mirror of
https://github.com/snailyp/gemini-balance.git
synced 2026-05-31 21:29:44 +08:00
379 lines
14 KiB
Python
379 lines
14 KiB
Python
from fastapi import FastAPI, HTTPException, Header, Request
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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import openai
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from typing import List, Optional, Union
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import logging
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from itertools import cycle
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import asyncio
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import uvicorn
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from app import config
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import requests
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from datetime import datetime, timezone
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import json
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import httpx
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import uuid
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import time
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# 配置日志
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
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)
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logger = logging.getLogger(__name__)
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app = FastAPI()
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# 允许跨域
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# API密钥配置
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API_KEYS = config.settings.API_KEYS
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# 创建一个循环迭代器
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key_cycle = cycle(API_KEYS)
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# 创建两个独立的锁
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key_cycle_lock = asyncio.Lock()
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failure_count_lock = asyncio.Lock()
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# 添加key失败计数记录
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key_failure_counts = {key: 0 for key in API_KEYS}
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MAX_FAILURES = 10 # 最大失败次数阈值
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MAX_RETRIES = 3 # 最大重试次数
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async def get_next_key():
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"""仅获取下一个key,不检查失败次数"""
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async with key_cycle_lock:
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return next(key_cycle)
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async def is_key_valid(key):
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"""检查key是否有效"""
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async with failure_count_lock:
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return key_failure_counts[key] < MAX_FAILURES
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async def reset_failure_counts():
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"""重置所有key的失败计数"""
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async with failure_count_lock:
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for key in key_failure_counts:
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key_failure_counts[key] = 0
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async def get_next_working_key():
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"""获取下一个可用的API key"""
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initial_key = await get_next_key()
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current_key = initial_key
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while True:
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if await is_key_valid(current_key):
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return current_key
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current_key = await get_next_key()
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if current_key == initial_key: # 已经循环了一圈
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await reset_failure_counts()
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return current_key
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async def handle_api_failure(api_key):
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"""处理API调用失败"""
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async with failure_count_lock:
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key_failure_counts[api_key] += 1
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if key_failure_counts[api_key] >= MAX_FAILURES:
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logger.warning(f"API key {api_key} has failed {MAX_FAILURES} times, switching to next key")
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# 在锁外获取新的key
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return await get_next_working_key()
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class ChatRequest(BaseModel):
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messages: List[dict]
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model: str = "gemini-1.5-flash-002"
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temperature: Optional[float] = 0.7
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stream: Optional[bool] = False
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tools: Optional[List[dict]] = []
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tool_choice: Optional[str] = "auto"
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class EmbeddingRequest(BaseModel):
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input: Union[str, List[str]]
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model: str = "text-embedding-004"
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encoding_format: Optional[str] = "float"
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async def verify_authorization(authorization: str = Header(None)):
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if not authorization:
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logger.error("Missing Authorization header")
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raise HTTPException(status_code=401, detail="Missing Authorization header")
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if not authorization.startswith("Bearer "):
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logger.error("Invalid Authorization header format")
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raise HTTPException(
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status_code=401, detail="Invalid Authorization header format"
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)
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token = authorization.replace("Bearer ", "")
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if token not in config.settings.ALLOWED_TOKENS:
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logger.error("Invalid token")
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raise HTTPException(status_code=401, detail="Invalid token")
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return token
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def get_gemini_models(api_key):
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base_url = "https://generativelanguage.googleapis.com/v1beta"
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url = f"{base_url}/models?key={api_key}"
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try:
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response = requests.get(url)
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if response.status_code == 200:
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gemini_models = response.json()
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return convert_to_openai_models_format(gemini_models)
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else:
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print(f"Error: {response.status_code}")
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print(response.text)
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return None
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except requests.RequestException as e:
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print(f"Request failed: {e}")
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return None
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def convert_to_openai_models_format(gemini_models):
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openai_format = {"object": "list", "data": []}
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for model in gemini_models.get("models", []):
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openai_model = {
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"id": model["name"].split("/")[-1], # 取最后一部分作为ID
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"object": "model",
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"created": int(datetime.now(timezone.utc).timestamp()), # 使用当前时间戳
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"owned_by": "google", # 假设所有Gemini模型都由Google拥有
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"permission": [], # Gemini API可能没有直接对应的权限信息
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"root": model["name"],
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"parent": None, # Gemini API可能没有直接对应的父模型信息
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}
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openai_format["data"].append(openai_model)
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return openai_format
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def convert_messages_to_gemini_format(messages):
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"""Convert OpenAI message format to Gemini format"""
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gemini_messages = []
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for message in messages:
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gemini_message = {
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"role": "user" if message["role"] == "user" else "model",
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"parts": [{"text": message["content"]}],
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}
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gemini_messages.append(gemini_message)
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return gemini_messages
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def convert_gemini_response_to_openai(response, model, stream=False):
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"""Convert Gemini response to OpenAI format"""
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if stream:
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# 处理流式响应
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chunk = response
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if not chunk["candidates"]:
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return None
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return {
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"id": "chatcmpl-" + str(uuid.uuid4()),
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {
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"content": chunk["candidates"][0]["content"]["parts"][0]["text"]
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},
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"finish_reason": None,
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}
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],
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}
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else:
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# 处理普通响应
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return {
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"id": "chatcmpl-" + str(uuid.uuid4()),
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"object": "chat.completion",
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"created": int(time.time()),
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"model": model,
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": response["candidates"][0]["content"]["parts"][0][
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"text"
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],
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},
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"finish_reason": "stop",
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}
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],
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"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
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}
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@app.get("/v1/models")
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@app.get("/hf/v1/models")
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async def list_models(authorization: str = Header(None)):
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await verify_authorization(authorization)
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api_key = await get_next_working_key()
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logger.info(f"Using API key: {api_key}")
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try:
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response = get_gemini_models(api_key)
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logger.info("Successfully retrieved models list")
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return response
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except Exception as e:
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logger.error(f"Error listing models: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/v1/chat/completions")
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@app.post("/hf/v1/chat/completions")
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async def chat_completion(request: ChatRequest, authorization: str = Header(None)):
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await verify_authorization(authorization)
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api_key = await get_next_working_key()
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logger.info(f"Chat completion request - Model: {request.model}")
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retries = 0
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while retries < MAX_RETRIES:
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try:
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logger.info(f"Attempt {retries + 1} with API key: {api_key}")
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if request.model in config.settings.MODEL_SEARCH:
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# Gemini API调用部分
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gemini_messages = convert_messages_to_gemini_format(request.messages)
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# 调用Gemini API
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payload = {
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"contents": gemini_messages,
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"generationConfig": {
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"temperature": request.temperature,
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},
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"tools": [{"googleSearch": {}}],
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}
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if request.stream:
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logger.info("Streaming response enabled")
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async def generate():
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nonlocal api_key, retries
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while retries < MAX_RETRIES:
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try:
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async with httpx.AsyncClient() as client:
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stream_url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse&key={api_key}"
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async with client.stream("POST", stream_url, json=payload) as response:
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if response.status_code == 429:
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logger.warning(f"Rate limit reached for key: {api_key}")
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api_key = await handle_api_failure(api_key)
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logger.info(f"Retrying with new API key: {api_key}")
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retries += 1
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if retries >= MAX_RETRIES:
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yield f"data: {json.dumps({'error': 'Max retries reached'})}\n\n"
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break
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continue
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if response.status_code != 200:
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logger.error(f"Error in streaming response: {response.status_code}")
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yield f"data: {json.dumps({'error': f'API error: {response.status_code}'})}\n\n"
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break
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async for line in response.aiter_lines():
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if line.startswith("data: "):
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try:
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chunk = json.loads(line[6:])
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openai_chunk = convert_gemini_response_to_openai(
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chunk, request.model, stream=True
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)
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if openai_chunk:
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yield f"data: {json.dumps(openai_chunk)}\n\n"
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except json.JSONDecodeError:
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continue
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yield "data: [DONE]\n\n"
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return
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except Exception as e:
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logger.error(f"Stream error: {str(e)}")
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api_key = await handle_api_failure(api_key)
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retries += 1
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if retries >= MAX_RETRIES:
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yield f"data: {json.dumps({'error': 'Max retries reached'})}\n\n"
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break
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continue
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return StreamingResponse(content=generate(), media_type="text/event-stream")
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else:
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# 非流式响应
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async with httpx.AsyncClient() as client:
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non_stream_url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:generateContent?key={api_key}"
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response = await client.post(non_stream_url, json=payload)
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gemini_response = response.json()
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logger.info("Chat completion successful")
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return convert_gemini_response_to_openai(gemini_response, request.model)
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# OpenAI API调用部分
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client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
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response = client.chat.completions.create(
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model=request.model,
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messages=request.messages,
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temperature=request.temperature,
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stream=request.stream if hasattr(request, "stream") else False,
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)
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if hasattr(request, "stream") and request.stream:
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logger.info("Streaming response enabled")
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async def generate():
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for chunk in response:
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yield f"data: {chunk.model_dump_json()}\n\n"
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logger.info("Chat completion successful")
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return StreamingResponse(content=generate(), media_type="text/event-stream")
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logger.info("Chat completion successful")
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return response
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except Exception as e:
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logger.error(f"Error in chat completion: {str(e)}")
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api_key = await handle_api_failure(api_key)
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retries += 1
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if retries >= MAX_RETRIES:
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logger.error("Max retries reached, giving up")
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raise HTTPException(status_code=500, detail="Max retries reached with all available API keys")
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logger.info(f"Retrying with new API key: {api_key}")
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continue
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raise HTTPException(status_code=500, detail="Unexpected error in chat completion")
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@app.post("/v1/embeddings")
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@app.post("/hf/v1/embeddings")
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async def embedding(request: EmbeddingRequest, authorization: str = Header(None)):
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await verify_authorization(authorization)
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api_key = await get_next_working_key()
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logger.info(f"Using API key: {api_key}")
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try:
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client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
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response = client.embeddings.create(input=request.input, model=request.model)
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logger.info("Embedding successful")
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return response
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except Exception as e:
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logger.error(f"Error in embedding: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/health")
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@app.get("/")
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async def health_check():
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logger.info("Health check endpoint called")
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return {"status": "healthy"}
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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