封装fastAPI openAI接口规范

fastapi_server/server.py

@@ -0,0 +1,163 @@
+from fastapi import FastAPI, HTTPException, Depends, Security
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from pydantic import BaseModel, Field
+from typing import List, Optional, Dict, Any, Union
+import os
+import sys
+import time
+import uvicorn
+from loguru import logger
+
+# 添加父目录到系统路径，以便导入lang_agent模块
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from lang_agent.pipeline import Pipeline, PipelineConfig
+
+# 定义OpenAI格式的请求模型
+class ChatMessage(BaseModel):
+    role: str = Field(..., description="消息角色，可以是 'system', 'user', 'assistant'")
+    content: str = Field(..., description="消息内容")
+
+class ChatCompletionRequest(BaseModel):
+    model: str = Field(default="qwen-flash", description="模型名称")
+    messages: List[ChatMessage] = Field(..., description="对话消息列表")
+    temperature: Optional[float] = Field(default=0.7, description="采样温度")
+    max_tokens: Optional[int] = Field(default=500, description="最大生成token数")
+    stream: Optional[bool] = Field(default=False, description="是否流式返回")
+    thread_id: Optional[int] = Field(default=3, description="线程ID，用于多轮对话")
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: str
+
+class ChatCompletionResponseUsage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+class ChatCompletionResponse(BaseModel):
+    id: str
+    object: str = "chat.completion"
+    created: int
+    model: str
+    choices: List[ChatCompletionResponseChoice]
+    usage: Optional[ChatCompletionResponseUsage] = None
+
+# 初始化FastAPI应用
+app = FastAPI(title="Lang Agent Chat API", description="使用OpenAI格式调用pipeline.invoke的聊天API")
+
+# 设置API密钥
+API_KEY = "123tangledup-ai"
+
+# 创建安全方案
+security = HTTPBearer()
+
+# 验证API密钥的依赖项
+async def verify_api_key(credentials: HTTPAuthorizationCredentials = Security(security)):
+    if credentials.credentials != API_KEY:
+        raise HTTPException(
+            status_code=401,
+            detail="无效的API密钥",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    return credentials
+
+# 添加CORS中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 初始化Pipeline
+pipeline_config = PipelineConfig()
+pipeline_config.llm_name = "qwen-flash"
+pipeline_config.llm_provider = "openai"
+pipeline_config.base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
+
+pipeline = Pipeline(pipeline_config)
+
+@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+async def chat_completions(
+    request: ChatCompletionRequest, 
+    credentials: HTTPAuthorizationCredentials = Depends(verify_api_key)
+):
+    """
+    使用OpenAI格式的聊天完成API
+    """
+    try:
+        # 提取用户消息
+        user_message = None
+        system_message = None
+        
+        for message in request.messages:
+            if message.role == "user":
+                user_message = message.content
+            elif message.role == "system":
+                system_message = message.content
+        
+        if not user_message:
+            raise HTTPException(status_code=400, detail="缺少用户消息")
+        
+        # 调用pipeline的chat方法
+        response_content = pipeline.chat(
+            inp=user_message,
+            as_stream=request.stream,
+            thread_id=request.thread_id
+        )
+        
+        # 构建响应
+        response = ChatCompletionResponse(
+            id=f"chatcmpl-{os.urandom(12).hex()}",
+            created=int(time.time()),
+            model=request.model,
+            choices=[
+                ChatCompletionResponseChoice(
+                    index=0,
+                    message=ChatMessage(role="assistant", content=response_content),
+                    finish_reason="stop"
+                )
+            ]
+        )
+        
+        return response
+    
+    except Exception as e:
+        logger.error(f"处理聊天请求时出错: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"内部服务器错误: {str(e)}")
+
+@app.get("/")
+async def root():
+    """
+    根路径，返回API信息
+    """
+    return {
+        "message": "Lang Agent Chat API",
+        "version": "1.0.0",
+        "description": "使用OpenAI格式调用pipeline.invoke的聊天API",
+        "authentication": "Bearer Token (API Key)",
+        "endpoints": {
+            "/v1/chat/completions": "POST - 聊天完成接口，兼容OpenAI格式，需要API密钥验证",
+            "/": "GET - API信息",
+            "/health": "GET - 健康检查接口"
+        }
+    }
+
+@app.get("/health")
+async def health_check():
+    """
+    健康检查接口
+    """
+    return {"status": "healthy"}
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "server:app",
+        host="0.0.0.0",
+        port=8488,
+        reload=True
+    )