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467 lines
9.3 KiB
Markdown
467 lines
9.3 KiB
Markdown
# BackendServer API 文档
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本文档描述后端服务器的 API 接口，供开发者扩展其他连接方式（如网络接口、WebSocket 等）。
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## 概述
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TrulyMEM 后端采用**请求-响应队列模式**，通过 `queue.Queue` 实现线程安全通信。后端在独立线程中运行，处理来自客户端的请求。
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### 核心组件
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| 组件 | 说明 |
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|------|------|
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| `BackendServer` | 后端服务器，独立线程运行 |
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| `BackendClient` | 客户端封装，提供便捷方法 |
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| `MessageType` | 请求类型枚举 |
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| `BackendRequest` | 请求数据包 |
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| `BackendResponse` | 响应数据包 |
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---
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## 请求类型 (MessageType)
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```python
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class MessageType(Enum):
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PROCESS_MESSAGE = "process_message" # 处理消息
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EXECUTE_TOOL = "execute_tool" # 执行工具
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GET_STATUS = "get_status" # 获取状态
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GET_CONFIG = "get_config" # 获取配置
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SET_CONFIG = "set_config" # 设置配置
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GET_HISTORY = "get_history" # 获取历史
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SAVE_HISTORY = "save_history" # 保存历史
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SHUTDOWN = "shutdown" # 关闭服务
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```
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---
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## 数据包格式
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### BackendRequest
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```python
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@dataclass
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class BackendRequest:
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request_id: str # 请求唯一标识
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message_type: MessageType # 请求类型
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payload: Dict[str, Any] # 请求参数
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response_queue: queue.Queue # 响应队列（用于返回结果）
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```
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### BackendResponse
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```python
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@dataclass
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class BackendResponse:
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request_id: str # 对应的请求ID
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success: bool # 是否成功
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data: Any = None # 返回数据
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error: Optional[str] = None # 错误信息
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```
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---
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## API 接口详情
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### 1. PROCESS_MESSAGE - 处理消息
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发送用户消息，AI 将处理并返回回复（可能包含工具调用）。
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**请求参数：**
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```python
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payload = {
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"user_input": str # 用户输入的消息
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}
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```
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**响应数据：**
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```python
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data = {
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"content": str, # AI 回复内容
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"tool_calls": [ # 工具调用记录
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{
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"name": str, # 工具名称
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"arguments": dict,# 工具参数
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"result": str # 工具执行结果
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}
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],
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"rejected_tools": [ # 被拒绝的工具调用
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(str, str) # (工具名, 拒绝原因)
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]
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}
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```
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**示例：**
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```python
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from core import BackendClient
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client = BackendClient(server)
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result = client.process_message("你好，请记住我的名字是小明")
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# result = {"success": True, "data": {"content": "...", "tool_calls": [...]}}
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```
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---
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### 2. EXECUTE_TOOL - 执行工具
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直接执行指定的记忆工具。
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> **注意**：前端直接调用的工具**不受次数限制**，只有模型发起的工具调用才受限制。
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**请求参数：**
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```python
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payload = {
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"tool_name": str, # 工具名称
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"arguments": dict # 工具参数
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}
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```
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**响应数据：**
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```python
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data = {
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"result": str # 工具执行结果
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}
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```
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**示例：**
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```python
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result = client.execute_tool("memory_recall", {"query_intent": "用户信息"})
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```
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---
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### 3. GET_STATUS - 获取状态
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获取后端运行状态。
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**请求参数：**
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```python
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payload = {} # 无参数
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```
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**响应数据：**
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```python
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data = {
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"graph_initialized": bool, # 图数据库是否初始化
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"client_initialized": bool, # API 客户端是否初始化
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"running": bool # 后端是否运行中
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}
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```
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**示例：**
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```python
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status = client.get_status()
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# status = {"success": True, "data": {"running": True, ...}}
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```
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---
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### 4. GET_CONFIG - 获取配置
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获取当前 API 配置。
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**请求参数：**
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```python
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payload = {} # 无参数
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```
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**响应数据：**
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```python
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data = {
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"api_key": str, # API Key
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"base_url": str # API Base URL
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}
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```
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---
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### 5. SET_CONFIG - 设置配置
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更新 API 配置（API Key 和 Base URL）。
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**请求参数：**
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```python
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payload = {
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"api_key": str, # API Key
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"base_url": str # API Base URL (默认: https://api.deepseek.com)
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}
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```
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**响应数据：**
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```python
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data = {
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"status": "config_updated"
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}
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```
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**示例：**
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```python
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result = client.update_config(
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api_key="sk-xxxxx",
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base_url="https://api.deepseek.com"
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)
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```
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---
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### 6. GET_HISTORY - 获取消息历史
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获取保存的消息历史。
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**请求参数：**
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```python
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payload = {} # 无参数
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```
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**响应数据：**
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```python
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data = {
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"history": list # 消息历史列表
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}
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```
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---
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### 7. SAVE_HISTORY - 保存消息历史
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保存消息历史到内存。
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**请求参数：**
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```python
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payload = {
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"messages": list # 消息列表
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}
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```
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**响应数据：**
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```python
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data = {
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"status": "history_saved"
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}
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```
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---
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### 8. SHUTDOWN - 关闭服务
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关闭后端服务器。
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**请求参数：**
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```python
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payload = {} # 无参数
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```
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**响应数据：**
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```python
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data = {
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"status": "shutdown"
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}
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```
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---
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## 使用示例
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### 基础使用
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```python
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from core import BackendServer, BackendClient
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# 1. 创建并启动后端
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server = BackendServer(db_path="graph_memory.db", use_embedded_db=True)
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server.start(api_key="your-api-key", base_url="https://api.deepseek.com")
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# 2. 创建客户端
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client = BackendClient(server)
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# 3. 发送消息
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result = client.process_message("你好")
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print(result["data"]["content"])
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# 4. 关闭
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client.shutdown()
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```
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### 直接使用请求队列
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```python
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import queue
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from core import BackendServer, MessageType, BackendRequest, BackendResponse
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server = BackendServer()
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server.start(api_key="your-key")
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# 创建请求
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response_queue = queue.Queue()
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request = BackendRequest(
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request_id="req-001",
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message_type=MessageType.PROCESS_MESSAGE,
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payload={"user_input": "你好"},
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response_queue=response_queue
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)
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# 发送请求
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server._request_queue.put(request)
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# 等待响应
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response = response_queue.get(timeout=30.0)
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print(response.data)
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```
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---
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## 扩展指南
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### 扩展为 HTTP API
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```python
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from flask import Flask, request, jsonify
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from core import BackendServer, BackendClient
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app = Flask(__name__)
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server = BackendServer()
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client = BackendClient(server)
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@app.route("/message", methods=["POST"])
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def send_message():
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data = request.json
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result = client.process_message(data["message"])
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return jsonify(result)
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@app.route("/config", methods=["POST"])
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def update_config():
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data = request.json
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result = client.update_config(data["api_key"], data.get("base_url"))
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return jsonify(result)
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@app.route("/status", methods=["GET"])
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def get_status():
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result = client.get_status()
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return jsonify(result)
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if __name__ == "__main__":
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server.start()
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app.run(port=8080)
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```
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### 扩展为 WebSocket
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```python
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import asyncio
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import websockets
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import json
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from core import BackendServer, BackendClient
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server = BackendServer()
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client = BackendClient(server)
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async def handler(websocket):
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async for message in websocket:
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data = json.loads(message)
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msg_type = data["type"]
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if msg_type == "message":
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result = client.process_message(data["content"])
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elif msg_type == "config":
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result = client.update_config(data["api_key"], data.get("base_url"))
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elif msg_type == "status":
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result = client.get_status()
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else:
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result = {"success": False, "error": "unknown type"}
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await websocket.send(json.dumps(result))
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async def main():
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server.start()
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async with websockets.serve(handler, "localhost", 8765):
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await asyncio.Future() # run forever
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asyncio.run(main())
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```
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### 扩展为 gRPC
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```protobuf
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// truly_mem.proto
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syntax = "proto3";
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service TrulyMEM {
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rpc ProcessMessage(MessageRequest) returns (MessageResponse);
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rpc UpdateConfig(ConfigRequest) returns (ConfigResponse);
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rpc GetStatus(Empty) returns (StatusResponse);
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}
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message MessageRequest {
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string message = 1;
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}
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message MessageResponse {
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bool success = 1;
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string content = 2;
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string error = 3;
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}
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```
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---
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## 线程安全说明
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- `BackendServer` 使用 `threading.Lock` 保护共享资源
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- 所有请求通过 `queue.Queue` 传递，线程安全
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- 响应通过每个请求独立的 `response_queue` 返回
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- 默认超时时间：30 秒
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---
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## 工具调用限制
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### 限制范围
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| 调用方式 | 是否受限 | 说明 |
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|---------|---------|------|
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| 模型发起的工具调用 | ✅ 受限 | 通过 `PROCESS_MESSAGE` 触发，模型自动调用工具 |
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| 前端直接调用工具 | ❌ 不受限 | 通过 `EXECUTE_TOOL` 直接调用 |
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### 限制规则（仅限模型发起）
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| 类别 | 操作 | 每轮上限 |
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|------|------|---------|
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| 人设图 | 查询 | 1 次 |
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| 人设图 | 修改 | 1 次 |
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| 工作记忆链 | 查询 | 4 次 |
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| 工作记忆链 | 修改 | 2 次 |
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| 一般记忆 | 查询 | 20 次 |
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| 一般记忆 | 修改 | 10 次 |
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### 重置机制
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- 每次调用 `PROCESS_MESSAGE` 时，计数器自动重置
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- 前端直接调用 `EXECUTE_TOOL` 不会重置计数器
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---
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## 错误处理
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所有 API 返回统一格式：
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```python
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# 成功
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{
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"success": True,
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"data": {...}
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}
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# 失败
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{
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"success": False,
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"error": "错误描述"
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}
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```
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常见错误：
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| 错误信息 | 说明 |
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|---------|------|
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| `API Key 未配置` | 未设置 API Key |
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| `timeout` | 请求超时 |
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| `工具调用被拒绝: ...` | 工具调用频率超限 |
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