_name_list)} {msg["content"]}'
 
             # 统一消息派发
             handler_map = {
                 'text': lambda msg: self.bot.send_text_message(
                     to_wxid=target_id,
                     message=msg['content'],
-                    ats=at_targets
+                    ats= ["notify@all"] if "all" in at_targets else at_targets
                 ),
                 'image': lambda msg: self.bot.send_image_message(
                     to_wxid=target_id,
                     img_url=msg["image"],
-                    ats = at_targets
+                    ats = ["notify@all"] if "all" in at_targets else at_targets
                 ),
                 'WeChatEmoji': lambda msg: self.bot.send_emoji_message(
-                    to_wxid=target_id,
-                    emoji_md5=msg['emoji_md5'],
-                    emoji_size=msg['emoji_size']
+                    to_wxid=target_id, emoji_md5=msg['emoji_md5'], emoji_size=msg['emoji_size']
                 ),
-
                 'voice': lambda msg: self.bot.send_voice_message(
                     to_wxid=target_id,
                     voice_data=msg['data'],
-                    voice_duration=msg["duration"],
-                    voice_forma=msg["forma"],
+                    voice_duration=msg['duration'],
+                    voice_forma=msg['forma'],
                 ),
                 'WeChatAppMsg': lambda msg: self.bot.send_app_message(
                     to_wxid=target_id,
                     app_message=msg['app_msg'],
                     type=0,
                 ),
-                'at': lambda msg: None
+                'at': lambda msg: None,
             }
 
             if handler := handler_map.get(msg['type']):
                 handler(msg)
                 # self.ap.logger.warning(f"未处理的消息类型: {ret}")
             else:
-                self.ap.logger.warning(f"未处理的消息类型: {msg['type']}")
+                self.ap.logger.warning(f'未处理的消息类型: {msg["type"]}')
                 continue
 
-    async def send_message(
-            self,
-            target_type: str,
-            target_id: str,
-            message: platform_message.MessageChain
-    ):
+    async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
         """主动发送消息"""
         return await self._handle_message(message, target_id)
 
     async def reply_message(
-            self,
-            message_source: platform_events.MessageEvent,
-            message: platform_message.MessageChain,
-            quote_origin: bool = False
+        self,
+        message_source: platform_events.MessageEvent,
+        message: platform_message.MessageChain,
+        quote_origin: bool = False,
     ):
         """回复消息"""
         if message_source.source_platform_object:
@@ -709,58 +643,49 @@ class WeChatPadAdapter(adapter.MessagePlatformAdapter):
         pass
 
     def register_listener(
-            self,
-            event_type: typing.Type[platform_events.Event],
-            callback: typing.Callable[[platform_events.Event, adapter.MessagePlatformAdapter], None]
+        self,
+        event_type: typing.Type[platform_events.Event],
+        callback: typing.Callable[[platform_events.Event, adapter.MessagePlatformAdapter], None],
     ):
         self.listeners[event_type] = callback
 
     def unregister_listener(
-            self,
-            event_type: typing.Type[platform_events.Event],
-            callback: typing.Callable[[platform_events.Event, adapter.MessagePlatformAdapter], None]
+        self,
+        event_type: typing.Type[platform_events.Event],
+        callback: typing.Callable[[platform_events.Event, adapter.MessagePlatformAdapter], None],
     ):
         pass
 
     async def run_async(self):
-
-        if not self.config["admin_key"] and not self.config["token"]:
-            raise RuntimeError("无wechatpad管理密匙，请填入配置文件后重启")
+        if not self.config['admin_key'] and not self.config['token']:
+            raise RuntimeError('无wechatpad管理密匙，请填入配置文件后重启')
         else:
-            if self.config["token"]:
-                self.bot = WeChatPadClient(
-                    self.config['wechatpad_url'],
-                    self.config["token"]
-                )
+            if self.config['token']:
+                self.bot = WeChatPadClient(self.config['wechatpad_url'], self.config['token'])
                 data = self.bot.get_login_status()
                 self.ap.logger.info(data)
-                if data["Code"] == 300 and data["Text"] == "你已退出微信":
+                if data['Code'] == 300 and data['Text'] == '你已退出微信':
                     response = requests.post(
-                        f"{self.config['wechatpad_url']}/admin/GenAuthKey1?key={self.config['admin_key']}",
-                        json={"Count": 1, "Days": 365}
+                        f'{self.config["wechatpad_url"]}/admin/GenAuthKey1?key={self.config["admin_key"]}',
+                        json={'Count': 1, 'Days': 365},
                     )
                     if response.status_code != 200:
-                        raise Exception(f"获取token失败: {response.text}")
-                    self.config["token"] = response.json()["Data"][0]
+                        raise Exception(f'获取token失败: {response.text}')
+                    self.config['token'] = response.json()['Data'][0]
 
-            elif not self.config["token"]:
+            elif not self.config['token']:
                 response = requests.post(
-                    f"{self.config['wechatpad_url']}/admin/GenAuthKey1?key={self.config['admin_key']}",
-                    json={"Count": 1, "Days": 365}
+                    f'{self.config["wechatpad_url"]}/admin/GenAuthKey1?key={self.config["admin_key"]}',
+                    json={'Count': 1, 'Days': 365},
                 )
                 if response.status_code != 200:
-                    raise Exception(f"获取token失败: {response.text}")
-                self.config["token"] = response.json()["Data"][0]
+                    raise Exception(f'获取token失败: {response.text}')
+                self.config['token'] = response.json()['Data'][0]
 
-        self.bot = WeChatPadClient(
-            self.config['wechatpad_url'],
-            self.config["token"],
-            logger=self.logger
-        )
-        self.ap.logger.info(self.config["token"])
+        self.bot = WeChatPadClient(self.config['wechatpad_url'], self.config['token'], logger=self.logger)
+        self.ap.logger.info(self.config['token'])
         thread_1 = threading.Event()
 
-
         def wechat_login_process():
             # 不登录，这些先注释掉，避免登陆态尝试拉qrcode。
             # login_data =self.bot.get_login_qr()
@@ -768,67 +693,54 @@ class WeChatPadAdapter(adapter.MessagePlatformAdapter):
             # url = login_data['Data']["QrCodeUrl"]
             # self.ap.logger.info(login_data)
 
-
-            profile =self.bot.get_profile()
+            profile = self.bot.get_profile()
             self.ap.logger.info(profile)
 
-            self.bot_account_id = profile["Data"]["userInfo"]["nickName"]["str"]
-            self.config["wxid"] = profile["Data"]["userInfo"]["userName"]["str"]
+            self.bot_account_id = profile['Data']['userInfo']['nickName']['str']
+            self.config['wxid'] = profile['Data']['userInfo']['userName']['str']
             thread_1.set()
 
-
         # asyncio.create_task(wechat_login_process)
         threading.Thread(target=wechat_login_process).start()
 
         def connect_websocket_sync() -> None:
-
             thread_1.wait()
-            uri = f"{self.config['wechatpad_ws']}/GetSyncMsg?key={self.config['token']}"
-            self.ap.logger.info(f"Connecting to WebSocket: {uri}")
+            uri = f'{self.config["wechatpad_ws"]}/GetSyncMsg?key={self.config["token"]}'
+            self.ap.logger.info(f'Connecting to WebSocket: {uri}')
+
             def on_message(ws, message):
                 try:
                     data = json.loads(message)
-                    self.ap.logger.debug(f"Received message: {data}")
+                    self.ap.logger.debug(f'Received message: {data}')
                     # 这里需要确保ws_message是同步的，或者使用asyncio.run调用异步方法
                     asyncio.run(self.ws_message(data))
                 except json.JSONDecodeError:
-                    self.ap.logger.error(f"Non-JSON message: {message[:100]}...")
+                    self.ap.logger.error(f'Non-JSON message: {message[:100]}...')
 
             def on_error(ws, error):
-                self.ap.logger.error(f"WebSocket error: {str(error)[:200]}")
+                self.ap.logger.error(f'WebSocket error: {str(error)[:200]}')
 
             def on_close(ws, close_status_code, close_msg):
-                self.ap.logger.info("WebSocket closed, reconnecting...")
+                self.ap.logger.info('WebSocket closed, reconnecting...')
                 time.sleep(5)
                 connect_websocket_sync()  # 自动重连
 
             def on_open(ws):
-                self.ap.logger.info("WebSocket connected successfully!")
+                self.ap.logger.info('WebSocket connected successfully!')
 
             ws = websocket.WebSocketApp(
-                uri,
-                on_message=on_message,
-                on_error=on_error,
-                on_close=on_close,
-                on_open=on_open
-            )
-            ws.run_forever(
-                ping_interval=60,
-                ping_timeout=20
+                uri, on_message=on_message, on_error=on_error, on_close=on_close, on_open=on_open
             )
+            ws.run_forever(ping_interval=60, ping_timeout=20)
 
         # 直接调用同步版本（会阻塞）
         # connect_websocket_sync()
 
         # 这行代码会在WebSocket连接断开后才会执行
         # self.ap.logger.info("WebSocket client thread started")
-        thread = threading.Thread(
-            target=connect_websocket_sync,
-            name="WebSocketClientThread",
-            daemon=True
-        )
+        thread = threading.Thread(target=connect_websocket_sync, name='WebSocketClientThread', daemon=True)
         thread.start()
-        self.ap.logger.info("WebSocket client thread started")
+        self.ap.logger.info('WebSocket client thread started')
 
     async def kill(self) -> bool:
         pass

pkg/platform/sources/wecom.py

@@ -157,7 +157,7 @@ class WecomAdapter(adapter.MessagePlatformAdapter):
             token=config['token'],
             EncodingAESKey=config['EncodingAESKey'],
             contacts_secret=config['contacts_secret'],
-            logger=self.logger
+            logger=self.logger,
         )
 
     async def reply_message(
@@ -201,8 +201,8 @@ class WecomAdapter(adapter.MessagePlatformAdapter):
             self.bot_account_id = event.receiver_id
             try:
                 return await callback(await self.event_converter.target2yiri(event), self)
-            except Exception as e:
-                await self.logger.error(f"Error in wecom callback: {traceback.format_exc()}")
+            except Exception:
+                await self.logger.error(f'Error in wecom callback: {traceback.format_exc()}')
 
         if event_type == platform_events.FriendMessage:
             self.bot.on_message('text')(on_message)

pkg/platform/sources/wecomcs.py

@@ -145,7 +145,7 @@ class WecomCSAdapter(adapter.MessagePlatformAdapter):
             secret=config['secret'],
             token=config['token'],
             EncodingAESKey=config['EncodingAESKey'],
-            logger=self.logger
+            logger=self.logger,
         )
 
     async def reply_message(
@@ -178,8 +178,8 @@ class WecomCSAdapter(adapter.MessagePlatformAdapter):
             self.bot_account_id = event.receiver_id
             try:
                 return await callback(await self.event_converter.target2yiri(event), self)
-            except Exception as e:
-                await self.logger.error(f"Error in wecomcs callback: {traceback.format_exc()}")
+            except Exception:
+                await self.logger.error(f'Error in wecomcs callback: {traceback.format_exc()}')
 
         if event_type == platform_events.FriendMessage:
             self.bot.on_message('text')(on_message)

pkg/provider/modelmgr/entities.py

@@ -17,7 +17,7 @@ class LLMModelInfo(pydantic.BaseModel):
 
     token_mgr: token.TokenManager
 
-    requester: requester.LLMAPIRequester
+    requester: requester.ProviderAPIRequester
 
     tool_call_supported: typing.Optional[bool] = False
 

pkg/provider/modelmgr/modelmgr.py

@@ -18,7 +18,7 @@ class ModelManager:
 
     model_list: list[entities.LLMModelInfo]  # deprecated
 
-    requesters: dict[str, requester.LLMAPIRequester]  # deprecated
+    requesters: dict[str, requester.ProviderAPIRequester]  # deprecated
 
     token_mgrs: dict[str, token.TokenManager]  # deprecated
 
@@ -28,9 +28,11 @@ class ModelManager:
 
     llm_models: list[requester.RuntimeLLMModel]
 
+    embedding_models: list[requester.RuntimeEmbeddingModel]
+
     requester_components: list[engine.Component]
 
-    requester_dict: dict[str, type[requester.LLMAPIRequester]]  # cache
+    requester_dict: dict[str, type[requester.ProviderAPIRequester]]  # cache
 
     def __init__(self, ap: app.Application):
         self.ap = ap
@@ -38,6 +40,7 @@ class ModelManager:
         self.requesters = {}
         self.token_mgrs = {}
         self.llm_models = []
+        self.embedding_models = []
         self.requester_components = []
         self.requester_dict = {}
 
@@ -45,7 +48,7 @@ class ModelManager:
         self.requester_components = self.ap.discover.get_components_by_kind('LLMAPIRequester')
 
         # forge requester class dict
-        requester_dict: dict[str, type[requester.LLMAPIRequester]] = {}
+        requester_dict: dict[str, type[requester.ProviderAPIRequester]] = {}
         for component in self.requester_components:
             requester_dict[component.metadata.name] = component.get_python_component_class()
 
@@ -58,13 +61,11 @@ class ModelManager:
         self.ap.logger.info('Loading models from db...')
 
         self.llm_models = []
+        self.embedding_models = []
 
         # llm models
         result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.LLMModel))
-
         llm_models = result.all()
-
-        # load models
         for llm_model in llm_models:
             try:
                 await self.load_llm_model(llm_model)
@@ -73,11 +74,17 @@ class ModelManager:
             except Exception as e:
                 self.ap.logger.error(f'Failed to load model {llm_model.uuid}: {e}\n{traceback.format_exc()}')
 
+        # embedding models
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.EmbeddingModel))
+        embedding_models = result.all()
+        for embedding_model in embedding_models:
+            await self.load_embedding_model(embedding_model)
+
     async def init_runtime_llm_model(
         self,
         model_info: persistence_model.LLMModel | sqlalchemy.Row[persistence_model.LLMModel] | dict,
     ):
-        """初始化运行时模型"""
+        """初始化运行时 LLM 模型"""
         if isinstance(model_info, sqlalchemy.Row):
             model_info = persistence_model.LLMModel(**model_info._mapping)
         elif isinstance(model_info, dict):
@@ -101,14 +108,47 @@ class ModelManager:
 
         return runtime_llm_model
 
+    async def init_runtime_embedding_model(
+        self,
+        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row[persistence_model.EmbeddingModel] | dict,
+    ):
+        """初始化运行时 Embedding 模型"""
+        if isinstance(model_info, sqlalchemy.Row):
+            model_info = persistence_model.EmbeddingModel(**model_info._mapping)
+        elif isinstance(model_info, dict):
+            model_info = persistence_model.EmbeddingModel(**model_info)
+
+        requester_inst = self.requester_dict[model_info.requester](ap=self.ap, config=model_info.requester_config)
+
+        await requester_inst.initialize()
+
+        runtime_embedding_model = requester.RuntimeEmbeddingModel(
+            model_entity=model_info,
+            token_mgr=token.TokenManager(
+                name=model_info.uuid,
+                tokens=model_info.api_keys,
+            ),
+            requester=requester_inst,
+        )
+
+        return runtime_embedding_model
+
     async def load_llm_model(
         self,
         model_info: persistence_model.LLMModel | sqlalchemy.Row[persistence_model.LLMModel] | dict,
     ):
-        """加载模型"""
+        """加载 LLM 模型"""
         runtime_llm_model = await self.init_runtime_llm_model(model_info)
         self.llm_models.append(runtime_llm_model)
 
+    async def load_embedding_model(
+        self,
+        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row[persistence_model.EmbeddingModel] | dict,
+    ):
+        """加载 Embedding 模型"""
+        runtime_embedding_model = await self.init_runtime_embedding_model(model_info)
+        self.embedding_models.append(runtime_embedding_model)
+
     async def get_model_by_name(self, name: str) -> entities.LLMModelInfo:  # deprecated
         """通过名称获取模型"""
         for model in self.model_list:
@@ -116,23 +156,44 @@ class ModelManager:
                 return model
         raise ValueError(f'无法确定模型 {name} 的信息')
 
-    async def get_model_by_uuid(self, uuid: str) -> entities.LLMModelInfo:
-        """通过uuid获取模型"""
+    async def get_model_by_uuid(self, uuid: str) -> requester.RuntimeLLMModel:
+        """通过uuid获取 LLM 模型"""
         for model in self.llm_models:
             if model.model_entity.uuid == uuid:
                 return model
-        raise ValueError(f'model {uuid} not found')
+        raise ValueError(f'LLM model {uuid} not found')
+
+    async def get_embedding_model_by_uuid(self, uuid: str) -> requester.RuntimeEmbeddingModel:
+        """通过uuid获取 Embedding 模型"""
+        for model in self.embedding_models:
+            if model.model_entity.uuid == uuid:
+                return model
+        raise ValueError(f'Embedding model {uuid} not found')
 
     async def remove_llm_model(self, model_uuid: str):
-        """移除模型"""
+        """移除 LLM 模型"""
         for model in self.llm_models:
             if model.model_entity.uuid == model_uuid:
                 self.llm_models.remove(model)
                 return
 
-    def get_available_requesters_info(self) -> list[dict]:
+    async def remove_embedding_model(self, model_uuid: str):
+        """移除 Embedding 模型"""
+        for model in self.embedding_models:
+            if model.model_entity.uuid == model_uuid:
+                self.embedding_models.remove(model)
+                return
+
+    def get_available_requesters_info(self, model_type: str) -> list[dict]:
         """获取所有可用的请求器"""
-        return [component.to_plain_dict() for component in self.requester_components]
+        if model_type != '':
+            return [
+                component.to_plain_dict()
+                for component in self.requester_components
+                if model_type in component.spec['support_type']
+            ]
+        else:
+            return [component.to_plain_dict() for component in self.requester_components]
 
     def get_available_requester_info_by_name(self, name: str) -> dict | None:
         """通过名称获取请求器信息"""

pkg/provider/modelmgr/requester.py

@@ -20,22 +20,45 @@ class RuntimeLLMModel:
     token_mgr: token.TokenManager
     """api key管理器"""
 
-    requester: LLMAPIRequester
+    requester: ProviderAPIRequester
     """请求器实例"""
 
     def __init__(
         self,
         model_entity: persistence_model.LLMModel,
         token_mgr: token.TokenManager,
-        requester: LLMAPIRequester,
+        requester: ProviderAPIRequester,
     ):
         self.model_entity = model_entity
         self.token_mgr = token_mgr
         self.requester = requester
 
 
-class LLMAPIRequester(metaclass=abc.ABCMeta):
-    """LLM API请求器"""
+class RuntimeEmbeddingModel:
+    """运行时 Embedding 模型"""
+
+    model_entity: persistence_model.EmbeddingModel
+    """模型数据"""
+
+    token_mgr: token.TokenManager
+    """api key管理器"""
+
+    requester: ProviderAPIRequester
+    """请求器实例"""
+
+    def __init__(
+        self,
+        model_entity: persistence_model.EmbeddingModel,
+        token_mgr: token.TokenManager,
+        requester: ProviderAPIRequester,
+    ):
+        self.model_entity = model_entity
+        self.token_mgr = token_mgr
+        self.requester = requester
+
+
+class ProviderAPIRequester(metaclass=abc.ABCMeta):
+    """Provider API请求器"""
 
     name: str = None
 
@@ -74,3 +97,22 @@ class LLMAPIRequester(metaclass=abc.ABCMeta):
             llm_entities.Message: 返回消息对象
         """
         pass
+
+    async def invoke_embedding(
+        self,
+        model: RuntimeEmbeddingModel,
+        input_text: list[str],
+        extra_args: dict[str, typing.Any] = {},
+    ) -> list[list[float]]:
+        """调用 Embedding API
+
+        Args:
+            query (core_entities.Query): 请求上下文
+            model (RuntimeEmbeddingModel): 使用的模型信息
+            input_text (list[str]): 输入文本
+            extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
+
+        Returns:
+            list[list[float]]: 返回的 embedding 向量
+        """
+        pass

pkg/provider/modelmgr/requesters/302aichatcmpl.yaml

@@ -22,6 +22,9 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
+    - text-embedding
 execution:
   python:
     path: ./302aichatcmpl.py

pkg/provider/modelmgr/requesters/anthropicmsgs.py

@@ -15,7 +15,7 @@ from ...tools import entities as tools_entities
 from ....utils import image
 
 
-class AnthropicMessages(requester.LLMAPIRequester):
+class AnthropicMessages(requester.ProviderAPIRequester):
     """Anthropic Messages API 请求器"""
 
     client: anthropic.AsyncAnthropic

pkg/provider/modelmgr/requesters/anthropicmsgs.yaml

@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./anthropicmsgs.py

pkg/provider/modelmgr/requesters/bailianchatcmpl.yaml

@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./bailianchatcmpl.py

pkg/provider/modelmgr/requesters/chatcmpl.py

@@ -13,7 +13,7 @@ from ... import entities as llm_entities
 from ...tools import entities as tools_entities
 
 
-class OpenAIChatCompletions(requester.LLMAPIRequester):
+class OpenAIChatCompletions(requester.ProviderAPIRequester):
     """OpenAI ChatCompletion API 请求器"""
 
     client: openai.AsyncClient
@@ -141,3 +141,39 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
             raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
         except openai.APIError as e:
             raise errors.RequesterError(f'请求错误: {e.message}')
+
+    async def invoke_embedding(
+        self,
+        model: requester.RuntimeEmbeddingModel,
+        input_text: list[str],
+        extra_args: dict[str, typing.Any] = {},
+    ) -> list[list[float]]:
+        """调用 Embedding API"""
+        self.client.api_key = model.token_mgr.get_token()
+
+        args = {
+            'model': model.model_entity.name,
+            'input': input_text,
+        }
+
+        if model.model_entity.extra_args:
+            args.update(model.model_entity.extra_args)
+
+        args.update(extra_args)
+
+        try:
+            resp = await self.client.embeddings.create(**args)
+
+            return [d.embedding for d in resp.data]
+        except asyncio.TimeoutError:
+            raise errors.RequesterError('请求超时')
+        except openai.BadRequestError as e:
+            raise errors.RequesterError(f'请求参数错误: {e.message}')
+        except openai.AuthenticationError as e:
+            raise errors.RequesterError(f'无效的 api-key: {e.message}')
+        except openai.NotFoundError as e:
+            raise errors.RequesterError(f'请求路径错误: {e.message}')
+        except openai.RateLimitError as e:
+            raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
+        except openai.APIError as e:
+            raise errors.RequesterError(f'请求错误: {e.message}')

pkg/provider/modelmgr/requesters/chatcmpl.yaml

@@ -22,6 +22,9 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
+    - text-embedding
 execution:
   python:
     path: ./chatcmpl.py

pkg/provider/modelmgr/requesters/compsharechatcmpl.yaml

@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./compsharechatcmpl.py

pkg/provider/modelmgr/requesters/deepseekchatcmpl.yaml

@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./deepseekchatcmpl.py

pkg/provider/modelmgr/requesters/geminichatcmpl.yaml

@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./geminichatcmpl.py

pkg/provider/modelmgr/requesters/giteeaichatcmpl.yaml

@@ -22,6 +22,9 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
+    - text-embedding
 execution:
   python:
     path: ./giteeaichatcmpl.py

pkg/provider/modelmgr/requesters/lmstudiochatcmpl.yaml

@@ -22,6 +22,9 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
+    - text-embedding
 execution:
   python:
     path: ./lmstudiochatcmpl.py

pkg/provider/modelmgr/requesters/modelscopechatcmpl.py

@@ -14,7 +14,7 @@ from ... import entities as llm_entities
 from ...tools import entities as tools_entities
 
 
-class ModelScopeChatCompletions(requester.LLMAPIRequester):
+class ModelScopeChatCompletions(requester.ProviderAPIRequester):
     """ModelScope ChatCompletion API 请求器"""
 
     client: openai.AsyncClient

pkg/provider/modelmgr/requesters/modelscopechatcmpl.yaml

@@ -29,6 +29,8 @@ spec:
       type: int
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./modelscopechatcmpl.py

pkg/provider/modelmgr/requesters/moonshotchatcmpl.yaml

@@ -14,7 +14,7 @@ spec:
         zh_Hans: 基础 URL
       type: string
       required: true
-      default: "https://api.moonshot.com/v1"
+      default: "https://api.moonshot.ai/v1"
     - name: timeout
       label:
         en_US: Timeout
@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./moonshotchatcmpl.py

pkg/provider/modelmgr/requesters/newapichatcmpl.py

@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+import typing
+import openai
+
+from . import chatcmpl
+
+
+class NewAPIChatCompletions(chatcmpl.OpenAIChatCompletions):
+    """New API ChatCompletion API 请求器"""
+
+    client: openai.AsyncClient
+
+    default_config: dict[str, typing.Any] = {
+        'base_url': 'http://localhost:3000/v1',
+        'timeout': 120,
+    }

pkg/provider/modelmgr/requesters/newapichatcmpl.yaml

@@ -0,0 +1,31 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: new-api-chat-completions
+  label:
+    en_US: New API
+    zh_Hans: New API
+  icon: newapi.png
+spec:
+  config:
+    - name: base_url
+      label:
+        en_US: Base URL
+        zh_Hans: 基础 URL
+      type: string
+      required: true
+      default: "http://localhost:3000/v1"
+    - name: timeout
+      label:
+        en_US: Timeout
+        zh_Hans: 超时时间
+      type: integer
+      required: true
+      default: 120
+  support_type:
+    - llm
+    - text-embedding
+execution:
+  python:
+    path: ./newapichatcmpl.py
+    attr: NewAPIChatCompletions

pkg/provider/modelmgr/requesters/ollamachat.py

@@ -17,7 +17,7 @@ from ....core import entities as core_entities
 REQUESTER_NAME: str = 'ollama-chat'
 
 
-class OllamaChatCompletions(requester.LLMAPIRequester):
+class OllamaChatCompletions(requester.ProviderAPIRequester):
     """Ollama平台 ChatCompletion API请求器"""
 
     client: ollama.AsyncClient
@@ -129,3 +129,15 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
             )
         except asyncio.TimeoutError:
             raise errors.RequesterError('请求超时')
+
+    async def invoke_embedding(
+        self,
+        model: requester.RuntimeEmbeddingModel,
+        input_text: list[str],
+        extra_args: dict[str, typing.Any] = {},
+    ) -> list[list[float]]:
+        return await self.client.embed(
+            model=model.model_entity.name,
+            input=input_text,
+            **extra_args,
+        )

pkg/provider/modelmgr/requesters/ollamachat.yaml

@@ -22,6 +22,9 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
+    - text-embedding
 execution:
   python:
     path: ./ollamachat.py

pkg/provider/modelmgr/requesters/openrouterchatcmpl.yaml

@@ -22,6 +22,9 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
+    - text-embedding
 execution:
   python:
     path: ./openrouterchatcmpl.py

pkg/provider/modelmgr/requesters/ppiochatcmpl.yaml

@@ -29,6 +29,9 @@ spec:
       type: int
       required: true
       default: 120
+  support_type:
+    - llm
+    - text-embedding
 execution:
   python:
     path: ./ppiochatcmpl.py

pkg/provider/modelmgr/requesters/qhaigcchatcmpl.py

@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+import openai
+import typing
+
+from . import chatcmpl
+
+
+class QHAIGCChatCompletions(chatcmpl.OpenAIChatCompletions):
+    """启航 AI ChatCompletion API 请求器"""
+
+    client: openai.AsyncClient
+
+    default_config: dict[str, typing.Any] = {
+        'base_url': 'https://api.qhaigc.com/v1',
+        'timeout': 120,
+    }

pkg/provider/modelmgr/requesters/qhaigcchatcmpl.yaml

@@ -0,0 +1,38 @@
+apiVersion: v1
+kind: LLMAPIRequester
+metadata:
+  name: qhaigc-chat-completions
+  label:
+    en_US: QH AI
+    zh_Hans: 启航 AI
+  icon: qhaigc.png
+spec:
+  config:
+    - name: base_url
+      label:
+        en_US: Base URL
+        zh_Hans: 基础 URL
+      type: string
+      required: true
+      default: "https://api.qhaigc.net/v1"
+    - name: args
+      label:
+        en_US: Args
+        zh_Hans: 附加参数
+      type: object
+      required: true
+      default: {}
+    - name: timeout
+      label:
+        en_US: Timeout
+        zh_Hans: 超时时间
+      type: int
+      required: true
+      default: 120
+  support_type:
+    - llm
+    - text-embedding
+execution:
+  python:
+    path: ./qhaigcchatcmpl.py
+    attr: QHAIGCChatCompletions

pkg/provider/modelmgr/requesters/siliconflowchatcmpl.yaml

@@ -22,6 +22,9 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
+    - text-embedding
 execution:
   python:
     path: ./siliconflowchatcmpl.py

pkg/provider/modelmgr/requesters/volcarkchatcmpl.yaml

@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./volcarkchatcmpl.py

pkg/provider/modelmgr/requesters/xaichatcmpl.yaml

@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./xaichatcmpl.py

pkg/provider/modelmgr/requesters/zhipuaichatcmpl.yaml

@@ -22,6 +22,8 @@ spec:
       type: integer
       required: true
       default: 120
+  support_type:
+    - llm
 execution:
   python:
     path: ./zhipuaichatcmpl.py

pkg/provider/runners/localagent.py

@@ -1,13 +1,28 @@
 from __future__ import annotations
 
 import json
+import copy
 import typing
-
 from .. import runner
 from ...core import entities as core_entities
 from .. import entities as llm_entities
 
 
+rag_combined_prompt_template = """
+The following are relevant context entries retrieved from the knowledge base. 
+Please use them to answer the user's message. 
+Respond in the same language as the user's input.
+
+<context>
+{rag_context}
+</context>
+
+<user_message>
+{user_message}
+</user_message>
+"""
+
+
 @runner.runner_class('local-agent')
 class LocalAgentRunner(runner.RequestRunner):
     """本地Agent请求运行器"""
@@ -16,7 +31,54 @@ class LocalAgentRunner(runner.RequestRunner):
         """运行请求"""
         pending_tool_calls = []
 
-        req_messages = query.prompt.messages.copy() + query.messages.copy() + [query.user_message]
+        kb_uuid = query.pipeline_config['ai']['local-agent']['knowledge-base']
+
+        if kb_uuid == '__none__':
+            kb_uuid = None
+
+        user_message = copy.deepcopy(query.user_message)
+
+        user_message_text = ''
+
+        if isinstance(user_message.content, str):
+            user_message_text = user_message.content
+        elif isinstance(user_message.content, list):
+            for ce in user_message.content:
+                if ce.type == 'text':
+                    user_message_text += ce.text
+                    break
+
+        if kb_uuid and user_message_text:
+            # only support text for now
+            kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
+
+            if not kb:
+                self.ap.logger.warning(f'Knowledge base {kb_uuid} not found')
+                raise ValueError(f'Knowledge base {kb_uuid} not found')
+
+            result = await kb.retrieve(user_message_text)
+
+            final_user_message_text = ''
+
+            if result:
+                rag_context = '\n\n'.join(
+                    f'[{i + 1}] {entry.metadata.get("text", "")}' for i, entry in enumerate(result)
+                )
+                final_user_message_text = rag_combined_prompt_template.format(
+                    rag_context=rag_context, user_message=user_message_text
+                )
+
+            else:
+                final_user_message_text = user_message_text
+
+            self.ap.logger.debug(f'Final user message text: {final_user_message_text}')
+
+            for ce in user_message.content:
+                if ce.type == 'text':
+                    ce.text = final_user_message_text
+                    break
+
+        req_messages = query.prompt.messages.copy() + query.messages.copy() + [user_message]
 
         # 首次请求
         msg = await query.use_llm_model.requester.invoke_llm(

pkg/rag/knowledge/kbmgr.py

@@ -0,0 +1,212 @@
+from __future__ import annotations
+import traceback
+import uuid
+from .services import parser, chunker
+from pkg.core import app
+from pkg.rag.knowledge.services.embedder import Embedder
+from pkg.rag.knowledge.services.retriever import Retriever
+import sqlalchemy
+from ...entity.persistence import rag as persistence_rag
+from pkg.core import taskmgr
+from ...entity.rag import retriever as retriever_entities
+
+
+class RuntimeKnowledgeBase:
+    ap: app.Application
+
+    knowledge_base_entity: persistence_rag.KnowledgeBase
+
+    parser: parser.FileParser
+
+    chunker: chunker.Chunker
+
+    embedder: Embedder
+
+    retriever: Retriever
+
+    def __init__(self, ap: app.Application, knowledge_base_entity: persistence_rag.KnowledgeBase):
+        self.ap = ap
+        self.knowledge_base_entity = knowledge_base_entity
+        self.parser = parser.FileParser(ap=self.ap)
+        self.chunker = chunker.Chunker(ap=self.ap)
+        self.embedder = Embedder(ap=self.ap)
+        self.retriever = Retriever(ap=self.ap)
+        # 传递kb_id给retriever
+        self.retriever.kb_id = knowledge_base_entity.uuid
+
+    async def initialize(self):
+        pass
+
+    async def _store_file_task(self, file: persistence_rag.File, task_context: taskmgr.TaskContext):
+        try:
+            # set file status to processing
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_rag.File)
+                .where(persistence_rag.File.uuid == file.uuid)
+                .values(status='processing')
+            )
+
+            task_context.set_current_action('Parsing file')
+            # parse file
+            text = await self.parser.parse(file.file_name, file.extension)
+            if not text:
+                raise Exception(f'No text extracted from file {file.file_name}')
+
+            task_context.set_current_action('Chunking file')
+            # chunk file
+            chunks_texts = await self.chunker.chunk(text)
+            if not chunks_texts:
+                raise Exception(f'No chunks extracted from file {file.file_name}')
+
+            task_context.set_current_action('Embedding chunks')
+
+            embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(
+                self.knowledge_base_entity.embedding_model_uuid
+            )
+            # embed chunks
+            await self.embedder.embed_and_store(
+                kb_id=self.knowledge_base_entity.uuid,
+                file_id=file.uuid,
+                chunks=chunks_texts,
+                embedding_model=embedding_model,
+            )
+
+            # set file status to completed
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_rag.File)
+                .where(persistence_rag.File.uuid == file.uuid)
+                .values(status='completed')
+            )
+
+        except Exception as e:
+            self.ap.logger.error(f'Error storing file {file.uuid}: {e}')
+            traceback.print_exc()
+            # set file status to failed
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_rag.File)
+                .where(persistence_rag.File.uuid == file.uuid)
+                .values(status='failed')
+            )
+
+            raise
+
+    async def store_file(self, file_id: str) -> str:
+        # pre checking
+        if not await self.ap.storage_mgr.storage_provider.exists(file_id):
+            raise Exception(f'File {file_id} not found')
+
+        file_uuid = str(uuid.uuid4())
+        kb_id = self.knowledge_base_entity.uuid
+        file_name = file_id
+        extension = file_name.split('.')[-1]
+
+        file_obj_data = {
+            'uuid': file_uuid,
+            'kb_id': kb_id,
+            'file_name': file_name,
+            'extension': extension,
+            'status': 'pending',
+        }
+
+        file_obj = persistence_rag.File(**file_obj_data)
+
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(file_obj_data))
+
+        # run background task asynchronously
+        ctx = taskmgr.TaskContext.new()
+        wrapper = self.ap.task_mgr.create_user_task(
+            self._store_file_task(file_obj, task_context=ctx),
+            kind='knowledge-operation',
+            name=f'knowledge-store-file-{file_id}',
+            label=f'Store file {file_id}',
+            context=ctx,
+        )
+        return wrapper.id
+
+    async def retrieve(self, query: str) -> list[retriever_entities.RetrieveResultEntry]:
+        embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(
+            self.knowledge_base_entity.embedding_model_uuid
+        )
+        return await self.retriever.retrieve(self.knowledge_base_entity.uuid, query, embedding_model)
+
+    async def delete_file(self, file_id: str):
+        # delete vector
+        await self.ap.vector_db_mgr.vector_db.delete_by_file_id(self.knowledge_base_entity.uuid, file_id)
+
+        # delete chunk
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_rag.Chunk).where(persistence_rag.Chunk.file_id == file_id)
+        )
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file_id)
+        )
+
+    async def dispose(self):
+        await self.ap.vector_db_mgr.vector_db.delete_collection(self.knowledge_base_entity.uuid)
+
+
+class RAGManager:
+    ap: app.Application
+
+    knowledge_bases: list[RuntimeKnowledgeBase]
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+        self.knowledge_bases = []
+
+    async def initialize(self):
+        await self.load_knowledge_bases_from_db()
+
+    async def load_knowledge_bases_from_db(self):
+        self.ap.logger.info('Loading knowledge bases from db...')
+
+        self.knowledge_bases = []
+
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
+
+        knowledge_bases = result.all()
+
+        for knowledge_base in knowledge_bases:
+            try:
+                await self.load_knowledge_base(knowledge_base)
+            except Exception as e:
+                self.ap.logger.error(
+                    f'Error loading knowledge base {knowledge_base.uuid}: {e}\n{traceback.format_exc()}'
+                )
+
+    async def load_knowledge_base(
+        self,
+        knowledge_base_entity: persistence_rag.KnowledgeBase | sqlalchemy.Row | dict,
+    ) -> RuntimeKnowledgeBase:
+        if isinstance(knowledge_base_entity, sqlalchemy.Row):
+            knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity._mapping)
+        elif isinstance(knowledge_base_entity, dict):
+            knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity)
+
+        runtime_knowledge_base = RuntimeKnowledgeBase(ap=self.ap, knowledge_base_entity=knowledge_base_entity)
+
+        await runtime_knowledge_base.initialize()
+
+        self.knowledge_bases.append(runtime_knowledge_base)
+
+        return runtime_knowledge_base
+
+    async def get_knowledge_base_by_uuid(self, kb_uuid: str) -> RuntimeKnowledgeBase | None:
+        for kb in self.knowledge_bases:
+            if kb.knowledge_base_entity.uuid == kb_uuid:
+                return kb
+        return None
+
+    async def remove_knowledge_base_from_runtime(self, kb_uuid: str):
+        for kb in self.knowledge_bases:
+            if kb.knowledge_base_entity.uuid == kb_uuid:
+                self.knowledge_bases.remove(kb)
+                return
+
+    async def delete_knowledge_base(self, kb_uuid: str):
+        for kb in self.knowledge_bases:
+            if kb.knowledge_base_entity.uuid == kb_uuid:
+                await kb.dispose()
+                self.knowledge_bases.remove(kb)
+                return

pkg/rag/knowledge/services/base_service.py

@@ -0,0 +1,15 @@
+# 封装异步操作
+import asyncio
+
+
+class BaseService:
+    def __init__(self):
+        pass
+
+    async def _run_sync(self, func, *args, **kwargs):
+        """
+        在单独的线程中运行同步函数。
+        如果第一个参数是 session，则在 to_thread 中获取新的 session。
+        """
+
+        return await asyncio.to_thread(func, *args, **kwargs)

pkg/rag/knowledge/services/chunker.py

@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+import json
+from typing import List
+from pkg.rag.knowledge.services import base_service
+from pkg.core import app
+
+
+class Chunker(base_service.BaseService):
+    """
+    A class for splitting long texts into smaller, overlapping chunks.
+    """
+
+    def __init__(self, ap: app.Application, chunk_size: int = 500, chunk_overlap: int = 50):
+        self.ap = ap
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+        if self.chunk_overlap >= self.chunk_size:
+            self.ap.logger.warning(
+                'Chunk overlap is greater than or equal to chunk size. This may lead to empty or malformed chunks.'
+            )
+
+    def _split_text_sync(self, text: str) -> List[str]:
+        """
+        Synchronously splits a long text into chunks with specified overlap.
+        This is a CPU-bound operation, intended to be run in a separate thread.
+        """
+        if not text:
+            return []
+        # words = text.split()
+        # chunks = []
+        # current_chunk = []
+
+        # for word in words:
+        #     current_chunk.append(word)
+        #     if len(current_chunk) > self.chunk_size:
+        #         chunks.append(" ".join(current_chunk[:self.chunk_size]))
+        #         current_chunk = current_chunk[self.chunk_size - self.chunk_overlap:]
+
+        # if current_chunk:
+        #     chunks.append(" ".join(current_chunk))
+
+        # A more robust chunking strategy (e.g., using recursive character text splitter)
+        from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=self.chunk_size,
+            chunk_overlap=self.chunk_overlap,
+            length_function=len,
+            is_separator_regex=False,
+        )
+        return text_splitter.split_text(text)
+
+    async def chunk(self, text: str) -> List[str]:
+        """
+        Asynchronously chunks a given text into smaller pieces.
+        """
+        self.ap.logger.info(f'Chunking text (length: {len(text)})...')
+        # Run the synchronous splitting logic in a separate thread
+        chunks = await self._run_sync(self._split_text_sync, text)
+        self.ap.logger.info(f'Text chunked into {len(chunks)} pieces.')
+        self.ap.logger.debug(f'Chunks: {json.dumps(chunks, indent=4, ensure_ascii=False)}')
+        return chunks

pkg/rag/knowledge/services/embedder.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+import uuid
+from typing import List
+from pkg.rag.knowledge.services.base_service import BaseService
+from ....entity.persistence import rag as persistence_rag
+from ....core import app
+from ....provider.modelmgr.requester import RuntimeEmbeddingModel
+import sqlalchemy
+
+
+class Embedder(BaseService):
+    def __init__(self, ap: app.Application) -> None:
+        super().__init__()
+        self.ap = ap
+
+    async def embed_and_store(
+        self, kb_id: str, file_id: str, chunks: List[str], embedding_model: RuntimeEmbeddingModel
+    ) -> list[persistence_rag.Chunk]:
+        # save chunk to db
+        chunk_entities: list[persistence_rag.Chunk] = []
+        chunk_ids: list[str] = []
+
+        for chunk_text in chunks:
+            chunk_uuid = str(uuid.uuid4())
+            chunk_ids.append(chunk_uuid)
+            chunk_entity = persistence_rag.Chunk(uuid=chunk_uuid, file_id=file_id, text=chunk_text)
+            chunk_entities.append(chunk_entity)
+
+        chunk_dicts = [
+            self.ap.persistence_mgr.serialize_model(persistence_rag.Chunk, chunk) for chunk in chunk_entities
+        ]
+
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.Chunk).values(chunk_dicts))
+
+        # get embeddings
+        embeddings_list: list[list[float]] = await embedding_model.requester.invoke_embedding(
+            model=embedding_model,
+            input_text=chunks,
+            extra_args={},  # TODO: add extra args
+        )
+
+        # save embeddings to vdb
+        await self.ap.vector_db_mgr.vector_db.add_embeddings(kb_id, chunk_ids, embeddings_list, chunk_dicts)
+
+        self.ap.logger.info(f'Successfully saved {len(chunk_entities)} embeddings to Knowledge Base.')
+
+        return chunk_entities

pkg/rag/knowledge/services/parser.py

@@ -0,0 +1,291 @@
+from __future__ import annotations
+
+import PyPDF2
+import io
+from docx import Document
+import chardet
+from typing import Union, Callable, Any
+import markdown
+from bs4 import BeautifulSoup
+import re
+import asyncio  # Import asyncio for async operations
+from pkg.core import app
+
+
+class FileParser:
+    """
+    A robust file parser class to extract text content from various document formats.
+    It supports TXT, PDF, DOCX, XLSX, CSV, Markdown, HTML, and EPUB files.
+    All core file reading operations are designed to be run synchronously in a thread pool
+    to avoid blocking the asyncio event loop.
+    """
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+
+    async def _run_sync(self, sync_func: Callable, *args: Any, **kwargs: Any) -> Any:
+        """
+        Runs a synchronous function in a separate thread to prevent blocking the event loop.
+        This is a general utility method for wrapping blocking I/O operations.
+        """
+        try:
+            return await asyncio.to_thread(sync_func, *args, **kwargs)
+        except Exception as e:
+            self.ap.logger.error(f'Error running synchronous function {sync_func.__name__}: {e}')
+            raise
+
+    async def parse(self, file_name: str, extension: str) -> Union[str, None]:
+        """
+        Parses the file based on its extension and returns the extracted text content.
+        This is the main asynchronous entry point for parsing.
+
+        Args:
+            file_name (str): The name of the file to be parsed, get from ap.storage_mgr
+
+        Returns:
+            Union[str, None]: The extracted text content as a single string, or None if parsing fails.
+        """
+
+        file_extension = extension.lower()
+        parser_method = getattr(self, f'_parse_{file_extension}', None)
+
+        if parser_method is None:
+            self.ap.logger.error(f'Unsupported file format: {file_extension} for file {file_name}')
+            return None
+
+        try:
+            # Pass file_path to the specific parser methods
+            return await parser_method(file_name)
+        except Exception as e:
+            self.ap.logger.error(f'Failed to parse {file_extension} file {file_name}: {e}')
+            return None
+
+    # --- Helper for reading files with encoding detection ---
+    async def _read_file_content(self, file_name: str) -> Union[str, bytes]:
+        """
+        Reads a file with automatic encoding detection, ensuring the synchronous
+        file read operation runs in a separate thread.
+        """
+
+        # def _read_sync():
+        #     with open(file_path, 'rb') as file:
+        #         raw_data = file.read()
+        #         detected = chardet.detect(raw_data)
+        #         encoding = detected['encoding'] or 'utf-8'
+
+        #     if mode == 'r':
+        #         return raw_data.decode(encoding, errors='ignore')
+        #     return raw_data  # For binary mode
+
+        # return await self._run_sync(_read_sync)
+        file_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        detected = chardet.detect(file_bytes)
+        encoding = detected['encoding'] or 'utf-8'
+
+        return file_bytes.decode(encoding, errors='ignore')
+
+    # --- Specific Parser Methods ---
+
+    async def _parse_txt(self, file_name: str) -> str:
+        """Parses a TXT file and returns its content."""
+        self.ap.logger.info(f'Parsing TXT file: {file_name}')
+        return await self._read_file_content(file_name)
+
+    async def _parse_pdf(self, file_name: str) -> str:
+        """Parses a PDF file and returns its text content."""
+        self.ap.logger.info(f'Parsing PDF file: {file_name}')
+
+        # def _parse_pdf_sync():
+        #     text_content = []
+        #     with open(file_name, 'rb') as file:
+        #         pdf_reader = PyPDF2.PdfReader(file)
+        #         for page in pdf_reader.pages:
+        #             text = page.extract_text()
+        #             if text:
+        #                 text_content.append(text)
+        #     return '\n'.join(text_content)
+
+        # return await self._run_sync(_parse_pdf_sync)
+
+        pdf_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        def _parse_pdf_sync():
+            pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_bytes))
+            text_content = []
+            for page in pdf_reader.pages:
+                text = page.extract_text()
+                if text:
+                    text_content.append(text)
+            return '\n'.join(text_content)
+
+        return await self._run_sync(_parse_pdf_sync)
+
+    async def _parse_docx(self, file_name: str) -> str:
+        """Parses a DOCX file and returns its text content."""
+        self.ap.logger.info(f'Parsing DOCX file: {file_name}')
+
+        docx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        def _parse_docx_sync():
+            doc = Document(io.BytesIO(docx_bytes))
+            text_content = [paragraph.text for paragraph in doc.paragraphs if paragraph.text.strip()]
+            return '\n'.join(text_content)
+
+        return await self._run_sync(_parse_docx_sync)
+
+    async def _parse_doc(self, file_name: str) -> str:
+        """Handles .doc files, explicitly stating lack of direct support."""
+        self.ap.logger.warning(f'Direct .doc parsing is not supported for {file_name}. Please convert to .docx first.')
+        raise NotImplementedError('Direct .doc parsing not supported. Please convert to .docx first.')
+
+    # async def _parse_xlsx(self, file_name: str) -> str:
+    #     """Parses an XLSX file, returning text from all sheets."""
+    #     self.ap.logger.info(f'Parsing XLSX file: {file_name}')
+
+    #     xlsx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+    #     def _parse_xlsx_sync():
+    #         excel_file = pd.ExcelFile(io.BytesIO(xlsx_bytes))
+    #         all_sheet_content = []
+    #         for sheet_name in excel_file.sheet_names:
+    #             df = pd.read_excel(io.BytesIO(xlsx_bytes), sheet_name=sheet_name)
+    #             sheet_text = f'--- Sheet: {sheet_name} ---\n{df.to_string(index=False)}\n'
+    #             all_sheet_content.append(sheet_text)
+    #         return '\n'.join(all_sheet_content)
+
+    #     return await self._run_sync(_parse_xlsx_sync)
+
+    # async def _parse_csv(self, file_name: str) -> str:
+    #     """Parses a CSV file and returns its content as a string."""
+    #     self.ap.logger.info(f'Parsing CSV file: {file_name}')
+
+    #     csv_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+    #     def _parse_csv_sync():
+    #         # pd.read_csv can often detect encoding, but explicit detection is safer
+    #         # raw_data = self._read_file_content(
+    #         #     file_name, mode='rb'
+    #         # )  # Note: this will need to be await outside this sync function
+    #         # _ = raw_data
+    #         # For simplicity, we'll let pandas handle encoding internally after a raw read.
+    #         # A more robust solution might pass encoding directly to pd.read_csv after detection.
+    #         detected = chardet.detect(io.BytesIO(csv_bytes))
+    #         encoding = detected['encoding'] or 'utf-8'
+    #         df = pd.read_csv(io.BytesIO(csv_bytes), encoding=encoding)
+    #         return df.to_string(index=False)
+
+    #     return await self._run_sync(_parse_csv_sync)
+
+    async def _parse_md(self, file_name: str) -> str:
+        """Parses a Markdown file, converting it to structured plain text."""
+        self.ap.logger.info(f'Parsing Markdown file: {file_name}')
+
+        md_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        def _parse_markdown_sync():
+            md_content = io.BytesIO(md_bytes).read().decode('utf-8', errors='ignore')
+            html_content = markdown.markdown(
+                md_content, extensions=['extra', 'codehilite', 'tables', 'toc', 'fenced_code']
+            )
+            soup = BeautifulSoup(html_content, 'html.parser')
+            text_parts = []
+            for element in soup.children:
+                if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
+                    level = int(element.name[1])
+                    text_parts.append('#' * level + ' ' + element.get_text().strip())
+                elif element.name == 'p':
+                    text = element.get_text().strip()
+                    if text:
+                        text_parts.append(text)
+                elif element.name in ['ul', 'ol']:
+                    for li in element.find_all('li'):
+                        text_parts.append(f'* {li.get_text().strip()}')
+                elif element.name == 'pre':
+                    code_block = element.get_text().strip()
+                    if code_block:
+                        text_parts.append(f'```\n{code_block}\n```')
+                elif element.name == 'table':
+                    table_str = self._extract_table_to_markdown_sync(element)  # Call sync helper
+                    if table_str:
+                        text_parts.append(table_str)
+                elif element.name:
+                    text = element.get_text(separator=' ', strip=True)
+                    if text:
+                        text_parts.append(text)
+            cleaned_text = re.sub(r'\n\s*\n', '\n\n', '\n'.join(text_parts))
+            return cleaned_text.strip()
+
+        return await self._run_sync(_parse_markdown_sync)
+
+    async def _parse_html(self, file_name: str) -> str:
+        """Parses an HTML file, extracting structured plain text."""
+        self.ap.logger.info(f'Parsing HTML file: {file_name}')
+
+        html_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        def _parse_html_sync():
+            html_content = io.BytesIO(html_bytes).read().decode('utf-8', errors='ignore')
+            soup = BeautifulSoup(html_content, 'html.parser')
+            for script_or_style in soup(['script', 'style']):
+                script_or_style.decompose()
+            text_parts = []
+            for element in soup.body.children if soup.body else soup.children:
+                if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
+                    level = int(element.name[1])
+                    text_parts.append('#' * level + ' ' + element.get_text().strip())
+                elif element.name == 'p':
+                    text = element.get_text().strip()
+                    if text:
+                        text_parts.append(text)
+                elif element.name in ['ul', 'ol']:
+                    for li in element.find_all('li'):
+                        text = li.get_text().strip()
+                        if text:
+                            text_parts.append(f'* {text}')
+                elif element.name == 'table':
+                    table_str = self._extract_table_to_markdown_sync(element)  # Call sync helper
+                    if table_str:
+                        text_parts.append(table_str)
+                elif element.name:
+                    text = element.get_text(separator=' ', strip=True)
+                    if text:
+                        text_parts.append(text)
+            cleaned_text = re.sub(r'\n\s*\n', '\n\n', '\n'.join(text_parts))
+            return cleaned_text.strip()
+
+        return await self._run_sync(_parse_html_sync)
+
+    def _add_toc_items_sync(self, toc_list: list, text_content: list, level: int):
+        """Recursively adds TOC items to text_content (synchronous helper)."""
+        indent = '  ' * level
+        for item in toc_list:
+            if isinstance(item, tuple):
+                chapter, subchapters = item
+                text_content.append(f'{indent}- {chapter.title}')
+                self._add_toc_items_sync(subchapters, text_content, level + 1)
+            else:
+                text_content.append(f'{indent}- {item.title}')
+
+    def _extract_table_to_markdown_sync(self, table_element: BeautifulSoup) -> str:
+        """Helper to convert a BeautifulSoup table element into a Markdown table string (synchronous)."""
+        headers = [th.get_text().strip() for th in table_element.find_all('th')]
+        rows = []
+        for tr in table_element.find_all('tr'):
+            cells = [td.get_text().strip() for td in tr.find_all('td')]
+            if cells:
+                rows.append(cells)
+
+        if not headers and not rows:
+            return ''
+
+        table_lines = []
+        if headers:
+            table_lines.append(' | '.join(headers))
+            table_lines.append(' | '.join(['---'] * len(headers)))
+
+        for row_cells in rows:
+            padded_cells = row_cells + [''] * (len(headers) - len(row_cells)) if headers else row_cells
+            table_lines.append(' | '.join(padded_cells))
+
+        return '\n'.join(table_lines)

pkg/rag/knowledge/services/retriever.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+from . import base_service
+from ....core import app
+from ....provider.modelmgr.requester import RuntimeEmbeddingModel
+from ....entity.rag import retriever as retriever_entities
+
+
+class Retriever(base_service.BaseService):
+    def __init__(self, ap: app.Application):
+        super().__init__()
+        self.ap = ap
+
+    async def retrieve(
+        self, kb_id: str, query: str, embedding_model: RuntimeEmbeddingModel, k: int = 5
+    ) -> list[retriever_entities.RetrieveResultEntry]:
+        self.ap.logger.info(
+            f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}"
+        )
+
+        query_embedding: list[float] = await embedding_model.requester.invoke_embedding(
+            model=embedding_model,
+            input_text=[query],
+            extra_args={},  # TODO: add extra args
+        )
+
+        chroma_results = await self.ap.vector_db_mgr.vector_db.search(kb_id, query_embedding[0], k)
+
+        # 'ids' is always returned by ChromaDB, even if not explicitly in 'include'
+        matched_chroma_ids = chroma_results.get('ids', [[]])[0]
+        distances = chroma_results.get('distances', [[]])[0]
+        chroma_metadatas = chroma_results.get('metadatas', [[]])[0]
+
+        if not matched_chroma_ids:
+            self.ap.logger.info('No relevant chunks found in Chroma.')
+            return []
+
+        result: list[retriever_entities.RetrieveResultEntry] = []
+
+        for i, id in enumerate(matched_chroma_ids):
+            entry = retriever_entities.RetrieveResultEntry(
+                id=id,
+                metadata=chroma_metadatas[i],
+                distance=distances[i],
+            )
+            result.append(entry)
+
+        return result

pkg/utils/constants.py

@@ -1,7 +1,7 @@
-semantic_version = 'v4.0.9'
+semantic_version = 'v4.1.2'
 
-required_database_version = 3
-"""标记本版本所需要的数据库结构版本，用于判断数据库迁移"""
+required_database_version = 4
+"""Tag the version of the database schema, used to check if the database needs to be migrated"""
 
 debug_mode = False
 

pkg/vector/mgr.py

@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+from ..core import app
+from .vdb import VectorDatabase
+from .vdbs.chroma import ChromaVectorDatabase
+
+
+class VectorDBManager:
+    ap: app.Application
+    vector_db: VectorDatabase = None
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+
+    async def initialize(self):
+        # 初始化 Chroma 向量数据库（可扩展为多种实现）
+        if self.vector_db is None:
+            self.vector_db = ChromaVectorDatabase(self.ap)

pkg/vector/vdb.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+import abc
+from typing import Any, Dict
+import numpy as np
+
+
+class VectorDatabase(abc.ABC):
+    @abc.abstractmethod
+    async def add_embeddings(
+        self,
+        collection: str,
+        ids: list[str],
+        embeddings_list: list[list[float]],
+        metadatas: list[dict[str, Any]],
+        documents: list[str],
+    ) -> None:
+        """向指定 collection 添加向量数据。"""
+        pass
+
+    @abc.abstractmethod
+    async def search(self, collection: str, query_embedding: np.ndarray, k: int = 5) -> Dict[str, Any]:
+        """在指定 collection 中检索最相似的向量。"""
+        pass
+
+    @abc.abstractmethod
+    async def delete_by_file_id(self, collection: str, file_id: str) -> None:
+        """根据 file_id 删除指定 collection 中的向量。"""
+        pass
+
+    @abc.abstractmethod
+    async def get_or_create_collection(self, collection: str):
+        """获取或创建 collection。"""
+        pass
+
+    @abc.abstractmethod
+    async def delete_collection(self, collection: str):
+        pass

pkg/vector/vdbs/chroma.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+import asyncio
+from typing import Any
+from chromadb import PersistentClient
+from pkg.vector.vdb import VectorDatabase
+from pkg.core import app
+import chromadb
+import chromadb.errors
+
+
+class ChromaVectorDatabase(VectorDatabase):
+    def __init__(self, ap: app.Application, base_path: str = './data/chroma'):
+        self.ap = ap
+        self.client = PersistentClient(path=base_path)
+        self._collections = {}
+
+    async def get_or_create_collection(self, collection: str) -> chromadb.Collection:
+        if collection not in self._collections:
+            self._collections[collection] = await asyncio.to_thread(
+                self.client.get_or_create_collection, name=collection
+            )
+            self.ap.logger.info(f"Chroma collection '{collection}' accessed/created.")
+        return self._collections[collection]
+
+    async def add_embeddings(
+        self,
+        collection: str,
+        ids: list[str],
+        embeddings_list: list[list[float]],
+        metadatas: list[dict[str, Any]],
+    ) -> None:
+        col = await self.get_or_create_collection(collection)
+        await asyncio.to_thread(col.add, embeddings=embeddings_list, ids=ids, metadatas=metadatas)
+        self.ap.logger.info(f"Added {len(ids)} embeddings to Chroma collection '{collection}'.")
+
+    async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> dict[str, Any]:
+        col = await self.get_or_create_collection(collection)
+        results = await asyncio.to_thread(
+            col.query,
+            query_embeddings=query_embedding,
+            n_results=k,
+            include=['metadatas', 'distances', 'documents'],
+        )
+        self.ap.logger.info(f"Chroma search in '{collection}' returned {len(results.get('ids', [[]])[0])} results.")
+        return results
+
+    async def delete_by_file_id(self, collection: str, file_id: str) -> None:
+        col = await self.get_or_create_collection(collection)
+        await asyncio.to_thread(col.delete, where={'file_id': file_id})
+        self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' with file_id: {file_id}")
+
+    async def delete_collection(self, collection: str):
+        if collection in self._collections:
+            del self._collections[collection]
+
+        try:
+            await asyncio.to_thread(self.client.delete_collection, name=collection)
+        except chromadb.errors.NotFoundError:
+            self.ap.logger.warning(f"Chroma collection '{collection}' not found.")
+            return
+        self.ap.logger.info(f"Chroma collection '{collection}' deleted.")

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langbot"
-version = "4.0.9"
+version = "4.1.0"
 description = "高稳定、支持扩展、多模态 - 大模型原生即时通信机器人平台"
 readme = "README.md"
 requires-python = ">=3.10.1"
@@ -50,6 +50,16 @@ dependencies = [
     "ruff>=0.11.9",
     "pre-commit>=4.2.0",
     "uv>=0.7.11",
+    "PyPDF2>=3.0.1",
+    "python-docx>=1.1.0",
+    "pandas>=2.2.2",
+    "chardet>=5.2.0",
+    "markdown>=3.6",
+    "beautifulsoup4>=4.12.3",
+    "ebooklib>=0.18",
+    "html2text>=2024.2.26",
+    "langchain>=0.2.0",
+    "chromadb>=0.4.24",
 ]
 keywords = [
     "bot",

templates/default-pipeline-config.json

@@ -44,7 +44,8 @@
                     "role": "system",
                     "content": "You are a helpful assistant."
                 }
-            ]
+            ],
+            "knowledge-base": ""
         },
         "dify-service-api": {
             "base-url": "https://api.dify.ai/v1",

templates/metadata/pipeline/ai.yaml

@@ -68,6 +68,16 @@ stages:
           zh_Hans: 除非您了解消息结构，否则请只使用 system 单提示词
         type: prompt-editor
         required: true
+      - name: knowledge-base
+        label:
+          en_US: Knowledge Base
+          zh_Hans: 知识库
+        description:
+          en_US: Configure the knowledge base to use for the agent, if not selected, the agent will directly use the LLM to reply
+          zh_Hans: 配置用于提升回复质量的知识库，若不选择，则直接使用大模型回复
+        type: knowledge-base-selector
+        required: false
+        default: ''
   - name: dify-service-api
     label:
       en_US: Dify Service API
@@ -298,3 +308,4 @@ stages:
         type: string
         required: false
         default: 'response'
+  

web/.env.example

@@ -0,0 +1 @@
+NEXT_PUBLIC_API_BASE_URL=http://localhost:5300

web/.gitignore

@@ -32,6 +32,7 @@ yarn-error.log*
 
 # env files (can opt-in for committing if needed)
 .env*
+!.env.example
 
 # vercel
 .vercel

web/README.md

@@ -1,36 +1,3 @@
-This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
+# Debug LangBot Frontend
 
-## Getting Started
-
-First, run the development server:
-
-```bash
-npm run dev
-# or
-yarn dev
-# or
-pnpm dev
-# or
-bun dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
-
-This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.
+Please refer to the [Development Guide](https://docs.langbot.app/en/develop/dev-config.html) for more information.

web/package-lock.json

@@ -12,23 +12,28 @@
         "@dnd-kit/sortable": "^10.0.0",
         "@hookform/resolvers": "^5.0.1",
         "@radix-ui/react-checkbox": "^1.3.1",
-        "@radix-ui/react-dialog": "^1.1.13",
+        "@radix-ui/react-dialog": "^1.1.14",
+        "@radix-ui/react-dropdown-menu": "^2.1.15",
         "@radix-ui/react-hover-card": "^1.1.13",
         "@radix-ui/react-label": "^2.1.6",
         "@radix-ui/react-popover": "^1.1.14",
         "@radix-ui/react-scroll-area": "^1.2.9",
         "@radix-ui/react-select": "^2.2.4",
-        "@radix-ui/react-slot": "^1.2.2",
+        "@radix-ui/react-separator": "^1.1.7",
+        "@radix-ui/react-slot": "^1.2.3",
         "@radix-ui/react-switch": "^1.2.4",
         "@radix-ui/react-tabs": "^1.1.11",
         "@radix-ui/react-toggle": "^1.1.8",
         "@radix-ui/react-toggle-group": "^1.1.9",
+        "@radix-ui/react-tooltip": "^1.2.7",
         "@tailwindcss/postcss": "^4.1.5",
+        "@tanstack/react-table": "^8.21.3",
         "axios": "^1.8.4",
         "class-variance-authority": "^0.7.1",
         "clsx": "^2.1.1",
         "i18next": "^25.1.2",
         "i18next-browser-languagedetector": "^8.1.0",
+        "input-otp": "^1.4.2",
         "lodash": "^4.17.21",
         "lucide-react": "^0.507.0",
         "next": "15.2.4",
@@ -1037,6 +1042,24 @@
         }
       }
     },
+    "node_modules/@radix-ui/react-collection/node_modules/@radix-ui/react-slot": {
+      "version": "1.2.2",
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+      }
+    },
     "node_modules/internal-slot": {
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/internal-slot/-/internal-slot-1.1.0.tgz",

web/package.json

@@ -4,8 +4,6 @@
   "private": true,
   "scripts": {
     "dev": "next dev --turbopack",
-    "dev:local": "NEXT_PUBLIC_API_BASE_URL=http://localhost:5300 next dev --turbopack",
-    "dev:local:win": "set NEXT_PUBLIC_API_BASE_URL=http://localhost:5300 && next dev --turbopack",
     "build": "next build",
     "start": "next start",
     "lint": "next lint",
@@ -17,12 +15,16 @@
       "prettier --write"
     ]
   },
+  "overrides": {
+    "@radix-ui/react-focus-scope": "1.1.7"
+  },
   "dependencies": {
     "@dnd-kit/core": "^6.3.1",
     "@dnd-kit/sortable": "^10.0.0",
     "@hookform/resolvers": "^5.0.1",
     "@radix-ui/react-checkbox": "^1.3.1",
     "@radix-ui/react-dialog": "^1.1.14",
+    "@radix-ui/react-dropdown-menu": "^2.1.15",
     "@radix-ui/react-hover-card": "^1.1.13",
     "@radix-ui/react-label": "^2.1.6",
     "@radix-ui/react-popover": "^1.1.14",
@@ -36,6 +38,7 @@
     "@radix-ui/react-toggle-group": "^1.1.9",
     "@radix-ui/react-tooltip": "^1.2.7",
     "@tailwindcss/postcss": "^4.1.5",
+    "@tanstack/react-table": "^8.21.3",
     "axios": "^1.8.4",
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",