4c05131536
- Revised mkdocs.yml to reflect the new site name and description, enhancing clarity for users. - Added a changelog.md to document important changes and updates for the project. - Introduced a roadmap.md to outline development plans and progress for future releases. - Expanded index.md with a comprehensive overview of the platform, including core features and installation instructions. - Enhanced concepts documentation with detailed explanations of assistants, engines, and their configurations. - Updated configuration documentation to provide clear guidance on environment setup and service configurations. - Added extra JavaScript for improved user experience in the documentation site.
357 lines
7.4 KiB
Markdown
357 lines
7.4 KiB
Markdown
# 系统架构
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本文档详细介绍 Realtime Agent Studio (RAS) 的系统架构设计。
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---
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## 整体架构
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RAS 采用前后端分离的微服务架构，主要由三个核心服务组成：
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```mermaid
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flowchart TB
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subgraph Client["客户端"]
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Browser[Web 浏览器]
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Mobile[移动应用]
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ThirdParty[第三方系统]
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end
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subgraph Frontend["前端服务"]
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WebApp[React 管理控制台]
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end
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subgraph Backend["后端服务"]
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API[API 服务<br/>FastAPI]
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Engine[实时交互引擎<br/>WebSocket]
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end
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subgraph Storage["数据存储"]
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DB[(SQLite/PostgreSQL)]
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FileStore[文件存储]
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end
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subgraph External["外部服务"]
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LLM[LLM 服务]
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ASR[ASR 服务]
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TTS[TTS 服务]
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end
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Browser --> WebApp
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Mobile -->|WebSocket| Engine
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ThirdParty -->|REST API| API
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WebApp -->|REST API| API
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WebApp -->|WebSocket| Engine
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API <--> DB
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API <--> FileStore
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Engine <--> API
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Engine --> LLM
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Engine --> ASR
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Engine --> TTS
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```
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---
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## 核心组件
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### 1. Web 前端 (React)
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管理控制台，提供可视化的配置和监控界面。
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| 功能模块 | 说明 |
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|---------|------|
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| 助手管理 | 创建、配置、测试智能助手 |
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| 资源库 | LLM/ASR/TTS 模型管理 |
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| 知识库 | RAG 文档上传与管理 |
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| 历史记录 | 会话日志查询与回放 |
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| 仪表盘 | 实时数据统计 |
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| 调试控制台 | WebSocket 实时测试 |
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### 2. API 服务 (FastAPI)
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RESTful API 后端，处理所有管理操作。
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```mermaid
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flowchart LR
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subgraph API["API 服务"]
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Router[路由层]
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Service[业务逻辑层]
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Model[数据模型层]
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end
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Client[客户端] --> Router
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Router --> Service
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Service --> Model
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Model --> DB[(数据库)]
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```
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**主要职责：**
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- 助手 CRUD 操作
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- 模型资源管理
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- 知识库管理
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- 会话记录存储
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- 认证与授权
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### 3. 实时交互引擎 (Engine)
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核心组件，处理实时音视频对话。
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```mermaid
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flowchart TB
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subgraph Engine["实时交互引擎"]
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WS[WebSocket Handler]
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SM[会话管理器]
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subgraph Pipeline["管线式引擎"]
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VAD[VAD 检测]
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ASR[语音识别]
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LLM[大语言模型]
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TTS[语音合成]
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end
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subgraph Multimodal["多模态引擎"]
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RT[Realtime Model<br/>GPT-4o / Gemini]
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end
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end
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Client[客户端] -->|音频流| WS
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WS --> SM
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SM --> Pipeline
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SM --> Multimodal
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Pipeline -->|文本/音频| WS
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Multimodal -->|文本/音频| WS
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```
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---
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## 引擎架构
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### 管线式全双工引擎
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传统方案，将语音交互拆分为三个独立阶段：
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```mermaid
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sequenceDiagram
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participant C as 客户端
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participant E as 引擎
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participant ASR as 语音识别
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participant LLM as 大语言模型
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participant TTS as 语音合成
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C->>E: 音频流 (PCM)
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E->>ASR: 语音转文字
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ASR-->>E: 转写文本
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E->>LLM: 生成回复
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LLM-->>E: 回复文本 (流式)
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E->>TTS: 文字转语音
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TTS-->>E: 音频流
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E->>C: 播放音频
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```
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**特点：**
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- 灵活选择各环节供应商
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- 可独立优化每个环节
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- 延迟约 500-1500ms
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### 原生多模态引擎
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使用端到端多模态模型（如 GPT-4o Realtime）：
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```mermaid
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sequenceDiagram
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participant C as 客户端
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participant E as 引擎
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participant RT as Realtime Model
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C->>E: 音频流
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E->>RT: 音频输入
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RT-->>E: 音频输出 (流式)
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E->>C: 播放音频
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```
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**特点：**
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- 更低延迟 (< 300ms)
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- 更自然的语音交互
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- 依赖特定模型供应商
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---
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## 数据流
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### WebSocket 会话流程
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```mermaid
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sequenceDiagram
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participant C as 客户端
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participant E as 引擎
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participant API as API 服务
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participant DB as 数据库
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C->>E: 连接 ws://.../ws?assistant_id=xxx
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E->>API: 获取助手配置
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API->>DB: 查询助手
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DB-->>API: 助手数据
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API-->>E: 配置信息
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C->>E: session.start
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E-->>C: session.started
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E-->>C: config.resolved
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loop 对话循环
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C->>E: 音频帧 (binary)
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E-->>C: input.speech_started
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E-->>C: transcript.delta
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E-->>C: transcript.final
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E-->>C: assistant.response.delta
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E-->>C: output.audio.start
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E-->>C: 音频帧 (binary)
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E-->>C: output.audio.end
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end
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C->>E: session.stop
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E->>API: 保存会话记录
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API->>DB: 存储
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E-->>C: session.stopped
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```
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### 智能打断流程
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```mermaid
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sequenceDiagram
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participant C as 客户端
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participant E as 引擎
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participant TTS as TTS 服务
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Note over E: 正在播放 TTS 音频
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E->>C: 音频帧...
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C->>E: 用户说话 (VAD 检测)
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E->>E: 触发打断
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E->>TTS: 停止合成
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E-->>C: output.audio.interrupted
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Note over E: 处理新的用户输入
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E-->>C: input.speech_started
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```
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---
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## 部署架构
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### 开发环境
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```mermaid
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flowchart LR
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subgraph Local["本地开发"]
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Web[npm run dev<br/>:3000]
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API[uvicorn<br/>:8080]
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Engine[python main.py<br/>:8000]
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DB[(SQLite)]
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end
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Web --> API
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Web --> Engine
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API --> DB
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Engine --> API
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```
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### 生产环境
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```mermaid
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flowchart TB
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subgraph Internet["互联网"]
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User[用户]
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end
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subgraph LoadBalancer["负载均衡"]
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Nginx[Nginx / Traefik]
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end
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subgraph Docker["Docker 集群"]
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Web1[Web 容器]
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Web2[Web 容器]
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API1[API 容器]
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API2[API 容器]
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Engine1[Engine 容器]
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Engine2[Engine 容器]
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end
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subgraph Storage["持久化存储"]
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PG[(PostgreSQL)]
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Redis[(Redis)]
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S3[对象存储]
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end
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User --> Nginx
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Nginx --> Web1
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Nginx --> Web2
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Nginx --> API1
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Nginx --> API2
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Nginx --> Engine1
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Nginx --> Engine2
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API1 --> PG
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API2 --> PG
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API1 --> Redis
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Engine1 --> Redis
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```
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---
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## 技术选型
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| 组件 | 技术 | 选型理由 |
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|------|------|---------|
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| **前端框架** | React 18 | 成熟生态，组件化开发 |
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| **状态管理** | Zustand | 轻量级，TypeScript 友好 |
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| **UI 组件** | Tailwind CSS | 原子化 CSS，快速开发 |
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| **后端框架** | FastAPI | 高性能，自动 API 文档 |
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| **WebSocket** | websockets | Python 异步 WebSocket |
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| **ORM** | SQLAlchemy | 功能完善，支持多数据库 |
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| **数据库** | SQLite/PostgreSQL | 开发简单/生产可靠 |
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---
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## 扩展性设计
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### 模型适配器模式
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```mermaid
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classDiagram
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class ModelAdapter {
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<<interface>>
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+generate(prompt) string
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+stream(prompt) AsyncIterator
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}
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class OpenAIAdapter {
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+generate(prompt) string
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+stream(prompt) AsyncIterator
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}
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class AzureAdapter {
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+generate(prompt) string
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+stream(prompt) AsyncIterator
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}
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class LocalAdapter {
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+generate(prompt) string
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+stream(prompt) AsyncIterator
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}
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ModelAdapter <|-- OpenAIAdapter
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ModelAdapter <|-- AzureAdapter
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ModelAdapter <|-- LocalAdapter
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```
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通过适配器模式，可以轻松接入新的模型供应商。
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---
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## 相关文档
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- [WebSocket 协议](../api-reference/websocket.md) - 详细的协议规范
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- [部署指南](../deployment/index.md) - 生产环境部署
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- [核心概念](../concepts/index.md) - 助手、管线等概念说明
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