diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml new file mode 100644 index 00000000..7ec21f40 --- /dev/null +++ b/.github/workflows/deploy.yml @@ -0,0 +1,66 @@ +# 构建 VitePress 站点并将其部署到 GitHub Pages 的示例工作流程 +# +name: Deploy VitePress site to Pages + +on: + # 在针对 `main` 分支的推送上运行。如果你 + # 使用 `master` 分支作为默认分支，请将其更改为 `master` + push: + branches: [main] + + # 允许你从 Actions 选项卡手动运行此工作流程 + workflow_dispatch: + +# 设置 GITHUB_TOKEN 的权限，以允许部署到 GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# 只允许同时进行一次部署，跳过正在运行和最新队列之间的运行队列 +# 但是，不要取消正在进行的运行，因为我们希望允许这些生产部署完成 +concurrency: + group: pages + cancel-in-progress: false + +jobs: + # 构建工作 + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 # 如果未启用 lastUpdated，则不需要 + # - uses: pnpm/action-setup@v3 # 如果使用 pnpm，请取消此区域注释 + # with: + # version: 9 + # - uses: oven-sh/setup-bun@v1 # 如果使用 Bun，请取消注释 + - name: Setup Node + uses: actions/setup-node@v4 + with: + node-version: 22 + cache: npm # 或 pnpm / yarn + - name: Setup Pages + uses: actions/configure-pages@v4 + - name: Install dependencies + run: npm ci # 或 pnpm install / yarn install / bun install + - name: Build with VitePress + run: npm run docs:build # 或 pnpm docs:build / yarn docs:build / bun run docs:build + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + path: docs/.vitepress/dist + + # 部署工作 + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + needs: build + runs-on: ubuntu-latest + name: Deploy + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 \ No newline at end of file diff --git a/docs/.vitepress/config.mts b/docs/.vitepress/config.mts index cfbcf67e..769b50c5 100644 --- a/docs/.vitepress/config.mts +++ b/docs/.vitepress/config.mts @@ -1,9 +1,17 @@ import { defineConfig } from 'vitepress' +import markdownItTaskCheckbox from 'markdown-it-task-checkbox' + // https://vitepress.dev/reference/site-config export default defineConfig({ + lang: 'zh-CN', title: "Yuxi-Know Docs", description: "文档中心", + markdown: { + config: (md) => { + md.use(markdownItTaskCheckbox) + } + }, themeConfig: { // https://vitepress.dev/reference/default-theme-config nav: [ @@ -30,5 +38,5 @@ export default defineConfig({ socialLinks: [ { icon: 'github', link: 'https://github.com/xerrors/Yuxi-Know' } ] - } + }, }) diff --git a/docs/changelog/update.md b/docs/changelog/update.md index 0705293a..bce826fc 100644 --- a/docs/changelog/update.md +++ b/docs/changelog/update.md @@ -1,4 +1,4 @@ -目前已有的开发计划包括： +# 开发计划 📝 0.3 version @@ -14,7 +14,6 @@ --- 💭 **Features Todo** -- [ ] 使用其他的聊天记录管理方法，解决两个问题，一个是上下文长度过长，一个是上下文的类型变得更加丰富，比如多模态等等。（现在是基于 LangGraph 的 Memory 实现的，v0.2.3 版本实现），暂定使用 [mem0](github.com/mem0ai/mem0) 来实现。但是目前了解下来，还不是我想要的那种方案。可能会基于这个实现一个 ThreadConvManager 这个类。 - [ ] 添加对于上传文件的支持：这里的复杂的地方就在于如何和历史记录结合在一起（v0.2.3 版本实现，放在记忆管理后面） - [ ] 知识图谱的上传和可视化，支持属性，标签的展示 - [ ] 集成智能体评估，首先使用命令行来实现，然后考虑放在 UI 里面展示 diff --git a/docs/intro/quick-start.md b/docs/intro/quick-start.md index 97eb7f18..416abddd 100644 --- a/docs/intro/quick-start.md +++ b/docs/intro/quick-start.md @@ -1,23 +1,33 @@ -# 快速开始 +# 快速开始指南 -## 📖 简介 +## 项目简介 -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。 +Yuxi-Know（语析）是一个基于知识图谱和向量数据库的智能知识库系统，融合了 RAG（检索增强生成）技术与知识图谱技术，为用户提供智能问答和知识管理服务。 -### ✨ 核心特性 +### 技术架构 -- 🤖 **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 +- **后端服务**: FastAPI + Python 3.12+ +- **前端界面**: Vue.js 3 + TypeScript +- **知识存储**: Milvus（向量数据库）+ Neo4j（图数据库） +- **智能体框架**: LangGraph +- **文档解析**: LightRAG + MinerU + PP-Structure-V3 +- **容器编排**: Docker Compose + +### 核心功能 + +- **智能问答**: 支持多种大语言模型，提供智能对话和问答服务 +- **知识库管理**: 支持多种存储形式（Chroma、Milvus、LightRAG） +- **知识图谱**: 自动构建和可视化知识图谱，支持图查询 +- **文档解析**: 支持 PDF、Word、图片等多种格式的智能解析 +- **权限管理**: 三级权限体系（超级管理员、管理员、普通用户） +- **内容安全**: 内置内容审查机制，保障服务合规性 + +## 演示视频
- 视频演示缩略图 - -

📽️ 点击查看视频演示 @@ -25,128 +35,205 @@

- -## 🚀 快速开始 +## 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。 +#### 硬件要求 +- **CPU**: 2 核心以上 +- **内存**: 4GB 以上（推荐 8GB） +- **存储**: 10GB 以上可用空间 +- **网络**: 稳定的互联网连接（用于下载模型和依赖） -### 一键启动 +#### 软件要求 +- **Docker**: 20.10+ 版本 +- **Docker Compose**: 2.0+ 版本 +- **操作系统**: Linux、macOS 或 Windows（支持 WSL2） -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。 +#### 可选配置 +- **GPU**: NVIDIA GPU（用于 OCR 服务和本地模型推理） +- **CUDA**: 11.8+ 或 12.6+（根据服务选择） -2. **配置 API 密钥** +::: tip 提示 +项目采用微服务架构，核心服务无需 GPU 支持。GPU 仅用于可选的 OCR 服务和本地模型推理，可通过环境变量配置外部服务。 +::: - 复制环境变量模板并编辑： - ```bash - cp src/.env.template src/.env - ``` +### 安装步骤 - **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） +#### 1. 获取项目代码 -3. **启动服务** - ```bash - docker compose up --build - ``` +```bash +# 克隆稳定版本 +git clone -b 0.2.1 https://github.com/xerrors/Yuxi-Know.git +cd Yuxi-Know +``` - 添加 `-d` 参数可后台运行。启动完成后访问：[http://localhost:5173](http://localhost:5173) +::: warning 版本说明 +- `0.2.1`: 当前稳定版本（推荐） +- `stable`: 旧版本稳定分支（与现版本不兼容） +- `main`: 最新开发版本（可能不稳定） +::: -4. **停止服务** - ```bash - docker compose down - ``` +#### 2. 配置环境变量 + +复制环境变量模板并编辑： + +```bash +cp src/.env.template src/.env +``` + +编辑 `src/.env` 文件，配置必需的 API 密钥： + +```env +# 必需配置 - 推荐使用硅基流动免费服务 +SILICONFLOW_API_KEY=sk-270ea********8bfa97.e3XOMd****Q1Sk +``` + +::: tip 免费获取 API Key +[硅基流动](https://cloud.siliconflow.cn/i/Eo5yTHGJ) 注册即送 14 元额度，支持多种开源模型。 +::: + +#### 3. 启动服务 + +```bash +# 构建并启动所有服务 +docker compose up --build + +# 后台运行（推荐） +docker compose up --build -d +``` + +#### 4. 访问系统 + +服务启动完成后，访问以下地址： + +- **Web 界面**: http://localhost:5173 +- **API 文档**: http://localhost:5050/docs + +#### 5. 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。 +#### 查看服务状态 + +```bash +# 查看所有容器状态 +docker ps + +# 查看后端服务日志 +docker logs api-dev -f + +# 查看前端服务日志 +docker logs web-dev -f +``` + +#### 常见问题
- Docker 镜像拉取失败 +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 +bash docker/pull_image.sh python:3.11-slim ``` -**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 +**离线部署方案**： + +```bash +# 在有网络的环境保存镜像 +bash docker/save_docker_images.sh # Linux/macOS +powershell -ExecutionPolicy Bypass -File docker/save_docker_images.ps1 # Windows + +# 传输到目标设备 scp docker_images_xxx.tar @: -# 在开发设备上执行 +# 在目标设备加载镜像 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。 +
+ +
+构建失败 + +如果构建失败，通常是网络问题，可以配置代理： ```bash export HTTP_PROXY=http://IP:PORT export HTTPS_PROXY=http://IP:PORT ``` + +如果已配置代理但构建失败，尝试移除代理后重试。 +
- Milvus 启动失败，执行 `docker compose up milvus -d && docker restart api-dev` +Milvus 启动失败 + +```bash +# 重启 Milvus 服务 +docker compose up milvus -d +docker restart api-dev +``` +
-## 🤖 模型配置 +## 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 | +系统支持多种大语言模型服务商，通过配置对应的 API 密钥即可使用： + +| 服务商 | 环境变量 | 特点 | |--------|----------|------| -| 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` | | +| 硅基流动 | `SILICONFLOW_API_KEY` | 🆓 免费额度，默认推荐 | +| OpenAI | `OPENAI_API_KEY` | GPT 系列模型 | +| DeepSeek | `DEEPSEEK_API_KEY` | 国产大模型 | +| OpenRouter | `OPENROUTER_API_KEY` | 多模型聚合平台 | +| 智谱清言 | `ZHIPUAI_API_KEY` | GLM 系列模型 | +| 阿里云百炼 | `DASHSCOPE_API_KEY` | 通义千问系列 | -
- 自定义模型供应商 +#### 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。 +如需添加新的模型供应商，请按以下步骤操作： -**示例**： +1. 编辑 `src/config/static/models.yaml` 文件 +2. 在 `.env` 文件中添加对应的环境变量 +3. 重新部署项目 + +**配置示例**： ```yaml -custom-provider-name-here: - name: custom-provider-name-here - default: custom-model-name-here +custom-provider-name: + name: custom-provider-name + default: custom-model-name base_url: "https://api.your-provider.com/v1" env: - - CUSTOM_API_KEY_ENV_NAME # e.g. OPENAI_API_KEY + - CUSTOM_API_KEY_ENV_NAME models: - - supported-model-name-here + - supported-model-name ``` -
-### 自定义嵌入模型和重排序模型 +### 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： +::: warning 重要说明 +从 v0.2 版本开始，项目采用微服务架构，模型部署与项目本身完全解耦。如需使用本地模型，需要先通过 vLLM 或 Ollama 部署为 API 服务。 +::: + +#### 本地模型部署 + +**1. 配置模型信息** + +在 `src/config/static/models.yaml` 或 `src/config/static/models.private.yaml` 中添加配置： ```yaml EMBED_MODEL_INFO: - # 其余的 vllm/Qwen/Qwen3-Embedding-0.6B: name: Qwen/Qwen3-Embedding-0.6B dimension: 1024 @@ -160,38 +247,46 @@ RERANKER_LIST: api_key: no_api_key ``` -
-启动脚本示例 +**2. 启动模型服务** ```bash -# 启动 Qwen Embedding 模型 +# 启动嵌入模型 vllm serve Qwen/Qwen3-Embedding-0.6B \ --task embed \ --dtype auto \ --port 8000 -# 启动 BAAI reranker 模型 +# 启动重排序模型 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。 +系统理论上兼容任何 OpenAI 兼容的模型服务，包括： + +- **vLLM**: 高性能推理服务 +- **Ollama**: 本地模型管理 +- **API 中转服务**: 各种代理和聚合服务 + +在 Web 界面的"设置"页面中可以添加本地模型地址。 -## 📚 功能详解 +## 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。 +系统支持多种知识库存储形式，满足不同场景需求： +| 存储类型 | 特点 | 适用场景 | +|----------|------|----------| +| **Chroma** | 轻量级向量数据库 | 小型项目、快速原型 | +| **Milvus** | 高性能向量数据库 | 大规模生产环境 | +| **LightRAG** | 图增强检索 | 复杂知识关系 | + +#### 知识库可视化 @@ -204,9 +299,32 @@ vllm serve BAAI/bge-reranker-v2-m3 \ ### 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。 +#### LightRAG 自动构建 -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。 +系统支持基于 [LightRAG](https://github.com/HKUDS/LightRAG) 的知识图谱自动构建： + +1. **创建 LightRAG 知识库**: 在知识库管理中选择 LightRAG 类型 +2. **上传文档**: 系统自动解析文档并构建知识图谱 +3. **图谱导入**: 构建的图谱自动导入 Neo4j 数据库 +4. **标签区分**: 使用不同标签区分不同来源的图谱数据 + +::: warning 使用限制 +- LightRAG 知识库可在知识库详情中可视化 +- 不支持在侧边栏图谱中直接检索 +- 图谱检索工具不支持 LightRAG 知识库 +- 查询需要使用对应的知识库作为工具 +::: + +#### 图谱构建模型 + +默认使用 `siliconflow` 的 `Qwen/Qwen3-30B-A3B-Instruct-2507` 模型，可通过环境变量自定义： + +```env +LIGHTRAG_LLM_PROVIDER=siliconflow +LIGHTRAG_LLM_NAME=Qwen/Qwen3-30B-A3B-Instruct-2507 +``` + +#### 图谱可视化
@@ -220,77 +338,151 @@ vllm serve BAAI/bge-reranker-v2-m3 \ -
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`。 +#### 外部图谱导入 +系统支持导入已有的知识图谱数据到 Neo4j 中： -**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。 +**数据格式**: JSONL 格式，每行一个三元组 ```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。 +**导入规则**: +- 节点自动添加 `Upload`、`Entity` 标签 +- 关系自动添加 `Relation` 标签 +- 通过 `name` 属性访问实体名称 +- 通过 `type` 属性访问关系名称 -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。 +**Neo4j 访问信息**: +- 默认账户: `neo4j` +- 默认密码: `0123456789` +- 管理界面: http://localhost:7474 -## 🔧 高级配置 +::: tip 测试数据 +可以使用 `test/data/A_Dream_of_Red_Mansions_tiny.jsonl` 文件进行测试导入。 +::: -### OCR 服务（可选） +#### 外部 Neo4j 接入 -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）。 +如需接入已有的 Neo4j 实例，可修改 `docker-compose.yml` 中的 `NEO4J_URI` 配置。 + +::: warning 注意事项 +确保每个节点都有 `Entity` 标签，每个关系都有 `RELATION` 类型，否则会影响图的检索与构建功能。 +::: + +## 高级配置 + +### OCR 服务 + +系统提供多种 OCR 服务选项，满足不同精度和性能需求： + +#### 基础 OCR 服务 + +使用 RapidOCR ONNX 版本，无需 GPU 支持： + +**1. 下载模型** ```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 +**2. 模型要求** -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）。 +确保以下文件存在： +- `PP-OCRv4/ch_PP-OCRv4_det_infer.onnx` +- `PP-OCRv4/ch_PP-OCRv4_rec_infer.onnx` -启用**MinerU**服务： +::: warning 权限问题 +如果提示 `[Errno 13] Permission denied`，需要使用 sudo 修改权限后执行。 +::: + +#### 高级 OCR 服务 + +为提升 PDF 解析准确性，可选择以下 GPU 加速服务： + +**MinerU 服务**（推荐） ```bash -# MinerU（需要 CUDA 12.6+ 的 sglang 镜像） +# 需要 CUDA 12.6+ 环境 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。 +**PP-Structure-V3 服务** ```bash -# PP-Structure-V3（需要 CUDA 11.8+） +# 需要 CUDA 11.8+ 环境 docker compose up paddlex --build ``` -### 自定义智能体应用开发 +配置文件位置: `docker/PP-StructureV3.yaml` -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。 +### 智能体开发 -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。 +系统基于 [LangGraph](https://github.com/langchain-ai/langgraph) 框架，支持自定义智能体应用开发。 -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： +#### 内置智能体 + +系统默认集成三个示例智能体： + +| 智能体 | 功能 | 位置 | +|--------|------|------| +| **基础智能体** | 简单对话功能 | `src/agents/chatbot/` | +| **ReAct 智能体** | 推理与行动循环 | `src/agents/react/` | +| **DeepResearch 智能体** | 深度研究分析 | `src/agents/deepresearch/` | + +#### 开发自定义智能体 + +**1. 创建智能体类** + +继承 `BaseAgent` 并实现 `get_graph` 方法： ```python -from .chatbot import ChatbotAgent +from .base import BaseAgent + +class CustomAgent(BaseAgent): + def get_graph(self): + # 返回 LangGraph 实例 + return graph_instance + + @property + def context_schema(self): + # 定义配置参数 + return schema +``` + +**2. 注册智能体** + +在 `src/agents/__init__.py` 中注册： + +```python +from .custom_agent import CustomAgent agent_manager = AgentManager() -agent_manager.register_agent(ChatbotAgent) +agent_manager.register_agent(CustomAgent) agent_manager.init_all_agents() ``` -### MySQL 数据库查询集成（Beta） +**3. 参考示例** -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： +查看 `src/agents/react/graph.py` 中的 `ReActAgent` 实现示例。 -```sh -# 基础配置示例 +### MySQL 数据库集成 + +系统支持智能体查询 MySQL 数据库，为数据分析提供强大支持。 + +#### 配置数据库连接 + +在环境变量中配置数据库信息： + +```env +# MySQL 数据库配置 MYSQL_HOST=192.168.1.100 MYSQL_USER=username MYSQL_PASSWORD=your_secure_password @@ -299,29 +491,43 @@ 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。 +#### 可用工具 -- **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 +在智能体配置中启用以下 MySQL 工具： -**安全特性** +| 工具名称 | 功能描述 | +|----------|----------| +| `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 | -- ✅ 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） +#### 安全特性 -**注意事项** +- ✅ **只读操作**: 仅允许 SELECT、SHOW、DESCRIBE、EXPLAIN 操作 +- ✅ **SQL 注入防护**: 严格的表名参数验证 +- ✅ **超时控制**: 默认 10 秒，最大 60 秒 +- ✅ **结果限制**: 默认 10000 字符，100 行，最大 1000 行 -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 +#### 使用建议 -### 图表可视化绘制 - MCP（Beta） +1. **权限设置**: 确保数据库用户只有只读权限 +2. **大表查询**: 建议使用 LIMIT 子句限制结果 +3. **复杂查询**: 可能需要调整超时时间 +4. **结果处理**: 查询结果过大会被自动截断并提示 -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`。 +### 图表可视化 - MCP + +系统支持基于 MCP（Model Context Protocol）的图表可视化功能。 + +#### 功能特点 + +- 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) +- 支持多种图表类型和数据可视化 +- 通过魔搭社区配置 Host 资源 + +#### 配置方法 + +在 `src/agents/common/mcp.py` 的 `MCP_SERVERS` 中添加配置： ```python # MCP Server configurations @@ -330,7 +536,6 @@ MCP_SERVERS = { "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", @@ -338,40 +543,115 @@ MCP_SERVERS = { } ``` -### 服务安全 +::: warning 配置注意 +记得将 `type` 字段修改为 `transport`。 +::: -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。 +### 内容安全 -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。 +系统内置内容审查机制，保障服务内容的合规性。 + +#### 功能特点 + +- **输入过滤**: 对用户输入进行关键词检测 +- **输出审查**: 对模型生成内容进行安全审查 +- **实时拦截**: 防止不当内容传播 + +#### 配置方法 + +管理员可在 `设置` → `基本设置` 页面中： + +- ✅ 一键启用/禁用内容审查功能 +- ✅ 自定义敏感词词库 +- ✅ 调整审查策略 + +#### 敏感词管理 + +敏感词词库位于 `src/config/static/bad_keywords.txt` 文件： + +- 每行一个关键词 +- 支持自定义修改 +- 实时生效，无需重启服务 -### 服务端口说明 +### 服务端口 -| 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）| +系统使用多个端口提供不同服务，以下是完整的端口映射： + +| 端口 | 服务 | 容器名称 | 说明 | +|------|------|----------|------| +| **5173** | Web 前端 | web-dev | 用户界面 | +| **5050** | API 后端 | api-dev | 核心服务 | +| **7474/7687** | Neo4j | graph | 图数据库 | +| **9000/9001** | MinIO | milvus-minio | 对象存储 | +| **19530/9091** | Milvus | milvus | 向量数据库 | +| **30000** | MinerU | mineru | PDF 解析（可选）| +| **8080** | PaddleX | paddlex-ocr | OCR 服务（可选）| +| **8081** | vLLM | - | 本地推理（可选）| + +::: tip 端口访问 +- Web 界面: http://localhost:5173 +- API 文档: http://localhost:5050/docs +- Neo4j 管理: http://localhost:7474 +::: -### 品牌信息配置 +### 品牌定制 -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。 +系统支持完整的品牌信息自定义，包括 Logo、组织名称、版权信息等。 -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。 +#### 配置方法 -### 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。 +**1. 复制模板文件** ```bash -# 上传知识库文件 +cp src/config/static/info.template.yaml src/config/static/info.local.yaml +``` + +**2. 编辑品牌信息** + +在 `src/config/static/info.local.yaml` 中配置： + +```yaml +# 组织信息 +organization_name: "您的组织名称" +logo_url: "/path/to/your/logo.png" + +# 版权信息 +copyright: "© 2024 您的组织名称" +``` + +**3. 环境变量配置** + +或在 `.env` 文件中指定配置文件路径： + +```env +YUXI_BRAND_FILE_PATH=src/config/static/info.local.yaml +``` + +#### 样式定制 + +系统配色主要保存在 `web/src/assets/css/base.css` 中： + +- 替换 `--main-*` 相关变量即可改变配色 +- 支持主题色、辅助色等完整定制 +- 实时预览，无需重启服务 + +::: tip 配置优先级 +`info.local.yaml` > `info.template.yaml`（默认） +::: + +### 批量处理脚本 + +系统提供便捷的批量处理脚本，支持文件上传和解析操作。 + +#### 文件上传脚本 + +使用 `scripts/batch_upload.py upload` 批量上传文件到知识库： + +```bash +# 批量上传文档 uv run scripts/batch_upload.py upload \ --db-id your_kb_id \ --directory path/to/your/data \ @@ -384,10 +664,20 @@ uv run scripts/batch_upload.py upload \ --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。 +**参数说明**: +- `--db-id`: 目标知识库 ID +- `--directory`: 文件目录路径 +- `--pattern`: 文件匹配模式 +- `--concurrency`: 并发处理数量 +- `--recursive`: 递归处理子目录 +- `--record-file`: 处理记录文件路径 + +#### 文件解析脚本 + +使用 `scripts/batch_upload.py trans` 将文件解析为 Markdown： ```bash -# 文件解析成 markdown +# 批量解析文档 uv run scripts/batch_upload.py trans \ --db-id your_kb_id \ --directory path/to/your/data \ @@ -400,13 +690,59 @@ uv run scripts/batch_upload.py trans \ --recursive ``` -## ❓ 常见问题 +**输出结果**: 解析后的 Markdown 文件将保存到指定输出目录。 -- 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。 +## 常见问题 -## 🤝 参与贡献 +### 服务管理 + +**Q: 如何查看后端服务日志？** + +```bash +# 查看后端日志 +docker logs api-dev -f + +# 查看前端日志 +docker logs web-dev -f + +# 查看所有服务状态 +docker ps +``` + +### OCR 服务 + +**Q: RapidOCR 模型未找到怎么办？** + +确认以下文件存在： +- `MODEL_DIR` 指向的目录存在 `SWHL/RapidOCR` +- 包含 `PP-OCRv4` 下的 `det_infer.onnx` 和 `rec_infer.onnx` 文件 + +**Q: MinerU/PaddleX 健康检查失败？** + +分别检查服务状态： +- MinerU: http://localhost:30000/health +- PaddleX: http://localhost:8080/ + +确认 GPU/驱动与 CUDA 版本匹配。 + +### 数据库连接 + +**Q: Milvus 启动失败？** + +```bash +# 重启 Milvus 服务 +docker compose up milvus -d +docker restart api-dev +``` + +**Q: Neo4j 连接问题？** + +检查默认账户信息： +- 用户名: `neo4j` +- 密码: `0123456789` +- 管理界面: http://localhost:7474 + +## 参与贡献 感谢所有贡献者的支持！ @@ -416,17 +752,26 @@ uv run scripts/batch_upload.py trans \ ### 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 +1. **Fork 项目**: 在 GitHub 上 Fork 本项目 +2. **创建分支**: `git checkout -b feature/amazing-feature` +3. **提交更改**: `git commit -m 'Add some amazing feature'` +4. **推送分支**: `git push origin feature/amazing-feature` +5. **创建 PR**: 在 GitHub 上创建 Pull Request -## 📊 Star History +### 开发指南 + +- 遵循项目代码规范 +- 添加必要的测试用例 +- 更新相关文档 +- 确保所有测试通过 + +## 项目信息 + +### 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 +### 许可证 本项目采用 MIT 许可证 - 查看 [LICENSE](LICENSE) 文件了解详情。 diff --git a/package-lock.json b/package-lock.json index 73350637..1a7223d3 100644 --- a/package-lock.json +++ b/package-lock.json @@ -5,7 +5,9 @@ "packages": { "": { "dependencies": { - "vitepress": "^1.6.4" + "markdown-it-task-checkbox": "^1.0.6", + "vitepress": "^1.6.4", + "vitepress-plugin-mermaid": "^2.0.17" } }, "node_modules/@algolia/abtesting": { @@ -248,6 +250,30 @@ "node": ">= 14.0.0" } }, + "node_modules/@antfu/install-pkg": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/@antfu/install-pkg/-/install-pkg-1.1.0.tgz", + "integrity": "sha512-MGQsmw10ZyI+EJo45CdSER4zEb+p31LpDAFp2Z3gkSd1yqVZGi0Ebx++YTEMonJy4oChEMLsxZ64j8FH6sSqtQ==", + "license": "MIT", + "peer": true, + "dependencies": { + "package-manager-detector": "^1.3.0", + "tinyexec": "^1.0.1" + }, + "funding": { + "url": "https://github.com/sponsors/antfu" + } + }, + "node_modules/@antfu/utils": { + "version": "9.3.0", + "resolved": "https://registry.npmjs.org/@antfu/utils/-/utils-9.3.0.tgz", + "integrity": "sha512-9hFT4RauhcUzqOE4f1+frMKLZrgNog5b06I7VmZQV1BkvwvqrbC8EBZf3L1eEL2AKb6rNKjER0sEvJiSP1FXEA==", + "license": "MIT", + "peer": true, + "funding": { + "url": "https://github.com/sponsors/antfu" + } + }, "node_modules/@babel/helper-string-parser": { 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