添加API密钥管理、模型服务和安全服务，并优化FastAPI应用程序配置

.vscode/launch.json

@@ -10,8 +10,12 @@
             "request": "launch",
             "module": "uvicorn",
             "args": [
-                "main:app",
+                "app.main:app",
-                "--reload"
+                "--reload",
+                "--host",
+                "127.0.0.1",
+                "--port",
+                "8000"
             ],
             "jinja": true
         }

app/api/routes.py

@@ -0,0 +1,98 @@
+from fastapi import APIRouter, Depends, Header
+from typing import Optional
+import logging
+from fastapi.responses import StreamingResponse
+
+from app.core.security import SecurityService
+from app.services.key_manager import KeyManager
+from app.services.model_service import ModelService
+from app.services.chat_service import ChatService
+from app.services.embedding_service import EmbeddingService
+from app.schemas.request_model import ChatRequest, EmbeddingRequest
+from app.core.config import settings
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+# 初始化服务
+security_service = SecurityService(settings.ALLOWED_TOKENS)
+key_manager = KeyManager(settings.API_KEYS)
+model_service = ModelService(settings.MODEL_SEARCH)
+chat_service = ChatService(settings.BASE_URL, key_manager)
+embedding_service = EmbeddingService(settings.BASE_URL)
+
+
+@router.get("/v1/models")
+@router.get("/hf/v1/models")
+async def list_models(
+    authorization: str = Header(None),
+    token: str = Depends(security_service.verify_authorization),
+):
+    logger.info("Handling models list request")
+    api_key = await key_manager.get_next_working_key()
+    logger.info(f"Using API key: {api_key}")
+    return model_service.get_gemini_models(api_key)
+
+
+@router.post("/v1/chat/completions")
+@router.post("/hf/v1/chat/completions")
+async def chat_completion(
+    request: ChatRequest,
+    authorization: str = Header(None),
+    token: str = Depends(security_service.verify_authorization),
+):
+    logger.info(f"Handling chat completion request for model: {request.model}")
+    api_key = await key_manager.get_next_working_key()
+    logger.info(f"Using API key: {api_key}")
+    retries = 0
+    MAX_RETRIES = 3
+
+    while retries < MAX_RETRIES:
+        try:
+            response = await chat_service.create_chat_completion(
+                messages=request.messages,
+                model=request.model,
+                temperature=request.temperature,
+                stream=request.stream,
+                api_key=api_key,
+                tools=request.tools,
+                tool_choice=request.tool_choice,
+            )
+            
+
+            # 处理流式响应
+            if request.stream:
+                return StreamingResponse(response, media_type="text/event-stream")
+            return response
+
+        except Exception as e:
+            logger.warning(
+                f"API call failed with error: {str(e)}. Attempt {retries + 1} of {MAX_RETRIES}"
+            )
+            api_key = await key_manager.handle_api_failure(api_key)
+            logger.info(f"Switched to new API key: {api_key}")
+            retries += 1
+            if retries >= MAX_RETRIES:
+                logger.error(f"Max retries ({MAX_RETRIES}) reached. Raising error")
+                raise
+
+
+@router.post("/v1/embeddings")
+@router.post("/hf/v1/embeddings")
+async def embedding(
+    request: EmbeddingRequest,
+    authorization: str = Header(None),
+    token: str = Depends(security_service.verify_authorization),
+):
+    logger.info(f"Handling embedding request for model: {request.model}")
+    api_key = await key_manager.get_next_working_key()
+    logger.info(f"Using API key: {api_key}")
+    try:
+        response = await embedding_service.create_embedding(
+            input_text=request.input, model=request.model, api_key=api_key
+        )
+        logger.info("Embedding request successful")
+        return response
+    except Exception as e:
+        logger.error(f"Embedding request failed: {str(e)}")
+        raise

app/config.py

@@ -1,20 +0,0 @@
-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
-    MODEL_SEARCH: List[str] = ["gemini-2.0-flash-exp"]
-
-    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"))

app/core/config.py

@@ -0,0 +1,16 @@
+from pydantic_settings import BaseSettings
+from typing import List
+
+
+class Settings(BaseSettings):
+    API_KEYS: List[str]
+    ALLOWED_TOKENS: List[str]
+    BASE_URL: str = "https://generativelanguage.googleapis.com/v1beta"
+    MODEL_SEARCH: List[str] = ["gemini-2.0-flash-exp"]
+    TOOLS_CODE_EXECUTION_ENABLED: bool = False
+
+    class Config:
+        env_file = ".env"
+
+
+settings = Settings()

app/core/security.py

@@ -0,0 +1,30 @@
+from fastapi import HTTPException, Header
+import logging
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+class SecurityService:
+    def __init__(self, allowed_tokens: list):
+        self.allowed_tokens = allowed_tokens
+
+    async def verify_authorization(
+        self, authorization: Optional[str] = Header(None)
+    ) -> str:
+        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 self.allowed_tokens:
+            logger.error("Invalid token")
+            raise HTTPException(status_code=401, detail="Invalid token")
+
+        return token

app/main.py

@@ -0,0 +1,38 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+import logging
+
+from app.api.routes import router
+import uvicorn
+
+# 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=["*"],
+)
+
+# 包含所有路由
+app.include_router(router)
+
+
+@app.get("/health")
+@app.get("/")
+async def health_check():
+    logger.info("Health check endpoint called")
+    return {"status": "healthy"}
+
+
+if __name__ == "__main__":
+
+    uvicorn.run(app, host="0.0.0.0", port=8001)

app/schemas/request_model.py

@@ -0,0 +1,17 @@
+from pydantic import BaseModel
+from typing import List, Optional, Union
+
+
+class ChatRequest(BaseModel):
+    messages: List[dict]
+    model: str = "gemini-1.5-flash-002"
+    temperature: Optional[float] = 0.7
+    stream: Optional[bool] = False
+    tools: Optional[List[dict]] = []
+    tool_choice: Optional[str] = "auto"
+
+
+class EmbeddingRequest(BaseModel):
+    input: Union[str, List[str]]
+    model: str = "text-embedding-004"
+    encoding_format: Optional[str] = "float"

app/services/chat_service.py

@@ -0,0 +1,299 @@
+import httpx
+import json
+import time
+import uuid
+import logging
+from typing import Dict, Any, Optional, AsyncGenerator, Union
+import openai
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+class ChatService:
+    def __init__(self, base_url: str, key_manager=None):
+        self.base_url = base_url
+        self.key_manager = key_manager
+
+    def convert_messages_to_gemini_format(self, messages: list) -> list:
+        """Convert OpenAI message format to Gemini format"""
+        converted_messages = []
+        for msg in messages:
+            role = "user" if msg["role"] == "user" else "model"
+            parts = []
+
+            # 处理文本内容
+            if isinstance(msg["content"], str):
+                parts.append({"text": msg["content"]})
+            # 处理包含图片的消息
+            elif isinstance(msg["content"], list):
+                for content in msg["content"]:
+                    if isinstance(content, str):
+                        parts.append({"text": content})
+                    elif isinstance(content, dict) and content["type"] == "text":
+                        parts.append({"text": content["text"]})
+                    elif isinstance(content, dict) and content["type"] == "image_url":
+                        # 处理图片URL
+                        image_url = content["image_url"]["url"]
+                        if image_url.startswith("data:image"):
+                            # 处理base64图片
+                            parts.append(
+                                {
+                                    "inline_data": {
+                                        "mime_type": "image/jpeg",
+                                        "data": image_url.split(",")[1],
+                                    }
+                                }
+                            )
+                        else:
+                            # 处理普通URL图片
+                            parts.append(
+                                {
+                                    "inline_data": {
+                                        "mime_type": "image/jpeg",
+                                        "data": image_url,
+                                    }
+                                }
+                            )
+
+            converted_messages.append({"role": role, "parts": parts})
+
+        return converted_messages
+
+    def convert_gemini_response_to_openai(
+        self, response: Dict[str, Any], model: str, stream: bool = False
+    ) -> Optional[Dict[str, Any]]:
+        """Convert Gemini response to OpenAI format"""
+        if stream:
+            if not response.get("candidates"):
+                return None
+
+            try:
+                candidate = response["candidates"][0]
+                content = candidate.get("content", {})
+                parts = content.get("parts", [])
+
+                if not parts:
+                    return None
+
+                if "text" in parts[0]:
+                    text = parts[0].get("text")
+                elif "executableCode" in parts[0]:
+                    text = self.format_code_block(parts[0]["executableCode"])
+                elif "executableCodeResult" in parts[0]:
+                    text = self.format_execution_result(parts[0]["executableCodeResult"])
+                else:
+                    text = ""
+
+                return {
+                    "id": f"chatcmpl-{uuid.uuid4()}",
+                    "object": "chat.completion.chunk",
+                    "created": int(time.time()),
+                    "model": model,
+                    "choices": [
+                        {
+                            "index": 0,
+                            "delta": {"content": text},
+                            "finish_reason": None,
+                        }
+                    ],
+                }
+            except Exception as e:
+                logger.error(f"Error converting Gemini response: {str(e)}")
+                logger.debug(f"Raw response: {response}")
+                return None
+        else:
+            return {
+                "id": f"chatcmpl-{uuid.uuid4()}",
+                "object": "chat.completion",
+                "created": int(time.time()),
+                "model": model,
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {
+                            "role": "assistant",
+                            "content": response["candidates"][0]["content"]["parts"][0][
+                                "text"
+                            ],
+                        },
+                        "finish_reason": "stop",
+                    }
+                ],
+                "usage": {
+                    "prompt_tokens": 0,
+                    "completion_tokens": 0,
+                    "total_tokens": 0,
+                },
+            }
+
+    async def create_chat_completion(
+        self,
+        messages: list,
+        model: str,
+        temperature: float,
+        stream: bool,
+        api_key: str,
+        tools: Optional[list] = None,
+        tool_choice: Optional[str] = None,
+    ) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
+        """Create chat completion using either Gemini or OpenAI API"""
+
+        if tools is None:
+            tools = []
+        if settings.TOOLS_CODE_EXECUTION_ENABLED:
+            tools.append({"code_execution": {}})
+        if model.endswith("-search"):
+            tools.append({"googleSearch": {}})
+        return await self._gemini_chat_completion(
+            messages, model, temperature, stream, api_key, tools
+        )
+        # else:
+        #     return await self._openai_chat_completion(
+        #         messages, model, temperature, stream, api_key, tools
+        #     )
+
+    async def _gemini_chat_completion(
+        self,
+        messages: list,
+        model: str,
+        temperature: float,
+        stream: bool,
+        api_key: str,
+        tools: Optional[list] = None,
+    ) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
+        """Handle Gemini API chat completion"""
+        if model.endswith("-search"):
+            gemini_model = model[:-7]  # Remove -search suffix
+        else:
+            gemini_model = model
+        gemini_messages = self.convert_messages_to_gemini_format(messages)
+
+        payload = {
+            "contents": gemini_messages,
+            "generationConfig": {"temperature": temperature},
+            "tools": tools,
+        }
+
+        if stream:
+
+            async def generate():
+                retries = 0
+                MAX_RETRIES = 3
+                current_api_key = api_key
+
+                while retries < MAX_RETRIES:
+                    try:
+                        async with httpx.AsyncClient() as client:
+                            stream_url = f"https://generativelanguage.googleapis.com/v1beta/models/{gemini_model}:streamGenerateContent?alt=sse&key={current_api_key}"
+                            async with client.stream(
+                                "POST", stream_url, json=payload
+                            ) as response:
+                                if response.status_code != 200:
+                                    if retries < MAX_RETRIES - 1:
+                                        logger.warning(
+                                            f"API error: {response.status_code}, attempting retry {retries + 1}"
+                                        )
+                                        current_api_key = (
+                                            await self.key_manager.handle_api_failure(
+                                                current_api_key
+                                            )
+                                        )
+                                        logger.info(
+                                            f"Switched to new API key: {current_api_key}"
+                                        )
+                                        retries += 1
+                                        continue
+                                    else:
+                                        logger.error(
+                                            f"Max retries reached. Final error: {response.status_code}"
+                                        )
+                                        yield f"data: {json.dumps({'error': f'API error: {response.status_code}'})}\n\n"
+                                        return
+
+                                async for line in response.aiter_lines():
+                                    if line.startswith("data: "):
+                                        try:
+                                            chunk = json.loads(line[6:])
+                                            openai_chunk = (
+                                                self.convert_gemini_response_to_openai(
+                                                    chunk, model, stream=True
+                                                )
+                                            )
+                                            if openai_chunk:
+                                                yield f"data: {json.dumps(openai_chunk)}\n\n"
+                                        except json.JSONDecodeError:
+                                            continue
+                                yield "data: [DONE]\n\n"
+                                return  # 成功完成,退出重试循环
+
+                    except Exception as e:
+                        if retries < MAX_RETRIES - 1:
+                            logger.warning(
+                                f"Stream error: {str(e)}, attempting retry {retries + 1}"
+                            )
+                            current_api_key = await self.key_manager.handle_api_failure(
+                                current_api_key
+                            )
+                            retries += 1
+                            continue
+                        else:
+                            logger.error(f"Max retries reached. Final error: {str(e)}")
+                            yield f"data: {json.dumps({'error': str(e)})}\n\n"
+                            return
+
+            return generate()
+        else:
+            async with httpx.AsyncClient() as client:
+                url = f"https://generativelanguage.googleapis.com/v1beta/models/{gemini_model}:generateContent?key={api_key}"
+                response = await client.post(url, json=payload)
+                gemini_response = response.json()
+                return self.convert_gemini_response_to_openai(gemini_response, model)
+
+    async def _openai_chat_completion(
+        self,
+        messages: list,
+        model: str,
+        temperature: float,
+        stream: bool,
+        api_key: str,
+        tools: Optional[list] = None,
+    ) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
+        """Handle OpenAI API chat completion"""
+        client = openai.OpenAI(api_key=api_key, base_url=self.base_url)
+        if tools:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=temperature,
+                stream=stream,
+                tools=tools,
+            )
+        else:
+            response = client.chat.completions.create(
+                model=model, messages=messages, temperature=temperature, stream=stream
+            )
+
+        if stream:
+
+            async def generate():
+                for chunk in response:
+                    yield f"data: {chunk.model_dump_json()}\n\n"
+
+            return generate()
+
+        return response
+
+    def format_code_block(self, code_data: dict) -> str:
+        """格式化代码块输出"""
+        language = code_data.get("language", "").lower()
+        code = code_data.get("code", "").strip()
+
+        return f"""\n```{language}\n{code}\n```\n"""
+
+
+    def format_execution_result(result_data: dict) -> str:
+        """格式化执行结果输出"""
+        outcome = result_data.get("outcome", "")
+        output = result_data.get("output", "").strip()
+        return f"""\n【执行结果】\n{output}\n"""

app/services/embedding_service.py

@@ -0,0 +1,22 @@
+import logging
+import openai
+from typing import Union, List, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingService:
+    def __init__(self, base_url: str):
+        self.base_url = base_url
+
+    async def create_embedding(
+        self, input_text: Union[str, List[str]], model: str, api_key: str
+    ) -> Dict[str, Any]:
+        """Create embeddings using OpenAI API"""
+        try:
+            client = openai.OpenAI(api_key=api_key, base_url=self.base_url)
+            response = client.embeddings.create(input=input_text, model=model)
+            return response
+        except Exception as e:
+            logger.error(f"Error creating embedding: {str(e)}")
+            raise

app/services/key_manager.py

@@ -0,0 +1,57 @@
+import asyncio
+from itertools import cycle
+import logging
+from typing import Dict
+
+logger = logging.getLogger(__name__)
+
+
+class KeyManager:
+    def __init__(self, api_keys: list):
+        self.api_keys = api_keys
+        self.key_cycle = cycle(api_keys)
+        self.key_cycle_lock = asyncio.Lock()
+        self.failure_count_lock = asyncio.Lock()
+        self.key_failure_counts: Dict[str, int] = {key: 0 for key in api_keys}
+        self.MAX_FAILURES = 10
+
+    async def get_next_key(self) -> str:
+        """获取下一个API key"""
+        async with self.key_cycle_lock:
+            return next(self.key_cycle)
+
+    async def is_key_valid(self, key: str) -> bool:
+        """检查key是否有效"""
+        async with self.failure_count_lock:
+            return self.key_failure_counts[key] < self.MAX_FAILURES
+
+    async def reset_failure_counts(self):
+        """重置所有key的失败计数"""
+        async with self.failure_count_lock:
+            for key in self.key_failure_counts:
+                self.key_failure_counts[key] = 0
+
+    async def get_next_working_key(self) -> str:
+        """获取下一个可用的API key"""
+        initial_key = await self.get_next_key()
+        current_key = initial_key
+
+        while True:
+            if await self.is_key_valid(current_key):
+                return current_key
+
+            current_key = await self.get_next_key()
+            if current_key == initial_key:
+                await self.reset_failure_counts()
+                return current_key
+
+    async def handle_api_failure(self, api_key: str) -> str:
+        """处理API调用失败"""
+        async with self.failure_count_lock:
+            self.key_failure_counts[api_key] += 1
+            if self.key_failure_counts[api_key] >= self.MAX_FAILURES:
+                logger.warning(
+                    f"API key {api_key} has failed {self.MAX_FAILURES} times"
+                )
+
+        return await self.get_next_working_key()

app/services/model_service.py

@@ -0,0 +1,55 @@
+import requests
+from datetime import datetime, timezone
+from typing import Optional, Dict, Any
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class ModelService:
+    def __init__(self, model_search: list):
+        self.model_search = model_search
+
+    def get_gemini_models(self, api_key: str) -> Optional[Dict[str, Any]]:
+        base_url = "https://generativelanguage.googleapis.com/v1beta"
+        url = f"{base_url}/models?key={api_key}"
+
+        try:
+            response = requests.get(url)
+            if response.status_code == 200:
+                gemini_models = response.json()
+                return self.convert_to_openai_models_format(gemini_models)
+            else:
+                logger.error(f"Error: {response.status_code}")
+                logger.error(response.text)
+                return None
+
+        except requests.RequestException as e:
+            logger.error(f"Request failed: {e}")
+            return None
+
+    def convert_to_openai_models_format(
+        self, gemini_models: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        openai_format = {"object": "list", "data": []}
+
+        for model in gemini_models.get("models", []):
+            model_id = model["name"].split("/")[-1]
+            openai_model = {
+                "id": model_id,
+                "object": "model",
+                "created": int(datetime.now(timezone.utc).timestamp()),
+                "owned_by": "google",
+                "permission": [],
+                "root": model["name"],
+                "parent": None,
+                "success": True,
+            }
+            openai_format["data"].append(openai_model)
+
+            if model_id in self.model_search:
+                search_model = openai_model.copy()
+                search_model["id"] = f"{model_id}-search"
+                openai_format["data"].append(search_model)
+
+        return openai_format

main.py

@@ -1,416 +0,0 @@
-from fastapi import FastAPI, HTTPException, Header
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
-import openai
-from typing import List, Optional, Union
-import logging
-from itertools import cycle
-import asyncio
-
-import uvicorn
-
-from app import config
-import requests
-from datetime import datetime, timezone
-import json
-import httpx
-import uuid
-import time
-
-# 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)
-
-# 创建两个独立的锁
-key_cycle_lock = asyncio.Lock()
-failure_count_lock = asyncio.Lock()
-
-# 添加key失败计数记录
-key_failure_counts = {key: 0 for key in API_KEYS}
-MAX_FAILURES = 10  # 最大失败次数阈值
-MAX_RETRIES = 3  # 最大重试次数
-
-
-async def get_next_key():
-    """仅获取下一个key,不检查失败次数"""
-    async with key_cycle_lock:
-        return next(key_cycle)
-
-
-async def is_key_valid(key):
-    """检查key是否有效"""
-    async with failure_count_lock:
-        return key_failure_counts[key] < MAX_FAILURES
-
-
-async def reset_failure_counts():
-    """重置所有key的失败计数"""
-    async with failure_count_lock:
-        for key in key_failure_counts:
-            key_failure_counts[key] = 0
-
-
-async def get_next_working_key():
-    """获取下一个可用的API key"""
-    initial_key = await get_next_key()
-    current_key = initial_key
-
-    while True:
-        if await is_key_valid(current_key):
-            return current_key
-
-        current_key = await get_next_key()
-        if current_key == initial_key:  # 已经循环了一圈
-            await reset_failure_counts()
-            return current_key
-
-
-async def handle_api_failure(api_key):
-    """处理API调用失败"""
-    async with failure_count_lock:
-        key_failure_counts[api_key] += 1
-        if key_failure_counts[api_key] >= MAX_FAILURES:
-            logger.warning(
-                f"API key {api_key} has failed {MAX_FAILURES} times, switching to next key"
-            )
-
-    # 在锁外获取新的key
-    return await get_next_working_key()
-
-
-class ChatRequest(BaseModel):
-    messages: List[dict]
-    model: str = "gemini-1.5-flash-002"
-    temperature: Optional[float] = 0.7
-    stream: Optional[bool] = False
-    tools: Optional[List[dict]] = []
-    tool_choice: Optional[str] = "auto"
-
-
-class EmbeddingRequest(BaseModel):
-    input: Union[str, List[str]]
-    model: str = "text-embedding-004"
-    encoding_format: Optional[str] = "float"
-
-
-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
-
-
-def get_gemini_models(api_key):
-    base_url = "https://generativelanguage.googleapis.com/v1beta"
-    url = f"{base_url}/models?key={api_key}"
-
-    try:
-        response = requests.get(url)
-        if response.status_code == 200:
-            gemini_models = response.json()
-            return convert_to_openai_models_format(gemini_models)
-        else:
-            print(f"Error: {response.status_code}")
-            print(response.text)
-            return None
-
-    except requests.RequestException as e:
-        print(f"Request failed: {e}")
-        return None
-
-
-def convert_to_openai_models_format(gemini_models):
-    openai_format = {"object": "list", "data": []}
-
-    # 添加常规模型
-    for model in gemini_models.get("models", []):
-        model_id = model["name"].split("/")[-1]
-        openai_model = {
-            "id": model_id,
-            "object": "model",
-            "created": int(datetime.now(timezone.utc).timestamp()),  # 使用当前时间戳
-            "owned_by": "google",  # 假设所有Gemini模型都由Google拥有
-            "permission": [],  # Gemini API可能没有直接对应的权限信息
-            "root": model["name"],
-            "parent": None,  # Gemini API可能没有直接对应的父模型信息
-        }
-        openai_format["data"].append(openai_model)
-
-        # 如果模型在 MODEL_SEARCH 中,添加带 search 后缀的版本
-        if model_id in config.settings.MODEL_SEARCH:
-            search_model = openai_model.copy()
-            search_model["id"] = f"{model_id}-search"
-            openai_format["data"].append(search_model)
-
-    return openai_format
-
-
-def convert_messages_to_gemini_format(messages):
-    """Convert OpenAI message format to Gemini format"""
-    gemini_messages = []
-    for message in messages:
-        gemini_message = {
-            "role": "user" if message["role"] == "user" else "model",
-            "parts": [{"text": message["content"]}],
-        }
-        gemini_messages.append(gemini_message)
-    return gemini_messages
-
-
-def convert_gemini_response_to_openai(response, model, stream=False):
-    """Convert Gemini response to OpenAI format"""
-    if stream:
-        # 处理流式响应
-        chunk = response
-        if not chunk["candidates"]:
-            return None
-
-        return {
-            "id": "chatcmpl-" + str(uuid.uuid4()),
-            "object": "chat.completion.chunk",
-            "created": int(time.time()),
-            "model": model,
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {
-                        "content": chunk["candidates"][0]["content"]["parts"][0]["text"]
-                    },
-                    "finish_reason": None,
-                }
-            ],
-        }
-    else:
-        # 处理普通响应
-        return {
-            "id": "chatcmpl-" + str(uuid.uuid4()),
-            "object": "chat.completion",
-            "created": int(time.time()),
-            "model": model,
-            "choices": [
-                {
-                    "index": 0,
-                    "message": {
-                        "role": "assistant",
-                        "content": response["candidates"][0]["content"]["parts"][0][
-                            "text"
-                        ],
-                    },
-                    "finish_reason": "stop",
-                }
-            ],
-            "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
-        }
-
-
-@app.get("/v1/models")
-@app.get("/hf/v1/models")
-async def list_models(authorization: str = Header(None)):
-    await verify_authorization(authorization)
-    api_key = await get_next_working_key()
-    logger.info(f"Using API key: {api_key}")
-    try:
-        response = get_gemini_models(api_key)
-        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")
-@app.post("/hf/v1/chat/completions")
-async def chat_completion(request: ChatRequest, authorization: str = Header(None)):
-    await verify_authorization(authorization)
-    api_key = await get_next_working_key()
-    logger.info(f"Chat completion request - Model: {request.model}")
-    retries = 0
-
-    while retries < MAX_RETRIES:
-        try:
-            logger.info(f"Attempt {retries + 1} with API key: {api_key}")
-
-            # 修改判断条件,检查模型名是否以 -search 结尾
-            if request.model.endswith("-search"):
-                # 去掉 -search 后缀
-                gemini_model = request.model[:-7]
-
-                # Gemini API调用部分
-                gemini_messages = convert_messages_to_gemini_format(request.messages)
-                # 调用Gemini API
-                payload = {
-                    "contents": gemini_messages,
-                    "generationConfig": {
-                        "temperature": request.temperature,
-                    },
-                    "tools": [{"googleSearch": {}}],
-                }
-
-                if request.stream:
-                    logger.info("Streaming response enabled")
-
-                    async def generate():
-                        nonlocal api_key, retries
-                        while retries < MAX_RETRIES:
-                            try:
-                                async with httpx.AsyncClient() as client:
-                                    stream_url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse&key={api_key}"
-                                    async with client.stream(
-                                        "POST", stream_url, json=payload
-                                    ) as response:
-                                        if response.status_code == 429:
-                                            logger.warning(
-                                                f"Rate limit reached for key: {api_key}"
-                                            )
-                                            api_key = await handle_api_failure(api_key)
-                                            logger.info(
-                                                f"Retrying with new API key: {api_key}"
-                                            )
-                                            retries += 1
-                                            if retries >= MAX_RETRIES:
-                                                yield f"data: {json.dumps({'error': 'Max retries reached'})}\n\n"
-                                                break
-                                            continue
-
-                                        if response.status_code != 200:
-                                            logger.error(
-                                                f"Error in streaming response: {response.status_code}"
-                                            )
-                                            yield f"data: {json.dumps({'error': f'API error: {response.status_code}'})}\n\n"
-                                            break
-
-                                        async for line in response.aiter_lines():
-                                            if line.startswith("data: "):
-                                                try:
-                                                    chunk = json.loads(line[6:])
-                                                    openai_chunk = convert_gemini_response_to_openai(
-                                                        chunk,
-                                                        request.model,
-                                                        stream=True,
-                                                    )
-                                                    if openai_chunk:
-                                                        yield f"data: {json.dumps(openai_chunk)}\n\n"
-                                                except json.JSONDecodeError:
-                                                    continue
-                                        yield "data: [DONE]\n\n"
-                                        return
-                            except Exception as e:
-                                logger.error(f"Stream error: {str(e)}")
-                                api_key = await handle_api_failure(api_key)
-                                retries += 1
-                                if retries >= MAX_RETRIES:
-                                    yield f"data: {json.dumps({'error': 'Max retries reached'})}\n\n"
-                                    break
-                                continue
-
-                    return StreamingResponse(
-                        content=generate(), media_type="text/event-stream"
-                    )
-                else:
-                    # 非流式响应
-                    async with httpx.AsyncClient() as client:
-                        non_stream_url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:generateContent?key={api_key}"
-                        response = await client.post(non_stream_url, json=payload)
-                        gemini_response = response.json()
-                        logger.info("Chat completion successful")
-                        return convert_gemini_response_to_openai(
-                            gemini_response, request.model
-                        )
-
-            # OpenAI API调用部分
-            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,
-                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"
-
-                logger.info("Chat completion successful")
-                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)}")
-            api_key = await handle_api_failure(api_key)
-            retries += 1
-
-            if retries >= MAX_RETRIES:
-                logger.error("Max retries reached, giving up")
-                raise HTTPException(
-                    status_code=500,
-                    detail="Max retries reached with all available API keys",
-                )
-
-            logger.info(f"Retrying with new API key: {api_key}")
-            continue
-
-    raise HTTPException(status_code=500, detail="Unexpected error in chat completion")
-
-
-@app.post("/v1/embeddings")
-@app.post("/hf/v1/embeddings")
-async def embedding(request: EmbeddingRequest, authorization: str = Header(None)):
-    await verify_authorization(authorization)
-    api_key = await get_next_working_key()
-    logger.info(f"Using API key: {api_key}")
-
-    try:
-        client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
-        response = client.embeddings.create(input=request.input, model=request.model)
-        logger.info("Embedding successful")
-        return response
-    except Exception as e:
-        logger.error(f"Error in embedding: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@app.get("/health")
-@app.get("/")
-async def health_check():
-    logger.info("Health check endpoint called")
-    return {"status": "healthy"}
-
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)