1092 lines
30 KiB
Markdown
1092 lines
30 KiB
Markdown
# 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+ (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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- **代码执行**: Python 原生执行
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- **网页爬虫**:
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- `httpx` - HTTP 客户端
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- `beautifulsoup4` - HTML 解析
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- `lxml` - XML/HTML 解析器
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## 目录结构
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```
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luxx/
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├── __init__.py # FastAPI 应用工厂
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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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│ ├── __init__.py # 路由聚合
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│ ├── auth.py # 认证 (登录/注册)
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│ ├── agents.py # Agent 模板管理 (CRUD)
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│ ├── chat_rooms.py # 聊天室管理 (多 Agent 协作)
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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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│ ├── agents.py # Agent 模板管理 (CRUD)
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│ └── tools.py # 工具管理
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├── services/ # 服务层
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│ ├── __init__.py # 服务导出
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│ ├── chat.py # 聊天服务门面
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│ ├── chat_room.py # 多 Agent 聊天室编排器
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│ ├── agentic_loop.py # Agentic Loop 执行器
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│ ├── stream_context.py # 流式状态管理
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│ ├── events.py # SSE 事件工具
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│ ├── llm_response.py # LLM 响应数据类
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│ ├── task.py # 任务系统 (Task/TaskGraph/TaskService)
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│ ├── llm_client.py # LLM 客户端
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│ └── llm_adapters/ # LLM API 适配器
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│ ├── __init__.py # 适配器导出
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│ ├── base.py # ProviderAdapter 基类
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│ ├── openai_adapter.py # OpenAI/DeepSeek/GLM 适配器
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│ └── anthropic_adapter.py # Anthropic Claude 适配器
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├── tools/ # 工具系统
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│ ├── __init__.py # 工具注册入口
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│ ├── core.py # 核心类 (ToolRegistry, ToolDefinition, ToolResult, ToolContext)
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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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│ ├── __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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│ ├── file.py # 文件操作 (file_read, file_write, file_list, file_exists, file_grep)
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│ └── shell.py # Shell 命令 (shell_execute)
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└── utils/ # 工具函数
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├── __init__.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、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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- 环境变量替换：`${VAR_NAME}`
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- 单例模式全局访问
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- 默认值支持
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```yaml
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# config.yaml 示例
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app:
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secret_key: ${APP_SECRET_KEY}
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debug: true
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host: 0.0.0.0
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port: 8000
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database:
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type: sqlite
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url: sqlite:///./chat.db
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workspace:
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root: ./workspaces # 用户工作空间根目录
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auto_create: true # 自动创建用户目录
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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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tools:
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enable_cache: true
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cache_ttl: 300
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max_workers: 4
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max_iterations: 10
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logging:
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level: INFO
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```
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**工作空间隔离机制：**
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- 每个用户的工作空间路径基于 `user_id` 的 SHA256 哈希值
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- 格式：`{workspace_root}/{hash_of_user_id}`
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- 所有文件操作必须在用户工作空间内，防止路径穿越攻击
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```
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### 3. 数据库 (`database.py`)
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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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### 4. ORM 模型 (`models.py`)
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```mermaid
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erDiagram
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USER {
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int id PK
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string username UK
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string email UK
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string password_hash
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string role
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int permission_level "1=READ_ONLY, 2=WRITE, 3=EXECUTE, 4=ADMIN"
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string workspace_path "用户工作空间路径"
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boolean is_active
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datetime created_at
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}
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USER_SETTINGS {
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int id PK
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int user_id FK UK
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int default_provider_id FK "optional"
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float temperature
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int max_tokens
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boolean thinking_enabled
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text system_prompt
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datetime created_at
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datetime updated_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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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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float temperature
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int max_tokens
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boolean thinking_enabled
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datetime created_at
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datetime updated_at
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}
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MESSAGE {
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string id PK
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string conversation_id FK "optional"
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string room_id FK "optional"
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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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string sender_name "聊天室: 发送者名称"
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string sender_color "聊天室: 发送者颜色"
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int round_number "聊天室: 轮次编号"
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datetime created_at
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}
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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 name
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string provider_type
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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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AGENT {
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int id PK
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int user_id FK
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string name
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string role
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int provider_id FK "optional"
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string model
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text system_prompt
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string color
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datetime created_at
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datetime updated_at
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}
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CHAT_ROOM {
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string id PK
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int user_id FK
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string title
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text task
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string status "idle/running/paused/completed/error"
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int max_rounds
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int current_round
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datetime created_at
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datetime updated_at
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}
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ROOM_AGENT {
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int id PK
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string room_id FK
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int agent_id FK "optional"
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string name
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string role
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int provider_id FK "optional"
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string model
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text system_prompt
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string color
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int turn_order
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}
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USER ||--|| USER_SETTINGS : "has"
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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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USER ||--o{ AGENT : "creates"
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USER ||--o{ CHAT_ROOM : "creates"
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PROJECT ||--o{ CONVERSATION : "contains"
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LLM_PROVIDER ||--o{ CONVERSATION : "uses"
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LLM_PROVIDER ||--o{ AGENT : "used_by"
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LLM_PROVIDER ||--o{ ROOM_AGENT : "used_by"
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CONVERSATION ||--o{ MESSAGE : "has"
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CHAT_ROOM ||--o{ MESSAGE : "has"
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CHAT_ROOM ||--o{ ROOM_AGENT : "contains"
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AGENT ||--o{ ROOM_AGENT : "linked_to"
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```
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**用户权限级别 (permission_level)：**
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| 级别 | 名称 | 说明 |
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|------|------|------|
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| 1 | READ_ONLY | 只读权限 |
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| 2 | WRITE | 写入权限（文件写入） |
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| 3 | EXECUTE | 执行权限（代码执行、Shell命令） |
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| 4 | ADMIN | 管理员权限 |
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### Message Content JSON 结构
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`content` 字段统一使用 JSON 格式存储：
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**User 消息：**
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```json
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{
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"text": "用户输入的文本内容",
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"attachments": [
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{"name": "utils.py", "extension": "py", "content": "..."}
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]
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}
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```
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**Assistant 消息：**
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```json
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{
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"steps": [
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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": "...", "success": true}
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]
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}
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```
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`steps` 字段是**唯一数据源**，按 `index` 顺序排列。thinking、text、tool_call、tool_result 可以在多轮迭代中穿插出现。
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**注意**：`text` 和 `content` 字段通过解析 `steps` 中所有 `type: "text"` 的内容动态计算得出。
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### 5. 工具系统
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```mermaid
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classDiagram
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class ToolDefinition {
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+str name
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+str description
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+dict parameters
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+Callable handler
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+str category
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+CommandPermission required_permission
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+to_openai_format() dict
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}
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class ToolContext {
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+int user_id
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+str username
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+str workspace
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+int user_permission_level
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}
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class CommandPermission {
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<<enumeration>>
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READ_ONLY = 1
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WRITE = 2
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EXECUTE = 3
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ADMIN = 4
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}
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class ToolResult {
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+bool success
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+Any data
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+str error
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+to_dict() dict
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+ok(data) ToolResult$
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+fail(error) ToolResult$
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}
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class ToolRegistry {
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+_tools: Dict
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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, context) dict
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+remove(name) bool
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}
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class ToolExecutor {
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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) 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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#### 内置工具
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**代码执行 (code.py)**
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| 工具 | 功能 | 权限 |
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|------|------|------|
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| `python_execute` | 执行 Python 代码 | EXECUTE |
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| `python_eval` | 计算表达式 | EXECUTE |
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**文件操作 (file.py)**
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| 工具 | 功能 | 权限 |
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|------|------|------|
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| `file_read` | 读取文件内容 | READ_ONLY |
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| `file_write` | 写入文件内容 | WRITE |
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| `file_list` | 列出目录内容 | READ_ONLY |
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| `file_exists` | 检查文件是否存在 | READ_ONLY |
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| `file_grep` | 正则搜索文件内容 | READ_ONLY |
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**Shell 命令 (shell.py)**
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| 工具 | 功能 | 权限 |
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|------|------|------|
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| `shell_execute` | 执行 Shell 命令 | EXECUTE |
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**网页爬虫 (crawler.py)**
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| 工具 | 功能 | 权限 |
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|------|------|------|
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| `web_search` | DuckDuckGo HTML 搜索 | READ_ONLY |
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| `web_fetch` | 网页抓取 | READ_ONLY |
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| `batch_fetch` | 批量并发抓取 | READ_ONLY |
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**数据处理 (data.py)**
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| 工具 | 功能 | 权限 |
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|------|------|------|
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| `process_data` | JSON 转换、格式化 | READ_ONLY |
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#### 权限检查机制
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工具执行时自动检查用户权限：
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```
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工具要求的权限 <= 用户拥有的权限 → 允许执行
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工具要求的权限 > 用户拥有的权限 → 拒绝执行
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```
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#### 工具开发规范
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所有工具必须遵循统一的开发规范，确保错误处理和返回格式一致。
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**核心原则：装饰器自动处理一切，工具函数只写业务逻辑**
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```python
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from luxx.tools.factory import tool
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@tool(
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name="my_tool",
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description="工具描述",
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parameters={...},
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required_params=["arg1"], # 自动验证
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category="my_category"
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)
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def my_tool(arguments: dict):
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# 业务逻辑 - 只管返回数据
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data = fetch_data(arguments["arg1"])
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return {"items": data, "count": len(data)}
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# 或者直接抛出异常（装饰器自动捕获并转换）
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if invalid:
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raise ValueError("Invalid input")
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```
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**装饰器自动处理：**
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1. 必需参数验证（`required_params`）
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2. 所有异常捕获和转换
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3. 结果格式统一包装
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**返回格式转换**
|
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| 工具函数返回/抛出 | 装饰器转换为 |
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|-------------------|-------------|
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| `return {"result": "ok"}` | `{"success": true, "data": {...}, "error": null}` |
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| `raise ValueError("msg")` | `{"success": false, "data": null, "error": "ValueError: 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
|
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↓
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ToolRegistry.execute(name, args)
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↓
|
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@tool 装饰器
|
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├─ 验证 required_params
|
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├─ 执行工具函数 (try-except 包裹)
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├─ 捕获异常 → 转换为 error
|
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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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前端 ProcessBlock 显示
|
||
```
|
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|
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### 6. 服务层
|
||
|
||
#### LLM 适配器 (`services/llm_adapters/`)
|
||
|
||
适配器模式统一处理不同 LLM API 格式：
|
||
|
||
```mermaid
|
||
classDiagram
|
||
class ProviderAdapter {
|
||
<<abstract>>
|
||
+str provider_type
|
||
+build_request() tuple
|
||
+parse_stream_chunk() AsyncGenerator
|
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+parse_response() Dict
|
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+supports_thinking() bool
|
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+supports_tools() bool
|
||
}
|
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|
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class OpenAIAdapter {
|
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+str provider_type = "openai"
|
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+build_request() tuple
|
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+parse_stream_chunk() AsyncGenerator
|
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+parse_response() Dict
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+supports_tools() bool
|
||
}
|
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|
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class AnthropicAdapter {
|
||
+str provider_type = "anthropic"
|
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+build_request() tuple
|
||
+parse_stream_chunk() AsyncGenerator
|
||
+parse_response() Dict
|
||
+supports_thinking() bool
|
||
+supports_tools() bool
|
||
}
|
||
|
||
ProviderAdapter <|-- OpenAIAdapter
|
||
ProviderAdapter <|-- AnthropicAdapter
|
||
```
|
||
|
||
**支持的功能对比：**
|
||
|
||
| 适配器 | 工具调用 | Thinking/Reasoning | 流式响应 |
|
||
|--------|----------|-------------------|----------|
|
||
| OpenAI | ✅ | ✅ (DeepSeek) | ✅ |
|
||
| Anthropic | ✅ | ✅ | ✅ |
|
||
|
||
#### LLM 响应数据类 (`services/llm_response.py`)
|
||
|
||
```python
|
||
class StepType:
|
||
"""步骤类型常量"""
|
||
THINKING = "thinking"
|
||
TEXT = "text"
|
||
TOOL_CALL = "tool_call"
|
||
TOOL_RESULT = "tool_result"
|
||
|
||
|
||
@dataclass
|
||
class Step:
|
||
"""单个步骤 - 用于存储和传输"""
|
||
id: str
|
||
index: int
|
||
type: str # thinking, text, tool_call, tool_result
|
||
content: str = ""
|
||
name: str = "" # tool_call/tool_result
|
||
arguments: str = "" # tool_call
|
||
id_ref: str = "" # tool_result
|
||
success: bool = True
|
||
|
||
|
||
@dataclass
|
||
class ParsedDelta:
|
||
"""LLM 流式响应增量"""
|
||
thinking: str = "" # 思考内容（增量）
|
||
text: str = "" # 文本内容（增量）
|
||
tool_call: Optional[Dict] = None # 单个工具调用
|
||
usage: Dict[str, int] = {} # Token 用量
|
||
is_complete: bool = False
|
||
```
|
||
|
||
#### ChatService (`services/chat.py`)
|
||
核心聊天服务：
|
||
- Agentic Loop 迭代执行（最多 10 轮）
|
||
- 流式 SSE 响应
|
||
- 工具调用编排（并行执行）
|
||
- 消息历史管理
|
||
- Token 用量追踪
|
||
- 工作空间上下文传递
|
||
|
||
#### AgenticLoop (`services/agentic_loop.py`)
|
||
执行 Agentic Loop 的核心循环：
|
||
- 调用 LLM 获取响应（流式）
|
||
- 解析 ParsedDelta，更新步骤状态
|
||
- 管理 thinking/text/tool_call/tool_result 步骤
|
||
- 工具并行执行
|
||
- 最大迭代次数：10
|
||
|
||
```python
|
||
# 执行流程
|
||
async for delta in llm.stream_call(...):
|
||
events = self._process_delta(delta, context, total_usage)
|
||
yield from events
|
||
|
||
# 工具调用时
|
||
tool_results = self.tool_executor.process_tool_calls_parallel(...)
|
||
messages.append({"role": "assistant", ...})
|
||
messages.extend(tool_results)
|
||
```
|
||
|
||
#### StreamContext (`services/stream_context.py`)
|
||
流式状态管理：
|
||
- 追踪当前步骤类型和索引
|
||
- 累积 thinking 和 text 内容
|
||
- 管理 tool_calls 列表和 tool_results
|
||
- 生成 SSE 事件
|
||
- 构建完整消息内容
|
||
|
||
#### LLMClient (`services/llm_client.py`)
|
||
LLM API 客户端：
|
||
- 多提供商：OpenAI、DeepSeek、Anthropic
|
||
- 自动适配器选择
|
||
- 流式/同步调用
|
||
- 错误处理和重试
|
||
- Token 计数
|
||
|
||
### 7. 任务系统 (`services/task.py`)
|
||
|
||
用于自主任务执行和计划管理：
|
||
|
||
```mermaid
|
||
classDiagram
|
||
class Task {
|
||
+str id
|
||
+str name
|
||
+str goal
|
||
+TaskStatus status
|
||
+List~Step~ steps
|
||
+str metadata
|
||
+get_step() Step?
|
||
+is_complete() bool
|
||
}
|
||
|
||
class Step {
|
||
+str id
|
||
+str name
|
||
+str goal
|
||
+List~str~ depends_on
|
||
+StepStatus status
|
||
+Dict tool_calls
|
||
}
|
||
|
||
class TaskGraph {
|
||
+topological_sort() List~Step~
|
||
+get_ready_steps() List~Step~
|
||
+get_parallel_levels() List~List~Step~~
|
||
+detect_cycles() List~List~str~~
|
||
+validate() tuple
|
||
}
|
||
|
||
class TaskService {
|
||
+create_task() Task
|
||
+get_task() Task
|
||
+update_task_status() Task
|
||
+add_steps() List~Step~
|
||
+set_steps_from_plan() List~Step~
|
||
+build_graph() TaskGraph
|
||
}
|
||
|
||
class Planner {
|
||
+plan() List~Dict~
|
||
+plan_sync() List~Dict~
|
||
}
|
||
|
||
Task "1" o-- "*" Step
|
||
TaskService ..> TaskGraph
|
||
Planner ..> TaskService
|
||
```
|
||
|
||
**任务状态 (TaskStatus) - 五状态模型：**
|
||
- `PENDING` - 待处理/就绪
|
||
- `RUNNING` - 运行中
|
||
- `BLOCK` - 阻塞
|
||
- `TERMINATED` - 终止
|
||
- `COMPLETED` - 完成
|
||
|
||
**步骤状态 (StepStatus)：**
|
||
- `PENDING` - 待执行
|
||
- `RUNNING` - 执行中
|
||
- `COMPLETED` - 已完成
|
||
- `FAILED` - 失败
|
||
- `SKIPPED` - 跳过
|
||
|
||
**TaskGraph 功能：**
|
||
- `topological_sort()` - 拓扑排序获取执行顺序
|
||
- `get_ready_steps()` - 获取就绪步骤（依赖已满足）
|
||
- `get_parallel_levels()` - 获取并行执行层级
|
||
- `detect_cycles()` - 检测循环依赖
|
||
- `validate()` - 验证图结构
|
||
|
||
**Planner 功能：**
|
||
- 调用 LLM 自动分解目标为步骤
|
||
- 支持上下文（可用工具、约束条件）
|
||
- 返回结构化步骤列表
|
||
|
||
### 8. 任务执行服务 (`services/task_executor.py`)
|
||
|
||
将 Task/Plan 集成到 Agent 执行流程：
|
||
|
||
```mermaid
|
||
classDiagram
|
||
class TaskExecutorService {
|
||
+llm: LLMClient
|
||
+generate_plan() List~Dict~
|
||
+create_task_with_plan() Task
|
||
+get_execution_plan() Dict
|
||
}
|
||
|
||
class AgentWithPlan {
|
||
+chat_service
|
||
+execute_with_plan() AsyncGenerator
|
||
}
|
||
|
||
TaskExecutorService --> TaskService
|
||
AgentWithPlan --> TaskExecutorService
|
||
AgentWithPlan --> ChatService
|
||
```
|
||
|
||
**执行流程：**
|
||
1. 用户输入目标 → AgentWithPlan 接收
|
||
2. TaskExecutorService.generate_plan() 调用 LLM 分解目标
|
||
3. 创建 Task 并设置步骤
|
||
4. 按 TaskGraph 拓扑顺序执行每个步骤
|
||
5. 每个步骤作为独立 Agent 对话执行
|
||
6. 步骤完成后更新状态，继续下一个
|
||
|
||
**SSE 事件：**
|
||
- `planning` - 计划生成中
|
||
- `plan_created` - 计划已创建
|
||
- `step_start` - 步骤开始
|
||
- `step_complete` - 步骤完成
|
||
- `task_complete` - 任务完成
|
||
|
||
### 7. 认证系统 (`routes/auth.py`)
|
||
- JWT Bearer Token
|
||
- Bcrypt 密码哈希
|
||
- 用户注册/登录
|
||
|
||
### 8. Agent 系统 (`routes/agents.py`, `models.py`)
|
||
|
||
Agent 是独立的可复用 AI 助手模板，可以在聊天室中使用：
|
||
|
||
```mermaid
|
||
classDiagram
|
||
class Agent {
|
||
+int id
|
||
+int user_id
|
||
+str name
|
||
+str role
|
||
+int provider_id
|
||
+str model
|
||
+str system_prompt
|
||
+str color
|
||
+to_dict() dict
|
||
}
|
||
|
||
class LLMProvider {
|
||
+int id
|
||
+str name
|
||
+str api_key
|
||
+str default_model
|
||
}
|
||
|
||
Agent --> LLMProvider : uses
|
||
```
|
||
|
||
**Agent 字段说明：**
|
||
|
||
| 字段 | 类型 | 说明 |
|
||
|------|------|------|
|
||
| `id` | int | Agent 唯一标识 |
|
||
| `user_id` | int | 所属用户 ID |
|
||
| `name` | string | Agent 显示名称 |
|
||
| `role` | string | Agent 角色描述（如"后端开发"） |
|
||
| `provider_id` | int | 使用的 LLM 提供商 |
|
||
| `model` | string | 模型名称 |
|
||
| `system_prompt` | string | 系统提示词 |
|
||
| `color` | string | 显示颜色（Hex） |
|
||
|
||
### 9. 聊天室系统 (`routes/chat_rooms.py`, `services/chat_room.py`)
|
||
|
||
聊天室支持多个 Agent 同时参与讨论，协作完成复杂任务：
|
||
|
||
```mermaid
|
||
classDiagram
|
||
class ChatRoom {
|
||
+str id
|
||
+int user_id
|
||
+str title
|
||
+str task
|
||
+str status
|
||
+int max_rounds
|
||
+int current_round
|
||
+List~RoomAgent~ agents
|
||
}
|
||
|
||
class RoomAgent {
|
||
+int id
|
||
+str room_id
|
||
+int agent_id
|
||
+str name
|
||
+str role
|
||
+str model
|
||
+str system_prompt
|
||
+str color
|
||
+int turn_order
|
||
}
|
||
|
||
class ChatRoomOrchestrator {
|
||
-Dict~str, Task~ _running_rooms
|
||
+is_running(room_id) bool
|
||
+cancel(room_id) void
|
||
+run_room(room_id) AsyncGenerator
|
||
}
|
||
|
||
ChatRoom "1" o-- "*" RoomAgent : contains
|
||
ChatRoomOrchestrator ..> ChatRoom : orchestrates
|
||
ChatRoomOrchestrator ..> RoomAgent : executes
|
||
```
|
||
|
||
**聊天室状态：**
|
||
|
||
| 状态 | 说明 |
|
||
|------|------|
|
||
| `idle` | 空闲，未开始 |
|
||
| `running` | 运行中 |
|
||
| `paused` | 已暂停（被手动停止） |
|
||
| `completed` | 已完成（达到最大轮次） |
|
||
| `error` | 错误（执行出错） |
|
||
|
||
**聊天室执行流程：**
|
||
|
||
```
|
||
1. 用户调用 /chat-rooms/{id}/start
|
||
2. ChatRoomOrchestrator.run_room() 被调用
|
||
3. 按 turn_order 顺序执行每个 Agent 的发言轮次
|
||
4. 每个 Agent 根据历史消息和系统提示生成回复
|
||
5. SSE 流式推送消息块 (message_chunk)
|
||
6. 消息结束时推送 message_end
|
||
7. 一轮结束后推送 round_end
|
||
8. 达到 max_rounds 或被停止时结束
|
||
```
|
||
|
||
**SSE 事件流：**
|
||
|
||
```
|
||
room_started → round_start → (message_start → message_chunk* → message_end → message) × N_agents
|
||
→ round_end → (重复下一轮) → room_completed
|
||
```
|
||
|
||
### 10. API 路由
|
||
|
||
| 路由 | 方法 | 说明 |
|
||
|------|------|------|
|
||
| `/auth/register` | POST | 用户注册 |
|
||
| `/auth/login` | POST | 用户登录 |
|
||
| `/auth/logout` | POST | 用户登出 |
|
||
| `/auth/me` | GET | 当前用户信息 |
|
||
| `/auth/users` | GET | 用户列表（管理员） |
|
||
| `/auth/users/{user_id}` | PUT | 更新用户权限 |
|
||
| `/conversations` | GET | 会话列表（分页） |
|
||
| `/conversations` | POST | 创建会话 |
|
||
| `/conversations/{id}` | GET | 会话详情 |
|
||
| `/conversations/{id}` | PUT | 更新会话 |
|
||
| `/conversations/{id}` | DELETE | 删除会话 |
|
||
| `/messages` | GET | 消息列表 |
|
||
| `/messages` | POST | 发送消息（同步） |
|
||
| `/messages/stream` | POST | 发送消息（流式 SSE） |
|
||
| `/messages/{id}` | DELETE | 删除消息 |
|
||
| `/providers` | GET | LLM 提供商列表 |
|
||
| `/providers` | POST | 创建提供商 |
|
||
| `/providers/{id}` | GET | 提供商详情 |
|
||
| `/providers/{id}` | PUT | 更新提供商 |
|
||
| `/providers/{id}` | DELETE | 删除提供商 |
|
||
| `/providers/{id}/test` | POST | 测试提供商连接 |
|
||
| `/agents` | GET | Agent 列表 |
|
||
| `/agents` | POST | 创建 Agent |
|
||
| `/agents/{id}` | GET | Agent 详情 |
|
||
| `/agents/{id}` | PUT | 更新 Agent |
|
||
| `/agents/{id}` | DELETE | 删除 Agent |
|
||
| `/chat-rooms` | GET | 聊天室列表 |
|
||
| `/chat-rooms` | POST | 创建聊天室 |
|
||
| `/chat-rooms/{id}` | GET | 聊天室详情 |
|
||
| `/chat-rooms/{id}` | PUT | 更新聊天室 |
|
||
| `/chat-rooms/{id}` | DELETE | 删除聊天室 |
|
||
| `/chat-rooms/{id}/start` | POST | 启动聊天室 |
|
||
| `/chat-rooms/{id}/stop` | POST | 停止聊天室 |
|
||
| `/chat-rooms/{id}/reset` | POST | 重置聊天室 |
|
||
| `/chat-rooms/{id}/messages` | GET | 聊天室消息 |
|
||
| `/chat-rooms/{id}/agents` | POST | 添加 Agent |
|
||
| `/chat-rooms/{id}/agents/{agent_id}` | PUT | 更新 Agent |
|
||
| `/chat-rooms/{id}/agents/{agent_id}` | DELETE | 移除 Agent |
|
||
| `/tools` | GET | 可用工具列表 |
|
||
| `/tools/{name}` | GET | 工具详情 |
|
||
| `/tools/{name}/execute` | POST | 执行工具 |
|
||
| `/tasks` | GET | 任务列表 |
|
||
| `/tasks` | POST | 创建任务 |
|
||
| `/tasks/{id}` | GET | 任务详情 |
|
||
| `/tasks/{id}` | PUT | 更新任务 |
|
||
| `/tasks/{id}` | DELETE | 删除任务 |
|
||
| `/tasks/{id}/steps` | POST | 添加步骤 |
|
||
| `/tasks/{id}/steps/{step_id}` | PUT | 更新步骤 |
|
||
| `/tasks/{id}/graph` | GET | 获取任务图 |
|
||
| `/tasks/{id}/ready-steps` | GET | 获取就绪步骤 |
|
||
| `/tasks/{id}/execute` | POST | 执行任务 |
|
||
| `/tasks/{id}/export` | GET | 导出任务 |
|
||
| `/tasks/import` | POST | 导入任务 |
|
||
| `/health` | GET | 健康检查 |
|
||
| `/` | GET | 服务信息 |
|
||
|
||
## 数据流
|
||
|
||
### 消息处理流程
|
||
|
||
```mermaid
|
||
sequenceDiagram
|
||
participant Client
|
||
participant API as POST /messages/stream
|
||
participant CS as ChatService
|
||
participant AL as AgenticLoop
|
||
participant Parser as LLMResponseParser
|
||
participant LLM as LLM API
|
||
participant TE as ToolExecutor
|
||
|
||
Client->>API: POST {content, tools, thinking_enabled}
|
||
API->>CS: stream_response()
|
||
CS->>AL: execute()
|
||
|
||
loop MAX_ITERATIONS (10)
|
||
AL->>LLM: stream_call(messages, tools)
|
||
LLM-->>AL: SSE Stream
|
||
|
||
AL->>Parser: parse_chunk()
|
||
Parser-->>AL: ParsedDelta {thinking, text, tool_calls}
|
||
|
||
alt tool_calls
|
||
AL->>TE: process_tool_calls_parallel()
|
||
TE-->>AL: tool_results
|
||
AL->>AL: 追加到 messages
|
||
end
|
||
end
|
||
|
||
AL->>CS: done event
|
||
CS->>CS: _save_message()
|
||
CS->>API: SSE Stream
|
||
API-->>Client: 流式响应
|
||
```
|
||
|
||
## SSE 事件
|
||
|
||
### 普通会话 SSE 事件
|
||
|
||
| 事件 | 说明 |
|
||
|------|------|
|
||
| `process_step` | 结构化步骤（thinking/text/tool_call/tool_result），携带 `id`、`index` 确保渲染顺序 |
|
||
| `done` | 响应完成，携带 message_id、token_count、usage |
|
||
| `error` | 错误信息 |
|
||
|
||
### 聊天室 SSE 事件
|
||
|
||
| 事件 | 说明 |
|
||
|------|------|
|
||
| `room_started` | 聊天室启动，携带 room_id 和 task |
|
||
| `round_start` | 回合开始，携带 round 和 max_rounds |
|
||
| `message_start` | 消息开始，携带 msg_id、sender_name、sender_color、round_number |
|
||
| `message_chunk` | 消息内容增量，携带 content 和 accumulated |
|
||
| `message_end` | 消息结束，携带 msg_id、content、token_count |
|
||
| `message` | 完整消息（用于存储和显示） |
|
||
| `round_end` | 回合结束 |
|
||
| `room_completed` | 聊天室完成 |
|
||
| `room_paused` | 聊天室暂停 |
|
||
| `agent_error` | Agent 执行错误 |
|
||
| `error` | 通用错误 |
|
||
|
||
### process_step 事件格式
|
||
|
||
```json
|
||
{"type": "process_step", "step": {"id": "step-0", "index": 0, "type": "thinking", "content": "..."}}
|
||
{"type": "process_step", "step": {"id": "step-1", "index": 1, "type": "text", "content": "回复文本..."}}
|
||
{"type": "process_step", "step": {"id": "step-2", "index": 2, "type": "tool_call", "id_ref": "call_abc", "name": "web_search", "arguments": "{\"query\": \"...\"}"}}
|
||
{"type": "process_step", "step": {"id": "step-3", "index": 3, "type": "tool_result", "id_ref": "call_abc", "name": "web_search", "content": "{...}", "success": true}}
|
||
```
|
||
|
||
| 字段 | 说明 |
|
||
|------|------|
|
||
| `id` | 步骤唯一标识（格式 `step-{index}`） |
|
||
| `index` | 步骤序号，确保按正确顺序显示 |
|
||
| `type` | 步骤类型：`thinking` / `text` / `tool_call` / `tool_result` |
|
||
| `id_ref` | 工具调用引用 ID（仅 tool_call/tool_result） |
|
||
| `name` | 工具名称（仅 tool_call/tool_result） |
|
||
| `arguments` | 工具调用参数 JSON 字符串（仅 tool_call） |
|
||
| `content` | 内容（thinking 的思考内容、text 的文本、tool_result 的返回结果） |
|
||
| `success` | 工具执行是否成功（仅 tool_result） |
|
||
|
||
### done 事件格式
|
||
|
||
```json
|
||
{"type": "done", "message_id": "uuid", "token_count": 1234, "usage": {"prompt_tokens": 100, "completion_tokens": 200, "total_tokens": 300}}
|
||
```
|
||
|
||
## 配置示例
|
||
|
||
### config.yaml
|
||
|
||
```yaml
|
||
app:
|
||
secret_key: ${APP_SECRET_KEY}
|
||
debug: true
|
||
host: 0.0.0.0
|
||
port: 8000
|
||
|
||
database:
|
||
type: sqlite
|
||
url: sqlite:///./chat.db
|
||
|
||
workspace:
|
||
root: ./workspaces # 用户工作空间根目录
|
||
auto_create: true # 自动创建用户工作空间
|
||
|
||
llm:
|
||
provider: deepseek
|
||
api_key: ${DEEPSEEK_API_KEY}
|
||
api_url: https://api.deepseek.com/v1
|
||
|
||
tools:
|
||
enable_cache: true
|
||
cache_ttl: 300
|
||
max_workers: 4
|
||
max_iterations: 10
|
||
|
||
logging:
|
||
level: INFO
|
||
```
|
||
|
||
## 环境变量
|
||
|
||
| 变量 | 说明 | 示例 |
|
||
|------|------|------|
|
||
| `APP_SECRET_KEY` | 应用密钥 | `your-secret-key` |
|
||
| `DEEPSEEK_API_KEY` | DeepSeek API | `sk-xxxx` |
|
||
| `DATABASE_URL` | 数据库连接 | `sqlite:///./chat.db` |
|
||
|
||
## LLM 适配器配置
|
||
|
||
### OpenAI 兼容 (DeepSeek/GLM 等)
|
||
|
||
```yaml
|
||
llm:
|
||
provider: openai
|
||
api_key: ${API_KEY}
|
||
api_url: https://api.deepseek.com/v1 # 或其他兼容端点
|
||
```
|
||
|
||
### Anthropic Claude
|
||
|
||
```yaml
|
||
llm:
|
||
provider: anthropic
|
||
api_key: ${ANTHROPIC_API_KEY}
|
||
api_url: https://api.anthropic.com/v1
|
||
```
|
||
|
||
## 项目结构说明
|
||
|
||
### 入口文件
|
||
|
||
- `run.py` - 启动 Uvicorn 服务器
|
||
|
||
### 响应格式
|
||
|
||
所有 API 统一使用响应封装：
|
||
|
||
```json
|
||
// 成功
|
||
{"success": true, "data": {...}, "message": "操作成功"}
|
||
|
||
// 错误
|
||
{"success": false, "error": "错误信息", "code": 404}
|
||
```
|
||
|
||
### 工具缓存机制
|
||
|
||
ToolExecutor 支持结果缓存：
|
||
- TTL: 5 分钟（可配置）
|
||
- 缓存 Key: `{tool_name}:{sorted_arguments_json}`
|
||
- 调用历史记录最近 1000 条
|
||
|
||
### 流式响应特点
|
||
|
||
1. 实时返回 thinking_content（模型思考过程）
|
||
2. 实时返回 text 增量更新
|
||
3. 工具调用并行执行，结果批量返回
|
||
4. 最终 `done` 事件包含完整 message_id 和 token 用量
|
||
|
||
### 工作空间隔离
|
||
|
||
每个用户的工作空间完全隔离：
|
||
- 用户目录基于 user_id 的 SHA256 哈希生成
|
||
- 所有文件操作强制在用户工作空间内
|
||
- 支持权限级别控制文件操作能力
|
||
|
||
### MessageBuilder
|
||
|
||
用于构建发送给 LLM 的消息列表：
|
||
- `add_system()` - 添加系统消息
|
||
- `add_user()` - 添加用户消息（JSON 格式）
|
||
- `add_assistant()` - 添加助手消息
|
||
- `add_tool_result()` - 添加工具结果消息
|
||
- `extract_text()` - 从 JSON 内容中提取文本
|