diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 7ec21f40..359e378a 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -32,21 +32,20 @@ jobs: 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 + cache: npm + cache-dependency-path: docs/package-lock.json - name: Setup Pages uses: actions/configure-pages@v4 - name: Install dependencies - run: npm ci # 或 pnpm install / yarn install / bun install + run: npm ci + working-directory: docs - name: Build with VitePress - run: npm run docs:build # 或 pnpm docs:build / yarn docs:build / bun run docs:build + run: npm run build + working-directory: docs - name: Upload artifact uses: actions/upload-pages-artifact@v3 with: diff --git a/AGENTS.md b/AGENTS.md index 9c6059bc..8942d44c 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -46,4 +46,4 @@ docker compose exec api uv run python test/your_script.py # 放在 test 文件 **其他**： - 如果需要新建说明文档（仅开发者可见，非必要不创建），则保存在 `docs/vibe` 文件夹下面 -- 代码更新后要检查文档部分是否有需要更新的地方，文档的目录定义在 `docs/.vitepress/config.mts` 中。文档应该更新最新版（`docs/latest`） +- 代码更新后要检查文档部分是否有需要更新的地方，文档的目录定义在 `docs/.vitepress/config.mts` 中 diff --git a/CLAUDE.md b/CLAUDE.md index f112a4cd..68ef9780 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -48,4 +48,4 @@ docker compose exec api uv run python test/your_script.py # 放在 test 文件 - 使用 YUXI_SUPER_ADMIN_NAME / YUXI_SUPER_ADMIN_PASSWORD 调试接口 - 如果需要新建说明文档（仅开发者可见，非必要不创建），则保存在 `docs/vibe` 文件夹下面 -- 代码更新后要检查文档部分是否有需要更新的地方，文档的目录定义在 `docs/.vitepress/config.mts` 中。文档应该更新最新版（`docs/latest`） +- 代码更新后要检查文档部分是否有需要更新的地方，文档的目录定义在 `docs/.vitepress/config.mts` 中 diff --git a/LICENSE b/LICENSE index ba26789d..d77b8ae1 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2025 The Project Contributors +Copyright (c) 2025 Yuxi Project Contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/Makefile b/Makefile index 4e90863e..8b743474 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ -.PHONY: up down logs lint format format_diff router-tests +.PHONY: up down logs lint format PYTEST_ARGS ?= @@ -33,6 +33,3 @@ format: cd backend && uv run ruff check package --fix cd backend && uv run ruff check --select I package --fix docker compose exec -T web pnpm run format - -router-tests: - docker compose exec -T api uv run --group test pytest test/api $(PYTEST_ARGS) diff --git a/docs/.vitepress/config.mts b/docs/.vitepress/config.mts index b4a4a2b0..3001ef10 100644 --- a/docs/.vitepress/config.mts +++ b/docs/.vitepress/config.mts @@ -8,7 +8,6 @@ export default defineConfig({ title: "Yuxi", description: "语析", base: '/Yuxi-Know/', - srcDir: './', ignoreDeadLinks: [ /localhost/ ], @@ -21,95 +20,63 @@ export default defineConfig({ // https://vitepress.dev/reference/default-theme-config logo: "/favicon.svg", nav: [ + { text: '快速开始', link: '/intro/quick-start' }, + { text: '智能体开发', link: '/agents/agents-config' } + ], + + sidebar: [ { - text: 'Version', + text: '简介', items: [ - { text: 'Latest (开发版)', link: '/latest/intro/quick-start' }, - { text: 'v0.4.0 (稳定版)', link: '/v0.4.0/intro/quick-start' } + { text: '什么是 Yuxi？', link: '/intro/project-overview' }, + { text: '快速开始', link: '/intro/quick-start' }, + { text: '模型配置', link: '/intro/model-config' }, + { text: '知识库与知识图谱', link: '/intro/knowledge-base' }, + { text: '知识库评估', link: '/intro/evaluation' } + ] + }, + { + text: '智能体开发', + items: [ + { text: '智能体配置', link: '/agents/agents-config' }, + { text: '上下文配置', link: '/agents/context-config' }, + { text: '工具系统', link: '/agents/tools-system' }, + { text: '中间件', link: '/agents/middleware' }, + { text: 'MCP 集成', link: '/agents/mcp-integration' }, + { text: 'Skills 管理', link: '/agents/skills-management' }, + { text: 'SubAgents 管理', link: '/agents/subagents-management' } + ] + }, + { + text: '高级配置', + items: [ + { text: '配置系统详解', link: '/advanced/configuration' }, + { text: '文档解析', link: '/advanced/document-processing' }, + { text: '品牌自定义', link: '/advanced/branding' }, + { text: '其他配置', link: '/advanced/misc' }, + { text: '生产部署', link: '/advanced/deployment' } + ] + }, + { + text: '更新日志', + items: [ + { text: '路线图', link: '/changelog/roadmap' }, + { text: '参与贡献', link: '/changelog/contributing' }, + { text: '常见问题', link: '/changelog/faq' }, + { text: '迁移至 v0.5', link: '/changelog/migrate_to_v0-5' } ] } ], - sidebar: { - '/latest/': [ - { - text: '简介', - items: [ - { text: '什么是 Yuxi？', link: '/latest/intro/project-overview' }, - { text: '快速开始', link: '/latest/intro/quick-start' }, - { text: '模型配置', link: '/latest/intro/model-config' }, - { text: '知识库与知识图谱', link: '/latest/intro/knowledge-base' }, - { text: '知识库评估', link: '/latest/intro/evaluation' } - ] - }, - { - text: '智能体开发', - items: [ - { text: '智能体配置', link: '/latest/agents/agents-config' }, - { text: '上下文配置', link: '/latest/agents/context-config' }, - { text: '工具系统', link: '/latest/agents/tools-system' }, - { text: '中间件', link: '/latest/agents/middleware' }, - { text: 'MCP 集成', link: '/latest/agents/mcp-integration' }, - { text: 'Skills 管理', link: '/latest/agents/skills-management' }, - { text: 'SubAgents 管理', link: '/latest/agents/subagents-management' } - ] - }, - { - text: '高级配置', - items: [ - { text: '配置系统详解', link: '/latest/advanced/configuration' }, - { text: '文档解析', link: '/latest/advanced/document-processing' }, - { text: '品牌自定义', link: '/latest/advanced/branding' }, - { text: '其他配置', link: '/latest/advanced/misc' }, - { text: '生产部署', link: '/latest/advanced/deployment' } - ] - }, - { - text: '更新日志', - items: [ - { text: '路线图', link: '/latest/changelog/roadmap' }, - { text: '参与贡献', link: '/latest/changelog/contributing' }, - { text: '常见问题', link: '/latest/changelog/faq' }, - { text: '迁移至 v0.5', link: '/latest/changelog/migrate_to_v0-5' } - ] - } - ], - '/v0.4.0/': [ - { - text: '简介', - items: [ - { text: '什么是 Yuxi？', link: '/v0.4.0/intro/project-overview' }, - { text: '快速开始', link: '/v0.4.0/intro/quick-start' }, - { text: '模型配置', link: '/v0.4.0/intro/model-config' }, - { text: '知识库与知识图谱', link: '/v0.4.0/intro/knowledge-base' }, - { text: '知识库评估', link: '/v0.4.0/intro/evaluation' } - ] - }, - { - text: '高级配置', - items: [ - { text: '配置系统详解', link: '/v0.4.0/advanced/configuration' }, - { text: '文档解析', link: '/v0.4.0/advanced/document-processing' }, - { text: '智能体', link: '/v0.4.0/advanced/agents-config' }, - { text: '品牌自定义', link: '/v0.4.0/advanced/branding' }, - { text: '其他配置', link: '/v0.4.0/advanced/misc' }, - { text: '生产部署', link: '/v0.4.0/advanced/deployment' } - ] - } - ], - }, - socialLinks: [ { icon: 'github', link: 'https://github.com/xerrors/Yuxi-Know' } ], - footer: { message: '本项目基于 MIT License 开源，欢迎使用和贡献。', copyright: 'Copyright © 2025-present Yuxi' }, - editLink: { pattern: 'https://github.com/xerrors/Yuxi-Know/edit/main/docs/:path', text: '在 GitHub 上编辑此页' @@ -127,7 +94,6 @@ export default defineConfig({ provider: 'local' }, - docFooter: { prev: '上一页', next: '下一页' diff --git a/docs/latest/advanced/branding.md b/docs/advanced/branding.md similarity index 100% rename from docs/latest/advanced/branding.md rename to docs/advanced/branding.md diff --git a/docs/latest/advanced/configuration.md b/docs/advanced/configuration.md similarity index 100% rename from docs/latest/advanced/configuration.md rename to docs/advanced/configuration.md diff --git a/docs/latest/advanced/deployment.md b/docs/advanced/deployment.md similarity index 100% rename from docs/latest/advanced/deployment.md rename to docs/advanced/deployment.md diff --git a/docs/latest/advanced/document-processing.md b/docs/advanced/document-processing.md similarity index 100% rename from docs/latest/advanced/document-processing.md rename to docs/advanced/document-processing.md diff --git a/docs/latest/advanced/misc.md b/docs/advanced/misc.md similarity index 100% rename from docs/latest/advanced/misc.md rename to docs/advanced/misc.md diff --git a/docs/latest/agents/agents-config.md b/docs/agents/agents-config.md similarity index 100% rename from docs/latest/agents/agents-config.md rename to docs/agents/agents-config.md diff --git a/docs/latest/agents/context-config.md b/docs/agents/context-config.md similarity index 100% rename from docs/latest/agents/context-config.md rename to docs/agents/context-config.md diff --git a/docs/latest/agents/mcp-integration.md b/docs/agents/mcp-integration.md similarity index 100% rename from docs/latest/agents/mcp-integration.md rename to docs/agents/mcp-integration.md diff --git a/docs/latest/agents/middleware.md b/docs/agents/middleware.md similarity index 100% rename from docs/latest/agents/middleware.md rename to docs/agents/middleware.md diff --git a/docs/latest/agents/skills-management.md b/docs/agents/skills-management.md similarity index 100% rename from docs/latest/agents/skills-management.md rename to docs/agents/skills-management.md diff --git a/docs/latest/agents/subagents-management.md b/docs/agents/subagents-management.md similarity index 100% rename from docs/latest/agents/subagents-management.md rename to docs/agents/subagents-management.md diff --git a/docs/latest/agents/tools-system.md b/docs/agents/tools-system.md similarity index 100% rename from docs/latest/agents/tools-system.md rename to docs/agents/tools-system.md diff --git a/docs/latest/changelog/contributing.md b/docs/changelog/contributing.md similarity index 100% rename from docs/latest/changelog/contributing.md rename to docs/changelog/contributing.md diff --git a/docs/latest/changelog/faq.md b/docs/changelog/faq.md similarity index 95% rename from docs/latest/changelog/faq.md rename to docs/changelog/faq.md index 0931b63e..88dd37db 100644 --- a/docs/latest/changelog/faq.md +++ b/docs/changelog/faq.md @@ -10,10 +10,10 @@ ```bash # Linux/macOS -bash docker/pull_image.sh +bash scripts/pull_image.sh # Windows PowerShell -powershell -ExecutionPolicy Bypass -File docker/pull_image.ps1 +powershell -ExecutionPolicy Bypass -File scripts/pull_image.ps1 ``` **构建失败问题**： diff --git a/docs/latest/changelog/migrate_to_v0-5.md b/docs/changelog/migrate_to_v0-5.md similarity index 100% rename from docs/latest/changelog/migrate_to_v0-5.md rename to docs/changelog/migrate_to_v0-5.md diff --git a/docs/latest/changelog/roadmap.md b/docs/changelog/roadmap.md similarity index 93% rename from docs/latest/changelog/roadmap.md rename to docs/changelog/roadmap.md index 0b08dd9e..82c9c952 100644 --- a/docs/latest/changelog/roadmap.md +++ b/docs/changelog/roadmap.md @@ -57,8 +57,8 @@ ## v0.4 ### 新增 -- 新增对于上传附件的智能体中间件，详见[文档](https://xerrors.github.io/Yuxi-Know/latest/advanced/agents-config.html#%E6%96%87%E4%BB%B6%E4%B8%8A%E4%BC%A0%E4%B8%AD%E9%97%B4%E4%BB%B6) -- 新增多模态模型支持（当前仅支持图片），详见[文档](https://xerrors.github.io/Yuxi-Know/latest/advanced/agents-config.html#%E5%A4%9A%E6%A8%A1%E6%80%81%E5%9B%BE%E7%89%87%E6%94%AF%E6%8C%81) +- 新增对于上传附件的智能体中间件，详见[文档](https://xerrors.github.io/Yuxi-Know/advanced/agents-config.html#%E6%96%87%E4%BB%B6%E4%B8%8A%E4%BC%A0%E4%B8%AD%E9%97%B4%E4%BB%B6) +- 新增多模态模型支持（当前仅支持图片），详见[文档](https://xerrors.github.io/Yuxi-Know/advanced/agents-config.html#%E5%A4%9A%E6%A8%A1%E6%80%81%E5%9B%BE%E7%89%87%E6%94%AF%E6%8C%81) - 新建 DeepAgents 智能体（深度分析智能体），支持 todo，files 等渲染，支持文件的下载。 - 新增基于知识库文件生成思维导图功能（[#335](https://github.com/xerrors/Yuxi-Know/pull/335#issuecomment-3530976425)） - 新增基于知识库文件生成示例问题功能（[#335](https://github.com/xerrors/Yuxi-Know/pull/335#issuecomment-3530976425)） @@ -66,10 +66,10 @@ - 新增自定义模型支持、新增 dashscope rerank/embeddings 模型的支持 - 新增文档解析的图片支持，已支持 MinerU Officical、Docs、Markdown Zip格式 - 新增暗色模式支持并调整整体 UI（[#343](https://github.com/xerrors/Yuxi-Know/pull/343)） -- 新增知识库评估功能，支持导入评估基准或者自动构建评估基准（目前仅支持Milvus类型知识库）详见[文档](https://xerrors.github.io/Yuxi-Know/latest/intro/evaluation.html) +- 新增知识库评估功能，支持导入评估基准或者自动构建评估基准（目前仅支持Milvus类型知识库）详见[文档](https://xerrors.github.io/Yuxi-Know/intro/evaluation.html) - 新增同名文件处理逻辑：遇到同名文件则在上传区域提示，是否删除旧文件 - 新增生产环境部署脚本，固定 python 依赖版本，提升部署稳定性 -- 优化图谱可视化方式，统一图谱数据结构，统一使用基于 G6 的可视化方式，同时支持上传带属性的图谱文件，详见[文档](https://xerrors.github.io/Yuxi-Know/latest/intro/knowledge-base.html#_1-%E4%BB%A5%E4%B8%89%E5%85%83%E7%BB%84%E5%BD%A2%E5%BC%8F%E5%AF%BC%E5%85%A5) +- 优化图谱可视化方式，统一图谱数据结构，统一使用基于 G6 的可视化方式，同时支持上传带属性的图谱文件，详见[文档](https://xerrors.github.io/Yuxi-Know/intro/knowledge-base.html#_1-%E4%BB%A5%E4%B8%89%E5%85%83%E7%BB%84%E5%BD%A2%E5%BC%8F%E5%AF%BC%E5%85%A5) - 优化 DBManager / ConversationManager，支持异步操作 - 优化 知识库详情页面，更加简洁清晰，增强文件下载功能 diff --git a/docs/latest/changelog/run-architecture-migration-v4.md b/docs/changelog/run-architecture-migration-v4.md similarity index 100% rename from docs/latest/changelog/run-architecture-migration-v4.md rename to docs/changelog/run-architecture-migration-v4.md diff --git a/docs/index.md b/docs/index.md index fdacedb2..49d90dd8 100644 --- a/docs/index.md +++ b/docs/index.md @@ -11,11 +11,8 @@ hero: alt: VitePress actions: - theme: brand - text: Latest 文档 - link: /latest/intro/quick-start - - theme: alt - text: v0.4.0 文档 - link: /v0.4.0/intro/quick-start + text: 快速开始 + link: /intro/quick-start features: - title: 🤖 智能体与模型 diff --git a/docs/latest/intro/evaluation.md b/docs/intro/evaluation.md similarity index 100% rename from docs/latest/intro/evaluation.md rename to docs/intro/evaluation.md diff --git a/docs/latest/intro/knowledge-base.md b/docs/intro/knowledge-base.md similarity index 100% rename from docs/latest/intro/knowledge-base.md rename to docs/intro/knowledge-base.md diff --git a/docs/latest/intro/model-config.md b/docs/intro/model-config.md similarity index 100% rename from docs/latest/intro/model-config.md rename to docs/intro/model-config.md diff --git a/docs/latest/intro/project-overview.md b/docs/intro/project-overview.md similarity index 100% rename from docs/latest/intro/project-overview.md rename to docs/intro/project-overview.md diff --git a/docs/latest/intro/quick-start.md b/docs/intro/quick-start.md similarity index 96% rename from docs/latest/intro/quick-start.md rename to docs/intro/quick-start.md index cf3b1da4..4e10a059 100644 --- a/docs/latest/intro/quick-start.md +++ b/docs/intro/quick-start.md @@ -119,13 +119,13 @@ docker logs web-dev -f ```bash # 手动拉取基础镜像 -bash docker/pull_image.sh python:3.12-slim +bash scripts/pull_image.sh python:3.12-slim ``` **离线环境部署方案**： ```bash -# 在有网络的环境导出镜像 +# 在有网络的环境导出镜像，注意检查镜像列表，不一定是最新的。 bash docker/save_docker_images.sh # 传输到目标机器 diff --git a/docs/package.json b/docs/package.json new file mode 100644 index 00000000..302c9a90 --- /dev/null +++ b/docs/package.json @@ -0,0 +1,11 @@ +{ + "scripts": { + "dev": "vitepress dev", + "build": "vitepress build", + "preview": "vitepress preview" + }, + "dependencies": { + "markdown-it-task-checkbox": "^1.0.6", + "vitepress": "^1.6.4" + } +} diff --git a/docs/v0.4.0/advanced/agents-config.md b/docs/v0.4.0/advanced/agents-config.md deleted file mode 100644 index 400240c4..00000000 --- a/docs/v0.4.0/advanced/agents-config.md +++ /dev/null @@ -1,272 +0,0 @@ -# 智能体 - -## 智能体开发 - -系统基于 [LangGraph](https://github.com/langchain-ai/langgraph) 并通过统一的 `AgentManager` 管理所有智能体。`backend/package/yuxi/agents/__init__.py` 会在启动时遍历 `backend/package/yuxi/agents` 目录，对每个包含 `__init__.py` 的子包执行自动发现：所有继承 `BaseAgent` 的类都会被注册并立即初始化，因此只要代码落位正确，就不需要再手动登记或修改管理器。 - -仓库预置了若干可直接运行的智能体：`chatbot` 聚焦对话与动态工具调度，`reporter` 演示报告类链路。这些目录展示了上下文类、Graph 构造方式、子智能体引用以及中间件组合的范例，新增功能时可以直接复用。 - -### 智能体元数据配置 - -每个智能体可以通过在智能体目录下创建 `metadata.toml` 文件来配置元数据信息。这个文件使用 TOML 格式，包含以下字段： - -- `name`: 智能体显示名称 -- `description`: 智能体功能描述 -- `examples`: 示例问题列表（数组格式） - -例如，`backend/package/yuxi/agents/chatbot/metadata.toml`： - -<<< @/../backend/package/yuxi/agents/chatbot/metadata.toml - -**注意**：`metadata.toml` 文件是可选的，如果没有提供，系统将使用智能体类的基本属性。 - -### 创建新的智能体 - -在 `backend/package/yuxi/agents` 下新建一个包，保持与现有目录一致的结构：放置 Graph 构造逻辑（通常命名为 `graph.py`），并在包内的 `__init__.py` 中暴露主类。 - -智能体类必须继承 `yuxi.agents.common.BaseAgent`，同时实现异步的 `get_graph` 方法来返回编译后的 LangGraph 实例，并配置好 `checkpointer`，否则无法从历史对话中恢复。 - -需要额外上下文字段时，可继承 `BaseContext` 构建自己的配置表单，再把类绑定到 `context_schema`，平台会在 `saves/agents/` 下生成默认配置。 - -案例 基于MySQL工具，以及自定义 MCP Server 的数据库报表助手。 - -<<< @/../backend/package/yuxi/agents/reporter/graph.py - - - -智能体实例的生命周期交给管理器处理，会在自动发现时完成初始化并缓存单例，以便快速响应请求。在容器内热重载时，只要保存文件即可触发重新导入；需要强制刷新可调用 `agent_manager.get_agent(, reload=True)`。 - -更多动态工具选择与 MCP 注册的例子，见 `backend/package/yuxi/agents/chatbot/graph.py` 中的中间件组合。 - -### 拓展现有智能体 - -智能体保持为 LangGraph 的标准节点组合，因此可以在原有 `graph.py` 中添加节点、条件与消息转换器。复用现成上下文时，只需扩展当前 `context_schema` 的字段；若功能差异较大，可以创建新的上下文类并替换 `context_schema`。 - -对工具、模型或提示语的调整建议封装到中间件或独立函数里，既方便多智能体共用，又能保持 `BaseAgent` 的基础接口稳定。变更提交后无需手动刷新注册表，只要确保包结构未改变，智能体会在热重载中自动更新。 - -### 子智能体与中间件 - -子智能体集中放在 `backend/package/yuxi/agents/common/subagents` 目录，典型例子是 `calc_agent`，它通过 LangChain 的 `create_agent` 构建计算器能力并以工具暴露给主图。新增子智能体时沿用这一结构：在目录内编写封装函数与 `@tool` 装饰器，导出后即可被任意智能体调用。 - -中间件位于 `backend/package/yuxi/agents/common/middlewares`，包含上下文感知提示词、模型选择、动态工具加载以及附件注入等实现。如果需要编写新的中间件，请遵循 LangChain 官方文档中对 `AgentMiddleware`、`ModelRequest`、`ModelResponse` 等接口的定义，完成后在该目录的 `__init__.py` 暴露入口，主智能体即可在 `middleware` 列表中引用。 - -#### 文件上传中间件 - -文件上传功能通过 `inject_attachment_context` 中间件实现（位于 `backend/package/yuxi/agents/common/middlewares/attachment_middleware.py`）。该中间件基于 LangChain 1.0 的 `AgentMiddleware` 标准实现，具有以下特点： - -1. **状态扩展**：定义 `AttachmentState` 扩展 `AgentState`，添加可选的 `attachments` 字段 -2. **自动注入**：在模型调用前，从 `request.state` 中读取附件并转换为 `SystemMessage` -3. **向后兼容**：不使用文件上传的智能体不受影响 - -##### 为智能体启用文件上传 - -只需两步： - -**步骤 1：声明能力**（让前端显示上传按钮） - -```python -class MyAgent(BaseAgent): - capabilities = ["file_upload"] -``` - -**步骤 2：添加中间件**（让智能体能够处理附件内容） - -```python -from yuxi.agents.common.middlewares import inject_attachment_context - -async def get_graph(self): - graph = create_agent( - model=load_chat_model("..."), - tools=get_tools(), - middleware=[ - inject_attachment_context, # 添加附件中间件 - context_aware_prompt, # 其他中间件... - # ... - ], - checkpointer=await self._get_checkpointer(), - ) - return graph -``` - -##### 工作流程 - -1. **前端上传**：用户在聊天界面上传文档（txt、md、docx、html） -2. **API 解析**：后端将文档转换为 Markdown 格式并存储到数据库（超过 32k 会被截断） -3. **自动加载**：API 层在调用 agent 前从数据库加载附件数据 -4. **中间件注入**：`inject_attachment_context` 自动将附件内容注入为系统消息 -5. **模型处理**：LLM 接收到附件内容和用户问题，进行综合回答 - -这种设计确保了附件功能的可选性和可扩展性，任何智能体都可以通过添加中间件快速启用文件上传能力。 - -## 内置工具与 MCP 集成 - -系统会根据配置自动组装工具集合，涵盖知识图谱查询、向量检索生成的动态工具、MySQL 只读查询能力、Tavily 搜索以及所有注册的 MCP 工具。 - -工具的启用状态和描述由配置文件或环境变量决定，当依赖缺失时会被中间件自动忽略，从而避免在图中加载不可用能力。MCP Server 的接入方式保持不变，只需在 `backend/package/yuxi/agents/common/mcp.py` 的 `MCP_SERVERS` 中填入服务地址与 `transport` 类型，如需更多范式可参阅 LangChain 官方文档。 - -### MCP 服务器配置方式 - -系统支持四种 MCP 服务器配置方式，可根据具体场景选择： - -#### 1. 远程 HTTP 服务器 - -```python -MCP_SERVERS = { - "sequentialthinking": { - "url": "https://remote.mcpservers.org/sequentialthinking/mcp", - "transport": "streamable_http", - } -} -``` - -**特点**： -- 通过 HTTP 远程访问，无需本地安装，适合公开可用的 MCP 服务 -- 启动速度快，无需本地依赖 - -#### 2. 使用 npx 运行 Node.js 包 - -```python -MCP_SERVERS = { - "mcp-server-chart": { - "command": "npx", - "args": ["-y", "@antv/mcp-server-chart"], - "transport": "stdio" - }, -} -``` - -**特点**： -- 使用 npx 直接运行 Node.js 包，`-y` 参数自动下载并运行指定包 -- 适合 Node.js 生态的 MCP 服务，需要确保 npx 可以使用 - -#### 3. 使用 uvx 运行 Python 包 - -```python -MCP_SERVERS = { - "mysql-mcp-server": { - "command": "uvx", - "args": ["mysql_mcp_server"], - "env": { - "MYSQL_DATABASE": "your_database", - "MYSQL_HOST": "localhost", - "MYSQL_PASSWORD": "your_password", - "MYSQL_PORT": "3306", - "MYSQL_USER": "your_username" - }, - "transport": "stdio" - } -} -``` - -**特点**： -- 使用 uvx 运行已发布的 Python 包，自动管理虚拟环境和依赖 -- 适合 PyPI 上已发布的 MCP 服务 - -#### 4. 使用 uv 运行本地仓库 - -```python -MCP_SERVERS = { - "arxiv-mcp-server": { - "command": "uv", - "args": [ - "tool", - "run", - "arxiv-mcp-server", - "--storage-path", "backend/package/yuxi/agents/mcp_repos/arxiv-mcp-server" - ], - "transport": "stdio" - } -} -``` - -**特点**： -- 直接运行本地 git 仓库中的 MCP 服务 -- 加载速度快，支持热重载，适合开发调试和自定义 MCP 服务 -- 需要先 git clone 对应仓库到指定路径 - -### 配置参数说明 - -- `url`: 远程 HTTP 服务器的 URL（仅 streamable_http 传输） -- `command`: 启动 MCP 服务的命令 -- `args`: 启动参数列表 -- `env`: 环境变量配置，用于数据库连接等敏感信息 -- `transport`: 传输协议，支持 `stdio`（本地）和 `streamable_http`（远程） - -### 动态工具加载 - -系统支持动态加载 MCP 工具： - -```python -from yuxi.agents.common.mcp import get_mcp_tools, add_mcp_server - -# 获取特定服务器的工具 -tools = await get_mcp_tools("sequentialthinking") - -# 动态添加新的 MCP 服务器 -add_mcp_server("custom-server", { - "url": "https://your-mcp-server.com/mcp", - "transport": "streamable_http" -}) - -# 获取所有 MCP 工具 -all_tools = await get_all_mcp_tools() -``` -### MySQL 数据库 - -在 数据库报表助手（SqlReporterAgent） 中，可以通过配置下面环境变量，让 Agent 能够连接到 MySQL 数据库。并通过执行 SQL 查询，获取数据库中的数据。 - -设置数据库连接时，在 `.env` 中提供以下字段： - -```env -MYSQL_HOST=192.168.1.100 -MYSQL_USER=username -MYSQL_PASSWORD=your_secure_password -MYSQL_DATABASE=database_name -MYSQL_DATABASE_DESCRIPTION=业务主库（可选提示） -MYSQL_PORT=3306 -MYSQL_CHARSET=utf8mb4 -``` - -所有查询限定在只读范围（SELECT、SHOW、DESCRIBE、EXPLAIN），请求会经过表名校验与超时控制，默认限制 60 秒与 100 行输出，并可通过配置调整上限。连接信息会反馈给 LangGraph，智能体可以自动陈述数据库用途并选择更准确的检索策略。详见代码部分 `backend/package/yuxi/agents/common/toolkits/mysql/` - -### 多模态图片支持 - -系统支持接收图片作为输入，与文本结合形成多模态查询。图片支持的核心特性如下： - -#### 1. 图片上传与处理 -- 通过 `/chat/image/upload` 接口上传图片 -- 自动处理图片格式转换和压缩 -- 返回 base64 编码的图片数据 -- 图片大小限制为 10MB -- 支持的图片格式：JPEG、PNG、WebP、GIF、BMP -- 自动压缩超过 5MB 的图片 - -当发送包含图片的请求时，消息格式为： -```json -{ - "query": "这张图片里有什么？", - "image_content": "", - "config": {}, - "meta": {} -} -``` - -智能体会自动识别多模态消息并将其传递给支持图片的模型。如果模型不支持图片，会自动忽略图片内容，只处理文本部分。系统会将图片转换为符合模型要求的格式（通常是 base64 编码的 JPEG 或 PNG），确保与主流多模态模型兼容。 - -目前仅支持上传单个图片，图片以 base64 编码形式存储在数据库。系统会自动处理图片的格式转换和压缩，并生成缩略图以优化性能。 - -### 图片上传响应格式 - -```json -{ - "success": true, - "image_content": "", - "thumbnail_content": "", - "width": 1024, - "height": 768, - "format": "JPEG", - "mime_type": "image/jpeg" -} -``` - -系统会将图片信息与用户查询一同传递给支持多模态的模型，并自动适配模型要求的格式。 diff --git a/docs/v0.4.0/advanced/branding.md b/docs/v0.4.0/advanced/branding.md deleted file mode 100644 index 514e927f..00000000 --- a/docs/v0.4.0/advanced/branding.md +++ /dev/null @@ -1,60 +0,0 @@ -# 品牌自定义 - -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。 - -## 配置方法 - -### 1. 复制模板文件 - -```bash -cp backend/package/yuxi/config/static/info.template.yaml backend/package/yuxi/config/static/info.local.yaml -``` - -### 2. 编辑品牌信息 - -在 `backend/package/yuxi/config/static/info.local.yaml` 中配置： - -<<< @/../backend/package/yuxi/config/static/info.template.yaml - -上述中提到的 ICON 预设了下面这些，如果需要更多的 ICONS，可以手动从 `lucide-vue-next` 中引入。 - -<<< @/../web/src/views/HomeView.vue#icon_mapping{js} - -### 3. 环境变量配置 - -在 `.env` 文件中指定配置文件路径： - -```bash -YUXI_BRAND_FILE_PATH=backend/package/yuxi/config/static/info.local.yaml -``` - -::: tip 配置优先级 -`info.local.yaml` > `info.template.yaml`（默认） -::: - - -## 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 - -**主要变量**: - -```css -:root { - --main-color: #1890ff; /* 主色调 */ - --main-1000: #f0f2f5; /* 色板 */ - --main-900: #e6f7ff; /* 色板 */ - /* ... 其他色板 */ -} - -``` - -**此外**，`web/src/stores/theme.js` 中也包含了主题相关的配置（需要修改 `colorPrimary`），可根据需要修改。 - -## 修改首页 - -首页提供了一个插槽组件 `web/src/components/ProjectOverview.vue`，可以在该组件中自定义项目介绍，当前为空文件。（借助 AI 编程可以设计出更好看的首页的） diff --git a/docs/v0.4.0/advanced/configuration.md b/docs/v0.4.0/advanced/configuration.md deleted file mode 100644 index 6f8866ca..00000000 --- a/docs/v0.4.0/advanced/configuration.md +++ /dev/null @@ -1,185 +0,0 @@ -# 配置系统详解 - -## 概述 - -Yuxi-Know 从 v0.3.x 版本开始采用了全新的配置系统，基于 Pydantic BaseModel 和 TOML 格式，提供了类型安全、智能提示和选择性持久化等现代化特性。 - -## 架构设计 - -### 配置层次结构 - -``` -配置系统架构 -├── 默认配置 (代码定义) -│ ├── backend/package/yuxi/config/static/models.py (模型配置) -│ └── backend/package/yuxi/config/app.py (应用配置) -├── 用户配置 (TOML 文件) -│ └── saves/config/base.toml (仅保存用户修改) -└── 环境变量 (运行时覆盖) - └── .env 文件 -``` - -### 核心组件 - -#### 1. Config 类 (`backend/package/yuxi/config/app.py`) - -主配置类，继承自 Pydantic BaseModel，提供： - -- **类型验证**: 自动检查配置项类型 -- **默认值管理**: 内置合理的默认配置 -- **选择性持久化**: 仅保存用户修改的配置项 -- **向后兼容**: 支持旧的字典式访问方式 - -```python -class Config(BaseModel): - # 功能开关 - enable_reranker: bool = Field(default=False, description="是否开启重排序") - enable_content_guard: bool = Field(default=False, description="是否启用内容审查") - - # 模型配置 - default_model: str = Field(default="siliconflow/deepseek-ai/DeepSeek-V3.2") - embed_model: str = Field(default="siliconflow/BAAI/bge-m3") - - # 运行时状态 (不持久化) - model_provider_status: dict[str, bool] = Field(exclude=True) -``` - -#### 2. 模型配置类 (`backend/package/yuxi/config/static/models.py`) - -定义了三种类型的模型配置： - -- **ChatModelProvider**: 聊天模型提供商 -- **EmbedModelInfo**: 嵌入模型信息 -- **RerankerInfo**: 重排序模型信息 - -```python -class ChatModelProvider(BaseModel): - name: str = Field(..., description="提供商显示名称") - url: str = Field(..., description="提供商文档或模型列表 URL") - base_url: str = Field(..., description="API 基础 URL") - default: str = Field(..., description="默认模型名称") - env: str = Field(..., description="API Key 环境变量名") - models: list[str] = Field(default_factory=list, description="支持的模型列表") -``` - -添加配置： - - -```python -# 1. 在 DEFAULT_CHAT_MODEL_PROVIDERS 中添加 -"new-provider": ChatModelProvider( - name="新提供商", - url="https://provider.com/docs", - base_url="https://api.provider.com/v1", - default="default-model", - env="NEW_PROVIDER_API_KEY", - models=["model1", "model2"], -), - -# 2. 在 .env 中配置 API Key -# NEW_PROVIDER_API_KEY=your_api_key - -# 3. 重启服务或重新加载配置 -``` - -## 配置管理特性 - -系统只会保存用户修改过的配置项： - -```python -# 用户只修改了 enable_reranker -config.enable_reranker = True -config.save() # 只保存 enable_reranker 到 TOML 文件 - -# TOML 文件内容 -# enable_reranker = true -``` - -### 默认模型配置 (`backend/package/yuxi/config/static/models.py`) - -包含所有支持的模型提供商的默认配置，开发者可以直接修改此文件添加新的模型： - -```python -DEFAULT_CHAT_MODEL_PROVIDERS: dict[str, ChatModelProvider] = { - "siliconflow": ChatModelProvider( - name="SiliconFlow", - url="https://cloud.siliconflow.cn/models", - base_url="https://api.siliconflow.cn/v1", - default="deepseek-ai/DeepSeek-V3.2", - env="SILICONFLOW_API_KEY", - models=[ - "deepseek-ai/DeepSeek-V3.2", - "Qwen/Qwen3-235B-A22B-Instruct-2507", - # ... - ], - ), - # 更多提供商... -} -``` - -### 用户配置 (`saves/config/base.toml`) - -只包含用户修改过的配置项，使用 TOML 格式： - -```toml -# 用户只修改了这些配置项 -enable_reranker = true -default_agent_id = "MyCustomAgent" -enable_content_guard = true - -# 模型配置修改 -[model_names.siliconflow] -models = [ - "deepseek-ai/DeepSeek-V3.2", - "custom-model-name", -] -``` - -## 高级配置 - -### 动态配置更新 - -```python -from yuxi.config import config - -# 更新配置 -config.enable_reranker = True -config.default_agent_id = "CustomAgent" - -# 更新模型列表 -config.model_names["siliconflow"].models.append("new-model") - -# 保存配置 -config.save() - -# 或者只保存特定提供商的模型配置 -config._save_models_to_file("siliconflow") -``` - -### 配置验证 - -```python -# 验证配置 -from yuxi.config import config - -# 检查模型提供商可用性 -for provider, status in config.model_provider_status.items(): - print(f"{provider}: {'✅' if status else '❌'}") - -# 获取可用模型列表 -available_models = config.get_model_choices() -available_embed_models = config.get_embed_model_choices() -available_rerankers = config.get_reranker_choices() -``` - -### 配置导出 - -```python -# 导出完整配置（包含运行时状态） -full_config = config.dump_config() - -# 导出用户配置（仅保存到文件的部分） -user_config = { - field: getattr(config, field) - for field in config._user_modified_fields -} diff --git a/docs/v0.4.0/advanced/deployment.md b/docs/v0.4.0/advanced/deployment.md deleted file mode 100644 index c3868d4b..00000000 --- a/docs/v0.4.0/advanced/deployment.md +++ /dev/null @@ -1,69 +0,0 @@ -# 生产部署指南 - -本指南介绍了如何在生产环境中部署 Yuxi-Know。 - -## 前置要求 - -- **Docker Engine** (v24.0+) -- **Docker Compose** (v2.20+) -- **NVIDIA Container Toolkit** (如果在生产环境使用 GPU 服务) - -注意事项： - -1. 生产环境和开发环境最好是两台独立的机器，不然会存在端口和资源的冲突问题。 -2. 虽然名为“生产环境”，但实际上只是做了一些基本的配置而已，真要上线业务，需要根据实际情况进行调整。 - -## 部署步骤 - -### 1. 配置环境变量 - -为了避免与开发环境的冲突，建议在生产环境中使用 `.env.prod` 文件。请确保你已经从模板创建了该文件并填写了必要的密钥。 - -```bash -cp .env.template .env.prod -``` - -编辑 `.env.prod` 文件，设置强密码并配置必要的 API 密钥： - -- `NEO4J_PASSWORD`: 修改默认密码 -- `MINIO_ACCESS_KEY` / `MINIO_SECRET_KEY`: 修改默认密钥 -- `SILICONFLOW_API_KEY` 等模型密钥 - -### 2. 启动服务 - -使用 `docker-compose.prod.yml` 文件启动生产环境： - -```bash -# 仅启动核心服务 (CPU 模式) -docker compose -f docker-compose.prod.yml up -d --build - -# 启动所有服务 (包含 GPU OCR 服务) -docker compose -f docker-compose.prod.yml --profile all up -d --build -``` - -### 3. 验证部署 - -- **Web 访问**: `http://localhost` (直接通过 80 端口访问，无需 :5173) -- **API 健康检查**: `curl http://localhost/api/system/health` - -## 维护与更新 - -### 更新代码并重新部署 - -```bash -# 拉取最新代码 -git pull - -# 重新构建并启动 -docker compose -f docker-compose.prod.yml up -d --build -``` - -### 查看日志 - -```bash -# 查看 API 日志 -docker logs -f api-prod - -# 查看 Nginx 访问日志 -docker logs -f web-prod -``` diff --git a/docs/v0.4.0/advanced/document-processing.md b/docs/v0.4.0/advanced/document-processing.md deleted file mode 100644 index 3fb3e7df..00000000 --- a/docs/v0.4.0/advanced/document-processing.md +++ /dev/null @@ -1,120 +0,0 @@ -# 文档处理与 OCR - -系统提供 4 种文档处理选项： - -- **RapidOCR**: CPU 友好，无需 GPU，适合基础文字识别 -- **MinerU**: 本地化高精度 VLM 解析，适合复杂 PDF 和表格文档 -- **MinerU Official**: 官方云服务 API，无需本地部署，开箱即用 -- **PaddleX**: 结构化解析，适合表格、票据等特殊格式 - -## 支持的文件类型 - -### 常规文档格式 -- **文本文档**: `.txt`, `.md`, `.html`, `.htm` -- **Word 文档**: `.docx` -- **PDF 文档**: `.pdf` -- **电子表格**: `.csv`, `.xls`, `.xlsx` -- **JSON 数据**: `.json` - -### 图像格式（需要 OCR） -- **常见图片**: `.jpg`, `.jpeg`, `.png`, `.bmp`, `.tiff`, `.tif`, `.gif`, `.webp` - -### ZIP 压缩包 -- **ZIP 文档**: `.zip` - 支持包含 Markdown 文件和图片的压缩包 - - 自动提取和处理 ZIP 包中的 `.md` 文件 - - 自动处理 ZIP 包中的图片文件并上传到对象存储（MINIO） - - 图片链接会自动替换为可访问的 URL - - 优先处理名为 `full.md` 的文件，否则使用第一个 `.md` 文件 - - 支持图片目录的智能识别（`images/`、`../images/` 等） - -::: tip 图片显示 -文档中的图片会自动上传到对象存储并替换为可访问的 URL。但是如果想要在外部正常显示图片，需要配置 `HOST_IP` 环境变量，将其设置为您的服务器 IP 地址。 -::: - -## 快速配置 - -### 1. 基础 OCR (RapidOCR) - -```bash -# 下载模型 -hf download SWHL/RapidOCR --local-dir ./models/SWHL/RapidOCR - -# 启动服务 -docker compose up -d api -``` - -需要确保 `MODEL_DIR` 环境变量指向 RapidOCR 上层目录，例如 `./models`。 - -### 2. 高精度 OCR (MinerU) - -需要在 `.env` 文件中配置： - -```bash -MINERU_VL_SERVER=http://localhost:30000 # 对应 docker compose 中的 mineru-vllm-server 服务 -MINERU_API_URI=http://localhost:30001 # 对应 docker compose 中的 mineru-api 服务 -``` - -然后启动相关服务 - -```bash -# 需要 GPU，启动 MinerU 服务 -docker compose up mineru-vllm-server mineru-api -d - -# 启动主服务 -docker compose up api -d -``` - -::: tip 处理超时 -文档解析超时时间默认 600 秒，可通过 `MINERU_TIMEOUT` 环境变量调整。 -::: - - -### 3. 官方云服务 (MinerU Official) - -API 密钥可以从 [MinerU 官网](https://mineru.net) 申请。 - -然后在 `.env` 文件中添加 - -```bash -# 设置 API 密钥环境变量 -MINERU_API_KEY="your-api-key-here" -``` - -然后使用 `docker compose up api -d` 重启后端服务。 - -### 4. 结构化解析 (PaddleX) - -```bash -# 需要 GPU，启动 PaddleX 服务 -docker compose up -d paddlex - -# 启动主服务 -docker compose up -d api -``` - -## 处理器选择 - -| 处理器 | 适用场景 | 硬件要求 | 特点 | -|--------|----------|------------|------| -| **RapidOCR** | 基础文字识别 | CPU | 速度快，资源占用低 | -| **MinerU** | 复杂 PDF、表格、公式 | GPU | 精度高，版面分析好 | -| **MinerU Official** | 复杂文档解析（云服务） | 无特殊要求 | 官方云服务，开箱即用，有 API 配额 | -| **PaddleX** | 表格、票据、结构化文档 | GPU | 专业版面解析 | - -## 参数说明 - -### enable_ocr 选项 - -对应网页中的 `使用 OCR` 选项 - -- `disable`: 不启用 OCR（PDF 按文本提取，图片**必须选择 OCR 方式**） -- `onnx_rapid_ocr`: RapidOCR 处理 -- `mineru_ocr`: MinerU HTTP API 处理 -- `mineru_official`: MinerU 官方云服务 API 处理 -- `paddlex_ocr`: PaddleX 处理 - -### 注意事项 -- **图片文件必须启用 OCR**，否则无法提取内容 -- MinerU 和 PaddleX 需要 GPU 支持 -- MinerU Official 需要设置 `MINERU_API_KEY` 环境变量 -- RapidOCR 适合 CPU 环境和基础识别需求 diff --git a/docs/v0.4.0/advanced/misc.md b/docs/v0.4.0/advanced/misc.md deleted file mode 100644 index 422f16ac..00000000 --- a/docs/v0.4.0/advanced/misc.md +++ /dev/null @@ -1,53 +0,0 @@ -# 其他配置 - -## 内容安全 - -系统内置内容审查机制（默认是关闭状态），保障服务内容的合规性。目前配置了关键词过滤以及 LLM 对内容进行审查。管理员可在 `设置` → `基本设置` 页面中进行配置并选择安全模型。 - -检测流程为，接收到用户输入之后，就对用户的输入进行检测是否合规，同时在流式传输的过程中进行实时检测（仅关键词）。当流式输出结束之后，则开始检测整个内容。 -**注意**，使用 LLM 检测虽然可以大大缓解提示词注入带来的问题，但也会在用户交互上带来延迟影响，需要考虑是否启用。 - -对于关键词检测，敏感词词库位于 `backend/package/yuxi/config/static/bad_keywords.txt` 文件，每行一个关键词，实时生效，无需重启服务。 - -对于 LLM 检测，Prompt 可以看到 `backend/package/yuxi/plugins/guard.py`： - -<<< @/../backend/package/yuxi/plugins/guard.py#guard_prompt - -## 网页搜索 - -系统内置了基于 Tavily 的联网搜索能力，配置完成后，大模型会自动在需要时调用对应的工具，为回答提供实时网页信息。 - - -1. 前往 [Tavily 官网](https://app.tavily.com/) 注册并在控制台创建 API Key。 -2. 在项目根目录的 `.env`（或 `docker-compose.yml` 中的对应环境变量段）写入： - ```env - TAVILY_API_KEY=sk-xxxxxxxxxxxxxxxx - ``` -3. 重新加载服务使密钥生效，推荐执行： - ```bash - docker compose up -d api-dev web-dev - ``` - 若服务已运行，则使用 `docker compose restart api-dev` 即可。 - -完成以上步骤后，在智能体的工具配置区域即可看到这个工具，展示 Tavily 返回的实时结果。若需要关闭该能力，删除或清空 `TAVILY_API_KEY` 后再次重启服务即可。 - -## 服务端口 - -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` -::: diff --git a/docs/v0.4.0/intro/evaluation.md b/docs/v0.4.0/intro/evaluation.md deleted file mode 100644 index fa7e3850..00000000 --- a/docs/v0.4.0/intro/evaluation.md +++ /dev/null @@ -1,57 +0,0 @@ -# 知识库评估使用与开发指南 - -知识库评估功能用于测试 RAG 系统的检索和生成质量。通过预设的测试问题和标准答案（或自动生成评估），量化评估系统在不同场景下的表现。 - -**适用场景**：验证知识库上线前的效果、对比不同配置下的检索效果、定期监控知识库质量变化、调优检索参数。 - -**注意**：当前版本支持 Milvus 类型的知识库。 - -## 如何创建评估基准 - -### 1. 上传评估文件 - -准备 JSONL 格式的文件，每行一个测试样本： - -```json -{"query": "什么是人工智能？", "gold_chunk_ids": ["chunk_001", "chunk_002"], "gold_answer": "人工智能是计算机科学的一个分支"} -{"query": "机器学习的主要类型有哪些？", "gold_chunk_ids": ["chunk_005"], "gold_answer": "主要包括监督学习、无监督学习和强化学习"} -{"query": "深度学习的应用领域", "gold_chunk_ids": ["chunk_010", "chunk_011"]} -``` - -**字段说明**： -- `query`（必需）：测试问题，用于触发 RAG 系统的检索 -- `gold_chunk_ids`（可选）：相关文档块的 ID 列表，用于验证检索效果 -- `gold_answer`（可选）：标准答案，用于验证生成效果 - -::: tip 数据集构建 -可以使用 [EasyDataset](https://github.com/ConardLi/easy-dataset) 从文档批量生成问答对、可视化编辑、导出多种格式和数据质量检查。挺好用的，推荐。注意导出的时候的字段需要修改为 `query`、`gold_answer`。 -::: - - -### 2. 自动生成评估基准 - -Yuxi 也实现了一个简易的、可以基于现有知识库自动生成测试数据。流程是：随机采样一个 chunk → 用嵌入模型找相似 chunk → 用 LLM 生成问题和答案。 - -**推荐参数设置**： -- 问题数量：10-50 个 -- 相似文档数：每个问题 2-5 个 - -## 运行评估任务 - -1. 选择评估基准后在知识库页面点击"评估"标签 - -2. 配置参数： - - **答案生成模型（可选）**：如果选择了，则会基于检索的 chunk 生成答案，然后用评判模型评估答案的准确性 - - **评判模型（可选）**：如果选择了，则会用评判模型评估答案的准确性，判断是否与标准答案一致，因此选择评判模型时，必须选择答案生成模型。 - -3. 点击"开始评估"： -系统会逐个处理测试问题，执行检索和生成，计算各项指标。评估在后台运行，可以继续其他操作。 - -**主要指标**： - -| 指标 | 含义 | 如何看待 | -|------|------|----------| -| Recall@1 | 第一个结果包含正确文档的比例 | 最重要的指标，反映用户第一眼看到的准确率 | -| Recall@5 | 前5个结果包含正确文档的比例 | 综合检索效果，应该大于 0.8 | -| F1@K | 精确率和召回率的调和平均 | 平衡指标，用于对比不同配置 | -| 答案准确性 | 生成答案是否与标准答案一致 | 检查 LLM 理解和表达能力 | diff --git a/docs/v0.4.0/intro/knowledge-base.md b/docs/v0.4.0/intro/knowledge-base.md deleted file mode 100644 index b9bb10b7..00000000 --- a/docs/v0.4.0/intro/knowledge-base.md +++ /dev/null @@ -1,117 +0,0 @@ -# 知识库与知识图谱 - -项目中的知识库与知识图谱，即是知识管理组织的方式，同时会被封装为工具供 AgenticRAG 系统调用。 - -## 创建知识库 - -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 | 已弃用，建议使用 Milvus | -| **Milvus** | 高性能向量数据库 | 大规模生产环境、高性能查询 | -| **LightRAG** | 图增强检索 | 复杂知识关系，构建成本较高 | - -访问 Web 界面：`http://localhost:5173`，进入"知识库管理"页面，点击"新建知识库"，填写知识库信息。 - -这里需要**注意**的是，这里的知识库的标题和描述都会作为智能体选择工具的依据，因此尽量详尽的描述该知识库。 - - -### LightRAG 知识库说明 - -在本项目中，系统支持基于 [LightRAG](https://github.com/HKUDS/LightRAG) 的知识图谱自动构建，能够从文档中自动提取实体和关系，构建结构化知识图谱。 - -**LightRAG 图谱 vs 全局知识图谱的区别：** - -- **LightRAG 图谱**（知识库专属）：针对单个知识库由 LightRAG 自动抽取实体/关系，用于该库内的图增强检索与可视化。通过特殊的 label（知识库ID）与全局图谱区分，不会混入全局数据。 - -- **全局知识图谱**（系统级）：通过三元组文件上传的图谱数据，提供系统级的知识图谱查询和可视化能力，会作为工具供 LLM 使用。 - -两者共享同一个 Neo4j 实例，但完全隔离，互不影响。 - -LightRAG 知识库可在知识库详情、知识图谱中可视化。由于免费版的 neo4j 只能创建一个图数据库，因此实际上 LightRAG 的节点和边依然是和知识图谱本身构建在了同一个 Neo4j 数据库中，但是使用了特殊的 label `{知识库ID}` 做区分。 - -同时项目支持原 LightRAG 的所有环境变量，只需要在项目的 `.env` 文件中配置即可。比如当本地计算资源有限时，可以配置 `EMBEDDING_TIMEOUT=60`, `LLM_TIMEOUT=180` 增加超时时间。 - -## 文档管理 - -本系统的“上传 → 解析入库 → 检索/可视化”流程既可通过 Web 界面完成，也可使用 API/脚本批量处理。详见[文档解析](../advanced/document-processing.md) - -接口查询：`GET /api/knowledge/files/supported-types` - -**上传与入库** - -1) 上传文件（返回服务端保存路径） -- `POST /api/knowledge/files/upload?db_id=<可选>` -- 成功返回：`file_path`（后续入库使用）、`content_hash`（内容去重） - -2) 解析并入库（异步任务） -- `POST /api/knowledge/databases/{db_id}/documents` -- 返回：`status=queued` 与 `task_id`，可在任务中心查看进度 - -去重策略：系统按“内容哈希”判断是否已存在相同文件，避免重复入库。 - -## 知识图谱 - -本项目存在两类“图谱相关”能力： - -- 上传的知识图谱（Neo4j）：提供三元组检索和系统级可视化。会作为工具供 LLM 使用。 -- LightRAG 知识库内图谱：针对某个知识库由 LightRAG 自动抽取实体/关系，用于该库内的图增强检索与可视化；与上传的图谱共享同一 Neo4j 实例，但通过特殊 label 区分，不作为全局图谱使用。 - - -### 1. 以三元组形式导入 - -系统支持通过网页导入 `jsonl` 格式的知识图谱数据，支持**简单三元组**和**带属性三元组**两种格式。 - -**简单格式（兼容旧版）**： - -```jsonl -{"h": "北京", "t": "中国", "r": "首都"} -{"h": "上海", "t": "中国", "r": "直辖市"} -``` - -**扩展格式（支持属性）**： - -支持 `h`（头节点）、`t`（尾节点）和 `r`（关系）为对象结构，其中： -- 节点对象必须包含 `name` 字段。 -- 关系对象必须包含 `type` 字段。 -- 其他字段将作为**属性**存储在 Neo4j 中。 - -```jsonl -{"h": {"name": "孙悟空", "title": "齐天大圣", "weapon": "如意金箍棒"}, "t": {"name": "唐僧", "species": "人"}, "r": {"type": "徒弟", "order": 1}} -{"h": "猪八戒", "t": {"name": "唐僧"}, "r": {"type": "徒弟", "order": 2}} -``` - -**格式说明**： -- 每行一个数据项。 -- 系统自动验证数据格式，并自动导入到 Neo4j 数据库。 -- 自动添加 `Upload`、`Entity` 标签（节点）和 `RELATION` 类型（关系）。 -- 自动处理重复实体和关系，并合并属性。 - -Neo4j 访问信息可以参考 `docker-compose.yml` 中配置对应的环境变量来覆盖。 - -- **默认账户**: `neo4j` -- **默认密码**: `0123456789` -- **管理界面**: `http://localhost:7474` -- **连接地址**: bolt://localhost:7687 - -::: tip 测试数据 -可以使用以下文件进行测试导入： -- 简单格式：`test/data/A_Dream_of_Red_Mansions_tiny.jsonl` -- 扩展属性格式：`test/data/complex_graph_test.jsonl` -::: - -### 2. 接入已有 Neo4j 实例 - -如需接入已有的 Neo4j 实例，可修改 `.env` 中的配置： - -<<< @/../.env.template#neo4j{bash} - -同时记得注释掉下面的 neo4j 服务： - -<<< @/../docker-compose.yml#neo4j - - -::: 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。 -::: diff --git a/docs/v0.4.0/intro/model-config.md b/docs/v0.4.0/intro/model-config.md deleted file mode 100644 index 867ebd7b..00000000 --- a/docs/v0.4.0/intro/model-config.md +++ /dev/null @@ -1,227 +0,0 @@ -# 模型配置 - -## 对话模型 - -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](https://cloud.siliconflow.cn/i/Eo5yTHGJ) | `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 | - -其余还支持火山豆包、Together、vLLM、Ollama 等。 - -### 配置方法 - -在 `.env` 文件中添加对应的环境变量： - - - -::: 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。 -::: - -<<< @/../.env.template#model_provider{bash 5} - -### 默认对话模型格式 - -系统的默认对话模型可以在设置页面配置，也可以通过配置项 `default_model` 指定，格式统一为 `模型提供商/模型名称`，例如： - -```yaml -default_model: siliconflow/deepseek-ai/DeepSeek-V3.2 -``` - -## 自定义模型供应商 - -::: tip 配置系统升级 (v0.3.x) -从 `v0.3.x` 版本开始，模型配置系统已升级为基于 Pydantic BaseModel 的类型安全配置，支持 TOML 格式的用户配置文件。 - -- **默认配置**: `backend/package/yuxi/config/static/models.py` (Python 代码) -- **用户配置**: `saves/config/base.toml` (TOML 格式，仅保存用户修改) -- **自定义供应商**: `saves/config/custom_providers.toml` (独立配置文件) - ::: - -系统提供了完整的自定义供应商管理功能，支持通过 Web 界面直接添加、编辑、测试和删除自定义模型供应商。 - -### 使用方法 - -系统支持任何 OpenAI 兼容的云服务提供商 - -#### 1. Web 界面操作（推荐） - -访问 **系统设置 > 模型配置**，在"自定义供应商"部分点击 **添加自定义供应商**。这里的密钥可以直接填写也可以填写对应的环境变量名称。 - -#### 2. 配置文件操作 - -如需通过配置文件管理，编辑 `saves/config/custom_providers.toml`： - -```toml -[model_names.local-vllm] -name = "本地 vLLM 服务" -url = "https://docs.vllm.ai" -base_url = "http://localhost:8000/v1" -default = "Qwen/Qwen2.5-7B-Instruct" -env = "LOCAL_VLLM_API_KEY" -models = [ - "Qwen/Qwen2.5-7B-Instruct", - "Qwen/Qwen2.5-14B-Instruct", -] -custom = true - -[model_names.local-ollama] -name = "本地 Ollama" -url = "https://ollama.com" -base_url = "http://localhost:11434/v1" -default = "llama3.2" -env = "NO_API_KEY" -models = ["llama3.2", "qwen2.5"] -custom = true -``` - -然后在 `.env` 文件中添加对应的环境变量： - -```env -LOCAL_VLLM_API_KEY=your_api_key_here -``` - -### API 端点 - -系统提供以下 API 端点管理自定义供应商： - -- `GET /api/system/custom-providers` - 获取所有自定义供应商 -- `POST /api/system/custom-providers` - 添加自定义供应商 -- `PUT /api/system/custom-providers/{provider_id}` - 更新自定义供应商 -- `DELETE /api/system/custom-providers/{provider_id}` - 删除自定义供应商 -- `POST /api/system/custom-providers/{provider_id}/test` - 测试供应商连接 - -### 常见配置示例 - -#### vLLM 本地服务 - -```toml -[model_names.vllm-local] -name = "vLLM 本地服务" -base_url = "http://localhost:8000/v1" -default = "Qwen/Qwen2.5-7B-Instruct" -env = "NO_API_KEY" -models = [ - "Qwen/Qwen2.5-7B-Instruct", - "Qwen/Qwen2.5-14B-Instruct", - "meta-llama/Llama-3.1-8B-Instruct" -] -``` - -#### Ollama 本地服务 - -```toml -[model_names.ollama-local] -name = "Ollama 本地服务" -base_url = "http://localhost:11434/v1" -default = "llama3.2" -env = "NO_API_KEY" -models = [ - "llama3.2:latest", - "qwen2.5:latest", - "codellama:latest" -] -``` - -#### 第三方 API 中转服务 - -```toml -[model_names.api-proxy] -name = "API 中转服务" -base_url = "https://api-proxy.example.com/v1" -default = "gpt-3.5-turbo" -env = "API_PROXY_KEY" -models = [ - "gpt-3.5-turbo", - "gpt-4", - "claude-3-sonnet" -] -``` - -### 故障排除 - -1. **测试连接失败**: 检查 API 地址格式和 API 密钥配置 -2. **模型不可用**: 确认模型名称拼写和服务端是否支持该模型 -3. **权限错误**: 确保用户具有管理员权限 -4. **配置未生效**: 检查环境变量配置和服务重启状态 - -## 嵌入模型和重排序模型 - -#### 1. 配置模型信息 - -在 `backend/package/yuxi/config/static/models.py` 中的默认配置部分添加： - -```python -# 默认嵌入模型配置 -DEFAULT_EMBED_MODELS: dict[str, EmbedModelInfo] = { - # ... 现有配置 ... - - "vllm/Qwen/Qwen3-Embedding-0.6B": EmbedModelInfo( - name="Qwen/Qwen3-Embedding-0.6B", - dimension=1024, - base_url="http://localhost:8000/v1/embeddings", - api_key="no_api_key", - ), -} - -# 默认重排序模型配置 -DEFAULT_RERANKERS: dict[str, RerankerInfo] = { - # ... 现有配置 ... - - "vllm/BAAI/bge-reranker-v2-m3": RerankerInfo( - name="BAAI/bge-reranker-v2-m3", - base_url="http://localhost:8000/v1/rerank", - api_key="no_api_key", - ), -} -``` - -#### 2. 动态配置（可选） - -你也可以通过代码动态添加本地模型： - -```python -from yuxi.config import config -from yuxi.config.static.models import EmbedModelInfo, RerankerInfo - -# 添加本地嵌入模型 -config.embed_model_names["local/embed-model"] = EmbedModelInfo( - name="local-embed-model", - dimension=1024, - base_url="http://localhost:8000/v1/embeddings", - api_key="no_api_key", -) - -# 添加本地重排序模型 -config.reranker_names["local/reranker-model"] = RerankerInfo( - name="local-reranker-model", - base_url="http://localhost:8000/v1/rerank", - api_key="no_api_key", -) - -# 保存配置 -config.save() -``` - -#### 3. 启动模型服务 - -```bash -# 启动嵌入模型 -vllm serve Qwen/Qwen3-Embedding-0.6B \ - --task embed \ - --dtype auto \ - --port 8000 - -# 启动重排序模型 -vllm serve BAAI/bge-reranker-v2-m3 \ - --task score \ - --dtype fp16 \ - --port 8000 -``` diff --git a/docs/v0.4.0/intro/project-overview.md b/docs/v0.4.0/intro/project-overview.md deleted file mode 100644 index 494c3b17..00000000 --- a/docs/v0.4.0/intro/project-overview.md +++ /dev/null @@ -1,24 +0,0 @@ -# 项目简介 - -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。 - -**特点**：技术栈简单，易于上手，使用 MIT 开源协议，非常适合二次开发使用。 - -### 技术栈选择 - -- **后端服务**: [FastAPI](https://github.com/tiangolo/fastapi) + Python 3.12+ -- **前端界面**: [Vue.js 3](https://github.com/vuejs/vue) + [Ant Design Vue](https://github.com/vueComponent/ant-design-vue) -- **数据库存储**: [SQLite](https://github.com/sqlite/sqlite) + [MinIO](https://github.com/minio/minio) -- **知识存储**: [Milvus](https://github.com/milvus-io/milvus)、[Chroma](https://github.com/chroma-core/chroma)（向量数据库）+ [Neo4j](https://github.com/neo4j/neo4j)（图数据库） -- **智能体框架**: [LangGraph](https://github.com/langchain-ai/langgraph) -- **文档解析**: [LightRAG](https://github.com/HKUDS/LightRAG) + [MinerU](https://github.com/HKUDS/MinerU) + [PP-Structure-V3](https://github.com/PaddlePaddle/PaddleOCR) -- **容器编排**: [Docker Compose](https://github.com/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 diff --git a/docs/v0.4.0/intro/quick-start.md b/docs/v0.4.0/intro/quick-start.md deleted file mode 100644 index 326ad268..00000000 --- a/docs/v0.4.0/intro/quick-start.md +++ /dev/null @@ -1,178 +0,0 @@ -# 快速开始指南 - -::: tip 提示 -除了此文档网站外，用户还可以在 [Zread](https://zread.ai/xerrors/Yuxi-Know) 或 [DeepWiki](https://deepwiki.com/xerrors/Yuxi-Know) 平台查看自动生成的详细项目文档。 -::: - - -## 快速开始 - - -### 安装步骤 - -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。 - -#### 1. 获取项目代码 - -```bash -# 克隆稳定版本 -git clone --branch v0.4.0 --depth 1 https://github.com/xerrors/Yuxi-Know.git -cd Yuxi-Know -``` - -::: warning 版本说明 -- `v0.4.0`: 稳定版本 -- `main`: 最新开发版本（不稳定，新特性可能会导致新 bug） -::: - -#### 2. 项目启动 - -**方法 1**：使用 init 脚本（推荐） - -我们提供了自动化的初始化脚本，可以帮您完成环境配置和 Docker 镜像拉取： - -```bash -# Linux/macOS -./scripts/init.sh - -# Windows PowerShell -.\scripts\init.ps1 -``` - -脚本会： -- 检查并创建 `.env` 文件 -- 提示您输入 `SILICONFLOW_API_KEY`（必需） -- 提示您输入 `TAVILY_API_KEY`（可选，用于搜索服务） -- 自动拉取所有必需的 Docker 镜像 - -::: tip API Key 获取 -- [硅基流动](https://cloud.siliconflow.cn/i/Eo5yTHGJ) 注册即送 14 元额度 -- [Tavily](https://app.tavily.com/) 获取搜索服务 API Key（可选） -::: - -**方法 2**：手动配置环境变量 - -复制环境变量模板并编辑： - -```bash -cp .env.template .env -``` - -编辑 `.env` 文件，配置必需的 API 密钥，这里强烈建议先使用硅基流动的 API 和模型（DeepSeek）验证平台的功能无误后，再尝试切换到自己的模型： - - -<<< @/../.env.template#model_provider{bash 5} - - -::: 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 -``` - -## 对话 - -项目第一次启动后，会要求填写超级管理员账号和密码，请确保填写正确。 - -然后在智能体页面可以进行对话，在右侧可以配置提示词、模型、工具等参数。 - -![agent.png](/images/agent.png) - - - -## 故障排除 - -#### 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 - -如果拉取镜像失败，可以尝试手动拉取： - -```bash -# Linux/macOS -bash docker/pull_image.sh python:3.12-slim - -# Windows PowerShell -powershell -ExecutionPolicy Bypass -File docker/pull_image.ps1 python:3.12-slim -``` - -**离线镜像拉取方案**： - -```bash -# 在有网络的环境保存镜像（镜像名称需要确认是否和实际一致） -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 @: - -# 在目标设备加载镜像 -docker load -i docker_images_xxx.tar -``` - -
- -
-构建失败 - -如果构建失败，通常是网络问题，可以配置代理： - -```bash -# Linux / macOS -export HTTP_PROXY=http://IP:PORT -export HTTPS_PROXY=http://IP:PORT - -# Windows PowerShell -$env:HTTP_PROXY="http://IP:PORT" -$env: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 - -```bash -# 重启 Milvus 服务 -docker compose up milvus -d -docker restart api-dev -``` - -
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"markdown-it-task-checkbox": "^1.0.6", - "vitepress": "^1.6.4" - } -} diff --git a/scripts/init.ps1 b/scripts/init.ps1 index c3d79fb5..a9060e2f 100644 --- a/scripts/init.ps1 +++ b/scripts/init.ps1 @@ -68,13 +68,14 @@ $images = @( "nginx:alpine", "quay.io/coreos/etcd:v3.5.5", "postgres:16" + "redis:7-alpine" ) # Pull each image foreach ($image in $images) { Write-Host "🔄 Pulling ${image}..." -ForegroundColor Yellow try { - & docker/pull_image.ps1 $image + & scripts/pull_image.ps1 $image if ($LASTEXITCODE -eq 0) { Write-Host "✅ Successfully pulled ${image}" -ForegroundColor Green } else { diff --git a/docker/pull_image.ps1 b/scripts/pull_image.ps1 similarity index 100% rename from docker/pull_image.ps1 rename to scripts/pull_image.ps1 diff --git a/docker/pull_image.sh b/scripts/pull_image.sh similarity index 100% rename from docker/pull_image.sh rename to scripts/pull_image.sh