fix: 修正Dockerfile中TOOLS_CODE_EXECUTION_ENABLED环境变量的拼写错误

将TOOLS_CODE_EXECUTION_ENABLED环境变量的值从"fasle"更正为"false"，修复了拼写错误。

.env.example

@@ -2,7 +2,8 @@ API_KEYS=["AIzaSyxxxxxxxxxxxxxxxxxxx","AIzaSyxxxxxxxxxxxxxxxxxxx"]
 ALLOWED_TOKENS=["sk-123456"]
 # AUTH_TOKEN=sk-123456
 MODEL_SEARCH=["gemini-2.0-flash-exp","gemini-2.0-pro-exp"]
-TOOLS_CODE_EXECUTION_ENABLED=true
+MODEL_IMAGE=["gemini-2.0-flash-exp"]
+TOOLS_CODE_EXECUTION_ENABLED=false
 SHOW_SEARCH_LINK=true
 SHOW_THINKING_PROCESS=true
 BASE_URL=https://generativelanguage.googleapis.com/v1beta
@@ -12,4 +13,12 @@ PAID_KEY=AIzaSyxxxxxxxxxxxxxxxxxxx
 CREATE_IMAGE_MODEL=imagen-3.0-generate-002
 UPLOAD_PROVIDER=smms
 SMMS_SECRET_TOKEN=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
+PICGO_API_KEY=xxxx
+##########################################################################
+#########################stream_optimizer 相关配置########################
+STREAM_MIN_DELAY=0.016
+STREAM_MAX_DELAY=0.024
+STREAM_SHORT_TEXT_THRESHOLD=10
+STREAM_LONG_TEXT_THRESHOLD=50
+STREAM_CHUNK_SIZE=5
 ##########################################################################

Dockerfile

@@ -3,14 +3,14 @@ FROM python:3.10-slim
 WORKDIR /app
 
 # 复制所需文件到容器中
-COPY ./app /app/app
 COPY ./requirements.txt /app
 
 RUN pip install --no-cache-dir -r requirements.txt
+COPY ./app /app/app
 ENV API_KEYS='["your_api_key_1"]'
 ENV ALLOWED_TOKENS='["your_token_1"]'
 ENV BASE_URL=https://generativelanguage.googleapis.com/v1beta
-ENV TOOLS_CODE_EXECUTION_ENABLED=true
+ENV TOOLS_CODE_EXECUTION_ENABLED=false
 ENV MODEL_SEARCH='["gemini-2.0-flash-exp"]'
 
 # Expose port

README.md

@@ -76,6 +76,13 @@
     # 图片上传配置
     UPLOAD_PROVIDER="smms"  # 图片上传提供商，目前支持smms
     SMMS_SECRET_TOKEN="your-smms-token"  # SM.MS图床的API Token
+
+    # stream_optimizer 相关配置
+    STREAM_MIN_DELAY=0.016
+    STREAM_MAX_DELAY=0.024
+    STREAM_SHORT_TEXT_THRESHOLD=10
+    STREAM_LONG_TEXT_THRESHOLD=50
+    STREAM_CHUNK_SIZE=5
     ```
 
    ### 配置说明
@@ -136,6 +143,24 @@
       - 用途: 用于图片上传到 SM.MS 图床
       - 获取方式: 需要在 SM.MS 官网注册并获取
 
+   #### 流式输出优化配置
+
+    - `STREAM_MIN_DELAY`: 最小延迟时间
+      - 默认值: `0.016`（秒）
+      - 说明: 长文本输出时使用的最小延迟时间，值越小输出速度越快
+    - `STREAM_MAX_DELAY`: 最大延迟时间
+      - 默认值: `0.024`（秒）
+      - 说明: 短文本输出时使用的最大延迟时间，值越大输出速度越慢
+    - `STREAM_SHORT_TEXT_THRESHOLD`: 短文本阈值
+      - 默认值: `10`（字符）
+      - 说明: 小于此长度的文本被视为短文本，将使用最大延迟输出
+    - `STREAM_LONG_TEXT_THRESHOLD`: 长文本阈值
+      - 默认值: `50`（字符）
+      - 说明: 大于此长度的文本被视为长文本，将使用最小延迟并分块输出
+    - `STREAM_CHUNK_SIZE`: 长文本分块大小
+      - 默认值: `5`（字符）
+      - 说明: 长文本分块输出时，每个块的大小
+
 ### ▶️ 运行
 
 #### 使用 Docker (推荐)

app/api/gemini_routes.py

@@ -1,6 +1,6 @@
 from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import StreamingResponse, JSONResponse
-
+from copy import deepcopy
 from app.core.config import settings
 from app.core.logger import get_gemini_logger
 from app.core.security import SecurityService
@@ -23,7 +23,7 @@ async def get_key_manager():
 async def get_next_working_key_wrapper(key_manager: KeyManager = Depends(get_key_manager)):
     return await key_manager.get_next_working_key()
 
-model_service = ModelService(settings.MODEL_SEARCH)
+model_service = ModelService(settings.MODEL_SEARCH,settings.MODEL_IMAGE)
 
 
 @router.get("/models")
@@ -36,12 +36,40 @@ async def list_models(_=Depends(security_service.verify_key),
     api_key = await key_manager.get_next_working_key()
     logger.info(f"Using API key: {api_key}")
     models_json = model_service.get_gemini_models(api_key)
-    models_json["models"].append({"name": "models/gemini-2.0-flash-exp-search", "version": "2.0",
+
-                                  "displayName": "Gemini 2.0 Flash Search Experimental",
+    # 模型名称以及对应的详细信息
-                                  "description": "Gemini 2.0 Flash Search Experimental", "inputTokenLimit": 32767,
+    model_mapping = {x.get("name", "").split("/", maxsplit=1)[1]: x for x in models_json["models"]}
-                                  "outputTokenLimit": 8192,
+
-                                  "supportedGenerationMethods": ["generateContent", "countTokens"], "temperature": 1,
+    # 添加搜索模型
-                                  "topP": 0.95, "topK": 64, "maxTemperature": 2})
+    if settings.MODEL_SEARCH:
+        for name in settings.MODEL_SEARCH:
+            model = model_mapping.get(name, None)
+            if not model:
+                continue
+
+            item = deepcopy(model)
+            item["name"] = f"models/{name}-search"
+            display_name = f'{item.get("displayName")} For Search'
+            item["displayName"] = display_name
+            item["description"] = display_name
+
+            models_json["models"].append(item)
+
+    # 添加图像生成模型
+    if settings.MODEL_IMAGE:
+        for name in settings.MODEL_IMAGE:
+            model = model_mapping.get(name, None)
+            if not model:
+                continue
+
+            item = deepcopy(model)
+            item["name"] = f"models/{name}-image"
+            display_name = f'{item.get("displayName")} For Image'
+            item["displayName"] = display_name
+            item["description"] = display_name
+
+            models_json["models"].append(item)
+            
     return models_json
 
 
@@ -62,8 +90,11 @@ async def generate_content(
     logger.info(f"Request: \n{request.model_dump_json(indent=2)}")
     logger.info(f"Using API key: {api_key}")
 
+    if not model_service.check_model_support(model_name):
+        raise HTTPException(status_code=400, detail=f"Model {model_name} is not supported")
+
     try:
-        response = chat_service.generate_content(
+        response = await chat_service.generate_content(
             model=model_name,
             request=request,
             api_key=api_key
@@ -92,6 +123,9 @@ async def stream_generate_content(
     logger.info(f"Request: \n{request.model_dump_json(indent=2)}")
     logger.info(f"Using API key: {api_key}")
 
+    if not model_service.check_model_support(model_name):
+        raise HTTPException(status_code=400, detail=f"Model {model_name} is not supported")
+
     try:
         response_stream = chat_service.stream_generate_content(
             model=model_name,
@@ -122,7 +156,7 @@ async def verify_key(api_key: str):
                 )
             ]
         )
-        response = chat_service.generate_content(settings.TEST_MODEL,gemini_requset, api_key)
+        response = await chat_service.generate_content(settings.TEST_MODEL,gemini_requset, api_key)
         if response:
             return JSONResponse({"status": "valid"})
         return JSONResponse({"status": "invalid"})

app/api/openai_routes.py

@@ -17,7 +17,7 @@ logger = get_openai_logger()
 
 # 初始化服务
 security_service = SecurityService(settings.ALLOWED_TOKENS, settings.AUTH_TOKEN)
-model_service = ModelService(settings.MODEL_SEARCH)
+model_service = ModelService(settings.MODEL_SEARCH,settings.MODEL_IMAGE)
 embedding_service = EmbeddingService(settings.BASE_URL)
 image_create_service = ImageCreateService()
 
@@ -61,6 +61,10 @@ async def chat_completion(
     logger.info(f"Handling chat completion request for model: {request.model}")
     logger.info(f"Request: \n{request.model_dump_json(indent=2)}")
     logger.info(f"Using API key: {api_key}")
+
+    if not model_service.check_model_support(request.model):
+        raise HTTPException(status_code=400, detail=f"Model {request.model} is not supported")
+       
     try:
         # 如果model是imagen3,使用paid_key
         if request.model == f"{settings.CREATE_IMAGE_MODEL}-chat":

app/core/config.py

@@ -7,6 +7,7 @@ class Settings(BaseSettings):
     ALLOWED_TOKENS: List[str]
     BASE_URL: str = "https://generativelanguage.googleapis.com/v1beta"
     MODEL_SEARCH: List[str] = ["gemini-2.0-flash-exp"]
+    MODEL_IMAGE: List[str] = ["gemini-2.0-flash-exp"]
     TOOLS_CODE_EXECUTION_ENABLED: bool = False
     SHOW_SEARCH_LINK: bool = True
     SHOW_THINKING_PROCESS: bool = True
@@ -16,7 +17,15 @@ class Settings(BaseSettings):
     CREATE_IMAGE_MODEL: str = "imagen-3.0-generate-002"
     UPLOAD_PROVIDER: str = "smms"
     SMMS_SECRET_TOKEN: str = ""
+    PICGO_API_KEY: str = ""
     TEST_MODEL: str = "gemini-1.5-flash"
+    
+    # 流式输出优化器配置
+    STREAM_MIN_DELAY: float = 0.016
+    STREAM_MAX_DELAY: float = 0.024
+    STREAM_SHORT_TEXT_THRESHOLD: int = 10
+    STREAM_LONG_TEXT_THRESHOLD: int = 50
+    STREAM_CHUNK_SIZE: int = 5
 
     def __init__(self):
         super().__init__()

app/core/uploader.py

@@ -149,6 +149,116 @@ class QiniuUploader(ImageUploader):
         pass
     
     
+class PicGoUploader(ImageUploader):
+    """Chevereto API 图片上传器"""
+    
+    def __init__(self, api_key: str, api_url: str = "https://www.picgo.net/api/1/upload"):
+        """
+        初始化 Chevereto 上传器
+        
+        Args:
+            api_key: Chevereto API 密钥
+            api_url: Chevereto API 上传地址
+        """
+        self.api_key = api_key
+        self.api_url = api_url
+        
+    def upload(self, file: bytes, filename: str) -> UploadResponse:
+        """
+        上传图片到 Chevereto 服务
+        
+        Args:
+            file: 图片文件二进制数据
+            filename: 文件名
+            
+        Returns:
+            UploadResponse: 上传响应对象
+            
+        Raises:
+            UploadError: 上传失败时抛出异常
+        """
+        try:
+            # 准备请求头
+            headers = {
+                "X-API-Key": self.api_key
+            }
+            
+            # 准备文件数据
+            files = {
+                "source": (filename, file)
+            }
+            
+            # 发送请求
+            response = requests.post(
+                self.api_url,
+                headers=headers,
+                files=files
+            )
+            
+            # 检查响应状态
+            response.raise_for_status()
+            
+            # 解析响应
+            result = response.json()
+            
+            # 验证上传是否成功
+            if result.get("status_code") != 200:
+                error_message = "Upload failed"
+                if "error" in result:
+                    error_message = result["error"].get("message", error_message)
+                raise UploadError(
+                    message=error_message,
+                    error_type=UploadErrorType.SERVER_ERROR,
+                    status_code=result.get("status_code"),
+                    details=result.get("error")
+                )
+                
+            # 从响应中提取图片信息
+            image_data = result.get("image", {})
+            
+            # 构建图片元数据
+            image_metadata = ImageMetadata(
+                width=image_data.get("width", 0),
+                height=image_data.get("height", 0),
+                filename=image_data.get("filename", filename),
+                size=image_data.get("size", 0),
+                url=image_data.get("url", ""),
+                delete_url=image_data.get("delete_url", None)
+            )
+            
+            return UploadResponse(
+                success=True,
+                code="success",
+                message=result.get("success", {}).get("message", "Upload success"),
+                data=image_metadata
+            )
+            
+        except requests.RequestException as e:
+            # 处理网络请求相关错误
+            raise UploadError(
+                message=f"Upload request failed: {str(e)}",
+                error_type=UploadErrorType.NETWORK_ERROR,
+                original_error=e
+            )
+        except (KeyError, ValueError, TypeError) as e:
+            # 处理响应解析错误
+            raise UploadError(
+                message=f"Invalid response format: {str(e)}",
+                error_type=UploadErrorType.PARSE_ERROR,
+                original_error=e
+            )
+        except UploadError:
+            # 重新抛出已经是 UploadError 类型的异常
+            raise
+        except Exception as e:
+            # 处理其他未预期的错误
+            raise UploadError(
+                message=f"Upload failed: {str(e)}",
+                error_type=UploadErrorType.UNKNOWN,
+                original_error=e
+            )
+
+    
 class ImageUploaderFactory:
     @staticmethod
     def create(provider: str, **credentials) -> ImageUploader:
@@ -159,5 +269,7 @@ class ImageUploaderFactory:
                 credentials["access_key"], 
                 credentials["secret_key"]
             )
+        elif provider == "picgo":
+            api_url = credentials.get("api_url", "https://www.picgo.net/api/1/upload")
+            return PicGoUploader(credentials["api_key"], api_url)
         raise ValueError(f"Unknown provider: {provider}")
-    

app/schemas/gemini_models.py

@@ -33,8 +33,8 @@ class GeminiContent(BaseModel):
 
 
 class GeminiRequest(BaseModel):
-    contents: List[GeminiContent]
+    contents: List[GeminiContent] = []
     tools: Optional[List[Dict[str, Any]]] = []
     safetySettings: Optional[List[SafetySetting]] = None
-    generationConfig: Optional[GenerationConfig] = None
+    generationConfig: Optional[GenerationConfig] = {}
     systemInstruction: Optional[SystemInstruction] = None

app/services/chat/api_client.py

@@ -24,13 +24,21 @@ class GeminiApiClient(ApiClient):
         self.base_url = base_url
         self.timeout = timeout
 
-    def generate_content(self, payload: Dict[str, Any], model: str, api_key: str) -> Dict[str, Any]:
+    def _get_real_model(self, model: str) -> str:
-        timeout = httpx.Timeout(self.timeout, read=self.timeout)
         if model.endswith("-search"):
             model = model[:-7]
-        with httpx.Client(timeout=timeout) as client:
+        if model.endswith("-image"):
+            model = model[:-6]
+
+        return model
+
+    async def generate_content(self, payload: Dict[str, Any], model: str, api_key: str) -> Dict[str, Any]:
+        timeout = httpx.Timeout(self.timeout, read=self.timeout)
+        model = self._get_real_model(model)
+
+        async with httpx.AsyncClient(timeout=timeout) as client:
             url = f"{self.base_url}/models/{model}:generateContent?key={api_key}"
-            response = client.post(url, json=payload)
+            response = await client.post(url, json=payload)
             if response.status_code != 200:
                 error_content = response.text
                 raise Exception(f"API call failed with status code {response.status_code}, {error_content}")
@@ -38,8 +46,8 @@ class GeminiApiClient(ApiClient):
 
     async def stream_generate_content(self, payload: Dict[str, Any], model: str, api_key: str) -> AsyncGenerator[str, None]:
         timeout = httpx.Timeout(self.timeout, read=self.timeout)
-        if model.endswith("-search"):
+        model = self._get_real_model(model)
-            model = model[:-7]
+        
         async with httpx.AsyncClient(timeout=timeout) as client:
             url = f"{self.base_url}/models/{model}:streamGenerateContent?alt=sse&key={api_key}"
             async with client.stream(method="POST", url=url, json=payload) as response:

app/services/chat/message_converter.py

@@ -1,14 +1,16 @@
 # app/services/chat/message_converter.py
 
 from abc import ABC, abstractmethod
-from typing import List, Dict, Any
+from typing import Any, Dict, List, Optional
+
+SUPPORTED_ROLES = ["user", "model", "system"]
 
 
 class MessageConverter(ABC):
     """消息转换器基类"""
 
     @abstractmethod
-    def convert(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    def convert(self, messages: List[Dict[str, Any]]) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
         pass
 
 
@@ -30,24 +32,48 @@ def _convert_image(image_url: str) -> Dict[str, Any]:
 class OpenAIMessageConverter(MessageConverter):
     """OpenAI消息格式转换器"""
 
-    def convert(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    def convert(self, messages: List[Dict[str, Any]]) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
         converted_messages = []
-        for msg in messages:
+        system_instruction_parts = []
-            role = "user" if msg["role"] == "user" else "model"
-            parts = []
 
-            if isinstance(msg["content"], str):
+        for idx, msg in enumerate(messages):
+            role = msg.get("role", "")
+            if role not in SUPPORTED_ROLES:
+                if role == "tool":
+                    role = "user"
+                else:
+                    # 如果是最后一条消息，则认为是用户消息
+                    if idx == len(messages) - 1:
+                        role = "user"
+                    else:
+                        role = "model"
+
+            parts = []
+            if isinstance(msg["content"], str) and msg["content"]:
+                # 请求 gemini 接口时如果包含 content 字段但内容为空时会返回 400 错误，所以需要判断是否为空并移除
                 parts.append({"text": msg["content"]})
             elif isinstance(msg["content"], list):
                 for content in msg["content"]:
-                    if isinstance(content, str):
+                    if isinstance(content, str) and content:
                         parts.append({"text": content})
                     elif isinstance(content, dict):
-                        if content["type"] == "text":
+                        if content["type"] == "text" and content["text"]:
                             parts.append({"text": content["text"]})
                         elif content["type"] == "image_url":
                             parts.append(_convert_image(content["image_url"]["url"]))
 
-            converted_messages.append({"role": role, "parts": parts})
+            if parts:
+                if role == "system":
+                    system_instruction_parts.extend(parts)
+                else:
+                    converted_messages.append({"role": role, "parts": parts})
 
-        return converted_messages
+        system_instruction = (
+            None
+            if not system_instruction_parts
+            else {
+                "role": "system",
+                "parts": system_instruction_parts,
+            }
+        )
+        return converted_messages, system_instruction

app/services/chat/response_handler.py

@@ -1,10 +1,15 @@
 # app/services/chat/response_handler.py
 
+import base64
+import json
+import random
+import string
 from abc import ABC, abstractmethod
-from typing import Dict, Any, Optional
+from typing import Dict, Any, List, Optional
 import time
 import uuid
 from app.core.config import settings
+from app.core.uploader import ImageUploaderFactory
 
 
 class ResponseHandler(ABC):
@@ -29,40 +34,38 @@ class GeminiResponseHandler(ResponseHandler):
 
 
 def _handle_openai_stream_response(response: Dict[str, Any], model: str, finish_reason: str) -> Dict[str, Any]:
-    text = _extract_text(response, model, stream=True)
+    text, tool_calls = _extract_result(response, model, stream=True, gemini_format=False)
+    if not text and not tool_calls:
+        delta = {}
+    else:
+        delta = {"content": text, "role": "assistant"}
+        if tool_calls:
+            delta["tool_calls"] = tool_calls
+
     return {
         "id": f"chatcmpl-{uuid.uuid4()}",
         "object": "chat.completion.chunk",
         "created": int(time.time()),
         "model": model,
-        "choices": [{
+        "choices": [{"index": 0, "delta": delta, "finish_reason": finish_reason}],
-            "index": 0,
-            "delta": {"content": text} if text else {},
-            "finish_reason": finish_reason
-        }]
     }
 
 
 def _handle_openai_normal_response(response: Dict[str, Any], model: str, finish_reason: str) -> Dict[str, Any]:
-    text = _extract_text(response, model, stream=False)
+    text, tool_calls = _extract_result(response, model, stream=False, gemini_format=False)
     return {
         "id": f"chatcmpl-{uuid.uuid4()}",
         "object": "chat.completion",
         "created": int(time.time()),
         "model": model,
-        "choices": [{
+        "choices": [
-            "index": 0,
+            {
-            "message": {
+                "index": 0,
-                "role": "assistant",
+                "message": {"role": "assistant", "content": text, "tool_calls": tool_calls},
-                "content": text
+                "finish_reason": finish_reason,
-            },
+            }
-            "finish_reason": finish_reason
+        ],
-        }],
+        "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
-        "usage": {
-            "prompt_tokens": 0,
-            "completion_tokens": 0,
-            "total_tokens": 0
-        }
     }
 
 
@@ -127,74 +130,15 @@ def _handle_openai_normal_image_response(image_str: str,model: str,finish_reason
     }
 
 
-def _extract_text(response: Dict[str, Any], model: str, stream: bool = False) -> str:
+def _extract_result(response: Dict[str, Any], model: str, stream: bool = False, gemini_format: bool = False) -> tuple[str, List[Dict[str, Any]]]:
-    text = ""
+    text, tool_calls = "", []
     if stream:
         if response.get("candidates"):
             candidate = response["candidates"][0]
             content = candidate.get("content", {})
             parts = content.get("parts", [])
-            # if "thinking" in model:
+            if not parts:
-            #     if settings.SHOW_THINKING_PROCESS:
+                return "", []
-            #         if len(parts) == 1:
-            #             if self.thinking_first:
-            #                 self.thinking_first = False
-            #                 self.thinking_status = True
-            #                 text = "> thinking\n\n" + parts[0].get("text")
-            #             else:
-            #                 text = parts[0].get("text")
-
-            #         if len(parts) == 2:
-            #             self.thinking_status = False
-            #             if self.thinking_first:
-            #                 self.thinking_first = False
-            #                 text = (
-            #                     "> thinking\n\n"
-            #                     + parts[0].get("text")
-            #                     + "\n\n---\n> output\n\n"
-            #                     + parts[1].get("text")
-            #                 )
-            #             else:
-            #                 text = (
-            #                     parts[0].get("text")
-            #                     + "\n\n---\n> output\n\n"
-            #                     + parts[1].get("text")
-            #                 )
-            #     else:
-            #         if len(parts) == 1:
-            #             if self.thinking_first:
-            #                 self.thinking_first = False
-            #                 self.thinking_status = True
-            #                 text = ""
-            #             elif self.thinking_status:
-            #                 text = ""
-            #             else:
-            #                 text = parts[0].get("text")
-
-            #         if len(parts) == 2:
-            #             self.thinking_status = False
-            #             if self.thinking_first:
-            #                 self.thinking_first = False
-            #                 text = parts[1].get("text")
-            #             else:
-            #                 text = parts[1].get("text")
-            # else:
-            #     if "text" in parts[0]:
-            #         text = parts[0].get("text")
-            #     elif "executableCode" in parts[0]:
-            #         text = _format_code_block(parts[0]["executableCode"])
-            #     elif "codeExecution" in parts[0]:
-            #         text = _format_code_block(parts[0]["codeExecution"])
-            #     elif "executableCodeResult" in parts[0]:
-            #         text = _format_execution_result(
-            #             parts[0]["executableCodeResult"]
-            #         )
-            #     elif "codeExecutionResult" in parts[0]:
-            #         text = _format_execution_result(
-            #             parts[0]["codeExecutionResult"]
-            #         )
-            #     else:
-            #         text = ""
             if "text" in parts[0]:
                 text = parts[0].get("text")
             elif "executableCode" in parts[0]:
@@ -209,9 +153,12 @@ def _extract_text(response: Dict[str, Any], model: str, stream: bool = False) ->
                 text = _format_execution_result(
                     parts[0]["codeExecutionResult"]
                 )
+            elif "inlineData" in parts[0]:
+                text = _extract_image_data(parts[0])
             else:
                 text = ""
             text = _add_search_link_text(model, candidate, text)
+            tool_calls = _extract_tool_calls(parts, gemini_format)
     else:
         if response.get("candidates"):
             candidate = response["candidates"][0]
@@ -233,24 +180,90 @@ def _extract_text(response: Dict[str, Any], model: str, stream: bool = False) ->
                         text = candidate["content"]["parts"][0]["text"]
             else:
                 text = ""
-                for part in candidate["content"]["parts"]:
+                if "parts" in candidate["content"]:
-                    text += part["text"]
+                    for part in candidate["content"]["parts"]:
+                        if "text" in part:
+                            text += part["text"]
+                        elif "inlineData" in part:
+                            text += _extract_image_data(part)
+
+
             text = _add_search_link_text(model, candidate, text)
+            tool_calls = _extract_tool_calls(candidate["content"]["parts"], gemini_format)
         else:
             text = "暂无返回"
+    return text, tool_calls
+
+def _extract_image_data(part: dict) -> str:
+    image_uploader = None
+    if settings.UPLOAD_PROVIDER == "smms":
+        image_uploader = ImageUploaderFactory.create(provider=settings.UPLOAD_PROVIDER,api_key=settings.SMMS_SECRET_TOKEN)
+    elif settings.UPLOAD_PROVIDER == "picgo":
+        image_uploader = ImageUploaderFactory.create(provider=settings.UPLOAD_PROVIDER,api_key=settings.PICGO_API_KEY)
+    current_date = time.strftime("%Y/%m/%d")
+    filename = f"{current_date}/{uuid.uuid4().hex[:8]}.png"
+    base64_data = part["inlineData"]["data"]
+                #将base64_data转成bytes数组
+    bytes_data = base64.b64decode(base64_data)
+    upload_response = image_uploader.upload(bytes_data,filename)
+    if upload_response.success:
+        text = f"\n![image]({upload_response.data.url})\n"
+    else:
+        text = ""
     return text
+    
+def _extract_tool_calls(parts: List[Dict[str, Any]], gemini_format: bool) -> List[Dict[str, Any]]:
+    """提取工具调用信息"""
+    if not parts or not isinstance(parts, list):
+        return []
+
+    letters = string.ascii_lowercase + string.digits
+
+    tool_calls = list()
+    for i in range(len(parts)):
+        part = parts[i]
+        if not part or not isinstance(part, dict):
+            continue
+
+        item = part.get("functionCall", {})
+        if not item or not isinstance(item, dict):
+            continue
+
+        if gemini_format:
+            tool_calls.append(part)
+        else:
+            id = f"call_{''.join(random.sample(letters, 32))}"
+            name = item.get("name", "")
+            arguments = json.dumps(item.get("args", None) or {})
+
+            tool_calls.append(
+                {
+                    "index": i,
+                    "id": id,
+                    "type": "function",
+                    "function": {"name": name, "arguments": arguments},
+                }
+            )
+
+    return tool_calls
 
 
 def _handle_gemini_stream_response(response: Dict[str, Any], model: str, stream: bool) -> Dict[str, Any]:
-    text = _extract_text(response, model, stream=stream)
+    text, tool_calls = _extract_result(response, model, stream=stream, gemini_format=True)
-    content = {"parts": [{"text": text}], "role": "model"}
+    if tool_calls:
+        content = {"parts": tool_calls, "role": "model"}
+    else:
+        content = {"parts": [{"text": text}], "role": "model"}
     response["candidates"][0]["content"] = content
     return response
 
 
 def _handle_gemini_normal_response(response: Dict[str, Any], model: str, stream: bool) -> Dict[str, Any]:
-    text = _extract_text(response, model, stream=stream)
+    text, tool_calls = _extract_result(response, model, stream=stream, gemini_format=True)
-    content = {"parts": [{"text": text}], "role": "model"}
+    if tool_calls:
+        content = {"parts": tool_calls, "role": "model"}
+    else:
+        content = {"parts": [{"text": text}], "role": "model"}
     response["candidates"][0]["content"] = content
     return response
 

app/services/chat/stream_optimizer.py

@@ -0,0 +1,132 @@
+# app/services/chat/stream_optimizer.py
+
+import asyncio
+import math
+from typing import Any, List, AsyncGenerator, Callable
+from app.core.logger import get_openai_logger, get_gemini_logger
+from app.core.config import settings
+
+logger_openai = get_openai_logger()
+logger_gemini = get_gemini_logger()
+
+
+class StreamOptimizer:
+    """流式输出优化器
+    
+    提供流式输出优化功能，包括智能延迟调整和长文本分块输出。
+    """
+    
+    def __init__(self, 
+                 logger=None, 
+                 min_delay: float = 0.016, 
+                 max_delay: float = 0.024,
+                 short_text_threshold: int = 10,
+                 long_text_threshold: int = 50,
+                 chunk_size: int = 5):
+        """初始化流式输出优化器
+        
+        参数:
+            logger: 日志记录器
+            min_delay: 最小延迟时间（秒）
+            max_delay: 最大延迟时间（秒）
+            short_text_threshold: 短文本阈值（字符数）
+            long_text_threshold: 长文本阈值（字符数）
+            chunk_size: 长文本分块大小（字符数）
+        """
+        self.logger = logger
+        self.min_delay = min_delay
+        self.max_delay = max_delay
+        self.short_text_threshold = short_text_threshold
+        self.long_text_threshold = long_text_threshold
+        self.chunk_size = chunk_size
+    
+    def calculate_delay(self, text_length: int) -> float:
+        """根据文本长度计算延迟时间
+        
+        参数:
+            text_length: 文本长度
+            
+        返回:
+            延迟时间（秒）
+        """
+        if text_length <= self.short_text_threshold:
+            # 短文本使用较大延迟
+            return self.max_delay
+        elif text_length >= self.long_text_threshold:
+            # 长文本使用较小延迟
+            return self.min_delay
+        else:
+            # 中等长度文本使用线性插值计算延迟
+            # 使用对数函数使延迟变化更平滑
+            ratio = math.log(text_length / self.short_text_threshold) / math.log(self.long_text_threshold / self.short_text_threshold)
+            return self.max_delay - ratio * (self.max_delay - self.min_delay)
+    
+    def split_text_into_chunks(self, text: str) -> List[str]:
+        """将文本分割成小块
+        
+        参数:
+            text: 要分割的文本
+            
+        返回:
+            文本块列表
+        """
+        return [text[i:i+self.chunk_size] for i in range(0, len(text), self.chunk_size)]
+    
+    async def optimize_stream_output(self, 
+                                    text: str, 
+                                    create_response_chunk: Callable[[str], Any],
+                                    format_chunk: Callable[[Any], str]) -> AsyncGenerator[str, None]:
+        """优化流式输出
+        
+        参数:
+            text: 要输出的文本
+            create_response_chunk: 创建响应块的函数，接收文本，返回响应块
+            format_chunk: 格式化响应块的函数，接收响应块，返回格式化后的字符串
+            
+        返回:
+            异步生成器，生成格式化后的响应块
+        """
+        if not text:
+            return
+            
+        # 计算智能延迟时间
+        delay = self.calculate_delay(len(text))
+        if self.logger:
+            self.logger.info(f"Text length: {len(text)}, delay: {delay:.4f}s")
+        
+        # 根据文本长度决定输出方式
+        if len(text) >= self.long_text_threshold:
+            # 长文本：分块输出
+            chunks = self.split_text_into_chunks(text)
+            if self.logger:
+                self.logger.info(f"Long text: splitting into {len(chunks)} chunks")
+            for chunk_text in chunks:
+                chunk_response = create_response_chunk(chunk_text)
+                yield format_chunk(chunk_response)
+                await asyncio.sleep(delay)
+        else:
+            # 短文本：逐字符输出
+            for char in text:
+                char_chunk = create_response_chunk(char)
+                yield format_chunk(char_chunk)
+                await asyncio.sleep(delay)
+
+
+# 创建默认的优化器实例，可以直接导入使用
+openai_optimizer = StreamOptimizer(
+    logger=logger_openai,
+    min_delay=settings.STREAM_MIN_DELAY,
+    max_delay=settings.STREAM_MAX_DELAY,
+    short_text_threshold=settings.STREAM_SHORT_TEXT_THRESHOLD,
+    long_text_threshold=settings.STREAM_LONG_TEXT_THRESHOLD,
+    chunk_size=settings.STREAM_CHUNK_SIZE
+)
+
+gemini_optimizer = StreamOptimizer(
+    logger=logger_gemini,
+    min_delay=settings.STREAM_MIN_DELAY,
+    max_delay=settings.STREAM_MAX_DELAY,
+    short_text_threshold=settings.STREAM_SHORT_TEXT_THRESHOLD,
+    long_text_threshold=settings.STREAM_LONG_TEXT_THRESHOLD,
+    chunk_size=settings.STREAM_CHUNK_SIZE
+)

app/services/gemini_chat_service.py

@@ -4,6 +4,7 @@ import json
 from typing import Dict, Any, AsyncGenerator, List
 from app.core.logger import get_gemini_logger
 from app.services.chat.api_client import GeminiApiClient
+from app.services.chat.stream_optimizer import gemini_optimizer
 from app.schemas.gemini_models import GeminiRequest
 from app.core.config import settings
 from app.services.chat.response_handler import GeminiResponseHandler
@@ -31,6 +32,12 @@ def _build_tools(model: str, payload: Dict[str, Any]) -> List[Dict[str, Any]]:
         tools.append({"code_execution": {}})
     if model.endswith("-search"):
         tools.append({"googleSearch": {}})
+
+    if payload and isinstance(payload, dict) and "tools" in payload:
+        items = payload.get("tools", [])
+        if items and isinstance(items, list):
+            tools.extend(items)
+
     return tools
 
 
@@ -55,14 +62,19 @@ def _get_safety_settings(model: str) -> List[Dict[str, str]]:
 
 def _build_payload(model: str, request: GeminiRequest) -> Dict[str, Any]:
     """构建请求payload"""
-    payload = request.model_dump()
+    request_dict = request.model_dump()
-    return {
+    payload = {
-        "contents": payload.get("contents", []),
+        "contents": request_dict.get("contents", []),
-        "tools": _build_tools(model, payload),
+        "tools": _build_tools(model, request_dict),
         "safetySettings": _get_safety_settings(model),
-        "generationConfig": payload.get("generationConfig", {}),
+        "generationConfig": request_dict.get("generationConfig", {}),
-        "systemInstruction": payload.get("systemInstruction", [])
+        "systemInstruction": request_dict.get("systemInstruction", "")
     }
+    
+    if model.endswith("-image"):
+        payload.pop("systemInstruction")
+        payload["generationConfig"]["responseModalities"] = ["Text","Image"]
+    return payload
 
 
 class GeminiChatService:
@@ -72,11 +84,31 @@ class GeminiChatService:
         self.api_client = GeminiApiClient(base_url)
         self.key_manager = key_manager
         self.response_handler = GeminiResponseHandler()
+        
+    def _extract_text_from_response(self, response: Dict[str, Any]) -> str:
+        """从响应中提取文本内容"""
+        if not response.get("candidates"):
+            return ""
+            
+        candidate = response["candidates"][0]
+        content = candidate.get("content", {})
+        parts = content.get("parts", [])
+        
+        if parts and "text" in parts[0]:
+            return parts[0].get("text", "")
+        return ""
+        
+    def _create_char_response(self, original_response: Dict[str, Any], text: str) -> Dict[str, Any]:
+        """创建包含指定文本的响应"""
+        response_copy = json.loads(json.dumps(original_response))  # 深拷贝
+        if response_copy.get("candidates") and response_copy["candidates"][0].get("content", {}).get("parts"):
+            response_copy["candidates"][0]["content"]["parts"][0]["text"] = text
+        return response_copy
 
-    def generate_content(self, model: str, request: GeminiRequest, api_key: str) -> Dict[str, Any]:
+    async def generate_content(self, model: str, request: GeminiRequest, api_key: str) -> Dict[str, Any]:
         """生成内容"""
         payload = _build_payload(model, request)
-        response = self.api_client.generate_content(payload, model, api_key)
+        response = await self.api_client.generate_content(payload, model, api_key)
         return self.response_handler.handle_response(response, model, stream=False)
 
     async def stream_generate_content(self, model: str, request: GeminiRequest, api_key: str) -> AsyncGenerator[str, None]:
@@ -90,8 +122,21 @@ class GeminiChatService:
                     # print(line)
                     if line.startswith("data:"):
                         line = line[6:]
-                        line = json.dumps(self.response_handler.handle_response(json.loads(line), model, stream=True))
+                        response_data = self.response_handler.handle_response(json.loads(line), model, stream=True)
-                        yield "data: " + line + "\n\n"
+                        text = self._extract_text_from_response(response_data)
+                        
+                        # 如果有文本内容，使用流式输出优化器处理
+                        if text:
+                            # 使用流式输出优化器处理文本输出
+                            async for optimized_chunk in gemini_optimizer.optimize_stream_output(
+                                text,
+                                lambda t: self._create_char_response(response_data, t),
+                                lambda c: "data: " + json.dumps(c) + "\n\n"
+                            ):
+                                yield optimized_chunk
+                        else:
+                            # 如果没有文本内容（如工具调用等），整块输出
+                            yield "data: " + json.dumps(response_data) + "\n\n"
                 logger.info("Streaming completed successfully")
                 break
             except Exception as e:

app/services/model_service.py

@@ -7,8 +7,9 @@ from app.core.config import settings
 logger = get_model_logger()
 
 class ModelService:
-    def __init__(self, model_search: list):
+    def __init__(self, model_search: list, model_image: list):
         self.model_search = model_search
+        self.model_image = model_image
         self.base_url = "https://generativelanguage.googleapis.com/v1beta"
 
     def get_gemini_models(self, api_key: str) -> Optional[Dict[str, Any]]:
@@ -57,9 +58,27 @@ class ModelService:
                 search_model = openai_model.copy()
                 search_model["id"] = f"{model_id}-search"
                 openai_format["data"].append(search_model)
+            if model_id in self.model_image:
+                image_model = openai_model.copy()
+                image_model["id"] = f"{model_id}-image"
+                openai_format["data"].append(image_model)
 
         if settings.CREATE_IMAGE_MODEL:
             image_model = openai_model.copy()
             image_model["id"] = f"{settings.CREATE_IMAGE_MODEL}-chat"
             openai_format["data"].append(image_model)
         return openai_format
+
+    def check_model_support(self, model: str) -> bool:
+        if not model or not isinstance(model, str):
+            return False
+
+        model = model.strip()
+        if model.endswith("-search"):
+            model = model[:-7]
+            return model in settings.MODEL_SEARCH
+        if model.endswith("-image"):
+            model = model[:-6]
+            return model in settings.MODEL_IMAGE
+
+        return True

app/services/openai_chat_service.py

@@ -1,11 +1,13 @@
 # app/services/chat_service.py
 
+from copy import deepcopy
 import json
-from typing import Dict, Any, AsyncGenerator, List, Union
+from typing import Dict, Any, AsyncGenerator, List, Optional, Union
 from app.core.logger import get_openai_logger
 from app.services.chat.message_converter import OpenAIMessageConverter
 from app.services.chat.response_handler import OpenAIResponseHandler
 from app.services.chat.api_client import GeminiApiClient
+from app.services.chat.stream_optimizer import openai_optimizer
 from app.schemas.openai_models import ChatRequest, ImageGenerationRequest
 from app.core.config import settings
 from app.services.image_create_service import ImageCreateService
@@ -33,12 +35,38 @@ def _build_tools(
 
     if (
             settings.TOOLS_CODE_EXECUTION_ENABLED
-            and not (model.endswith("-search") or "-thinking" in model)
+            and not (model.endswith("-search") or "-thinking" in model or model.endswith("-image"))
             and not _has_image_parts(messages)
     ):
         tools.append({"code_execution": {}})
     if model.endswith("-search"):
         tools.append({"googleSearch": {}})
+
+    # 将 request 中的 tools 合并到 tools 中
+    if request.tools:
+        function_declarations = []
+        for tool in request.tools:
+            if not tool or not isinstance(tool, dict):
+                continue
+
+            if tool.get("type", "") == "function" and tool.get("function"):
+                function = deepcopy(tool.get("function"))
+                parameters = function.get("parameters", {})
+                if parameters.get("type") == "object" and not parameters.get("properties", {}):
+                    function.pop("parameters", None)
+
+                function_declarations.append(function)
+
+        if function_declarations:
+            # 按照 function 的 name 去重
+            names, functions = set(), []
+            for item in function_declarations:
+                if item.get("name") not in names:
+                    names.add(item.get("name"))
+                    functions.append(item)
+
+            tools.append({"functionDeclarations": functions})
+            
     return tools
 
 
@@ -67,10 +95,10 @@ def _get_safety_settings(model: str) -> List[Dict[str, str]]:
 
 
 def _build_payload(
-        request: ChatRequest, messages: List[Dict[str, Any]]
+    request: ChatRequest, messages: List[Dict[str, Any]], instruction: Optional[Dict[str, Any]] = None
 ) -> Dict[str, Any]:
     """构建请求payload"""
-    return {
+    payload = {
         "contents": messages,
         "generationConfig": {
             "temperature": request.temperature,
@@ -82,6 +110,19 @@ def _build_payload(
         "tools": _build_tools(request, messages),
         "safetySettings": _get_safety_settings(request.model),
     }
+    if request.model.endswith("-image"):
+        payload["generationConfig"]["responseModalities"] = ["Text","Image"]
+        
+    if (
+        instruction
+        and isinstance(instruction, dict)
+        and instruction.get("role") == "system"
+        and instruction.get("parts")
+        and not request.model.endswith("-image")
+    ):
+        payload["systemInstruction"] = instruction
+
+    return payload
 
 
 class OpenAIChatService:
@@ -92,6 +133,23 @@ class OpenAIChatService:
         self.api_client = GeminiApiClient(base_url)
         self.key_manager = key_manager
         self.image_create_service = ImageCreateService()
+        
+    def _extract_text_from_openai_chunk(self, chunk: Dict[str, Any]) -> str:
+        """从OpenAI响应块中提取文本内容"""
+        if not chunk.get("choices"):
+            return ""
+            
+        choice = chunk["choices"][0]
+        if "delta" in choice and "content" in choice["delta"]:
+            return choice["delta"]["content"]
+        return ""
+        
+    def _create_char_openai_chunk(self, original_chunk: Dict[str, Any], text: str) -> Dict[str, Any]:
+        """创建包含指定文本的OpenAI响应块"""
+        chunk_copy = json.loads(json.dumps(original_chunk))  # 深拷贝
+        if chunk_copy.get("choices") and "delta" in chunk_copy["choices"][0]:
+            chunk_copy["choices"][0]["delta"]["content"] = text
+        return chunk_copy
 
     async def create_chat_completion(
             self,
@@ -100,20 +158,20 @@ class OpenAIChatService:
     ) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
         """创建聊天完成"""
         # 转换消息格式
-        messages = self.message_converter.convert(request.messages)
+        messages, instruction = self.message_converter.convert(request.messages)
 
         # 构建请求payload
-        payload = _build_payload(request, messages)
+        payload = _build_payload(request, messages, instruction)
 
         if request.stream:
             return self._handle_stream_completion(request.model, payload, api_key)
-        return self._handle_normal_completion(request.model, payload, api_key)
+        return await self._handle_normal_completion(request.model, payload, api_key)
 
-    def _handle_normal_completion(
+    async def _handle_normal_completion(
             self, model: str, payload: Dict[str, Any], api_key: str
     ) -> Dict[str, Any]:
         """处理普通聊天完成"""
-        response = self.api_client.generate_content(payload, model, api_key)
+        response = await self.api_client.generate_content(payload, model, api_key)
         return self.response_handler.handle_response(
             response, model, stream=False, finish_reason="stop"
         )
@@ -136,7 +194,19 @@ class OpenAIChatService:
                             chunk, model, stream=True, finish_reason=None
                         )
                         if openai_chunk:
-                            yield f"data: {json.dumps(openai_chunk)}\n\n"
+                            # 提取文本内容
+                            text = self._extract_text_from_openai_chunk(openai_chunk)
+                            if text:
+                                # 使用流式输出优化器处理文本输出
+                                async for optimized_chunk in openai_optimizer.optimize_stream_output(
+                                    text,
+                                    lambda t: self._create_char_openai_chunk(openai_chunk, t),
+                                    lambda c: f"data: {json.dumps(c)}\n\n"
+                                ):
+                                    yield optimized_chunk
+                            else:
+                                # 如果没有文本内容（如工具调用等），整块输出
+                                yield f"data: {json.dumps(openai_chunk)}\n\n"
                 yield f"data: {json.dumps(self.response_handler.handle_response({}, model, stream=True, finish_reason='stop'))}\n\n"
                 yield "data: [DONE]\n\n"
                 logger.info("Streaming completed successfully")
@@ -178,7 +248,19 @@ class OpenAIChatService:
                 image_data, model, stream=True, finish_reason=None
             )
             if openai_chunk:
-                yield f"data: {json.dumps(openai_chunk)}\n\n"
+                # 提取文本内容
+                text = self._extract_text_from_openai_chunk(openai_chunk)
+                if text:
+                    # 使用流式输出优化器处理文本输出
+                    async for optimized_chunk in openai_optimizer.optimize_stream_output(
+                        text,
+                        lambda t: self._create_char_openai_chunk(openai_chunk, t),
+                        lambda c: f"data: {json.dumps(c)}\n\n"
+                    ):
+                        yield optimized_chunk
+                else:
+                    # 如果没有文本内容（如图片URL等），整块输出
+                    yield f"data: {json.dumps(openai_chunk)}\n\n"
         yield f"data: {json.dumps(self.response_handler.handle_response({}, model, stream=True, finish_reason='stop'))}\n\n"
         yield "data: [DONE]\n\n"
         logger.info("Image chat streaming completed successfully")
@@ -189,4 +271,4 @@ class OpenAIChatService:
         
         return self.response_handler.handle_image_chat_response(
             image_data, model, stream=False, finish_reason="stop"
-        )
+        )

docker-compose.yml

@@ -0,0 +1,9 @@
+version: '3'
+
+services:
+  gemini-balance:
+    build: .
+    ports:
+      - "8000:8000"
+    env_file:
+      - .env