diff --git a/docs/.vitepress/config.mts b/docs/.vitepress/config.mts new file mode 100644 index 00000000..cfbcf67e --- /dev/null +++ b/docs/.vitepress/config.mts @@ -0,0 +1,34 @@ +import { defineConfig } from 'vitepress' + +// https://vitepress.dev/reference/site-config +export default defineConfig({ + title: "Yuxi-Know Docs", + description: "文档中心", + themeConfig: { + // https://vitepress.dev/reference/default-theme-config + nav: [ + { text: 'Home', link: '/' }, + { text: '简介', link: '/intro/quick-start' }, + { text: '更新日志', link: '/changelog/update' } + ], + + sidebar: [ + { + text: '简介', + items: [ + { text: '快速开始', link: '/intro/quick-start' } + ] + }, + { + text: '更新日志', + items: [ + { text: '更新日志', link: '/changelog/update' } + ] + } + ], + + socialLinks: [ + { icon: 'github', link: 'https://github.com/xerrors/Yuxi-Know' } + ] + } +}) diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..1971558c --- /dev/null +++ b/docs/index.md @@ -0,0 +1,31 @@ +--- +# https://vitepress.dev/reference/default-theme-home-page +layout: home + +hero: + name: "Yuxi-Know" + text: "智能知识库与知识图谱问答系统" + tagline: 基于 LangGraph + Vue.js + FastAPI + LightRAG 架构构建的智能问答平台 + actions: + - theme: brand + text: 快速开始 + link: /intro/quick-start + - theme: alt + text: 在线演示 + link: https://www.bilibili.com/video/BV1ETedzREgY/ + +features: + - title: 🤖 智能体与模型 + details: 支持主流大模型及 vLLM、Ollama 等，支持自定义智能体开发，兼容 LangGraph 部署 + - title: 📚 灵活知识库 + details: 支持 LightRAG、Milvus、Chroma 等存储形式，配置 MinerU、PP-Structure-V3 文档解析引擎 + - title: 🕸️ 知识图谱 + details: 支持 LightRAG 自动图谱构建，以及自定义图谱问答，可接入现有知识图谱 + - title: 👥 权限安全 + details: 支持超级管理员、管理员、普通用户三级权限体系，并配置内容审查以及守卫模型 + - title: 🔧 易于部署 + details: 基于 Docker Compose 一键部署，支持热重载开发，无需显卡即可运行 + - title: 🎯 生产就绪 + details: 完整的测试套件、API 文档、监控日志，适合企业级部署和使用 +--- + diff --git a/docs/intro/quick-start.md b/docs/intro/quick-start.md new file mode 100644 index 00000000..97eb7f18 --- /dev/null +++ b/docs/intro/quick-start.md @@ -0,0 +1,441 @@ +# 快速开始 + +## 📖 简介 + +语析是一个功能强大的智能问答平台，融合了 RAG 知识库与知识图谱技术，基于 LangGraph + Vue.js + FastAPI + LightRAG 架构构建。 + +### ✨ 核心特性 + +- 🤖 **模型与智能体** - 支持主流大模型及 vLLM、Ollama 等，支持自定义智能体开发，兼容 LangGraph 部署 +- 📚 **灵活知识库** - 支持 LightRAG、Milvus、Chroma 等存储形式，配置 MinerU、PP-Structure-V3 文档解析引擎 +- 🕸️ **知识图谱** - 支持 LightRAG 自动图谱构建，以及自定义图谱问答，可接入现有知识图谱 +- 👥 **权限安全** - 支持超级管理员、管理员、普通用户三级权限体系，并配置内容审查以及守卫模型 + +
+ + + 视频演示缩略图 + + + +

+ + 📽️ 点击查看视频演示 + +

+
+ + +## 🚀 快速开始 + +### 系统要求 + +项目本身不需要显卡支持，可以运行在任何设备上。对于模型推理或者文档解析服务都可以和项目本身解耦，通过环境变量配置对应的服务即可。同时项目也提供了本地部署基于 vllm 的脚本。 + +### 一键启动 + +1. **克隆项目** + ```bash + git clone -b 0.2.1 https://github.com/xerrors/Yuxi-Know.git + cd Yuxi-Know + ``` + 如果想要使用之前的稳定版（与现版本不兼容），可以使用 `stable` 分支，`main` 分支是最新的开发版本。 + +2. **配置 API 密钥** + + 复制环境变量模板并编辑： + ```bash + cp src/.env.template src/.env + ``` + + **必需配置**（推荐使用硅基流动免费服务）： + ```env + SILICONFLOW_API_KEY=sk-270ea********8bfa97.e3XOMd****Q1Sk + ``` + > 💡 [免费获取 SiliconFlow API Key](https://cloud.siliconflow.cn/i/Eo5yTHGJ)（注册即送 14 元额度） + +3. **启动服务** + ```bash + docker compose up --build + ``` + + 添加 `-d` 参数可后台运行。启动完成后访问：[http://localhost:5173](http://localhost:5173) + +4. **停止服务** + ```bash + docker compose down + ``` + +### 故障排除 + +如果 docker 已经正常启动，则使用 `docker logs api-dev -f` 查看后端日志。 + +
+ Docker 镜像拉取失败 + +如果拉取镜像失败，可以尝试手动拉取： + +```bash +bash docker/pull_image.sh python:3.11-slim # 替换后面的 repo_name 即可 +``` +**如果**依然存在镜像拉取问题，可以先在另一个可访问 Docker 的设备上拉取镜像后，使用下面的脚本保存为 tar 文件，再拷贝到开发设备使用。 +```bash +# 保存镜像，会生成 docker_images_xxx.tar +bash docker/save_docker_images.sh # Linux 或 macOS +powershell -ExecutionPolicy Bypass -File docker/save_docker_images.ps1 # Windows PowerShell + +# 拷贝 docker_images_xxx.tar 文件到开发设备 +scp docker_images_xxx.tar @: + +# 在开发设备上执行 +docker load -i docker_images_xxx.tar +``` +
+
+ 镜像拉取完成，但是构建失败 + +如果拉取镜像完成，但是构建失败，基本都是因为依赖的镜像问题，可以在环境变量中配置如下。如果是已经配置了代理，但是构建失败，也可以尝试移除代理后重试。 + +```bash +export HTTP_PROXY=http://IP:PORT +export HTTPS_PROXY=http://IP:PORT +``` +
+ +
+ Milvus 启动失败，执行 `docker compose up milvus -d && docker restart api-dev` +
+ + +## 🤖 模型配置 + +### 对话模型 + +支持多种 API 服务商，配置对应的 `API_KEY` 即可使用： +| 服务商 | 环境变量 | 备注 | +|--------|----------|------| +| 硅基流动 | `SILICONFLOW_API_KEY` | 🆓 免费，默认 | +| OpenAI | `OPENAI_API_KEY` | | +| DeepSeek | `DEEPSEEK_API_KEY` | | +| OpenRouter | `OPENROUTER_API_KEY` | | +| 智谱清言 | `ZHIPUAI_API_KEY` | | +| 阿里云百炼 | `DASHSCOPE_API_KEY` | | + +
+ 自定义模型供应商 + +在 [src/config/static/models.yaml](src/config/static/models.yaml) 中添加新的模型配置，然后在 `.env` 文件中添加对应的环境变量，最后重新部署项目即可。 + +**示例**： + +```yaml +custom-provider-name-here: + name: custom-provider-name-here + default: custom-model-name-here + base_url: "https://api.your-provider.com/v1" + env: + - CUSTOM_API_KEY_ENV_NAME # e.g. OPENAI_API_KEY + models: + - supported-model-name-here +``` +
+ +### 自定义嵌入模型和重排序模型 + +需要注意的是，从 v0.2 版本开始，项目将模型部署和项目本身做了完全解耦，因此无论是 Embedding 还是 Reranker，如果需要使用本地模型，都需要使用 vllm /ollama 转换为 api 服务后，在 src/config/static/models.yaml 或者 src/config/static/models.private.yaml （优先）中 添加类似下面的信息： + +```yaml +EMBED_MODEL_INFO: + # 其余的 + vllm/Qwen/Qwen3-Embedding-0.6B: + name: Qwen/Qwen3-Embedding-0.6B + dimension: 1024 + base_url: http://localhost:8000/v1/embeddings + api_key: no_api_key + +RERANKER_LIST: + vllm/BAAI/bge-reranker-v2-m3: + name: BAAI/bge-reranker-v2-m3 + base_url: http://localhost:8000/v1/rerank + api_key: no_api_key +``` + +
+启动脚本示例 + +```bash +# 启动 Qwen Embedding 模型 +vllm serve Qwen/Qwen3-Embedding-0.6B \ + --task embed \ + --dtype auto \ + --port 8000 + +# 启动 BAAI reranker 模型 +vllm serve BAAI/bge-reranker-v2-m3 \ + --task score \ + --dtype fp16 \ + --port 8000 +``` + +
+ + + +### OpenAI 兼容模型 + +项目理论上兼容任何 OpenAI 兼容的模型，包括但不限于 vLLM、Ollama 或者其他 API 中转或者代理服务。在 Web 界面的"设置"中添加本地模型地址。 + + +## 📚 功能详解 + +### 知识库管理 + +现在支持的知识库类型包括 `Chroma`、`Milvus`、`LightRAG`。[LightRAG](https://github.com/HKUDS/LightRAG) 是轻量级 GraphRAG 方法。 + + + + + + + + + +
知识图谱可视化知识库可视化
+ +### 知识图谱 + +在 v0.2 版本中，项目支持了基于 [LightRAG](https://github.com/HKUDS/LightRAG) 的知识图谱构建方法。需要在知识库中创建一个基于 LightRAG 的知识库，然后上传文档。构建的知识图谱会自动导入到 Neo4j 中，并使用不同的 label 做区分。需要说明的是，基于 LightRAG 的知识库可以在知识库详情中可视化，但是不能在侧边栏的图谱中检索，知识图谱检索工具也不支持基于 LightRAG 的知识库进行检索。基于 LightRAG 方法构建的图谱查询，需要使用对应的知识库作为查询工具。 + +默认使用的图谱构建模型是 `siliconflow` 的 `Qwen/Qwen3-30B-A3B-Instruct-2507`，可以使用 `LIGHTRAG_LLM_PROVIDER` 和 `LIGHTRAG_LLM_NAME` 覆盖。 + + + + + + + + + + + + + + + + +
知识图谱可视化知识库可视化Neo4J管理端
知识图谱可视化知识库可视化Neo4J管理端
+ + +除此之外，也支持将已有的知识图谱按照下面的格式导入 Neo4j 中，上传后，节点会自动添加 `Upload`、`Entity` 标签，关系会自动添加 `Relation` 标签。可以通过 `name` 属性访问实体的名称，使用 `type` 属性访问边的名称。默认账户密码是 `neo4j` / `0123456789`。 + + +**数据格式**：支持 JSONL 格式导入，可以先尝试 [test/data/A_Dream_of_Red_Mansions_tiny.jsonl](test/data/A_Dream_of_Red_Mansions_tiny.jsonl) 文件。 + +```jsonl +{"h": "北京", "t": "中国", "r": "首都"} +{"h": "上海", "t": "中国", "r": "直辖市"} +``` + +此外，也可以通过修改 `docker-compose.yml` 中的 `NEO4J_URI` 配置来接入已有的 Neo4j 实例，**但是**最好确保每个节点都有 `Entity` 标签，每个关系都有 `RELATION` 类型，否则会影响到图的检索与构建。 + +注：在“图谱”页面，只能看到上传的节点和边，基于 LightRAG 构建的图谱不会展示在里面，完整的图谱可以去 Neo4j 管理页面查看。 + +## 🔧 高级配置 + +### OCR 服务（可选） + +对于**基础的 OCR 服务**（RapidOCR ONNX 版本），可以使用 SWHL/RapidOCR 的 ONNX 版本，但是需要提前将模型下载到 `$MODEL_DIR` 目录下（默认 `src/.env.template` 中为 `models`）。在容器内实际路径为 `/models`（由 `MODEL_DIR_IN_DOCKER` 指向）。 + +```bash +huggingface-cli download SWHL/RapidOCR --local-dir ${MODEL_DIR:-./models}/SWHL/RapidOCR +``` + +- 模型完整性要求：需要存在 `PP-OCRv4/ch_PP-OCRv4_det_infer.onnx` 和 `PP-OCRv4/ch_PP-OCRv4_rec_infer.onnx` +- 如果提示 `[Errno 13] Permission denied` 则需要使用 sudo 修改权限之后再执行 + +提升 PDF 解析准确性，可以选择使用 MinerU 或 PP-Structure-V3 服务（需要 NVIDIA GPU）。 + +启用**MinerU**服务： + +```bash +# MinerU（需要 CUDA 12.6+ 的 sglang 镜像） +docker compose up mineru --build +``` + +启用**PP-Structure-V3**服务，配置文件可在 [docker/PP-StructureV3.yaml](docker/PP-StructureV3.yaml) 中修改。 + +```bash +# PP-Structure-V3（需要 CUDA 11.8+） +docker compose up paddlex --build +``` + +### 自定义智能体应用开发 + +目前该项目默认集成了三个 Demo 智能体，包含基础智能体、ReAct、DeepResearch 三个案例 Demo，均使用 [LangGraph](https://github.com/langchain-ai/langgraph) 开发。代码位于 [src/agents](src/agents) 目录。在 [src/agents/react/graph.py](src/agents/react/graph.py) 中定义了 `ReActAgent` 示例。 + +如果需要自定义智能体应用，需要实现一个继承于 `BaseAgent` 的类，并实现 `get_graph` 方法返回一个 graph 实例。智能体的 `context_schema` 定义了配置参数。 + +注册智能体的方式请参考已有实现：[src/agents/__init__.py](src/agents/__init__.py)。例如： + +```python +from .chatbot import ChatbotAgent + +agent_manager = AgentManager() +agent_manager.register_agent(ChatbotAgent) +agent_manager.init_all_agents() +``` + +### MySQL 数据库查询集成（Beta） + +项目目前已经支持智能体查询 MySQL 数据库，如果想要接入数据库，则可以在环境变量中配置如下信息： + +```sh +# 基础配置示例 +MYSQL_HOST=192.168.1.100 +MYSQL_USER=username +MYSQL_PASSWORD=your_secure_password +MYSQL_DATABASE=database_name +MYSQL_PORT=3306 +MYSQL_CHARSET=utf8mb4 +``` + +然后在智能体配置的工具中，勾选上 `mysql` 开头的几个工具即可。 + +- **mysql_list_tables**: 获取数据库中的所有表名 +- **mysql_describe_table**: 获取指定表的详细结构信息 +- **mysql_query**: 执行只读的 SQL 查询语句 + +**安全特性** + +- ✅ 只允许 SELECT、SHOW、DESCRIBE、EXPLAIN 操作 +- ✅ 表名参数验证，严格的 SQL 注入防护 +- ✅ 查询超时控制（默认10秒，最大60秒） +- ✅ 结果大小、行数限制（默认10000字符，100行，最大1000行） + +**注意事项** + +1. 确保数据库用户只有只读权限 +2. 大表查询建议使用 LIMIT 子句 +3. 复杂查询可能需要调整超时时间 +4. 查询结果过大会被自动截断并提示 + +### 图表可视化绘制 - MCP（Beta） + +这是基于 @antvis 团队开发的 [可视化图表-MCP-Server](https://www.modelscope.cn/mcp/servers/@antvis/mcp-server-chart)，可以在魔搭社区中配置 Host 资源后，在 src/agents/common/mcp.py 的 `MCP_SERVERS` 中添加 mcp-server，需要注意的是记得将 `type` 字段修改为 `transport`。 + +```python +# MCP Server configurations +MCP_SERVERS = { + "sequentialthinking": { + "url": "https://remote.mcpservers.org/sequentialthinking/mcp", + "transport": "streamable_http", + }, + + "mcp-server-chart": { + "url": "https://mcp.api-inference.modelscope.net/9993ae42524c4c/mcp", + "transport": "streamable_http", + } +} +``` + +### 服务安全 + +为了保障服务内容的合规性，系统内置了一套内容审查机制。对用户的输入和模型生成的输出进行关键词过滤，防止不当内容的传播。 + +管理员可以在 `设置` -> `基本设置` 页面一键启用或禁用内容审查功能。**敏感词词库**位于 `src/config/static/bad_keywords.txt` 文件，可以根据需要自行修改，每行一个关键词。 + + + +### 服务端口说明 + +| 端口 | 服务 | 说明 | +|------|------|------| +| 5173 | Web 前端 | 用户界面（容器名：web-dev） | +| 5050 | API 后端 | 核心服务（容器名：api-dev） | +| 7474/7687 | Neo4j | 图数据库（容器名：graph） | +| 9000/9001 | MinIO | 对象存储（容器名：milvus-minio） | +| 19530/9091 | Milvus | 向量数据库（容器名：milvus） | +| 30000 | MinerU | PDF 解析（容器名：mineru，可选）| +| 8080 | PaddleX | OCR 服务（容器名：paddlex-ocr，可选）| +| 8081 | vLLM | 本地推理（可选，scripts/vllm/run.sh 默认端口）| + + +### 品牌信息配置 + +在主页和登录页面的很多信息，比如 Logo、组织名称、版权信息等，都可以复制 [src/config/static/info.template.yaml](src/config/static/info.template.yaml)，并新建一个 `src/config/static/info.local.yaml`（或者在 .env 文件中配置 `YUXI_BRAND_FILE_PATH` 指向这个文件），在这个文件中配置。在项目启动时，会加载这个文件，然后根据文件中的配置渲染到前端页面中。如果 `src/config/static/info.local.yaml` 不存在，会默认使用 [src/config/static/info.template.yaml](src/config/static/info.template.yaml) 中的配置。 + +系统的配色方面，主要保存在 [web/src/assets/css/base.css](web/src/assets/css/base.css) 中。只要替换其中的 `--main-*` 相关变量，就可以改变系统的配色。 + +### 预设脚本 + +**文件上传脚本**，使用 `scripts/batch_upload.py` 脚本上传文件到知识库中，调用示例如下，会将 `path/to/your/data` 目录下所有 `*.docx` 文件上传到 `your_kb_id` 知识库中。 + +```bash +# 上传知识库文件 +uv run scripts/batch_upload.py upload \ + --db-id your_kb_id \ + --directory path/to/your/data \ + --pattern "*.docx" \ + --base-url http://127.0.0.1:5050/api \ + --username your_username \ + --password your_password \ + --concurrency 4 \ + --recursive \ + --record-file scripts/tmp/batch_processed_files.txt +``` + +**文件解析脚本**，还可以使用 `scripts/batch_upload.py trans` 脚本将文件解析成 markdown 文件，调用示例如下，会将 `path/to/your/data` 目录下所有 `*.docx` 文件解析成 markdown 文件，输出到 `path/to/output_markdown` 目录中。 + +```bash +# 文件解析成 markdown +uv run scripts/batch_upload.py trans \ + --db-id your_kb_id \ + --directory path/to/your/data \ + --output-dir path/to/output_markdown \ + --pattern "*.docx" \ + --base-url http://127.0.0.1:5050/api \ + --username your_username \ + --password your_password \ + --concurrency 4 \ + --recursive +``` + +## ❓ 常见问题 + +- 如何查看后端日志？运行 `docker logs api-dev -f` +- RapidOCR 模型未找到怎么办？确认 `MODEL_DIR` 指向的目录存在 `SWHL/RapidOCR`，且包含 `PP-OCRv4` 下的 det/rec onnx 文件。 +- MinerU/PaddleX 健康检查失败？分别检查 `http://localhost:30000/health` 与 `http://localhost:8080/` 是否 200，确认 GPU/驱动与 CUDA 版本匹配。 + +## 🤝 参与贡献 + +感谢所有贡献者的支持！ + + + 贡献者名单 + + +### 如何贡献 + +1. Fork 本项目 +2. 创建特性分支 (`git checkout -b feature/amazing-feature`) +3. 提交更改 (`git commit -m 'Add some amazing feature'`) +4. 推送到分支 (`git push origin feature/amazing-feature`) +5. 创建 Pull Request + +## 📊 Star History + +[![Star History Chart](https://api.star-history.com/svg?repos=xerrors/Yuxi-Know)](https://star-history.com/#xerrors/Yuxi-Know) + +## 📄 许可证 + +本项目采用 MIT 许可证 - 查看 [LICENSE](LICENSE) 文件了解详情。 + +--- + +
+ +**如果这个项目对您有帮助，请不要忘记给我们一个 ⭐️** + +[报告问题](https://github.com/xerrors/Yuxi-Know/issues) | [功能请求](https://github.com/xerrors/Yuxi-Know/issues) | [讨论](https://github.com/xerrors/Yuxi-Know/discussions) + +
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