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Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+# 复制所需文件到容器中
+COPY ./app /app/app
+COPY ./main.py /app
+COPY ./requirements.txt /app
+
+RUN pip install --no-cache-dir -r requirements.txt
+ENV API_KEYS=["your_api_key_1"]
+ENV ALLOWED_TOKENS=["your_token_1"]
+ENV BASE_URL=https://api.groq.com/openai/v1
+
+# Expose port
+EXPOSE 8000
+
+# Run the application
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

app/config.py

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+from pydantic_settings import BaseSettings
+import os
+from typing import List
+
+class Settings(BaseSettings):
+    API_KEYS: List[str]
+    ALLOWED_TOKENS: List[str]
+    BASE_URL: str
+    
+    class Config:
+        env_file = ".env"
+        env_file_encoding = "utf-8"
+        case_sensitive = True
+        # 同时从环境变量和.env文件获取配置
+        env_nested_delimiter = "__"
+        extra = "ignore"
+
+# 优先从环境变量获取,如果没有则从.env文件获取
+settings = Settings(_env_file=os.getenv("ENV_FILE", ".env"))

main.py

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+from fastapi import FastAPI, HTTPException, Header, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+import openai
+from typing import List, Optional
+import logging
+from itertools import cycle
+import asyncio
+
+import uvicorn
+
+from app import config
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+# 允许跨域
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# API密钥配置
+API_KEYS = config.settings.API_KEYS
+
+# 创建一个循环迭代器
+key_cycle = cycle(API_KEYS)
+key_lock = asyncio.Lock()
+
+
+class ChatRequest(BaseModel):
+    messages: List[dict]
+    model: str = "llama-3.2-90b-text-preview"
+    temperature: Optional[float] = 0.7
+    max_tokens: Optional[int] = 8000
+    stream: Optional[bool] = False
+
+
+async def verify_authorization(authorization: str = Header(None)):
+    if not authorization:
+        logger.error("Missing Authorization header")
+        raise HTTPException(status_code=401, detail="Missing Authorization header")
+    if not authorization.startswith("Bearer "):
+        logger.error("Invalid Authorization header format")
+        raise HTTPException(
+            status_code=401, detail="Invalid Authorization header format"
+        )
+    token = authorization.replace("Bearer ", "")
+    if token not in config.settings.ALLOWED_TOKENS:
+        logger.error("Invalid token")
+        raise HTTPException(status_code=401, detail="Invalid token")
+    return token
+
+
+@app.get("/v1/models")
+async def list_models(authorization: str = Header(None)):
+    await verify_authorization(authorization)
+    async with key_lock:
+        api_key = next(key_cycle)
+        logger.info(f"Using API key: {api_key[:8]}...")
+    try:
+        client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
+        response = client.models.list()
+        logger.info("Successfully retrieved models list")
+        return response
+    except Exception as e:
+        logger.error(f"Error listing models: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/v1/chat/completions")
+async def chat_completion(request: ChatRequest, authorization: str = Header(None)):
+    await verify_authorization(authorization)
+    async with key_lock:
+        api_key = next(key_cycle)
+        logger.info(f"Using API key: {api_key[:8]}...")
+
+    try:
+        logger.info(f"Chat completion request - Model: {request.model}")
+        client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
+        response = client.chat.completions.create(
+            model=request.model,
+            messages=request.messages,
+            temperature=request.temperature,
+            max_tokens=request.max_tokens,
+            stream=request.stream if hasattr(request, "stream") else False,
+        )
+
+        if hasattr(request, "stream") and request.stream:
+            logger.info("Streaming response enabled")
+
+            async def generate():
+                for chunk in response:
+                    yield f"data: {chunk.model_dump_json()}\n\n"
+
+            return StreamingResponse(content=generate(), media_type="text/event-stream")
+
+        logger.info("Chat completion successful")
+        return response
+
+    except Exception as e:
+        logger.error(f"Error in chat completion: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/health")
+async def health_check(authorization: str = Header(None)):
+    await verify_authorization(authorization)
+    logger.info("Health check endpoint called")
+    return {"status": "healthy"}
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+fastapi
+openai
+pydantic
+pydantic_settings
+uvicorn