Merge pull request #3013 from xinnan-tech/version-num

Bump to 0.9.2

.gitignore

@@ -3,6 +3,9 @@ __pycache__/
 .idea/
 *.py[cod]
 *$py.class
+.vscode
+.claude
+AGENTS.md
 
 # C extensions
 *.so

Dockerfile-web

@@ -23,7 +23,8 @@ RUN apk update && \
         bash \
         fontconfig \
         ttf-dejavu \
-    && rm -rf /var/cache/apk/*
+    && rm -rf /var/cache/apk/* \
+    && mkdir -p /run/nginx /var/log/nginx /var/tmp/nginx /etc/nginx/conf.d
 
 # 复制项目自带的中文字体
 COPY main/manager-web/public/generator/static/fonts/*.ttf /usr/share/fonts/

README.md

@@ -21,6 +21,7 @@
   <a href="./README_en.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
   <a href="./README_vi.md"><img alt="Tiếng Việt" src="https://img.shields.io/badge/Tiếng Việt-DFE0E5"></a>
   <a href="./README_de.md"><img alt="Deutsch" src="https://img.shields.io/badge/Deutsch-DFE0E5"></a>
+  <a href="./README_pt_BR.md"><img alt="Português (Brasil)" src="https://img.shields.io/badge/Português (Brasil)-DFE0E5"></a>
   <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/releases">
     <img alt="GitHub Contributors" src="https://img.shields.io/github/v/release/xinnan-tech/xiaozhi-esp32-server?logo=docker" />
   </a>
@@ -242,7 +243,7 @@ Websocket接口地址: wss://2662r3426b.vicp.fun/xiaozhi/v1/
 | 智能对话 | 支持多种LLM(大语言模型)，实现智能对话 |
 | 视觉感知 | 支持多种VLLM(视觉大模型)，实现多模态交互 |
 | 意图识别 | 支持外挂的大模型意图识别、大模型自主函数调用，提供插件化意图处理机制 |
-| 记忆系统 | 支持本地短期记忆、mem0ai接口记忆，具备记忆总结功能 |
+| 记忆系统 | 支持本地短期记忆、mem0ai接口记忆、PowerMem智能记忆，具备记忆总结功能 |
 | 知识库 | 支持RAGFlow知识库，让大模型判断需要调度知识库后再回答 |
 | 工具调用 | 支持客户端IOT协议、客户MCP协议、服务端MCP协议、MCP接入点协议、自定义工具函数 |
 | 指令下发 | 依托MQTT协议，支持从智控台将MCP指令下发到ESP32设备 |
@@ -330,6 +331,7 @@ Websocket接口地址: wss://2662r3426b.vicp.fun/xiaozhi/v1/
 |   类型   |      平台名称       | 使用方式 |   收费模式    | 备注 |
 |:------:|:---------------:|:----:|:---------:|:--:|
 | Memory |     mem0ai      | 接口调用 | 1000次/月额度 |    |
+| Memory |     [powermem](./docs/powermem-integration.md)    | 本地总结 | 取决于LLM和DB |  OceanBase开源，支持智能检索  |
 | Memory | mem_local_short | 本地总结 |    免费     |    |
 | Memory |     nomem       | 无记忆模式 |    免费     |    |
 

README_de.md

@@ -21,6 +21,7 @@ Unterstützt MQTT+UDP-Protokoll, Websocket-Protokoll, MCP-Endpunkte und Stimmabd
   <a href="./README_en.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
   <a href="./README_vi.md"><img alt="Tiếng Việt" src="https://img.shields.io/badge/Tiếng Việt-DFE0E5"></a>
   <a href="./README_de.md"><img alt="Deutsch" src="https://img.shields.io/badge/Deutsch-DBEDFA"></a>
+  <a href="./README_pt_BR.md"><img alt="Português (Brasil)" src="https://img.shields.io/badge/Português (Brasil)-DFE0E5"></a>
   <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/releases">
     <img alt="GitHub Contributors" src="https://img.shields.io/github/v/release/xinnan-tech/xiaozhi-esp32-server?logo=docker" />
   </a>
@@ -240,7 +241,7 @@ Dieses Projekt bietet die folgenden Testwerkzeuge, um Ihnen bei der Überprüfun
 | Intelligenter Dialog | Unterstützt mehrere LLM (große Sprachmodelle), implementiert intelligenten Dialog |
 | Visuelle Wahrnehmung | Unterstützt mehrere VLLM (Vision Large Models), implementiert multimodale Interaktion |
 | Absichtserkennung | Unterstützt LLM-Absichtserkennung, Function Call-Funktionsaufruf, bietet plugin-basierten Absichtsverarbeitungsmechanismus |
-| Gedächtnissystem | Unterstützt lokales Kurzzeitgedächtnis, mem0ai-Schnittstellengedächtnis, mit Gedächtniszusammenfassungsfunktion |
+| Gedächtnissystem | Unterstützt lokales Kurzzeitgedächtnis, mem0ai-Schnittstellengedächtnis, PowerMem intelligentes Gedächtnis, mit Gedächtniszusammenfassungsfunktion |
 | Wissensdatenbank | Unterstützt RAGFlow-Wissensdatenbank, ermöglicht großem Modell die Bewertung, ob Wissensdatenbank benötigt wird, bevor geantwortet wird |
 | Werkzeugaufruf | Unterstützt Client-IOT-Protokoll, Client-MCP-Protokoll, Server-MCP-Protokoll, MCP-Endpunktprotokoll, benutzerdefinierte Werkzeugfunktionen |
 | Befehlsübermittlung | Basierend auf MQTT-Protokoll, unterstützt die Übermittlung von MCP-Befehlen von der intelligenten Steuerkonsole an ESP32-Geräte |
@@ -258,7 +259,7 @@ Wenn Sie ein Softwareentwickler sind, finden Sie hier einen [Offenen Brief an En
 ---
 
 ## Produktökosystem 👬
-Xiaozhi ist ein Ökosystem. Wenn Sie dieses Produkt verwenden, können Sie sich auch andere [hervorragende Projekte](https://github.com/78/xiaozhi-esp32?tab=readme-ov-file#related-open-source-projects) in diesem Ökosystem ansehen
+Xiaozhi ist ein Ökosystem. Wenn Sie dieses Produkt verwenden, können Sie sich auch andere [hervorragende Projekte](https://github.com/78/xiaozhi-esp32/blob/main/README_zh.md#%E7%9B%B8%E5%85%B3%E5%BC%80%E6%BA%90%E9%A1%B9%E7%9B%AE) in diesem Ökosystem ansehen
 
 ---
 
@@ -328,6 +329,7 @@ Tatsächlich kann jedes VLLM, das OpenAI-Schnittstellenaufrufe unterstützt, int
 | Typ | Plattformname | Verwendungsmethode | Preismodell | Hinweise |
 |:------:|:---------------:|:----:|:---------:|:--:|
 | Memory | mem0ai | Schnittstellenaufrufe | 1000 Mal/Monat Kontingent | |
+| Memory | [powermem](./docs/powermem-integration.md) | Lokale Zusammenfassung | Abhängig von LLM und DB | OceanBase Open Source, unterstützt intelligente Abfrage |
 | Memory | mem_local_short | Lokale Zusammenfassung | Kostenlos | |
 | Memory | nomem | Kein Gedächtnismodus | Kostenlos | |
 

README_en.md

@@ -21,6 +21,7 @@ Support for MQTT+UDP protocol, Websocket protocol, MCP access point, voiceprint
   <a href="./README_en.md"><img alt="README in English" src="https://img.shields.io/badge/English-DBEDFA"></a>
   <a href="./README_vi.md"><img alt="Tiếng Việt" src="https://img.shields.io/badge/Tiếng Việt-DFE0E5"></a>
   <a href="./README_de.md"><img alt="Deutsch" src="https://img.shields.io/badge/Deutsch-DFE0E5"></a>
+  <a href="./README_pt_BR.md"><img alt="Português (Brasil)" src="https://img.shields.io/badge/Português (Brasil)-DFE0E5"></a>
   <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/releases">
     <img alt="GitHub Contributors" src="https://img.shields.io/github/v/release/xinnan-tech/xiaozhi-esp32-server?logo=docker" />
   </a>
@@ -182,8 +183,9 @@ This project provides two deployment methods. Please choose based on your specif
 | Deployment Method | Features | Applicable Scenarios | Deployment Docs | Configuration Requirements | Video Tutorials | 
 |---------|------|---------|---------|---------|---------|
 | **Simplified Installation** | Intelligent dialogue, single agent management | Low-configuration environments, data stored in config files, no database required | [①Docker Version](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%B8%80docker%E5%8F%AA%E8%BF%90%E8%A1%8Cserver) / [②Source Code Deployment](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E5%8F%AA%E8%BF%90%E8%A1%8Cserver)| 2 cores 4GB if using `FunASR`, 2 cores 2GB if all APIs | - | 
-| **Full Module Installation** | Intelligent dialogue, multi-user management, multi-agent management, intelligent console interface operation | Complete functionality experience, data stored in database |[①Docker Version](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%B8%80docker%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [②Source Code Deployment](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [③Source Code Deployment Auto-Update Tutorial](./docs/dev-ops-integration.md) | 4 cores 8GB if using `FunASR`, 2 cores 4GB if all APIs| [Local Source Code Startup Video Tutorial](https://www.bilibili.com/video/BV1wBJhz4Ewe) | 
+| **Full Module Installation** | Intelligent dialogue, multi-user management, multi-agent management, intelligent console interface operation | Complete functionality experience, data stored in database |[①Docker Version](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%B8%80docker%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [②Source Code Deployment](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [③Source Code Deployment Auto-Update Tutorial](./docs/dev-ops-integration.md) | 4 cores 8GB if using `FunASR`, 2 cores 4GB if all APIs| [Local Source Code Startup Video Tutorial](https://www.bilibili.com/video/BV1wBJhz4Ewe) |
 
+For frequently asked questions and related tutorials, please refer to [this link](./docs/FAQ.md)
 
 > 💡 Note: Below is a test platform deployed with the latest code. You can burn and test if needed. Concurrent users: 6, data will be cleared daily.
 
@@ -215,14 +217,15 @@ Websocket Interface Address: wss://2662r3426b.vicp.fun/xiaozhi/v1/
 | Intent(Intent Recognition) | function_call(Function calling) | function_call(Function calling) |
 | Memory(Memory function) | mem_local_short(Local short-term memory) | mem_local_short(Local short-term memory) |
 
+If you are concerned about the latency of each component, please refer to the [Xiaozhi Component Performance Test Report](https://github.com/xinnan-tech/xiaozhi-performance-research), and test in your own environment following the test methods in the report.
+
 #### 🔧 Testing Tools
 This project provides the following testing tools to help you verify the system and choose suitable models:
 
 | Tool Name | Location | Usage Method | Function Description |
 |:---:|:---|:---:|:---:|
 | Audio Interaction Test Tool | main》xiaozhi-server》test》test_page.html | Open directly with Google Chrome | Tests audio playback and reception functions, verifies if Python-side audio processing is normal |
-| Model Response Test Tool 1 | main》xiaozhi-server》performance_tester.py | Execute `python performance_tester.py` | Tests response speed of three core modules: ASR(speech recognition), LLM(large model), TTS(speech synthesis) |
+| Model Response Test Tool | main》xiaozhi-server》performance_tester.py | Execute `python performance_tester.py` | Tests response speed of three core modules: ASR(speech recognition), LLM(large model), VLLM(vision model), TTS(speech synthesis) |
-| Model Response Test Tool 2 | main》xiaozhi-server》performance_tester_vllm.py | Execute `python performance_tester_vllm.py` | Tests VLLM(vision model) response speed |
 
 > 💡 Note: When testing model speed, only models with configured keys will be tested.
 
@@ -238,10 +241,10 @@ This project provides the following testing tools to help you verify the system
 | Intelligent Dialogue | Supports multiple LLM(large language models), implements intelligent dialogue |
 | Visual Perception | Supports multiple VLLM(vision large models), implements multimodal interaction |
 | Intent Recognition | Supports LLM intent recognition, Function Call function calling, provides plugin-based intent processing mechanism |
-| Memory System | Supports local short-term memory, mem0ai interface memory, with memory summarization functionality |
+| Memory System | Supports local short-term memory, mem0ai interface memory, PowerMem intelligent memory, with memory summarization functionality |
 | Knowledge Base | Supports RAGFlow knowledge base, enabling LLM to judge whether to schedule the knowledge base after receiving the user's question, and then answer the question |
-| Command Delivery | Supports MCP command delivery to ESP32 devices via MQTT protocol from Smart Console |
 | Tool Calling | Supports client IOT protocol, client MCP protocol, server MCP protocol, MCP endpoint protocol, custom tool functions |
+| Command Delivery | Supports MCP command delivery to ESP32 devices via MQTT protocol from Smart Console |
 | Management Backend | Provides Web management interface, supports user management, system configuration and device management; Supports Simplified Chinese, Traditional Chinese and English display |
 | Testing Tools | Provides performance testing tools, vision model testing tools, and audio interaction testing tools |
 | Deployment Support | Supports Docker deployment and local deployment, provides complete configuration file management |
@@ -249,20 +252,14 @@ This project provides the following testing tools to help you verify the system
 
 ### Under Development 🚧
 
-To learn about specific development plan progress, [click here](https://github.com/users/xinnan-tech/projects/3)
+To learn about specific development plan progress, [click here](https://github.com/users/xinnan-tech/projects/3). For frequently asked questions and related tutorials, please refer to [this link](./docs/FAQ.md)
 
 If you are a software developer, here is an [Open Letter to Developers](docs/contributor_open_letter.md). Welcome to join!
 
 ---
 
 ## Product Ecosystem 👬
-Xiaozhi is an ecosystem. When using this product, you can also check out other [excellent projects](https://github.com/78/xiaozhi-esp32?tab=readme-ov-file#related-open-source-projects) in this ecosystem
+Xiaozhi is an ecosystem. When using this product, you can also check out other [excellent projects](https://github.com/78/xiaozhi-esp32/blob/main/README_zh.md#%E7%9B%B8%E5%85%B3%E5%BC%80%E6%BA%90%E9%A1%B9%E7%9B%AE) in this ecosystem
-
-| Project Name | Project Address | Project Description |
-|:---------------------|:--------|:--------|
-| Xiaozhi Android Client | [xiaozhi-android-client](https://github.com/TOM88812/xiaozhi-android-client) | An Android and iOS voice dialogue application based on xiaozhi-server, supporting real-time voice interaction and text dialogue.<br/>Currently a Flutter version, connecting iOS and Android platforms. |
-| Xiaozhi Desktop Client | [py-xiaozhi](https://github.com/Huang-junsen/py-xiaozhi) | This project provides a Python-based AI client for beginners, allowing users to experience Xiaozhi AI functionality through code even without physical hardware conditions. |
-| Xiaozhi Java Server | [xiaozhi-esp32-server-java](https://github.com/joey-zhou/xiaozhi-esp32-server-java) | Xiaozhi open-source backend service Java version is a Java-based open-source project.<br/>It includes frontend and backend services, aiming to provide users with a complete backend service solution. |
 
 ---
 
@@ -276,8 +273,10 @@ Xiaozhi is an ecosystem. When using this product, you can also check out other [
 | Dify interface calls | Dify | - |
 | FastGPT interface calls | FastGPT | - |
 | Coze interface calls | Coze | - |
+| Xinference interface calls | Xinference | - |
+| HomeAssistant interface calls | HomeAssistant | - |
 
-In fact, any LLM that supports OpenAI interface calls can be integrated and used, including Xinference and HomeAssistant interfaces.
+In fact, any LLM that supports OpenAI interface calls can be integrated and used.
 
 ---
 
@@ -296,7 +295,7 @@ In fact, any VLLM that supports OpenAI interface calls can be integrated and use
 | Usage Method | Supported Platforms | Free Platforms |
 |:---:|:---:|:---:|
 | Interface calls | EdgeTTS, iFLYTEK, Volcano Engine, Tencent Cloud, Alibaba Cloud and Bailian, CosyVoiceSiliconflow, TTS302AI, CozeCnTTS, GizwitsTTS, ACGNTTS, OpenAITTS, Lingxi Streaming TTS, MinimaxTTS | Lingxi Streaming TTS, EdgeTTS, CosyVoiceSiliconflow(partial) |
-| Local services | FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3, MinimaxTTS | FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3, MinimaxTTS |
+| Local services | FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3, Index-TTS, PaddleSpeech | Index-TTS, PaddleSpeech, FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3 |
 
 ---
 
@@ -330,7 +329,9 @@ In fact, any VLLM that supports OpenAI interface calls can be integrated and use
 | Type | Platform Name | Usage Method | Pricing Model | Notes |
 |:------:|:---------------:|:----:|:---------:|:--:|
 | Memory | mem0ai | Interface calls | 1000 times/month quota | |
+| Memory | [powermem](./docs/powermem-integration.md) | Local summarization | Depends on LLM and DB | OceanBase open source, supports intelligent retrieval |
 | Memory | mem_local_short | Local summarization | Free | |
+| Memory | nomem | No memory mode | Free | |
 
 ---
 
@@ -340,6 +341,7 @@ In fact, any VLLM that supports OpenAI interface calls can be integrated and use
 |:------:|:-------------:|:----:|:-------:|:---------------------:|
 | Intent | intent_llm | Interface calls | Based on LLM pricing | Recognizes intent through large models, strong generalization |
 | Intent | function_call | Interface calls | Based on LLM pricing | Completes intent through large model function calling, fast speed, good effect |
+| Intent | nointent | No intent mode | Free | Does not perform intent recognition, directly returns dialogue result |
 
 ---
 

README_pt_BR.md

@@ -0,0 +1,376 @@
+[![Banners](docs/images/banner1.png)](https://github.com/xinnan-tech/xiaozhi-esp32-server)
+
+<h1 align="center">Serviço Backend Xiaozhi xiaozhi-esp32-server</h1>
+
+<p align="center">
+Este projeto é baseado na teoria e tecnologia de inteligência simbiótica humano-máquina para desenvolver sistemas inteligentes de hardware e software para terminais<br/>fornecendo serviços de backend para o projeto de hardware inteligente de código aberto
+<a href="https://github.com/78/xiaozhi-esp32">xiaozhi-esp32</a><br/>
+Implementado usando Python, Java e Vue de acordo com o <a href="https://ccnphfhqs21z.feishu.cn/wiki/M0XiwldO9iJwHikpXD5cEx71nKh">Protocolo de Comunicação Xiaozhi</a><br/>
+Suporte ao protocolo MQTT+UDP, protocolo WebSocket, ponto de acesso MCP, reconhecimento de impressão vocal e base de conhecimento
+</p>
+
+<p align="center">
+<a href="./docs/FAQ.md">Perguntas Frequentes</a>
+· <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/issues">Reportar Problemas</a>
+· <a href="./README.md#%E9%83%A8%E7%BD%B2%E6%96%87%E6%A1%A3">Documentação de Implantação</a>
+· <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/releases">Notas de Lançamento</a>
+</p>
+
+<p align="center">
+  <a href="./README.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DFE0E5"></a>
+  <a href="./README_en.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
+  <a href="./README_vi.md"><img alt="Tiếng Việt" src="https://img.shields.io/badge/Tiếng Việt-DFE0E5"></a>
+  <a href="./README_de.md"><img alt="Deutsch" src="https://img.shields.io/badge/Deutsch-DFE0E5"></a>
+  <a href="./README_pt_BR.md"><img alt="Português (Brasil)" src="https://img.shields.io/badge/Português (Brasil)-DBEDFA"></a>
+  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/releases">
+    <img alt="GitHub Contributors" src="https://img.shields.io/github/v/release/xinnan-tech/xiaozhi-esp32-server?logo=docker" />
+  </a>
+  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/blob/main/LICENSE">
+    <img alt="GitHub pull requests" src="https://img.shields.io/badge/license-MIT-white?labelColor=black" />
+  </a>
+  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server">
+    <img alt="stars" src="https://img.shields.io/github/stars/xinnan-tech/xiaozhi-esp32-server?color=ffcb47&labelColor=black" />
+  </a>
+</p>
+
+<p align="center">
+Liderado pela Equipe do Professor Siyuan Liu (Universidade de Tecnologia do Sul da China)
+</br>
+刘思源教授团队主导研发（华南理工大学）
+</br>
+<img src="./docs/images/hnlg.jpg" alt="Universidade de Tecnologia do Sul da China (华南理工大学)" width="50%">
+</p>
+
+---
+
+## Público-Alvo 👥
+
+Este projeto requer dispositivos de hardware ESP32 para funcionar. Se você adquiriu hardware relacionado ao ESP32, conectou-se com sucesso ao serviço backend implantado pelo Brother Xia e deseja construir seu próprio serviço backend `xiaozhi-esp32` de forma independente, então este projeto é perfeito para você.
+
+Quer ver os efeitos de uso? Clique nos vídeos abaixo 🎥
+
+<table>
+  <tr>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1FMFyejExX" target="_blank">
+         <picture>
+           <img alt="Experiência de velocidade de resposta" src="docs/images/demo9.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1vchQzaEse" target="_blank">
+         <picture>
+           <img alt="Segredo da otimização de velocidade" src="docs/images/demo6.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1C1tCzUEZh" target="_blank">
+         <picture>
+           <img alt="Cenário médico complexo" src="docs/images/demo1.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1zUW5zJEkq" target="_blank">
+         <picture>
+           <img alt="Envio de comandos MQTT" src="docs/images/demo4.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1Exu3zqEDe" target="_blank">
+         <picture>
+           <img alt="Reconhecimento de impressão vocal" src="docs/images/demo14.png" />
+         </picture>
+        </a>
+    </td>
+  </tr>
+  <tr>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1pNXWYGEx1" target="_blank">
+         <picture>
+           <img alt="Controle de interruptores de eletrodomésticos" src="docs/images/demo5.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1ZQKUzYExM" target="_blank">
+         <picture>
+           <img alt="Ponto de acesso MCP" src="docs/images/demo13.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+      <a href="https://www.bilibili.com/video/BV1TJ7WzzEo6" target="_blank">
+         <picture>
+           <img alt="Tarefas com múltiplos comandos" src="docs/images/demo11.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1VC96Y5EMH" target="_blank">
+         <picture>
+           <img alt="Reproduzir música" src="docs/images/demo7.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1Z8XuYZEAS" target="_blank">
+         <picture>
+           <img alt="Plugin de clima" src="docs/images/demo8.png" />
+         </picture>
+        </a>
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <a href="https://www.bilibili.com/video/BV12J7WzBEaH" target="_blank">
+         <picture>
+           <img alt="Interrupção em tempo real" src="docs/images/demo10.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+      <a href="https://www.bilibili.com/video/BV1Co76z7EvK" target="_blank">
+         <picture>
+           <img alt="Fotografar e identificar objetos" src="docs/images/demo12.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV1CDKWemEU6" target="_blank">
+         <picture>
+           <img alt="Timbre de voz personalizado" src="docs/images/demo2.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV12yA2egEaC" target="_blank">
+         <picture>
+           <img alt="Comunicação em cantonês" src="docs/images/demo3.png" />
+         </picture>
+        </a>
+    </td>
+    <td>
+        <a href="https://www.bilibili.com/video/BV17LXWYvENb" target="_blank">
+         <picture>
+           <img alt="Transmissão de notícias" src="docs/images/demo0.png" />
+         </picture>
+        </a>
+    </td>
+  </tr>
+</table>
+
+---
+
+## Avisos ⚠️
+
+1. Este projeto é um software de código aberto. Este software não possui parceria comercial com nenhum provedor de serviços de API de terceiros (incluindo, mas não se limitando a reconhecimento de fala, modelos de linguagem, síntese de voz e outras plataformas) com os quais se conecta, e não fornece nenhuma forma de garantia quanto à qualidade de serviço ou segurança financeira desses provedores. Recomenda-se que os usuários priorizem provedores de serviço com licenças comerciais relevantes e leiam cuidadosamente seus termos de serviço e políticas de privacidade. Este software não armazena nenhuma chave de conta, não participa de fluxos de fundos e não assume o risco de perda de fundos recarregados.
+
+2. A funcionalidade deste projeto não está completa e não passou por avaliação de segurança de rede. Por favor, não o utilize em ambientes de produção. Se você implantar este projeto para fins de aprendizado em um ambiente de rede pública, certifique-se de que as medidas de proteção necessárias estejam em vigor.
+
+---
+
+## Documentação de Implantação
+
+![Banners](docs/images/banner2.png)
+
+Este projeto oferece dois métodos de implantação. Por favor, escolha de acordo com suas necessidades específicas:
+
+#### 🚀 Seleção do Método de Implantação
+| Método de Implantação | Funcionalidades | Cenários Aplicáveis | Documentação de Implantação | Requisitos de Configuração | Tutoriais em Vídeo |
+|---------|------|---------|---------|---------|---------|
+| **Instalação Simplificada** | Diálogo inteligente, gerenciamento de agente único | Ambientes de baixa configuração, dados armazenados em arquivos de configuração, sem necessidade de banco de dados | [①Versão Docker](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%B8%80docker%E5%8F%AA%E8%BF%90%E8%A1%8Cserver) / [②Implantação via Código-Fonte](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E5%8F%AA%E8%BF%90%E8%A1%8Cserver)| 2 núcleos 4GB se usar `FunASR`, 2 núcleos 2GB se todas APIs | - |
+| **Instalação de Módulo Completo** | Diálogo inteligente, gerenciamento multiusuário, gerenciamento de múltiplos agentes, operação de interface do console inteligente | Experiência com funcionalidade completa, dados armazenados em banco de dados |[①Versão Docker](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%B8%80docker%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [②Implantação via Código-Fonte](./docs/Deployment_all.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E8%BF%90%E8%A1%8C%E5%85%A8%E6%A8%A1%E5%9D%97) / [③Tutorial de Atualização Automática via Código-Fonte](./docs/dev-ops-integration.md) | 4 núcleos 8GB se usar `FunASR`, 2 núcleos 4GB se todas APIs| [Tutorial em Vídeo de Inicialização via Código-Fonte Local](https://www.bilibili.com/video/BV1wBJhz4Ewe) |
+
+Perguntas frequentes e tutoriais relacionados podem ser consultados [neste link](./docs/FAQ.md)
+
+> 💡 Nota: Abaixo está uma plataforma de teste implantada com o código mais recente. Você pode gravar e testar se necessário. Usuários simultâneos: 6, os dados serão limpos diariamente.
+
+```
+Endereço do Console de Controle Inteligente: https://2662r3426b.vicp.fun
+Endereço do Console de Controle Inteligente (H5): https://2662r3426b.vicp.fun/h5/index.html
+
+Ferramenta de Teste de Serviço: https://2662r3426b.vicp.fun/test/
+Endereço da Interface OTA: https://2662r3426b.vicp.fun/xiaozhi/ota/
+Endereço da Interface WebSocket: wss://2662r3426b.vicp.fun/xiaozhi/v1/
+```
+
+#### 🚩 Descrição e Recomendações de Configuração
+> [!Note]
+> Este projeto oferece dois esquemas de configuração:
+>
+> 1. `Configurações Gratuitas Nível Básico`: Adequado para uso pessoal e doméstico, todos os componentes utilizam soluções gratuitas, sem necessidade de pagamento adicional.
+>
+> 2. `Configuração de Streaming`: Adequado para demonstrações, treinamentos, cenários com mais de 2 usuários simultâneos, etc. Utiliza tecnologia de processamento em streaming para velocidade de resposta mais rápida e melhor experiência.
+>
+> A partir da versão `0.5.2`, o projeto suporta configuração de streaming. Em comparação com versões anteriores, a velocidade de resposta é melhorada em aproximadamente `2,5 segundos`, melhorando significativamente a experiência do usuário.
+
+| Nome do Módulo | Configurações Gratuitas Nível Básico | Configuração de Streaming |
+|:---:|:---:|:---:|
+| ASR(Reconhecimento de Fala) | FunASR(Local) | 👍XunfeiStreamASR(Xunfei Streaming) |
+| LLM(Modelo de Linguagem) | glm-4-flash(Zhipu) | 👍qwen-flash(Alibaba Bailian) |
+| VLLM(Modelo de Visão) | glm-4v-flash(Zhipu) | 👍qwen2.5-vl-3b-instructh(Alibaba Bailian) |
+| TTS(Síntese de Voz) | ✅LinkeraiTTS(Lingxi streaming) | 👍HuoshanDoubleStreamTTS(Volcano Streaming) |
+| Intent(Reconhecimento de Intenção) | function_call(Chamada de função) | function_call(Chamada de função) |
+| Memory(Função de Memória) | mem_local_short(Memória local de curto prazo) | mem_local_short(Memória local de curto prazo) |
+
+Se você está preocupado com o tempo de resposta de cada componente, consulte o [Relatório de Teste de Desempenho dos Componentes Xiaozhi](https://github.com/xinnan-tech/xiaozhi-performance-research), e teste em seu próprio ambiente seguindo os métodos de teste do relatório.
+
+#### 🔧 Ferramentas de Teste
+Este projeto fornece as seguintes ferramentas de teste para ajudá-lo a verificar o sistema e escolher modelos adequados:
+
+| Nome da Ferramenta | Localização | Método de Uso | Descrição da Função |
+|:---:|:---|:---:|:---:|
+| Ferramenta de Teste de Interação por Áudio | main》xiaozhi-server》test》test_page.html | Abrir diretamente com Google Chrome | Testa as funções de reprodução e recepção de áudio, verifica se o processamento de áudio no lado Python está normal |
+| Ferramenta de Teste de Resposta de Modelo | main》xiaozhi-server》performance_tester.py | Execute `python performance_tester.py` | Testa a velocidade de resposta dos três módulos principais: ASR(reconhecimento de fala), LLM(modelo de linguagem), VLLM(modelo de visão), TTS(síntese de voz) |
+
+> 💡 Nota: Ao testar a velocidade dos modelos, apenas os modelos com chaves configuradas serão testados.
+
+---
+## Lista de Funcionalidades ✨
+### Implementado ✅
+![请参考-全模块安装架构图](docs/images/deploy2.png)
+| Módulo de Funcionalidade | Descrição |
+|:---:|:---|
+| Arquitetura Principal | Baseado em [gateway MQTT+UDP](https://github.com/xinnan-tech/xiaozhi-esp32-server/blob/main/docs/mqtt-gateway-integration.md), servidores WebSocket e HTTP, fornece sistema completo de gerenciamento de console e autenticação |
+| Interação por Voz | Suporta ASR em streaming (reconhecimento de fala), TTS em streaming (síntese de voz), VAD (detecção de atividade vocal), suporta reconhecimento multilíngue e processamento de voz |
+| Reconhecimento de Impressão Vocal | Suporta registro, gerenciamento e reconhecimento de impressão vocal de múltiplos usuários, processa em paralelo com o ASR, reconhecimento de identidade do falante em tempo real e repassa ao LLM para respostas personalizadas |
+| Diálogo Inteligente | Suporta múltiplos LLM (modelos de linguagem de grande porte), implementa diálogo inteligente |
+| Percepção Visual | Suporta múltiplos VLLM (modelos de visão de grande porte), implementa interação multimodal |
+| Reconhecimento de Intenção | Suporta reconhecimento de intenção por LLM, Function Call (chamada de função), fornece mecanismo de processamento de intenção baseado em plugins |
+| Sistema de Memória | Suporta memória local de curto prazo, memória via interface mem0ai, memória inteligente PowerMem, com funcionalidade de resumo de memória |
+| Base de Conhecimento | Suporta base de conhecimento RAGFlow, permitindo que o LLM julgue se deve acionar a base de conhecimento após receber a pergunta do usuário, e então responda à pergunta |
+| Chamada de Ferramentas | Suporta protocolo IOT do cliente, protocolo MCP do cliente, protocolo MCP do servidor, protocolo de endpoint MCP, funções de ferramentas personalizadas |
+| Envio de Comandos | Suporta envio de comandos MCP para dispositivos ESP32 via protocolo MQTT a partir do Console Inteligente |
+| Backend de Gerenciamento | Fornece interface de gerenciamento Web, suporta gerenciamento de usuários, configuração do sistema e gerenciamento de dispositivos; Suporta exibição em Chinês Simplificado, Chinês Tradicional e Inglês |
+| Ferramentas de Teste | Fornece ferramentas de teste de desempenho, ferramentas de teste de modelo de visão e ferramentas de teste de interação por áudio |
+| Suporte à Implantação | Suporta implantação via Docker e implantação local, fornece gerenciamento completo de arquivos de configuração |
+| Sistema de Plugins | Suporta extensões de plugins funcionais, desenvolvimento de plugins personalizados e carregamento dinâmico de plugins |
+
+### Em Desenvolvimento 🚧
+
+Para conhecer o progresso específico do plano de desenvolvimento, [clique aqui](https://github.com/users/xinnan-tech/projects/3). Perguntas frequentes e tutoriais relacionados podem ser consultados [neste link](./docs/FAQ.md)
+
+Se você é um desenvolvedor de software, aqui está uma [Carta Aberta aos Desenvolvedores](docs/contributor_open_letter.md). Seja bem-vindo a participar!
+
+---
+
+## Ecossistema do Produto 👬
+Xiaozhi é um ecossistema. Ao utilizar este produto, você também pode conferir outros [projetos excelentes](https://github.com/78/xiaozhi-esp32/blob/main/README_zh.md#%E7%9B%B8%E5%85%B3%E5%BC%80%E6%BA%90%E9%A1%B9%E7%9B%AE) neste ecossistema
+
+---
+
+## Lista de Plataformas/Componentes Suportados 📋
+### LLM Modelos de Linguagem
+
+| Método de Uso | Plataformas Suportadas | Plataformas Gratuitas |
+|:---:|:---:|:---:|
+| Chamadas via interface OpenAI | Alibaba Bailian, Volcano Engine, DeepSeek, Zhipu, Gemini, iFLYTEK | Zhipu, Gemini |
+| Chamadas via interface Ollama | Ollama | - |
+| Chamadas via interface Dify | Dify | - |
+| Chamadas via interface FastGPT | FastGPT | - |
+| Chamadas via interface Coze | Coze | - |
+| Chamadas via interface Xinference | Xinference | - |
+| Chamadas via interface HomeAssistant | HomeAssistant | - |
+
+Na verdade, qualquer LLM que suporte chamadas via interface openai pode ser integrado e utilizado.
+
+---
+
+### VLLM Modelos de Visão
+
+| Método de Uso | Plataformas Suportadas | Plataformas Gratuitas |
+|:---:|:---:|:---:|
+| Chamadas via interface OpenAI | Alibaba Bailian, Zhipu ChatGLMVLLM | Zhipu ChatGLMVLLM |
+
+Na verdade, qualquer VLLM que suporte chamadas via interface OpenAI pode ser integrado e utilizado.
+
+---
+
+### TTS Síntese de Voz
+
+| Método de Uso | Plataformas Suportadas | Plataformas Gratuitas |
+|:---:|:---:|:---:|
+| Chamadas via interface | EdgeTTS, iFLYTEK, Volcano Engine, Tencent Cloud, Alibaba Cloud e Bailian, CosyVoiceSiliconflow, TTS302AI, CozeCnTTS, GizwitsTTS, ACGNTTS, OpenAITTS, Lingxi Streaming TTS, MinimaxTTS | Lingxi Streaming TTS, EdgeTTS, CosyVoiceSiliconflow(parcial) |
+| Serviços locais | FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3, Index-TTS, PaddleSpeech | Index-TTS, PaddleSpeech, FishSpeech, GPT_SOVITS_V2, GPT_SOVITS_V3 |
+
+---
+
+### VAD Detecção de Atividade Vocal
+
+| Tipo | Nome da Plataforma | Método de Uso | Modelo de Preço | Observações |
+|:---:|:---------:|:----:|:----:|:--:|
+| VAD | SileroVAD | Uso local | Gratuito | |
+
+---
+
+### ASR Reconhecimento de Fala
+
+| Método de Uso | Plataformas Suportadas | Plataformas Gratuitas |
+|:---:|:---:|:---:|
+| Uso local | FunASR, SherpaASR | FunASR, SherpaASR |
+| Chamadas via interface | FunASRServer, Volcano Engine, iFLYTEK, Tencent Cloud, Alibaba Cloud, Baidu Cloud, OpenAI ASR | FunASRServer |
+
+---
+
+### Reconhecimento de Impressão Vocal
+
+| Método de Uso | Plataformas Suportadas | Plataformas Gratuitas |
+|:---:|:---:|:---:|
+| Uso local | 3D-Speaker | 3D-Speaker |
+
+---
+
+### Armazenamento de Memória
+
+| Tipo | Nome da Plataforma | Método de Uso | Modelo de Preço | Observações |
+|:------:|:---------------:|:----:|:---------:|:--:|
+| Memória | mem0ai | Chamadas via interface | Cota de 1000 vezes/mês | |
+| Memória | [powermem](./docs/powermem-integration.md) | Resumo local | Depende do LLM e BD | OceanBase de código aberto, suporta busca inteligente |
+| Memória | mem_local_short | Resumo local | Gratuito | |
+| Memória | nomem | Modo sem memória | Gratuito | |
+
+---
+
+### Reconhecimento de Intenção
+
+| Tipo | Nome da Plataforma | Método de Uso | Modelo de Preço | Observações |
+|:------:|:-------------:|:----:|:-------:|:---------------------:|
+| Intenção | intent_llm | Chamadas via interface | Baseado no preço do LLM | Reconhece intenção através de modelos de linguagem, forte generalização |
+| Intenção | function_call | Chamadas via interface | Baseado no preço do LLM | Completa a intenção através de chamada de função do modelo de linguagem, velocidade rápida, bom resultado |
+| Intenção | nointent | Modo sem intenção | Gratuito | Não realiza reconhecimento de intenção, retorna diretamente o resultado do diálogo |
+
+---
+
+### RAG Geração Aumentada por Recuperação
+
+| Tipo | Nome da Plataforma | Método de Uso | Modelo de Preço | Observações |
+|:------:|:-------------:|:----:|:-------:|:---------------------:|
+| RAG | ragflow | Chamadas via interface | Cobrado com base nos tokens consumidos para fatiamento e segmentação de palavras | Utiliza o recurso de geração aumentada por recuperação do RagFlow para fornecer respostas de diálogo mais precisas |
+
+---
+
+## Agradecimentos 🙏
+
+| Logo | Projeto/Empresa | Descrição |
+|:---:|:---:|:---|
+| <img src="./docs/images/logo_bailing.png" width="160"> | [Robô de Diálogo por Voz Bailing](https://github.com/wwbin2017/bailing) | Este projeto foi inspirado pelo [Robô de Diálogo por Voz Bailing](https://github.com/wwbin2017/bailing) e implementado com base nele |
+| <img src="./docs/images/logo_tenclass.png" width="160"> | [Tenclass](https://www.tenclass.com/) | Agradecimentos à [Tenclass](https://www.tenclass.com/) por formular protocolos de comunicação padrão, soluções de compatibilidade multidispositivo e demonstrações práticas de cenários de alta concorrência para o ecossistema Xiaozhi; fornecendo suporte completo de documentação técnica para este projeto |
+| <img src="./docs/images/logo_xuanfeng.png" width="160"> | [Xuanfeng Technology (玄凤科技)](https://github.com/Eric0308) | Agradecimentos à [Xuanfeng Technology](https://github.com/Eric0308) por contribuir com o framework de chamada de função, protocolo de comunicação MCP e implementação do mecanismo de chamada baseado em plugins. Através de um sistema padronizado de agendamento de instruções e capacidades de expansão dinâmica, melhora significativamente a eficiência de interação e extensibilidade funcional dos dispositivos de frontend (IoT) |
+| <img src="./docs/images/logo_junsen.png" width="160"> | [huangjunsen](https://github.com/huangjunsen0406) | Agradecimentos a [huangjunsen](https://github.com/huangjunsen0406) por contribuir com o módulo `Console de Controle Inteligente Mobile`, que permite controle eficiente e interação em tempo real em dispositivos móveis, melhorando significativamente a conveniência operacional e a eficiência de gerenciamento do sistema em cenários móveis. |
+| <img src="./docs/images/logo_huiyuan.png" width="160"> | [Huiyuan Design (汇远设计)](http://ui.kwd988.net/) | Agradecimentos à [Huiyuan Design](http://ui.kwd988.net/) por fornecer soluções visuais profissionais para este projeto, utilizando sua experiência prática de design atendendo mais de mil empresas para potencializar a experiência do usuário deste produto |
+| <img src="./docs/images/logo_qinren.png" width="160"> | [Xi'an Qinren Information Technology (西安勤人信息科技)](https://www.029app.com/) | Agradecimentos à [Xi'an Qinren Information Technology](https://www.029app.com/) por aprofundar o sistema visual deste projeto, garantindo consistência e extensibilidade do estilo de design geral em aplicações de múltiplos cenários |
+| <img src="./docs/images/logo_contributors.png" width="160"> | [Contribuidores de Código](https://github.com/xinnan-tech/xiaozhi-esp32-server/graphs/contributors) | Agradecimentos a [todos os contribuidores de código](https://github.com/xinnan-tech/xiaozhi-esp32-server/graphs/contributors), seus esforços tornaram o projeto mais robusto e poderoso. |
+
+
+<a href="https://star-history.com/#xinnan-tech/xiaozhi-esp32-server&Date">
+
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=xinnan-tech/xiaozhi-esp32-server&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=xinnan-tech/xiaozhi-esp32-server&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=xinnan-tech/xiaozhi-esp32-server&type=Date" />
+ </picture>
+</a>

README_vi.md

@@ -21,6 +21,7 @@ Hỗ trợ giao thức MQTT+UDP, giao thức Websocket, điểm truy cập MCP,
   <a href="./README_en.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
   <a href="./README_vi.md"><img alt="Tiếng Việt" src="https://img.shields.io/badge/Tiếng Việt-DBEDFA"></a>
   <a href="./README_de.md"><img alt="Deutsch" src="https://img.shields.io/badge/Deutsch-DFE0E5"></a>
+  <a href="./README_pt_BR.md"><img alt="Português (Brasil)" src="https://img.shields.io/badge/Português (Brasil)-DFE0E5"></a>
   <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/releases">
     <img alt="GitHub Contributors" src="https://img.shields.io/github/v/release/xinnan-tech/xiaozhi-esp32-server?logo=docker" />
   </a>
@@ -241,7 +242,7 @@ Dự án này cung cấp các công cụ kiểm tra sau để giúp bạn xác m
 | Đối thoại thông minh | Hỗ trợ nhiều LLM(Mô hình ngôn ngữ lớn), thực hiện đối thoại thông minh |
 | Cảm nhận thị giác | Hỗ trợ nhiều VLLM(Mô hình lớn thị giác), thực hiện tương tác đa phương thức |
 | Nhận dạng ý định | Hỗ trợ nhận dạng ý định mô hình lớn gắn ngoài, gọi hàm tự chủ mô hình lớn, cung cấp cơ chế xử lý ý định dạng plugin |
-| Hệ thống bộ nhớ | Hỗ trợ bộ nhớ ngắn hạn cục bộ, bộ nhớ giao diện mem0ai, có chức năng tóm tắt bộ nhớ |
+| Hệ thống bộ nhớ | Hỗ trợ bộ nhớ ngắn hạn cục bộ, bộ nhớ giao diện mem0ai, bộ nhớ thông minh PowerMem, có chức năng tóm tắt bộ nhớ |
 | Kho tri thức | Hỗ trợ kho tri thức RAGFlow, cho phép mô hình lớn đánh giá cần gọi kho tri thức trước khi trả lời |
 | Gọi công cụ | Hỗ trợ giao thức IOT phía client, giao thức MCP phía client, giao thức MCP phía server, giao thức điểm truy cập MCP, hàm công cụ tùy chỉnh |
 | Gửi lệnh | Dựa vào giao thức MQTT, hỗ trợ gửi lệnh MCP từ bảng điều khiển thông minh xuống thiết bị ESP32 |
@@ -259,7 +260,7 @@ Nếu bạn là một nhà phát triển phần mềm, đây có một [Lá thư
 ---
 
 ## Hệ sinh thái sản phẩm 👬
-Xiaozhi là một hệ sinh thái, khi bạn sử dụng sản phẩm này, bạn cũng có thể xem các [dự án xuất sắc](https://github.com/78/xiaozhi-esp32?tab=readme-ov-file#related-open-source-projects) khác trong hệ sinh thái này
+Xiaozhi là một hệ sinh thái, khi bạn sử dụng sản phẩm này, bạn cũng có thể xem các [dự án xuất sắc](https://github.com/78/xiaozhi-esp32/blob/main/README_zh.md#%E7%9B%B8%E5%85%B3%E5%BC%80%E6%BA%90%E9%A1%B9%E7%9B%AE) khác trong hệ sinh thái này
 
 ---
 
@@ -329,6 +330,7 @@ Trên thực tế, bất kỳ VLLM nào hỗ trợ gọi giao diện openai đ
 |   Loại   |      Tên nền tảng       | Phương pháp sử dụng |   Mô hình thu phí    | Ghi chú |
 |:------:|:---------------:|:----:|:---------:|:--:|
 | Memory |     mem0ai      | Gọi giao diện | Hạn mức 1000 lần/tháng |    |
+| Memory |     [powermem](./docs/powermem-integration.md)    | Tóm tắt cục bộ | Phụ thuộc vào LLM và DB |  OceanBase mã nguồn mở, hỗ trợ tìm kiếm thông minh  |
 | Memory | mem_local_short | Tóm tắt cục bộ |    Miễn phí     |    |
 | Memory |     nomem       | Chế độ không có bộ nhớ |    Miễn phí     |    |
 

docs/FAQ.md

@@ -82,6 +82,7 @@ VAD:
 8、[新闻插件源配置指南](./newsnow_plugin_config.md)<br/>
 9、[知识库ragflow集成指南](./ragflow-integration.md)<br/>
 10、[如何部署上下文源](./context-provider-integration.md)<br/>
+11、[如何集成PowerMem智能记忆](./powermem-integration.md)<br/>
 
 ### 11、语音克隆、本地语音部署相关教程
 1、[如何在智控台克隆音色](./huoshan-streamTTS-voice-cloning.md)<br/>

docs/mqtt-gateway-integration.md

@@ -127,7 +127,7 @@ pm2 restart xz-mqtt
 ```
 192.168.0.7:8884
 ```
-4. 在智控台顶部，点击`参数管理`，搜索`server.mqtt_manager_api`，点击编辑，填入你在`.env`文件中设置的`PUBLIC_IP`+`:`+`UDP_PORT`。类似这样
+4. 在智控台顶部，点击`参数管理`，搜索`server.mqtt_manager_api`，点击编辑，填入你在`.env`文件中设置的`PUBLIC_IP`+`:`+`API_PORT`。类似这样
 ```
 192.168.0.7:8007
 ```
@@ -154,7 +154,7 @@ curl 'http://localhost:8002/xiaozhi/ota/' \
 pm2 logs xz-mqtt
 ```
 
-## 第三部分：全模块运行实现小智硬件MQTT+UDP连接
+## 第三部分：单模块运行xiaozhi-server实现小智硬件MQTT+UDP连接
 
 打开你的`data/.config.yaml`文件，在`server`下找到`mqtt_gateway`填入你在`.env`文件中设置的`PUBLIC_IP`+`:`+`MQTT_PORT`。类似这样
 ```
@@ -184,4 +184,4 @@ curl 'http://localhost:8002/xiaozhi/ota/' \
 唤醒后留意mqtt-gateway的日志，确认是否有连接成功的日志。
 ```
 pm2 logs xz-mqtt
-```
+```

docs/powermem-integration.md

@@ -0,0 +1,345 @@
+# PowerMem 记忆组件集成指南
+
+## 简介
+
+[PowerMem](https://www.powermem.ai/) 是由 OceanBase 开源的 Agent 记忆组件，通过本地 LLM 进行记忆总结和智能检索，为 AI 代理提供高效的记忆管理功能。
+
+费用说明：PowerMem 本身开源免费，实际费用取决于您选择的 LLM 和数据库：
+- 使用 SQLite + 免费 LLM（如智谱 glm-4-flash）= **完全免费**
+- 使用云端 LLM 或云端数据库 = 按对应服务收费
+
+> 💡 **最佳性能提示**：PowerMem 配合 OceanBase 使用可实现最大性能释放，SQLite 仅建议在资源不足的情况下使用。
+
+- **GitHub**: https://github.com/oceanbase/powermem
+- **官网**: https://www.powermem.ai/
+- **使用示例**: https://github.com/oceanbase/powermem/tree/main/examples
+
+## 功能特性
+
+- **本地总结**：通过 LLM 在本地进行记忆总结和提取
+- **用户画像**：通过 `UserMemory` 自动提取用户信息（姓名、职业、兴趣等），持续更新用户画像
+- **智能遗忘**：基于艾宾浩斯遗忘曲线，自动"遗忘"过时噪声信息
+- **多种存储后端**：支持 OceanBase（推荐，最佳性能）、SeekDB（推荐，AI应用存储一体）、PostgreSQL、SQLite（轻量备选）
+- **多种 LLM 支持**：通义千问、智谱（glm-4-flash 免费）、OpenAI 等
+- **智能检索**：基于向量搜索的语义检索能力
+- **私有部署**：完全支持本地私有化部署
+- **异步操作**：高效的异步记忆管理
+
+## 安装
+
+PowerMem 已添加到项目依赖中，如果需要手动安装：
+
+```bash
+pip install powermem
+```
+
+## 配置说明
+
+### 基础配置
+
+在 `config.yaml` 中配置 PowerMem：
+
+```yaml
+selected_module:
+  Memory: powermem
+
+Memory:
+  powermem:
+    type: powermem
+    # 是否启用用户画像功能
+    # 用户画像支持: oceanbase、seekdb、sqlite (powermem 0.3.0+)
+    enable_user_profile: true
+    
+    # ========== LLM 配置 ==========
+    llm:
+      provider: openai  # 可选: qwen, openai, zhipu 等
+      config:
+        api_key: 你的LLM API密钥
+        model: qwen-plus
+        # openai_base_url: https://api.openai.com/v1  # 可选，自定义服务地址
+    
+    # ========== Embedding 配置 ==========
+    embedder:
+      provider: openai  # 可选: qwen, openai 等
+      config:
+        api_key: 你的嵌入模型API密钥
+        model: text-embedding-v4
+        openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
+        # embedding_dims: 1024  # 向量维度，非1536时需配置
+    
+    # ========== Database 配置 ==========
+    vector_store:
+      provider: sqlite  # 可选: oceanbase(推荐), seekdb(推荐), postgres, sqlite(轻量)
+      config: {}  # SQLite 无需额外配置
+```
+
+### 配置参数详解
+
+#### LLM 配置
+
+| 参数 | 说明 | 可选值 |
+|------|------|--------|
+| `llm.provider` | LLM 提供商 | `qwen`, `openai`, `zhipu` 等 |
+| `llm.config.api_key` | API 密钥 | - |
+| `llm.config.model` | 模型名称 | 根据提供商选择 |
+| `llm.config.openai_base_url` | 自定义服务地址（可选） | - |
+
+#### Embedding 配置
+
+| 参数 | 说明 | 可选值 |
+|------|------|--------|
+| `embedder.provider` | 嵌入模型提供商 | `qwen`, `openai` 等 |
+| `embedder.config.api_key` | API 密钥 | - |
+| `embedder.config.model` | 模型名称 | 根据提供商选择 |
+| `embedder.config.openai_base_url` | 自定义服务地址（可选） | - |
+
+#### Database 配置
+
+| 参数 | 说明 | 可选值 |
+|------|------|--------|
+| `vector_store.provider` | 存储后端类型 | `oceanbase`(推荐), `seekdb`(推荐), `postgres`, `sqlite`(轻量) |
+| `vector_store.config` | 数据库连接配置 | 根据 provider 设置 |
+
+### 记忆模式说明
+
+PowerMem 支持两种记忆模式：
+
+| 模式 | 配置 | 功能 | 存储要求 |
+|------|------|------|----------|
+| **普通记忆** | `enable_user_profile: false` | 对话记忆存储与检索 | 支持所有数据库 |
+| **用户画像** | `enable_user_profile: true` | 记忆 + 自动提取用户画像 | oceanbase、seekdb、sqlite |
+
+> 📌 **版本说明**：PowerMem 0.3.0+ 版本，用户画像功能支持 OceanBase、SeekDB、SQLite 三种存储后端。
+
+### 使用通义千问（推荐）
+
+1. 访问 [阿里云百炼平台](https://bailian.console.aliyun.com/) 注册账号
+2. 在 [API Key 管理](https://bailian.console.aliyun.com/?apiKey=1#/api-key) 页面获取 API 密钥
+3. 配置如下：
+
+```yaml
+Memory:
+  powermem:
+    type: powermem
+    enable_user_profile: true
+    llm:
+      provider: qwen
+      config:
+        api_key: sk-xxxxxxxxxxxxxxxx
+        model: qwen-plus
+    embedder:
+      provider: openai
+      config:
+        api_key: sk-xxxxxxxxxxxxxxxx
+        model: text-embedding-v4
+        openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
+    vector_store:
+      provider: sqlite
+      config: {}
+```
+
+### 使用智谱免费 LLM（完全免费方案）
+
+智谱提供免费的 glm-4-flash 模型，配合 SQLite 可实现完全免费使用：
+
+1. 访问 [智谱AI开放平台](https://bigmodel.cn/) 注册账号
+2. 在 [API Keys](https://bigmodel.cn/usercenter/proj-mgmt/apikeys) 页面获取 API 密钥
+3. 配置如下：
+
+```yaml
+Memory:
+  powermem:
+    type: powermem
+    enable_user_profile: true
+    llm:
+      provider: openai  # 使用 openai 兼容模式
+      config:
+        api_key: xxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxx
+        model: glm-4-flash
+        openai_base_url: https://open.bigmodel.cn/api/paas/v4/
+    embedder:
+      provider: openai
+      config:
+        api_key: xxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxx
+        model: embedding-3
+        openai_base_url: https://open.bigmodel.cn/api/paas/v4/
+    vector_store:
+      provider: sqlite
+      config: {}
+```
+
+### 使用 OpenAI
+
+```yaml
+Memory:
+  powermem:
+    type: powermem
+    enable_user_profile: true
+    llm:
+      provider: openai
+      config:
+        api_key: sk-xxxxxxxxxxxxxxxx
+        model: gpt-4o-mini
+        openai_base_url: https://api.openai.com/v1
+    embedder:
+      provider: openai
+      config:
+        api_key: sk-xxxxxxxxxxxxxxxx
+        model: text-embedding-3-small
+        openai_base_url: https://api.openai.com/v1
+    vector_store:
+      provider: sqlite
+      config: {}
+```
+
+### 使用 OceanBase（最佳性能方案）
+
+OceanBase 是 PowerMem 的最佳搭档，可实现最大性能释放：
+
+1. 部署 OceanBase 数据库（支持开源本地部署或使用云服务）
+   - 开源部署：https://github.com/oceanbase/oceanbase
+   - 云服务：https://www.oceanbase.com/
+2. 配置如下：
+
+```yaml
+Memory:
+  powermem:
+    type: powermem
+    enable_user_profile: true
+    llm:
+      provider: qwen
+      config:
+        api_key: sk-xxxxxxxxxxxxxxxx
+        model: qwen-plus
+    embedder:
+      provider: openai
+      config:
+        api_key: sk-xxxxxxxxxxxxxxxx
+        model: text-embedding-v4
+        openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
+    vector_store:
+      provider: oceanbase
+      config:
+        host: 127.0.0.1
+        port: 2881
+        user: root@test
+        password: your_password
+        db_name: powermem
+        collection_name: memories  # 默认值
+        embedding_model_dims: 1536  # 嵌入向量维度，必需参数
+```
+
+## 设备记忆隔离
+
+PowerMem 会自动使用设备 ID（`device_id`）作为 `user_id` 进行记忆隔离。这意味着：
+
+- 每个设备拥有独立的记忆空间
+- 不同设备之间的记忆完全隔离
+- 同一设备的多次对话可以共享记忆上下文
+
+## 用户画像（UserMemory）
+
+PowerMem 提供 `UserMemory` 类，可自动从对话中提取用户画像信息。
+
+> 📌 **版本说明**：PowerMem 0.3.0+ 版本，用户画像功能支持 OceanBase、SeekDB、SQLite 三种存储后端。
+
+### 启用用户画像
+
+在配置中设置 `enable_user_profile: true` 即可启用：
+
+```yaml
+Memory:
+  powermem:
+    type: powermem
+    enable_user_profile: true  # 启用用户画像
+    llm:
+      provider: qwen
+      config:
+        api_key: sk-xxxxxxxxxxxxxxxx
+        model: qwen-plus
+    embedder:
+      provider: openai
+      config:
+        api_key: sk-xxxxxxxxxxxxxxxx
+        model: text-embedding-v4
+        openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
+    vector_store:
+      provider: sqlite  # 用户画像支持: oceanbase、seekdb、sqlite
+      config: {}
+```
+
+### 用户画像能力
+
+| 能力 | 说明 |
+|------|------|
+| **信息提取** | 自动从对话中提取姓名、年龄、职业、兴趣等 |
+| **持续更新** | 随着对话进行，不断完善用户画像 |
+| **画像检索** | 将用户画像与记忆搜索结合，提升检索相关性 |
+| **智能遗忘** | 基于艾宾浩斯遗忘曲线，淡化过时信息 |
+
+### 工作原理
+
+启用用户画像后，小智在查询记忆时会自动返回：
+1. **用户画像**：用户的基本信息、兴趣爱好等
+2. **相关记忆**：与当前对话相关的历史记忆
+
+> ✅ **版本说明**：PowerMem 0.3.0+ 版本，用户画像功能支持 OceanBase、SeekDB、SQLite 三种存储后端。
+
+## 与其他记忆组件的对比
+
+| 特性 | PowerMem | mem0ai | mem_local_short |
+|------|----------|--------|-----------------|
+| 工作方式 | 本地总结 | 云端接口 | 本地总结 |
+| 存储位置 | 本地/云端DB | 云端 | 本地YAML |
+| 费用 | 取决于LLM和DB | 1000次/月免费 | 完全免费 |
+| 智能检索 | ✅ 向量搜索 | ✅ 向量搜索 | ❌ 全量返回 |
+| 用户画像 | ✅ UserMemory | ❌ | ❌ |
+| 智能遗忘 | ✅ 遗忘曲线 | ❌ | ❌ |
+| 私有部署 | ✅ 支持 | ❌ 仅云端 | ✅ 支持 |
+| 数据库支持 | OceanBase(推荐)/SeekDB/PostgreSQL/SQLite | - | YAML 文件 |
+
+## 常见问题
+
+### 1. API 密钥错误
+
+如果出现 `API key is required` 错误，请检查：
+- `llm_api_key` 和 `embedding_api_key` 是否正确填写
+- API 密钥是否有效
+
+### 2. 模型不存在
+
+如果出现模型不存在的错误，请确认：
+- `llm_model` 和 `embedding_model` 名称是否正确
+- 对应的模型服务是否已开通
+
+### 3. 连接超时
+
+如果出现连接超时，可以尝试：
+- 检查网络连接
+- 如果使用代理，配置 `llm_base_url` 和 `embedding_base_url`
+
+## 测试验证
+
+可以在虚拟环境中测试 PowerMem 是否正常工作：
+
+```bash
+# 激活虚拟环境
+source .venv/bin/activate
+
+# 测试 PowerMem 导入
+python -c "from powermem import AsyncMemory; print('PowerMem 导入成功')"
+
+# 测试 UserMemory 导入（用户画像功能）
+python -c "from powermem import UserMemory; print('UserMemory 导入成功')"
+```
+
+## 更多资源
+
+- [PowerMem 官方文档](https://www.powermem.ai/)
+- [PowerMem GitHub 仓库](https://github.com/oceanbase/powermem)
+- [PowerMem 使用示例](https://github.com/oceanbase/powermem/tree/main/examples)
+- [OceanBase 官网](https://www.oceanbase.com/)
+- [OceanBase GitHub](https://github.com/oceanbase/oceanbase)
+- [SeekDB GitHub](https://github.com/oceanbase/seekdb)（AI原生搜索数据库）
+- [阿里云百炼平台](https://bailian.console.aliyun.com/)
+

main/manager-api/rule/doc/测试验证/知识库模块全量集成测试报告.md

@@ -0,0 +1,45 @@
+# 知识库模块全量集成测试报告
+
+## 1. 测试背景
+针对 `KnowledgeBaseController` 和 `KnowledgeFilesController` 共 14 个接口进行了深度集成测试。主要解决了本地影子库与 RAGFlow 远程服务之间的状态对齐、数据反序列化兼容性以及批量操作逻辑安全性问题。
+
+## 2. 修复的核心 Bug 清单 (Hotfixes)
+
+| 模块 | 问题类型 | 修复方案 | 验证结果 |
+| :--- | :--- | :--- | :--- |
+| **DTO** | `positions` 反序列化失败 | 类型从 `List<Integer>` 提升为 `Object`，支持嵌套数组 | ✅ 已验证 |
+| **DTO** | 日期格式不兼容 | 针对 RAGFlow 的 RFC 1123 格式，将 `Date` 改为 `String` 透传 | ✅ 已验证 |
+| **请求** | 检索参数 `null` 拒绝 | 增加 `@JsonInclude(NON_NULL)`，跳过可选字段的空值序列化 | ✅ 已验证 |
+| **同步** | 状态自愈死锁 | 增加 `CANCEL/FAIL` 状态的 60s 低频同步机制，防止逻辑错误锁定 | ✅ 已验证 |
+| **逻辑** | 删除守卫逻辑错误 | 将拦截条件从 `status="1"` 修正为 `run="RUNNING"` | ✅ 已验证 |
+
+## 3. 全量接口测试统计
+
+### KnowledgeBaseController (7/7)
+- [x] 分页查询 (`GET /datasets`)
+- [x] 详情获取 (`GET /datasets/{id}`)
+- [x] 创建知识库 (`POST /datasets`)
+- [x] 修改配置 (`PUT /datasets/{id}`)
+- [x] 物理删除 (`DELETE /datasets/{id}`)
+- [x] 批量删除 (`DELETE /datasets/batch`)
+- [x] 模型列表获取 (`GET /datasets/rag-models`)
+
+### KnowledgeFilesController (7/7)
+- [x] 文档列表与同步 (`GET /datasets/{id}/documents`)
+- [x] 状态过滤查询 (`GET /datasets/{id}/documents/status/{s}`)
+- [x] 文档上传 (`POST /datasets/{id}/documents`)
+- [x] 触发解析 (`POST /datasets/{id}/chunks`)
+- [x] 切片详情 (`GET /datasets/{id}/documents/{docId}/chunks`)
+- [x] 召回测试 (`POST /datasets/{id}/retrieval-test`)
+- [x] 批量删除文档 (`DELETE /datasets/{id}/documents`)
+
+## 4. 自动化审计结论
+通过执行 `comprehensive_audit.ps1` 自动化脚本，模拟了“创建->上传->解析->同步->检索->删除”的完整生产链路。
+- **解析成功率**：100%
+- **数据准确性**：DTO 转换无异常，坐标及得分提取正常
+- **系统安全性**：解析中拦截机制生效
+- **结论**：**准生产就绪 (Production Ready)**
+
+---
+*报告生成时间：2026-02-13*
+*审核：dora--1206563805@qq.com*

main/manager-api/src/main/java/xiaozhi/common/config/RestTemplateConfig.java

@@ -2,7 +2,9 @@ package xiaozhi.common.config;
 
 import org.springframework.context.annotation.Bean;
 import org.springframework.context.annotation.Configuration;
+import org.springframework.http.client.JdkClientHttpRequestFactory;
 import org.springframework.web.client.RestTemplate;
+import java.time.Duration;
 
 /**
  * RestTemplate配置
@@ -12,6 +14,8 @@ public class RestTemplateConfig {
 
     @Bean
     public RestTemplate restTemplate() {
-        return new RestTemplate();
+        JdkClientHttpRequestFactory factory = new JdkClientHttpRequestFactory();
+        factory.setReadTimeout(Duration.ofSeconds(30));
+        return new RestTemplate(factory);
     }
 }

main/manager-api/src/main/java/xiaozhi/common/config/SwaggerConfig.java

@@ -79,6 +79,22 @@ public class SwaggerConfig {
                 .build();
     }
 
+    @Bean
+    public GroupedOpenApi knowledgeApi() {
+        return GroupedOpenApi.builder()
+                .group("knowledge")
+                .pathsToMatch("/datasets/**")
+                .build();
+    }
+
+    @Bean
+    public GroupedOpenApi botApi() {
+        return GroupedOpenApi.builder()
+                .group("bot")
+                .pathsToMatch("/api/v1/**")
+                .build();
+    }
+
     @Bean
     public OpenAPI customOpenAPI() {
         return new OpenAPI().info(new Info()

main/manager-api/src/main/java/xiaozhi/common/constant/Constant.java

@@ -304,7 +304,7 @@ public interface Constant {
     /**
      * 版本号
      */
-    public static final String VERSION = "0.9.1";
+    public static final String VERSION = "0.9.2";
 
     /**
      * 无效固件URL

main/manager-api/src/main/java/xiaozhi/common/exception/ErrorCode.java

@@ -245,4 +245,10 @@ public interface ErrorCode {
     int DEVICE_NOT_EXIST = 10194; // 设备不存在
     int OTA_UPLOAD_COUNT_EXCEED = 10195; // OTA上传次数超过限制
 
+    // 智能体标签相关错误码
+    int AGENT_TAG_NAME_DUPLICATE = 10196; // 标签名称已存在
+    int AGENT_TAG_NAME_EMPTY = 10197; // 标签名称不能为空
+    int AGENT_TAG_NOT_EXIST = 10198; // 标签不存在
+
+    int RAG_DOCUMENT_PARSING_DELETE_ERROR = 10199; // 文档解析中，禁止删除
 }

main/manager-api/src/main/java/xiaozhi/common/exception/RenException.java

@@ -13,23 +13,25 @@ public class RenException extends RuntimeException {
     private String msg;
 
     public RenException(int code) {
+        super(MessageUtils.getMessage(code));
         this.code = code;
         this.msg = MessageUtils.getMessage(code);
     }
 
     public RenException(int code, String... params) {
+        super(MessageUtils.getMessage(code, params));
         this.code = code;
         this.msg = MessageUtils.getMessage(code, params);
     }
 
     public RenException(int code, Throwable e) {
-        super(e);
+        super(MessageUtils.getMessage(code), e);
         this.code = code;
         this.msg = MessageUtils.getMessage(code);
     }
 
     public RenException(int code, Throwable e, String... params) {
-        super(e);
+        super(MessageUtils.getMessage(code, params), e);
         this.code = code;
         this.msg = MessageUtils.getMessage(code, params);
     }

main/manager-api/src/main/java/xiaozhi/common/utils/Sm2DecryptUtil.java

@@ -12,28 +12,29 @@ import xiaozhi.modules.sys.service.SysParamsService;
  * 封装了重复的SM2解密、验证码提取和验证逻辑
  */
 public class Sm2DecryptUtil {
-    
+
     /**
      * 验证码长度
      */
     private static final int CAPTCHA_LENGTH = 5;
-    
+
     /**
      * 解密SM2加密内容，提取验证码并验证
+     * 
      * @param encryptedPassword SM2加密的密码字符串
-     * @param captchaId 验证码ID
+     * @param captchaId         验证码ID
-     * @param captchaService 验证码服务
+     * @param captchaService    验证码服务
-     * @param sysParamsService 系统参数服务
+     * @param sysParamsService  系统参数服务
      * @return 解密后的实际密码
      */
-    public static String decryptAndValidateCaptcha(String encryptedPassword, String captchaId, 
+    public static String decryptAndValidateCaptcha(String encryptedPassword, String captchaId,
-                                                 CaptchaService captchaService, SysParamsService sysParamsService) {
+            CaptchaService captchaService, SysParamsService sysParamsService) {
         // 获取SM2私钥
         String privateKeyStr = sysParamsService.getValue(Constant.SM2_PRIVATE_KEY, true);
         if (StringUtils.isBlank(privateKeyStr)) {
             throw new RenException(ErrorCode.SM2_KEY_NOT_CONFIGURED);
         }
-        
+
         // 使用SM2私钥解密密码
         String decryptedContent;
         try {
@@ -41,19 +42,20 @@ public class Sm2DecryptUtil {
         } catch (Exception e) {
             throw new RenException(ErrorCode.SM2_DECRYPT_ERROR);
         }
-        
+
         // 分离验证码和密码：前5位是验证码，后面是密码
         if (decryptedContent.length() > CAPTCHA_LENGTH) {
             String embeddedCaptcha = decryptedContent.substring(0, CAPTCHA_LENGTH);
             String actualPassword = decryptedContent.substring(CAPTCHA_LENGTH);
-            
+
-            // 验证嵌入的验证码是否正确
             boolean embeddedCaptchaValid = captchaService.validate(captchaId, embeddedCaptcha, true);
             if (!embeddedCaptchaValid) {
                 throw new RenException(ErrorCode.SMS_CAPTCHA_ERROR);
             }
-            
+
             return actualPassword;
+        } else if (decryptedContent.length() > 0) {
+            throw new RenException(ErrorCode.SMS_CAPTCHA_ERROR);
         } else {
             throw new RenException(ErrorCode.SM2_DECRYPT_ERROR);
         }

main/manager-api/src/main/java/xiaozhi/modules/agent/controller/AgentController.java

@@ -42,6 +42,9 @@ import xiaozhi.modules.agent.dto.AgentMemoryDTO;
 import xiaozhi.modules.agent.dto.AgentUpdateDTO;
 import xiaozhi.modules.agent.entity.AgentEntity;
 import xiaozhi.modules.agent.entity.AgentTemplateEntity;
+import xiaozhi.modules.agent.dto.AgentTagDTO;
+import xiaozhi.modules.agent.entity.AgentTagEntity;
+import xiaozhi.modules.agent.service.AgentTagService;
 import xiaozhi.modules.agent.service.AgentChatAudioService;
 import xiaozhi.modules.agent.service.AgentChatHistoryService;
 import xiaozhi.modules.agent.service.AgentChatSummaryService;
@@ -69,6 +72,7 @@ public class AgentController {
     private final AgentContextProviderService agentContextProviderService;
     private final AgentChatSummaryService agentChatSummaryService;
     private final RedisUtils redisUtils;
+    private final AgentTagService agentTagService;
 
     @GetMapping("/list")
     @Operation(summary = "获取用户智能体列表")
@@ -77,7 +81,7 @@ public class AgentController {
             @RequestParam(value = "keyword", required = false) String keyword,
             @RequestParam(value = "searchType", defaultValue = "name") String searchType) {
         UserDetail user = SecurityUser.getUser();
-        
+
         // 直接调用整合后的getUserAgents方法，无需再区分搜索和普通查询
         List<AgentDTO> agents = agentService.getUserAgents(user.getId(), keyword, searchType);
         return new Result<List<AgentDTO>>().ok(agents);
@@ -275,6 +279,50 @@ public class AgentController {
                 .body(audioData);
     }
 
+    @PostMapping("/tag")
+    @Operation(summary = "创建标签")
+    @RequiresPermissions("sys:role:normal")
+    public Result<AgentTagEntity> createTag(@RequestBody Map<String, String> params) {
+        String tagName = params.get("tagName");
+        if (StringUtils.isBlank(tagName)) {
+            return new Result<AgentTagEntity>().error("标签名称不能为空");
+        }
+        AgentTagEntity tag = agentTagService.saveTag(tagName);
+        return new Result<AgentTagEntity>().ok(tag);
+    }
 
+    @GetMapping("/tag/list")
+    @Operation(summary = "获取所有标签列表")
+    @RequiresPermissions("sys:role:normal")
+    public Result<List<AgentTagDTO>> getAllTags() {
+        List<AgentTagDTO> tags = agentTagService.getAllTags();
+        return new Result<List<AgentTagDTO>>().ok(tags);
+    }
+
+    @DeleteMapping("/tag/{id}")
+    @Operation(summary = "删除标签")
+    @RequiresPermissions("sys:role:normal")
+    public Result<Void> deleteTag(@PathVariable String id) {
+        agentTagService.deleteTag(id);
+        return new Result<Void>().ok(null);
+    }
+
+    @GetMapping("/{id}/tags")
+    @Operation(summary = "获取智能体的标签")
+    @RequiresPermissions("sys:role:normal")
+    public Result<List<AgentTagDTO>> getAgentTags(@PathVariable String id) {
+        List<AgentTagDTO> tags = agentTagService.getTagsByAgentId(id);
+        return new Result<List<AgentTagDTO>>().ok(tags);
+    }
+
+    @PutMapping("/{id}/tags")
+    @Operation(summary = "保存智能体的标签")
+    @RequiresPermissions("sys:role:normal")
+    public Result<Void> saveAgentTags(@PathVariable String id, @RequestBody Map<String, Object> params) {
+        List<String> tagIds = (List<String>) params.get("tagIds");
+        List<String> tagNames = (List<String>) params.get("tagNames");
+        agentTagService.saveAgentTags(id, tagIds, tagNames);
+        return new Result<Void>().ok(null);
+    }
 
 }

main/manager-api/src/main/java/xiaozhi/modules/agent/dao/AgentTagDao.java

@@ -0,0 +1,24 @@
+package xiaozhi.modules.agent.dao;
+
+import org.apache.ibatis.annotations.Mapper;
+import org.apache.ibatis.annotations.Param;
+import xiaozhi.common.dao.BaseDao;
+import xiaozhi.modules.agent.entity.AgentTagEntity;
+
+import java.util.List;
+
+@Mapper
+public interface AgentTagDao extends BaseDao<AgentTagEntity> {
+
+    List<AgentTagEntity> selectByAgentId(@Param("agentId") String agentId);
+
+    List<AgentTagEntity> selectByAgentIds(@Param("agentIds") List<String> agentIds);
+
+    List<AgentTagEntity> selectAll();
+
+    List<String> selectAgentIdsByTagName(@Param("tagName") String tagName);
+
+    List<AgentTagEntity> selectByTagNames(@Param("tagNames") List<String> tagNames);
+
+    int batchInsert(@Param("list") List<AgentTagEntity> tagList);
+}

main/manager-api/src/main/java/xiaozhi/modules/agent/dao/AgentTagRelationDao.java

@@ -0,0 +1,18 @@
+package xiaozhi.modules.agent.dao;
+
+import org.apache.ibatis.annotations.Mapper;
+import org.apache.ibatis.annotations.Param;
+import xiaozhi.common.dao.BaseDao;
+import xiaozhi.modules.agent.entity.AgentTagRelationEntity;
+
+import java.util.List;
+
+@Mapper
+public interface AgentTagRelationDao extends BaseDao<AgentTagRelationEntity> {
+
+    int deleteByAgentId(@Param("agentId") String agentId);
+
+    int insertRelation(AgentTagRelationEntity relation);
+
+    int batchInsertRelation(@Param("list") List<AgentTagRelationEntity> relations);
+}

main/manager-api/src/main/java/xiaozhi/modules/agent/dto/AgentDTO.java

@@ -5,6 +5,7 @@ import java.util.List;
 
 import io.swagger.v3.oas.annotations.media.Schema;
 import lombok.Data;
+import xiaozhi.modules.agent.dto.AgentTagDTO;
 
 /**
  * 智能体数据传输对象
@@ -46,4 +47,7 @@ public class AgentDTO {
 
     @Schema(description = "设备数量", example = "10")
     private Integer deviceCount;
+
+    @Schema(description = "标签列表")
+    private List<AgentTagDTO> tags;
 }

main/manager-api/src/main/java/xiaozhi/modules/agent/dto/AgentTagDTO.java

@@ -0,0 +1,20 @@
+package xiaozhi.modules.agent.dto;
+
+import io.swagger.v3.oas.annotations.media.Schema;
+import lombok.Data;
+
+import java.io.Serializable;
+import java.util.List;
+
+@Data
+@Schema(description = "智能体标签DTO")
+public class AgentTagDTO implements Serializable {
+
+    private static final long serialVersionUID = 1L;
+
+    @Schema(description = "标签ID")
+    private String id;
+
+    @Schema(description = "标签名称")
+    private String tagName;
+}

main/manager-api/src/main/java/xiaozhi/modules/agent/dto/AgentUpdateDTO.java

@@ -1,6 +1,7 @@
 package xiaozhi.modules.agent.dto;
 
 import java.io.Serializable;
+import java.math.BigDecimal;
 import java.util.HashMap;
 import java.util.List;
 
@@ -41,6 +42,18 @@ public class AgentUpdateDTO implements Serializable {
     @Schema(description = "音色标识", example = "voice_02", nullable = true)
     private String ttsVoiceId;
 
+    @Schema(description = "音色语言", example = "普通话", nullable = true)
+    private String ttsLanguage;
+
+    @Schema(description = "TTS音量", example = "50", nullable = true)
+    private Integer ttsVolume;
+
+    @Schema(description = "TTS语速", example = "50", nullable = true)
+    private Integer ttsRate;
+
+    @Schema(description = "TTS音调", example = "50", nullable = true)
+    private Integer ttsPitch;
+
     @Schema(description = "记忆模型标识", example = "mem_model_02", nullable = true)
     private String memModelId;
 

main/manager-api/src/main/java/xiaozhi/modules/agent/entity/AgentEntity.java

@@ -1,5 +1,6 @@
 package xiaozhi.modules.agent.entity;
 
+import java.math.BigDecimal;
 import java.util.Date;
 
 import com.baomidou.mybatisplus.annotation.IdType;
@@ -45,6 +46,18 @@ public class AgentEntity {
     @Schema(description = "音色标识")
     private String ttsVoiceId;
 
+    @Schema(description = "音色语言")
+    private String ttsLanguage;
+
+    @Schema(description = "TTS音量")
+    private Integer ttsVolume;
+
+    @Schema(description = "TTS语速")
+    private Integer ttsRate;
+
+    @Schema(description = "TTS音调")
+    private Integer ttsPitch;
+
     @Schema(description = "记忆模型标识")
     private String memModelId;
 

main/manager-api/src/main/java/xiaozhi/modules/agent/entity/AgentTagEntity.java

@@ -0,0 +1,41 @@
+package xiaozhi.modules.agent.entity;
+
+import java.util.Date;
+
+import com.baomidou.mybatisplus.annotation.IdType;
+import com.baomidou.mybatisplus.annotation.TableId;
+import com.baomidou.mybatisplus.annotation.TableName;
+
+import io.swagger.v3.oas.annotations.media.Schema;
+import lombok.Data;
+
+@Data
+@TableName("ai_agent_tag")
+@Schema(description = "智能体标签")
+public class AgentTagEntity {
+
+    @TableId(type = IdType.ASSIGN_UUID)
+    @Schema(description = "主键")
+    private String id;
+
+    @Schema(description = "标签名称")
+    private String tagName;
+
+    @Schema(description = "排序")
+    private Integer sort;
+
+    @Schema(description = "创建者")
+    private Long creator;
+
+    @Schema(description = "创建时间")
+    private Date createdAt;
+
+    @Schema(description = "更新者")
+    private Long updater;
+
+    @Schema(description = "更新时间")
+    private Date updatedAt;
+
+    @Schema(description = "删除标记")
+    private Integer deleted;
+}

main/manager-api/src/main/java/xiaozhi/modules/agent/entity/AgentTagRelationEntity.java

@@ -0,0 +1,41 @@
+package xiaozhi.modules.agent.entity;
+
+import java.util.Date;
+
+import com.baomidou.mybatisplus.annotation.IdType;
+import com.baomidou.mybatisplus.annotation.TableId;
+import com.baomidou.mybatisplus.annotation.TableName;
+
+import io.swagger.v3.oas.annotations.media.Schema;
+import lombok.Data;
+
+@Data
+@TableName("ai_agent_tag_relation")
+@Schema(description = "智能体标签关联")
+public class AgentTagRelationEntity {
+
+    @TableId(type = IdType.ASSIGN_UUID)
+    @Schema(description = "主键")
+    private String id;
+
+    @Schema(description = "智能体ID")
+    private String agentId;
+
+    @Schema(description = "标签ID")
+    private String tagId;
+
+    @Schema(description = "排序")
+    private Integer sort;
+
+    @Schema(description = "创建者")
+    private Long creator;
+
+    @Schema(description = "创建时间")
+    private Date createdAt;
+
+    @Schema(description = "更新者")
+    private Long updater;
+
+    @Schema(description = "更新时间")
+    private Date updatedAt;
+}

main/manager-api/src/main/java/xiaozhi/modules/agent/entity/AgentTemplateEntity.java

@@ -1,6 +1,7 @@
 package xiaozhi.modules.agent.entity;
 
 import java.io.Serializable;
+import java.math.BigDecimal;
 import java.util.Date;
 
 import com.baomidou.mybatisplus.annotation.IdType;
@@ -64,6 +65,26 @@ public class AgentTemplateEntity implements Serializable {
      */
     private String ttsVoiceId;
 
+    /**
+     * 音色语言
+     */
+    private String ttsLanguage;
+
+    /**
+     * TTS音量
+     */
+    private Integer ttsVolume;
+
+    /**
+     * TTS语速
+     */
+    private Integer ttsRate;
+
+    /**
+     * TTS音调
+     */
+    private Integer ttsPitch;
+
     /**
      * 记忆模型标识
      */

main/manager-api/src/main/java/xiaozhi/modules/agent/service/AgentTagService.java

@@ -0,0 +1,25 @@
+package xiaozhi.modules.agent.service;
+
+import java.util.List;
+
+import xiaozhi.modules.agent.dto.AgentTagDTO;
+import xiaozhi.modules.agent.entity.AgentTagEntity;
+
+public interface AgentTagService {
+
+    AgentTagEntity saveTag(String tagName);
+
+    void deleteTag(String tagId);
+
+    List<AgentTagDTO> getTagsByAgentId(String agentId);
+
+    void saveAgentTags(String agentId, List<String> tagIds, List<String> tagNames);
+
+    void deleteAgentTags(String agentId);
+
+    List<AgentTagDTO> getTagsByAgentIds(List<String> agentIds);
+
+    List<AgentTagDTO> getAllTags();
+
+    List<String> getAgentIdsByTagName(String tagName);
+}

main/manager-api/src/main/java/xiaozhi/modules/agent/service/impl/AgentServiceImpl.java

@@ -31,17 +31,21 @@ import xiaozhi.common.utils.ConvertUtils;
 import xiaozhi.common.utils.JsonUtils;
 import xiaozhi.common.utils.ToolUtil;
 import xiaozhi.modules.agent.dao.AgentDao;
+import xiaozhi.modules.agent.dao.AgentTagDao;
 import xiaozhi.modules.agent.dto.AgentCreateDTO;
 import xiaozhi.modules.agent.dto.AgentDTO;
+import xiaozhi.modules.agent.dto.AgentTagDTO;
 import xiaozhi.modules.agent.dto.AgentUpdateDTO;
 import xiaozhi.modules.agent.entity.AgentContextProviderEntity;
 import xiaozhi.modules.agent.entity.AgentEntity;
 import xiaozhi.modules.agent.entity.AgentPluginMapping;
+import xiaozhi.modules.agent.entity.AgentTagEntity;
 import xiaozhi.modules.agent.entity.AgentTemplateEntity;
 import xiaozhi.modules.agent.service.AgentChatHistoryService;
 import xiaozhi.modules.agent.service.AgentContextProviderService;
 import xiaozhi.modules.agent.service.AgentPluginMappingService;
 import xiaozhi.modules.agent.service.AgentService;
+import xiaozhi.modules.agent.service.AgentTagService;
 import xiaozhi.modules.agent.service.AgentTemplateService;
 import xiaozhi.modules.agent.vo.AgentInfoVO;
 import xiaozhi.modules.device.entity.DeviceEntity;
@@ -59,6 +63,7 @@ import xiaozhi.modules.timbre.service.TimbreService;
 @AllArgsConstructor
 public class AgentServiceImpl extends BaseServiceImpl<AgentDao, AgentEntity> implements AgentService {
     private final AgentDao agentDao;
+    private final AgentTagDao agentTagDao;
     private final TimbreService timbreModelService;
     private final ModelConfigService modelConfigService;
     private final RedisUtils redisUtils;
@@ -68,6 +73,7 @@ public class AgentServiceImpl extends BaseServiceImpl<AgentDao, AgentEntity> imp
     private final AgentTemplateService agentTemplateService;
     private final ModelProviderService modelProviderService;
     private final AgentContextProviderService agentContextProviderService;
+    private final AgentTagService agentTagService;
 
     @Override
     public PageData<AgentEntity> adminAgentList(Map<String, Object> params) {
@@ -151,8 +157,16 @@ public class AgentServiceImpl extends BaseServiceImpl<AgentDao, AgentEntity> imp
                     return new ArrayList<>();
                 }
             } else {
-                // 按名称搜索
+                // 按名称搜索（默认）：同时搜索智能体名称和标签名
-                queryWrapper.like("agent_name", keyword);
+                List<String> tagAgentIds = agentTagService.getAgentIdsByTagName(keyword);
+                if (ToolUtil.isNotEmpty(tagAgentIds)) {
+                    queryWrapper.and(wrapper -> wrapper
+                            .like("agent_name", keyword)
+                            .or()
+                            .in("id", tagAgentIds));
+                } else {
+                    queryWrapper.like("agent_name", keyword);
+                }
             }
         }
 
@@ -193,6 +207,19 @@ public class AgentServiceImpl extends BaseServiceImpl<AgentDao, AgentEntity> imp
         // 获取设备数量
         dto.setDeviceCount(getDeviceCountByAgentId(agent.getId()));
 
+        // 获取标签列表
+        List<AgentTagEntity> tags = agentTagDao.selectByAgentId(agent.getId());
+        if (ToolUtil.isNotEmpty(tags)) {
+            dto.setTags(tags.stream().map(this::convertTagToDTO).collect(Collectors.toList()));
+        }
+
+        return dto;
+    }
+
+    private AgentTagDTO convertTagToDTO(AgentTagEntity entity) {
+        AgentTagDTO dto = new AgentTagDTO();
+        dto.setId(entity.getId());
+        dto.setTagName(entity.getTagName());
         return dto;
     }
 
@@ -282,6 +309,18 @@ public class AgentServiceImpl extends BaseServiceImpl<AgentDao, AgentEntity> imp
         if (dto.getTtsVoiceId() != null) {
             existingEntity.setTtsVoiceId(dto.getTtsVoiceId());
         }
+        if (dto.getTtsLanguage() != null) {
+            existingEntity.setTtsLanguage(dto.getTtsLanguage());
+        }
+        if (dto.getTtsVolume() != null) {
+            existingEntity.setTtsVolume(dto.getTtsVolume());
+        }
+        if (dto.getTtsRate() != null) {
+            existingEntity.setTtsRate(dto.getTtsRate());
+        }
+        if (dto.getTtsPitch() != null) {
+            existingEntity.setTtsPitch(dto.getTtsPitch());
+        }
         if (dto.getMemModelId() != null) {
             existingEntity.setMemModelId(dto.getMemModelId());
         }

main/manager-api/src/main/java/xiaozhi/modules/agent/service/impl/AgentTagServiceImpl.java

@@ -0,0 +1,198 @@
+package xiaozhi.modules.agent.service.impl;
+
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.UUID;
+import java.util.stream.Collectors;
+
+import org.springframework.stereotype.Service;
+import org.springframework.transaction.annotation.Transactional;
+
+import com.baomidou.mybatisplus.core.conditions.query.QueryWrapper;
+
+import lombok.AllArgsConstructor;
+import xiaozhi.common.exception.ErrorCode;
+import xiaozhi.common.exception.RenException;
+import xiaozhi.common.service.impl.BaseServiceImpl;
+import xiaozhi.modules.agent.dao.AgentTagDao;
+import xiaozhi.modules.agent.dao.AgentTagRelationDao;
+import xiaozhi.modules.agent.dto.AgentTagDTO;
+import xiaozhi.modules.agent.entity.AgentTagEntity;
+import xiaozhi.modules.agent.entity.AgentTagRelationEntity;
+import xiaozhi.modules.agent.service.AgentTagService;
+
+@Service
+@AllArgsConstructor
+public class AgentTagServiceImpl extends BaseServiceImpl<AgentTagDao, AgentTagEntity> implements AgentTagService {
+
+    private final AgentTagRelationDao agentTagRelationDao;
+
+    @Override
+    public AgentTagEntity saveTag(String tagName) {
+        if (tagName == null || tagName.trim().isEmpty()) {
+            throw new RenException(ErrorCode.AGENT_TAG_NAME_EMPTY);
+        }
+
+        QueryWrapper<AgentTagEntity> wrapper = new QueryWrapper<>();
+        wrapper.eq("tag_name", tagName);
+        wrapper.eq("deleted", 0);
+        AgentTagEntity existTag = baseDao.selectOne(wrapper);
+        if (existTag != null) {
+            return existTag;
+        }
+
+        AgentTagEntity tag = new AgentTagEntity();
+        tag.setId(UUID.randomUUID().toString().replace("-", ""));
+        tag.setTagName(tagName);
+        tag.setSort(0);
+        tag.setCreatedAt(new Date());
+        tag.setUpdatedAt(new Date());
+        tag.setDeleted(0);
+        baseDao.insert(tag);
+        return tag;
+    }
+
+    @Override
+    public void deleteTag(String tagId) {
+        AgentTagEntity tag = baseDao.selectById(tagId);
+        if (tag != null) {
+            tag.setDeleted(1);
+            tag.setUpdatedAt(new Date());
+            baseDao.updateById(tag);
+        }
+    }
+
+    @Override
+    public List<AgentTagDTO> getTagsByAgentId(String agentId) {
+        List<AgentTagEntity> tags = baseDao.selectByAgentId(agentId);
+        return tags.stream().map(this::convertToDTO).collect(Collectors.toList());
+    }
+
+    @Override
+    @Transactional(rollbackFor = Exception.class)
+    public void saveAgentTags(String agentId, List<String> tagIds, List<String> tagNames) {
+        agentTagRelationDao.deleteByAgentId(agentId);
+
+        List<AgentTagEntity> currentTags = baseDao.selectByAgentId(agentId);
+        List<String> currentTagNames = currentTags.stream()
+                .map(AgentTagEntity::getTagName)
+                .collect(Collectors.toList());
+
+        List<String> allTagIds = new ArrayList<>();
+        List<String> newTagNames = new ArrayList<>();
+
+        if (tagNames != null && !tagNames.isEmpty()) {
+            Set<String> addedTagNames = new HashSet<>();
+            for (String tagName : tagNames) {
+                if (tagName == null || tagName.trim().isEmpty()) {
+                    throw new RenException(ErrorCode.AGENT_TAG_NAME_EMPTY);
+                }
+
+                if (currentTagNames.contains(tagName) || addedTagNames.contains(tagName)) {
+                    throw new RenException(ErrorCode.AGENT_TAG_NAME_DUPLICATE);
+                }
+                addedTagNames.add(tagName);
+                newTagNames.add(tagName);
+            }
+        }
+
+        List<AgentTagEntity> existTags = new ArrayList<>();
+        if (!newTagNames.isEmpty()) {
+            existTags = baseDao.selectByTagNames(newTagNames);
+        }
+
+        Map<String, AgentTagEntity> existTagMap = existTags.stream()
+                .collect(Collectors.toMap(AgentTagEntity::getTagName, t -> t, (a, b) -> a));
+
+        List<AgentTagEntity> tagsToInsert = new ArrayList<>();
+        for (String tagName : newTagNames) {
+            AgentTagEntity existTag = existTagMap.get(tagName);
+            if (existTag != null) {
+                allTagIds.add(existTag.getId());
+            } else {
+                AgentTagEntity tag = new AgentTagEntity();
+                tag.setId(UUID.randomUUID().toString().replace("-", ""));
+                tag.setTagName(tagName);
+                tag.setSort(0);
+                tag.setDeleted(0);
+                tag.setCreatedAt(new Date());
+                tag.setUpdatedAt(new Date());
+                tagsToInsert.add(tag);
+            }
+        }
+
+        if (!tagsToInsert.isEmpty()) {
+            baseDao.batchInsert(tagsToInsert);
+            for (AgentTagEntity tag : tagsToInsert) {
+                allTagIds.add(tag.getId());
+            }
+        }
+
+        if (tagIds != null && !tagIds.isEmpty()) {
+            List<AgentTagEntity> tagIdEntities = baseDao.selectBatchIds(tagIds);
+            for (AgentTagEntity tag : tagIdEntities) {
+                if (tag != null && (currentTagNames.contains(tag.getTagName()) ||
+                        newTagNames.contains(tag.getTagName()))) {
+                    throw new RenException(ErrorCode.AGENT_TAG_NAME_DUPLICATE);
+                }
+            }
+            allTagIds.addAll(tagIds);
+        }
+
+        if (allTagIds.isEmpty()) {
+            return;
+        }
+
+        List<AgentTagRelationEntity> relations = new ArrayList<>();
+        Date now = new Date();
+        int sort = 0;
+        for (String tagId : allTagIds) {
+            AgentTagRelationEntity relation = new AgentTagRelationEntity();
+            relation.setId(UUID.randomUUID().toString().replace("-", ""));
+            relation.setAgentId(agentId);
+            relation.setTagId(tagId);
+            relation.setSort(sort++);
+            relation.setCreatedAt(now);
+            relation.setUpdatedAt(now);
+            relations.add(relation);
+        }
+        agentTagRelationDao.batchInsertRelation(relations);
+    }
+
+    @Override
+    @Transactional(rollbackFor = Exception.class)
+    public void deleteAgentTags(String agentId) {
+        agentTagRelationDao.deleteByAgentId(agentId);
+    }
+
+    @Override
+    public List<AgentTagDTO> getTagsByAgentIds(List<String> agentIds) {
+        if (agentIds == null || agentIds.isEmpty()) {
+            return List.of();
+        }
+        List<AgentTagEntity> tags = baseDao.selectByAgentIds(agentIds);
+        return tags.stream().map(this::convertToDTO).collect(Collectors.toList());
+    }
+
+    @Override
+    public List<AgentTagDTO> getAllTags() {
+        List<AgentTagEntity> tags = baseDao.selectAll();
+        return tags.stream().map(this::convertToDTO).collect(Collectors.toList());
+    }
+
+    @Override
+    public List<String> getAgentIdsByTagName(String tagName) {
+        return baseDao.selectAgentIdsByTagName(tagName);
+    }
+
+    private AgentTagDTO convertToDTO(AgentTagEntity entity) {
+        AgentTagDTO dto = new AgentTagDTO();
+        dto.setId(entity.getId());
+        dto.setTagName(entity.getTagName());
+        return dto;
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/config/service/impl/ConfigServiceImpl.java

@@ -87,6 +87,10 @@ public class ConfigServiceImpl implements ConfigService {
                 null,
                 null,
                 null,
+                null,
+                null,
+                null,
+                null,
                 agent.getVadModelId(),
                 agent.getAsrModelId(),
                 null,
@@ -135,15 +139,24 @@ public class ConfigServiceImpl implements ConfigService {
         String voice = null;
         String referenceAudio = null;
         String referenceText = null;
+        String language = null;
         TimbreDetailsVO timbre = timbreService.get(agent.getTtsVoiceId());
         if (timbre != null) {
             voice = timbre.getTtsVoice();
             referenceAudio = timbre.getReferenceAudio();
             referenceText = timbre.getReferenceText();
+            // 优先使用用户选择的语言，如果没有则使用音色支持的第一个语言
+            if (StringUtils.isNotBlank(agent.getTtsLanguage())) {
+                language = agent.getTtsLanguage();
+            } else if (StringUtils.isNotBlank(timbre.getLanguages())) {
+                language = timbre.getLanguages().split("、")[0].trim();
+            }
         } else {
             VoiceCloneEntity voice_print = cloneVoiceService.selectById(agent.getTtsVoiceId());
             if (voice_print != null) {
                 voice = voice_print.getVoiceId();
+                // 优先使用用户选择的语言，如果没有则使用默认值
+                language = StringUtils.isNotBlank(agent.getTtsLanguage()) ? agent.getTtsLanguage() : "普通话";
             }
         }
         // 构建返回数据
@@ -208,6 +221,10 @@ public class ConfigServiceImpl implements ConfigService {
                 voice,
                 referenceAudio,
                 referenceText,
+                language,
+                agent.getTtsVolume(),
+                agent.getTtsRate(),
+                agent.getTtsPitch(),
                 agent.getVadModelId(),
                 agent.getAsrModelId(),
                 agent.getLlmModelId(),
@@ -385,6 +402,10 @@ public class ConfigServiceImpl implements ConfigService {
             String voice,
             String referenceAudio,
             String referenceText,
+            String language,
+            Integer ttsVolume,
+            Integer ttsRate,
+            Integer ttsPitch,
             String vadModelId,
             String asrModelId,
             String llmModelId,
@@ -423,6 +444,14 @@ public class ConfigServiceImpl implements ConfigService {
                         ((Map<String, Object>) model.getConfigJson()).put("ref_audio", referenceAudio);
                     if (referenceText != null)
                         ((Map<String, Object>) model.getConfigJson()).put("ref_text", referenceText);
+                    if (language != null)
+                        ((Map<String, Object>) model.getConfigJson()).put("language", language);
+                    if (ttsVolume != null)
+                        ((Map<String, Object>) model.getConfigJson()).put("ttsVolume", ttsVolume);
+                    if (ttsRate != null)
+                        ((Map<String, Object>) model.getConfigJson()).put("ttsRate", ttsRate);
+                    if (ttsPitch != null)
+                        ((Map<String, Object>) model.getConfigJson()).put("ttsPitch", ttsPitch);
 
                     // 火山引擎声音克隆需要替换resource_id
                     Map<String, Object> map = (Map<String, Object>) model.getConfigJson();

main/manager-api/src/main/java/xiaozhi/modules/device/service/impl/DeviceServiceImpl.java

@@ -4,6 +4,7 @@ import java.nio.charset.StandardCharsets;
 import java.security.InvalidKeyException;
 import java.security.NoSuchAlgorithmException;
 import java.time.Instant;
+import java.util.ArrayList;
 import java.util.Base64;
 import java.util.Date;
 import java.util.HashMap;
@@ -38,6 +39,8 @@ import cn.hutool.crypto.digest.DigestUtil;
 import cn.hutool.http.ContentType;
 import cn.hutool.http.Header;
 import cn.hutool.http.HttpRequest;
+import cn.hutool.json.JSONArray;
+import cn.hutool.json.JSONObject;
 import cn.hutool.json.JSONUtil;
 import jakarta.servlet.http.HttpServletRequest;
 import lombok.AllArgsConstructor;
@@ -700,40 +703,82 @@ public class DeviceServiceImpl extends BaseServiceImpl<DeviceDao, DeviceEntity>
         // 构建完整的URL
         String url = StrUtil.format("http://{}/api/commands/{}", mqttGatewayUrl, clientId);
 
-        // 构建请求体
+        // 存储所有工具列表
-        Map<String, Object> payload = MapUtil
+        List<Object> allTools = new ArrayList<>();
-                .builder(new HashMap<String, Object>())
+        String cursor = null;
-                .put("jsonrpc", "2.0")
-                .put("id", 2)
-                .put("method", "tools/list")
-                .put("params", MapUtil.builder(new HashMap<String, Object>())
-                        .put("withUserTools", true)
-                        .build())
-                .build();
 
-        Map<String, Object> requestBody = MapUtil
+        // 循环获取分页数据
-                .builder(new HashMap<String, Object>())
+        while (true) {
-                .put("type", "mcp")
+            // 构建params
-                .put("payload", payload)
+            Map<String, Object> paramsMap = MapUtil.builder(new HashMap<String, Object>())
-                .build();
+                    .put("withUserTools", true)
-
+                    .build();
-        // 发送请求
+            // 如果有cursor，添加到请求参数中
-        String resultMessage = HttpRequest.post(url)
+            if (StringUtils.isNotBlank(cursor)) {
-                .header(Header.CONTENT_TYPE, ContentType.JSON.getValue())
+                paramsMap.put("cursor", cursor);
-                .header(Header.AUTHORIZATION, "Bearer " + generateBearerToken())
-                .body(JSONUtil.toJsonStr(requestBody))
-                .timeout(10000) // 超时，毫秒
-                .execute().body();
-
-        // 解析响应
-        if (StringUtils.isNotBlank(resultMessage)) {
-            cn.hutool.json.JSONObject jsonObject = JSONUtil.parseObj(resultMessage);
-            if (jsonObject.getBool("success", false)) {
-                return jsonObject.get("data");
             }
+
+            // 构建请求体
+            Map<String, Object> payload = MapUtil
+                    .builder(new HashMap<String, Object>())
+                    .put("jsonrpc", "2.0")
+                    .put("id", 2)
+                    .put("method", "tools/list")
+                    .put("params", paramsMap)
+                    .build();
+
+            Map<String, Object> requestBody = MapUtil
+                    .builder(new HashMap<String, Object>())
+                    .put("type", "mcp")
+                    .put("payload", payload)
+                    .build();
+
+            // 发送请求
+            String resultMessage = HttpRequest.post(url)
+                    .header(Header.CONTENT_TYPE, ContentType.JSON.getValue())
+                    .header(Header.AUTHORIZATION, "Bearer " + generateBearerToken())
+                    .body(JSONUtil.toJsonStr(requestBody))
+                    .timeout(10000) // 超时，毫秒
+                    .execute().body();
+
+            // 解析响应
+            if (StringUtils.isBlank(resultMessage)) {
+                break;
+            }
+
+            JSONObject jsonObject = JSONUtil.parseObj(resultMessage);
+            if (!jsonObject.getBool("success", false)) {
+                break;
+            }
+
+            JSONObject data = jsonObject.getJSONObject("data");
+            if (data == null) {
+                break;
+            }
+
+            // 获取当前页的工具列表
+            JSONArray tools = data.getJSONArray("tools");
+            if (tools != null && !tools.isEmpty()) {
+                allTools.addAll(tools);
+            }
+
+            // 获取下一页的cursor
+            String nextCursor = data.getStr("nextCursor");
+            if (StringUtils.isBlank(nextCursor)) {
+                // 没有下一页了
+                break;
+            }
+            cursor = nextCursor;
         }
 
-        return null;
+        // 构建返回结果
+        if (allTools.isEmpty()) {
+            return null;
+        }
+
+        Map<String, Object> resultData = new HashMap<>();
+        resultData.put("tools", allTools);
+        return resultData;
     }
 
     @Override

main/manager-api/src/main/java/xiaozhi/modules/device/service/impl/OtaServiceImpl.java

@@ -66,7 +66,7 @@ public class OtaServiceImpl extends BaseServiceImpl<OtaDao, OtaEntity> implement
         // 同类固件只保留最新的一条
         List<OtaEntity> otaList = baseDao.selectList(queryWrapper);
         if (otaList != null && otaList.size() > 0) {
-            OtaEntity otaBefore = otaList.getFirst();
+            OtaEntity otaBefore = otaList.get(0);
             entity.setId(otaBefore.getId());
             baseDao.updateById(entity);
             return true;

main/manager-api/src/main/java/xiaozhi/modules/knowledge/config/RAGTaskConfig.java

@@ -0,0 +1,13 @@
+package xiaozhi.modules.knowledge.config;
+
+import org.springframework.context.annotation.Configuration;
+import org.springframework.scheduling.annotation.EnableScheduling;
+
+/**
+ * 知识库模块定时任务配置
+ * 启用 Spring Schedule 能力
+ */
+@Configuration
+@EnableScheduling
+public class RAGTaskConfig {
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/controller/KnowledgeBaseController.java

@@ -26,6 +26,7 @@ import xiaozhi.common.utils.Result;
 import xiaozhi.common.utils.ToolUtil;
 import xiaozhi.modules.knowledge.dto.KnowledgeBaseDTO;
 import xiaozhi.modules.knowledge.service.KnowledgeBaseService;
+import xiaozhi.modules.knowledge.service.KnowledgeManagerService;
 import xiaozhi.modules.model.entity.ModelConfigEntity;
 import xiaozhi.modules.security.user.SecurityUser;
 
@@ -36,6 +37,7 @@ import xiaozhi.modules.security.user.SecurityUser;
 public class KnowledgeBaseController {
 
     private final KnowledgeBaseService knowledgeBaseService;
+    private final KnowledgeManagerService knowledgeManagerService;
 
     @GetMapping
     @Operation(summary = "分页查询知识库列表")
@@ -96,6 +98,8 @@ public class KnowledgeBaseController {
             throw new RenException(ErrorCode.NO_PERMISSION);
         }
 
+        // [FIX] 注入 ID，防止 Service 层找不到记录
+        knowledgeBaseDTO.setId(existingKnowledgeBase.getId());
         knowledgeBaseDTO.setDatasetId(datasetId);
         KnowledgeBaseDTO resp = knowledgeBaseService.update(knowledgeBaseDTO);
         return new Result<KnowledgeBaseDTO>().ok(resp);
@@ -117,7 +121,8 @@ public class KnowledgeBaseController {
             throw new RenException(ErrorCode.NO_PERMISSION);
         }
 
-        knowledgeBaseService.deleteByDatasetId(datasetId);
+        // [Architecture Fix] 通过编排层级联删除，防止孤儿数据并解决循环依赖
+        knowledgeManagerService.deleteDatasetWithFiles(datasetId);
         return new Result<>();
     }
 
@@ -133,15 +138,16 @@ public class KnowledgeBaseController {
         // 获取当前登录用户ID
         Long currentUserId = SecurityUser.getUserId();
         List<String> idList = Arrays.asList(ids.split(","));
-        List<KnowledgeBaseDTO> knowledgeBaseDTOs = Optional.ofNullable(knowledgeBaseService.getByDatasetIdList(idList)).orElseGet(ArrayList::new);
+        List<KnowledgeBaseDTO> knowledgeBaseDTOs = Optional.ofNullable(knowledgeBaseService.getByDatasetIdList(idList))
+                .orElseGet(ArrayList::new);
         if (ToolUtil.isNotEmpty(knowledgeBaseDTOs)) {
-            knowledgeBaseDTOs.forEach(item->{
+            knowledgeBaseDTOs.forEach(item -> {
                 // 检查权限：用户只能删除自己创建的知识库
                 if (item.getCreator() == null || !item.getCreator().equals(currentUserId)) {
                     throw new RenException(ErrorCode.NO_PERMISSION);
                 }
-                //删除
+                // [Architecture Fix] 通过编排层级联删除
-                knowledgeBaseService.deleteByDatasetId(item.getDatasetId());
+                knowledgeManagerService.deleteDatasetWithFiles(item.getDatasetId());
             });
         }
         return new Result<>();

main/manager-api/src/main/java/xiaozhi/modules/knowledge/controller/KnowledgeFilesController.java

@@ -4,6 +4,7 @@ import java.util.List;
 import java.util.Map;
 
 import org.apache.shiro.authz.annotation.RequiresPermissions;
+import org.springdoc.core.annotations.ParameterObject;
 import org.springframework.web.bind.annotation.*;
 import org.springframework.web.multipart.MultipartFile;
 
@@ -11,7 +12,6 @@ import com.fasterxml.jackson.core.type.TypeReference;
 import com.fasterxml.jackson.databind.ObjectMapper;
 
 import io.swagger.v3.oas.annotations.Operation;
-import io.swagger.v3.oas.annotations.Parameter;
 import io.swagger.v3.oas.annotations.tags.Tag;
 import lombok.AllArgsConstructor;
 import xiaozhi.common.exception.ErrorCode;
@@ -20,6 +20,9 @@ import xiaozhi.common.page.PageData;
 import xiaozhi.common.utils.Result;
 import xiaozhi.modules.knowledge.dto.KnowledgeBaseDTO;
 import xiaozhi.modules.knowledge.dto.KnowledgeFilesDTO;
+import xiaozhi.modules.knowledge.dto.document.ChunkDTO;
+import xiaozhi.modules.knowledge.dto.document.DocumentDTO;
+import xiaozhi.modules.knowledge.dto.document.RetrievalDTO;
 import xiaozhi.modules.knowledge.service.KnowledgeBaseService;
 import xiaozhi.modules.knowledge.service.KnowledgeFilesService;
 import xiaozhi.modules.security.user.SecurityUser;
@@ -57,13 +60,13 @@ public class KnowledgeFilesController {
     public Result<PageData<KnowledgeFilesDTO>> getPageList(
             @PathVariable("dataset_id") String datasetId,
             @RequestParam(required = false) String name,
-            @RequestParam(required = false) Integer status,
+            @RequestParam(required = false) String status,
             @RequestParam(required = false, defaultValue = "1") Integer page,
             @RequestParam(required = false, defaultValue = "10") Integer page_size) {
         // 验证知识库权限
         validateKnowledgeBasePermission(datasetId);
 
-        //组装参数
+        // 组装参数
         KnowledgeFilesDTO knowledgeFilesDTO = new KnowledgeFilesDTO();
         knowledgeFilesDTO.setDatasetId(datasetId);
         knowledgeFilesDTO.setName(name);
@@ -77,12 +80,12 @@ public class KnowledgeFilesController {
     @RequiresPermissions("sys:role:normal")
     public Result<PageData<KnowledgeFilesDTO>> getPageListByStatus(
             @PathVariable("dataset_id") String datasetId,
-            @PathVariable("status") Integer status,
+            @PathVariable("status") String status,
             @RequestParam(required = false, defaultValue = "1") Integer page,
             @RequestParam(required = false, defaultValue = "10") Integer page_size) {
         // 验证知识库权限
         validateKnowledgeBasePermission(datasetId);
-        //组装参数
+        // 组装参数
         KnowledgeFilesDTO knowledgeFilesDTO = new KnowledgeFilesDTO();
         knowledgeFilesDTO.setDatasetId(datasetId);
         knowledgeFilesDTO.setStatus(status);
@@ -111,16 +114,29 @@ public class KnowledgeFilesController {
         return new Result<KnowledgeFilesDTO>().ok(resp);
     }
 
-    @DeleteMapping("/documents/{document_id}")
+    @DeleteMapping("/documents")
-    @Operation(summary = "删除单个文档")
+    @Operation(summary = "批量删除文档")
-    @Parameter(name = "document_id", description = "文档ID", required = true)
     @RequiresPermissions("sys:role:normal")
     public Result<Void> delete(@PathVariable("dataset_id") String datasetId,
+            @RequestBody DocumentDTO.BatchIdReq req) {
+        // 验证知识库权限
+        validateKnowledgeBasePermission(datasetId);
+
+        knowledgeFilesService.deleteDocuments(datasetId, req);
+        return new Result<>();
+    }
+
+    @DeleteMapping("/documents/{document_id}")
+    @Operation(summary = "删除单个文档")
+    @RequiresPermissions("sys:role:normal")
+    public Result<Void> deleteSingle(@PathVariable("dataset_id") String datasetId,
             @PathVariable("document_id") String documentId) {
         // 验证知识库权限
         validateKnowledgeBasePermission(datasetId);
 
-        knowledgeFilesService.deleteByDocumentId(documentId, datasetId);
+        DocumentDTO.BatchIdReq req = new DocumentDTO.BatchIdReq();
+        req.setIds(java.util.Collections.singletonList(documentId));
+        knowledgeFilesService.deleteDocuments(datasetId, req);
         return new Result<>();
     }
 
@@ -148,64 +164,53 @@ public class KnowledgeFilesController {
     @GetMapping("/documents/{document_id}/chunks")
     @Operation(summary = "列出指定文档的切片")
     @RequiresPermissions("sys:role:normal")
-    public Result<Map<String, Object>> listChunks(@PathVariable("dataset_id") String datasetId,
+    public Result<ChunkDTO.ListVO> listChunks(
+            @PathVariable("dataset_id") String datasetId,
             @PathVariable("document_id") String documentId,
-            @RequestParam(required = false) String keywords,
+            @ParameterObject ChunkDTO.ListReq req) {
-            @RequestParam(required = false, defaultValue = "1") Integer page,
+
-            @RequestParam(required = false, defaultValue = "1024") Integer page_size,
+        // 验证权限 (内部已包含知识库存在性校验与归属权校验)
-            @RequestParam(required = false) String id) {
-        // 验证知识库权限
         validateKnowledgeBasePermission(datasetId);
 
-        Map<String, Object> result = knowledgeFilesService.listChunks(datasetId, documentId, keywords, page, page_size, id);
+        // 设置默认值
-        return new Result<Map<String, Object>>().ok(result);
+        if (req.getPage() == null)
+            req.setPage(1);
+        if (req.getPageSize() == null)
+            req.setPageSize(50);
+
+        // 调用服务层获取强类型切片列表
+        ChunkDTO.ListVO result = knowledgeFilesService.listChunks(datasetId, documentId, req);
+        return new Result<ChunkDTO.ListVO>().ok(result);
     }
 
-    /**
-     * 召回测试
-     */
     @PostMapping("/retrieval-test")
     @Operation(summary = "召回测试")
     @RequiresPermissions("sys:role:normal")
-    public Result<Map<String, Object>> retrievalTest(@PathVariable("dataset_id") String datasetId,
+    public Result<RetrievalDTO.ResultVO> retrievalTest(
-            @RequestBody Map<String, Object> params) {
+            @PathVariable("dataset_id") String datasetId,
+            @RequestBody RetrievalDTO.TestReq req) {
+
         // 验证知识库权限
         validateKnowledgeBasePermission(datasetId);
 
-        try {
+        // 业务下沉逻辑：如果未指定知识库ID，则设为当前路径中的 datasetId
-            // 提取参数
+        if (req.getDatasetIds() == null || req.getDatasetIds().isEmpty()) {
-            String question = (String) params.get("question");
+            req.setDatasetIds(java.util.Arrays.asList(datasetId));
-            if (question == null || question.trim().isEmpty()) {
-                return new Result<Map<String, Object>>().error("问题不能为空");
-            }
-
-            List<String> datasetIds = (List<String>) params.get("dataset_ids");
-            List<String> documentIds = (List<String>) params.get("document_ids");
-            Integer page = (Integer) params.get("page");
-            Integer pageSize = (Integer) params.get("page_size");
-            Float similarityThreshold = (Float) params.get("similarity_threshold");
-            Float vectorSimilarityWeight = (Float) params.get("vector_similarity_weight");
-            Integer topK = (Integer) params.get("top_k");
-            String rerankId = (String) params.get("rerank_id");
-            Boolean keyword = (Boolean) params.get("keyword");
-            Boolean highlight = (Boolean) params.get("highlight");
-            List<String> crossLanguages = (List<String>) params.get("cross_languages");
-            Map<String, Object> metadataCondition = (Map<String, Object>) params.get("metadata_condition");
-
-            // 如果未指定数据集ID，使用当前数据集
-            if (datasetIds == null || datasetIds.isEmpty()) {
-                datasetIds = java.util.Arrays.asList(datasetId);
-            }
-
-            Map<String, Object> result = knowledgeFilesService.retrievalTest(
-                    question, datasetIds, documentIds, page, pageSize, similarityThreshold,
-                    vectorSimilarityWeight, topK, rerankId, keyword, highlight, crossLanguages, metadataCondition);
-
-            return new Result<Map<String, Object>>().ok(result);
-        } catch (Exception e) {
-            return new Result<Map<String, Object>>().error("召回测试失败: " + e.getMessage());
         }
+
+        // [Reinforce] 强管控分页参数，防止 RAGFlow 端出现 Negative Slicing 报错
+        if (req.getPage() == null || req.getPage() < 1) {
+            req.setPage(1);
+        }
+        if (req.getPageSize() == null || req.getPageSize() < 1) {
+            req.setPageSize(100);
+        }
+
+        // 调用检索服务，返回强类型聚合对象
+        RetrievalDTO.ResultVO result = knowledgeFilesService.retrievalTest(req);
+        return new Result<RetrievalDTO.ResultVO>().ok(result);
     }
+
     /**
      * 解析JSON字符串为Map对象
      */

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dao/DocumentDao.java

@@ -0,0 +1,12 @@
+package xiaozhi.modules.knowledge.dao;
+
+import org.apache.ibatis.annotations.Mapper;
+import xiaozhi.common.dao.BaseDao;
+import xiaozhi.modules.knowledge.entity.DocumentEntity;
+
+/**
+ * 文档 DAO
+ */
+@Mapper
+public interface DocumentDao extends BaseDao<DocumentEntity> {
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dao/KnowledgeBaseDao.java

@@ -19,4 +19,17 @@ public interface KnowledgeBaseDao extends BaseDao<KnowledgeBaseEntity> {
      */
     void deletePluginMappingByKnowledgeBaseId(@Param("knowledgeBaseId") String knowledgeBaseId);
 
+    /**
+     * 通用维度原子更新知识库统计信息
+     * 
+     * @param datasetId  数据集ID
+     * @param docDelta   文档数增量
+     * @param chunkDelta 分块数增量
+     * @param tokenDelta Token数增量
+     */
+    void updateStatsAfterChange(@Param("datasetId") String datasetId,
+            @Param("docDelta") Integer docDelta,
+            @Param("chunkDelta") Long chunkDelta,
+            @Param("tokenDelta") Long tokenDelta);
+
 }

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_API接口分类表.md

@@ -0,0 +1,102 @@
+# RAGFlow API Interface Classification
+
+## 1. External APIs (三方接入体系)
+**Path Prefix:** `/api/v1`
+**Authentication:** API Key (`@token_required`)
+**Primary Use:** External system integration, SDK usage.
+
+| Interface Type | Python File Path | Class/Function Name | URL Pattern | Notes |
+|---|---|---|---|---|
+| **External** | `api/apps/sdk/session.py` | `agent_bot_completions` | `/api/v1/agentbots/<agent_id>/completions` | Agent Bot completion |
+| **External** | `api/apps/sdk/session.py` | `begin_inputs` | `/api/v1/agentbots/<agent_id>/inputs` | Get Agent Bot inputs |
+| **External** | `api/apps/sdk/agents.py` | `list_agents` | `/api/v1/agents` | List Agents |
+| **External** | `api/apps/sdk/agents.py` | `create_agent` | `/api/v1/agents` | Create Agent |
+| **External** | `api/apps/sdk/agents.py` | `update_agent` | `/api/v1/agents/<agent_id>` | Update Agent |
+| **External** | `api/apps/sdk/agents.py` | `delete_agent` | `/api/v1/agents/<agent_id>` | Delete Agent |
+| **External** | `api/apps/sdk/session.py` | `agent_completions` | `/api/v1/agents/<agent_id>/completions` | Agent completion |
+| **External** | `api/apps/sdk/session.py` | `create_agent_session` | `/api/v1/agents/<agent_id>/sessions` | Create Agent Session |
+| **External** | `api/apps/sdk/session.py` | `list_agent_session` | `/api/v1/agents/<agent_id>/sessions` | List Agent Sessions |
+| **External** | `api/apps/sdk/session.py` | `delete_agent_session` | `/api/v1/agents/<agent_id>/sessions` | Delete Agent Session |
+| **External** | `api/apps/sdk/session.py` | `agents_completion_openai_compatibility` | `/api/v1/agents_openai/<agent_id>/chat/completions` | OpenAI compatible Agent completion |
+| **External** | `api/apps/sdk/session.py` | `chatbot_completions` | `/api/v1/chatbots/<dialog_id>/completions` | Chatbot completion |
+| **External** | `api/apps/sdk/session.py` | `chatbots_inputs` | `/api/v1/chatbots/<dialog_id>/info` | Chatbot info |
+| **External** | `api/apps/sdk/chat.py` | `create` | `/api/v1/chats` | Create Chat |
+| **External** | `api/apps/sdk/chat.py` | `delete_chats` | `/api/v1/chats` | Delete Chat |
+| **External** | `api/apps/sdk/chat.py` | `list_chat` | `/api/v1/chats` | List Chats |
+| **External** | `api/apps/sdk/chat.py` | `update` | `/api/v1/chats/<chat_id>` | Update Chat |
+| **External** | `api/apps/sdk/session.py` | `chat_completion` | `/api/v1/chats/<chat_id>/completions` | Chat completion |
+| **External** | `api/apps/sdk/session.py` | `create` | `/api/v1/chats/<chat_id>/sessions` | Create Chat Session |
+| **External** | `api/apps/sdk/session.py` | `list_session` | `/api/v1/chats/<chat_id>/sessions` | List Chat Sessions |
+| **External** | `api/apps/sdk/session.py` | `delete` | `/api/v1/chats/<chat_id>/sessions` | Delete Chat Session |
+| **External** | `api/apps/sdk/session.py` | `update` | `/api/v1/chats/<chat_id>/sessions/<session_id>` | Update Chat Session |
+| **External** | `api/apps/sdk/session.py` | `chat_completion_openai_like` | `/api/v1/chats_openai/<chat_id>/chat/completions` | OpenAI compatible Chat completion |
+| **External** | `api/apps/sdk/dataset.py` | `create` | `/api/v1/datasets` | Create Dataset |
+| **External** | `api/apps/sdk/dataset.py` | `delete` | `/api/v1/datasets` | Delete Dataset |
+| **External** | `api/apps/sdk/dataset.py` | `list_datasets` | `/api/v1/datasets` | List Datasets |
+| **External** | `api/apps/sdk/dataset.py` | `update` | `/api/v1/datasets/<dataset_id>` | Update Dataset |
+| **External** | `api/apps/sdk/doc.py` | `parse` | `/api/v1/datasets/<dataset_id>/chunks` | Parse Document Chunks |
+| **External** | `api/apps/sdk/doc.py` | `stop_parsing` | `/api/v1/datasets/<dataset_id>/chunks` | Stop Parsing |
+| **External** | `api/apps/sdk/doc.py` | `upload` | `/api/v1/datasets/<dataset_id>/documents` | Upload Document |
+| **External** | `api/apps/sdk/doc.py` | `list_docs` | `/api/v1/datasets/<dataset_id>/documents` | List Documents |
+| **External** | `api/apps/sdk/doc.py` | `delete` | `/api/v1/datasets/<dataset_id>/documents` | Delete Document |
+| **External** | `api/apps/sdk/doc.py` | `update_doc` | `/api/v1/datasets/<dataset_id>/documents/<document_id>` | Update Document |
+| **External** | `api/apps/sdk/doc.py` | `download` | `/api/v1/datasets/<dataset_id>/documents/<document_id>` | Download Document |
+| **External** | `api/apps/sdk/doc.py` | `list_chunks` | `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks` | List Chunks |
+| **External** | `api/apps/sdk/doc.py` | `add_chunk` | `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks` | Add Chunk |
+| **External** | `api/apps/sdk/doc.py` | `update_chunk` | `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks/<chunk_id>` | Update Chunk |
+| **External** | `api/apps/sdk/dataset.py` | `knowledge_graph` | `/api/v1/datasets/<dataset_id>/knowledge_graph` | Knowledge Graph |
+| **External** | `api/apps/sdk/dataset.py` | `delete_knowledge_graph` | `/api/v1/datasets/<dataset_id>/knowledge_graph` | Delete Knowledge Graph |
+| **External** | `api/apps/sdk/doc.py` | `metadata_summary` | `/api/v1/datasets/<dataset_id>/metadata/summary` | Metadata Summary |
+| **External** | `api/apps/sdk/doc.py` | `metadata_batch_update` | `/api/v1/datasets/<dataset_id>/metadata/update` | Batch Update Metadata |
+| **External** | `api/apps/sdk/dataset.py` | `run_graphrag` | `/api/v1/datasets/<dataset_id>/run_graphrag` | Run GraphRAG |
+| **External** | `api/apps/sdk/dataset.py` | `run_raptor` | `/api/v1/datasets/<dataset_id>/run_raptor` | Run Raptor |
+| **External** | `api/apps/sdk/dataset.py` | `trace_graphrag` | `/api/v1/datasets/<dataset_id>/trace_graphrag` | Trace GraphRAG |
+| **External** | `api/apps/sdk/dataset.py` | `trace_raptor` | `/api/v1/datasets/<dataset_id>/trace_raptor` | Trace Raptor |
+| **External** | `api/apps/sdk/dify_retrieval.py` | `retrieval` | `/api/v1/dify/retrieval` | Dify Retrieval |
+| **External** | `api/apps/sdk/files.py` | `get_all_parent_folders` | `/api/v1/file/all_parent_folder` | Get All Parent Folders |
+| **External** | `api/apps/sdk/files.py` | `convert` | `/api/v1/file/convert` | File Convert |
+| **External** | `api/apps/sdk/files.py` | `create` | `/api/v1/file/create` | File Create |
+| **External** | `api/apps/sdk/files.py` | `download_attachment` | `/api/v1/file/download/<attachment_id>` | Download Attachment |
+| **External** | `api/apps/sdk/files.py` | `get` | `/api/v1/file/get/<file_id>` | Get File |
+| **External** | `api/apps/sdk/files.py` | `list_files` | `/api/v1/file/list` | List Files |
+| **External** | `api/apps/sdk/files.py` | `move` | `/api/v1/file/mv` | Move File |
+| **External** | `api/apps/sdk/files.py` | `get_parent_folder` | `/api/v1/file/parent_folder` | Get Parent Folder |
+| **External** | `api/apps/sdk/files.py` | `rename` | `/api/v1/file/rename` | Rename File |
+| **External** | `api/apps/sdk/files.py` | `rm` | `/api/v1/file/rm` | Remove File |
+| **External** | `api/apps/sdk/files.py` | `get_root_folder` | `/api/v1/file/root_folder` | Get Root Folder |
+| **External** | `api/apps/sdk/files.py` | `upload` | `/api/v1/file/upload` | Upload File |
+| **External** | `api/apps/sdk/doc.py` | `retrieval_test` | `/api/v1/retrieval` | Retrieval Test |
+| **External** | `api/apps/sdk/session.py` | `ask_about_embedded` | `/api/v1/searchbots/ask` | Searchbot Ask |
+| **External** | `api/apps/sdk/session.py` | `detail_share_embedded` | `/api/v1/searchbots/detail` | Searchbot Detail |
+| **External** | `api/apps/sdk/session.py` | `mindmap` | `/api/v1/searchbots/mindmap` | Searchbot Mindmap |
+| **External** | `api/apps/sdk/session.py` | `related_questions_embedded` | `/api/v1/searchbots/related_questions` | Searchbot Related Questions |
+| **External** | `api/apps/sdk/session.py` | `retrieval_test_embedded` | `/api/v1/searchbots/retrieval_test` | Searchbot Retrieval Test |
+| **External** | `api/apps/sdk/session.py` | `ask_about` | `/api/v1/sessions/ask` | Session Ask |
+| **External** | `api/apps/sdk/session.py` | `related_questions` | `/api/v1/sessions/related_questions` | Session Related Questions |
+| **External** | `api/apps/sdk/agents.py` | `webhook` | `/api/v1/webhook_test/<agent_id>` | Webhook Test |
+| **External** | `api/apps/sdk/agents.py` | `webhook_trace` | `/api/v1/webhook_trace/<agent_id>` | Webhook Trace |
+| **External** | `api/apps/sdk/doc.py` | `rm_chunk` | `/api/v1datasets/<dataset_id>/documents/<document_id>/chunks` | Remove Chunk |
+
+
+## 2. Internal APIs (内部前端体系)
+**Path Prefix:** `/v1/<app_name>` matches file `api/apps/<app_name>_app.py`
+**Authentication:** Session/Cookie (`@login_required`)
+**Primary Use:** RAGFlow Web Frontend.
+
+**Selected Core Interfaces:**
+
+| Interface Type | Python File Path | Class/Function Name | URL Pattern | Notes |
+|---|---|---|---|---|
+| Internal | `api/apps/user_app.py` | `login` | `/v1/user/login` | User Login (Frontend) |
+| Internal | `api/apps/user_app.py` | `log_out` | `/v1/user/logout` | User Logout |
+| Internal | `api/apps/user_app.py` | `user_add` | `/v1/user/register` | User Registration |
+| Internal | `api/apps/user_app.py` | `user_profile` | `/v1/user/info` | User Profile Info |
+| Internal | `api/apps/api_app.py` | `new_token` | `/v1/api/new_token` | Generate new API Token |
+| Internal | `api/apps/conversation_app.py` | `set_conversation` | `/v1/conversation/set` | Create/Update Conversation |
+| Internal | `api/apps/conversation_app.py` | `completion` | `/v1/conversation/completion` | Chat Conversation Completion |
+| Internal | `api/apps/kb_app.py` | `list_kbs` | `/v1/kb/list` | List Knowledge Bases |
+| Internal | `api/apps/kb_app.py` | `create` | `/v1/kb/create` | Create Knowledge Base |
+| Internal | `api/apps/document_app.py` | `upload` | `/v1/document/upload` | Upload Document to KB |
+| Internal | `api/apps/document_app.py` | `parse` | `/v1/document/parse` | Parse Document |
+
+*(For a complete list of all 200+ internal APIs, please refer to the `api_endpoints.txt` file or the full scan results)*

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_Agent_Dify接口详解.md

@@ -0,0 +1,279 @@
+# RAGFlow Agent 与 Dify 兼容接口详解 (Agent & Dify Compatibility)
+
+## 1. Dify 兼容检索 - `retrieval`
+**接口描述**: 模拟 Dify API 格式的知识库检索接口。此接口主要用于让现有的 Dify 客户端或系统能够方便地接入 RAGFlow 的知识库检索能力。它支持文本检索、混合检索以及通过元数据过滤文档。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/dify/retrieval`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| knowledge_id | string | 是 | - | **知识库 ID**。 |
+| query | string | 是 | - | **查询文本**。用户输入的检索问题。 |
+| use_kg | boolean | 否 | false | **使用知识图谱**。是否结合知识图谱进行检索。 |
+| retrieval_setting | object | 否 | {} | **检索配置**。包含相似度阈值和 Top-K。 |
+| metadata_condition | object | 否 | {} | **元数据过滤条件**。用于筛选特定文档。 |
+
+#### 参数详情 (Detail Objects)
+**retrieval_setting**:
+```json
+{
+  "score_threshold": 0.5, // 相似度阈值 (default: 0.0)
+  "top_k": 5              // 返回数量 (default: 1024)
+}
+```
+
+**metadata_condition**:
+```json
+{
+  "logic": "and", // 逻辑关系 (and/or)
+  "conditions": [
+    {
+      "name": "author",           // 字段名
+      "comparison_operator": "eq",// 运算符 (eq, ne, gt, lt 等)
+      "value": "Alice"            // 字段值
+    }
+  ]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "records": [
+      {
+        "content": "RAGFlow 是一个基于深度文档理解的检索增强生成引擎...",
+        "score": 0.92,
+        "title": "RAGFlow_Introduction.pdf",
+        "metadata": {
+          "doc_id": "doc_uuid_123",
+          "author": "Alice",
+          "publish_year": "2024"
+        }
+      },
+      {
+        "content": "DeepDOC 模型能够精准识别复杂的表格结构...",
+        "score": 0.88,
+        "title": "DeepDOC_Tech_Report.pdf",
+        "metadata": {
+          "doc_id": "doc_uuid_456",
+          "author": "Bob"
+        }
+      }
+    ]
+  }
+}
+```
+
+---
+
+## 2. 创建 Agent 会话 - `create_agent_session`
+**接口描述**: 创建一个新的 Agent 会话 (Session)。会话是用户与 Agent 交互的上下文容器，保存了历史对话记录和 DSL（领域特定语言）状态。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/agents/<agent_id>/sessions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | **Agent ID**。 |
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| user_id | string | 否 | **用户标识**。用于区分不同终端用户的会话。若不传，默认为当前 Tenant ID。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "session_uuid_new_123",
+    "agent_id": "agent_uuid_abc",
+    "user_id": "user_123",
+    "source": "agent",
+    "dsl": { ... }, // 完整的 Agent DSL 定义
+    "messages": [
+      {
+        "role": "assistant",
+        "content": "你好！我是你的智能助手，有什么可以帮你的吗？" // Prologue (开场白)
+      }
+    ]
+  }
+}
+```
+
+---
+
+## 3. 获取 Agent 会话列表 - `list_agent_session`
+**接口描述**: 分页获取指定 Agent 下的会话列表。支持按 ID 或 User ID 过滤。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/agents/<agent_id>/sessions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | **Agent ID**。 |
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| page | int | 否 | 1 | **页码**。 |
+| page_size | int | 否 | 30 | **每页数量**。 |
+| orderby | string | 否 | "update_time" | **排序字段**。 |
+| desc | boolean | 否 | true | **是否降序**。 |
+| id | string | 否 | - | **会话 ID**。精确筛选。 |
+| user_id | string | 否 | - | **用户标识**。筛选特定用户的会话。 |
+| dsl | boolean | 否 | true | **包含 DSL**。是否在返回结果中包含完整的 DSL 结构 (数据量较大)。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    {
+      "id": "session_uuid_123",
+      "agent_id": "agent_uuid_abc",
+      "user_id": "user_123",
+      "create_time": 1715000000000,
+      "update_time": 1715000050000,
+      "source": "agent",
+      "messages": [
+        {
+          "role": "assistant",
+          "content": "Hi there!"
+        },
+        {
+          "role": "user",
+          "content": "What is RAG?"
+        }
+      ]
+    }
+  ]
+}
+```
+
+---
+
+## 4. 删除 Agent 会话 - `delete_agent_session`
+**接口描述**: 批量删除 Agent 会话。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/agents/<agent_id>/sessions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | **Agent ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| ids | array<string> | 否 | **会话 ID 列表**。若不传该参数，将尝试删除(或清空)该 Agent 下的所有会话（需谨慎）。 |
+
+**Request Example**:
+```json
+{
+  "ids": ["session_id_1", "session_id_2"]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "success_count": 2,
+    "errors": []
+  }
+}
+```
+
+---
+
+## 5. Agent 对话 (流式) - `agent_completions`
+**接口描述**: 向 Agent 发送用户问题并获取回复。这是 Agent 交互的核心接口，支持 **Server-Sent Events (SSE)** 流式响应。Agent 会根据编排好的 DSL 流程执行（可能涉及多个节点、知识库检索、LLM 推理等），并实时推送执行过程和最终结果。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/agents/<agent_id>/completions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | **Agent ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| session_id | string | 是 | - | **会话 ID**。必须是 `create_agent_session` 返回的 ID。 |
+| question | string | 是 | - | **用户问题**。 |
+| stream | boolean | 否 | true | **是否流式响应**。强烈建议设为 `true` 以获得更好的用户体验。 |
+| return_trace | boolean | 否 | false | **返回执行轨迹**。如果为 `true`，流式响应中将包含各个节点的执行过程数据 (Trace)。 |
+
+### 响应参数 (Stream Response)
+**Content-Type**: `text/event-stream`
+
+响应是一个 SSE 流，每一行以 `data:` 开头，包含一个 JSON 对象。
+
+**Event Types**:
+- `message`: 普通文本消息片段。
+- `node_finished`: (当 `return_trace=true` 时) 节点执行完成事件，包含节点输出数据。
+- `message_end`: 消息结束。
+- `[DONE]`: 流结束标志。
+
+#### Stream Chunk Examples:
+
+**1. 文本生成片段 (message)**:
+```text
+data:{"code": 0, "message": "success", "data": {"content": "Hello", "reference": {}, "id": "msg_uuid_1"}, "event": "message"}
+
+data:{"code": 0, "message": "success", "data": {"content": " world", "reference": {}, "id": "msg_uuid_1"}, "event": "message"}
+```
+
+**2. 节点执行轨迹 (node_finished, return_trace=true)**:
+```text
+data:{"code": 0, "message": "success", "data": {"component_id": "retrieval_node_1", "content": "...", "trace": [...]}, "event": "node_finished"}
+```
+
+**3. 最终结束 (DONE)**:
+```text
+data:[DONE]
+```
+
+#### Non-Stream Response (stream=false)
+如果不使用流式响应，将等待 Agent 全流程执行完毕后一次性返回 JSON。
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "content": "Hello world! This is the final answer.",
+    "reference": {
+        "chunk_id_1": { ... } // 引用来源
+    },
+    "trace": [ ... ] // 如果 return_trace=true
+  }
+}
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_Agent接口详解.md

@@ -0,0 +1,233 @@
+## 1. 获取 Agent 列表 - `list_agents`
+**接口描述**: 分页查询当前租户下的所有 Agent 列表，支持按 ID 或标题筛选。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/agents`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| page | int | 否 | 1 | 页码 |
+| page_size | int | 否 | 30 | 每页条数 |
+| orderby | string | 否 | update_time | 排序字段 (create_time, update_time, title) |
+| desc | boolean | 否 | True | 是否降序排列 (True: 降序, False: 升序) |
+| id | string | 否 | - | 按 Agent ID 精确筛选 |
+| title | string | 否 | - | 按 Agent 标题精确筛选 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    {
+      "id": "e0d34e2c-...",
+      "title": "My Assistant",
+      "description": "A helpful AI assistant",
+      "dsl": { ... }, // Agent 的 DSL 流程定义
+      "user_id": "tenant_123",
+      "avatar": "", // 头像 Base64 或 URL
+      "canvas_category": "Agent",
+      "create_time": 1715623400000,
+      "update_time": 1715624500000
+    }
+  ]
+}
+```
+
+---
+
+## 2. 创建 Agent - `create_agent`
+**接口描述**: 创建一个新的 Agent，必须包含标题和 DSL 定义。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/agents`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| title | string | 是 | - | Agent 的名称 (必须唯一) |
+| dsl | object | 是 | - | Agent 的流程定义 (节点、连线配置) |
+| description | string | 否 | - | Agent 的功能描述 |
+| avatar | string | 否 | - | Agent 头像 (Base64 字符串或 URL) |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": true
+}
+```
+
+---
+
+## 3. 更新 Agent - `update_agent`
+**接口描述**: 更新指定 Agent 的配置信息，支持增量更新（仅传递需要修改的字段）。
+**请求方法**: `PUT`
+**接口地址**: `/api/v1/agents/<agent_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | 要更新的 Agent ID |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| title | string | 否 | - | 新的 Agent 名称 |
+| dsl | object | 否 | - | 新的 DSL 流程定义 |
+| description | string | 否 | - | 新的功能描述 |
+| avatar | string | 否 | - | 新的头像 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": true
+}
+```
+
+---
+
+## 4. 删除 Agent - `delete_agent`
+**接口描述**: 根据 ID 删除指定的 Agent。此操作不可恢复。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/agents/<agent_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | 要删除的 Agent ID |
+
+#### Body Parameters (JSON)
+无
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": true
+}
+```
+
+---
+
+## 5. Webhook 测试触发 - `webhook`
+**接口描述**: 用于测试 Agent 的 Webhook 触发功能。该接口模拟外部系统调用，触发 Agent 按照配置的 "Begin" 节点逻辑开始执行。支持同步等待结果或流式返回（取决于 Agent 配置）。
+**请求方法**: `POST` (支持 GET/PUT/DELETE 等，取决于 Canvas 配置)
+**接口地址**: `/api/v1/webhook_test/<agent_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | Agent 的唯一标识符 |
+
+#### Query / Headers / Body Parameters
+**说明**: 此接口的参数完全动态，取决于 Agent 画布中 **"Begin" (开始)** 节点的 **Webhook** 配置。
+- 如果配置了 Query 参数验证，则需在 URL 中传递对应参数。
+- 如果配置了 Header 验证，则需传递对应 Header。
+- **Body**: 通常为 JSON 格式，包含 Agent 运行所需的变量（inputs）或上下文数据。
+
+**Body Example (JSON)**:
+```json
+{
+  "inputs": {
+    "topic": "AI Trends",
+    "style": "professional"
+  },
+  "query": "Start generation"
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json` (或 `text/event-stream`)
+
+**即时响应模式 (Immediately)**:
+```json
+{
+  "code": 0,
+  "data": {
+    "content": "生成的回答内容...",
+    "usage": { ... }
+  }
+}
+```
+
+**流式响应模式 (SSE)**:
+如果不使用 `webhook_test` 而是生产环境 `webhook` 且配置为 SSE，则返回流式数据。但在 `webhook_test` 接口中，通常配合 `webhook_trace` 进行异步调试。
+
+---
+
+## 6. Webhook 执行轨迹查询 - `webhook_trace`
+**接口描述**: 轮询查询 Agent 在 Webhook 测试触发后的执行日志和中间状态。采用长轮询或游标机制，实时获取执行进度。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/webhook_trace/<agent_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | Agent 的唯一标识符 |
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| since_ts | float | 否 | 当前时间 | 起始时间戳。返回此时间之后的日志事件。首次调用可不传（获取当前时间作为游标）。 |
+| webhook_id | string | 否 | - | Webhook 会话 ID。用于锁定特定的某次执行记录。首次轮询时不传，接口会返回新生成的 ID。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "webhook_id": "YWdlbnxxxx...",  // 当前追踪的会话 ID (加密串)
+    "finished": false,              // 执行是否已结束 (true/false)
+    "next_since_ts": 1715629999.5,  // 下一次轮询应使用的 since_ts
+    "events": [                     // 本次轮询获取到的新事件列表
+      {
+        "ts": 1715629998.1,
+        "event": "message",         // 事件类型: message, start_to_think, finished, error 等
+        "data": {
+            "content": "思考中...",
+            "reference": []
+        }
+      }
+    ]
+  }
+}
+```
+
+### 💡 最佳实践 (调试流程)
+1.  **初始化**: 调用 `GET /webhook_trace/<id>` (不带参数)，获取 `next_since_ts` (记为 `T0`)。
+2.  **触发**: 调用 `POST /webhook_test/<id>` 发送测试数据。
+3.  **首帧捕获**: 循环调用 `GET /webhook_trace/<id>?since_ts=T0`，直到返回 `webhook_id` (记为 `WID`) 和第一批 `events`。
+4.  **持续追踪**: 使用 `WID` 和响应中的 `next_since_ts` 持续轮询，直到 `data.finished == true`。

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_Chat_Completion接口详解.md

@@ -0,0 +1,164 @@
+# RAGFlow 对话交互接口详解 (Chat Completion & OpenAI Compatibility)
+
+## 5. 对话助手对话 (流式) - `chat_completion`
+**接口描述**: 发送问题给对话助手 (Assistant/Chat) 并获取回复。这是 RAGFlow 最核心的原生对话接口，支持 **Server-Sent Events (SSE)** 流式响应。它会根据助手绑定的知识库进行 RAG 检索生成。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/chats/<chat_id>/completions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| chat_id | string | 是 | **助手 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| session_id | string | 是 | - | **会话 ID**。从 `create_chat_session` 获取。 |
+| question | string | 是 | - | **用户问题**。 |
+| stream | boolean | 否 | true | **是否流式响应**。 |
+| quote | boolean | 否 | false | **返回引用**。是否在响应中包含检索到的引用片段。 |
+| doc_ids | string | 否 | - | **限定文档 ID**。多个 ID 用逗号分隔，仅检索指定文档。 |
+| metadata_condition | object | 否 | {} | **元数据过滤**。用于限定检索范围。 |
+
+### 响应参数 (Stream Response)
+**Content-Type**: `text/event-stream`
+
+每一行数据以 `data:` 开头，包含一个 JSON 对象。
+
+**Event Example**:
+```text
+data:{"code": 0, "message": "success", "data": {"answer": "Hello", "reference": {}}}
+
+data:{"code": 0, "message": "success", "data": {"answer": " world!", "reference": {}}}
+
+data:{"code": 0, "message": "success", "data": {"answer": "", "reference": {"chunk_1": {...}}}} // 引用数据
+```
+
+### 响应参数 (Non-Stream Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "answer": "Hello world! This is the generated response.",
+    "reference": {
+      "chunk_id_1": {
+        "content_with_weight": "Original text...",
+        "doc_name": "manual.pdf"
+      }
+    }
+  }
+}
+```
+
+---
+
+## 6. OpenAI 兼容对话 - `chat_completion_openai_like`
+**接口描述**: 提供与 **OpenAI API (`/v1/chat/completions`)** 完全兼容的接口。允许开发者使用 LangChain、OpenAI Python SDK 或其他支持 OpenAI 协议的工具直接调用 RAGFlow，实现无缝迁移。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/chats_openai/<chat_id>/chat/completions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| chat_id | string | 是 | **助手 ID**。在此上下文中充当 "Base URL" 的一部分。 |
+
+#### Body Parameters (JSON - OpenAI Standard)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| messages | array | 是 | **消息列表**。包含 `role` (system/user/assistant) 和 `content`。 |
+| model | string | 是 | **模型名称**。可以是任意非空字符串 (RAGFlow 会使用助手预设的模型)。 |
+| stream | boolean | 否 | **是否流式**。默认为 `true`。 |
+
+**Request Example**:
+```json
+{
+  "model": "ragflow_default",
+  "messages": [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Explain quantum physics."}
+  ],
+  "stream": true
+}
+```
+
+### 响应参数 (Stream Response - OpenAI Format)
+**Content-Type**: `text/event-stream`
+
+严格遵循 OpenAI Chunk 格式：
+
+```text
+data: {"id": "chatcmpl-123", "object": "chat.completion.chunk", "created": 1715000000, "model": "model", "choices": [{"index": 0, "delta": {"role": "assistant", "content": ""}, "finish_reason": null}]}
+
+data: {"id": "chatcmpl-123", "object": "chat.completion.chunk", "created": 1715000001, "model": "model", "choices": [{"index": 0, "delta": {"content": "Quantum"}, "finish_reason": null}]}
+
+data: {"id": "chatcmpl-123", "object": "chat.completion.chunk", "created": 1715000002, "model": "model", "choices": [{"index": 0, "delta": {"content": " physics"}, "finish_reason": null}]}
+
+data: [DONE]
+```
+
+---
+
+## 7. 嵌入式 Chatbot 对话 - `chatbot_completions`
+**接口描述**: 专为 **嵌入式窗口 (Embed Window)** 设计的公开对话接口。它通常用于将 RAGFlow 助手作为客服窗口嵌入到第三方网站。与普通接口不同，它通过 `Authorization` Header 传递 **Beta Token** (即 API Key) 进行鉴权，且通常面向最终用户。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/chatbots/<dialog_id>/completions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dialog_id | string | 是 | **助手 ID** (Dialog ID)。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| question | string | 是 | - | **用户问题**。 |
+| stream | boolean | 否 | true | **是否流式**。 |
+| session_id | string | 否 | - | **会话 ID**。用于维持上下文。 |
+| quote | boolean | 否 | false | **返回引用**。 |
+
+### 响应参数 (Stream Response)
+**Content-Type**: `text/event-stream`
+
+与 `chat_completion` 类似，返回 RAGFlow 原生 SSE 格式。
+
+```text
+data:{"code": 0, "message": "success", "data": {"answer": "Here is the answer...", "reference": {}}}
+```
+
+---
+
+## 8. Chatbot 初始化信息 - `chatbots_inputs`
+**接口描述**: 获取嵌入式 Chatbot 的初始化配置信息。通常在前端组件加载时调用，用于展示助手的头像、名称、开场白 (Prologue) 等信息。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/chatbots/<dialog_id>/info`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dialog_id | string | 是 | **助手 ID**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "title": "IT Support Bot",          // 助手名称
+    "avatar": "http://...",             // 头像 URL
+    "prologue": "Hi! How can I help?"   // 开场白
+  }
+}
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_Chat_Session接口详解.md

@@ -0,0 +1,208 @@
+# RAGFlow 聊天助手会话管理接口详解 (Chat Assistant Session Management)
+
+## 1. 创建会话 - `create_chat_session`
+**接口描述**: 为指定的聊天助手 (Chat/Assistant) 创建一个新的会话。系统会自动加载该助手的开场白 (Prologue) 作为第一条消息。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/chats/<chat_id>/sessions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| chat_id | string | 是 | **助手 ID** (Assistant/Dialog ID)。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| name | string | 否 | "New session" | **会话名称**。 |
+| user_id | string | 否 | - | **用户标识**。用于区分不同终端用户的会话。 |
+
+**Request Example**:
+```json
+{
+  "name": "Consulting regarding RAG",
+  "user_id": "client_001"
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "session_uuid_123",
+    "chat_id": "chat_uuid_abc",
+    "name": "Consulting regarding RAG",
+    "user_id": "client_001",
+    "create_time": 1715000000000,
+    "create_date": "2024-05-01 10:00:00",
+    "update_time": 1715000000000,
+    "update_date": "2024-05-01 10:00:00",
+    "messages": [
+      {
+        "role": "assistant",
+        "content": "Hi! I am your AI assistant. How can I help you today?" // 自动加载的开场白
+      }
+    ]
+  }
+}
+```
+
+---
+
+## 2. 获取会话列表 - `list_chat_session`
+**接口描述**: 分页获取指定助手下的会话列表。支持按名称或用户 ID 过滤。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/chats/<chat_id>/sessions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| chat_id | string | 是 | **助手 ID**。 |
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| page | int | 否 | 1 | **页码**。 |
+| page_size | int | 否 | 30 | **每页数量**。 |
+| orderby | string | 否 | "create_time" | **排序字段**。 |
+| desc | boolean | 否 | true | **是否降序**。 |
+| name | string | 否 | - | **会话名称搜索**。 |
+| id | string | 否 | - | **会话 ID 精确筛选**。 |
+| user_id | string | 否 | - | **用户标识筛选**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    {
+      "id": "session_uuid_123",
+      "chat_id": "chat_uuid_abc",
+      "name": "Consulting regarding RAG",
+      "user_id": "client_001",
+      "create_time": 1715000000000,
+      "create_date": "2024-05-01 10:00:00",
+      "update_time": 1715000050000,
+      "update_date": "2024-05-01 10:00:50",
+      "messages": [
+        {
+          "role": "assistant",
+          "content": "Hi! I am your AI assistant. How can I help you today?"
+        },
+        {
+          "role": "user",
+          "content": "What is RAGFlow?"
+        }
+      ]
+    },
+    {
+      "id": "session_uuid_456",
+      "chat_id": "chat_uuid_abc",
+      "name": "New session",
+      "user_id": "client_002",
+      "create_time": 1714900000000,
+      "create_date": "2024-04-30 09:00:00",
+      "update_time": 1714900000000,
+      "update_date": "2024-04-30 09:00:00",
+      "messages": [ ... ]
+    }
+  ]
+}
+```
+
+---
+
+## 3. 更新会话 - `update_chat_session`
+**接口描述**: 更新会话信息。目前主要用于 **重命名** 会话。注意：不能通过此接口修改消息记录 (`messages`)。
+**请求方法**: `PUT`
+**接口地址**: `/api/v1/chats/<chat_id>/sessions/<session_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| chat_id | string | 是 | **助手 ID**。 |
+| session_id | string | 是 | **会话 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| name | string | 否 | **新的会话名称**。不可为空字符串。 |
+
+**Request Example**:
+```json
+{
+  "name": "RAG Technical Discussion"
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": null
+}
+```
+
+---
+
+## 4. 删除会话 - `delete_chat_session`
+**接口描述**: 批量删除指定助手下的会话。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/chats/<chat_id>/sessions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| chat_id | string | 是 | **助手 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| ids | array<string> | 否 | **待删除的会话 ID 列表**。若不传该参数，将尝试删除该助手下的**所有会话**（请极其谨慎使用）。 |
+
+**Request Example**:
+```json
+{
+  "ids": ["session_uuid_123", "session_uuid_456"]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": null // 若全部删除成功
+}
+```
+
+**Response (部分成功时)**:
+```json
+{
+  "code": 0,
+  "message": "Partially deleted 1 sessions with 1 errors",
+  "data": {
+    "success_count": 1,
+    "errors": ["The chat doesn't own the session session_uuid_999"]
+  }
+}
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_Chat接口详解.md

@@ -0,0 +1,213 @@
+## 1. 创建助手应用 - `create`
+**接口描述**: 创建一个新的对话助手（Chat Assistant）。支持配置关联知识库、LLM 模型参数、提示词（Prompt）以及开场白等高级设置。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/chats`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| name | string | 是 | - | 助手应用名称 (租户内唯一) |
+| avatar | string | 否 | - | 助手头像 (URL 或 Base64 字符串) |
+| description | string | 否 | "A helpful Assistant" | 助手的功能描述 |
+| dataset_ids | array | 否 | [] | 关联的知识库 ID 列表 (必须是当前租户有权限访问的知识库) |
+| llm | object | 否 | - | LLM 模型生成配置 (如模型名称、温度等) |
+| prompt | object | 否 | - | 提示词引擎与检索配置 (包含 System Prompt, Opener, Rerank 等) |
+
+**`llm` 对象详细结构**:
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| model_name | string | 是 | - | 模型名称 (例如: `deepseek-chat`, `gpt-4`, `qwen-turbo`) |
+| temperature | float | 否 | 0.1 | 温度系数 (0.0 ~ 1.0)，越高越随机，越低越确定 |
+| top_p | float | 否 | 0.3 | 核采样概率阈值 |
+| max_tokens | int | 否 | 512 | 单次回答的最大 Token 数限制 |
+| presence_penalty | float | 否 | 0.4 | 话题新鲜度惩罚 (-2.0 ~ 2.0)，正值鼓励讨论新话题 |
+| frequency_penalty | float | 否 | 0.7 | 频率惩罚 (-2.0 ~ 2.0)，正值减少重复词汇 |
+
+**`prompt` 对象详细结构**:
+*注意：此对象包含“提示词配置”与“检索策略配置”两部分。*
+
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| prompt | string | 否 | (内置默认提示词) | **System Prompt (系统提示词)**。给大模型的角色指令，例如 "你是一个客服..."。可使用变量占位符 `{knowledge}`。 |
+| opener | string | 否 | "Hi! I'm your assistant..." | **开场白**。用户进入对话窗口时，助手自动发送的第一条欢迎语。 |
+| show_quote | boolean | 否 | true | **显示引用**。回答中是否标注来源文档 (e.g., [1])。 |
+| variables | array | 否 | `[{"key": "knowledge", "optional": false}]` | **变量列表**。定义用于填充 System Prompt 的变量。`knowledge` 为保留变量，代表检索到的知识片段。 |
+| rerank_model | string | 否 | - | **重排序模型 ID**。配置后会对检索结果进行二次精排 (如 `BAAI/bge-reranker-v2-m3`)。 |
+| keywords_similarity_weight | float | 否 | 0.7 | **关键字权重** (0.0 ~ 1.0)。控制混合检索的比例。更接近 1.0 侧重关键字匹配，更接近 0.0 侧重向量语义匹配。 |
+| similarity_threshold | float | 否 | 0.2 | **相似度阈值** (0.0 ~ 1.0)。低于此相似度的文档块将被过滤，不喂给大模型。 |
+| top_n | int | 否 | 6 | **Top N**。最终截取并输入给大模型的文档块数量。 |
+| empty_response | string | 否 | "Sorry! No relevant..." | **空结果回复**。当没有检索到相关知识库内容时的兜底回复。 |
+| tts | boolean | 否 | false | **启用 TTS**。是否将助手的文本回答自动转为语音播放。 |
+| refine_multiturn | boolean | 否 | true | **多轮对话优化**。是否根据历史上下文重写用户问题 (Query Rewrite) 以提高检索准确率。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "e0d34e2c-1234-5678-9xxx-xxxxxxxxxxxx",
+    "name": "企业知识库助手",
+    "avatar": "http://example.com/avatar.png",
+    "description": "用于回答员工内部问题的 AI",
+    "dataset_ids": ["kb_123", "kb_456"],
+    "llm": {
+      "model_name": "deepseek-chat",
+      "temperature": 0.1,
+      "top_p": 0.3,
+      "max_tokens": 512,
+      "presence_penalty": 0.4,
+      "frequency_penalty": 0.7
+    },
+    "prompt": {
+      "prompt": "你是一个智能助手，请根据以下知识回答问题：\n{knowledge}",
+      "opener": "你好！有什么可以帮你的？",
+      "show_quote": true,
+      "variables": [
+        { "key": "knowledge", "optional": false }
+      ],
+      "rerank_model": "",
+      "keywords_similarity_weight": 0.7,
+      "similarity_threshold": 0.2,
+      "top_n": 8,
+      "empty_response": "抱歉，知识库中没有找到相关答案。",
+      "tts": false,
+      "refine_multiturn": true
+    },
+    "create_time": 1715623400000,
+    "update_time": 1715624500000
+  }
+}
+```
+
+---
+
+## 2. 获取助手列表 - `list_chat`
+**接口描述**: 获取当前租户下的所有助手应用列表。支持分页、排序及按名称/ID筛选。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/chats`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| page | int | 否 | 1 | 页码 |
+| page_size | int | 否 | 30 | 每页条数 |
+| orderby | string | 否 | create_time | 排序字段 (`create_time`, `update_time`) |
+| desc | boolean | 否 | true | 是否降序排列 (`true`: 降序, `false`: 升序) |
+| name | string | 否 | - | 按名称模糊搜索 (支持 partial match) |
+| id | string | 否 | - | 按 ID 精确筛选 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    {
+      "id": "e0d34e2c-...",
+      "name": "客服机器人",
+      "avatar": "http://...",
+      "datasets": [
+        {
+          "id": "kb_1",
+          "name": "产品手册",
+          "avatar": "",
+          "chunk_num": 100
+        }
+      ],
+      "llm": { ... },     // (结构同 create 接口响应)
+      "prompt": { ... },  // (结构同 create 接口响应)
+      "create_time": 1715623400000
+    }
+  ]
+}
+```
+
+---
+
+## 3. 更新助手配置 - `update`
+**接口描述**: 更新指定助手应用的配置信息。支持全量或增量更新部分字段。
+**请求方法**: `PUT`
+**接口地址**: `/api/v1/chats/<chat_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| chat_id | string | 是 | 助手应用 ID |
+
+#### Body Parameters (JSON)
+*(以下所有字段均为可选，仅传递需要修改的字段即可)*
+
+| 参数名 | 类型 | 默认值 | 说明 |
+|---|---|---|---|
+| name | string | - | 新的助手名称 |
+| avatar | string | - | 新的头像 URL 或 Base64 |
+| dataset_ids | array | - | **全量替换**关联的知识库 ID 列表 |
+| llm | object | - | 更新 LLM 配置。需包含 `model_name`，其他字段覆盖更新。 |
+| prompt | object | - | 更新提示词配置。支持增量更新 (e.g. 只改 `opener`)。 |
+| show_quotation | boolean | - | 是否显示引用来源 (此字段直接位于根对象下，对应 prompt.show_quote) |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": null
+}
+```
+
+---
+
+## 4. 批量删除助手 - `delete_chats`
+**接口描述**: 批量删除一个或多个助手应用。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/chats`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| ids | array<string> | 是 | 要删除的助手应用 ID 列表。**⚠️ 注意：若列表为空或不传，虽然后端有全量删除逻辑，但在实际业务中应严谨传递 ID。** |
+
+**Request Example**:
+```json
+{
+  "ids": ["chat_id_1001", "chat_id_1002"]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+     "success_count": 2, // 成功删除的数量
+     "errors": []        // 失败原因列表 (如 ID 不存在)
+  }
+}
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_Dataset接口详解.md

@@ -0,0 +1,420 @@
+## 1. 创建知识库 - `create`
+**接口描述**: 创建一个新的知识库（Dataset），用于存储和检索文档数据。支持配置嵌入模型（Embedding Model）、解析方法、权限范围等。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/datasets`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| name | string | 是 | - | **知识库名称**。在同一个租户（Tenant）内必须唯一。 |
+| avatar | string | 否 | "" | **知识库头像**。Base64 编码的图片字符串。 |
+| description | string | 否 | "" | **描述信息**。用于说明知识库的用途或内容概要。 |
+| embedding_model | string | 否 | (系统默认) | **嵌入模型名称** (例如 `BAAI/bge-large-zh-v1.5`)。若不传，则自动使用系统设置的默认 Embedding 模型。 |
+| permission | string | 否 | "me" | **可见权限**。`me`: 仅自己可见；`team`: 团队内所有成员可见。 |
+| chunk_method | string | 否 | "naive" | **默认分块解析方法**。当上传文件未指定解析方式时使用。可选值: `naive` (通用), `manual` (手动), `qa` (Q&A拆分), `table` (表格), `paper` (论文), `book` (书籍), `laws` (法律), `presentation` (PPT), `picture` (图片), `one` (单文档), `email` (邮件)。 |
+| parser_config | object | 否 | (见下文) | **解析器详细配置**。根据 `chunk_method` 的不同而变化。 |
+
+**`parser_config` 默认配置参数 (Naive 通用模式)**:
+| 参数名 | 类型 | 默认值 | 说明 |
+|---|---|---|---|
+| chunk_token_num | int | 512 | **切片最大 Token 数**。超过该长度会被截断到下一块。 |
+| delimiter | string | "\\n" | **分段分隔符**。用于识别段落边界。 |
+| layout_recognize | string | "DeepDOC" | **布局识别模型**。用于处理复杂文档结构 (如 `DeepDOC` 或 `Simple`)。 |
+| html4excel | boolean | false | **Excel转HTML**。是否将 Excel 表格转为 HTML 格式进行解析。 |
+| auto_keywords | int | 0 | **自动关键词抽取**。0 表示不抽取；N>0 表示为每个切片抽取 N 个关键词。 |
+| auto_questions | int | 0 | **自动问题生成**。0 表示不生成；N>0 表示为每个切片生成 N 个相关问题。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "kb_uuid_12345678",
+    "name": "企业产品手册",
+    "avatar": "",
+    "tenant_id": "tenant_001",
+    "description": "存放所有产品相关的说明文档",
+    "embedding_model": "BAAI/bge-large-zh-v1.5",
+    "permission": "me",
+    "chunk_method": "naive",
+    "parser_config": {
+      "chunk_token_num": 512,
+      "delimiter": "\n",
+      "layout_recognize": "DeepDOC",
+      "html4excel": false,
+      "auto_keywords": 0,
+      "auto_questions": 0
+    },
+    "chunk_count": 0,
+    "document_count": 0,
+    "create_time": 1715623400000,
+    "update_time": 1715624500000
+  }
+}
+```
+
+---
+
+## 2. 删除知识库 - `delete`
+**接口描述**: 批量删除一个或多个知识库。删除知识库将连带删除其中的所有文档和索引数据，**不可恢复**。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/datasets`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| ids | array<string> | 是 | **ID 列表**。指定要删除的知识库 ID。如果传递 `null`，则会**清空当前租户下所有**知识库（高危操作，请谨慎使用）。 |
+
+**Request Example**:
+```json
+{
+  "ids": ["kb_id_101", "kb_id_102"]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "Successfully deleted 2 datasets, 0 failed...",
+  "data": {
+     "success_count": 2, // 成功删除的数量
+     "errors": []        // 失败的 ID 及原因列表
+  }
+}
+```
+
+---
+
+## 3. 获取知识库列表 - `list_datasets`
+**接口描述**: 获取当前用户（及团队）有权限访问的知识库列表。支持分页、排序和筛选。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/datasets`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| page | int | 否 | 1 | **页码**。从 1 开始。 |
+| page_size | int | 否 | 30 | **每页条数**。 |
+| orderby | string | 否 | "create_time" | **排序字段**。可选值: `create_time` (创建时间), `update_time` (更新时间), `document_count` (文档数)。 |
+| desc | boolean | 否 | true | **是否降序**。`true`: 降序 (最新的在前); `false`: 升序。 |
+| name | string | 否 | - | **名称筛选**。支持模糊匹配。 |
+| id | string | 否 | - | **ID 筛选**。精确匹配知识库 ID。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    {
+      "id": "kb_uuid_123",
+      "name": "HR 政策库",
+      "document_count": 12,    // 包含的文档数量
+      "token_num": 10240,      // 总 Token 数
+      "chunk_count": 150,      // 总切片数
+      "create_time": 1715623400000,
+      "permission": "team",
+      "embedding_model": "BAAI/bge-large-zh-v1.5"
+    }
+  ],
+  "total": 1 // 匹配查询条件的总记录数 (用户分页计算)
+}
+```
+
+---
+
+## 4. 更新知识库配置 - `update`
+**接口描述**: 更新指定知识库的配置信息。注意：如果知识库内已有解析过的切片，通常不允许修改嵌入模型 (`embedding_model`)。
+**请求方法**: `PUT`
+**接口地址**: `/api/v1/datasets/<dataset_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | 知识库 ID |
+
+#### Body Parameters (JSON)
+*(以下所有字段均为可选，仅传递需要修改的字段即可)*
+
+| 参数名 | 类型 | 默认值 | 说明 |
+|---|---|---|---|
+| name | string | - | **新名称**。需保持租户内唯一。 |
+| avatar | string | - | **新头像**。Base64 字符串。 |
+| description | string | - | **新描述**。 |
+| permission | string | - | **新权限**。`me` 或 `team`。 |
+| embedding_model | string | - | **嵌入模型**。**注意**: 仅当知识库为空（chunk_count=0）时才允许修改。 |
+| chunk_method | string | - | **默认解析方法**。修改后将应用于后续新上传的文件 (旧文件解析方式不变)。 |
+| parser_config | object | - | **解析器配置**。全量覆盖旧配置 (结构参考 create 接口)。 |
+| pagerank | int | 0 | **PageRank 权重**。仅在使用 Elasticsearch 引擎且需调整图谱权重时设置。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+      "id": "kb_uuid_...",
+      "name": "新名称",
+      "update_time": 1715629999000,
+      ...
+  }
+}
+```
+
+---
+
+## 5. 获取知识图谱数据 - `knowledge_graph`
+**接口描述**: 获取知识库构建的知识图谱数据，包含节点（Nodes）和边（Edges），用于前端可视化展示（如 ECharts 力导向图）。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/datasets/<dataset_id>/knowledge_graph`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | 知识库 ID |
+
+#### Query Parameters
+无
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "graph": {
+      "nodes": [
+        {
+          "id": "node_1",
+          "label": "人工智能",      // 节点显示的文本
+          "pagerank": 0.05,       // PageRank 权重 (决定节点大小)
+          "color": "#fcb",        // 节点颜色
+          "img": ""               // 节点图标 (如有)
+        },
+        {
+          "id": "node_2",
+          "label": "机器学习",
+          "pagerank": 0.03,
+          "color": "#e2b"
+        }
+      ],
+      "edges": [
+        {
+          "source": "node_1",     // 起始节点 ID
+          "target": "node_2",     // 目标节点 ID
+          "weight": 0.8,          // 边权重 (决定连线粗细)
+          "label": "includes"     // 关系名称 (显示在连线上)
+        }
+      ]
+    },
+    "mind_map": {                 // 思维导图结构的保留字段 (通常用于脑图展示)
+        "root": {
+            "id": "root_node",
+            "children": [...]
+        }
+    }
+  }
+}
+```
+
+---
+
+## 6. 清空知识图谱数据 - `delete_knowledge_graph`
+**接口描述**: 删除指定知识库中已生成的知识图谱索引数据（包括所有实体节点和关系边）。
+**注意**: 此操作**不会**删除原始文档或普通的向量索引，仅仅是重置图谱结构。如果需要重新生成图谱，请再次调用 `chunk` 相关接口或使用 `run_graphrag`。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/datasets/<dataset_id>/knowledge_graph`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | 知识库 ID |
+
+#### Body Parameters
+无
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": true
+}
+```
+
+---
+
+## 7. 运行/触发 GraphRAG 索引任务 - `run_graphrag`
+**接口描述**: 触发后台异步任务，对知识库中的文档进行 GraphRAG 索引构建。此过程会使用 LLM 抽取实体（Entities）和关系（Relationships），并构建全局社区摘要。
+**前提条件**: 知识库中必须包含已解析的文档。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/datasets/<dataset_id>/run_graphrag`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | 知识库 ID |
+
+#### Body Parameters (JSON)
+*(Body 可为空 `{}`, 后续版本将扩展以下配置参数)*
+
+| 参数名 | 类型 | 默认值 | 说明 |
+|---|---|---|---|
+| entity_types | array | ["organization", "person", "geo", "event"] | **(预留)** 指定要抽取的实体类型列表。 |
+| method | string | "light" | **(预留)** 构建模式: `light` (轻量级), `general` (标准), `complex` (深度)。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "graphrag_task_id": "task_uuid_12345678" // 异步任务 ID，用于后续追踪进度
+  }
+}
+```
+
+---
+
+## 8. 运行/触发 RAPTOR 递归摘要任务 - `run_raptor`
+**接口描述**: 触发后台异步任务，对知识库中的文档运行 RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) 算法。
+**功能说明**: 该算法会递归地对文档块进行聚类和摘要，生成多层级的树状索引，显著提升对长文档和复杂问题的回答能力。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/datasets/<dataset_id>/run_raptor`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | 知识库 ID |
+
+#### Body Parameters (JSON)
+*(Body 可为空 `{}`, 后续版本将扩展以下配置参数)*
+
+| 参数名 | 类型 | 默认值 | 说明 |
+|---|---|---|---|
+| max_cluster | int | 64 | **(预留)** 最大聚类数。 |
+| prompt | string | (内置摘要提示词) | **(预留)** 用于生成摘要的 Prompt。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "raptor_task_id": "task_uuid_87654321" // 异步任务 ID
+  }
+}
+```
+
+---
+
+## 9. 查询 GraphRAG 任务进度 - `trace_graphrag`
+**接口描述**: 查询指定知识库当前 **GraphRAG** 索引构建任务的实时状态。支持长轮询机制监测进度。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/datasets/<dataset_id>/trace_graphrag`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | 知识库 ID |
+
+#### Query Parameters
+无
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "task_uuid_12345678",        // 任务 ID
+    "doc_id": "doc_uuid_...",          // 当前正在处理的文档 ID (如果是多文档任务)
+    "from_page": 0,                    // 当前处理的起始页码
+    "to_page": 10,                     // 当前处理的结束页码
+    "progress": 0.45,                  // **总进度** (0.0 ~ 1.0)。0.0: 未开始/刚开始; 1.0: 完成; -1.0: 失败。
+    "progress_msg": "Extracting entities from chunk 25...", // **当前状态描述**。用于前端展示 Loading 提示。
+    "create_time": 1715623400000,
+    "update_time": 1715624500000
+  }
+}
+```
+
+---
+
+## 10. 查询 RAPTOR 任务进度 - `trace_raptor`
+**接口描述**: 查询指定知识库当前 **RAPTOR** 递归摘要任务的实时状态。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/datasets/<dataset_id>/trace_raptor`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | 知识库 ID |
+
+#### Query Parameters
+无
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "task_uuid_87654321",
+    "progress": 1.0,                   // 进度值。1.0 表示树构建完成。
+    "progress_msg": "Tree construction completed.", // 状态消息。
+    "create_time": 1715629000000
+  }
+}
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_Document接口详解.md

@@ -0,0 +1,757 @@
+## 1. 上传文档 - `upload`
+**接口描述**: 向指定的知识库上传一个或多个文档文件。上传后，文档将立即被存入文件系统/对象存储，并在数据库中创建记录。默认解析状态为 `UNSTART` (未开始)，解析配置将继承自 KnowledgeBase 的默认设置。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+**Content-Type**: `multipart/form-data`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。指定文档归属的知识库。 |
+
+#### Form Data Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| file | file | 是 | **文件二进制流**。支持多文件上传 (Multiple Files)。<br>支持格式: PDF, DOCX, TXT, MD, CS, HTML, CSV, XLSX, PPTX 等。<br>单文件大小限制请参考系统配置 (默认通常为 10MB/100MB)。 |
+| parent_path | string | 否 | **父级目录路径**。类似于文件系统的文件夹结构，默认为 `/`。如果指定 (如 `/docs/v1/`)，文档将在该虚拟路径下列出。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    {
+      "id": "e457f92e3c0411ef8d4c0242ac120003",
+      "thumbnail": null,
+      "dataset_id": "d1234567890abcdef1234567890abcde",
+      "chunk_method": "naive",
+      "pipeline_id": null,
+      "parser_config": {
+        "chunk_token_num": 512,
+        "delimiter": "\\n",
+        "layout_recognize": "DeepDOC",
+        "html4excel": false,
+        "auto_keywords": 0,
+        "auto_questions": 0,
+        "topn_tags": 3,
+        "raptor": {
+          "use_raptor": false
+        },
+        "graphrag": {
+          "use_graphrag": false
+        }
+      },
+      "source_type": "local",
+      "type": "pdf",
+      "created_by": "user_id_123",
+      "name": "UserGuide_v2.pdf",
+      "location": "UserGuide_v2.pdf",
+      "size": 102400,
+      "token_count": 0,
+      "chunk_count": 0,
+      "progress": 0.0,
+      "progress_msg": "",
+      "process_begin_at": null,
+      "process_duration": 0.0,
+      "meta_fields": {},
+      "suffix": "pdf",
+      "run": "UNSTART",
+      "status": "1",
+      "create_time": 1715623400123,
+      "create_date": "2024-05-13 10:03:20",
+      "update_time": 1715623400123,
+      "update_date": "2024-05-13 10:03:20"
+    }
+  ]
+}
+```
+
+---
+
+## 2. 获取文档列表 - `list_docs`
+**接口描述**: 查询知识库下的文档列表。支持分页检索、关键词搜索、状态筛选等功能。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| page | int | 否 | 1 | **页码**。从 1 开始计数。 |
+| page_size | int | 否 | 30 | **每页数量**。 |
+| orderby | string | 否 | "create_time" | **排序字段**。支持 `create_time` (创建时间), `name` (文件名), `size` (大小) 等。 |
+| desc | boolean | 否 | true | **是否降序**。`true` (最新/最大在前), `false` (最旧/最小在前)。 |
+| id | string | 否 | - | **精确筛选 ID**。仅返回指定 ID 的文档。 |
+| name | string | 否 | - | **精确筛选文件名**。仅返回指定名称的文档。 |
+| keywords | string | 否 | - | **模糊搜索**。匹配文档名称包含该关键词的记录。 |
+| suffix | array | 否 | - | **文件后缀筛选** (如 `pdf`, `docx`)。 |
+| run | array | 否 | - | **运行状态筛选**。可选值: `UNSTART`, `RUNNING`, `CANCEL`, `DONE`, `FAIL`。 |
+| create_time_from | int | 否 | 0 | **起始时间戳** (毫秒)。查询在此时间之后创建的文档。 |
+| create_time_to | int | 否 | 0 | **结束时间戳** (毫秒)。查询在此时间之前创建的文档。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "total": 128,
+    "docs": [
+      {
+        "id": "e457f92e3c0411ef8d4c0242ac120003",
+        "thumbnail": null,
+        "dataset_id": "d1234567890abcdef1234567890abcde",
+        "chunk_method": "naive",
+        "pipeline_id": null,
+        "parser_config": {
+          "chunk_token_num": 512,
+          "delimiter": "\\n",
+          "layout_recognize": "DeepDOC",
+          "html4excel": false,
+          "auto_keywords": 0,
+          "auto_questions": 0,
+          "topn_tags": 3,
+          "raptor": {
+            "use_raptor": false
+          },
+          "graphrag": {
+            "use_graphrag": false
+          }
+        },
+        "source_type": "local",
+        "type": "pdf",
+        "created_by": "user_id_123",
+        "name": "UserGuide_v2.pdf",
+        "location": "UserGuide_v2.pdf",
+        "size": 102400,
+        "token_count": 45000,
+        "chunk_count": 120,
+        "progress": 1.0,
+        "progress_msg": "Parsing finished",
+        "process_begin_at": "2024-05-13 10:05:00",
+        "process_duration": 45.2,
+        "meta_fields": {
+            "author": "RAGFlow Team",
+            "version": "2.0"
+        },
+        "suffix": "pdf",
+        "run": "DONE",
+        "status": "1",
+        "create_time": 1715623400123,
+        "create_date": "2024-05-13 10:03:20",
+        "update_time": 1715623450000,
+        "update_date": "2024-05-13 10:05:45"
+      }
+    ]
+  }
+}
+```
+
+---
+
+## 3. 更新文档信息 - `update_doc`
+**接口描述**: 更新文档的名称、状态或解析配置。
+**特别注意**: 如果修改了 `chunk_method` 或 `parser_config`，后端会自动将 `run` 状态重置为 `UNSTART`，并清除已有的 chunk 数据，等待重新解析。
+**请求方法**: `PUT`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents/<document_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+| document_id | string | 是 | **文档 ID**。 |
+
+#### Body Parameters (JSON)
+*(仅需传递要修改的字段)*
+
+| 参数名 | 类型 | 说明 |
+|---|---|---|
+| name | string | **新文档名称**。需包含文件后缀且不能改变原始文件类型 (如从 `.pdf` 改为 `.txt` 会导致错误)。 |
+| enabled | boolean | **启用/禁用**。`true`: 启用 (DEFAULT, 对应 status="1"); `false`: 禁用 (对应 status="0")。禁用后该文档不参与检索。 |
+| chunk_method | string | **解析方法**。可选值: `naive`, `manual`, `qa`, `table`, `paper`, `book`, `laws`, `presentation`, `picture`, `one`, `knowledge_graph`, `email`。 |
+| parser_config | object | **解析器详细配置**。应与 `chunk_method` 匹配。以下列出 `naive` (通用) 方法的完整配置参数。 |
+
+**parser_config (Naive 模式全量参数)**:
+| 参数名 | 类型 | 默认值 | 说明 |
+|---|---|---|---|
+| chunk_token_num | int | 512 | **切片最大 Token 数**。 |
+| delimiter | string | "\\n" | **分段符**。支持转义字符。 |
+| layout_recognize | string | "DeepDOC" | **布局识别模型**。可选 `DeepDOC` 或 `Simple`。 |
+| html4excel | boolean | false | **Excel转HTML**。是否将 Excel 解析为 HTML 表格。 |
+| auto_keywords | int | 0 | **自动关键词数量**。0 表示不抽取。 |
+| auto_questions | int | 0 | **自动问题数量**。0 表示不生成。 |
+| topn_tags | int | 3 | **自动标签数量**。 |
+| raptor | object | `{ "use_raptor": false }` | **RAPTOR 配置**。设置 `use_raptor: true` 可开启递归摘要索引。 |
+| graphrag | object | `{ "use_graphrag": false }` | **GraphRAG 配置**。设置 `use_graphrag: true` 可开启图谱增强。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "e457f92e3c0411ef8d4c0242ac120003",
+    "thumbnail": null,
+    "dataset_id": "d1234567890abcdef1234567890abcde",
+    "chunk_method": "naive",
+    "pipeline_id": null,
+    "parser_config": {
+      "chunk_token_num": 1024,
+      "delimiter": "\\n",
+      "layout_recognize": "DeepDOC",
+      "html4excel": false,
+      "auto_keywords": 0,
+      "auto_questions": 0,
+      "topn_tags": 3,
+      "raptor": {
+        "use_raptor": false
+      },
+      "graphrag": {
+        "use_graphrag": false
+      }
+    },
+    "source_type": "local",
+    "type": "pdf",
+    "created_by": "user_id_123",
+    "name": "Renamed_Guide.pdf",
+    "location": "UserGuide_v2.pdf",
+    "size": 102400,
+    "token_count": 45000,
+    "chunk_count": 0,
+    "progress": 0.0,
+    "progress_msg": "",
+    "process_begin_at": null,
+    "process_duration": 0.0,
+    "meta_fields": {},
+    "suffix": "pdf",
+    "run": "UNSTART",
+    "status": "0",
+    "create_time": 1715623400123,
+    "create_date": "2024-05-13 10:03:20",
+    "update_time": 1715629999000,
+    "update_date": "2024-05-13 12:00:00"
+  }
+}
+```
+
+---
+
+## 4. 删除文档 - `delete`
+**接口描述**: 物理删除一个或多个文档。此操作不可恢复，将同时删除数据库记录、MinIO 中的源文件以及 Elasticsearch 中的所有相关切片索引。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| ids | array<string> | 是 | **文档 ID 列表**。必须指定要删除的文档 ID。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": null
+}
+```
+
+---
+
+## 5. 下载/预览原始文件 - `download`
+**接口描述**: 获取文档的原始二进制文件流。响应头将会包含 `Content-Disposition` 字段，指示浏览器以附件形式下载。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents/<document_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+| document_id | string | 是 | **文档 ID**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/octet-stream`
+**Content-Disposition**: `attachment; filename="UserGuide_v2.pdf"`
+
+*(直接返回文件的二进制数据流)*
+
+
+## 6. 触发/重试文档解析 - `parse`
+**接口描述**: 手动触发文档的解析任务。通常在上传文件后、或修改了解析配置（如 `chunk_method`）后调用此接口。支持批量触发。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/datasets/<dataset_id>/chunks`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| document_ids | array<string> | 是 | **文档 ID 列表**。指定需要（重新）解析的文档 ID。 |
+
+**Request Example**:
+```json
+{
+    "document_ids": ["doc_id_1", "doc_id_2"]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": null
+}
+```
+
+---
+
+## 7. 停止文档解析 - `stop_parsing`
+**接口描述**: 停止当前正在进行的文档解析任务。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/datasets/<dataset_id>/chunks`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| document_ids | array<string> | 是 | **文档 ID 列表**。指定要停止解析的任务。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": null
+}
+```
+
+---
+
+## 8. 获取切片列表 - `list_chunks`
+**接口描述**: 获取指定文档已解析出的切片（Chunk）列表。支持分页和关键词搜索。返回结果包含文档的详细元数据和具体的切片内容。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+| document_id | string | 是 | **文档 ID**。 |
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| page | int | 否 | 1 | **页码**。 |
+| page_size | int | 否 | 30 | **每页数量**。 |
+| keywords | string | 否 | - | **搜索关键词**。在切片内容中进行全文检索。 |
+| id | string | 否 | - | **精确切片 ID**。若指定，则只返回该 ID 对应的切片。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "total": 150,
+    "chunks": [
+      {
+        "id": "e457f92e3c0411ef8d4c0242ac120003_0",
+        "content": "RAGFlow 是一款基于深度文档理解的开源 RAG（检索增强生成）引擎。它旨在为各种规模的企业提供精简的 RAG 工作流。RAGFlow 结合了传统文档处理的稳健性与现代大语言模型（LLM）的生成能力，确保在处理复杂格式数据（如 PDF 表格、扫描件等）时依然能保持极高的召回率和准确性。",
+        "document_id": "doc_uuid_123",
+        "docnm_kwd": "RAGFlow_UserGuide_v2.pdf",
+        "important_keywords": ["RAGFlow", "开源", "深度文档理解", "LLM"],
+        "questions": ["什么是 RAGFlow?", "RAGFlow 的主要特点是什么?"],
+        "image_id": "",
+        "dataset_id": "kb_uuid_456",
+        "available": true,
+        "positions": [1]
+      },
+      {
+        "id": "e457f92e3c0411ef8d4c0242ac120003_1",
+        "content": "主要特性：\n1. **深度文档解析**：内置 DeepDOC 识别引擎，精准还原表格、段落结构。\n2. **多路召回**：支持关键词 + 向量的混合检索。\n3. **可视化编排**：提供基于 Graph 的工作流编排能力。",
+        "document_id": "doc_uuid_123",
+        "docnm_kwd": "RAGFlow_UserGuide_v2.pdf",
+        "important_keywords": ["DeepDOC", "混合检索", "可视化编排"],
+        "questions": [],
+        "image_id": "img_uuid_789",
+        "dataset_id": "kb_uuid_456",
+        "available": true,
+        "positions": [2]
+      }
+    ],
+    "doc": {
+        "id": "doc_uuid_123",
+        "name": "RAGFlow_UserGuide_v2.pdf",
+        "chunk_count": 150,
+        "token_count": 45000,
+        "chunk_method": "naive",
+        "run": "DONE",
+        "status": "1",
+        "progress": 1.0,
+        "progress_msg": "Parsing finished",
+        "process_begin_at": "2024-05-13 10:05:00",
+        "process_duration": 45.2,
+        "meta_fields": {
+             "author": "RAGFlow Team",
+             "version": "2.0"
+        },
+        "create_time": 1715623400123,
+        "create_date": "2024-05-13 10:03:20",
+        "update_time": 1715623450000,
+        "update_date": "2024-05-13 10:05:45",
+        "dataset_id": "kb_uuid_456"
+    }
+  }
+}
+```
+
+---
+
+## 9. 手动新增切片 - `add_chunk`
+**接口描述**: 向指定文档中手动添加一个新的切片。系统会自动计算该切片的向量嵌入 (Embedding)。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+| document_id | string | 是 | **文档 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| content | string | 是 | **切片内容**。手动输入的文本内容。 |
+| important_keywords | array<string> | 否 | **重要关键词**。用于关键词检索增强。 |
+| questions | array<string> | 否 | **预设问题**。用于 Q&A 检索模式增强。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "chunk": {
+        "id": "new_chunk_uuid_999",
+        "content": "这是管理员手动添加的一条补充切片，用于修正文档中缺失的关键信息。",
+        "document_id": "doc_uuid_123",
+        "docnm_kwd": "RAGFlow_UserGuide_v2.pdf",
+        "important_keywords": ["手动添加", "补充信息"],
+        "questions": ["如何手动添加切片?"],
+        "image_id": "",
+        "dataset_id": "kb_uuid_456",
+        "available": true,
+        "positions": []
+    }
+  }
+}
+```
+
+---
+
+## 10. 修改切片信息 - `update_chunk`
+**接口描述**: 修改已存在的切片内容、关键词、可用状态等。修改内容后，系统会自动重新计算向量。
+**请求方法**: `PUT`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks/<chunk_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+| document_id | string | 是 | **文档 ID**。 |
+| chunk_id | string | 是 | **切片 ID**。 |
+
+#### Body Parameters (JSON)
+*(以下字段均为可选，仅传递需修改的字段)*
+
+| 参数名 | 类型 | 说明 |
+|---|---|---|
+| content | string | **新的切片内容**。 |
+| important_keywords | array<string> | **更新关键词列表**。覆盖原有列表。 |
+| available | boolean | **启用/禁用**。`true`: 启用 (默认); `false`: 禁用 (检索时将忽略此切片)。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": null
+}
+```
+
+---
+
+## 11. 删除切片 - `rm_chunk`
+**接口描述**: 批量删除文档中的指定切片。
+**请求方法**: `DELETE`
+**接口地址**: `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+| document_id | string | 是 | **文档 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| chunk_ids | array<string> | 是 | **切片 ID 列表**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "deleted 2 chunks",
+  "data": null
+}
+```
+
+
+## 12. 获取元数据摘要 - `metadata_summary`
+**接口描述**: 获取知识库中所有文档的元数据摘要信息。通常用于前端展示知识库的数据分布概况，例如不同文件类型的数量统计、文件状态分布等。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/datasets/<dataset_id>/metadata/summary`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+
+#### Query Parameters
+无
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "summary": {
+      "total_doc_count": 120,
+      "total_token_count": 500000,
+      "file_type_distribution": {
+        "pdf": 80,
+        "docx": 30,
+        "txt": 10
+      },
+      "status_distribution": {
+        "1": 118, // 正常启用
+        "0": 2    // 禁用
+      },
+      "custom_metadata": {
+        "author": {
+            "Alice": 50,
+            "Bob": 30
+        },
+        "department": {
+            "HR": 20,
+            "Engineering": 100
+        }
+      }
+    }
+  }
+}
+```
+
+---
+
+## 13. 批量更新元数据 - `metadata_batch_update`
+**接口描述**: 对知识库中的文档进行批量元数据修改。支持基于复杂的条件筛选文档，然后执行批量更新或删除元数据字段的操作。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/datasets/<dataset_id>/metadata/update`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| dataset_id | string | 是 | **知识库 ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| selector | object | 否 | **筛选器**。定义要更新哪些文档。如果不传，可能作用于全量文档（请谨慎）。 |
+| updates | array | 否 | **更新操作列表**。包含 `key` 和 `value`。 |
+| deletes | array | 否 | **删除操作列表**。包含 `key`。 |
+
+**Request Example (复杂场景)**:
+```json
+{
+  "selector": {
+    "document_ids": ["doc_id_101", "doc_id_102"],
+    "metadata_condition": {
+       "logic": "and",
+       "conditions": [
+          {"key": "author", "value": "OldName", "operator": "eq"},
+          {"key": "status", "value": "draft", "operator": "eq"}
+       ]
+    }
+  },
+  "updates": [
+    {"key": "author", "value": "Admin"},
+    {"key": "reviewed_by", "value": "ManagerA"}
+  ],
+  "deletes": [
+    {"key": "temp_tag"},
+    {"key": "draft_flag"}
+  ]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "updated": 2,        // 实际更新成功的文档数量
+    "matched_docs": 2    // 匹配到的文档数量
+  }
+}
+```
+
+---
+
+## 14. 检索测试 (Hit Test) - `retrieval_test`
+**接口描述**: 在指定的知识库中进行模拟检索测试。此接口用于验证分段（Chunk）质量、检索参数（相似度阈值、Top K）的效果，是调试 RAG 效果的核心工具。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/retrieval`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+**注意**: 即使是简单的查询，由于包含较多配置参数，本接口也设计为 `POST` 请求。
+
+### 请求参数 (Request)
+#### Path Parameters
+无
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| dataset_ids | array<string> | 是 | - | **目标知识库 ID 列表**。支持跨多个知识库检索。 |
+| question | string | 是 | - | **用户查询问题**。 |
+| similarity_threshold | float | 否 | 0.2 | **相似度阈值**。低于此分数的 Chunk 将被过滤。 |
+| vector_similarity_weight | float | 否 | 0.3 | **向量权重**。混合检索时，向量检索结果的权重 (0~1)。剩余权重归于关键词检索。 |
+| top_k | int | 否 | 1024 | **初筛数量**。向量检索返回的候选切片数量。 |
+| rerank_id | string | 否 | - | **重排模型 ID**。若指定，将对检索结果进行 Rerank 二次排序。 |
+| highlight | boolean | 否 | true | **高亮匹配**。是否在返回内容中高亮关键词。 |
+| keyword | boolean | 否 | false | **关键词增强**。是否使用 LLM 提取问题关键词以增强检索。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "total": 15,
+    "chunks": [
+      {
+        "id": "e457f92e3c0411ef8d4c0242ac120003_12",
+        "content": "RAGFlow 支持多种文档解析模式，其中 DeepDOC 模式特别适合处理包含大量表格和扫描件的 PDF 文档。它使用深度学习模型识别文档布局，精准提取表格内容。",
+        "document_id": "doc_uuid_123",
+        "dataset_id": "kb_uuid_456",
+        "document_name": "RAGFlow_UserGuide_v2.pdf",
+        "document_keyword": "RAGFlow_UserGuide_v2.pdf",
+        "similarity": 0.88,
+        "vector_similarity": 0.85,
+        "term_similarity": 0.92,
+        "index": 12,
+        "highlight": "RAGFlow 支持多种<em>文档解析模式</em>，其中 <em>DeepDOC</em> 模式特别适合处理包含大量表格和扫描件的 PDF 文档。",
+        "important_keywords": ["DeepDOC", "PDF"],
+        "questions": ["DeepDOC 模式有什么用?"],
+        "image_id": "",
+        "positions": [12]
+      },
+      {
+        "id": "e457f92e3c0411ef8d4c0242ac120003_15",
+        "content": "如果文档主要由纯文本构成，建议使用 Naive 模式。该模式解析速度快，适合通用场景。",
+        "document_id": "doc_uuid_123",
+        "dataset_id": "kb_uuid_456",
+        "document_name": "RAGFlow_UserGuide_v2.pdf",
+        "document_keyword": "RAGFlow_UserGuide_v2.pdf",
+        "similarity": 0.45,
+        "vector_similarity": 0.40,
+        "term_similarity": 0.50,
+        "index": 15,
+        "highlight": "如果文档主要由纯文本构成，建议使用 <em>Naive</em> 模式。",
+        "important_keywords": ["Naive", "纯文本"],
+        "questions": [],
+        "image_id": "",
+        "positions": [15]
+      }
+    ],
+    "doc_aggs": [
+        {
+            "doc_name": "RAGFlow_UserGuide_v2.pdf",
+            "doc_id": "doc_uuid_123",
+            "count": 2
+        }
+    ]
+  }
+}
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_File接口详解.md

@@ -0,0 +1,503 @@
+# RAGFlow 文件管理接口详解 (File Management API)
+
+## 1. 上传文件 - `upload`
+**接口描述**: 上传一个或多个文件到指定文件夹。支持多文件上传 (Multipart)。上传成功后，文件将存储在 MinIO/S3 中，并返回文件元数据列表。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/file/upload`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+**Content-Type**: `multipart/form-data`
+
+### 请求参数 (Request)
+#### Form Data Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| file | file | 是 | **文件二进制流**。支持多文件上传。 |
+| parent_id | string | 否 | **父级目录 ID**。如果省略，默认上传到根目录 (root)。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    {
+      "id": "e457f92e3c0411ef8d4c0242ac120003",
+      "parent_id": "root_folder_id_123",
+      "tenant_id": "tenant_uuid_456",
+      "created_by": "user_uuid_789",
+      "type": "pdf",
+      "name": "ProjectReport.pdf",
+      "location": "ProjectReport.pdf",
+      "size": 204800,
+      "source_type": "",
+      "create_time": 1715623400123,
+      "create_date": "2024-05-13 10:03:20",
+      "update_time": 1715623400123,
+      "update_date": "2024-05-13 10:03:20"
+    }
+  ]
+}
+```
+
+---
+
+## 2. 新建文件夹 - `create`
+**接口描述**: 在指定父目录下创建一个新的文件夹（逻辑目录）。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/file/create`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| name | string | 是 | **文件夹名称**。同一目录下不可重名。 |
+| parent_id | string | 否 | **父级目录 ID**。省略则默认为根目录。 |
+| type | string | 是 | **类型**。固定值为 `FOLDER` 创建文件夹。 |
+
+**Request Example**:
+```json
+{
+  "name": "Year2024_Reports",
+  "parent_id": "root_folder_id_123",
+  "type": "FOLDER"
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "folder_uuid_abc",
+    "parent_id": "root_folder_id_123",
+    "tenant_id": "tenant_uuid_456",
+    "created_by": "user_uuid_789",
+    "name": "Year2024_Reports",
+    "location": "",
+    "size": 0,
+    "type": "folder",
+    "source_type": "",
+    "create_time": 1715623500000,
+    "create_date": "2024-05-13 10:05:00",
+    "update_time": 1715623500000,
+    "update_date": "2024-05-13 10:05:00"
+  }
+}
+```
+
+---
+
+## 3. 获取文件列表 - `list_files`
+**接口描述**: 分页获取指定文件夹下的文件和子文件夹列表。支持按名称模糊搜索。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/file/list`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| parent_id | string | 否 | (Root) | **父级目录 ID**。指定要查看的目录 ID。 |
+| keywords | string | 否 | - | **搜索关键词**。按文件名模糊搜索。 |
+| page | int | 否 | 1 | **页码**。 |
+| page_size | int | 否 | 15 | **每页数量**。 |
+| orderby | string | 否 | "create_time" | **排序字段**。 |
+| desc | boolean | 否 | true | **是否降序**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "total": 25,
+    "parent_folder": {
+      "id": "root_folder_id_123",
+      "parent_id": "",
+      "tenant_id": "tenant_uuid_456",
+      "created_by": "system",
+      "name": "ROOT",
+      "location": "",
+      "size": 0,
+      "type": "folder",
+      "source_type": "",
+      "create_time": 1710000000000,
+      "create_date": "2024-03-01 00:00:00",
+      "update_time": 1710000000000,
+      "update_date": "2024-03-01 00:00:00"
+    },
+    "files": [
+      {
+        "id": "folder_uuid_abc",
+        "parent_id": "root_folder_id_123",
+        "tenant_id": "tenant_uuid_456",
+        "created_by": "user_uuid_789",
+        "name": "Year2024_Reports",
+        "location": "",
+        "size": 0,
+        "type": "folder",
+        "source_type": "",
+        "create_time": 1715623500000,
+        "create_date": "2024-05-13 10:05:00",
+        "update_time": 1715623500000,
+        "update_date": "2024-05-13 10:05:00"
+      },
+      {
+        "id": "e457f92e3c0411ef8d4c0242ac120003",
+        "parent_id": "root_folder_id_123",
+        "tenant_id": "tenant_uuid_456",
+        "created_by": "user_uuid_789",
+        "name": "ProjectReport.pdf",
+        "location": "ProjectReport.pdf",
+        "size": 204800,
+        "type": "pdf",
+        "source_type": "",
+        "create_time": 1715623400123,
+        "create_date": "2024-05-13 10:03:20",
+        "update_time": 1715623400123,
+        "update_date": "2024-05-13 10:03:20"
+      }
+    ]
+  }
+}
+```
+
+---
+
+## 4. 获取文件流 (下载) - `get`
+**接口描述**: 通过文件 ID 下载文件内容。不同于获取元数据，该接口直接返回文件的二进制流（Octet-stream 或 Image 等）。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/file/get/<file_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| file_id | string | 是 | **文件 ID**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/octet-stream` (或具体 MIME 类型如 `image/png`)
+
+*(返回二进制文件流)*
+
+---
+
+## 5. 下载附件 - `download_attachment`
+**接口描述**: 这是一个通用的附件下载接口，通常用于系统内部引用或特定路径的下载。它使用 `attachment_id`（通常对应 MinIO 中的存储路径/Key）来检索文件。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/file/download/<attachment_id>`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| attachment_id | string | 是 | **附件 ID / 存储 Key**。通常对应底层存储的唯一标识符。 |
+
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| ext | string | 否 | "markdown" | **文件扩展名**。用于设置响应头中的 Content-Type。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/octet-stream` (或根据 ext 参数推断)
+
+*(返回二进制文件流)*
+
+
+## 6. 重命名文件/文件夹 - `rename`
+**接口描述**: 修改文件或文件夹的名称。对于文件，通常不允许修改扩展名（后缀）。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/file/rename`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| file_id | string | 是 | **目标文件/文件夹 ID**。 |
+| name | string | 是 | **新名称**。需符合文件命名规范，且同一目录下不可重名。 |
+
+**Request Example**:
+```json
+{
+  "file_id": "file_uuid_123",
+  "name": "New_Report_Final.pdf"
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": true
+}
+```
+
+---
+
+## 7. 移动文件/文件夹 - `move`
+**接口描述**: 批量移动文件或文件夹到指定的目录 (Move)。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/file/mv`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| src_file_ids | array<string> | 是 | **源文件/文件夹 ID 列表**。支持批量移动。 |
+| dest_file_id | string | 是 | **目标文件夹 ID**。必须是已存在的文件夹 ID。 |
+
+**Request Example**:
+```json
+{
+  "src_file_ids": ["file_id_1", "file_id_2"],
+  "dest_file_id": "folder_id_target"
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": true
+}
+```
+
+---
+
+## 8. 删除文件/文件夹 - `rm`
+**接口描述**: 批量删除文件或文件夹。如果是文件夹，将递归删除其下的所有内容。此操作不可恢复。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/file/rm`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| file_ids | array<string> | 是 | **待删除的文件/文件夹 ID 列表**。 |
+
+**Request Example**:
+```json
+{
+  "file_ids": ["file_uuid_to_delete_1", "folder_uuid_to_delete_2"]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": true
+}
+```
+
+---
+
+## 9. 文件转知识库文档 - `convert`
+**接口描述**: 将已上传的文件（File）导入到指定的知识库（Dataset）中，转换为文档（Document）并进行解析。这是一个“文件 -> 知识库”的桥接操作。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/file/convert`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| file_ids | array<string> | 是 | **源文件 ID 列表**。必须是已存在于文件管理系统中的 ID。 |
+| kb_ids | array<string> | 是 | **目标知识库 ID 列表**。文件将被同时导入到这些知识库中。 |
+
+**Request Example**:
+```json
+{
+  "file_ids": ["file_uuid_pdf_1", "file_uuid_txt_2"],
+  "kb_ids": ["dataset_uuid_A"]
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    {
+      "id": "mapping_uuid_1",
+      "file_id": "file_uuid_pdf_1",
+      "document_id": "doc_uuid_created_in_kb_A",
+      "create_time": 1715623600123,
+      "create_date": "2024-05-13 10:06:40",
+      "update_time": 1715623600123,
+      "update_date": "2024-05-13 10:06:40"
+    },
+    {
+      "id": "mapping_uuid_2",
+      "file_id": "file_uuid_txt_2",
+      "document_id": "doc_uuid_created_in_kb_A",
+      "create_time": 1715623600124,
+      "create_date": "2024-05-13 10:06:40",
+      "update_time": 1715623600124,
+      "update_date": "2024-05-13 10:06:40"
+    }
+  ]
+}
+```
+
+
+## 10. 获取根目录信息 - `get_root_folder`
+**接口描述**: 获取当前用户的根目录文件夹信息。每个用户（Tenant）都有且仅有一个系统自动创建的根目录。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/file/root_folder`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Query Parameters
+无
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "root_folder": {
+      "id": "root_folder_id_123",
+      "parent_id": "",
+      "tenant_id": "tenant_uuid_456",
+      "created_by": "system",
+      "name": "ROOT",
+      "location": "",
+      "size": 0,
+      "type": "folder",
+      "source_type": "",
+      "create_time": 1710000000000,
+      "create_date": "2024-03-01 00:00:00",
+      "update_time": 1710000000000,
+      "update_date": "2024-03-01 00:00:00"
+    }
+  }
+}
+```
+
+---
+
+## 11. 获取父目录信息 - `get_parent_folder`
+**接口描述**: 获取指定文件或文件夹的直接父级目录信息。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/file/parent_folder`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| file_id | string | 是 | **当前文件/文件夹 ID**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "parent_folder": {
+      "id": "root_folder_id_123",
+      "parent_id": "",
+      "tenant_id": "tenant_uuid_456",
+      "created_by": "system",
+      "name": "ROOT",
+      "location": "",
+      "size": 0,
+      "type": "folder",
+      "source_type": "",
+      "create_time": 1710000000000,
+      "create_date": "2024-03-01 00:00:00",
+      "update_time": 1710000000000,
+      "update_date": "2024-03-01 00:00:00"
+    }
+  }
+}
+```
+
+---
+
+## 12. 获取完整路径 (面包屑) - `get_all_parent_folders`
+**接口描述**: 获取指定文件或文件夹的所有上级目录列表，形成完整的路径链。返回的列表顺序通常是从根目录到直接父目录（有序）。此接口常用于前端展示“面包屑导航” (Breadcrumbs)。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/file/all_parent_folder`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| file_id | string | 是 | **目标文件/文件夹 ID**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "parent_folders": [
+      {
+        "id": "root_folder_id_123",
+        "parent_id": "",
+        "tenant_id": "tenant_uuid_456",
+        "created_by": "system",
+        "name": "ROOT",
+        "location": "",
+        "size": 0,
+        "type": "folder",
+        "source_type": "",
+        "create_time": 1710000000000,
+        "create_date": "2024-03-01 00:00:00",
+        "update_time": 1710000000000,
+        "update_date": "2024-03-01 00:00:00"
+      },
+      {
+         "id": "folder_project_a_id",
+         "parent_id": "root_folder_id_123",
+         "tenant_id": "tenant_uuid_456",
+         "created_by": "user_id_001",
+         "name": "Project A Docs",
+         "location": "",
+         "size": 0,
+         "type": "folder",
+         "source_type": "",
+         "create_time": 1715000000000,
+         "create_date": "2024-05-01 09:00:00",
+         "update_time": 1715000000000,
+         "update_date": "2024-05-01 09:00:00"
+      }
+    ]
+  }
+}
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_SearchBot_AgentBot接口详解.md

@@ -0,0 +1,228 @@
+# RAGFlow 搜索机器人 & AgentBot 接口详解 (SearchBot & AgentBot)
+
+## 1. 搜索机器人对话 - `ask_about_embedded`
+**接口描述**: 面向 **SearchBot (搜索机器人)** 的核心对话接口，通常用于嵌入式知识库问答场景。与普通 Chat 不同，它更侧重于从指定的 `kb_ids` 中直接检索答案，且鉴权使用 `Authorization: Bearer <Beta_Token>` (即 API Key)。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/searchbots/ask`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| question | string | 是 | - | **用户问题**。 |
+| kb_ids | array<string> | 是 | - | **知识库 ID 列表**。限定从哪些知识库中检索。 |
+| search_id | string | 否 | - | **搜索应用 ID**。如果指定，将使用该搜索应用的配置 (Search App Config)。 |
+
+**Request Example**:
+```json
+{
+  "question": "What is the refund policy?",
+  "kb_ids": ["dataset_uuid_1", "dataset_uuid_2"],
+  "search_id": "search_app_uuid_abc"
+}
+```
+
+### 响应参数 (Stream Response)
+**Content-Type**: `text/event-stream`
+
+```text
+data:{"code": 0, "message": "", "data": {"answer": "According to the ", "reference": {}}}
+
+data:{"code": 0, "message": "", "data": {"answer": "policy, refunds are processed within 7 days.", "reference": {"chunk_1": {"content_with_weight": "Refunds...", "doc_name": "policy.pdf"}}}}
+
+data:{"code": 0, "message": "", "data": true} // 结束标志
+```
+
+---
+
+## 2. 获取思维导图 - `mindmap`
+**接口描述**: 根据用户的查询或对话上下文，生成用于前端展示的思维导图数据结构。这通常用于帮助用户梳理复杂的搜索结果或知识结构。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/searchbots/mindmap`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| question | string | 是 | **用户问题/主题**。 |
+| kb_ids | array<string> | 是 | **知识库 ID 列表**。 |
+| search_id | string | 否 | **搜索应用 ID**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "root": {
+      "text": "Refund Policy", // 根节点文本
+      "children": [
+        {
+          "text": "Conditions",
+          "children": [
+            { "text": "Product defect" },
+            { "text": "Shipping error" }
+          ]
+        },
+        {
+          "text": "Timeline",
+          "children": [
+            { "text": "7-14 business days" }
+          ]
+        }
+      ]
+    }
+  }
+}
+```
+
+---
+
+## 3. 获取相关推荐问题 - `related_questions_embedded`
+**接口描述**: 根据用户当前的问题，生成一组相关的推荐问题 (Suggest Questions)。常用于搜索结果页底部的“猜你想问”。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/searchbots/related_questions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| question | string | 是 | **用户当前问题**。 |
+| search_id | string | 否 | **搜索应用 ID**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    "How to apply for a refund online?",
+    "What items are non-refundable?",
+    "Contact customer support"
+  ]
+}
+```
+
+---
+
+## 4. 获取 AgentBot 输入项 - `begin_inputs`
+**接口描述**: 获取 **AgentBot** (嵌入式 Agent) 的初始化信息，特别是前置输入项 (Prolog/Inputs)。这用于在用户开始对话前，展示一个表单让用户输入必要信息（如姓名、邮箱、API Key 等），这些信息会被传递给 Agent 的 `Begin` 节点。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/agentbots/<agent_id>/inputs`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | **Agent ID**。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "title": "Booking Assistant",
+    "avatar": "http://...",
+    "prologue": "Welcome! Please tell me your details.",
+    "inputs": { // `Begin` 节点定义的输入变量
+      "user_name": {
+        "type": "string",
+        "description": "Your Name",
+        "required": true
+      },
+      "email": {
+        "type": "string",
+        "description": "Contact Email",
+        "required": false
+      }
+    },
+    "mode": "chat"
+  }
+}
+```
+
+---
+
+## 5. AgentBot 对话交互 - `agent_bot_completions`
+**接口描述**: 面向 **AgentBot** 的嵌入式对话接口。与 `agent_completions` 类似，但它专为无需登录的 C 端用户设计，通过 API Key 鉴权。它支持完整的 Agent 流程执行和流式响应。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/agentbots/<agent_id>/completions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | **Agent ID**。 |
+
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| session_id | string | 是 | **会话 ID**。 |
+| inputs | object | 否 | **前置输入值**。对应 `begin_inputs` 中定义的变量，如 `{"user_name": "Alice"}`。 |
+| query | string | 否 | **用户输入**。 |
+| stream | boolean | 否 | **是否流式**。默认 `true`。 |
+
+**Request Example**:
+```json
+{
+  "session_id": "session_uuid_123",
+  "inputs": {
+    "user_name": "Bob"
+  },
+  "query": "I want to book a room.",
+  "stream": true
+}
+```
+
+### 响应参数 (Stream Response)
+**Content-Type**: `text/event-stream`
+
+```text
+data:{"event": "message", "data": {"content": "Hello Bob, ", "reference": {}}}
+
+data:{"event": "message", "data": {"content": "when do you want to check in?", "reference": {}}}
+```
+
+---
+
+## 6. Agent OpenAI 兼容接口 - `agents_completion_openai_compatibility`
+**接口描述**: 专门针对 Agent 的 **OpenAI 兼容** 接口。这使得外部工具可以像调用 OpenAI Chat Completion 一样调用 RAGFlow 配置好的复杂 Agent。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/agents_openai/<agent_id>/chat/completions`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Path Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| agent_id | string | 是 | **Agent ID**。 |
+
+#### Body Parameters (OpenAI Standard)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| messages | array | 是 | 包含 `role`, `content` 的消息数组。 |
+| model | string | 是 | 占位符，任意字符串。 |
+| stream | boolean | 否 | 默认 `true`。 |
+
+### 响应参数 (Stream Response - OpenAI Format)
+**Content-Type**: `text/event-stream`
+
+```text
+data: {"id": "agent-chat-uuid", "object": "chat.completion.chunk", "created": 1715000000, "model": "ragflow_agent", "choices": [{"index": 0, "delta": {"role": "assistant", "content": ""}, "finish_reason": null}]}
+
+data: {"id": "agent-chat-uuid", "object": "chat.completion.chunk", "created": 1715000001, "model": "ragflow_agent", "choices": [{"index": 0, "delta": {"content": "Processing your request..."}, "finish_reason": null}]}
+
+data: [DONE]
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_Session_Extra接口详解.md

@@ -0,0 +1,168 @@
+# RAGFlow SearchBot 补充与通用会话接口详解 (Session Extras)
+
+## 1. 获取引用详情 - `detail_share_embedded`
+**接口描述**: 当用户点击 SearchBot 回复中的引用标号 (e.g., [1]) 时，调用此接口获取该引用的详细内容（包括原文片段、来源文档名等）。此接口通常用于前端展示“引用来源”侧边栏或弹窗。它使用 API Key (Beta Token) 进行鉴权。
+**请求方法**: `GET`
+**接口地址**: `/api/v1/searchbots/detail`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Query Parameters
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| search_id | string | 是 | **搜索应用/SearchBot ID**。此接口需要验证调用者是否有权访问该 SearchBot。 |
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "id": "search_app_uuid_123",
+    "title": "IT Knowledge Base",
+    "description": "Tech support search bot",
+    "kb_ids": ["kb_uuid_1", "kb_uuid_2"],
+    "search_config": {
+      "top_k": 5,
+      "similarity_threshold": 0.5
+    },
+    // 注意：此接口目前主要返回 Search App 的详情配置，
+    // 前端通常使用 search_config 或其他信息来辅助展示引用。
+    // 具体引用内容的文本通常已包含在 `ask` 接口的 `reference` 字段中。
+  }
+}
+```
+
+---
+
+## 2. SearchBot 检索测试 - `retrieval_test_embedded`
+**接口描述**: 面向 SearchBot 的**检索效果测试**接口。它不通过 LLM 生成答案，而是直接返回 RAG 检索到的文档片段 (`chunks`)。这用于调试 SearchBot 的检索参数（如相似度阈值、Top-K）是否合理。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/searchbots/retrieval_test`
+**鉴权方式**: Header `Authorization: Bearer <API_KEY>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| kb_id | string/array | 是 | - | **知识库 ID** (或列表)。支持单个 ID 字符串或 ID 列表。 |
+| question | string | 是 | - | **测试查询词**。 |
+| page | int | 否 | 1 | **页码**。 |
+| size | int | 否 | 30 | **每页数量**。 |
+| doc_ids | array<string> | 否 | - | **限定文档 ID**。仅在指定文档中检索。 |
+| similarity_threshold | float | 否 | 0.0 | **相似度阈值**。 |
+| top_k | int | 否 | 1024 | **Top-K 数量**。 |
+| highlight | boolean | 否 | false | **高亮匹配**。是否在返回内容中标记匹配关键词。 |
+
+**Request Example**:
+```json
+{
+  "kb_id": ["dataset_uuid_1"],
+  "question": "refund policy",
+  "top_k": 5,
+  "highlight": true
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": {
+    "total": 12, // 命中总是
+    "chunks": [
+      {
+        "content_with_weight": "Refunds are processed within <em>7 days</em>...", // 支持高亮
+        "doc_name": "policy.pdf",
+        "doc_id": "doc_uuid_101",
+        "similarity": 0.92,
+        "img_id": ""
+      },
+      {
+        "content_with_weight": "Product return guidelines...",
+        "doc_name": "guidelines.docx",
+        "doc_id": "doc_uuid_102",
+        "similarity": 0.88
+      }
+    ],
+    "labels": [] // 如果启用了查询标签功能
+  }
+}
+```
+
+---
+
+## 3. 通用会话问答 - `ask_about`
+**接口描述**: **内部/测试用**的通用会话问答接口。与 `ask_embedded` 不同，此接口通常用于 RAGFlow 控制台内部的“调试”或“预览”功能，鉴权依赖用户的登录 Token (User Token)，且必须显式指定 `dataset_ids`。它不绑定特定的 Chat/Agent/SearchBot 配置。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/sessions/ask`
+**鉴权方式**: Header `Authorization: Bearer <USER_TOKEN>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| question | string | 是 | **用户问题**。 |
+| dataset_ids | array<string> | 是 | **知识库 ID 列表**。必须是当前用户有权访问的知识库。 |
+
+**Request Example**:
+```json
+{
+  "question": "Summary of report",
+  "dataset_ids": ["dataset_uuid_internal_1"]
+}
+```
+
+### 响应参数 (Stream Response)
+**Content-Type**: `text/event-stream`
+
+```text
+data:{"code": 0, "message": "", "data": {"answer": "Here is the summary:", "reference": {}}}
+
+data:{"code": 0, "message": "", "data": {"answer": " The report indicates...", "reference": {}}}
+
+data:{"code": 0, "message": "", "data": true} // 结束
+```
+
+---
+
+## 4. 通用相关问题 - `related_questions`
+**接口描述**: **内部/测试用**的通用相关问题推荐接口。根据用户的问题和行业背景，利用 LLM 生成推荐问题。通常用于内部测试台。
+**请求方法**: `POST`
+**接口地址**: `/api/v1/sessions/related_questions`
+**鉴权方式**: Header `Authorization: Bearer <USER_TOKEN>`
+
+### 请求参数 (Request)
+#### Body Parameters (JSON)
+| 参数名 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| question | string | 是 | - | **原始问题/关键词**。 |
+| industry | string | 否 | "" | **行业背景** (e.g., "Finance", "Healthcare")。帮助 LLM 生成更专业的推荐。 |
+
+**Request Example**:
+```json
+{
+  "question": "Data privacy",
+  "industry": "IT"
+}
+```
+
+### 响应参数 (Response)
+**Content-Type**: `application/json`
+
+```json
+{
+  "code": 0,
+  "message": "success",
+  "data": [
+    "GDPR compliance checklist",
+    "Data encryption standards",
+    "User consent management"
+  ]
+}
+```

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/RAGFlow_外部API清单_按文件分组.md

@@ -0,0 +1,98 @@
+# RAGFlow External API Reference (Grouped by File)
+
+## File: `api/apps/sdk/agents.py`
+| Function Name | URL Pattern | Notes |
+|---|---|---|
+| `list_agents` | `/api/v1/agents` | List Agents |
+| `create_agent` | `/api/v1/agents` | Create Agent |
+| `update_agent` | `/api/v1/agents/<agent_id>` | Update Agent |
+| `delete_agent` | `/api/v1/agents/<agent_id>` | Delete Agent |
+| `webhook` | `/api/v1/webhook_test/<agent_id>` | Webhook Test |
+| `webhook_trace` | `/api/v1/webhook_trace/<agent_id>` | Webhook Trace |
+
+## File: `api/apps/sdk/chat.py`
+| Function Name | URL Pattern | Notes |
+|---|---|---|
+| `create` | `/api/v1/chats` | Create Chat |
+| `delete_chats` | `/api/v1/chats` | Delete Chat |
+| `list_chat` | `/api/v1/chats` | List Chats |
+| `update` | `/api/v1/chats/<chat_id>` | Update Chat |
+
+## File: `api/apps/sdk/dataset.py`
+| Function Name | URL Pattern | Notes |
+|---|---|---|
+| `create` | `/api/v1/datasets` | Create Dataset |
+| `delete` | `/api/v1/datasets` | Delete Dataset |
+| `list_datasets` | `/api/v1/datasets` | List Datasets |
+| `update` | `/api/v1/datasets/<dataset_id>` | Update Dataset |
+| `knowledge_graph` | `/api/v1/datasets/<dataset_id>/knowledge_graph` | Knowledge Graph |
+| `delete_knowledge_graph` | `/api/v1/datasets/<dataset_id>/knowledge_graph` | Delete Knowledge Graph |
+| `run_graphrag` | `/api/v1/datasets/<dataset_id>/run_graphrag` | Run GraphRAG |
+| `run_raptor` | `/api/v1/datasets/<dataset_id>/run_raptor` | Run Raptor |
+| `trace_graphrag` | `/api/v1/datasets/<dataset_id>/trace_graphrag` | Trace GraphRAG |
+| `trace_raptor` | `/api/v1/datasets/<dataset_id>/trace_raptor` | Trace Raptor |
+
+## File: `api/apps/sdk/dify_retrieval.py`
+| Function Name | URL Pattern | Notes |
+|---|---|---|
+| `retrieval` | `/api/v1/dify/retrieval` | Dify Retrieval |
+
+## File: `api/apps/sdk/doc.py`
+| Function Name | URL Pattern | Notes |
+|---|---|---|
+| `parse` | `/api/v1/datasets/<dataset_id>/chunks` | Parse Document Chunks |
+| `stop_parsing` | `/api/v1/datasets/<dataset_id>/chunks` | Stop Parsing |
+| `upload` | `/api/v1/datasets/<dataset_id>/documents` | Upload Document |
+| `list_docs` | `/api/v1/datasets/<dataset_id>/documents` | List Documents |
+| `delete` | `/api/v1/datasets/<dataset_id>/documents` | Delete Document |
+| `update_doc` | `/api/v1/datasets/<dataset_id>/documents/<document_id>` | Update Document |
+| `download` | `/api/v1/datasets/<dataset_id>/documents/<document_id>` | Download Document |
+| `list_chunks` | `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks` | List Chunks |
+| `add_chunk` | `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks` | Add Chunk |
+| `update_chunk` | `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks/<chunk_id>` | Update Chunk |
+| `rm_chunk` | `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks` | Remove Chunk |
+| `metadata_summary` | `/api/v1/datasets/<dataset_id>/metadata/summary` | Metadata Summary |
+| `metadata_batch_update` | `/api/v1/datasets/<dataset_id>/metadata/update` | Batch Update Metadata |
+| `retrieval_test` | `/api/v1/retrieval` | Retrieval Test |
+
+## File: `api/apps/sdk/files.py`
+| Function Name | URL Pattern | Notes |
+|---|---|---|
+| `get_all_parent_folders` | `/api/v1/file/all_parent_folder` | Get All Parent Folders |
+| `convert` | `/api/v1/file/convert` | File Convert |
+| `create` | `/api/v1/file/create` | File Create |
+| `download_attachment` | `/api/v1/file/download/<attachment_id>` | Download Attachment |
+| `get` | `/api/v1/file/get/<file_id>` | Get File |
+| `list_files` | `/api/v1/file/list` | List Files |
+| `move` | `/api/v1/file/mv` | Move File |
+| `get_parent_folder` | `/api/v1/file/parent_folder` | Get Parent Folder |
+| `rename` | `/api/v1/file/rename` | Rename File |
+| `rm` | `/api/v1/file/rm` | Remove File |
+| `get_root_folder` | `/api/v1/file/root_folder` | Get Root Folder |
+| `upload` | `/api/v1/file/upload` | Upload File |
+
+## File: `api/apps/sdk/session.py`
+| Function Name | URL Pattern | Notes |
+|---|---|---|
+| `agent_bot_completions` | `/api/v1/agentbots/<agent_id>/completions` | Agent Bot completion |
+| `begin_inputs` | `/api/v1/agentbots/<agent_id>/inputs` | Get Agent Bot inputs |
+| `agent_completions` | `/api/v1/agents/<agent_id>/completions` | Agent completion |
+| `create_agent_session` | `/api/v1/agents/<agent_id>/sessions` | Create Agent Session |
+| `list_agent_session` | `/api/v1/agents/<agent_id>/sessions` | List Agent Sessions |
+| `delete_agent_session` | `/api/v1/agents/<agent_id>/sessions` | Delete Agent Session |
+| `agents_completion_openai_compatibility` | `/api/v1/agents_openai/<agent_id>/chat/completions` | OpenAI compatible Agent completion |
+| `chatbot_completions` | `/api/v1/chatbots/<dialog_id>/completions` | Chatbot completion |
+| `chatbots_inputs` | `/api/v1/chatbots/<dialog_id>/info` | Chatbot info |
+| `chat_completion` | `/api/v1/chats/<chat_id>/completions` | Chat completion |
+| `create` | `/api/v1/chats/<chat_id>/sessions` | Create Chat Session |
+| `list_session` | `/api/v1/chats/<chat_id>/sessions` | List Chat Sessions |
+| `delete` | `/api/v1/chats/<chat_id>/sessions` | Delete Chat Session |
+| `update` | `/api/v1/chats/<chat_id>/sessions/<session_id>` | Update Chat Session |
+| `chat_completion_openai_like` | `/api/v1/chats_openai/<chat_id>/chat/completions` | OpenAI compatible Chat completion |
+| `ask_about_embedded` | `/api/v1/searchbots/ask` | Searchbot Ask |
+| `detail_share_embedded` | `/api/v1/searchbots/detail` | Searchbot Detail |
+| `mindmap` | `/api/v1/searchbots/mindmap` | Searchbot Mindmap |
+| `related_questions_embedded` | `/api/v1/searchbots/related_questions` | Searchbot Related Questions |
+| `retrieval_test_embedded` | `/api/v1/searchbots/retrieval_test` | Searchbot Retrieval Test |
+| `ask_about` | `/api/v1/sessions/ask` | Session Ask |
+| `related_questions` | `/api/v1/sessions/related_questions` | Session Related Questions |

main/manager-api/src/main/java/xiaozhi/modules/knowledge/doc/README.md

@@ -0,0 +1,45 @@
+# RAGFlow API 接口文档索引 (Unofficial Detailed Guide)
+
+本文档汇集了 RAGFlow 核心模块的 API 详解。所有文档均遵循 **Zero Omissions (无省略)** 原则，全字段展开并包含中文注释。
+
+## 📚 1. 知识库与文档管理 (Knowledge & Documents)
+核心的数据管理模块，负责上传文件、解析文档与建立索引。
+
+- **[知识库管理 (Dataset)](./RAGFlow_Dataset接口详解.md)**
+  - 涵盖知识库的创建、列表查询、更新、删除等接口。
+- **[文档处理 (Document)](./RAGFlow_Document接口详解.md)**
+  - 涵盖文档的上传 (Upload)、解析配置更新 (Update)、解析状态查询 (Run Status)。
+  - **切片管理**: 解析后的 Chunk 列表查询、增删改查。
+  - **检索测试**: 直接对知识库进行召回测试 (Retrieval Test)。
+- **[文件管理 (File)](./RAGFlow_File接口详解.md)**
+  - 类似网盘的文件操作体系。
+  - **CRUD**: 上传、下载、列表。
+  - **目录**: 文件夹创建、面包屑导航 (`get_all_parent_folders`)。
+  - **操作**: 移动、重命名、删除、导入知识库 (`convert`).
+
+## 💬 2. 聊天助手 (Chat Assistant)
+RAGFlow 原生的对话助手体系，基于 Assistant (Dialog) 模型。
+
+- **[会话管理 (Chat Session)](./RAGFlow_Chat_Session接口详解.md)**
+  - 管理 `/chats/` 下的会话生命周期。
+  - 创建会话、获取历史记录、重命名、批量删除。
+- **[对话交互 (Chat Completion)](./RAGFlow_Chat_Completion接口详解.md)**
+  - **Core Chat**: 原生流式对话 (`/chats/<id>/completions`), 支持引用 (`quote`)。
+  - **OpenAI Compatible**: 完美兼容 OpenAI `/v1/chat/completions` 协议。
+  - **Embedded Bot**: 面向 C 端嵌入窗口的对话接口 (`/chatbots/`).
+
+## 🤖 3. Agent 与 机器人 (Agent & Bots)
+基于 Graph (DAG) 编排的复杂应用与各类机器人扩展。
+
+- **[Agent 与 Dify 兼容 (Agent & Dify)](./RAGFlow_Agent_Dify接口详解.md)**
+  - **Agent Session**: Agent 的会话管理与流式对话 (`agent_completions`)。
+  - **Dify Adapter**: 兼容 Dify 协议的检索接口 (`retrieval`).
+- **[SearchBot 与 AgentBot](./RAGFlow_SearchBot_AgentBot接口详解.md)**
+  - **SearchBot**: 纯搜索机器人，支持思维导图 (`mindmap`)、相关问题 (`related_questions`).
+  - **AgentBot**: 嵌入式 Agent，支持前置表单 (`begin_inputs`).
+  - **Agent OpenAI**: Agent 的 OpenAI 兼容接口。
+
+## 🛠️ 4. 其他 (Extras)
+- **[通用与补充接口 (Session Extras)](./RAGFlow_Session_Extra接口详解.md)**
+  - **引用详情**: 获取 SearchBot 引用来源 (`detail_share_embedded`).
+  - **通用问答**: 内部调试用的直接问答 (`ask_about`).

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/DocumentDTO.java

@@ -0,0 +1,68 @@
+package xiaozhi.modules.knowledge.dto;
+
+import java.io.Serializable;
+import java.util.Date;
+import java.util.Map;
+
+import io.swagger.v3.oas.annotations.media.Schema;
+import lombok.Data;
+
+/**
+ * 文档 DTO
+ */
+@Data
+@Schema(description = "知识库文档")
+public class DocumentDTO implements Serializable {
+    private static final long serialVersionUID = 1L;
+
+    @Schema(description = "本地ID")
+    private String id;
+
+    @Schema(description = "知识库ID")
+    private String datasetId;
+
+    @Schema(description = "RAGFlow文档ID")
+    private String documentId;
+
+    @Schema(description = "文档名称")
+    private String name;
+
+    @Schema(description = "文件大小")
+    private Long size;
+
+    @Schema(description = "文件类型")
+    private String type;
+
+    @Schema(description = "分块方法")
+    private String chunkMethod;
+
+    @Schema(description = "解析配置")
+    private Map<String, Object> parserConfig;
+
+    @Schema(description = "处理状态 (1:解析中 3:成功 4:失败)")
+    private Integer status;
+
+    @Schema(description = "错误信息")
+    private String error;
+
+    @Schema(description = "分块数量")
+    private Integer chunkCount;
+
+    @Schema(description = "Token数量")
+    private Long tokenCount;
+
+    @Schema(description = "是否启用")
+    private Integer enabled;
+
+    @Schema(description = "创建时间")
+    private Date createdAt;
+
+    @Schema(description = "更新时间")
+    private Date updatedAt;
+
+    @Schema(description = "上传进度 (虚拟字段)")
+    private Double progress;
+
+    @Schema(description = "缩略图/预览图 (虚拟字段)")
+    private String thumbnail;
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/KnowledgeBaseDTO.java

@@ -26,9 +26,30 @@ public class KnowledgeBaseDTO implements Serializable {
     @Schema(description = "知识库名称")
     private String name;
 
+    @Schema(description = "知识库头像(Base64)")
+    private String avatar;
+
     @Schema(description = "知识库描述")
     private String description;
 
+    @Schema(description = "嵌入模型名称")
+    private String embeddingModel;
+
+    @Schema(description = "权限设置: me/team")
+    private String permission;
+
+    @Schema(description = "分块方法")
+    private String chunkMethod;
+
+    @Schema(description = "解析器配置(JSON String)")
+    private String parserConfig;
+
+    @Schema(description = "分块总数")
+    private Long chunkCount;
+
+    @Schema(description = "总Token数")
+    private Long tokenNum;
+
     @Schema(description = "状态(0:禁用 1:启用)")
     private Integer status;
 

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/KnowledgeFilesDTO.java

@@ -6,10 +6,12 @@ import java.util.Date;
 import java.util.Map;
 
 import io.swagger.v3.oas.annotations.media.Schema;
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
 import lombok.Data;
 
 @Data
 @Schema(description = "知识库文档")
+@JsonIgnoreProperties(ignoreUnknown = true)
 public class KnowledgeFilesDTO implements Serializable {
 
     @Serial
@@ -35,7 +37,19 @@ public class KnowledgeFilesDTO implements Serializable {
     @Schema(description = "文件路径")
     private String filePath;
 
-    @Schema(description = "元数据字段")
+    @Schema(description = "解析进度 (0.0 ~ 1.0)")
+    private Double progress;
+
+    @Schema(description = "缩略图 (Base64 或 URL)")
+    private String thumbnail;
+
+    @Schema(description = "解析耗时 (单位: 秒)")
+    private Double processDuration;
+
+    @Schema(description = "来源类型 (local, s3, url 等)")
+    private String sourceType;
+
+    @Schema(description = "元数据字段 (Map 格式)")
     private Map<String, Object> metaFields;
 
     @Schema(description = "分块方法")
@@ -44,10 +58,10 @@ public class KnowledgeFilesDTO implements Serializable {
     @Schema(description = "解析器配置")
     private Map<String, Object> parserConfig;
 
-    @Schema(description = "状态")
+    @Schema(description = "可用状态 (1: 启用/正常, 0: 禁用/失效)")
-    private Integer status;
+    private String status;
 
-    @Schema(description = "文档解析状态")
+    @Schema(description = "运行状态 (UNSTART/RUNNING/CANCEL/DONE/FAIL)")
     private String run;
 
     @Schema(description = "创建者")
@@ -62,6 +76,15 @@ public class KnowledgeFilesDTO implements Serializable {
     @Schema(description = "更新时间")
     private Date updatedAt;
 
+    @Schema(description = "分块数量")
+    private Integer chunkCount;
+
+    @Schema(description = "Token数量")
+    private Long tokenCount;
+
+    @Schema(description = "解析错误信息")
+    private String error;
+
     // 文档解析状态常量定义
     private static final Integer STATUS_UNSTART = 0;
     private static final Integer STATUS_RUNNING = 1;

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/agent/AgentDTO.java

@@ -0,0 +1,421 @@
+package xiaozhi.modules.knowledge.dto.agent;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import java.util.Map;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import jakarta.validation.constraints.*;
+
+@Schema(description = "智能体 (Agent) 管理聚合 DTO")
+public class AgentDTO {
+
+    // ========== 1. Agent 管理 (CRUD) - 对应 RAGFlow_Agent接口详解 ==========
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Agent 创建请求")
+    public static class CreateReq implements Serializable {
+        @Schema(description = "Agent 标题", requiredMode = Schema.RequiredMode.REQUIRED, example = "My Agent")
+        @NotBlank(message = "Agent 标题不能为空")
+        @JsonProperty("title")
+        private String title;
+
+        @Schema(description = "DSL 定义 (画布 JSON)", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotNull(message = "DSL 定义不能为空")
+        @JsonProperty("dsl")
+        private Map<String, Object> dsl;
+
+        @Schema(description = "描述", example = "这是一个测试 Agent")
+        @JsonProperty("description")
+        private String description;
+
+        @Schema(description = "头像 URL", example = "http://example.com/avatar.png")
+        @JsonProperty("avatar")
+        private String avatar;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Agent 更新请求")
+    public static class UpdateReq implements Serializable {
+        @Schema(description = "Agent 标题", example = "Updated Agent")
+        @JsonProperty("title")
+        private String title;
+
+        @Schema(description = "DSL 定义 (画布 JSON)")
+        @JsonProperty("dsl")
+        private Map<String, Object> dsl;
+
+        @Schema(description = "描述")
+        @JsonProperty("description")
+        private String description;
+
+        @Schema(description = "头像 URL")
+        @JsonProperty("avatar")
+        private String avatar;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Agent 列表请求")
+    public static class ListReq implements Serializable {
+        @Schema(description = "页码", defaultValue = "1")
+        @JsonProperty("page")
+        @Builder.Default
+        private Integer page = 1;
+
+        @Schema(description = "每页大小", defaultValue = "10")
+        @JsonProperty("page_size")
+        @Builder.Default
+        private Integer pageSize = 10;
+
+        @Schema(description = "排序字段", defaultValue = "update_time")
+        @JsonProperty("orderby")
+        @Builder.Default
+        private String orderby = "update_time";
+
+        @Schema(description = "是否降序", defaultValue = "true")
+        @JsonProperty("desc")
+        @Builder.Default
+        private Boolean desc = true;
+
+        @Schema(description = "Agent ID 过滤")
+        @JsonProperty("id")
+        private String id;
+
+        @Schema(description = "标题模糊搜索")
+        @JsonProperty("title")
+        private String title;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Agent 响应对象")
+    public static class AgentVO implements Serializable {
+        @Schema(description = "Agent ID")
+        @JsonProperty("id")
+        private String id;
+
+        @Schema(description = "标题")
+        @JsonProperty("title")
+        private String title;
+
+        @Schema(description = "描述")
+        @JsonProperty("description")
+        private String description;
+
+        @Schema(description = "头像")
+        @JsonProperty("avatar")
+        private String avatar;
+
+        @Schema(description = "DSL 定义")
+        @JsonProperty("dsl")
+        private Map<String, Object> dsl;
+
+        @Schema(description = "创建者 ID")
+        @JsonProperty("user_id")
+        private String userId;
+
+        @Schema(description = "画布分类")
+        @JsonProperty("canvas_category")
+        private String canvasCategory;
+
+        @Schema(description = "创建时间 (时间戳)")
+        @JsonProperty("create_time")
+        private Long createTime;
+
+        @Schema(description = "更新时间 (时间戳)")
+        @JsonProperty("update_time")
+        private Long updateTime;
+    }
+
+    // ========== 2. Webhook 调试与追踪 - 对应 RAGFlow_Agent接口详解 ==========
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Webhook 触发请求 (参数动态)")
+    public static class WebhookTriggerReq implements Serializable {
+        @Schema(description = "输入变量", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotNull(message = "输入变量不能为空")
+        @JsonProperty("inputs")
+        private Map<String, Object> inputs;
+
+        @Schema(description = "查询词", example = "Hello")
+        @JsonProperty("query")
+        private String query;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Webhook 追踪请求")
+    public static class WebhookTraceReq implements Serializable {
+        @Schema(description = "时间戳游标", example = "1700000000.0")
+        @JsonProperty("since_ts")
+        private Double sinceTs;
+
+        @Schema(description = "Webhook ID")
+        @JsonProperty("webhook_id")
+        private String webhookId;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Webhook 追踪响应")
+    public static class WebhookTraceVO implements Serializable {
+        @Schema(description = "Webhook ID")
+        @JsonProperty("webhook_id")
+        private String webhookId;
+
+        @Schema(description = "是否结束")
+        @JsonProperty("finished")
+        private Boolean finished;
+
+        @Schema(description = "下一次查询的时间戳游标")
+        @JsonProperty("next_since_ts")
+        private Double nextSinceTs;
+
+        @Schema(description = "事件列表")
+        @JsonProperty("events")
+        private List<TraceEvent> events;
+
+        @Data
+        @Builder
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Schema(description = "追踪事件项")
+        public static class TraceEvent implements Serializable {
+            @Schema(description = "时间戳")
+            @JsonProperty("ts")
+            private Double ts;
+
+            @Schema(description = "事件类型")
+            @JsonProperty("event")
+            private String event;
+
+            @Schema(description = "事件数据")
+            @JsonProperty("data")
+            private Object data;
+        }
+    }
+
+    // ========== 3. Agent 会话 (Session) - 对应 RAGFlow_Agent_Dify接口详解 ==========
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Session 创建请求")
+    public static class SessionCreateReq implements Serializable {
+        @Schema(description = "用户 ID")
+        @JsonProperty("user_id")
+        private String userId;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Session 列表请求")
+    public static class SessionListReq implements Serializable {
+        @Schema(description = "页码", defaultValue = "1")
+        @JsonProperty("page")
+        @Builder.Default
+        private Integer page = 1;
+
+        @Schema(description = "每页大小", defaultValue = "10")
+        @JsonProperty("page_size")
+        @Builder.Default
+        private Integer pageSize = 10;
+
+        @Schema(description = "排序字段", defaultValue = "create_time")
+        @JsonProperty("orderby")
+        @Builder.Default
+        private String orderby = "create_time";
+
+        @Schema(description = "是否降序", defaultValue = "true")
+        @JsonProperty("desc")
+        @Builder.Default
+        private Boolean desc = true;
+
+        @Schema(description = "Session ID")
+        @JsonProperty("id")
+        private String id;
+
+        @Schema(description = "用户 ID")
+        @JsonProperty("user_id")
+        private String userId;
+
+        @Schema(description = "是否返回 DSL")
+        @JsonProperty("dsl")
+        @Builder.Default
+        private Boolean dsl = false;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Session 批量删除请求")
+    public static class SessionBatchDeleteReq implements Serializable {
+        @Schema(description = "会话 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("ids")
+        @NotEmpty(message = "ID列表不能为空")
+        private List<String> ids;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Session 响应对象")
+    public static class SessionVO implements Serializable {
+        @Schema(description = "Session ID")
+        @JsonProperty("id")
+        private String id;
+
+        @Schema(description = "Agent ID")
+        @JsonProperty("agent_id")
+        private String agentId;
+
+        @Schema(description = "用户 ID")
+        @JsonProperty("user_id")
+        private String userId;
+
+        @Schema(description = "来源")
+        @JsonProperty("source")
+        private String source;
+
+        @Schema(description = "DSL 定义")
+        @JsonProperty("dsl")
+        private Map<String, Object> dsl;
+
+        @Schema(description = "消息列表")
+        @JsonProperty("messages")
+        private List<Map<String, Object>> messages;
+    }
+
+    // ========== 4. Agent 对话 (Completion) - 对应 RAGFlow_Agent_Dify接口详解 ==========
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Completion 对话请求")
+    public static class CompletionReq implements Serializable {
+        @Schema(description = "会话 ID", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotBlank(message = "会话 ID 不能为空")
+        @JsonProperty("session_id")
+        private String sessionId;
+
+        @Schema(description = "用户问题")
+        @JsonProperty("question")
+        private String question;
+
+        @Schema(description = "是否流式返回", defaultValue = "true")
+        @JsonProperty("stream")
+        @Builder.Default
+        private Boolean stream = true;
+
+        @Schema(description = "是否返回追踪信息", defaultValue = "false")
+        @JsonProperty("return_trace")
+        @Builder.Default
+        private Boolean returnTrace = false;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Completion 对话响应")
+    public static class CompletionVO implements Serializable {
+        @Schema(description = "会话 ID")
+        @JsonProperty("id")
+        private String id;
+
+        @Schema(description = "回复内容")
+        @JsonProperty("content")
+        private String content;
+
+        @Schema(description = "引用来源")
+        @JsonProperty("reference")
+        private Map<String, Object> reference;
+
+        @Schema(description = "追踪信息")
+        @JsonProperty("trace")
+        private List<Object> trace;
+    }
+
+    // ========== 5. Dify 兼容检索 - 对应 RAGFlow_Agent_Dify接口详解 ==========
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Dify 兼容检索请求")
+    public static class DifyRetrievalReq implements Serializable {
+        @Schema(description = "知识库 ID")
+        @JsonProperty("knowledge_id")
+        private String knowledgeId;
+
+        @Schema(description = "查询词")
+        @JsonProperty("query")
+        private String query;
+
+        @Schema(description = "检索设置")
+        @JsonProperty("retrieval_setting")
+        private Map<String, Object> retrievalSetting;
+
+        @Schema(description = "元数据过滤条件")
+        @JsonProperty("metadata_condition")
+        private Map<String, Object> metadataCondition;
+
+        @Schema(description = "是否使用知识图谱")
+        @JsonProperty("use_kg")
+        private Boolean useKg;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "Dify 兼容检索响应")
+    public static class DifyRetrievalVO implements Serializable {
+        @Schema(description = "检索结果列表")
+        @JsonProperty("records")
+        private List<Record> records;
+
+        @Data
+        @Builder
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Schema(description = "检索记录")
+        public static class Record implements Serializable {
+            @Schema(description = "内容")
+            @JsonProperty("content")
+            private String content;
+
+            @Schema(description = "相似度分数")
+            @JsonProperty("score")
+            private Double score;
+
+            @Schema(description = "标题")
+            @JsonProperty("title")
+            private String title;
+
+            @Schema(description = "元数据")
+            @JsonProperty("metadata")
+            private Map<String, Object> metadata;
+        }
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/bot/BotDTO.java

@@ -0,0 +1,126 @@
+package xiaozhi.modules.knowledge.dto.bot;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import java.util.Map;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import jakarta.validation.constraints.*;
+
+@Schema(description = "外部机器人 (Bot) 聚合 DTO")
+public class BotDTO {
+
+    // ========== 1. SearchBot (检索机器人) ==========
+
+    // 对应 /api/v1/searchbots/ask
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "SearchBot 提问请求")
+    public static class SearchAskReq implements Serializable {
+        @Schema(description = "用户问题", requiredMode = Schema.RequiredMode.REQUIRED, example = "What is RAG?")
+        @NotBlank(message = "问题不能为空")
+        @JsonProperty("question")
+        private String question;
+
+        @Schema(description = "是否返回引用", defaultValue = "false")
+        @JsonProperty("quote")
+        @Builder.Default
+        private Boolean quote = false;
+
+        @Schema(description = "是否流式返回", defaultValue = "true")
+        @JsonProperty("stream")
+        @Builder.Default
+        private Boolean stream = true;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "SearchBot 提问响应")
+    public static class SearchAskVO implements Serializable {
+        @Schema(description = "回答内容")
+        @JsonProperty("answer")
+        private String answer;
+
+        @Schema(description = "引用来源 (Value 结构通常对应 RetrievalDTO.HitVO)")
+        @JsonProperty("reference")
+        private Map<String, Object> reference;
+    }
+
+    // 对应 /api/v1/searchbots/related_questions
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "相关问题请求")
+    public static class RelatedQuestionReq implements Serializable {
+        @Schema(description = "用户问题", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotBlank(message = "问题不能为空")
+        @JsonProperty("question")
+        private String question;
+    }
+
+    // 对应 /api/v1/searchbots/mindmap
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "思维导图请求")
+    public static class MindMapReq implements Serializable {
+        @Schema(description = "用户问题", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotBlank(message = "问题不能为空")
+        @JsonProperty("question")
+        private String question;
+    }
+
+    // ========== 2. AgentBot (嵌入式 Agent) ==========
+
+    // 对应 /api/v1/agentbots/{id}/inputs
+    @Data
+    @Builder
+    @AllArgsConstructor
+    @Schema(description = "AgentBot 输入参数请求")
+    public static class AgentInputsReq implements Serializable {
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "AgentBot 输入参数定义响应")
+    public static class AgentInputsVO implements Serializable {
+        @Schema(description = "表单变量定义列表")
+        @JsonProperty("variables")
+        private List<Map<String, Object>> variables;
+    }
+
+    // 对应 /api/v1/agentbots/{id}/completions
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "AgentBot 对话请求")
+    public static class AgentCompletionReq implements Serializable {
+        @Schema(description = "输入参数值")
+        @JsonProperty("inputs")
+        private Map<String, Object> inputs;
+
+        @Schema(description = "用户查询", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotBlank(message = "查询内容不能为空")
+        @JsonProperty("question")
+        private String question;
+
+        @Schema(description = "是否流式返回", defaultValue = "true")
+        @JsonProperty("stream")
+        @Builder.Default
+        private Boolean stream = true;
+
+        @Schema(description = "会话 ID")
+        @JsonProperty("session_id")
+        private String sessionId;
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/chat/ChatCompletionRequest.java

@@ -0,0 +1,50 @@
+package xiaozhi.modules.knowledge.dto.chat;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.Map;
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+
+import io.swagger.v3.oas.annotations.media.Schema;
+import lombok.Data;
+
+/**
+ * 聊天对话请求 DTO (OpenAI 兼容格式)
+ */
+@Data
+@Schema(description = "聊天对话请求")
+public class ChatCompletionRequest implements Serializable {
+
+    @Schema(description = "模型标识 (对应 agent_id 或 bot_id)", requiredMode = Schema.RequiredMode.REQUIRED)
+    @JsonProperty("model")
+    private String model;
+
+    @Schema(description = "对话消息列表", requiredMode = Schema.RequiredMode.REQUIRED)
+    @JsonProperty("messages")
+    private List<Message> messages;
+
+    @Schema(description = "是否流式返回", defaultValue = "false")
+    @JsonProperty("stream")
+    private Boolean stream = false;
+
+    @Schema(description = "温度系数 (0-1)", defaultValue = "0.7")
+    @JsonProperty("temperature")
+    private Double temperature;
+
+    @Schema(description = "Session ID (可选，用于延续会话)")
+    @JsonProperty("session_id")
+    private String sessionId;
+
+    @Schema(description = "其他RAGFlow特定参数 (可选)")
+    private Map<String, Object> extra;
+
+    @Data
+    public static class Message implements Serializable {
+        @Schema(description = "角色 (system, user, assistant)", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String role;
+
+        @Schema(description = "内容", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String content;
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/chat/ChatDTO.java

@@ -0,0 +1,523 @@
+package xiaozhi.modules.knowledge.dto.chat;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import java.util.Map;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import jakarta.validation.constraints.*;
+
+/**
+ * 对话管理聚合 DTO
+ * <p>
+ * 容器类，内含对话助手、会话和消息的所有请求/响应对象。
+ * </p>
+ */
+@Schema(description = "对话管理聚合 DTO")
+public class ChatDTO {
+
+    // ========== 1. 对话助手 (Assistant/Bot) 相关 ==========
+
+    /**
+     * 提示词配置
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "提示词配置")
+    public static class PromptConfig implements Serializable {
+
+        @Schema(description = "系统提示词", example = "你是一个专业的客服助手...")
+        @JsonProperty("prompt")
+        private String systemPrompt;
+
+        @Schema(description = "开场白", example = "您好，我是您的智能助手，请问有什么可以帮您？")
+        private String opener;
+
+        @Schema(description = "空结果回复", example = "抱歉，我没有找到相关信息。")
+        @JsonProperty("empty_response")
+        private String emptyResponse;
+
+        @Schema(description = "是否展示引用", example = "true")
+        @JsonProperty("show_quote")
+        private Boolean quote;
+
+        @Schema(description = "是否启用 TTS", example = "false")
+        private Boolean tts;
+
+        @Schema(description = "相似度阈值 (0.0 - 1.0)", example = "0.2")
+        @JsonProperty("similarity_threshold")
+        private Float similarityThreshold;
+
+        @Schema(description = "关键词相似度权重 (0.0 - 1.0)", example = "0.7")
+        @JsonProperty("keywords_similarity_weight")
+        private Float vectorSimilarityWeight;
+
+        @Schema(description = "检索 Top N", example = "6")
+        @JsonProperty("top_n")
+        private Integer topK;
+
+        @Schema(description = "Rerank 模型", example = "rerank_model_001")
+        @JsonProperty("rerank_model")
+        private String rerankId;
+
+        @Schema(description = "是否启用多轮对话优化", example = "false")
+        @JsonProperty("refine_multiturn")
+        private Boolean refineMultigraph;
+
+        @Schema(description = "变量列表")
+        private List<Map<String, Object>> variables;
+    }
+
+    /**
+     * LLM 配置
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "LLM 模型配置")
+    public static class LLMConfig implements Serializable {
+
+        @NotBlank(message = "模型名称不能为空")
+        @Schema(description = "模型名称", requiredMode = Schema.RequiredMode.REQUIRED, example = "gpt-4")
+        @JsonProperty("model_name")
+        private String modelName;
+
+        @Schema(description = "温度参数 (0.0 - 2.0)", example = "0.7")
+        private Float temperature;
+
+        @Schema(description = "Top P 采样", example = "0.9")
+        @JsonProperty("top_p")
+        private Float topP;
+
+        @Schema(description = "最大 Token 数", example = "4096")
+        @JsonProperty("max_tokens")
+        private Integer maxTokens;
+
+        @Schema(description = "存在惩罚", example = "0.0")
+        @JsonProperty("presence_penalty")
+        private Float presencePenalty;
+
+        @Schema(description = "频率惩罚", example = "0.0")
+        @JsonProperty("frequency_penalty")
+        private Float frequencyPenalty;
+    }
+
+    /**
+     * 创建助手请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "创建助手请求")
+    public static class AssistantCreateReq implements Serializable {
+
+        @NotBlank(message = "助手名称不能为空")
+        @Schema(description = "助手名称", requiredMode = Schema.RequiredMode.REQUIRED, example = "智能客服助手")
+        private String name;
+
+        @Schema(description = "助手头像 (Base64 编码)", example = "")
+        private String avatar;
+
+        @Schema(description = "关联的知识库 ID 列表", example = "[\"kb_001\", \"kb_002\"]")
+        @JsonProperty("dataset_ids")
+        private List<String> datasetIds;
+
+        @Schema(description = "助手描述", example = "这是一个智能客服助手")
+        private String description;
+
+        @Schema(description = "LLM 模型配置")
+        @JsonProperty("llm")
+        private LLMConfig llm;
+
+        @Schema(description = "提示词配置")
+        @JsonProperty("prompt")
+        private PromptConfig promptConfig;
+    }
+
+    /**
+     * 更新助手请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "更新助手请求")
+    public static class AssistantUpdateReq implements Serializable {
+
+        @Schema(description = "助手名称", example = "智能客服助手 V2")
+        private String name;
+
+        @Schema(description = "助手头像 (Base64 编码)", example = "")
+        private String avatar;
+
+        @Schema(description = "关联的知识库 ID 列表", example = "[\"kb_001\", \"kb_002\"]")
+        @JsonProperty("dataset_ids")
+        private List<String> datasetIds;
+
+        @Schema(description = "助手描述", example = "这是一个智能客服助手")
+        private String description;
+
+        @Schema(description = "LLM 模型配置")
+        @JsonProperty("llm")
+        private LLMConfig llm;
+
+        @Schema(description = "提示词配置")
+        @JsonProperty("prompt")
+        private PromptConfig promptConfig;
+    }
+
+    /**
+     * 查询助手列表请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "查询助手列表请求")
+    public static class AssistantListReq implements Serializable {
+
+        @Schema(description = "页码 (从 1 开始)", example = "1")
+        private Integer page;
+
+        @Schema(description = "每页数量", example = "30")
+        @JsonProperty("page_size")
+        private Integer pageSize;
+
+        @Schema(description = "按名称过滤 (模糊匹配)", example = "客服")
+        private String name;
+
+        @Schema(description = "排序字段: create_time / update_time", example = "create_time")
+        private String orderby;
+
+        @Schema(description = "是否降序", example = "true")
+        private Boolean desc;
+
+        @Schema(description = "按 ID 精确筛选", example = "assistant_001")
+        private String id;
+    }
+
+    /**
+     * 助手详情 VO
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "助手详情 VO")
+    public static class AssistantVO implements Serializable {
+
+        @Schema(description = "助手 ID", example = "assistant_001")
+        private String id;
+
+        @Schema(description = "租户 ID", example = "tenant_001")
+        @JsonProperty("tenant_id")
+        private String tenantId;
+
+        @Schema(description = "助手名称", example = "智能客服助手")
+        private String name;
+
+        @Schema(description = "助手头像", example = "")
+        private String avatar;
+
+        @Schema(description = "关联的知识库 ID 列表")
+        @JsonProperty("dataset_ids")
+        private List<String> datasetIds;
+
+        @Schema(description = "关联的知识库列表 (详情)")
+        private List<SimpleDatasetVO> datasets;
+
+        @Schema(description = "助手描述")
+        private String description;
+
+        @Schema(description = "LLM 模型配置")
+        @JsonProperty("llm")
+        private LLMConfig llm;
+
+        @Schema(description = "提示词配置")
+        @JsonProperty("prompt")
+        private PromptConfig promptConfig;
+
+        @Schema(description = "创建时间 (时间戳)", example = "1700000000000")
+        @JsonProperty("create_time")
+        private Long createTime;
+
+        @Schema(description = "更新时间 (时间戳)", example = "1700000001000")
+        @JsonProperty("update_time")
+        private Long updateTime;
+    }
+
+    /**
+     * 删除助手请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "删除助手请求")
+    public static class AssistantDeleteReq implements Serializable {
+
+        @Schema(description = "要删除的助手 ID 列表", example = "[\"assistant_001\", \"assistant_002\"]")
+        private List<String> ids;
+    }
+
+    // ========== 2. 会话 (Session) 相关 ==========
+
+    /**
+     * 创建会话请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "创建会话请求")
+    public static class SessionCreateReq implements Serializable {
+
+        @Schema(description = "会话名称", example = "技术咨询会话")
+        private String name;
+
+        @Schema(description = "用户 ID", example = "user_001")
+        @JsonProperty("user_id")
+        private String userId;
+    }
+
+    /**
+     * 更新会话请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "更新会话请求")
+    public static class SessionUpdateReq implements Serializable {
+
+        @Schema(description = "会话名称", example = "技术咨询会话 - 更新")
+        private String name;
+    }
+
+    /**
+     * 查询会话列表请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "查询会话列表请求")
+    public static class SessionListReq implements Serializable {
+
+        @Schema(description = "助手 ID", example = "assistant_001")
+        @JsonProperty("assistant_id")
+        private String assistantId;
+
+        @Schema(description = "页码 (从 1 开始)", example = "1")
+        private Integer page;
+
+        @Schema(description = "每页数量", example = "30")
+        @JsonProperty("page_size")
+        private Integer pageSize;
+
+        @Schema(description = "按名称过滤", example = "技术")
+        private String name;
+
+        @Schema(description = "排序字段", example = "create_time")
+        private String orderby;
+
+        @Schema(description = "是否降序", example = "true")
+        private Boolean desc;
+
+        @Schema(description = "会话 ID 精确筛选", example = "session_001")
+        private String id;
+
+        @Schema(description = "用户标识筛选", example = "user_001")
+        @JsonProperty("user_id")
+        private String userId;
+    }
+
+    /**
+     * 会话详情 VO
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "会话详情 VO")
+    public static class SessionVO implements Serializable {
+
+        @Schema(description = "会话 ID", example = "session_001")
+        private String id;
+
+        @Schema(description = "助手 ID", example = "assistant_001")
+        @JsonProperty("chat_id")
+        private String chatId;
+
+        @Schema(description = "助手 ID (兼容旧版)", example = "assistant_001")
+        @JsonProperty("assistant_id")
+        private String assistantId;
+
+        @Schema(description = "会话名称", example = "技术咨询会话")
+        private String name;
+
+        @Schema(description = "创建时间 (时间戳)", example = "1700000000000")
+        @JsonProperty("create_time")
+        private Long createTime;
+
+        @Schema(description = "更新时间 (时间戳)", example = "1700000001000")
+        @JsonProperty("update_time")
+        private Long updateTime;
+
+        @Schema(description = "创建日期", example = "2024-05-01 10:00:00")
+        @JsonProperty("create_date")
+        private String createDate;
+
+        @Schema(description = "更新日期", example = "2024-05-01 10:00:00")
+        @JsonProperty("update_date")
+        private String updateDate;
+
+        @Schema(description = "用户 ID", example = "user_001")
+        @JsonProperty("user_id")
+        private String userId;
+
+        @Schema(description = "对话历史消息列表")
+        private List<Map<String, Object>> messages;
+    }
+
+    /**
+     * 删除会话请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "删除会话请求")
+    public static class SessionDeleteReq implements Serializable {
+
+        @Schema(description = "要删除的会话 ID 列表", example = "[\"session_001\", \"session_002\"]")
+        private List<String> ids;
+    }
+
+    // ========== 3. 消息/对话 (Completion) 相关 ==========
+
+    /**
+     * 发送消息请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "发送消息请求")
+    public static class CompletionReq implements Serializable {
+
+        @NotBlank(message = "问题内容不能为空")
+        @Schema(description = "用户问题", requiredMode = Schema.RequiredMode.REQUIRED, example = "请介绍一下你们的产品")
+        private String question;
+
+        @Schema(description = "是否使用流式响应 (SSE)", example = "true")
+        @Builder.Default
+        private Boolean stream = true;
+
+        @NotBlank(message = "会话 ID 不能为空")
+        @Schema(description = "会话 ID (可选，不传则创建新会话)", example = "session_001")
+        @JsonProperty("session_id")
+        private String sessionId;
+
+        @Schema(description = "是否展示引用", example = "true")
+        private Boolean quote;
+
+        @Schema(description = "指定检索的文档 ID 列表 (逗号分隔)", example = "doc_001,doc_002")
+        @JsonProperty("doc_ids")
+        private String docIds;
+
+        @Schema(description = "元数据过滤条件")
+        @JsonProperty("metadata_condition")
+        private Map<String, Object> metadataCondition;
+    }
+
+    /**
+     * 消息响应 VO
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "消息响应 VO")
+    public static class CompletionVO implements Serializable {
+
+        @Schema(description = "AI 回答内容")
+        private String answer;
+
+        @Schema(description = "引用信息")
+        private Reference reference;
+
+        @Schema(description = "会话 ID", example = "session_001")
+        @JsonProperty("session_id")
+        private String sessionId;
+
+        @Schema(description = "任务 ID (用于流式响应追踪)", example = "task_001")
+        @JsonProperty("task_id")
+        private String taskId;
+
+        /**
+         * 引用信息 (检索命中结果)
+         */
+        @Data
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Builder
+        @Schema(description = "引用信息")
+        public static class Reference implements Serializable {
+
+            @Schema(description = "命中的文档块列表")
+            private List<xiaozhi.modules.knowledge.dto.document.RetrievalDTO.HitVO> chunks;
+
+            @Schema(description = "文档聚合信息")
+            @JsonProperty("doc_aggs")
+            private List<DocAgg> docAggs;
+        }
+
+        /**
+         * 文档聚合信息
+         */
+        @Data
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Builder
+        @Schema(description = "文档聚合信息")
+        public static class DocAgg implements Serializable {
+
+            @Schema(description = "文档 ID", example = "doc_001")
+            @JsonProperty("doc_id")
+            private String docId;
+
+            @Schema(description = "文档名称", example = "产品手册.pdf")
+            @JsonProperty("doc_name")
+            private String docName;
+
+            @Schema(description = "命中次数", example = "3")
+            private Integer count;
+        }
+    }
+
+    /**
+     * 简易知识库 VO (用于 Assistant 列表)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "简易知识库 VO")
+    public static class SimpleDatasetVO implements Serializable {
+        @Schema(description = "知识库 ID")
+        private String id;
+        @Schema(description = "知识库名称")
+        private String name;
+        @Schema(description = "头像")
+        private String avatar;
+        @Schema(description = "分块数量")
+        @JsonProperty("chunk_num")
+        private Integer chunkNum;
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/common/CommonDTO.java

@@ -0,0 +1,79 @@
+package xiaozhi.modules.knowledge.dto.common;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import java.util.Map;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import jakarta.validation.constraints.*;
+
+@Schema(description = "通用扩展功能 DTO")
+public class CommonDTO {
+
+    // ========== 1. 引用详情 (detail_share_embedded) ==========
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "引用详情请求")
+    public static class ReferenceDetailReq implements Serializable {
+        @Schema(description = "切片 ID", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotBlank(message = "切片 ID 不能为空")
+        @JsonProperty("chunk_id")
+        private String chunkId;
+
+        @Schema(description = "知识库 ID")
+        @JsonProperty("knowledge_id")
+        private String knowledgeId;
+    }
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "引用详情响应")
+    public static class ReferenceDetailVO implements Serializable {
+        @Schema(description = "切片 ID")
+        @JsonProperty("chunk_id")
+        private String chunkId;
+
+        @Schema(description = "完整内容")
+        @JsonProperty("content_with_weight")
+        private String contentWithWeight;
+
+        @Schema(description = "文档名称")
+        @JsonProperty("doc_name")
+        private String docName;
+
+        @Schema(description = "图片 ID 列表")
+        @JsonProperty("img_id")
+        private String imageId; // 注意：RAGFlow 有时返回 String 有时返回 List，需根据实际情况确认，暂定 String 用于 ID
+
+        @Schema(description = "文档 ID")
+        @JsonProperty("doc_id")
+        private String docId;
+    }
+
+    // ========== 2. 通用问答 (ask_about) - 调试用 ==========
+
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "通用问答请求 (调试用)")
+    public static class AskAboutReq implements Serializable {
+        @Schema(description = "用户问题", requiredMode = Schema.RequiredMode.REQUIRED, example = "What is this dataset about?")
+        @NotBlank(message = "问题不能为空")
+        @JsonProperty("question")
+        private String question;
+
+        @Schema(description = "数据集 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotEmpty(message = "数据集列表不能为空")
+        @JsonProperty("dataset_ids")
+        private List<String> datasetIds;
+    }
+
+    // 响应通常复用 String 或者简单的 Map 结构，视具体实现而定，暂不定义专用 VO
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/dataset/DatasetDTO.java

@@ -0,0 +1,447 @@
+package xiaozhi.modules.knowledge.dto.dataset;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import java.util.Map;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import jakarta.validation.constraints.*;
+
+/**
+ * 知识库管理聚合 DTO
+ * <p>
+ * 容器类，内含知识库模块所有请求/响应对象的静态内部类定义。
+ * </p>
+ */
+@Schema(description = "知识库管理聚合 DTO")
+@JsonIgnoreProperties(ignoreUnknown = true)
+public class DatasetDTO {
+
+    // ========== 通用内部类 ==========
+
+    /**
+     * 解析器配置
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "解析器配置")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class ParserConfig implements Serializable {
+
+        @Schema(description = "分块 token 数量", example = "128")
+        @JsonProperty("chunk_token_num")
+        private Integer chunkTokenNum;
+
+        @Schema(description = "分隔符", example = "\\n!?;。；！？")
+        private String delimiter;
+
+        @Schema(description = "布局识别模型: DeepDOC / Simple", example = "DeepDOC")
+        @JsonProperty("layout_recognize")
+        private String layoutRecognize;
+
+        @Schema(description = "是否将 Excel 转为 HTML", example = "false")
+        private Boolean html4excel;
+
+        @Schema(description = "自动生成关键词数量 (0 表示关闭)", example = "0")
+        @JsonProperty("auto_keywords")
+        private Integer autoKeywords;
+
+        @Schema(description = "自动生成问题数量 (0 表示关闭)", example = "0")
+        @JsonProperty("auto_questions")
+        private Integer autoQuestions;
+    }
+
+    // ========== 请求类 ==========
+
+    /**
+     * 创建知识库请求 (映射接口 1: create)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "创建知识库请求")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class CreateReq implements Serializable {
+
+        @NotBlank(message = "知识库名称不能为空")
+        @Schema(description = "知识库名称", requiredMode = Schema.RequiredMode.REQUIRED, example = "my_dataset")
+        private String name;
+
+        @Schema(description = "知识库头像 (Base64 编码)", example = "")
+        private String avatar;
+
+        @Schema(description = "知识库描述", example = "用于存储产品文档")
+        private String description;
+
+        @Schema(description = "嵌入模型名称", example = "BAAI/bge-large-zh-v1.5")
+        @JsonProperty("embedding_model")
+        private String embeddingModel;
+
+        @Schema(description = "权限设置: me / team", example = "me")
+        private String permission;
+
+        @Schema(description = "分块方法: naive / manual / qa / table / paper / book / laws / presentation / picture / one / knowledge_graph / email", example = "naive")
+        @JsonProperty("chunk_method")
+        private String chunkMethod;
+
+        @Schema(description = "解析器配置")
+        @JsonProperty("parser_config")
+        private ParserConfig parserConfig;
+    }
+
+    /**
+     * 更新知识库请求 (映射接口 4: update)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "更新知识库请求")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class UpdateReq implements Serializable {
+
+        @Schema(description = "知识库名称", example = "updated_dataset")
+        private String name;
+
+        @Schema(description = "知识库头像 (Base64 编码)", example = "")
+        private String avatar;
+
+        @Schema(description = "知识库描述", example = "更新后的描述")
+        private String description;
+
+        @Schema(description = "权限设置: me / team", example = "team")
+        private String permission;
+
+        @Schema(description = "嵌入模型名称", example = "BAAI/bge-large-zh-v1.5")
+        @JsonProperty("embedding_model")
+        private String embeddingModel;
+
+        @Schema(description = "分块方法: naive / manual / qa / table / paper / book / laws / presentation / picture / one / knowledge_graph / email", example = "naive")
+        @JsonProperty("chunk_method")
+        private String chunkMethod;
+
+        @Schema(description = "解析器配置")
+        @JsonProperty("parser_config")
+        private ParserConfig parserConfig;
+
+        @Schema(description = "PageRank 权重 (0-100)", example = "50")
+        private Integer pagerank;
+    }
+
+    /**
+     * 查询知识库列表请求 (映射接口 3: list_datasets)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "查询知识库列表请求")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class ListReq implements Serializable {
+
+        @Schema(description = "页码 (从 1 开始)", example = "1")
+        private Integer page;
+
+        @Schema(description = "每页数量", example = "30")
+        @JsonProperty("page_size")
+        private Integer pageSize;
+
+        @Schema(description = "排序字段: create_time / update_time", example = "create_time")
+        private String orderby;
+
+        @Schema(description = "是否降序", example = "true")
+        private Boolean desc;
+
+        @Schema(description = "按名称过滤 (模糊匹配)", example = "my_dataset")
+        private String name;
+
+        @Schema(description = "按知识库 ID 过滤", example = "abc123")
+        private String id;
+    }
+
+    /**
+     * 批量删除知识库请求 (映射接口 2: delete)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "批量删除知识库请求")
+    public static class BatchIdReq implements Serializable {
+
+        @NotNull(message = "知识库 ID 列表不能为空")
+        @Size(min = 1, message = "至少需要一个知识库 ID")
+        @Schema(description = "知识库 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED, example = "[\"id1\", \"id2\"]")
+        private List<String> ids;
+    }
+
+    /**
+     * 运行 GraphRAG 请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "运行 GraphRAG 请求")
+    public static class RunGraphRagReq implements Serializable {
+
+        @Schema(description = "实体类型列表", example = "[\"person\", \"organization\"]")
+        @JsonProperty("entity_types")
+        private List<String> entityTypes;
+
+        @Schema(description = "构建方法: light / fast / full", example = "light")
+        private String method;
+    }
+
+    /**
+     * 运行 RAPTOR 请求
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "运行 RAPTOR 请求")
+    public static class RunRaptorReq implements Serializable {
+
+        @Schema(description = "最大聚类数", example = "64")
+        @JsonProperty("max_cluster")
+        private Integer maxCluster;
+
+        @Schema(description = "自定义提示词", example = "请总结以下内容...")
+        private String prompt;
+    }
+
+    /**
+     * 异步任务 ID 响应 VO (映射接口 7/8: run_graphrag/run_raptor)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "异步任务 ID 响应")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class TaskIdVO implements Serializable {
+
+        @Schema(description = "GraphRAG 任务 ID", example = "task_uuid_12345678")
+        @JsonProperty("graphrag_task_id")
+        private String graphragTaskId;
+
+        @Schema(description = "RAPTOR 任务 ID", example = "task_uuid_87654321")
+        @JsonProperty("raptor_task_id")
+        private String raptorTaskId;
+    }
+
+    // ========== 响应类 ==========
+
+    /**
+     * 知识库详情 VO (映射接口 1/3 的返回数据项)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "知识库详情 VO")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class InfoVO implements Serializable {
+
+        @Schema(description = "知识库 ID", example = "abc123")
+        private String id;
+
+        @Schema(description = "知识库名称", example = "my_dataset")
+        private String name;
+
+        @Schema(description = "知识库头像 (Base64 编码)", example = "")
+        private String avatar;
+
+        @Schema(description = "租户 ID", example = "tenant_001")
+        @JsonProperty("tenant_id")
+        private String tenantId;
+
+        @Schema(description = "知识库描述", example = "用于存储产品文档")
+        private String description;
+
+        @Schema(description = "嵌入模型名称", example = "BAAI/bge-large-zh-v1.5")
+        @JsonProperty("embedding_model")
+        private String embeddingModel;
+
+        @Schema(description = "权限设置: me / team", example = "me")
+        private String permission;
+
+        @Schema(description = "分块方法", example = "naive")
+        @JsonProperty("chunk_method")
+        private String chunkMethod;
+
+        @Schema(description = "解析器配置")
+        @JsonProperty("parser_config")
+        private ParserConfig parserConfig;
+
+        @Schema(description = "分块总数", example = "1024")
+        @JsonProperty("chunk_count")
+        private Long chunkCount;
+
+        @Schema(description = "文档总数", example = "50")
+        @JsonProperty("document_count")
+        private Long documentCount;
+
+        @Schema(description = "创建时间 (时间戳)", example = "1700000000000")
+        @JsonProperty("create_time")
+        private Long createTime;
+
+        @Schema(description = "更新时间 (时间戳)", example = "1700000001000")
+        @JsonProperty("update_time")
+        private Long updateTime;
+
+        @Schema(description = "总 Token 数", example = "102400")
+        @JsonProperty("token_num")
+        private Long tokenNum;
+
+        @Schema(description = "创建日期 (格式: yyyy-MM-dd HH:mm:ss)")
+        @JsonProperty("create_date")
+        private String createDate;
+
+        @Schema(description = "最后更新日期 (格式: yyyy-MM-dd HH:mm:ss)")
+        @JsonProperty("update_date")
+        private String updateDate;
+    }
+
+    /**
+     * 批量操作响应 VO
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "批量操作响应 VO")
+    public static class BatchOperationVO implements Serializable {
+
+        @Schema(description = "成功操作数量", example = "5")
+        @JsonProperty("success_count")
+        private Integer successCount;
+
+        @Schema(description = "错误列表")
+        private List<Object> errors;
+    }
+
+    // ========== 知识图谱相关 ==========
+
+    /**
+     * 知识图谱数据 VO (映射接口 5: knowledge_graph)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "知识图谱数据 VO")
+    public static class GraphVO implements Serializable {
+
+        @Schema(description = "图谱节点列表")
+        private List<Node> nodes;
+
+        @Schema(description = "图谱边列表")
+        private List<Edge> edges;
+
+        @Schema(description = "思维导图数据")
+        @JsonProperty("mind_map")
+        private Map<String, Object> mindMap;
+
+        /**
+         * 图谱节点
+         */
+        @Data
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Builder
+        @Schema(description = "图谱节点")
+        @JsonIgnoreProperties(ignoreUnknown = true)
+        public static class Node implements Serializable {
+
+            @Schema(description = "节点 ID", example = "node_001")
+            private String id;
+
+            @Schema(description = "节点标签", example = "产品")
+            private String label;
+
+            @Schema(description = "PageRank 值", example = "0.85")
+            private Double pagerank;
+
+            @Schema(description = "节点颜色", example = "#FF5733")
+            private String color;
+
+            @Schema(description = "节点图片 URL", example = "https://example.com/icon.png")
+            private String img;
+        }
+
+        /**
+         * 图谱边
+         */
+        @Data
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Builder
+        @Schema(description = "图谱边")
+        @JsonIgnoreProperties(ignoreUnknown = true)
+        public static class Edge implements Serializable {
+
+            @Schema(description = "源节点 ID", example = "node_001")
+            private String source;
+
+            @Schema(description = "目标节点 ID", example = "node_002")
+            private String target;
+
+            @Schema(description = "边权重", example = "0.75")
+            private Double weight;
+
+            @Schema(description = "边标签 (关系描述)", example = "属于")
+            private String label;
+        }
+    }
+
+    // ========== 异步任务追踪 (GraphRAG/RAPTOR) ==========
+
+    /**
+     * 异步任务追踪 VO (映射接口 9/10: 任务进度返回)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "异步任务追踪 VO")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class TaskTraceVO implements Serializable {
+
+        @Schema(description = "任务 ID", example = "task_001")
+        private String id;
+
+        @Schema(description = "文档 ID", example = "doc_001")
+        @JsonProperty("doc_id")
+        private String docId;
+
+        @Schema(description = "起始页码", example = "1")
+        @JsonProperty("from_page")
+        private Integer fromPage;
+
+        @Schema(description = "结束页码", example = "10")
+        @JsonProperty("to_page")
+        private Integer toPage;
+
+        @Schema(description = "进度百分比 (0.0 - 1.0)", example = "0.75")
+        private Double progress;
+
+        @Schema(description = "进度消息", example = "正在处理第 5 页...")
+        @JsonProperty("progress_msg")
+        private String progressMsg;
+
+        @Schema(description = "创建时间 (时间戳)", example = "1700000000000")
+        @JsonProperty("create_time")
+        private Long createTime;
+
+        @Schema(description = "更新时间 (时间戳)", example = "1700000001000")
+        @JsonProperty("update_time")
+        private Long updateTime;
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/document/ChunkDTO.java

@@ -0,0 +1,182 @@
+package xiaozhi.modules.knowledge.dto.document;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import jakarta.validation.constraints.*;
+
+/**
+ * 切片管理聚合 DTO
+ */
+@Schema(description = "切片管理聚合 DTO")
+@JsonIgnoreProperties(ignoreUnknown = true)
+public class ChunkDTO {
+
+    /**
+     * 新增切片请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "新增切片请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class AddReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "切片内容", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotBlank(message = "切片内容不能为空")
+        private String content;
+
+        @Schema(description = "重要关键词列表")
+        @JsonProperty("important_keywords")
+        private List<String> importantKeywords;
+
+        @Schema(description = "预设问题列表")
+        private List<String> questions;
+    }
+
+    /**
+     * 更新切片请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "更新切片请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class UpdateReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "新的切片内容")
+        private String content;
+
+        @Schema(description = "更新关键词列表 (覆盖原有列表)")
+        @JsonProperty("important_keywords")
+        private List<String> importantKeywords;
+
+        @Schema(description = "启用/禁用 (true: 启用, false: 禁用)")
+        private Boolean available;
+    }
+
+    /**
+     * 获取切片列表请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "获取切片列表请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class ListReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "页码 (默认 1)")
+        private Integer page;
+
+        @Schema(description = "每页数量 (默认 30)")
+        @JsonProperty("page_size")
+        private Integer pageSize;
+
+        @Schema(description = "搜索关键词 (全文检索)")
+        private String keywords;
+
+        @Schema(description = "精确切片 ID")
+        private String id;
+    }
+
+    /**
+     * 批量删除切片请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "批量删除切片请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class RemoveReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "切片 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("chunk_ids")
+        @NotEmpty(message = "切片ID列表不能为空")
+        private List<String> chunkIds;
+    }
+
+    /**
+     * 文档切片信息 VO
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "文档切片信息")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class InfoVO implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "切片 ID (通常为 document_id + 索引)", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String id;
+
+        @Schema(description = "切片文本内容 (全文检索的主要对象)", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String content;
+
+        @Schema(description = "所属文档 ID", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("document_id")
+        private String documentId;
+
+        @Schema(description = "文档名称 / 关键词")
+        @JsonProperty("docnm_kwd")
+        private String docnmKwd;
+
+        @Schema(description = "重要关键词列表 (用于关键词增强检索)")
+        @JsonProperty("important_keywords")
+        private List<String> importantKeywords;
+
+        @Schema(description = "预设问题列表 (用于 Q&A 模式增强)")
+        private List<String> questions;
+
+        @Schema(description = "关联的图片 ID")
+        @JsonProperty("image_id")
+        private String imageId;
+
+        @Schema(description = "所属知识库 ID")
+        @JsonProperty("dataset_id")
+        private String datasetId;
+
+        @Schema(description = "切片是否可用 (true: 参与检索, false: 被禁用)")
+        private Boolean available;
+
+        @Schema(description = "切片在原文中的位置索引列表 (RAGFlow返回嵌套数组, 如 [[start, end, filename]])")
+        private List<List<Object>> positions;
+
+        @Schema(description = "Token ID 列表")
+        @JsonProperty("token")
+        private List<Integer> token;
+    }
+
+    /**
+     * 分片列表聚合响应
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "分片列表聚合响应")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class ListVO implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "切片信息列表")
+        private List<InfoVO> chunks;
+
+        @Schema(description = "关联的文档详细信息")
+        private DocumentDTO.InfoVO doc;
+
+        @Schema(description = "总记录数")
+        private Long total;
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/document/DocumentDTO.java

@@ -0,0 +1,407 @@
+package xiaozhi.modules.knowledge.dto.document;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import java.util.Map;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import com.fasterxml.jackson.annotation.JsonAlias;
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import jakarta.validation.constraints.*;
+
+/**
+ * 文档管理聚合 DTO
+ */
+@Schema(description = "文档管理聚合 DTO")
+@JsonIgnoreProperties(ignoreUnknown = true)
+public class DocumentDTO {
+
+    /**
+     * 上传文档请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "上传文档请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class UploadReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "知识库 ID (必须指定归属)", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("dataset_id")
+        @NotBlank(message = "知识库ID不能为空")
+        private String datasetId;
+
+        @Schema(description = "文件名 (如果指定，则覆盖原始文件名)")
+        private String name;
+
+        @Schema(description = "分块方法")
+        @JsonProperty("chunk_method")
+        private DocumentDTO.InfoVO.ChunkMethod chunkMethod;
+
+        @Schema(description = "解析参数配置")
+        @JsonProperty("parser_config")
+        private DocumentDTO.InfoVO.ParserConfig parserConfig;
+
+        @Schema(description = "虚拟文件夹路径 (默认为 /)")
+        @JsonProperty("parent_path")
+        private String parentPath;
+
+        @Schema(description = "元数据字段")
+        @JsonProperty("meta")
+        private Map<String, Object> metaFields;
+
+        @Schema(description = "文件二进制流 (支持 PDF, DOCX, TXT, MD 等多种格式)", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotNull(message = "上传文件不能为空")
+        private org.springframework.web.multipart.MultipartFile file;
+    }
+
+    /**
+     * 更新文档请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "更新文档请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class UpdateReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "新文档名称 (必须包含文件后缀，且不能更改原始类型)")
+        private String name;
+
+        @Schema(description = "启用/禁用状态 (true: 启用, false: 禁用; 禁用后不参与检索)")
+        private Boolean enabled;
+
+        @Schema(description = "新解析方法 (修改此项会重置解析状态)")
+        @JsonProperty("chunk_method")
+        private InfoVO.ChunkMethod chunkMethod;
+
+        @Schema(description = "新解析器详细配置 (应与 chunk_method 配套使用)")
+        @JsonProperty("parser_config")
+        private InfoVO.ParserConfig parserConfig;
+    }
+
+    /**
+     * 获取文档列表请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "获取文档列表请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class ListReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "页码 (默认: 1)")
+        private Integer page;
+
+        @Schema(description = "每页数量 (默认: 30)")
+        @JsonProperty("page_size")
+        private Integer pageSize;
+
+        @Schema(description = "排序字段 (可选: create_time, name, size; 默认: create_time)")
+        private String orderby;
+
+        @Schema(description = "是否降序排列 (true: 最新/最大在前; false: 最旧/最小在前; 默认: true)")
+        private Boolean desc;
+
+        @Schema(description = "精确筛选: 文档 ID")
+        private String id;
+
+        @Schema(description = "精确筛选: 文档完整名称 (含后缀)")
+        private String name;
+
+        @Schema(description = "模糊搜索: 文档名称关键词")
+        private String keywords;
+
+        @Schema(description = "筛选: 文件后缀列表 (如 ['pdf', 'docx'])")
+        private List<String> suffix;
+
+        @Schema(description = "筛选: 运行状态列表")
+        private List<InfoVO.RunStatus> run;
+
+        @Schema(description = "筛选: 起始创建时间 (时间戳, 毫秒)")
+        @JsonProperty("create_time_from")
+        private Long createTimeFrom;
+
+        @Schema(description = "筛选: 结束创建时间 (时间戳, 毫秒)")
+        @JsonProperty("create_time_to")
+        private Long createTimeTo;
+    }
+
+    /**
+     * 批量文档操作请求参数 (用于删除、解析等)
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "批量文档操作请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class BatchIdReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "文档 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("ids") // 为了兼容性，也可以考虑支持 document_ids，但这里统一叫 ids
+        @JsonAlias("document_ids")
+        @NotEmpty(message = "文档ID列表不能为空")
+        private List<String> ids;
+    }
+
+    /**
+     * 知识库文档信息 VO
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "知识库文档信息")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class InfoVO implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "文档 ID (唯一标识)", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String id;
+
+        @Schema(description = "文档缩略图 URL (Base64 或 链接)")
+        private String thumbnail;
+
+        @Schema(description = "所属知识库 ID", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("dataset_id")
+        private String datasetId;
+
+        @Schema(description = "文档解析方法 (决定了文档如何被切片)")
+        @JsonProperty("chunk_method")
+        private ChunkMethod chunkMethod;
+
+        @Schema(description = "关联的 ETL Pipeline ID (如有)")
+        @JsonProperty("pipeline_id")
+        private String pipelineId;
+
+        @Schema(description = "文档解析器的详细配置")
+        @JsonProperty("parser_config")
+        private ParserConfig parserConfig;
+
+        @Schema(description = "来源类型 (如 local, s3, url 等)")
+        @JsonProperty("source_type")
+        private String sourceType;
+
+        @Schema(description = "文档文件类型 (如 pdf, docx, txt)", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String type;
+
+        @Schema(description = "创建者用户 ID")
+        @JsonProperty("created_by")
+        private String createdBy;
+
+        @Schema(description = "文档名称 (包含扩展名)", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String name;
+
+        @Schema(description = "文件存储路径或位置标识")
+        private String location;
+
+        @Schema(description = "文件大小 (单位: Bytes)")
+        private Long size;
+
+        @Schema(description = "包含的 Token 总数 (解析后统计)")
+        @JsonProperty("token_count")
+        private Long tokenCount;
+
+        @Schema(description = "包含的切片 (Chunk) 总数")
+        @JsonProperty("chunk_count")
+        private Long chunkCount;
+
+        @Schema(description = "解析进度 (0.0 ~ 1.0, 1.0 表示完成)")
+        private Double progress;
+
+        @Schema(description = "当前进度描述或错误信息")
+        @JsonProperty("progress_msg")
+        private String progressMsg;
+
+        @Schema(description = "开始处理的时间戳 (RAGFlow返回RFC1123格式)")
+        @JsonProperty("process_begin_at")
+        private String processBeginAt;
+
+        @Schema(description = "处理总耗时 (单位: 秒)")
+        @JsonProperty("process_duration")
+        private Double processDuration;
+
+        @Schema(description = "自定义元数据字段 (Key-Value 键值对)")
+        @JsonProperty("meta_fields")
+        private Map<String, Object> metaFields;
+
+        @Schema(description = "文件后缀名 (不含点)")
+        private String suffix;
+
+        @Schema(description = "文档解析运行状态")
+        private RunStatus run;
+
+        @Schema(description = "文档可用状态 (1: 启用/正常, 0: 禁用/失效)", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String status;
+
+        @Schema(description = "创建时间 (时间戳, 毫秒)", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("create_time")
+        private Long createTime;
+
+        @Schema(description = "创建日期 (RAGFlow返回RFC1123格式)")
+        @JsonProperty("create_date")
+        private String createDate;
+
+        @Schema(description = "最后更新时间 (时间戳, 毫秒)")
+        @JsonProperty("update_time")
+        private Long updateTime;
+
+        @Schema(description = "最后更新日期 (RAGFlow返回RFC1123格式)")
+        @JsonProperty("update_date")
+        private String updateDate;
+
+        /**
+         * 解析方法枚举 (ChunkMethod)
+         */
+        public enum ChunkMethod {
+            @Schema(description = "通用模式: 适用于大多数纯文本或混合文档")
+            @JsonProperty("naive")
+            NAIVE,
+            @Schema(description = "手动模式: 允许用户手动编辑切片")
+            @JsonProperty("manual")
+            MANUAL,
+            @Schema(description = "问答模式: 专门优化 Q&A 格式的文档")
+            @JsonProperty("qa")
+            QA,
+            @Schema(description = "表格模式: 专门优化 Excel 或 CSV 等表格数据")
+            @JsonProperty("table")
+            TABLE,
+            @Schema(description = "论文模式: 针对学术论文排版优化")
+            @JsonProperty("paper")
+            PAPER,
+            @Schema(description = "书籍模式: 针对书籍章节结构优化")
+            @JsonProperty("book")
+            BOOK,
+            @Schema(description = "法律法规模式: 针对法律条文结构优化")
+            @JsonProperty("laws")
+            LAWS,
+            @Schema(description = "演示文稿模式: 针对 PPT 等演示文件优化")
+            @JsonProperty("presentation")
+            PRESENTATION,
+            @Schema(description = "图片模式: 针对图片内容进行 OCR 和描述")
+            @JsonProperty("picture")
+            PICTURE,
+            @Schema(description = "整体模式: 将整个文档作为一个切片")
+            @JsonProperty("one")
+            ONE,
+            @Schema(description = "知识图谱模式: 提取实体关系构建图谱")
+            @JsonProperty("knowledge_graph")
+            KNOWLEDGE_GRAPH,
+            @Schema(description = "邮件模式: 针对邮件格式优化")
+            @JsonProperty("email")
+            EMAIL;
+        }
+
+        /**
+         * 运行状态枚举 (RunStatus)
+         */
+        public enum RunStatus {
+            @Schema(description = "未开始: 等待解析队列")
+            @JsonProperty("UNSTART")
+            UNSTART,
+            @Schema(description = "进行中: 正在解析或索引")
+            @JsonProperty("RUNNING")
+            RUNNING,
+            @Schema(description = "已取消: 用户手动取消")
+            @JsonProperty("CANCEL")
+            CANCEL,
+            @Schema(description = "已完成: 解析成功")
+            @JsonProperty("DONE")
+            DONE,
+            @Schema(description = "失败: 解析过程中出错")
+            @JsonProperty("FAIL")
+            FAIL;
+        }
+
+        /**
+         * 布局识别模型枚举
+         */
+        public enum LayoutRecognize {
+            @Schema(description = "深度文档理解模型: 适合复杂排版")
+            @JsonProperty("DeepDOC")
+            DeepDOC,
+            @Schema(description = "简单规则模型: 适合纯文本")
+            @JsonProperty("Simple")
+            Simple;
+        }
+
+        @Data
+        @Builder
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Schema(description = "文档解析器参数配置")
+        @JsonIgnoreProperties(ignoreUnknown = true)
+        public static class ParserConfig implements Serializable {
+            private static final long serialVersionUID = 1L;
+
+            @Schema(description = "切片最大 Token 数 (建议值: 512, 1024, 2048)")
+            @JsonProperty("chunk_token_num")
+            private Integer chunkTokenNum;
+
+            @Schema(description = "分段分隔符 (支持转义字符, 如 \\n)")
+            private String delimiter;
+
+            @Schema(description = "布局识别模型 (DeepDOC/Simple)")
+            @JsonProperty("layout_recognize")
+            private LayoutRecognize layoutRecognize;
+
+            @Schema(description = "是否将 Excel 转换为 HTML 表格")
+            @JsonProperty("html4excel")
+            private Boolean html4excel;
+
+            @Schema(description = "自动提取关键词数量 (0 表示不提取)")
+            @JsonProperty("auto_keywords")
+            private Integer autoKeywords;
+
+            @Schema(description = "自动生成问题数量 (0 表示不生成)")
+            @JsonProperty("auto_questions")
+            private Integer autoQuestions;
+
+            @Schema(description = "自动生成标签数量")
+            @JsonProperty("topn_tags")
+            private Integer topnTags;
+
+            @Schema(description = "RAPTOR 高级索引配置")
+            private RaptorConfig raptor;
+
+            @Schema(description = "GraphRAG 知识图谱配置")
+            @JsonProperty("graphrag")
+            private GraphRagConfig graphRag;
+
+            @Data
+            @Builder
+            @NoArgsConstructor
+            @AllArgsConstructor
+            @Schema(description = "RAPTOR (递归摘要索引) 配置")
+            @JsonIgnoreProperties(ignoreUnknown = true)
+            public static class RaptorConfig implements Serializable {
+                private static final long serialVersionUID = 1L;
+                @Schema(description = "是否启用 RAPTOR 索引")
+                @JsonProperty("use_raptor")
+                private Boolean useRaptor;
+            }
+
+            @Data
+            @Builder
+            @NoArgsConstructor
+            @AllArgsConstructor
+            @Schema(description = "GraphRAG (图增强检索) 配置")
+            @JsonIgnoreProperties(ignoreUnknown = true)
+            public static class GraphRagConfig implements Serializable {
+                private static final long serialVersionUID = 1L;
+                @Schema(description = "是否启用 GraphRAG 索引")
+                @JsonProperty("use_graphrag")
+                private Boolean useGraphRag;
+            }
+        }
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/document/RetrievalDTO.java

@@ -0,0 +1,307 @@
+package xiaozhi.modules.knowledge.dto.document;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import java.util.Map;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import com.fasterxml.jackson.annotation.JsonInclude;
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import jakarta.validation.constraints.*;
+
+/**
+ * 检索与元数据管理聚合 DTO
+ */
+@Schema(description = "检索与元数据管理聚合 DTO")
+@JsonIgnoreProperties(ignoreUnknown = true)
+public class RetrievalDTO {
+
+    /**
+     * 文档聚合信息 (VO)
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "文档聚合信息")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class DocAggVO implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "文档名称")
+        @JsonProperty("doc_name")
+        private String docName;
+
+        @Schema(description = "文档 ID")
+        @JsonProperty("doc_id")
+        private String docId;
+
+        @Schema(description = "数量")
+        private Integer count;
+    }
+
+    /**
+     * 检索测试请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "检索测试请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    @JsonInclude(JsonInclude.Include.NON_NULL)
+    public static class TestReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "知识库 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("dataset_ids")
+        @NotEmpty(message = "知识库ID列表不能为空")
+        private List<String> datasetIds;
+
+        @Schema(description = "文档 ID 列表 (可选，用于限定检索范围)")
+        @JsonProperty("document_ids")
+        private List<String> documentIds;
+
+        @Schema(description = "检索问题", requiredMode = Schema.RequiredMode.REQUIRED)
+        @NotBlank(message = "检索问题不能为空")
+        private String question;
+
+        @Schema(description = "页码 (默认 1)")
+        private Integer page;
+
+        @Schema(description = "每页数量 (默认 10)")
+        @JsonProperty("page_size")
+        private Integer pageSize;
+
+        @Schema(description = "相似度阈值 (默认 0.2)")
+        @JsonProperty("similarity_threshold")
+        private Float similarityThreshold;
+
+        @Schema(description = "向量相似度权重 (默认 0.3)")
+        @JsonProperty("vector_similarity_weight")
+        private Float vectorSimilarityWeight;
+
+        @Schema(description = "返回 Top K 切片 (默认 1024)")
+        @JsonProperty("top_k")
+        private Integer topK;
+
+        @Schema(description = "重排序模型 ID")
+        @JsonProperty("rerank_id")
+        private String rerankId;
+
+        @Schema(description = "是否高亮关键词")
+        private Boolean highlight;
+
+        @Schema(description = "是否启用关键词检索")
+        private Boolean keyword;
+
+        @Schema(description = "跨语言翻译列表 (可选)")
+        @JsonProperty("cross_languages")
+        private List<String> crossLanguages;
+
+        @Schema(description = "元数据过滤条件 (JSON 对象)")
+        @JsonProperty("metadata_condition")
+        private Map<String, Object> metadataCondition;
+    }
+
+    /**
+     * 检索命中结果 (VO)
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "检索命中切片详情")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class HitVO implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "切片 ID", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String id;
+
+        @Schema(description = "切片内容", requiredMode = Schema.RequiredMode.REQUIRED)
+        private String content;
+
+        @Schema(description = "所属文档 ID", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("document_id")
+        private String documentId;
+
+        @Schema(description = "所属知识库 ID")
+        @JsonProperty("dataset_id")
+        private String datasetId;
+
+        @Schema(description = "文档名称")
+        @JsonProperty("document_name")
+        private String documentName;
+
+        @Schema(description = "文档关键词")
+        @JsonProperty("document_keyword")
+        private String documentKeyword;
+
+        @Schema(description = "综合相似度", requiredMode = Schema.RequiredMode.REQUIRED)
+        private Float similarity;
+
+        @Schema(description = "向量相似度")
+        @JsonProperty("vector_similarity")
+        private Float vectorSimilarity;
+
+        @Schema(description = "关键词相似度")
+        @JsonProperty("term_similarity")
+        private Float termSimilarity;
+
+        @Schema(description = "索引位置")
+        private Integer index;
+
+        @Schema(description = "高亮内容")
+        private String highlight;
+
+        @Schema(description = "重要关键词列表")
+        @JsonProperty("important_keywords")
+        private List<String> importantKeywords;
+
+        @Schema(description = "预设问题列表")
+        private List<String> questions;
+
+        @Schema(description = "图片 ID")
+        @JsonProperty("image_id")
+        private String imageId;
+
+        @Schema(description = "位置索引 (RAGFlow返回嵌套数组, 如 [[start, end, filename]])")
+        private Object positions;
+    }
+
+    /**
+     * 知识库元数据摘要 (VO)
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "知识库元数据摘要信息")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class MetaSummaryVO implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "文档总数", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("total_doc_count")
+        private Long totalDocCount;
+
+        @Schema(description = "Token 总数", requiredMode = Schema.RequiredMode.REQUIRED)
+        @JsonProperty("total_token_count")
+        private Long totalTokenCount;
+
+        @Schema(description = "文件类型分布 (key: 文件后缀, value: 数量)")
+        @JsonProperty("file_type_distribution")
+        private Map<String, Long> fileTypeDistribution;
+
+        @Schema(description = "文状态分布 (key: 状态码, value: 数量)")
+        @JsonProperty("status_distribution")
+        private Map<String, Long> statusDistribution;
+
+        @Schema(description = "自定义元数据统计 (key: 字段名, value: 数量/值)")
+        @JsonProperty("custom_metadata")
+        private Map<String, Object> customMetadata;
+    }
+
+    /**
+     * 批量更新元数据请求参数
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "批量更新元数据请求参数")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class MetaBatchReq implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "筛选器: 用于指定要更新的文档范围 (默认全部)")
+        private Selector selector;
+
+        @Schema(description = "新增或更新的元数据列表")
+        private List<UpdateItem> updates;
+
+        @Schema(description = "需要删除的元数据键列表")
+        private List<DeleteItem> deletes;
+
+        /**
+         * 文档筛选器
+         */
+        @Data
+        @Builder
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Schema(description = "元数据更新筛选器")
+        @JsonIgnoreProperties(ignoreUnknown = true)
+        public static class Selector implements Serializable {
+            private static final long serialVersionUID = 1L;
+
+            @Schema(description = "指定文档 ID 列表")
+            @JsonProperty("document_ids")
+            private List<String> documentIds;
+
+            @Schema(description = "元数据条件匹配 (key: 字段名, value: 匹配值)")
+            @JsonProperty("metadata_condition")
+            private Map<String, Object> metadataCondition;
+        }
+
+        /**
+         * 更新项
+         */
+        @Data
+        @Builder
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Schema(description = "元数据更新项")
+        @JsonIgnoreProperties(ignoreUnknown = true)
+        public static class UpdateItem implements Serializable {
+            private static final long serialVersionUID = 1L;
+
+            @Schema(description = "元数据键名", requiredMode = Schema.RequiredMode.REQUIRED)
+            private String key;
+
+            @Schema(description = "元数据值", requiredMode = Schema.RequiredMode.REQUIRED)
+            private Object value;
+        }
+
+        /**
+         * 删除项
+         */
+        @Data
+        @Builder
+        @NoArgsConstructor
+        @AllArgsConstructor
+        @Schema(description = "元数据删除项")
+        @JsonIgnoreProperties(ignoreUnknown = true)
+        public static class DeleteItem implements Serializable {
+            private static final long serialVersionUID = 1L;
+
+            @Schema(description = "需删除的元数据键名", requiredMode = Schema.RequiredMode.REQUIRED)
+            private String key;
+        }
+    }
+
+    /**
+     * 召回测试结果聚合响应
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Schema(description = "召回测试结果聚合响应")
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public static class ResultVO implements Serializable {
+        private static final long serialVersionUID = 1L;
+
+        @Schema(description = "检索命中的切片列表")
+        private List<HitVO> chunks;
+
+        @Schema(description = "文档分布统计")
+        @JsonProperty("doc_aggs")
+        private List<DocAggVO> docAggs;
+
+        @Schema(description = "总命中记录数")
+        private Long total;
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/dto/file/FileDTO.java

@@ -0,0 +1,363 @@
+package xiaozhi.modules.knowledge.dto.file;
+
+import lombok.*;
+import io.swagger.v3.oas.annotations.media.Schema;
+import java.io.Serializable;
+import java.util.List;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import jakarta.validation.constraints.*;
+import org.springframework.web.multipart.MultipartFile;
+
+/**
+ * 文件管理聚合 DTO
+ * <p>
+ * 容器类，内含文件模块所有请求/响应对象的静态内部类定义。
+ * </p>
+ */
+@Schema(description = "文件管理聚合 DTO")
+public class FileDTO {
+
+    // ========== 请求类 ==========
+
+    /**
+     * 文件上传请求 (对应接口 1: upload)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "文件上传请求")
+    public static class UploadReq implements Serializable {
+
+        @NotNull(message = "文件不能为空")
+        @Schema(description = "上传的文件", requiredMode = Schema.RequiredMode.REQUIRED)
+        private MultipartFile file;
+
+        @Schema(description = "父文件夹 ID (为空则上传到根目录)", example = "folder_001")
+        @JsonProperty("parent_id")
+        private String parentId;
+    }
+
+    /**
+     * 新建文件夹请求 (对应接口 2: create)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "新建文件夹请求")
+    public static class CreateReq implements Serializable {
+
+        @NotBlank(message = "文件夹名称不能为空")
+        @Schema(description = "文件夹名称", requiredMode = Schema.RequiredMode.REQUIRED, example = "新建文件夹")
+        private String name;
+
+        @Schema(description = "父文件夹 ID (为空则创建在根目录)", example = "folder_001")
+        @JsonProperty("parent_id")
+        private String parentId;
+
+        @NotBlank(message = "类型不能为空")
+        @Schema(description = "类型: FOLDER", requiredMode = Schema.RequiredMode.REQUIRED, example = "FOLDER")
+        @Builder.Default
+        private String type = "FOLDER";
+    }
+
+    /**
+     * 重命名请求 (对应接口 6: rename)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "重命名请求")
+    public static class RenameReq implements Serializable {
+
+        @NotBlank(message = "文件 ID 不能为空")
+        @Schema(description = "文件/文件夹 ID", requiredMode = Schema.RequiredMode.REQUIRED, example = "file_001")
+        @JsonProperty("file_id")
+        private String fileId;
+
+        @NotBlank(message = "新名称不能为空")
+        @Schema(description = "新名称", requiredMode = Schema.RequiredMode.REQUIRED, example = "重命名后的文件")
+        private String name;
+    }
+
+    /**
+     * 移动请求 (对应接口 7: move)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "移动请求")
+    public static class MoveReq implements Serializable {
+
+        @NotEmpty(message = "源文件 ID 列表不能为空")
+        @Schema(description = "源文件/文件夹 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED, example = "[\"file_001\", \"file_002\"]")
+        @JsonProperty("src_file_ids")
+        private List<String> srcFileIds;
+
+        @NotBlank(message = "目标文件夹 ID 不能为空")
+        @Schema(description = "目标文件夹 ID", requiredMode = Schema.RequiredMode.REQUIRED, example = "folder_002")
+        @JsonProperty("dest_file_id")
+        private String destFileId;
+    }
+
+    /**
+     * 批量删除请求 (对应接口 8: rm)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "批量删除请求")
+    public static class RemoveReq implements Serializable {
+
+        @NotEmpty(message = "文件 ID 列表不能为空")
+        @Schema(description = "文件/文件夹 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED, example = "[\"file_001\", \"file_002\"]")
+        @JsonProperty("file_ids")
+        private List<String> fileIds;
+    }
+
+    /**
+     * 导入知识库请求 (对应接口 9: convert)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "导入知识库请求")
+    public static class ConvertReq implements Serializable {
+
+        @NotEmpty(message = "文件 ID 列表不能为空")
+        @Schema(description = "文件 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED, example = "[\"file_001\", \"file_002\"]")
+        @JsonProperty("file_ids")
+        private List<String> fileIds;
+
+        @NotEmpty(message = "知识库 ID 列表不能为空")
+        @Schema(description = "目标知识库 ID 列表", requiredMode = Schema.RequiredMode.REQUIRED, example = "[\"kb_001\"]")
+        @JsonProperty("kb_ids")
+        private List<String> kbIds;
+    }
+
+    /**
+     * 列表查询请求 (对应接口 3: list_files)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "列表查询请求")
+    public static class ListReq implements Serializable {
+
+        @Schema(description = "父文件夹 ID (为空则查询根目录)", example = "folder_001")
+        @JsonProperty("parent_id")
+        private String parentId;
+
+        @Schema(description = "关键词搜索", example = "文档")
+        private String keywords;
+
+        @Schema(description = "页码 (从 1 开始)", example = "1")
+        private Integer page;
+
+        @Schema(description = "每页数量", example = "30")
+        @JsonProperty("page_size")
+        private Integer pageSize;
+
+        @Schema(description = "排序字段: create_time / update_time / name / size", example = "create_time")
+        private String orderby;
+
+        @Schema(description = "是否降序", example = "true")
+        private Boolean desc;
+    }
+
+    // ========== 响应类 ==========
+
+    /**
+     * 文件/文件夹基础信息 VO
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "文件/文件夹基础信息")
+    public static class InfoVO implements Serializable {
+
+        @Schema(description = "文件/文件夹 ID", example = "file_001")
+        private String id;
+
+        @Schema(description = "父文件夹 ID", example = "folder_001")
+        @JsonProperty("parent_id")
+        private String parentId;
+
+        @Schema(description = "租户 ID", example = "tenant_001")
+        @JsonProperty("tenant_id")
+        private String tenantId;
+
+        @Schema(description = "创建者 ID", example = "user_001")
+        @JsonProperty("created_by")
+        private String createdBy;
+
+        @Schema(description = "类型: FOLDER / FILE", example = "FOLDER")
+        private String type;
+
+        @Schema(description = "名称", example = "我的文件夹")
+        private String name;
+
+        @Schema(description = "路径位置", example = "/root/folder")
+        private String location;
+
+        @Schema(description = "文件大小 (字节)", example = "1024")
+        private Long size;
+
+        @Schema(description = "来源类型", example = "local")
+        @JsonProperty("source_type")
+        private String sourceType;
+
+        @Schema(description = "创建时间 (时间戳)", example = "1700000000000")
+        @JsonProperty("create_time")
+        private Long createTime;
+
+        @Schema(description = "创建日期 (格式化)", example = "2024-01-15 10:30:00")
+        @JsonProperty("create_date")
+        private String createDate;
+
+        @Schema(description = "更新时间 (时间戳)", example = "1700000001000")
+        @JsonProperty("update_time")
+        private Long updateTime;
+
+        @Schema(description = "更新日期 (格式化)", example = "2024-01-15 11:00:00")
+        @JsonProperty("update_date")
+        private String updateDate;
+
+        @Schema(description = "文件扩展名", example = "pdf")
+        private String extension;
+    }
+
+    /**
+     * 列表响应 VO (对应接口 3: list_files)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "文件列表响应")
+    public static class ListVO implements Serializable {
+
+        @Schema(description = "总记录数", example = "100")
+        private Long total;
+
+        @Schema(description = "当前父文件夹信息")
+        @JsonProperty("parent_folder")
+        private InfoVO parentFolder;
+
+        @Schema(description = "文件/文件夹列表")
+        private List<InfoVO> files;
+
+        @Schema(description = "面包屑导航路径")
+        private List<InfoVO> breadcrumb;
+    }
+
+    /**
+     * 转换结果项 VO (对应接口 9: convert)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "文件转换结果项")
+    public static class ConvertVO implements Serializable {
+
+        @Schema(description = "转换记录 ID", example = "convert_001")
+        private String id;
+
+        @Schema(description = "源文件 ID", example = "file_001")
+        @JsonProperty("file_id")
+        private String fileId;
+
+        @Schema(description = "目标文档 ID", example = "doc_001")
+        @JsonProperty("document_id")
+        private String documentId;
+
+        @Schema(description = "创建时间 (时间戳)", example = "1700000000000")
+        @JsonProperty("create_time")
+        private Long createTime;
+
+        @Schema(description = "创建日期 (格式化)", example = "2024-01-15 10:30:00")
+        @JsonProperty("create_date")
+        private String createDate;
+
+        @Schema(description = "更新时间 (时间戳)", example = "1700000001000")
+        @JsonProperty("update_time")
+        private Long updateTime;
+
+        @Schema(description = "更新日期 (格式化)", example = "2024-01-15 11:00:00")
+        @JsonProperty("update_date")
+        private String updateDate;
+    }
+
+    /**
+     * 转换状态 VO (对应接口 10: get_convert_status)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "文件转换状态")
+    public static class ConvertStatusVO implements Serializable {
+
+        @Schema(description = "转换状态: pending / processing / completed / failed", example = "completed")
+        private String status;
+
+        @Schema(description = "转换进度 (0.0 - 1.0)", example = "1.0")
+        private Float progress;
+
+        @Schema(description = "状态消息", example = "转换完成")
+        private String message;
+    }
+
+    /**
+     * 面包屑 VO (对应接口 12: all_parent_folder)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "面包屑导航 (所有父文件夹)")
+    public static class BreadcrumbVO implements Serializable {
+
+        @Schema(description = "父文件夹列表 (从根到当前的路径)")
+        @JsonProperty("parent_folders")
+        private List<InfoVO> parentFolders;
+    }
+
+    /**
+     * 根目录信息 VO (对应接口 10: get_root_folder)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "根目录信息")
+    public static class RootFolderVO implements Serializable {
+
+        @Schema(description = "根文件夹信息")
+        @JsonProperty("root_folder")
+        private InfoVO rootFolder;
+    }
+
+    /**
+     * 父目录信息 VO (对应接口 11: get_parent_folder)
+     */
+    @Data
+    @NoArgsConstructor
+    @AllArgsConstructor
+    @Builder
+    @Schema(description = "父目录信息")
+    public static class ParentFolderVO implements Serializable {
+
+        @Schema(description = "父文件夹信息")
+        @JsonProperty("parent_folder")
+        private InfoVO parentFolder;
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/entity/DocumentEntity.java

@@ -0,0 +1,97 @@
+package xiaozhi.modules.knowledge.entity;
+
+import java.io.Serializable;
+import java.util.Date;
+
+import com.baomidou.mybatisplus.annotation.FieldFill;
+import com.baomidou.mybatisplus.annotation.IdType;
+import com.baomidou.mybatisplus.annotation.TableField;
+import com.baomidou.mybatisplus.annotation.TableId;
+import com.baomidou.mybatisplus.annotation.TableName;
+
+import io.swagger.v3.oas.annotations.media.Schema;
+import lombok.Data;
+
+/**
+ * 文档表 (Shadow DB for RAGFlow Documents)
+ * 对应表名: ai_knowledge_document
+ */
+@Data
+@TableName(value = "ai_rag_knowledge_document", autoResultMap = true)
+@Schema(description = "知识库文档表")
+public class DocumentEntity implements Serializable {
+    private static final long serialVersionUID = 1L;
+
+    @TableId(type = IdType.ASSIGN_UUID)
+    @Schema(description = "本地唯一ID")
+    private String id;
+
+    @Schema(description = "知识库ID (关联 ai_rag_dataset.dataset_id)")
+    private String datasetId;
+
+    @Schema(description = "RAGFlow文档ID (远程ID)")
+    private String documentId;
+
+    @Schema(description = "文档名称")
+    private String name;
+
+    @Schema(description = "文件大小(Bytes)")
+    private Long size;
+
+    @Schema(description = "文件类型(pdf/doc/txt等)")
+    private String type;
+
+    @Schema(description = "分块方法")
+    private String chunkMethod;
+
+    @Schema(description = "解析配置(JSON String)")
+    private String parserConfig;
+
+    @Schema(description = "可用状态 (1: 启用/正常, 0: 禁用/失效)")
+    private String status;
+
+    @Schema(description = "运行状态 (UNSTART/RUNNING/CANCEL/DONE/FAIL)")
+    private String run;
+
+    @Schema(description = "解析进度 (0.0 ~ 1.0)")
+    private Double progress;
+
+    @Schema(description = "缩略图 (Base64 或 URL)")
+    private String thumbnail;
+
+    @Schema(description = "解析耗时 (单位: 秒)")
+    private Double processDuration;
+
+    @Schema(description = "自定义元数据 (JSON 格式)")
+    private String metaFields;
+
+    @Schema(description = "来源类型 (local, s3, url 等)")
+    private String sourceType;
+
+    @Schema(description = "解析错误信息")
+    private String error;
+
+    @Schema(description = "分块数量")
+    private Integer chunkCount;
+
+    @Schema(description = "Token数量")
+    private Long tokenCount;
+
+    @Schema(description = "是否启用 (0:禁用 1:启用)")
+    private Integer enabled;
+
+    @Schema(description = "创建者")
+    @TableField(fill = FieldFill.INSERT)
+    private Long creator;
+
+    @Schema(description = "创建时间")
+    @TableField(fill = FieldFill.INSERT)
+    private Date createdAt;
+
+    @Schema(description = "更新时间")
+    @TableField(fill = FieldFill.UPDATE)
+    private Date updatedAt;
+
+    @Schema(description = "最新同步时间")
+    private Date lastSyncAt;
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/entity/KnowledgeBaseEntity.java

@@ -23,15 +23,43 @@ public class KnowledgeBaseEntity {
     @Schema(description = "知识库ID")
     private String datasetId;
 
-    @Schema(description = "RAG模型配置ID")
+//    @Deprecated
+    @Schema(description = "RAG模型配置ID (连接RAGFlow的凭证指针)")
     private String ragModelId;
 
+    @Schema(description = "租户ID")
+    private String tenantId;
+
     @Schema(description = "知识库名称")
     private String name;
 
+    @Schema(description = "知识库头像(Base64)")
+    private String avatar;
+
     @Schema(description = "知识库描述")
     private String description;
 
+    @Schema(description = "嵌入模型名称")
+    private String embeddingModel;
+
+    @Schema(description = "权限设置: me/team")
+    private String permission;
+
+    @Schema(description = "分块方法")
+    private String chunkMethod;
+
+    @Schema(description = "解析器配置(JSON String)")
+    private String parserConfig;
+
+    @Schema(description = "分块总数")
+    private Long chunkCount;
+
+    @Schema(description = "文档总数")
+    private Long documentCount;
+
+    @Schema(description = "总Token数")
+    private Long tokenNum;
+
     @Schema(description = "状态(0:禁用 1:启用)")
     private Integer status;
 

main/manager-api/src/main/java/xiaozhi/modules/knowledge/rag/KnowledgeBaseAdapter.java

@@ -3,10 +3,14 @@ package xiaozhi.modules.knowledge.rag;
 import java.util.List;
 import java.util.Map;
 
-import org.springframework.web.multipart.MultipartFile;
+import xiaozhi.modules.knowledge.dto.dataset.DatasetDTO;
 
 import xiaozhi.common.page.PageData;
 import xiaozhi.modules.knowledge.dto.KnowledgeFilesDTO;
+import xiaozhi.modules.knowledge.dto.document.DocumentDTO;
+import xiaozhi.modules.knowledge.dto.document.ChunkDTO;
+import xiaozhi.modules.knowledge.dto.document.RetrievalDTO;
+import java.util.function.Consumer;
 
 /**
  * 知识库API适配器抽象基类
@@ -46,35 +50,24 @@ public abstract class KnowledgeBaseAdapter {
          * @return 分页数据
          */
         public abstract PageData<KnowledgeFilesDTO> getDocumentList(String datasetId,
-                        Map<String, Object> queryParams,
+                        DocumentDTO.ListReq req);
-                        Integer page,
-                        Integer limit);
 
         /**
          * 根据文档ID获取文档详情
          * 
-         * @param datasetId 知识库ID
+         * @param datasetId  知识库ID
-         * @return 文档详情
+         * @param documentId 文档ID
+         * @return 文档详情 (强类型 InfoVO)
          */
-        public abstract KnowledgeFilesDTO getDocumentById(String datasetId, String documentId);
+        public abstract DocumentDTO.InfoVO getDocumentById(String datasetId, String documentId);
 
         /**
          * 上传文档到知识库
          * 
-         * @param datasetId    知识库ID
+         * @param req 上传请求参数
-         * @param file         上传的文件
-         * @param name         文档名称
-         * @param metaFields   元数据字段
-         * @param chunkMethod  分块方法
-         * @param parserConfig 解析器配置
          * @return 上传的文档信息
          */
-        public abstract KnowledgeFilesDTO uploadDocument(String datasetId,
+        public abstract KnowledgeFilesDTO uploadDocument(DocumentDTO.UploadReq req);
-                        MultipartFile file,
-                        String name,
-                        Map<String, Object> metaFields,
-                        String chunkMethod,
-                        Map<String, Object> parserConfig);
 
         /**
          * 根据状态分页查询文档列表
@@ -91,12 +84,12 @@ public abstract class KnowledgeBaseAdapter {
                         Integer limit);
 
         /**
-         * 删除文档
+         * 删除文档 (支持批量删除)
          * 
-         * @param datasetId  知识库ID
+         * @param datasetId 知识库ID
-         * @param documentId 文档ID
+         * @param req       包含文档ID列表的请求对象
          */
-        public abstract void deleteDocument(String datasetId, String documentId);
+        public abstract void deleteDocument(String datasetId, DocumentDTO.BatchIdReq req);
 
         /**
          * 解析文档（切块）
@@ -112,32 +105,21 @@ public abstract class KnowledgeBaseAdapter {
          * 
          * @param datasetId  知识库ID
          * @param documentId 文档ID
-         * @param keywords   关键词过滤
+         * @param req        列表请求参数 (分页、关键词等)
-         * @param page       页码
+         * @return 切片列表VO
-         * @param pageSize   每页数量
-         * @param chunkId    切片ID
-         * @return 切片列表信息
          */
-        public abstract Map<String, Object> listChunks(String datasetId,
+        public abstract ChunkDTO.ListVO listChunks(String datasetId,
                         String documentId,
-                        String keywords,
+                        ChunkDTO.ListReq req);
-                        Integer page,
-                        Integer pageSize,
-                        String chunkId);
 
         /**
          * 召回测试 - 从知识库中检索相关切片
          * 
-         * @param question        用户查询
+         * @param req 检索测试请求参数
-         * @param datasetIds      数据集ID列表
-         * @param documentIds     文档ID列表
-         * @param retrievalParams 检索参数
          * @return 召回测试结果
          */
-        public abstract Map<String, Object> retrievalTest(String question,
+        public abstract RetrievalDTO.ResultVO retrievalTest(
-                        List<String> datasetIds,
+                        RetrievalDTO.TestReq req);
-                        List<String> documentIds,
-                        Map<String, Object> retrievalParams);
 
         /**
          * 测试连接
@@ -170,25 +152,27 @@ public abstract class KnowledgeBaseAdapter {
         /**
          * 创建数据集
          * 
-         * @param createParams 创建参数
+         * @param req 创建参数
-         * @return 数据集ID
+         * @return 数据集详情
          */
-        public abstract String createDataset(Map<String, Object> createParams);
+        public abstract DatasetDTO.InfoVO createDataset(DatasetDTO.CreateReq req);
 
         /**
          * 更新数据集
          * 
-         * @param datasetId    数据集ID
+         * @param datasetId 数据集ID
-         * @param updateParams 更新参数
+         * @param req       更新参数
+         * @return 数据集详情
          */
-        public abstract void updateDataset(String datasetId, Map<String, Object> updateParams);
+        public abstract DatasetDTO.InfoVO updateDataset(String datasetId, DatasetDTO.UpdateReq req);
 
         /**
          * 删除数据集
          * 
-         * @param datasetId 数据集ID
+         * @param req 删除请求参数（包含ID列表）
+         * @return 批量操作结果
          */
-        public abstract void deleteDataset(String datasetId);
+        public abstract DatasetDTO.BatchOperationVO deleteDataset(DatasetDTO.BatchIdReq req);
 
         /**
          * 获取数据集的文档数量
@@ -197,4 +181,35 @@ public abstract class KnowledgeBaseAdapter {
          * @return 文档数量
          */
         public abstract Integer getDocumentCount(String datasetId);
+
+        /**
+         * 发送流式请求 (SSE)
+         * 
+         * @param endpoint API端点
+         * @param body     请求体
+         * @param onData   数据回调
+         */
+        public abstract void postStream(String endpoint, Object body, Consumer<String> onData);
+
+        /**
+         * SearchBot 提问
+         *
+         * @param config RAG配置
+         * @param body   请求体
+         * @param onData 数据回调
+         * @return 响应对象
+         */
+        public abstract Object postSearchBotAsk(Map<String, Object> config, Object body,
+                        Consumer<String> onData);
+
+        /**
+         * AgentBot 对话
+         *
+         * @param config  RAG配置
+         * @param agentId Agent ID
+         * @param body    请求体
+         * @param onData  数据回调
+         */
+        public abstract void postAgentBotCompletion(Map<String, Object> config, String agentId, Object body,
+                        Consumer<String> onData);
 }

main/manager-api/src/main/java/xiaozhi/modules/knowledge/rag/KnowledgeBaseAdapterFactory.java

@@ -22,6 +22,9 @@ public class KnowledgeBaseAdapterFactory {
     // 适配器实例缓存
     private static final Map<String, KnowledgeBaseAdapter> adapterCache = new ConcurrentHashMap<>();
 
+    // 最大缓存实例数，防止内存泄露 (Issue 9)
+    private static final int MAX_CACHE_SIZE = 50;
+
     static {
         // 注册内置适配器类型
         registerAdapter("ragflow", xiaozhi.modules.knowledge.rag.impl.RAGFlowAdapter.class);
@@ -61,7 +64,13 @@ public class KnowledgeBaseAdapterFactory {
         // 创建新的适配器实例
         KnowledgeBaseAdapter adapter = createAdapter(adapterType, config);
 
-        // 缓存适配器实例
+        // 缓存适配器实例 (带容量限制检查)
+        if (adapterCache.size() >= MAX_CACHE_SIZE) {
+            log.warn("适配器缓存已达上限 ({})，执行内存保护性清除", MAX_CACHE_SIZE);
+            // 简单处理：直接清空，生产环境下建议使用 LRU
+            adapterCache.clear();
+        }
+
         adapterCache.put(cacheKey, adapter);
         log.info("创建并缓存适配器实例: {}", cacheKey);
 

main/manager-api/src/main/java/xiaozhi/modules/knowledge/rag/RAGFlowClient.java

@@ -0,0 +1,278 @@
+package xiaozhi.modules.knowledge.rag;
+
+import java.time.Duration;
+import java.nio.charset.StandardCharsets;
+import java.util.Collections;
+import java.util.Map;
+
+import org.springframework.boot.web.client.RestTemplateBuilder;
+import org.springframework.http.HttpEntity;
+import org.springframework.http.HttpHeaders;
+import org.springframework.http.HttpMethod;
+import org.springframework.http.MediaType;
+import org.springframework.http.ResponseEntity;
+import org.springframework.util.MultiValueMap;
+import org.springframework.web.client.RestTemplate;
+import org.springframework.http.client.SimpleClientHttpRequestFactory;
+
+import java.text.SimpleDateFormat;
+import java.util.TimeZone;
+import java.util.Locale;
+import java.net.URLEncoder;
+import java.io.UnsupportedEncodingException;
+import java.net.http.HttpClient;
+import java.net.http.HttpRequest;
+import java.net.http.HttpResponse;
+import java.net.URI;
+import java.io.OutputStream;
+import java.io.ByteArrayOutputStream;
+import java.io.IOException;
+import java.util.function.Consumer;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+
+import lombok.extern.slf4j.Slf4j;
+import xiaozhi.common.exception.ErrorCode;
+import xiaozhi.common.exception.RenException;
+
+/**
+ * RAGFlow HTTP Client
+ * 统一处理HTTP通信、鉴权、超时与错误解析
+ */
+@Slf4j
+public class RAGFlowClient {
+
+    private final String baseUrl;
+    private final String apiKey;
+    private final RestTemplate restTemplate;
+    private final ObjectMapper objectMapper;
+
+    // 默认超时时间 (秒)
+    private static final int DEFAULT_TIMEOUT = 30;
+
+    public RAGFlowClient(String baseUrl, String apiKey) {
+        this(baseUrl, apiKey, DEFAULT_TIMEOUT);
+    }
+
+    public RAGFlowClient(String baseUrl, String apiKey, int timeoutSeconds) {
+        this.baseUrl = baseUrl.endsWith("/") ? baseUrl.substring(0, baseUrl.length() - 1) : baseUrl;
+        this.apiKey = apiKey;
+        this.objectMapper = new ObjectMapper();
+        // [Reinforce] 兼容 RAGFlow 返回的 RFC 1123 日期格式 (如: Tue, 10 Feb 2026 10:27:35 GMT)
+        this.objectMapper
+                .setDateFormat(new SimpleDateFormat("EEE, dd MMM yyyy HH:mm:ss 'GMT'", Locale.US));
+        this.objectMapper.setTimeZone(TimeZone.getTimeZone("GMT"));
+
+        // 优先从 Spring 上下文中获取池化的 RestTemplate Bean (Issue 3: 连接池化)
+        RestTemplate pooledTemplate = null;
+        try {
+            pooledTemplate = xiaozhi.common.utils.SpringContextUtils.getBean(RestTemplate.class);
+        } catch (Exception e) {
+            log.warn("无法从 SpringContext 获取池化 RestTemplate，将退化为简单连接模式: {}", e.getMessage());
+        }
+
+        if (false) { // Force new RestTemplate for debugging
+            this.restTemplate = pooledTemplate;
+            log.debug("RAGFlowClient 已成功挂载全局池化 RestTemplate");
+        } else {
+            // 兜底方案：配置超时并创建简单 RestTemplate
+            log.info("RAGFlowClient 初始化: 使用独立 RestTemplate (Debug Mode)");
+            SimpleClientHttpRequestFactory factory = new SimpleClientHttpRequestFactory();
+            factory.setConnectTimeout(timeoutSeconds * 1000);
+            factory.setReadTimeout(timeoutSeconds * 1000);
+            this.restTemplate = new RestTemplate(factory);
+        }
+    }
+
+    /**
+     * 发送 GET 请求
+     */
+    public Map<String, Object> get(String endpoint, Map<String, Object> queryParams) {
+        String url = buildUrl(endpoint, queryParams);
+        log.debug("GET {}", url);
+        return execute(url, HttpMethod.GET, null);
+    }
+
+    /**
+     * 发送 POST 请求 (JSON)
+     */
+    public Map<String, Object> post(String endpoint, Object body) {
+        String url = buildUrl(endpoint, null);
+        log.info("RAGFlow Client POST Request: URL={}, BodyType={}", url,
+                body != null ? body.getClass().getName() : "null");
+        try {
+            return execute(url, HttpMethod.POST, body);
+        } catch (Exception e) {
+            log.error("RAGFlow Client POST Failed: URL={}", url, e);
+            throw e;
+        }
+    }
+
+    /**
+     * 发送 DELETE 请求
+     */
+    public Map<String, Object> delete(String endpoint, Object body) {
+        String url = buildUrl(endpoint, null);
+        log.debug("DELETE {}", url);
+        return execute(url, HttpMethod.DELETE, body);
+    }
+
+    /**
+     * 发送 PUT 请求
+     */
+    public Map<String, Object> put(String endpoint, Object body) {
+        String url = buildUrl(endpoint, null);
+        log.debug("PUT {}", url);
+        return execute(url, HttpMethod.PUT, body);
+    }
+
+    /**
+     * 发送 Multipart 请求 (文件上传)
+     */
+    public Map<String, Object> postMultipart(String endpoint, MultiValueMap<String, Object> parts) {
+        String url = buildUrl(endpoint, null);
+        log.debug("POST MULTIPART {}", url);
+
+        HttpHeaders headers = new HttpHeaders();
+        headers.setContentType(MediaType.MULTIPART_FORM_DATA);
+        headers.setBearerAuth(apiKey);
+        // 为了防止中文文件名乱码，某些环境可能需要设置 Charset，但在 Multipart 中通常由 Part header 控制
+
+        HttpEntity<MultiValueMap<String, Object>> requestEntity = new HttpEntity<>(parts, headers);
+
+        return doExecute(url, HttpMethod.POST, requestEntity);
+    }
+
+    private Map<String, Object> execute(String url, HttpMethod method, Object body) {
+        HttpHeaders headers = new HttpHeaders();
+        headers.setContentType(MediaType.APPLICATION_JSON);
+        headers.setBearerAuth(apiKey);
+        // 强制 UTF-8
+        headers.setAcceptCharset(Collections.singletonList(StandardCharsets.UTF_8));
+
+        HttpEntity<Object> requestEntity = new HttpEntity<>(body, headers);
+        return doExecute(url, method, requestEntity);
+    }
+
+    private Map<String, Object> doExecute(String url, HttpMethod method, HttpEntity<?> requestEntity) {
+        try {
+            ResponseEntity<String> response = restTemplate.exchange(url, method, requestEntity, String.class);
+
+            if (!response.getStatusCode().is2xxSuccessful()) {
+                log.error("RAGFlow API Error Status: {}", response.getStatusCode());
+                throw new RenException(ErrorCode.RAG_API_ERROR, "HTTP " + response.getStatusCode());
+            }
+
+            String responseBody = response.getBody();
+            if (responseBody == null) {
+                throw new RenException(ErrorCode.RAG_API_ERROR, "Empty Response");
+            }
+
+            @SuppressWarnings("unchecked")
+            Map<String, Object> map = objectMapper.readValue(responseBody, Map.class);
+
+            Integer code = (Integer) map.get("code");
+            if (code != null && code != 0) {
+                String msg = (String) map.get("message");
+                log.error("RAGFlow Business Error: code={}, msg={}", code, msg);
+                throw new RenException(ErrorCode.RAG_API_ERROR, msg != null ? msg : "Unknown RAGFlow Error");
+            }
+
+            // 返回 data 字段，如果 data 不存在则返回整个 map (视具体情况，通常 RAGFlow 返回 code=0, data=...)
+            // 兼容性处理：如果 external caller 需要 check code，这里已经 check 过了。
+            // 统一返回 wrap 了 code 的 map 还是只返回 data?
+            // 根据分析报告，旧逻辑 check code==0 后取 data.
+            // 这里我们返回整个 Map，让 Adapter 决定怎么取，或者我们直接在这里剥离？
+            // 建议：为了灵活性，返回全量 Map，但在 Client 层做 code!=0 的抛错。
+            return map;
+
+        } catch (RenException re) {
+            throw re;
+        } catch (Exception e) {
+            log.error("RAGFlow Client Execute Error! URL: {}, Method: {}, Body Type: {}", url, method,
+                    requestEntity.getBody() != null ? requestEntity.getBody().getClass().getName() : "null");
+            log.error("Full exception stack trace: ", e);
+            throw new RenException(ErrorCode.RAG_API_ERROR, "Request Failed: " + e.getMessage());
+        }
+    }
+
+    private String buildUrl(String endpoint, Map<String, Object> queryParams) {
+        StringBuilder sb = new StringBuilder(baseUrl);
+        if (!endpoint.startsWith("/")) {
+            sb.append("/");
+        }
+        sb.append(endpoint);
+
+        if (queryParams != null && !queryParams.isEmpty()) {
+            sb.append("?");
+            queryParams.forEach((k, v) -> {
+                if (v != null) {
+                    try {
+                        sb.append(k).append("=")
+                                .append(URLEncoder.encode(v.toString(),
+                                        StandardCharsets.UTF_8.name()))
+                                .append("&");
+                    } catch (UnsupportedEncodingException e) {
+                        log.warn("参数编码失败: k={}, v={}", k, v);
+                        sb.append(k).append("=").append(v).append("&");
+                    }
+                }
+            });
+            // 移除最后一个 &
+            sb.setLength(sb.length() - 1);
+        }
+        return sb.toString();
+    }
+
+    /**
+     * 发送流式 POST 请求 (SSE)
+     * 使用 Java 21 HttpClient 实现
+     *
+     * @param endpoint API端点
+     * @param body     请求体
+     * @param onData   数据回调（每收到一行数据调用一次）
+     */
+    public void postStream(String endpoint, Object body, Consumer<String> onData) {
+        try {
+            String url = buildUrl(endpoint, null);
+            log.debug("POST STREAM {}", url);
+
+            String jsonBody = objectMapper.writeValueAsString(body);
+
+            HttpClient httpClient = HttpClient.newBuilder()
+                    .connectTimeout(Duration.ofSeconds(DEFAULT_TIMEOUT))
+                    .build();
+
+            HttpRequest request = HttpRequest.newBuilder()
+                    .uri(URI.create(url))
+                    .header("Content-Type", "application/json")
+                    .header("Authorization", "Bearer " + apiKey)
+                    .POST(HttpRequest.BodyPublishers.ofString(jsonBody, StandardCharsets.UTF_8))
+                    .build();
+
+            // 发送请求并处理流式响应
+            httpClient.send(request, HttpResponse.BodyHandlers.ofInputStream())
+                    .body()
+                    .transferTo(new OutputStream() {
+                        private final ByteArrayOutputStream buffer = new ByteArrayOutputStream();
+
+                        @Override
+                        public void write(int b) throws IOException {
+                            if (b == '\n') {
+                                String line = buffer.toString(StandardCharsets.UTF_8);
+                                if (!line.trim().isEmpty()) {
+                                    onData.accept(line);
+                                }
+                                buffer.reset();
+                            } else {
+                                buffer.write(b);
+                            }
+                        }
+                    });
+
+        } catch (Exception e) {
+            log.error("RAGFlow Stream Request Error", e);
+            throw new RenException(ErrorCode.RAG_API_ERROR, "Stream Request Failed: " + e.getMessage());
+        }
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/service/KnowledgeBaseService.java

@@ -93,4 +93,14 @@ public interface KnowledgeBaseService extends BaseService<KnowledgeBaseEntity> {
      * @return RAG模型列表
      */
     List<ModelConfigEntity> getRAGModels();
+
+    /**
+     * 更新知识库统计信息 (用于被文件服务回调)
+     * 
+     * @param datasetId  知识库ID
+     * @param docDelta   文档数增量
+     * @param chunkDelta 分块数增量
+     * @param tokenDelta Token数增量
+     */
+    void updateStatistics(String datasetId, Integer docDelta, Long chunkDelta, Long tokenDelta);
 }

main/manager-api/src/main/java/xiaozhi/modules/knowledge/service/KnowledgeFilesService.java

@@ -7,6 +7,9 @@ import org.springframework.web.multipart.MultipartFile;
 
 import xiaozhi.common.page.PageData;
 import xiaozhi.modules.knowledge.dto.KnowledgeFilesDTO;
+import xiaozhi.modules.knowledge.dto.document.ChunkDTO;
+import xiaozhi.modules.knowledge.dto.document.RetrievalDTO;
+import xiaozhi.modules.knowledge.dto.document.DocumentDTO;
 
 /**
  * 知识库文档服务接口
@@ -28,9 +31,9 @@ public interface KnowledgeFilesService {
          * 
          * @param documentId 文档ID
          * @param datasetId  知识库ID
-         * @return 文档详情
+         * @return 文档详情 (强类型 InfoVO)
          */
-        KnowledgeFilesDTO getByDocumentId(String documentId, String datasetId);
+        DocumentDTO.InfoVO getByDocumentId(String documentId, String datasetId);
 
         /**
          * 上传文档到知识库
@@ -48,12 +51,12 @@ public interface KnowledgeFilesService {
                         Map<String, Object> parserConfig);
 
         /**
-         * 根据文档ID和知识库ID删除文档
+         * 批量删除文档
          * 
-         * @param documentId 文档ID
+         * @param datasetId 知识库ID
-         * @param datasetId  知识库ID
+         * @param req       删除请求参数 (含文档ID列表)
          */
-        void deleteByDocumentId(String documentId, String datasetId);
+        void deleteDocuments(String datasetId, DocumentDTO.BatchIdReq req);
 
         /**
          * 获取RAG配置信息
@@ -77,36 +80,44 @@ public interface KnowledgeFilesService {
          * 
          * @param datasetId  知识库ID
          * @param documentId 文档ID
-         * @param keywords   关键词过滤
+         * @param req        切片列表请求参数
-         * @param page       页码
-         * @param pageSize   每页数量
-         * @param chunkId    切片ID
          * @return 切片列表信息
          */
-        Map<String, Object> listChunks(String datasetId, String documentId, String keywords,
+        ChunkDTO.ListVO listChunks(String datasetId, String documentId, ChunkDTO.ListReq req);
-                        Integer page, Integer pageSize, String chunkId);
 
         /**
-         * 召回测试 - 从指定数据集或文档中检索相关切片
+         * 召回测试
          * 
-         * @param question               用户查询或查询关键词
+         * @param req 检索测试请求参数
-         * @param datasetIds             数据集ID列表
-         * @param documentIds            文档ID列表
-         * @param page                   页码
-         * @param pageSize               每页数量
-         * @param similarityThreshold    最小相似度阈值
-         * @param vectorSimilarityWeight 向量相似度权重
-         * @param topK                   参与向量余弦计算的切片数量
-         * @param rerankId               重排模型ID
-         * @param keyword                是否启用关键词匹配
-         * @param highlight              是否启用高亮显示
-         * @param crossLanguages         跨语言翻译列表
-         * @param metadataCondition      元数据过滤条件
          * @return 召回测试结果
          */
-        Map<String, Object> retrievalTest(String question, List<String> datasetIds, List<String> documentIds,
+        RetrievalDTO.ResultVO retrievalTest(RetrievalDTO.TestReq req);
-                        Integer page, Integer pageSize, Float similarityThreshold,
+
-                        Float vectorSimilarityWeight, Integer topK, String rerankId,
+        /**
-                        Boolean keyword, Boolean highlight, List<String> crossLanguages,
+         * 保存文档影子记录
-                        Map<String, Object> metadataCondition);
+         */
+        void saveDocumentShadow(String datasetId, KnowledgeFilesDTO result, String originalName, String chunkMethod,
+                        Map<String, Object> parserConfig);
+
+        /**
+         * 批量删除文档影子记录并同步统计数据
+         * 
+         * @param documentIds 文档ID列表
+         * @param datasetId   数据集ID
+         * @param chunkDelta  待扣减的总分块数
+         * @param tokenDelta  待扣减的总Token数
+         */
+        void deleteDocumentShadows(List<String> documentIds, String datasetId, Long chunkDelta, Long tokenDelta);
+
+        /**
+         * 根据数据集ID清理所有关联文档 (级联删除专用)
+         * 
+         * @param datasetId 数据集ID
+         */
+        void deleteDocumentsByDatasetId(String datasetId);
+
+        /**
+         * 同步所有处于 RUNNING 状态的文档 (供定时任务调用)
+         */
+        void syncRunningDocuments();
 }

main/manager-api/src/main/java/xiaozhi/modules/knowledge/service/KnowledgeManagerService.java

@@ -0,0 +1,24 @@
+package xiaozhi.modules.knowledge.service;
+
+import java.util.List;
+
+/**
+ * 知识库模块领域编排服务
+ * 用于处理跨 KnowledgeBase 和 KnowledgeFiles 的复杂业务流程，彻底解决 Service 间的循环依赖问题。
+ */
+public interface KnowledgeManagerService {
+
+    /**
+     * 级联删除知识库及其下属所有文档 (包括本地 DB 和 RAGFlow 远程数据)
+     * 
+     * @param datasetId 知识库 ID
+     */
+    void deleteDatasetWithFiles(String datasetId);
+
+    /**
+     * 批量级联删除知识库
+     * 
+     * @param datasetIds 知识库 ID 列表
+     */
+    void batchDeleteDatasetsWithFiles(List<String> datasetIds);
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/service/impl/KnowledgeBaseServiceImpl.java

@@ -1,19 +1,14 @@
 package xiaozhi.modules.knowledge.service.impl;
 
-import java.io.Serializable;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import org.apache.commons.lang3.StringUtils;
-import org.springframework.stereotype.Service;
-import org.springframework.web.client.RestTemplate;
 import com.baomidou.mybatisplus.core.conditions.query.QueryWrapper;
 import com.baomidou.mybatisplus.core.metadata.IPage;
 import com.baomidou.mybatisplus.extension.plugins.pagination.Page;
-import com.fasterxml.jackson.databind.ObjectMapper;
-
 import lombok.AllArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.lang3.StringUtils;
+import org.springframework.stereotype.Service;
+import org.springframework.transaction.annotation.Transactional;
+import org.springframework.beans.BeanUtils;
 import xiaozhi.common.constant.Constant;
 import xiaozhi.common.exception.ErrorCode;
 import xiaozhi.common.exception.RenException;
@@ -22,10 +17,10 @@ import xiaozhi.common.redis.RedisKeys;
 import xiaozhi.common.redis.RedisUtils;
 import xiaozhi.common.service.impl.BaseServiceImpl;
 import xiaozhi.common.utils.ConvertUtils;
-import xiaozhi.common.utils.MessageUtils;
+import xiaozhi.common.utils.JsonUtils;
-import xiaozhi.common.utils.ToolUtil;
 import xiaozhi.modules.knowledge.dao.KnowledgeBaseDao;
 import xiaozhi.modules.knowledge.dto.KnowledgeBaseDTO;
+import xiaozhi.modules.knowledge.dto.dataset.DatasetDTO;
 import xiaozhi.modules.knowledge.entity.KnowledgeBaseEntity;
 import xiaozhi.modules.knowledge.rag.KnowledgeBaseAdapter;
 import xiaozhi.modules.knowledge.rag.KnowledgeBaseAdapterFactory;
@@ -35,6 +30,15 @@ import xiaozhi.modules.model.entity.ModelConfigEntity;
 import xiaozhi.modules.model.service.ModelConfigService;
 import xiaozhi.modules.security.user.SecurityUser;
 
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * 知识库服务实现类 (Refactored)
+ * 集成 RAGFlow Adapter 与 Shadow DB 模式
+ */
 @Service
 @AllArgsConstructor
 @Slf4j
@@ -46,200 +50,50 @@ public class KnowledgeBaseServiceImpl extends BaseServiceImpl<KnowledgeBaseDao,
     private final ModelConfigDao modelConfigDao;
     private final RedisUtils redisUtils;
 
-    @Override
-    public KnowledgeBaseEntity selectById(Serializable datasetId) {
-        if (datasetId == null) {
-            return null;
-        }
-
-        // 先从Redis获取缓存
-        String key = RedisKeys.getKnowledgeBaseCacheKey(datasetId.toString());
-        KnowledgeBaseEntity cachedEntity = (KnowledgeBaseEntity) redisUtils.get(key);
-        if (cachedEntity != null) {
-            return cachedEntity;
-        }
-
-        // 如果缓存中没有，则从数据库获取
-        KnowledgeBaseEntity entity = knowledgeBaseDao.selectById(datasetId);
-        if (entity == null) {
-            return null;
-        }
-
-        // 存入Redis缓存
-        redisUtils.set(key, entity);
-
-        return entity;
-    }
-
     @Override
     public PageData<KnowledgeBaseDTO> getPageList(KnowledgeBaseDTO knowledgeBaseDTO, Integer page, Integer limit) {
-        long curPage = page;
+        Page<KnowledgeBaseEntity> pageInfo = new Page<>(page, limit);
-        long pageSize = limit;
-        Page<KnowledgeBaseEntity> pageInfo = new Page<>(curPage, pageSize);
-
         QueryWrapper<KnowledgeBaseEntity> queryWrapper = new QueryWrapper<>();
 
-        // 添加查询条件
         if (knowledgeBaseDTO != null) {
             queryWrapper.like(StringUtils.isNotBlank(knowledgeBaseDTO.getName()), "name", knowledgeBaseDTO.getName());
             queryWrapper.eq(knowledgeBaseDTO.getStatus() != null, "status", knowledgeBaseDTO.getStatus());
             queryWrapper.eq("creator", knowledgeBaseDTO.getCreator());
         }
-
-        // 添加排序规则：按创建时间降序
         queryWrapper.orderByDesc("created_at");
 
-        IPage<KnowledgeBaseEntity> knowledgeBaseEntityIPage = knowledgeBaseDao.selectPage(pageInfo, queryWrapper);
+        IPage<KnowledgeBaseEntity> iPage = knowledgeBaseDao.selectPage(pageInfo, queryWrapper);
+        PageData<KnowledgeBaseDTO> pageData = getPageData(iPage, KnowledgeBaseDTO.class);
 
-        // 获取分页数据
+        // Enrich with Document Count from RAG (Optional / Lazy)
-        PageData<KnowledgeBaseDTO> pageData = getPageData(knowledgeBaseEntityIPage, KnowledgeBaseDTO.class);
-
-        // 为每个知识库获取文档数量
         if (pageData != null && pageData.getList() != null) {
-            for (KnowledgeBaseDTO knowledgeBase : pageData.getList()) {
+            for (KnowledgeBaseDTO dto : pageData.getList()) {
-                try {
+                enrichDocumentCount(dto);
-                    Integer documentCount = getDocumentCountFromRAG(knowledgeBase.getDatasetId(),
-                            knowledgeBase.getRagModelId());
-                    knowledgeBase.setDocumentCount(documentCount);
-                } catch (Exception e) {
-                    // 构建详细的错误信息，包含异常类型和消息
-                    String baseErrorMessage = e.getClass().getSimpleName() + " - 获取知识库文档数量失败";
-                    String errorMessage = baseErrorMessage + (e.getMessage() != null ? ": " + e.getMessage() : "");
-                    log.warn("知识库 {} {}", knowledgeBase.getDatasetId(), errorMessage);
-                    knowledgeBase.setDocumentCount(0); // 设置默认值
-                }
             }
         }
-
         return pageData;
     }
 
+    private void enrichDocumentCount(KnowledgeBaseDTO dto) {
+        try {
+            if (StringUtils.isNotBlank(dto.getDatasetId()) && StringUtils.isNotBlank(dto.getRagModelId())) {
+                KnowledgeBaseAdapter adapter = getAdapterByModelId(dto.getRagModelId());
+                if (adapter != null) {
+                    dto.setDocumentCount(adapter.getDocumentCount(dto.getDatasetId()));
+                }
+            }
+        } catch (Exception e) {
+            log.warn("无法获取知识库 {} 的文档计数: {}", dto.getName(), e.getMessage());
+            dto.setDocumentCount(0);
+        }
+    }
+
     @Override
     public KnowledgeBaseDTO getById(String id) {
-        if (StringUtils.isBlank(id)) {
-            throw new RenException(ErrorCode.IDENTIFIER_NOT_NULL);
-        }
-
         KnowledgeBaseEntity entity = knowledgeBaseDao.selectById(id);
         if (entity == null) {
             throw new RenException(ErrorCode.Knowledge_Base_RECORD_NOT_EXISTS);
         }
-
-        return ConvertUtils.sourceToTarget(entity, KnowledgeBaseDTO.class);
-    }
-
-    @Override
-    public KnowledgeBaseDTO save(KnowledgeBaseDTO knowledgeBaseDTO) {
-        if (knowledgeBaseDTO == null) {
-            throw new RenException(ErrorCode.PARAMS_GET_ERROR);
-        }
-
-        // 检查是否存在同名知识库
-        checkDuplicateKnowledgeBaseName(knowledgeBaseDTO, null);
-
-        String datasetId = null;
-        // 调用RAG API创建数据集
-        try {
-            Map<String, Object> ragConfig = getValidatedRAGConfig(knowledgeBaseDTO.getRagModelId());
-            datasetId = createDatasetInRAG(
-                    knowledgeBaseDTO.getName(),
-                    knowledgeBaseDTO.getDescription(),
-                    ragConfig);
-        } catch (Exception e) {
-            // 如果RAG API调用失败，直接抛出异常
-            throw e;
-        }
-
-        // 验证数据集ID是否已存在
-        KnowledgeBaseEntity existingEntity = knowledgeBaseDao.selectOne(
-                new QueryWrapper<KnowledgeBaseEntity>().eq("dataset_id", datasetId));
-        if (existingEntity != null) {
-            // 如果datasetId已存在，删除RAG中的数据集并抛出异常
-            try {
-                Map<String, Object> ragConfig = getValidatedRAGConfig(knowledgeBaseDTO.getRagModelId());
-                deleteDatasetInRAG(datasetId, ragConfig);
-            } catch (Exception deleteException) {
-                // 提供更详细的错误信息，包括异常类型和消息
-                String errorMessage = "删除重复datasetId的RAG数据集失败: " + deleteException.getClass().getSimpleName();
-                if (deleteException.getMessage() != null) {
-                    errorMessage += " - " + deleteException.getMessage();
-                }
-                log.warn(errorMessage, deleteException);
-            }
-            throw new RenException(ErrorCode.DB_RECORD_EXISTS);
-        }
-
-        // 创建本地实体并保存
-        KnowledgeBaseEntity entity = ConvertUtils.sourceToTarget(knowledgeBaseDTO, KnowledgeBaseEntity.class);
-        entity.setDatasetId(datasetId);
-        knowledgeBaseDao.insert(entity);
-
-        return ConvertUtils.sourceToTarget(entity, KnowledgeBaseDTO.class);
-    }
-
-    @Override
-    public KnowledgeBaseDTO update(KnowledgeBaseDTO knowledgeBaseDTO) {
-        if (knowledgeBaseDTO == null || StringUtils.isBlank(knowledgeBaseDTO.getId())) {
-            throw new RenException(ErrorCode.IDENTIFIER_NOT_NULL);
-        }
-
-        // 检查记录是否存在
-        KnowledgeBaseEntity existingEntity = knowledgeBaseDao.selectById(knowledgeBaseDTO.getId());
-        if (existingEntity == null) {
-            throw new RenException(ErrorCode.Knowledge_Base_RECORD_NOT_EXISTS);
-        }
-
-        // 检查是否存在同名知识库（排除当前记录）
-        checkDuplicateKnowledgeBaseName(knowledgeBaseDTO, knowledgeBaseDTO.getId());
-
-        // 验证数据集ID是否与其他记录冲突
-        if (StringUtils.isNotBlank(knowledgeBaseDTO.getDatasetId())) {
-            KnowledgeBaseEntity conflictEntity = knowledgeBaseDao.selectOne(
-                    new QueryWrapper<KnowledgeBaseEntity>()
-                            .eq("dataset_id", knowledgeBaseDTO.getDatasetId())
-                            .ne("id", knowledgeBaseDTO.getId()));
-            if (conflictEntity != null) {
-                throw new RenException(ErrorCode.DB_RECORD_EXISTS);
-            }
-        }
-
-        boolean needRagValidation = StringUtils.isNotBlank(knowledgeBaseDTO.getDatasetId())
-                && StringUtils.isNotBlank(knowledgeBaseDTO.getRagModelId());
-
-        if (needRagValidation) {
-            try {
-                // 先校验RAG配置
-                Map<String, Object> ragConfig = getValidatedRAGConfig(knowledgeBaseDTO.getRagModelId());
-
-                // 调用RAG API更新数据集
-                updateDatasetInRAG(
-                        knowledgeBaseDTO.getDatasetId(),
-                        knowledgeBaseDTO.getName(),
-                        knowledgeBaseDTO.getDescription(),
-                        ragConfig);
-
-                log.info("RAG API更新成功，datasetId: {}", knowledgeBaseDTO.getDatasetId());
-            } catch (Exception e) {
-                // 提供更详细的错误信息，包括异常类型和消息
-                String errorMessage = "更新RAG数据集失败: " + e.getClass().getSimpleName();
-                if (e.getMessage() != null) {
-                    errorMessage += " - " + e.getMessage();
-                }
-                log.error(errorMessage, e);
-                throw e;
-            }
-        } else {
-            log.warn("datasetId或ragModelId为空，跳过RAG更新");
-        }
-
-        KnowledgeBaseEntity entity = ConvertUtils.sourceToTarget(knowledgeBaseDTO, KnowledgeBaseEntity.class);
-        knowledgeBaseDao.updateById(entity);
-
-        // 删除缓存
-        if (entity.getDatasetId() != null) {
-            redisUtils.delete(RedisKeys.getKnowledgeBaseCacheKey(entity.getId()));
-        }
-
         return ConvertUtils.sourceToTarget(entity, KnowledgeBaseDTO.class);
     }
 
@@ -248,400 +102,316 @@ public class KnowledgeBaseServiceImpl extends BaseServiceImpl<KnowledgeBaseDao,
         if (StringUtils.isBlank(datasetId)) {
             throw new RenException(ErrorCode.PARAMS_GET_ERROR);
         }
-
+        // [Production Fix] 兼容性查找：优先通过 dataset_id 找，找不到通过主键 id 找，确保前端传哪种 UUID 都能命中
-        KnowledgeBaseEntity entity = knowledgeBaseDao.selectOne(
+        KnowledgeBaseEntity entity = knowledgeBaseDao
-                new QueryWrapper<KnowledgeBaseEntity>().eq("dataset_id", datasetId));
+                .selectOne(new QueryWrapper<KnowledgeBaseEntity>()
-
+                        .eq("dataset_id", datasetId)
+                        .or()
+                        .eq("id", datasetId));
         if (entity == null) {
             throw new RenException(ErrorCode.Knowledge_Base_RECORD_NOT_EXISTS);
         }
+        return ConvertUtils.sourceToTarget(entity, KnowledgeBaseDTO.class);
+    }
+
+    @Override
+    @Transactional(rollbackFor = Exception.class)
+    public KnowledgeBaseDTO save(KnowledgeBaseDTO dto) {
+        // 1. Validation
+        checkDuplicateName(dto.getName(), null);
+        KnowledgeBaseAdapter adapter = null;
+
+        // 2. RAG Creation
+        String datasetId = null;
+        try {
+            // 若未指定 RAG 模型，自动使用系统默认
+            if (StringUtils.isBlank(dto.getRagModelId())) {
+                List<ModelConfigEntity> models = getRAGModels();
+                if (models != null && !models.isEmpty()) {
+                    dto.setRagModelId(models.get(0).getId());
+                } else {
+                    throw new RenException(ErrorCode.RAG_CONFIG_NOT_FOUND, "未指定且无可用默认 RAG 模型");
+                }
+            }
+
+            Map<String, Object> ragConfig = getValidatedRAGConfig(dto.getRagModelId());
+            adapter = KnowledgeBaseAdapterFactory.getAdapter((String) ragConfig.get("type"),
+                    ragConfig);
+
+            DatasetDTO.CreateReq createReq = ConvertUtils.sourceToTarget(dto, DatasetDTO.CreateReq.class);
+            createReq.setName(SecurityUser.getUser().getUsername() + "_" + dto.getName());
+
+            DatasetDTO.InfoVO ragResponse = adapter.createDataset(createReq);
+            if (ragResponse == null || StringUtils.isBlank(ragResponse.getId())) {
+                throw new RenException(ErrorCode.RAG_API_ERROR, "RAG创建返回无效: 缺失ID");
+            }
+            datasetId = ragResponse.getId();
+
+            // 3. Local Save (Shadow)
+            KnowledgeBaseEntity entity = ConvertUtils.sourceToTarget(dto, KnowledgeBaseEntity.class);
+
+            // [Production Fix] 统一本地 ID 与 RAGFlow ID，防止前端调用 /delete 或 /update 时因 ID 混淆（本地
+            // UUID vs RAG UUID）导致 10163 错误
+            entity.setId(datasetId);
+            entity.setDatasetId(datasetId);
+            entity.setStatus(1); // Default Enabled
+
+            // ✅ FULL PERSISTENCE: 严格全量回写 (User Requirement)
+            // 使用强类型 DTO 属性获取，不再从 Map 中手动解析 Key
+            entity.setTenantId(ragResponse.getTenantId());
+            entity.setChunkMethod(ragResponse.getChunkMethod());
+            entity.setEmbeddingModel(ragResponse.getEmbeddingModel());
+            entity.setPermission(ragResponse.getPermission());
+
+            if (StringUtils.isBlank(entity.getAvatar())) {
+                entity.setAvatar(ragResponse.getAvatar());
+            }
+
+            // Parse Config (JSON)
+            if (ragResponse.getParserConfig() != null) {
+                entity.setParserConfig(JsonUtils.toJsonString(ragResponse.getParserConfig()));
+            }
+
+            // Numeric fields
+            entity.setChunkCount(ragResponse.getChunkCount() != null ? ragResponse.getChunkCount() : 0L);
+            entity.setDocumentCount(ragResponse.getDocumentCount() != null ? ragResponse.getDocumentCount() : 0L);
+            entity.setTokenNum(ragResponse.getTokenNum() != null ? ragResponse.getTokenNum() : 0L);
+
+            // 清空 creator/updater，让 FieldMetaObjectHandler 从 SecurityUser 自动填充
+            // ConvertUtils 会把 DTO 中的 creator=0 拷贝过来，导致 strictInsertFill 跳过填充
+            entity.setCreator(null);
+            entity.setUpdater(null);
+
+            knowledgeBaseDao.insert(entity);
+            return ConvertUtils.sourceToTarget(entity, KnowledgeBaseDTO.class);
+        } catch (Exception e) {
+            log.error("RAG创建或本地保存失败", e);
+            // 如果datasetId已生成但在保存本地时失败，尝试回滚RAG (Best Effort)
+            if (StringUtils.isNotBlank(datasetId)) {
+                try {
+                    if (adapter != null)
+                        adapter.deleteDataset(
+                                DatasetDTO.BatchIdReq.builder().ids(Collections.singletonList(datasetId)).build());
+                } catch (Exception rollbackEx) {
+                    log.error("RAG回滚失败: {}", datasetId, rollbackEx);
+                }
+            }
+            if (e instanceof RenException) {
+                throw (RenException) e;
+            }
+            throw new RenException(ErrorCode.RAG_API_ERROR, "创建知识库失败: " + e.getMessage());
+        }
+    }
+
+    @Override
+    @Transactional(rollbackFor = Exception.class)
+    @SuppressWarnings("deprecation")
+    public KnowledgeBaseDTO update(KnowledgeBaseDTO dto) {
+        log.info("Update Service Called: ID={}, DatasetID={}", dto.getId(), dto.getDatasetId());
+        KnowledgeBaseEntity entity = knowledgeBaseDao.selectById(dto.getId());
+        if (entity == null) {
+            log.error("Update failed: Entity not found for ID={}", dto.getId());
+            throw new RenException(ErrorCode.Knowledge_Base_RECORD_NOT_EXISTS);
+        }
+
+        checkDuplicateName(dto.getName(), dto.getId());
+
+        // 验证数据集ID是否与其他记录冲突
+        if (StringUtils.isNotBlank(dto.getDatasetId())) {
+            KnowledgeBaseEntity conflictEntity = knowledgeBaseDao.selectOne(
+                    new QueryWrapper<KnowledgeBaseEntity>()
+                            .eq("dataset_id", dto.getDatasetId())
+                            .ne("id", dto.getId()));
+            if (conflictEntity != null) {
+                throw new RenException(ErrorCode.DB_RECORD_EXISTS);
+            }
+        }
+
+        // RAG Update if needed
+        if (StringUtils.isNotBlank(entity.getDatasetId()) && StringUtils.isNotBlank(dto.getRagModelId())) {
+            try {
+                // 🤖 AUTO-FILL: 若 DTO 未传 ragModelId (极少情况)，尝试复用 Entity 中的
+                if (StringUtils.isBlank(dto.getRagModelId())) {
+                    dto.setRagModelId(entity.getRagModelId());
+                }
+
+                // [FIX] 智能补全：如果 DTO 里的关键字段为空，则使用 Entity 里的旧值
+                // 确保发给 RAGFlow 的请求包含所有必填项 (Partial Update Support)
+                if (StringUtils.isBlank(dto.getPermission())) {
+                    dto.setPermission(entity.getPermission());
+                }
+                if (StringUtils.isBlank(dto.getChunkMethod())) {
+                    dto.setChunkMethod(entity.getChunkMethod());
+                }
+
+                KnowledgeBaseAdapter adapter = getAdapterByModelId(dto.getRagModelId());
+                if (adapter != null) {
+                    DatasetDTO.UpdateReq updateReq = ConvertUtils.sourceToTarget(dto, DatasetDTO.UpdateReq.class);
+
+                    // 1. 必填/核心字段前缀处理
+                    if (StringUtils.isNotBlank(dto.getName())) {
+                        updateReq.setName(SecurityUser.getUser().getUsername() + "_" + dto.getName());
+                    }
+
+                    // 2. 解析器配置支持 (如果 DTO 里有字符串形式的配置，尝试转换，但优先建议 DTO 化)
+                    if (StringUtils.isNotBlank(dto.getParserConfig())) {
+                        try {
+                            DatasetDTO.ParserConfig parserConfig = JsonUtils.parseObject(dto.getParserConfig(),
+                                    DatasetDTO.ParserConfig.class);
+                            updateReq.setParserConfig(parserConfig);
+                        } catch (Exception e) {
+                            log.warn("解析 parser_config 失败，跳过同步", e);
+                        }
+                    }
+
+                    adapter.updateDataset(entity.getDatasetId(), updateReq);
+                    log.info("RAG更新成功: {}", entity.getDatasetId());
+                }
+            } catch (Exception e) {
+                log.error("RAG更新失败", e);
+                // 恢复事务一致性：RAG失败则整体回滚
+                if (e instanceof RenException) {
+                    throw (RenException) e;
+                }
+                throw new RenException(ErrorCode.RAG_API_ERROR, "RAG更新失败: " + e.getMessage());
+            }
+        }
+
+        BeanUtils.copyProperties(dto, entity);
+        knowledgeBaseDao.updateById(entity);
+
+        // Clean cache
+        redisUtils.delete(RedisKeys.getKnowledgeBaseCacheKey(entity.getId()));
 
         return ConvertUtils.sourceToTarget(entity, KnowledgeBaseDTO.class);
     }
 
-    /**
-     * 根据知识库ID集合查询知识库
-     * @param datasetIdList 知识库ID集合
-     * @return
-     */
-    @Override
-    public List<KnowledgeBaseDTO> getByDatasetIdList(List<String> datasetIdList) {
-        //判断参数
-        if (ToolUtil.isEmpty(datasetIdList)) {
-            throw new RenException(ErrorCode.PARAMS_GET_ERROR);
-        }
-        //批量查询
-        List<KnowledgeBaseEntity> entityList = knowledgeBaseDao.selectList(
-                new QueryWrapper<KnowledgeBaseEntity>().in("dataset_id", datasetIdList));
-        if (ToolUtil.isEmpty(entityList)) {
-            throw new RenException(ErrorCode.Knowledge_Base_RECORD_NOT_EXISTS);
-        }
-        return ConvertUtils.sourceToTarget(entityList, KnowledgeBaseDTO.class);
-    }
-
     @Override
+    @Transactional(rollbackFor = Exception.class)
     public void deleteByDatasetId(String datasetId) {
         if (StringUtils.isBlank(datasetId)) {
             throw new RenException(ErrorCode.PARAMS_GET_ERROR);
         }
 
-        log.info("=== 开始通过datasetId删除操作 ===");
+        KnowledgeBaseEntity entity = knowledgeBaseDao
-        log.info("删除datasetId: {}", datasetId);
+                .selectOne(new QueryWrapper<KnowledgeBaseEntity>().eq("dataset_id", datasetId));
-
-        KnowledgeBaseEntity entity = knowledgeBaseDao.selectOne(
-                new QueryWrapper<KnowledgeBaseEntity>().eq("dataset_id", datasetId));
 
+        // 1. 恢复 404 校验：找不到记录抛异常
         if (entity == null) {
             log.warn("记录不存在，datasetId: {}", datasetId);
             throw new RenException(ErrorCode.Knowledge_Base_RECORD_NOT_EXISTS);
         }
-        redisUtils.delete(RedisKeys.getKnowledgeBaseCacheKey(entity.getId()));
-
         log.info("找到记录: ID={}, datasetId={}, ragModelId={}",
                 entity.getId(), entity.getDatasetId(), entity.getRagModelId());
 
-        // 先调用RAG API删除数据集
+        // 2. RAG Delete (Strict Mode)
+        // 恢复严格一致性：RAG 删除失败则抛出异常，触发事务回滚，不允许已删除本地但保留远程的脏数据
         boolean apiDeleteSuccess = false;
-        if (StringUtils.isNotBlank(entity.getDatasetId()) && StringUtils.isNotBlank(entity.getRagModelId())) {
+        if (StringUtils.isNotBlank(entity.getRagModelId()) && StringUtils.isNotBlank(entity.getDatasetId())) {
             try {
-                log.info("开始调用RAG API删除数据集");
+                KnowledgeBaseAdapter adapter = getAdapterByModelId(entity.getRagModelId());
-                // 在删除前进行RAG配置校验
+                if (adapter != null) {
-                Map<String, Object> ragConfig = getValidatedRAGConfig(entity.getRagModelId());
+                    adapter.deleteDataset(
-                deleteDatasetInRAG(entity.getDatasetId(), ragConfig);
+                            DatasetDTO.BatchIdReq.builder().ids(Collections.singletonList(datasetId)).build());
-                log.info("RAG API删除调用完成");
+                }
                 apiDeleteSuccess = true;
             } catch (Exception e) {
-                // 提供更详细的错误信息，包括异常类型和消息
+                log.error("RAG删除失败，触发回滚", e);
-                String errorMessage = "删除RAG数据集失败: " + e.getClass().getSimpleName();
+                if (e instanceof RenException) {
-                if (e.getMessage() != null) {
+                    throw (RenException) e;
-                    errorMessage += " - " + e.getMessage();
                 }
-                log.error(errorMessage, e);
+                throw new RenException(ErrorCode.RAG_API_ERROR, "RAG删除失败: " + e.getMessage());
-                throw e;
             }
         } else {
             log.warn("datasetId或ragModelId为空，跳过RAG删除");
             apiDeleteSuccess = true; // 没有RAG数据集，视为成功
         }
 
-        // API删除成功后再删除本地记录
+        // 3. Local Delete (Safe Order)
+        // 恢复正确顺序：先删子表 (Plugin Mapping)，再删主表 (Entity)
         if (apiDeleteSuccess) {
             log.info("开始删除ai_agent_plugin_mapping表中与知识库ID '{}' 相关的映射记录", entity.getId());
-
+            log.info("开始删除关联数据, entityId: {}", entity.getId());
-            // 先删除相关的插件映射记录
             knowledgeBaseDao.deletePluginMappingByKnowledgeBaseId(entity.getId());
             log.info("插件映射记录删除完成");
-
             int deleteCount = knowledgeBaseDao.deleteById(entity.getId());
             log.info("本地数据库删除结果: {}", deleteCount > 0 ? "成功" : "失败");
+            redisUtils.delete(RedisKeys.getKnowledgeBaseCacheKey(entity.getId()));
         }
+    }
 
-        log.info("=== 通过datasetId删除操作结束 ===");
+    @Override
+    public List<KnowledgeBaseDTO> getByDatasetIdList(List<String> datasetIdList) {
+        if (datasetIdList == null || datasetIdList.isEmpty()) {
+            return Collections.emptyList();
+        }
+        // [Production Fix] 批量兼容性查找
+        QueryWrapper<KnowledgeBaseEntity> queryWrapper = new QueryWrapper<>();
+        queryWrapper.in("dataset_id", datasetIdList).or().in("id", datasetIdList);
+        List<KnowledgeBaseEntity> list = knowledgeBaseDao.selectList(queryWrapper);
+        return ConvertUtils.sourceToTarget(list, KnowledgeBaseDTO.class);
     }
 
     @Override
     public Map<String, Object> getRAGConfig(String ragModelId) {
-        if (StringUtils.isBlank(ragModelId)) {
+        return getValidatedRAGConfig(ragModelId);
-            throw new RenException(ErrorCode.PARAMS_GET_ERROR);
-        }
-
-        // 从缓存获取模型配置
-        ModelConfigEntity modelConfig = modelConfigService.getModelByIdFromCache(ragModelId);
-        if (modelConfig == null || modelConfig.getConfigJson() == null) {
-            throw new RenException(ErrorCode.RAG_CONFIG_NOT_FOUND);
-        }
-
-        // 验证是否为RAG类型配置
-        if (!Constant.RAG_CONFIG_TYPE.equals(modelConfig.getModelType().toUpperCase())) {
-            throw new RenException(ErrorCode.RAG_CONFIG_TYPE_ERROR);
-        }
-
-        Map<String, Object> config = modelConfig.getConfigJson();
-
-        // 验证必要的配置参数
-        validateRagConfig(config);
-
-        // 返回配置信息
-        return config;
     }
 
     @Override
     public Map<String, Object> getRAGConfigByDatasetId(String datasetId) {
-        if (StringUtils.isBlank(datasetId)) {
+        KnowledgeBaseEntity entity = knowledgeBaseDao
-            throw new RenException(ErrorCode.RAG_DATASET_ID_NOT_NULL);
+                .selectOne(new QueryWrapper<KnowledgeBaseEntity>().eq("dataset_id", datasetId));
-        }
+        if (entity == null || StringUtils.isBlank(entity.getRagModelId())) {
-
-        // 根据datasetId查询知识库信息
-        KnowledgeBaseDTO knowledgeBase = getByDatasetId(datasetId);
-        if (knowledgeBase == null) {
-            log.warn("未找到datasetId为{}的知识库", datasetId);
-            throw new RenException(ErrorCode.Knowledge_Base_RECORD_NOT_EXISTS);
-        }
-
-        // 如果知识库指定了ragModelId，使用该配置
-        String ragModelId = knowledgeBase.getRagModelId();
-        if (StringUtils.isBlank(ragModelId)) {
-            log.warn("知识库datasetId为{}未配置ragModelId", datasetId);
             throw new RenException(ErrorCode.RAG_CONFIG_NOT_FOUND);
         }
+        return getRAGConfig(entity.getRagModelId());
+    }
 
-        // 获取并返回RAG配置
+    @Override
-        return getRAGConfig(ragModelId);
+    @Transactional(rollbackFor = Exception.class)
+    public void updateStatistics(String datasetId, Integer docDelta, Long chunkDelta, Long tokenDelta) {
+        log.info("递增更新知识库统计: datasetId={}, docs={}, chunks={}, tokens={}", datasetId, docDelta, chunkDelta, tokenDelta);
+        knowledgeBaseDao.updateStatsAfterChange(datasetId, docDelta, chunkDelta, tokenDelta);
     }
 
     @Override
     public List<ModelConfigEntity> getRAGModels() {
-        // 查询RAG类型的模型配置
+        return modelConfigDao.selectList(new QueryWrapper<ModelConfigEntity>()
-        QueryWrapper<ModelConfigEntity> queryWrapper = new QueryWrapper<ModelConfigEntity>()
+                .select("id", "model_name", "config_json") // Explicitly select needed fields
-                .select("id", "model_name")
                 .eq("model_type", Constant.RAG_CONFIG_TYPE)
                 .eq("is_enabled", 1)
                 .orderByDesc("is_default")
-                .orderByDesc("create_date");
+                .orderByDesc("create_date"));
-
-        List<ModelConfigEntity> modelConfigs = modelConfigDao.selectList(queryWrapper);
-        return modelConfigs;
     }
 
-    /**
+    // --- Helpers ---
-     * 验证RAG配置中是否包含必要的参数
+
-     */
+    private void checkDuplicateName(String name, String excludeId) {
-    private void validateRagConfig(Map<String, Object> config) {
+        if (StringUtils.isBlank(name))
-        if (config == null) {
+            return;
+        QueryWrapper<KnowledgeBaseEntity> qw = new QueryWrapper<>();
+        qw.eq("name", name).eq("creator", SecurityUser.getUserId());
+        if (excludeId != null)
+            qw.ne("id", excludeId);
+        if (knowledgeBaseDao.selectCount(qw) > 0) {
+            throw new RenException(ErrorCode.KNOWLEDGE_BASE_NAME_EXISTS);
+        }
+    }
+
+    private KnowledgeBaseAdapter getAdapterByModelId(String modelId) {
+        Map<String, Object> config = getValidatedRAGConfig(modelId);
+        return KnowledgeBaseAdapterFactory.getAdapter((String) config.get("type"), config);
+    }
+
+    private Map<String, Object> getValidatedRAGConfig(String modelId) {
+        ModelConfigEntity configEntity = modelConfigService.getModelByIdFromCache(modelId);
+        if (configEntity == null || configEntity.getConfigJson() == null) {
             throw new RenException(ErrorCode.RAG_CONFIG_NOT_FOUND);
         }
-
+        Map<String, Object> config = new HashMap<>(configEntity.getConfigJson());
-        // 从配置中提取必要的参数
+        if (!config.containsKey("type")) {
-        String baseUrl = (String) config.get("base_url");
+            config.put("type", "ragflow");
-        String apiKey = (String) config.get("api_key");
-
-        // 验证base_url是否存在且非空
-        if (StringUtils.isBlank(baseUrl)) {
-            throw new RenException(ErrorCode.RAG_API_ERROR_URL_NULL);
-        }
-
-        // 验证api_key是否存在且非空
-        if (StringUtils.isBlank(apiKey)) {
-            throw new RenException(ErrorCode.RAG_API_ERROR_API_KEY_NULL);
-        }
-
-        // 检查api_key是否包含占位符
-        if (apiKey.contains("你")) {
-            throw new RenException(ErrorCode.RAG_API_ERROR_API_KEY_INVALID);
-        }
-
-        // 验证base_url格式
-        if (!baseUrl.startsWith("http://") && !baseUrl.startsWith("https://")) {
-            throw new RenException(ErrorCode.RAG_API_ERROR_URL_INVALID);
         }
+        return config;
     }
-
-    /**
-     * 从RAG配置中提取适配器类型
-     * 
-     * @param config RAG配置
-     * @return 适配器类型
-     */
-    private String extractAdapterType(Map<String, Object> config) {
-        if (config == null) {
-            throw new RenException(ErrorCode.RAG_CONFIG_NOT_FOUND);
-        }
-
-        // 从配置中提取适配器类型
-        String adapterType = (String) config.get("type");
-
-        // 验证适配器类型是否存在且非空
-        if (StringUtils.isBlank(adapterType)) {
-            throw new RenException(ErrorCode.RAG_ADAPTER_TYPE_NOT_FOUND);
-        }
-
-        // 验证适配器类型是否已注册
-        if (!KnowledgeBaseAdapterFactory.isAdapterTypeRegistered(adapterType)) {
-            throw new RenException(ErrorCode.RAG_ADAPTER_TYPE_NOT_SUPPORTED,
-                    "不支持的适配器类型: " + adapterType);
-        }
-
-        return adapterType;
-    }
-
-    /**
-     * 使用适配器创建数据集
-     */
-    private String createDatasetInRAG(String name, String description, Map<String, Object> ragConfig) {
-        log.info("开始使用适配器创建数据集, name: {}", name);
-
-        try {
-            // 从RAG配置中提取适配器类型
-            String adapterType = extractAdapterType(ragConfig);
-
-            // 使用适配器工厂获取适配器实例
-            KnowledgeBaseAdapter adapter = KnowledgeBaseAdapterFactory.getAdapter(adapterType, ragConfig);
-
-            // 构建数据集创建参数
-            Map<String, Object> createParams = new HashMap<>();
-            String username = SecurityUser.getUser().getUsername();
-            createParams.put("name", username + "_" + name);
-            if (StringUtils.isNotBlank(description)) {
-                createParams.put("description", description);
-            }
-
-            // 调用适配器的创建数据集方法
-            String datasetId = adapter.createDataset(createParams);
-
-            log.info("数据集创建成功，datasetId: {}", datasetId);
-            return datasetId;
-
-        } catch (Exception e) {
-            // 直接传递底层适配器的详细错误信息
-            log.error("创建数据集失败", e);
-            if (e instanceof RenException) {
-                throw (RenException) e;
-            }
-            throw new RenException(ErrorCode.RAG_API_ERROR, e.getMessage());
-        }
-    }
-
-    /**
-     * 使用适配器更新数据集
-     */
-    private void updateDatasetInRAG(String datasetId, String name, String description,
-            Map<String, Object> ragConfig) {
-        log.info("开始使用适配器更新数据集，datasetId: {}, name: {}", datasetId, name);
-
-        try {
-            // 从RAG配置中提取适配器类型
-            String adapterType = extractAdapterType(ragConfig);
-
-            // 使用适配器工厂获取适配器实例
-            KnowledgeBaseAdapter adapter = KnowledgeBaseAdapterFactory.getAdapter(adapterType, ragConfig);
-
-            // 构建数据集更新参数
-            Map<String, Object> updateParams = new HashMap<>();
-            String username = SecurityUser.getUser().getUsername();
-            updateParams.put("name", username + "_" + name);
-            if (StringUtils.isNotBlank(description)) {
-                updateParams.put("description", description);
-            }
-
-            // 调用适配器的更新数据集方法
-            adapter.updateDataset(datasetId, updateParams);
-
-            log.info("数据集更新成功，datasetId: {}", datasetId);
-
-        } catch (Exception e) {
-            // 直接传递底层适配器的详细错误信息
-            log.error("更新数据集失败", e);
-            if (e instanceof RenException) {
-                throw (RenException) e;
-            }
-            throw new RenException(ErrorCode.RAG_API_ERROR, e.getMessage());
-        }
-    }
-
-    /**
-     * 使用适配器删除数据集
-     */
-    private void deleteDatasetInRAG(String datasetId, Map<String, Object> ragConfig) {
-        log.info("开始使用适配器删除数据集，datasetId: {}", datasetId);
-
-        try {
-            // 从RAG配置中提取适配器类型
-            String adapterType = extractAdapterType(ragConfig);
-
-            // 使用适配器工厂获取适配器实例
-            KnowledgeBaseAdapter adapter = KnowledgeBaseAdapterFactory.getAdapter(adapterType, ragConfig);
-
-            // 调用适配器的删除数据集方法
-            adapter.deleteDataset(datasetId);
-
-            log.info("数据集删除成功，datasetId: {}", datasetId);
-
-        } catch (Exception e) {
-            // 直接传递底层适配器的详细错误信息
-            log.error("删除数据集失败", e);
-            if (e instanceof RenException) {
-                throw (RenException) e;
-            }
-            throw new RenException(ErrorCode.RAG_API_ERROR, e.getMessage());
-        }
-    }
-
-    /**
-     * 获取RAG配置并验证
-     */
-    private Map<String, Object> getValidatedRAGConfig(String ragModelId) {
-        if (StringUtils.isBlank(ragModelId)) {
-            throw new RenException(ErrorCode.RAG_MODEL_ID_NOT_NULL);
-        }
-
-        Map<String, Object> ragConfig = getRAGConfig(ragModelId);
-
-        // 验证RAG配置参数
-        validateRagConfig(ragConfig);
-
-        return ragConfig;
-    }
-
-    /**
-     * 检查是否存在同名知识库
-     * 
-     * @param knowledgeBaseDTO 知识库DTO
-     * @param excludeId        排除的ID（更新时使用）
-     */
-    private void checkDuplicateKnowledgeBaseName(KnowledgeBaseDTO knowledgeBaseDTO, String excludeId) {
-        if (StringUtils.isNotBlank(knowledgeBaseDTO.getName())) {
-            Long currentUserId = SecurityUser.getUserId();
-            QueryWrapper<KnowledgeBaseEntity> queryWrapper = new QueryWrapper<KnowledgeBaseEntity>()
-                    .eq("name", knowledgeBaseDTO.getName())
-                    .eq("creator", currentUserId);
-
-            // 如果提供了排除ID，则排除该记录
-            if (StringUtils.isNotBlank(excludeId)) {
-                queryWrapper.ne("id", excludeId);
-            }
-
-            long count = knowledgeBaseDao.selectCount(queryWrapper);
-            if (count > 0) {
-                throw new RenException(ErrorCode.KNOWLEDGE_BASE_NAME_EXISTS,
-                        MessageUtils.getMessage(ErrorCode.KNOWLEDGE_BASE_NAME_EXISTS));
-            }
-        }
-    }
-
-    /**
-     * 从适配器获取知识库的文档数量
-     */
-    private Integer getDocumentCountFromRAG(String datasetId, String ragModelId) {
-        if (StringUtils.isBlank(datasetId) || StringUtils.isBlank(ragModelId)) {
-            log.warn("datasetId或ragModelId为空，无法获取文档数量");
-            return 0;
-        }
-
-        log.info("开始获取知识库 {} 的文档数量", datasetId);
-
-        try {
-            // 获取RAG配置
-            Map<String, Object> ragConfig = getValidatedRAGConfig(ragModelId);
-
-            // 从RAG配置中提取适配器类型
-            String adapterType = extractAdapterType(ragConfig);
-
-            // 使用适配器工厂获取适配器实例
-            KnowledgeBaseAdapter adapter = KnowledgeBaseAdapterFactory.getAdapter(adapterType, ragConfig);
-
-            // 调用适配器的获取文档数量方法
-            Integer documentCount = adapter.getDocumentCount(datasetId);
-
-            log.info("获取知识库 {} 的文档数量成功: {}", datasetId, documentCount);
-            return documentCount;
-
-        } catch (Exception e) {
-            // 构建详细的错误信息，包含异常类型和消息
-            String baseErrorMessage = e.getClass().getSimpleName() + " - 获取知识库文档数量失败";
-            String errorMessage = baseErrorMessage + (e.getMessage() != null ? ": " + e.getMessage() : "");
-            log.error(errorMessage, e);
-            return 0;
-        }
-    }
-
 }

main/manager-api/src/main/java/xiaozhi/modules/knowledge/service/impl/KnowledgeManagerServiceImpl.java

@@ -0,0 +1,47 @@
+package xiaozhi.modules.knowledge.service.impl;
+
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.stereotype.Service;
+import org.springframework.transaction.annotation.Transactional;
+import xiaozhi.modules.knowledge.service.KnowledgeBaseService;
+import xiaozhi.modules.knowledge.service.KnowledgeFilesService;
+import xiaozhi.modules.knowledge.service.KnowledgeManagerService;
+
+import java.util.List;
+
+@Service
+@Slf4j
+@RequiredArgsConstructor
+public class KnowledgeManagerServiceImpl implements KnowledgeManagerService {
+
+    private final KnowledgeBaseService knowledgeBaseService;
+    private final KnowledgeFilesService knowledgeFilesService;
+
+    @Override
+    @Transactional(rollbackFor = Exception.class)
+    public void deleteDatasetWithFiles(String datasetId) {
+        log.info("=== 级联删除开始: datasetId={} ===", datasetId);
+
+        // 1. 先调用文件服务，清理该数据集下的所有文档记录 (含 RAGFlow 端)
+        log.info("Step 1: 清理关联文档...");
+        knowledgeFilesService.deleteDocumentsByDatasetId(datasetId);
+
+        // 2. 再调用知识库服务，彻底注销数据集 (含 RAGFlow 端)
+        log.info("Step 2: 删除数据集主体...");
+        knowledgeBaseService.deleteByDatasetId(datasetId);
+
+        log.info("=== 级联删除成功: datasetId={} ===", datasetId);
+    }
+
+    @Override
+    @Transactional(rollbackFor = Exception.class)
+    public void batchDeleteDatasetsWithFiles(List<String> datasetIds) {
+        if (datasetIds == null || datasetIds.isEmpty())
+            return;
+        log.info("=== 批量级联删除开始: count={} ===", datasetIds.size());
+        for (String id : datasetIds) {
+            deleteDatasetWithFiles(id);
+        }
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/knowledge/task/DocumentStatusSyncTask.java

@@ -0,0 +1,39 @@
+package xiaozhi.modules.knowledge.task;
+
+import org.springframework.scheduling.annotation.Scheduled;
+import org.springframework.stereotype.Component;
+
+import lombok.AllArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import xiaozhi.modules.knowledge.service.KnowledgeFilesService;
+
+/**
+ * 知识库文档状态同步定时任务
+ * 
+ * 作用：
+ * 1. 自动扫描处于 "RUNNING" (解析中) 状态的文档
+ * 2. 调用 RAGFlow 接口获取最新状态
+ * 3. 状态翻转 (RUNNING -> SUCCESS/FAIL) 时，同步更新数据库
+ * 4. [关键] 解析成功时，补偿更新知识库的统计信息 (TokenCount)
+ */
+@Component
+@AllArgsConstructor
+@Slf4j
+public class DocumentStatusSyncTask {
+
+    private final KnowledgeFilesService knowledgeFilesService;
+
+    /**
+     * 每 30 秒执行一次同步
+     * 采用 fixedDelay，确保上一次执行完 30 秒后才开始下一次，防止积压
+     */
+    @Scheduled(fixedDelay = 30000)
+    public void syncRunningDocuments() {
+        try {
+            // log.debug("开始执行文档状态同步任务...");
+            knowledgeFilesService.syncRunningDocuments();
+        } catch (Exception e) {
+            log.error("文档状态同步任务异常", e);
+        }
+    }
+}

main/manager-api/src/main/java/xiaozhi/modules/model/controller/ModelController.java

@@ -129,6 +129,10 @@ public class ModelController {
         if (entity == null) {
             return new Result<Void>().error("模型配置不存在");
         }
+        // 不能关闭默认模型
+        if (status == 0 && entity.getIsDefault() > 0) {
+            return new Result<Void>().error("默认模型配置不允许关闭");
+        }
         // 不更新ConfigJson字段
         entity.setConfigJson(null);
         entity.setIsEnabled(status);

main/manager-api/src/main/java/xiaozhi/modules/model/dto/VoiceDTO.java

@@ -23,6 +23,9 @@ public class VoiceDTO implements Serializable {
     @Schema(description = "音频播放地址")
     private String voiceDemo;
     
+    @Schema(description = "语言类型")
+    private String languages;
+    
     @Schema(description = "是否为克隆音色")
     private Boolean isClone;
 
@@ -31,6 +34,7 @@ public class VoiceDTO implements Serializable {
         this.id = id;
         this.name = name;
         this.voiceDemo = null;
+        this.languages = null;
         this.isClone = false; // 默认不是克隆音色
     }
     
@@ -39,6 +43,7 @@ public class VoiceDTO implements Serializable {
         this.id = id;
         this.name = name;
         this.voiceDemo = voiceDemo;
+        this.languages = null;
         this.isClone = false;
     }
 

main/manager-api/src/main/java/xiaozhi/modules/sys/service/impl/SysUserServiceImpl.java

@@ -56,7 +56,7 @@ public class SysUserServiceImpl extends BaseServiceImpl<SysUserDao, SysUserEntit
         if (users == null || users.isEmpty()) {
             return null;
         }
-        SysUserEntity entity = users.getFirst();
+        SysUserEntity entity = users.get(0);
         return ConvertUtils.sourceToTarget(entity, SysUserDTO.class);
     }
 
@@ -197,7 +197,7 @@ public class SysUserServiceImpl extends BaseServiceImpl<SysUserDao, SysUserEntit
      */
     private String generatePassword() {
         StringBuilder password = new StringBuilder();
-        
+
         // 确保包含至少一个数字
         password.append("0123456789".charAt(random.nextInt(10)));
         // 确保包含至少一个小写字母
@@ -206,12 +206,12 @@ public class SysUserServiceImpl extends BaseServiceImpl<SysUserDao, SysUserEntit
         password.append("ABCDEFGHIJKLMNOPQRSTUVWXYZ".charAt(random.nextInt(26)));
         // 确保包含至少一个特殊符号
         password.append("!@#$%^&*()".charAt(random.nextInt(10)));
-        
+
         // 生成剩余的8个字符
         for (int i = 4; i < 12; i++) {
             password.append(CHARACTERS.charAt(random.nextInt(CHARACTERS.length())));
         }
-        
+
         // 打乱密码中字符的顺序
         char[] passwordChars = password.toString().toCharArray();
         for (int i = 0; i < passwordChars.length; i++) {
@@ -220,7 +220,7 @@ public class SysUserServiceImpl extends BaseServiceImpl<SysUserDao, SysUserEntit
             passwordChars[i] = passwordChars[randomIndex];
             passwordChars[randomIndex] = temp;
         }
-        
+
         return new String(passwordChars);
     }
 

main/manager-api/src/main/java/xiaozhi/modules/timbre/service/impl/TimbreServiceImpl.java

@@ -129,6 +129,7 @@ public class TimbreServiceImpl extends BaseServiceImpl<TimbreDao, TimbreEntity>
                 .map(entity -> {
                     VoiceDTO dto = new VoiceDTO(entity.getId(), entity.getName());
                     dto.setVoiceDemo(entity.getVoiceDemo());
+                    dto.setLanguages(entity.getLanguages()); // 设置语言类型
                     dto.setIsClone(false); // 设置为普通音色
                     return dto;
                 })
@@ -146,6 +147,7 @@ public class TimbreServiceImpl extends BaseServiceImpl<TimbreDao, TimbreEntity>
                 voiceDTO.setName(MessageUtils.getMessage(ErrorCode.VOICE_CLONE_PREFIX) + entity.getName());
                 // 保留从数据库查询到的voiceDemo字段
                 voiceDTO.setVoiceDemo(entity.getVoiceDemo());
+                voiceDTO.setLanguages(entity.getLanguages());
                 voiceDTO.setIsClone(true); // 设置为克隆音色
                 redisUtils.set(RedisKeys.getTimbreNameById(voiceDTO.getId()), voiceDTO.getName(),
                         RedisUtils.NOT_EXPIRE);

main/manager-api/src/main/java/xiaozhi/modules/voiceclone/dto/VoiceCloneDTO.java

@@ -17,4 +17,7 @@ public class VoiceCloneDTO {
 
     @Schema(description = "用户ID")
     private Long userId;
+
+    @Schema(description = "语言")
+    private String languages;
 }

main/manager-api/src/main/java/xiaozhi/modules/voiceclone/dto/VoiceCloneResponseDTO.java

@@ -28,6 +28,9 @@ public class VoiceCloneResponseDTO {
     @Schema(description = "声音id")
     private String voiceId;
 
+    @Schema(description = "语言")
+    private String languages;
+
     @Schema(description = "用户ID（关联用户表）")
     private Long userId;
 

main/manager-api/src/main/java/xiaozhi/modules/voiceclone/entity/VoiceCloneEntity.java

@@ -31,6 +31,9 @@ public class VoiceCloneEntity {
     @Schema(description = "声音id")
     private String voiceId;
 
+    @Schema(description = "语言")
+    private String languages;
+
     @Schema(description = "用户 ID（关联用户表）")
     private Long userId;
 

main/manager-api/src/main/java/xiaozhi/modules/voiceclone/service/impl/VoiceCloneServiceImpl.java

@@ -116,6 +116,7 @@ public class VoiceCloneServiceImpl extends BaseServiceImpl<VoiceCloneDao, VoiceC
             entity.setVoiceId(voiceId);
             entity.setName(namePrefix + "_" + index);
             entity.setUserId(dto.getUserId());
+            entity.setLanguages(dto.getLanguages());
             entity.setTrainStatus(0); // 默认训练中
             batchInsertList.add(entity);
         }

main/manager-api/src/main/resources/db/changelog/202602021555.sql

@@ -0,0 +1 @@
+INSERT INTO `sys_params` (id, param_code, param_value, value_type, param_type, remark) VALUES (502, 'system_error_response', '主人，小智现在有点忙，我们稍后再试吧。', 'string', 1, '系统错误时的回复');

main/manager-api/src/main/resources/db/changelog/202602051017.sql

@@ -0,0 +1,115 @@
+-- 新增powermem记忆模型供应器
+INSERT INTO `ai_model_provider` (`id`, `model_type`, `provider_code`, `name`, `fields`, `sort`, `creator`, `create_date`, `updater`, `update_date`)
+VALUES ('SYSTEM_Memory_powermem', 'Memory', 'powermem', 'PowerMem记忆', '[
+  {"key":"enable_user_profile","label":"启用用户画像","type":"boolean"},
+  {"key":"llm_provider","label":"LLM提供商","type":"string"},
+  {"key":"llm_api_key","label":"LLM API密钥","type":"string"},
+  {"key":"llm_model","label":"LLM模型","type":"string"},
+  {"key":"openai_base_url","label":"OpenAI基础URL","type":"string"},
+  {"key":"embedding_provider","label":"Embedding提供商","type":"string"},
+  {"key":"embedding_api_key","label":"Embedding API密钥","type":"string"},
+  {"key":"embedding_model","label":"Embedding模型","type":"string"},
+  {"key":"embedding_openai_base_url","label":"Embedding OpenAI基础URL","type":"string"},
+  {"key":"embedding_dims","label":"Embedding维度","type":"integer"},
+  {"key":"vector_store","label":"向量存储配置(JSON)","type":"dict"}
+]', 4, 1, NOW(), 1, NOW());
+
+-- 新增PowerMem记忆模型配置
+INSERT INTO `ai_model_config` VALUES (
+  'Memory_powermem',
+  'Memory',
+  'powermem',
+  'PowerMem记忆',
+  0,
+  1,
+  '{\"type\": \"powermem\", \"enable_user_profile\": true, \"llm_provider\": \"openai\", \"llm_api_key\": \"你的LLM API密钥\", \"llm_model\": \"qwen-plus\", \"openai_base_url\": \"\", \"embedding_provider\": \"openai\", \"embedding_api_key\": \"你的嵌入模型API密钥\", \"embedding_model\": \"text-embedding-v4\", \"embedding_openai_base_url\": \"https://api.openai.com/v1\", \"embedding_dims\": \"\", \"vector_store\": {\"provider\": \"sqlite\", \"config\": {}}}',
+  NULL,
+  NULL,
+  4,
+  NULL,
+  NULL,
+  NULL,
+  NULL
+);
+
+
+-- PowerMem记忆配置说明
+UPDATE `ai_model_config` SET
+`doc_link` = 'https://github.com/oceanbase/powermem',
+`remark` = 'PowerMem是OceanBase开源的agent记忆组件，通过本地LLM进行记忆总结
+GitHub: https://github.com/oceanbase/powermem
+官网: https://www.powermem.ai/
+使用示例: https://github.com/oceanbase/powermem/tree/main/examples
+
+【费用说明】
+PowerMem本身免费，实际费用取决于所选LLM和数据库：
+- 使用sqlite + 免费LLM(如glm-4-flash) = 完全免费
+- 使用云端LLM或云端数据库 = 按对应服务收费
+
+【enable_user_profile】用户画像功能
+- false: 使用普通记忆模式(AsyncMemory)
+- true: 使用用户画像模式(UserMemory)，自动提取用户信息
+- 用户画像功能支持: oceanbase、seekdb、sqlite (powermem 0.3.0+)
+
+【llm】LLM配置 - 用于记忆总结和用户画像提取
+  provider: LLM提供商，可选值：
+    - qwen: 通义千问 (https://bailian.console.aliyun.com/?apiKey=1#/api-key)
+    - openai: OpenAI兼容接口
+    - zhipu: 智谱AI (https://bigmodel.cn/usercenter/proj-mgmt/apikeys) - 推荐使用免费的glm-4-flash
+  config: LLM配置参数
+    - api_key: API密钥 (必填)
+    - model: 模型名称，如 qwen-plus、glm-4-flash 等
+    - openai_base_url: 自定义服务地址 (可选)，如 https://api.openai.com/v1
+  示例：
+    {"provider": "zhipu", "config": {"api_key": "your_key", "model": "glm-4-flash"}}
+    {"provider": "qwen", "config": {"api_key": "your_key", "model": "qwen-plus"}}
+
+【embedder】Embedding配置 - 用于向量化记忆内容
+  provider: 嵌入模型提供商，可选值：
+    - qwen: 通义千问
+    - openai: OpenAI兼容接口
+  config: Embedding配置参数
+    - api_key: API密钥 (必填)
+    - model: 模型名称，如 text-embedding-v4、text-embedding-3-small 等
+    - openai_base_url: 自定义服务地址 (可选)
+    - embedding_dims: 向量维度 (可选)，非1536时需配置
+  示例：
+    {"provider": "openai", "config": {"api_key": "your_key", "model": "text-embedding-v4", "openai_base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1"}}
+
+【vector_store】数据库存储配置 - 用于存储向量化的记忆
+  provider: 数据库类型，可选值：
+    - sqlite: 轻量级本地数据库 (推荐入门使用，无需额外配置)
+    - oceanbase: OceanBase数据库 (推荐生产使用，最佳性能)
+    - seekdb: SeekDB (推荐，AI应用存储一体)
+    - postgres: PostgreSQL数据库
+
+  SQLite配置 (无需额外配置):
+    {"provider": "sqlite", "config": {}}
+
+  OceanBase配置示例:
+    {"provider": "oceanbase", "config": {
+      "host": "127.0.0.1",
+      "port": 2881,
+      "user": "root@test",
+      "password": "your_password",
+      "db_name": "powermem",
+      "collection_name": "memories",
+      "embedding_model_dims": 1024
+    }}
+  注意：
+    - collection_name: 默认表名，如创建维度错误请删除此表或更改名称
+    - embedding_model_dims: 嵌入向量维度，需与embedder的模型维度匹配
+      例如智谱：embedding-2维度是1024，embedding-3维度是2048
+
+【推荐配置组合】
+1. 完全免费方案：
+   - LLM: zhipu + glm-4-flash (免费)
+   - Embedder: 通义千问 text-embedding-v4
+   - Database: sqlite
+
+2. 生产环境方案：
+   - LLM: qwen-plus 或其他商业模型
+   - Embedder: text-embedding-v4
+   - Database: oceanbase 或 seekdb
+'
+WHERE `id` = 'Memory_powermem';

main/manager-api/src/main/resources/db/changelog/202602051125.sql

@@ -0,0 +1,2 @@
+-- 删除无用业务表ai_voiceprint
+DROP TABLE IF EXISTS ai_voiceprint;

main/manager-api/src/main/resources/db/changelog/202602061650.sql

@@ -0,0 +1,48 @@
+-- 统一规范ai_tts_voice语言类型数据
+UPDATE ai_tts_voice
+SET languages = CASE
+    WHEN languages IN ('中文', '普通话','东北话','天津话','中文-北京口音','中文-青岛口音','中文-河南口音','中文-广西口音','辽宁','陕西','中文-四川口音','中文-台湾口音','中文-长沙口音') THEN '普通话'
+    WHEN languages IN ('中文及中英文混合', '中文、英文', '中文、美式英语','中文-北京口音、英文','中文(东北)及中英文混合') THEN '普通话、英语'
+    WHEN languages IN ('英式英文', '英式英语', '美式英语', '澳洲英语', '英文') THEN '英语'
+    WHEN languages = '日语' THEN '日语'
+    WHEN languages = '日语、西语' THEN '日语、西班牙语'
+    WHEN languages = '韩语' THEN '韩语'
+    WHEN languages IN ('粤语', '中文-广东口音') THEN '粤语'
+    WHEN languages = '中文(粤语)及中英文混合' THEN '粤语、英语'
+    WHEN languages = '粤语及粤英混合' THEN '粤语、英语'
+    ELSE languages
+END;
+
+-- 添加音色语言、音量、语速、音调字段到 ai_agent 表
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent' AND COLUMN_NAME = 'tts_language');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent` ADD COLUMN `tts_language` VARCHAR(50) NULL COMMENT ''音色语言'' AFTER `tts_voice_id`', 'SELECT ''Column tts_language already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent' AND COLUMN_NAME = 'tts_volume');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent` ADD COLUMN `tts_volume` INT NULL COMMENT ''TTS音量'' AFTER `tts_language`', 'SELECT ''Column tts_volume already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent' AND COLUMN_NAME = 'tts_rate');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent` ADD COLUMN `tts_rate` INT NULL COMMENT ''TTS语速'' AFTER `tts_volume`', 'SELECT ''Column tts_rate already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent' AND COLUMN_NAME = 'tts_pitch');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent` ADD COLUMN `tts_pitch` INT NULL COMMENT ''TTS音调'' AFTER `tts_rate`', 'SELECT ''Column tts_pitch already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+-- 添加音色语言、音量、语速、音调字段到 ai_agent_template 表
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent_template' AND COLUMN_NAME = 'tts_language');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent_template` ADD COLUMN `tts_language` VARCHAR(50) NULL COMMENT ''音色语言'' AFTER `tts_voice_id`', 'SELECT ''Column tts_language already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent_template' AND COLUMN_NAME = 'tts_volume');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent_template` ADD COLUMN `tts_volume` INT NULL COMMENT ''TTS音量'' AFTER `tts_language`', 'SELECT ''Column tts_volume already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent_template' AND COLUMN_NAME = 'tts_rate');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent_template` ADD COLUMN `tts_rate` INT NULL COMMENT ''TTS语速'' AFTER `tts_volume`', 'SELECT ''Column tts_rate already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent_template' AND COLUMN_NAME = 'tts_pitch');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent_template` ADD COLUMN `tts_pitch` INT NULL COMMENT ''TTS音调'' AFTER `tts_rate`', 'SELECT ''Column tts_pitch already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;

main/manager-api/src/main/resources/db/changelog/202602271137.sql

@@ -0,0 +1,42 @@
+-- 更新腾讯TTS供应器配置，增加speed、volume和format参数
+UPDATE `ai_model_provider`
+SET fields = '[{"key":"appid","label":"应用ID","type":"string"},{"key":"secret_id","label":"Secret ID","type":"string"},{"key":"secret_key","label":"Secret Key","type":"string"},{"key":"format","label":"音频格式","type":"string"},{"key":"speed","label":"语速","type":"number"},{"key":"volume","label":"音量","type":"number"},{"key":"output_dir","label":"输出目录","type":"string"},{"key":"voice","label":"音色ID","type":"string"},{"key":"region","label":"区域","type":"string"}]'
+WHERE id = 'SYSTEM_TTS_TencentTTS';
+
+-- 更新腾讯TTS模型配置，增加speed和volume参数，补充参数说明
+UPDATE `ai_model_config` SET 
+    `config_json` = JSON_SET(`config_json`, '$.speed', 0, '$.volume', 0),
+    `remark` = '腾讯TTS配置说明：
+1. 需要在腾讯云平台开通智能语音交互服务
+2. 支持多种音色，当前配置使用101001
+3. 需要网络连接
+4. 输出文件保存在tmp/目录
+申请步骤：
+1. 访问 https://console.cloud.tencent.com/cam/capi 获取密钥
+2. 访问 https://console.cloud.tencent.com/tts/resourcebundle 领取免费资源
+3. 创建新应用
+4. 获取appid、secret_id和secret_key
+5. 填入配置文件中
+音频参数：
+- format: 音频格式，支持pcm、wav、mp3
+- speed: 语速，范围-2~6，默认0
+- volume: 音量，范围-10~10，默认0'
+WHERE `id` = 'TTS_TencentTTS';
+
+-- 更新CozeCnTTS供应器配置，增加speed和loudness_rate参数
+UPDATE `ai_model_provider`
+SET fields = '[{"key":"voice","label":"音色","type":"string"},{"key":"access_token","label":"访问令牌","type":"string"},{"key":"speed","label":"语速","type":"number"},{"key":"loudness_rate","label":"音量增益","type":"number"},{"key":"output_dir","label":"输出目录","type":"string"},{"key":"response_format","label":"响应格式","type":"string"}]'
+WHERE id = 'SYSTEM_TTS_cozecn';
+
+-- 更新CozeCnTTS模型配置，增加speed和loudness_rate参数，补充参数说明
+UPDATE `ai_model_config` SET 
+    `config_json` = JSON_SET(`config_json`, '$.speed', 1, '$.loudness_rate', 0),
+    `remark` = 'Coze中文语音合成配置说明：
+1. 访问 https://www.coze.cn/ 注册并登录
+2. 创建应用并获取access_token
+3. 选择合适的音色ID
+音频参数：
+- response_format: 音频格式，支持pcm、wav、mp3
+- speed: 语速，范围0.5~2，默认1
+- loudness_rate: 音量增益，范围-50~100，默认0'
+WHERE `id` = 'TTS_CozeCnTTS';

main/manager-api/src/main/resources/db/changelog/202602281000.sql

@@ -0,0 +1,29 @@
+-- 标签表
+CREATE TABLE IF NOT EXISTS ai_agent_tag (
+    id VARCHAR(32) NOT NULL COMMENT '主键',
+    tag_name VARCHAR(64) NOT NULL COMMENT '标签名称',
+    sort INT UNSIGNED DEFAULT 0 COMMENT '排序',
+    creator BIGINT COMMENT '创建者',
+    created_at DATETIME COMMENT '创建时间',
+    updater BIGINT COMMENT '更新者',
+    updated_at DATETIME COMMENT '更新时间',
+    deleted TINYINT DEFAULT 0 COMMENT '删除标记',
+    PRIMARY KEY (id),
+    UNIQUE KEY uk_tag_name (tag_name),
+    INDEX idx_sort (sort)
+) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='智能体标签表';
+
+-- 智能体标签关联表
+CREATE TABLE IF NOT EXISTS ai_agent_tag_relation (
+    id VARCHAR(32) NOT NULL COMMENT '主键',
+    agent_id VARCHAR(32) NOT NULL COMMENT '智能体ID',
+    tag_id VARCHAR(32) NOT NULL COMMENT '标签ID',
+    creator BIGINT COMMENT '创建者',
+    created_at DATETIME COMMENT '创建时间',
+    updater BIGINT COMMENT '更新者',
+    updated_at DATETIME COMMENT '更新时间',
+    PRIMARY KEY (id),
+    UNIQUE KEY uk_agent_tag (agent_id, tag_id),
+    INDEX idx_agent_id (agent_id),
+    INDEX idx_tag_id (tag_id)
+) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='智能体标签关联表';

main/manager-api/src/main/resources/db/changelog/202603091051.sql

@@ -0,0 +1,11 @@
+-- 给智能体标签关联表添加排序字段
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent_tag_relation' AND COLUMN_NAME = 'sort');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_agent_tag_relation` ADD COLUMN `sort` INT UNSIGNED DEFAULT 0 COMMENT ''排序''', 'SELECT ''Column sort already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+-- 修复现有数据（仅当 sort 字段为默认值 0 时执行，避免覆盖用户已设置的顺序）
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_agent_tag_relation' AND COLUMN_NAME = 'sort');
+SET @sql = IF(@col_exists > 0 AND (SELECT COUNT(*) FROM ai_agent_tag_relation WHERE sort = 0) > 0, 
+    'UPDATE ai_agent_tag_relation r INNER JOIN (SELECT id, ROW_NUMBER() OVER (PARTITION BY agent_id ORDER BY created_at) AS row_num FROM ai_agent_tag_relation) t ON r.id = t.id SET r.sort = t.row_num', 
+    'SELECT ''No need to update or column does not exist'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;

main/manager-api/src/main/resources/db/changelog/202603101723.sql

@@ -0,0 +1,4 @@
+-- 给声音克隆表添加语言字段
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_voice_clone' AND COLUMN_NAME = 'languages');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_voice_clone` ADD COLUMN `languages` VARCHAR(50) DEFAULT NULL COMMENT ''语言'' AFTER `voice_id`', 'SELECT ''Column languages already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;

main/manager-api/src/main/resources/db/changelog/202603111131.sql

@@ -0,0 +1,69 @@
+-- 增加一下ragflow返回的参数（创建/查询知识库时返回）
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'tenant_id');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `tenant_id` varchar(32) DEFAULT NULL COMMENT ''租户ID''', 'SELECT ''Column tenant_id already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'avatar');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `avatar` text DEFAULT NULL COMMENT ''知识库头像 (Base64)''', 'SELECT ''Column avatar already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'embedding_model');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `embedding_model` varchar(50) DEFAULT NULL COMMENT ''嵌入模型名称''', 'SELECT ''Column embedding_model already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'permission');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `permission` varchar(20) DEFAULT ''me'' COMMENT ''权限设置：me/team''', 'SELECT ''Column permission already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'chunk_method');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `chunk_method` varchar(50) DEFAULT NULL COMMENT ''分块方法''', 'SELECT ''Column chunk_method already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'parser_config');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `parser_config` text DEFAULT NULL COMMENT ''解析器配置 (JSON)''', 'SELECT ''Column parser_config already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'chunk_count');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `chunk_count` bigint(20) DEFAULT 0 COMMENT ''分块总数''', 'SELECT ''Column chunk_count already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'document_count');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `document_count` bigint(20) DEFAULT 0 COMMENT ''文档总数''', 'SELECT ''Column document_count already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @col_exists = (SELECT COUNT(*) FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = 'ai_rag_dataset' AND COLUMN_NAME = 'token_num');
+SET @sql = IF(@col_exists = 0, 'ALTER TABLE `ai_rag_dataset` ADD COLUMN `token_num` bigint(20) DEFAULT 0 COMMENT ''总 Token 数''', 'SELECT ''Column token_num already exists'' AS msg');
+PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+-- 文档表 (Shadow DB for RAGFlow)
+-- 留存一份文档id，把ragflow远端文档id和本地id关联起来（只是备份一份元信息链接，实际上文件内容的存储还是在ragflow）
+DROP TABLE IF EXISTS `ai_rag_knowledge_document`;
+CREATE TABLE `ai_rag_knowledge_document` (
+     `id` varchar(36) NOT NULL COMMENT '本地唯一ID',
+     `dataset_id` varchar(36) NOT NULL COMMENT '知识库ID (关联 ai_rag_dataset)',
+     `document_id` varchar(64) NOT NULL COMMENT 'RAGFlow文档ID (远程ID)',
+     `name` varchar(255) DEFAULT NULL COMMENT '文档名称',
+     `size` bigint(20) DEFAULT NULL COMMENT '文件大小(Bytes)',
+     `type` varchar(20) DEFAULT NULL COMMENT '文件类型',
+     `chunk_method` varchar(50) DEFAULT NULL COMMENT '分块方法',
+     `parser_config` text COMMENT '解析配置(JSON)',
+     `status` varchar(10) DEFAULT '1' COMMENT '可用状态 (1:启用 0:禁用)',
+     `run` varchar(32) DEFAULT 'UNSTART' COMMENT '运行状态 (UNSTART/RUNNING/CANCEL/DONE/FAIL)',
+     `progress` double DEFAULT '0' COMMENT '解析进度 (0.0 ~ 1.0)',
+     `thumbnail` mediumtext COMMENT '缩略图 (Base64 或 URL)',
+     `process_duration` double DEFAULT '0' COMMENT '解析耗时 (单位: 秒)',
+     `meta_fields` text COMMENT '自定义元数据 (JSON)',
+     `source_type` varchar(32) DEFAULT 'local' COMMENT '来源类型 (local, s3, url 等)',
+     `error` text COMMENT '错误信息',
+     `chunk_count` int(11) DEFAULT '0' COMMENT '分块数量',
+     `token_count` bigint(20) DEFAULT '0' COMMENT 'Token数量',
+     `enabled` tinyint(1) DEFAULT '1' COMMENT '启用状态',
+     `creator` bigint(20) DEFAULT NULL COMMENT '创建者',
+     `created_at` datetime DEFAULT NULL COMMENT '创建时间',
+     `updated_at` datetime DEFAULT NULL COMMENT '更新时间',
+     `last_sync_at` datetime DEFAULT NULL COMMENT '最后同步时间',
+     PRIMARY KEY (`id`),
+     UNIQUE KEY `uk_doc_id` (`document_id`),
+     KEY `idx_dataset_id` (`dataset_id`),
+     KEY `idx_status` (`status`)
+) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='知识库文档表';

main/manager-api/src/main/resources/db/changelog/db.changelog-master.yaml

@@ -508,3 +508,66 @@ databaseChangeLog:
         - sqlFile:
             encoding: utf8
             path: classpath:db/changelog/202601261730.sql
+  - changeSet:
+      id: 202602021555
+      author: shengzhou1216
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202602021555.sql
+  - changeSet:
+      id: 202602051017
+      author: RanChen
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202602051017.sql
+  - changeSet:
+      id: 202602051125
+      author: DaGou12138
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202602051125.sql
+  - changeSet:
+      id: 202602061650
+      author: DaGou12138
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202602061650.sql
+  - changeSet:
+      id: 202602271137
+      author: RanChen
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202602271137.sql
+  - changeSet:
+      id: 202602281000
+      author: rainv123
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202602281000.sql
+  - changeSet:
+      id: 202603091051
+      author: rainv123
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202603091051.sql
+  - changeSet:
+      id: 202603101723
+      author: RanChen
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202603101723.sql
+  - changeSet:
+      id: 202603111131
+      author: DaGou12138
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202603111131.sql

main/manager-api/src/main/resources/i18n/messages.properties

@@ -201,4 +201,9 @@
 10192=\u9002\u914D\u5668\u7C7B\u578B\u672A\u627E\u5230
 10193=\u8BBE\u5907ID\u4E0D\u80FD\u4E3A\u7A7A
 10194=\u8BBE\u5907\u4E0D\u5B58\u5728\u6216\u4E0D\u5728\u7EBF
-10195=OTA\u4E0A\u4F20\u6B21\u6570\u8D85\u8FC7\u9650\u5236
+10195=OTA\u4E0A\u4F20\u6B21\u6570\u8D85\u8FC7\u9650\u5236
+10196=\u6807\u7B7E\u540D\u79F0\u5DF2\u5B58\u5728
+10197=\u6807\u7B7E\u540D\u79F0\u4E0D\u80FD\u4E3A\u7A7A
+10198=\u6807\u7B7E\u4E0D\u5B58\u5728
+10199=\u89E3\u6790\u4E2D\u6587\u4EF6\u6682\u4E0D\u652F\u6301\u6B64\u64CD\u4F5C
+

main/manager-api/src/main/resources/i18n/messages_de_DE.properties

@@ -201,4 +201,9 @@
 10192=Adaptertyp nicht gefunden
 10193=Adapter-ID darf nicht leer sein
 10194=Adapter nicht gefunden oder nicht online
-10195=OTA-Upload-Anzahl \u00FCberschreitet das Limit
+10195=OTA-Upload-Anzahl \u00FCberschreitet das Limit
+10196=Tag-Name existiert bereits
+10197=Tag-Name darf nicht leer sein
+10198=Tag nicht gefunden
+10199=Dateianalyse l\u00E4uft, dieser Vorgang wird nicht unterst\u00FCtzt
+

main/manager-api/src/main/resources/i18n/messages_en_US.properties

@@ -201,4 +201,9 @@
 10192=Adapter type not found
 10193=Device ID cannot be empty
 10194=Device not found or not online
-10195=OTA upload times exceed the limit
+10195=OTA upload times exceed the limit
+10196=Tag name already exists
+10197=Tag name cannot be empty
+10198=Tag not found
+10199=Parsing in progress, this operation is not supported
+

main/manager-api/src/main/resources/i18n/messages_pt_BR.properties

@@ -0,0 +1,208 @@
+#Portugu\u00EAs do Brasil
+500=Erro interno do servidor
+401=N\u00E3o autorizado
+403=Acesso negado, sem permiss\u00E3o
+
+10001={0} n\u00E3o pode ser vazio
+10002=O registro j\u00E1 existe no banco de dados
+10003=Falha ao obter par\u00E2metros
+10004=Conta ou senha incorretos
+10005=Conta desativada
+10006=Identificador \u00FAnico n\u00E3o pode ser vazio
+10007=C\u00F3digo de verifica\u00E7\u00E3o incorreto
+10008=Exclua primeiro o submenu ou bot\u00E3o
+10009=Senha atual incorreta
+10010=Conta ou senha incorretos, voc\u00EA ainda tem {0} tentativas
+
+10011=Erro na sele\u00E7\u00E3o do departamento superior
+10012=O menu superior n\u00E3o pode ser ele mesmo
+10013=Interface de permiss\u00E3o de dados, apenas par\u00E2metros do tipo Map
+10014=Exclua primeiro o departamento inferior
+10015=Exclua primeiro os usu\u00E1rios do departamento
+10016=Falha na implanta\u00E7\u00E3o, sem fluxo
+10017=Diagrama incorreto, verifique
+10018=Falha na exporta\u00E7\u00E3o, ID do modelo � {0}
+
+10019=Por favor, fa\u00E7a upload do arquivo
+10020=Token n\u00E3o pode ser vazio
+10021=Token expirado, fa\u00E7a login novamente
+10022=Conta bloqueada
+10023=Por favor, fa\u00E7a upload de arquivos nos formatos zip, bar, bpmn, bpmn20.xml
+
+10024=Falha no upload do arquivo {0}
+10025=Falha ao enviar SMS {0}
+10026=Modelo de e-mail n\u00E3o existe
+
+10027=Erro no servi\u00E7o Redis
+10028=Falha na tarefa agendada
+10029=N\u00E3o pode conter caracteres ilegais
+10030=Comprimento da senha insuficiente, m\u00EDnimo de {0} caracteres
+10031=A senha deve conter n\u00FAmeros, letras mai\u00FAsculas e min\u00FAsculas e caracteres especiais
+10032=Erro ao excluir dados
+10033=C\u00F3digo de verifica\u00E7\u00E3o do dispositivo incorreto
+
+10034=Valor do par\u00E2metro n\u00E3o pode ser vazio
+10035=Tipo do par\u00E2metro n\u00E3o pode ser vazio
+10036=Tipo de par\u00E2metro n\u00E3o suportado
+10037=Valor do par\u00E2metro deve ser um n\u00FAmero v\u00E1lido
+10038=Valor do par\u00E2metro deve ser true ou false
+10039=Valor do par\u00E2metro deve ser um formato de array JSON v\u00E1lido
+10040=Valor do par\u00E2metro deve ser um formato JSON v\u00E1lido
+
+10041=Dispositivo n\u00E3o encontrado
+10042={0}
+10043=Falha ao excluir dados
+10044=Usu\u00E1rio n\u00E3o logged in
+10045=Falha na conex\u00E3o WebSocket ou conex\u00E3o expirada
+10046=Erro ao salvar impress\u00E3o vocal, entre em contato com o administrador
+10047=Limite di\u00E1rio de envio atingido
+10048=Erro na inser\u00E7\u00E3o da senha antiga
+10049=LLM configurado n\u00E3o � openai ou Collama
+10050=Falha na gera\u00E7\u00E3o do token
+10051=Recurso n\u00E3o existe
+10052=Agente padr\u00E3o n\u00E3o encontrado
+10053=Agente n\u00E3o encontrado
+10054=Interface de impress\u00E3o vocal n\u00E3o configurada, configure primeiro o endere\u00E7o da interface no par\u00E2metro (server.voice_print)
+10055=Falha no envio do SMS
+10056=Falha ao estabelecer conex\u00E3o SMS
+10057=Falha ao criar impress\u00E3o vocal do agente
+10058=Falha ao atualizar a impress\u00E3o vocal correspondente do agente
+10059=Falha ao excluir a impress\u00E3o vocal correspondente do agente
+10060=Envio muito frequente, tente novamente ap\u00F3s {0} segundos
+10061=C\u00F3digo de ativa\u00E7\u00E3o n\u00E3o pode ser vazio
+10062=C\u00F3digo de ativa\u00E7\u00E3o incorreto
+10063=Dispositivo j\u00E1 ativado
+10064=Este modelo � o modelo padr\u00E3o, configure primeiro outro modelo como padr\u00E3o
+10065=Falha ao adicionar dados
+10066=Falha ao modificar dados
+10067=C\u00F3digo de verifica\u00E7\u00E3o gr\u00E1fico incorreto
+10068=Registro por telefone n\u00E3o habilitado, fun\u00E7\u00E3o de c\u00F3digo de verifica\u00E7\u00E3o por SMS indispon\u00EDvel
+10069=Nome de usu\u00E1rio n\u00E3o � n\u00FAmero de telefone, por favor insira novamente
+10070=Este n\u00FAmero de telefone j\u00E1 est\u00E1 registrado
+10071=N\u00FAmero de telefone inserido n\u00E3o est\u00E1 registrado
+10072=Registro de usu\u00E1rio comum n\u00E3o permitido atualmente
+10073=Registro por telefone n\u00E3o habilitado, fun\u00E7\u00E3o de recupera\u00E7\u00E3o de senha indispon\u00EDvel
+10074=Formato de n\u00FAmero de telefone incorreto
+10075=C\u00F3digo de verifica\u00E7\u00E3o do telefone incorreto
+10076=Tipo de dicion\u00E1rio n\u00E3o existe
+10077=C\u00F3digo de tipo de dicion\u00E1rio duplicado
+10078=Falha ao carregar recurso
+10079=Par\u00E2metros de sele\u00E7\u00E3o incompat\u00EDveis entre LLM e reconhecimento de inten\u00E7\u00E3o Intent
+10080=A pessoa ({0}) correspondente a esta impress\u00E3o vocal j\u00E1 est\u00E1 registrada, escolha outra voz para registrar
+10081=Erro ao excluir impress\u00E3o vocal
+10082=Esta modifica\u00E7\u00E3o n\u00E3o � permitida, esta voz j\u00E1 est\u00E1 registrada como impress\u00E3o vocal ({0})
+10083=Erro ao modificar impress\u00E3o vocal, entre em contato com o administrador
+10084=Erro no endere\u00E7o da interface de impress\u00E3o vocal, acesse o gerenciamento de par\u00E2metros para modificar o endere\u00E7o
+10085=Dados de \u00E1udio n\u00E3o pertencem a este agente
+10086=Dados de \u00E1udio est\u00E3o vazios, verifique os dados enviados
+10087=Falha ao salvar impress\u00E3o vocal, falha na requisi\u00E7\u00E3o
+10088=Falha ao salvar impress\u00E3o vocal, falha no processamento
+10089=Falha ao cancelar impress\u00E3o vocal, falha na requisi\u00E7\u00E3o
+10090=Falha ao cancelar impress\u00E3o vocal, falha no processamento
+10091=Provedor n\u00E3o existe
+10092=LLM configurado n\u00E3o existe
+10093=Esta configura\u00E7\u00E3o de modelo foi referenciada pelo agente {0}, n\u00E3o pode ser exclu\u00EDda
+10094=Este modelo LLM foi referenciado pela configura\u00E7\u00E3o de reconhecimento de inten\u00E7\u00E3o, n\u00E3o pode ser exclu\u00EDdo
+10095=Opera\u00E7\u00E3o de servidor inv\u00E1lida
+10096=Endere\u00E7o WebSocket do servidor n\u00E3o configurado
+10097=Endere\u00E7o WebSocket de destino n\u00E3o existe
+10098=Lista de endere\u00E7os WebSocket n\u00E3o pode ser vazia
+10099=Endere\u00E7o WebSocket n\u00E3o pode usar localhost ou 127.0.0.1
+10100=Formato de endere\u00E7o WebSocket incorreto
+10101=Falha no teste de conex\u00E3o WebSocket
+10102=Endere\u00E7o OTA n\u00E3o pode ser vazio
+10103=Endere\u00E7o OTA n\u00E3o pode usar localhost ou 127.0.0.1
+10104=Endere\u00E7o OTA deve come\u00E7ar com http ou https
+10105=Endere\u00E7o OTA deve terminar com /ota/
+10106=Falha ao acessar interface OTA
+10107=Formato de conte\u00FAdo retornado pela interface OTA incorreto
+10108=Falha na valida\u00E7\u00E3o da interface OTA
+10109=Endere\u00E7o MCP n\u00E3o pode ser vazio
+10110=Endere\u00E7o MCP n\u00E3o pode usar localhost ou 127.0.0.1
+10111=Endere\u00E7o MCP incorreto
+10112=Falha ao acessar interface MCP
+10113=Formato de conte\u00FAdo retornado pela interface MCP incorreto
+10114=Falha na valida\u00E7\u00E3o da interface MCP
+10115=Endere\u00E7o da interface de impress\u00E3o vocal n\u00E3o pode ser vazio
+10116=Endere\u00E7o da interface de impress\u00E3o vocal n\u00E3o pode usar localhost ou 127.0.0.1
+10117=Endere\u00E7o da interface de impress\u00E3o vocal incorreto
+10118=Endere\u00E7o da interface de impress\u00E3o vocal deve come\u00E7ar com http ou https
+10119=Falha ao acessar interface de impress\u00E3o vocal
+10120=Formato de conte\u00FAdo retornado pela interface de impress\u00E3o vocal incorreto
+10121=Falha na valida\u00E7\u00E3o da interface de impress\u00E3o vocal
+10122=Chave mqtt n\u00E3o pode ser vazia
+10123=Comprimento da chave mqtt inseguro, deve ter pelo menos 8 caracteres e conter letras mai\u00FAsculas e min\u00FAsculas
+10124=Comprimento da chave mqtt inseguro, deve conter letras mai\u00FAsculas e min\u00FAsculas
+10125=Sua chave mqtt cont\u00E9m senha fraca
+10128=Etiqueta de dicion\u00E1rio duplicada
+10129=Chave SM2 n\u00E3o configurada
+10130=Falha na descriptografia SM2
+10131=modelType e provideCode n\u00E3o podem ser vazios
+10132=Sem permiss\u00E3o para visualizar registros de chat deste agente
+10133=ID da sess\u00E3o n\u00E3o pode ser vazio
+10134=ID do agente n\u00E3o pode ser vazio
+10135=Falha ao baixar registros de chat
+10136=Link de download expirado ou inv\u00E1lido
+10137=Link de download inv\u00E1lido
+10138=Usu\u00E1rio
+10139=Agente
+10140=Arquivo de \u00E1udio n\u00E3o pode ser vazio
+10141=Apenas arquivos de \u00E1udio s\u00E3o suportados
+10142=Tamanho do arquivo de \u00E1udio n\u00E3o pode exceder 10MB
+10143=Falha no upload
+10144=Registro de clonagem de voz n\u00E3o existe
+10145=Informa\u00E7\u00F5es de recurso de tom de voz n\u00E3o podem ser vazias
+10146=Nome da plataforma n\u00E3o pode ser vazio
+10147=ID do tom de voz n\u00E3o pode ser vazio
+10148=N\u00FAmero da conta atribu\u00EDda n\u00E3o pode ser vazio
+10149=IDs de recursos de tom de voz a serem exclu\u00EDdos n\u00E3o podem ser vazios
+10150=Voc\u00EA n\u00E3o tem permiss\u00E3o para operar este registro
+10151=Por favor, fa\u00E7a upload do arquivo de \u00E1udio primeiro
+10152=Configura\u00E7\u00E3o do modelo n\u00E3o encontrada
+10153=Tipo de modelo n\u00E3o encontrado
+10154=Falha no treinamento: {0}
+10155=Configura\u00E7\u00E3o ausente do mecanismo Huoshan
+10156=Erro de formato de resposta, campo BaseResp ausente
+10157=Falha na requisi\u00E7\u00E3o
+10158=Clonagem de tom de voz:
+10159=ID do tom de voz j\u00E1 existe
+10160=Formato de ID do tom de voz do mecanismo Huoshan incorreto, deve come\u00E7ar com S_
+10161=Endere\u00E7o Mac j\u00E1 existe
+10162=Provedor de modelos n\u00E3o existe
+10163=Registro da base de conhecimento n\u00E3o existe
+10164=Configura\u00E7\u00E3o RAG n\u00E3o encontrada
+10165=Erro de tipo de configura\u00E7\u00E3o RAG
+10166=Configura\u00E7\u00E3o RAG padr\u00E3o n\u00E3o encontrada
+10167=Falha na chamada RAG, {0}
+10168=Falha no upload do arquivo
+10169=Voc\u00EA n\u00E3o tem permiss\u00E3o para operar este registro
+10170=Nome da base de conhecimento duplicado
+10171=base_url da configura\u00E7\u00E3o RAG n\u00E3o pode ser vazio
+10172=api_key da configura\u00E7\u00E3o RAG n\u00E3o pode ser vazio
+10173=api_key da configura\u00E7\u00E3o RAG n\u00E3o pode ser vazio, substitua pelo par\u00E2metro API obtido
+10174=Formato de base_url da configura\u00E7\u00E3o RAG incorreto, deve come\u00E7ar com http ou https
+10175=Endere\u00E7o mac n\u00E3o pode ser vazio
+10176=dataset_id da configura\u00E7\u00E3o RAG n\u00E3o pode ser vazio
+10177=model_id da configura\u00E7\u00E3o RAG n\u00E3o pode ser vazio
+10178=dataset_id e model_id da configura\u00E7\u00E3o RAG n\u00E3o podem ser vazios
+10179=Nome do arquivo n\u00E3o pode ser vazio
+10180=Conte\u00FAdo do arquivo n\u00E3o pode ser vazio
+10181=Nome da clonagem de tom de voz n\u00E3o pode ser vazio
+10182=\u00C1udio de clonagem de tom de voz n\u00E3o existe
+10183=Agente padr\u00E3o n\u00E3o encontrado
+10184=Tipo de adaptador n\u00E3o suportado
+10185=Falha na valida\u00E7\u00E3o da configura\u00E7\u00E3o RAG
+10186=Falha ao criar adaptador
+10187=Falha ao inicializar adaptador
+10188=Falha no teste de conex\u00E3o do adaptador
+10189=Falha na opera\u00E7\u00E3o do adaptador
+10190=Adaptador n\u00E3o encontrado
+10191=Erro de cache do adaptador
+10192=Tipo de adaptador n\u00E3o encontrado
+10193=ID do dispositivo n\u00E3o pode ser vazio
+10194=Dispositivo n\u00E3o existe ou est\u00E1 offline
+10195=Limite de uploads OTA excedido
+10196=Nome da etiqueta j\u00E1 existe
+10197=Nome da etiqueta n\u00E3o pode ser vazio
+10198=Etiqueta n\u00E3o existe
+10199=Esta opera\u00E7\u00E3o n\u00E3o � suportada na an\u00E1lise de arquivos chineses

main/manager-api/src/main/resources/i18n/messages_vi_VN.properties

@@ -201,4 +201,9 @@
 10192=Kh\u00F4ng t\u00ECm th\u1EA5y lo\u1EA1i b\u1ED9 chuy\u1EC3n \u0111\u1ED5i
 10193=ID thi\u1EBFt b\u1ECB kh\u00F4ng th\u1EC3 tr\u1ED1ng
 10194=Kh\u00F4ng t\u00ECm th\u1EA5y thi\u1EBFt b\u1ECB ho\u1EB7c thi\u1EBFt b\u1ECB kh\u00F4ng tr\u1EF1c tuy\u1EBFn
-10195=S\u1ED1 l\u1EA7n t\u1EA3i l\u00EAn OTA v\u01B0\u1EE3t qu\u00E1 gi\u1EDBi h\u1EA1n
+10195=S\u1ED1 l\u1EA7n t\u1EA3i l\u00EAn OTA v\u01B0\u1EE3t qu\u00E1 gi\u1EDBi h\u1EA1n
+10196=T\u00EAn th\u1EB9\uFFFD \u0111\u00E3 t\u1ED3n t\u1EA1i
+10197=T\u00EAn th\u1EB9\uFFFD kh\u00F4ng th\u1EC3 \u0111\u1EC3 tr\u1ED1ng
+10198=Kh\u00F4ng t\u00ECm th\u1EA5y th\u1EB9\uFFFD
+10199=T\u1EC7p \u0111ang \u0111\u01B0\u1EE3c ph\u00E2n t\u00EDch, thao t\u00E1c n\u00E0y kh\u00F4ng \u0111\u01B0\u1EE3c h\u1ED7 tr\u1EE3
+