docs: 更新文档
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# 项目架构
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# Luxx 项目架构
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## 技术栈
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- **框架**: FastAPI 0.109+
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- **数据库**: SQLAlchemy 2.0+
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- **数据库**: SQLAlchemy 2.0+ (同步模式)
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- **认证**: JWT (PyJWT)
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- **HTTP客户端**: httpx, requests
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- **配置**: YAML (PyYAML)
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```
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luxx/
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├── __init__.py # FastAPI 应用工厂
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├── run.py # 入口文件
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├── config.py # 配置管理(YAML)
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├── database.py # 数据库连接
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├── models.py # ORM 模型
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├── routes/ # API 路由层
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│ ├── auth.py # 认证
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│ ├── conversations.py # 会话管理
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│ ├── messages.py # 消息处理
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│ ├── __init__.py # 路由聚合
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│ ├── auth.py # 认证 (登录/注册)
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│ ├── conversations.py # 会话管理 (CRUD)
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│ ├── messages.py # 消息处理 (流式/同步)
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│ ├── providers.py # LLM 提供商管理
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│ └── tools.py # 工具管理
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├── services/ # 服务层
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│ ├── chat.py # 聊天服务
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│ ├── chat.py # 聊天服务 (Agentic Loop)
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│ └── llm_client.py # LLM 客户端
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├── tools/ # 工具系统
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│ ├── core.py # 核心类
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│ ├── factory.py # 装饰器
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│ ├── executor.py # 执行器
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│ ├── core.py # 核心类 (ToolRegistry, ToolDefinition, ToolResult)
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│ ├── factory.py # @tool 装饰器
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│ ├── executor.py # 工具执行器 (缓存/并行)
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│ ├── services.py # 工具服务层
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│ └── builtin/ # 内置工具
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│ ├── code.py # 代码执行
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│ ├── crawler.py # 网页爬虫
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│ ├── data.py # 数据处理
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│ └── weather.py # 天气查询
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│ └── services.py # 工具服务层
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│ ├── __init__.py # 工具注册入口
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│ ├── code.py # 代码执行 (python_execute, python_eval)
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│ ├── crawler.py # 网页爬虫 (web_search, web_fetch, batch_fetch)
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│ └── data.py # 数据处理 (process_data)
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└── utils/ # 工具函数
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└── helpers.py
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└── helpers.py # 密码哈希、ID生成、响应封装
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run.py # 应用入口文件
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config.yaml # 配置文件
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```
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## 核心组件
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### 1. 应用工厂 (`__init__.py`)
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FastAPI 应用入口,使用 lifespan 管理生命周期:
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- 启动:初始化数据库、加载工具
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- 启动:初始化数据库、注册内置工具、创建默认管理员用户
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- 关闭:清理资源
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```python
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# 默认管理员账号
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username: admin
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password: admin123
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```
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### 2. 配置管理 (`config.py`)
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使用 YAML 文件管理配置:
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- 配置文件:`config.yaml`
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@ -65,13 +74,18 @@ app:
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secret_key: ${APP_SECRET_KEY}
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debug: true
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database:
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type: sqlite
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url: sqlite:///./chat.db
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llm:
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provider: deepseek
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api_key: ${DEEPSEEK_API_KEY}
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api_url: https://api.deepseek.com/v1
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```
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### 3. 数据库 (`database.py`)
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- SQLAlchemy 异步支持
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- SQLAlchemy 同步支持
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- SQLite 默认数据库
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- 依赖注入获取会话
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@ -89,10 +103,20 @@ erDiagram
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datetime created_at
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}
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PROJECT {
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string id PK
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int user_id FK
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string name
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text description
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datetime created_at
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datetime updated_at
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}
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CONVERSATION {
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string id PK
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int user_id FK
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string project_id FK
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int provider_id FK "optional"
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string project_id FK "optional"
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string title
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string model
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text system_prompt
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@ -109,14 +133,10 @@ erDiagram
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string role
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longtext content "JSON 格式"
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int token_count
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text usage "JSON 格式"
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datetime created_at
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}
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USER ||--o{ CONVERSATION : "has"
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CONVERSATION ||--o{ MESSAGE : "has"
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USER ||--o{ LLM_PROVIDER : "configures"
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LLM_PROVIDER {
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int id PK
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int user_id FK
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string base_url
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string api_key
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string default_model
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int max_tokens
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boolean is_default
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boolean enabled
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datetime created_at
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datetime updated_at
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}
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USER ||--o{ PROJECT : "has"
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USER ||--o{ CONVERSATION : "has"
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USER ||--o{ LLM_PROVIDER : "configures"
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PROJECT ||--o{ CONVERSATION : "contains"
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LLM_PROVIDER ||--o{ CONVERSATION : "uses"
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CONVERSATION ||--o{ MESSAGE : "has"
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```
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### Message Content JSON 结构
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@ -157,7 +185,7 @@ erDiagram
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{"id": "step-0", "index": 0, "type": "thinking", "content": "..."},
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{"id": "step-1", "index": 1, "type": "text", "content": "..."},
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{"id": "step-2", "index": 2, "type": "tool_call", "id_ref": "call_xxx", "name": "...", "arguments": "..."},
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{"id": "step-3", "index": 3, "type": "tool_result", "id_ref": "call_xxx", "name": "...", "content": "..."}
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{"id": "step-3", "index": 3, "type": "tool_result", "id_ref": "call_xxx", "name": "...", "content": "...", "success": true}
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]
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}
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```
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@ -191,16 +219,21 @@ classDiagram
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+register(tool) void
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+get(name) ToolDefinition?
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+list_all() List~dict~
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+list_by_category(category) List~dict~
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+execute(name, arguments) dict
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+remove(name) bool
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}
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class ToolExecutor {
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+registry: ToolRegistry
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+enable_cache: bool
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+cache_ttl: int
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+max_workers: int
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+_cache: Dict
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+_call_history: List
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+process_tool_calls(tool_calls, context) list
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+process_tool_calls_parallel(tool_calls, context, max_workers) list
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+process_tool_calls_parallel(tool_calls, context) list
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+clear_cache() void
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+get_history(limit) List
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}
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```
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@ -213,7 +246,6 @@ classDiagram
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| `web_search` | DuckDuckGo HTML | DuckDuckGo HTML 搜索 |
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| `web_fetch` | 网页抓取 | httpx + BeautifulSoup,支持 text/links/structured |
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| `batch_fetch` | 批量抓取 | 并发获取多个页面 |
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| `get_weather` | 天气查询 | 支持城市名查询 |
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| `process_data` | 数据处理 | JSON 转换、格式化等 |
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#### 工具开发规范
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├─ 包装返回格式
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└─ 返回 ToolResult
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↓
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ToolExecutor 返回结果
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↓
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前端 ProcessBlock 显示
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```
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| 工具函数返回 | 装饰器转换为 |
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|-------------|-------------|
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| `{"result": "ok"}` | `{"success": true, "data": {...}, "error": null}` |
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| `{"error": "msg"}` | `{"success": false, "data": null, "error": "msg"}` |
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| `raise Exception()` | `{"success": false, "data": null, "error": "..."}` |
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**工具调用流程**
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```
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LLM 请求 → ToolRegistry.execute() → @tool 装饰器包装
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↓
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工具函数执行
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↓
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返回 ToolResult 或 dict
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↓
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自动转换为标准格式 {"success": bool, "data": any, "error": str}
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↓
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前端 ProcessBlock 显示结果
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```
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### 6. 服务层
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#### ChatService (`services/chat.py`)
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核心聊天服务:
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- Agentic Loop 迭代执行
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- Agentic Loop 迭代执行(最多 10 轮)
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- 流式 SSE 响应
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- 工具调用编排
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- 工具调用编排(并行执行)
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- 消息历史管理
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- 自动重试机制
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- 支持 thinking_content 提取
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- Token 用量追踪
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#### LLMClient (`services/llm_client.py`)
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LLM API 客户端:
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|------|------|------|
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| `/auth/register` | POST | 用户注册 |
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| `/auth/login` | POST | 用户登录 |
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| `/conversations` | GET/POST | 会话列表/创建 |
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| `/conversations/{id}` | GET/DELETE | 会话详情/删除 |
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| `/messages/stream` | POST | 流式消息发送 |
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| `/providers` | GET/POST | LLM 提供商列表/创建 |
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| `/providers/{id}` | GET/PUT/DELETE | 提供商详情/更新/删除 |
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| `/conversations` | GET | 会话列表(分页) |
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| `/conversations` | POST | 创建会话 |
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| `/conversations/{id}` | GET | 会话详情 |
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| `/conversations/{id}` | PUT | 更新会话 |
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| `/conversations/{id}` | DELETE | 删除会话 |
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| `/messages` | GET | 消息列表 |
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| `/messages` | POST | 发送消息(同步) |
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| `/messages/stream` | POST | 发送消息(流式 SSE) |
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| `/messages/{id}` | DELETE | 删除消息 |
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| `/providers` | GET | LLM 提供商列表 |
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| `/providers` | POST | 创建提供商 |
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| `/providers/{id}` | GET | 提供商详情 |
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| `/providers/{id}` | PUT | 更新提供商 |
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| `/providers/{id}` | DELETE | 删除提供商 |
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| `/providers/{id}/test` | POST | 测试提供商连接 |
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| `/tools` | GET | 可用工具列表 |
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| `/health` | GET | 健康检查 |
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| `/` | GET | 服务信息 |
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## 数据流
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@ -340,10 +367,10 @@ sequenceDiagram
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participant LLM as LLM API
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participant TE as ToolExecutor
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Client->>API: POST {content, tools}
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Client->>API: POST {content, tools, thinking_enabled}
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API->>CS: stream_response()
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loop MAX_ITERATIONS
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loop MAX_ITERATIONS (10)
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CS->>LLM: call(messages, tools)
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LLM-->>CS: SSE Stream
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@ -354,6 +381,7 @@ sequenceDiagram
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end
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end
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CS->>CS: _save_message()
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CS->>API: SSE Stream
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API-->>Client: 流式响应
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```
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@ -363,7 +391,7 @@ sequenceDiagram
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| 事件 | 说明 |
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|------|------|
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| `process_step` | 结构化步骤(thinking/text/tool_call/tool_result),携带 `id`、`index` 确保渲染顺序 |
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| `done` | 响应完成 |
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| `done` | 响应完成,携带 message_id、token_count、usage |
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| `error` | 错误信息 |
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### process_step 事件格式
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{"type": "process_step", "step": {"id": "step-0", "index": 0, "type": "thinking", "content": "..."}}
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{"type": "process_step", "step": {"id": "step-1", "index": 1, "type": "text", "content": "回复文本..."}}
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{"type": "process_step", "step": {"id": "step-2", "index": 2, "type": "tool_call", "id_ref": "call_abc", "name": "web_search", "arguments": "{\"query\": \"...\"}"}}
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{"type": "process_step", "step": {"id": "step-3", "index": 3, "type": "tool_result", "id_ref": "call_abc", "name": "web_search", "content": "{\"success\": true, ...}"}}
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{"type": "process_step", "step": {"id": "step-3", "index": 3, "type": "tool_result", "id_ref": "call_abc", "name": "web_search", "content": "{...}", "success": true}}
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```
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| 字段 | 说明 |
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| `name` | 工具名称(仅 tool_call/tool_result) |
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| `arguments` | 工具调用参数 JSON 字符串(仅 tool_call) |
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| `content` | 内容(thinking 的思考内容、text 的文本、tool_result 的返回结果) |
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| `success` | 工具执行是否成功(仅 tool_result) |
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### done 事件格式
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```json
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{"type": "done", "message_id": "uuid", "token_count": 1234, "usage": {"prompt_tokens": 100, "completion_tokens": 200, "total_tokens": 300}}
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```
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## 配置示例
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| `APP_SECRET_KEY` | 应用密钥 | `your-secret-key` |
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| `DEEPSEEK_API_KEY` | DeepSeek API | `sk-xxxx` |
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| `DATABASE_URL` | 数据库连接 | `sqlite:///./chat.db` |
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## 项目结构说明
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### 入口文件
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- `run.py` - 启动 Uvicorn 服务器
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### 响应格式
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所有 API 统一使用响应封装:
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```json
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// 成功
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{"success": true, "data": {...}, "message": "操作成功"}
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// 错误
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{"success": false, "error": "错误信息", "code": 404}
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```
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### 工具缓存机制
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ToolExecutor 支持结果缓存:
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- TTL: 5 分钟(可配置)
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- 缓存 Key: `{tool_name}:{sorted_arguments_json}`
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- 调用历史记录最近 1000 条
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### 流式响应特点
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1. 实时返回 thinking_content(模型思考过程)
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2. 实时返回 text 增量更新
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3. 工具调用串行执行,结果批量返回
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4. 最终 `done` 事件包含完整 message_id 和 token 用量
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