chore: 更新版本号至 2.1.2

- 新增 `SAFETY_SETTINGS` 配置项，允许用户通过环境变量或数据库配置 Gemini 模型的安全过滤级别。
- 更新后端服务 (`config.py`, `constants.py`, `gemini_routes.py`, `openai_routes.py`, `openai_chat_service.py`, `api_client.py`, `model_service.py`) 以支持和传递 `safety_settings` 参数。
- 在配置编辑器前端 (`config_editor.js`, `config_editor.html`) 添加了用于管理安全设置的用户界面。
- 将模型获取逻辑 (`model_service.py`, `api_client.py`) 改为异步。
- 优化 Service Worker (`service-worker.js`) 的缓存策略为 "cache then network"。

Bump version to 2.1.2

.env.example

@@ -23,6 +23,9 @@ CHECK_INTERVAL_HOURS=1
 TIMEZONE=Asia/Shanghai
 # 请求超时时间（秒）
 TIME_OUT=300
+# 代理服务器配置 (支持 http 和 socks5)
+# 示例: PROXIES=["http://user:pass@host:port", "socks5://host:port"]
+PROXIES=[]
 #########################image_generate 相关配置###########################
 PAID_KEY=AIzaSyxxxxxxxxxxxxxxxxxxx
 CREATE_IMAGE_MODEL=imagen-3.0-generate-002
@@ -44,3 +47,7 @@ STREAM_CHUNK_SIZE=5
 # 日志级别 (debug, info, warning, error, critical)，默认为 info
 LOG_LEVEL=info
 ##########################################################################
+
+# 安全设置 (JSON 字符串格式)
+# 注意：这里的示例值可能需要根据实际模型支持情况调整
+SAFETY_SETTINGS='[{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"}, {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"}, {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"}, {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"}]'

README.md

@@ -67,6 +67,7 @@ app/
     >镜像地址: docker pull ghcr.io/snailyp/gemini-balance:latest
 * **模型列表自动维护**: 支持openai和gemini模型列表获取，与newapi自动获取模型列表完美兼容，无需手动填写。
 * **支持移除不使用的模型**: 默认提供的模型太多，很多用不上，可以通过`FILTERED_MODELS`过滤掉。
+* **代理支持**: 支持配置 HTTP/SOCKS5 代理服务器 (`PROXIES`)，用于访问 Gemini API，方便在特殊网络环境下使用。支持批量添加代理。
 
 ## 🚀 快速开始
 
@@ -166,6 +167,7 @@ app/
 | `CHECK_INTERVAL_HOURS`       | 可选，检查禁用 Key 是否恢复的时间间隔 (小时)                   | `1`                                                   |
 | `TIMEZONE`                   | 可选，应用程序使用的时区                                       | `Asia/Shanghai`                                       |
 | `TIME_OUT`                   | 可选，请求超时时间 (秒)                                        | `300`                                                 |
+| `PROXIES`                    | 可选，代理服务器列表 (例如 `http://user:pass@host:port`, `socks5://host:port`) | `[]`                                                  |
 | `LOG_LEVEL`                  | 可选，日志级别，例如 DEBUG, INFO, WARNING, ERROR, CRITICAL     | `INFO`                                                |
 | **图像生成相关**             |                                                          |                                                       |
 | `PAID_KEY`                   | 可选，付费版API Key，用于图片生成等高级功能                    | `your-paid-api-key`                                   |
@@ -193,12 +195,16 @@ app/
 * `POST /models/{model_name}:generateContent`: 使用指定的 Gemini 模型生成内容。
 * `POST /models/{model_name}:streamGenerateContent`: 使用指定的 Gemini 模型流式生成内容。
 
-### OpenAI API 相关 (`(/hf)/v1`)
+### OpenAI API 相关
 
-* `GET /v1/models`: 列出可用的 OpenAI 模型。
-* `POST /v1/chat/completions`: 通过 OpenAI API 进行聊天补全。
-* `POST /v1/images/generations`: 通过 OpenAI API 生成图像。
-* `POST /v1/embeddings`: 通过 OpenAI API 创建文本嵌入。
+* `GET (/hf)/v1/models`: 列出可用的模型 (底层用的gemini格式)。
+* `POST (/hf)/v1/chat/completions`: 进行聊天补全 (底层用的gemini格式, 支持流式传输)。
+* `POST (/hf)/v1/embeddings`: 创建文本嵌入 (底层用的gemini格式)。
+* `POST (/hf)/v1/images/generations`: 生成图像 (底层用的gemini格式)。
+* `GET /openai/v1/models`: 列出可用的模型 (底层用的openai格式)。
+* `POST /openai/v1/chat/completions`: 进行聊天补全 (底层用的openai格式, 支持流式传输, 可防止截断，速度也快)。
+* `POST /openai/v1/embeddings`: 创建文本嵌入 (底层用的openai格式)。
+* `POST /openai/v1/images/generations`: 生成图像 (底层用的openai格式)。
 
 ## 🤝 贡献
 

VERSION

@@ -1 +1 @@
-2.1.0
+2.1.2

app/config/config.py

@@ -9,7 +9,7 @@ from pydantic import ValidationError
 from pydantic_settings import BaseSettings
 from sqlalchemy import insert, update, select
 
-from app.core.constants import API_VERSION, DEFAULT_CREATE_IMAGE_MODEL, DEFAULT_FILTER_MODELS, DEFAULT_MODEL, DEFAULT_STREAM_CHUNK_SIZE, DEFAULT_STREAM_LONG_TEXT_THRESHOLD, DEFAULT_STREAM_MAX_DELAY, DEFAULT_STREAM_MIN_DELAY, DEFAULT_STREAM_SHORT_TEXT_THRESHOLD, DEFAULT_TIMEOUT, MAX_RETRIES
+from app.core.constants import API_VERSION, DEFAULT_CREATE_IMAGE_MODEL, DEFAULT_FILTER_MODELS, DEFAULT_MODEL, DEFAULT_SAFETY_SETTINGS, DEFAULT_STREAM_CHUNK_SIZE, DEFAULT_STREAM_LONG_TEXT_THRESHOLD, DEFAULT_STREAM_MAX_DELAY, DEFAULT_STREAM_MIN_DELAY, DEFAULT_STREAM_SHORT_TEXT_THRESHOLD, DEFAULT_TIMEOUT, MAX_RETRIES
 from app.log.logger import Logger
 
 
@@ -30,7 +30,8 @@ class Settings(BaseSettings):
     TEST_MODEL: str = DEFAULT_MODEL
     TIME_OUT: int = DEFAULT_TIMEOUT
     MAX_RETRIES: int = MAX_RETRIES
-    
+    PROXIES: List[str] = [] # 新增：代理服务器列表
+
     # 模型相关配置
     SEARCH_MODELS: List[str] = ["gemini-2.0-flash-exp"]
     IMAGE_MODELS: List[str] = ["gemini-2.0-flash-exp"]
@@ -68,6 +69,7 @@ class Settings(BaseSettings):
 
     # 日志配置
     LOG_LEVEL: str = "INFO" # 默认日志级别
+    SAFETY_SETTINGS: List[Dict[str, str]] = DEFAULT_SAFETY_SETTINGS # 新增：安全设置
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -120,6 +122,32 @@ def _parse_db_value(key: str, db_value: str, target_type: Type) -> Any:
                     # Log other errors (ValueError, TypeError) or JSON errors without single quotes
                     logger.error(f"Could not parse '{db_value}' as Dict[str, float] for key '{key}': {e1}. Returning empty dict.")
             return parsed_dict # Return the parsed dict or an empty one if all attempts fail
+        # 处理 List[Dict[str, str]]
+        elif target_type == List[Dict[str, str]]:
+            try:
+                parsed = json.loads(db_value)
+                if isinstance(parsed, list):
+                    # 验证列表中的每个元素是否为字典，并且键和值都是字符串
+                    valid = all(
+                        isinstance(item, dict) and
+                        all(isinstance(k, str) for k in item.keys()) and
+                        all(isinstance(v, str) for v in item.values())
+                        for item in parsed
+                    )
+                    if valid:
+                        return parsed
+                    else:
+                        logger.warning(f"Invalid structure in List[Dict[str, str]] for key '{key}'. Value: {db_value}")
+                        return [] # 或者返回默认值？这里返回空列表
+                else:
+                     logger.warning(f"Parsed DB value for key '{key}' is not a list type. Value: {db_value}")
+                     return []
+            except json.JSONDecodeError:
+                logger.error(f"Could not parse '{db_value}' as JSON for List[Dict[str, str]] for key '{key}'. Returning empty list.")
+                return []
+            except Exception as e:
+                logger.error(f"Error parsing List[Dict[str, str]] for key '{key}': {e}. Value: {db_value}. Returning empty list.")
+                return []
         # 处理 bool
         elif target_type == bool:
             return db_value.lower() in ('true', '1', 'yes', 'on')

app/core/constants.py

@@ -40,3 +40,40 @@ DEFAULT_STREAM_CHUNK_SIZE = 5
 # 正则表达式模式
 IMAGE_URL_PATTERN = r'!\[(.*?)\]\((.*?)\)'
 DATA_URL_PATTERN = r'data:([^;]+);base64,(.+)'
+
+# Audio/Video Settings
+SUPPORTED_AUDIO_FORMATS = ["wav", "mp3", "flac", "ogg"]
+SUPPORTED_VIDEO_FORMATS = ["mp4", "mov", "avi", "webm"]
+MAX_AUDIO_SIZE_BYTES = 50 * 1024 * 1024  # Example: 50MB limit for Base64 payload
+MAX_VIDEO_SIZE_BYTES = 200 * 1024 * 1024 # Example: 200MB limit
+
+# Optional: Define MIME type mappings if needed, or handle directly in converter
+AUDIO_FORMAT_TO_MIMETYPE = {
+    "wav": "audio/wav",
+    "mp3": "audio/mpeg",
+    "flac": "audio/flac",
+    "ogg": "audio/ogg",
+}
+
+VIDEO_FORMAT_TO_MIMETYPE = {
+    "mp4": "video/mp4",
+    "mov": "video/quicktime",
+    "avi": "video/x-msvideo",
+    "webm": "video/webm",
+}
+
+GEMINI_2_FLASH_EXP_SAFETY_SETTINGS = [
+            {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "OFF"},
+            {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "OFF"},
+            {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "OFF"},
+            {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "OFF"},
+            {"category": "HARM_CATEGORY_CIVIC_INTEGRITY", "threshold": "OFF"},
+        ]
+
+DEFAULT_SAFETY_SETTINGS = [
+        {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "OFF"},
+        {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "OFF"},
+        {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "OFF"},
+        {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "OFF"},
+        {"category": "HARM_CATEGORY_CIVIC_INTEGRITY", "threshold": "BLOCK_NONE"},
+    ]

app/domain/openai_models.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel
-from typing import List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 from app.core.constants import DEFAULT_MODEL, DEFAULT_TEMPERATURE, DEFAULT_TOP_K, DEFAULT_TOP_P
 
@@ -9,11 +9,14 @@ class ChatRequest(BaseModel):
     model: str = DEFAULT_MODEL
     temperature: Optional[float] = DEFAULT_TEMPERATURE
     stream: Optional[bool] = False
-    tools: Optional[List[dict]] = []
     max_tokens: Optional[int] = None
     top_p: Optional[float] = DEFAULT_TOP_P
     top_k: Optional[int] = DEFAULT_TOP_K
-    stop: Optional[List[str]] = []
+    stop: Optional[Union[List[str],str]] = None
+    reasoning_effort: Optional[str] = None
+    tools: Optional[Union[List[Dict[str, Any]], Dict[str, Any]]] = []
+    tool_choice: Optional[str] = None
+    response_format: Optional[dict] = None
 
 
 class EmbeddingRequest(BaseModel):
@@ -23,10 +26,10 @@ class EmbeddingRequest(BaseModel):
 
 
 class ImageGenerationRequest(BaseModel):
-    model: str = "DALL-E-3"
+    model: str = "imagen-3.0-generate-002"
     prompt: str = ""
     n: int = 1
     size: Optional[str] = "1024x1024"
-    quality: Optional[str] = ""
-    style: Optional[str] = ""
-    response_format: Optional[str] = "url"
+    quality: Optional[str] = None
+    style: Optional[str] = None
+    response_format: Optional[str] = "b64_json"

app/handler/error_handler.py

@@ -0,0 +1,32 @@
+from contextlib import asynccontextmanager
+from fastapi import HTTPException
+import logging
+
+@asynccontextmanager
+async def handle_route_errors(logger: logging.Logger, operation_name: str, success_message: str = None, failure_message: str = None):
+    """
+    一个异步上下文管理器，用于统一处理 FastAPI 路由中的常见错误和日志记录。
+
+    Args:
+        logger: 用于记录日志的 Logger 实例。
+        operation_name: 操作的名称，用于日志记录和错误详情。
+        success_message: 操作成功时记录的自定义消息 (可选)。
+        failure_message: 操作失败时记录的自定义消息 (可选)。
+    """
+    default_success_msg = f"{operation_name} request successful"
+    default_failure_msg = f"{operation_name} request failed"
+
+    logger.info("-" * 50 + operation_name + "-" * 50)
+    try:
+        yield
+        logger.info(success_message or default_success_msg)
+    except HTTPException as http_exc:
+        # 如果已经是 HTTPException，直接重新抛出，保留原始状态码和详情
+        logger.error(f"{failure_message or default_failure_msg}: {http_exc.detail} (Status: {http_exc.status_code})")
+        raise http_exc
+    except Exception as e:
+        # 对于其他所有异常，记录错误并抛出标准的 500 错误
+        logger.error(f"{failure_message or default_failure_msg}: {str(e)}")
+        raise HTTPException(
+            status_code=500, detail=f"Internal server error during {operation_name}"
+        ) from e

app/handler/message_converter.py

@@ -1,61 +1,70 @@
-
-from abc import ABC, abstractmethod
+import base64
 import json
 import re
+from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional
-import requests
-import base64
 
-from app.core.constants import DATA_URL_PATTERN, IMAGE_URL_PATTERN, SUPPORTED_ROLES
+import requests
+
+from app.core.constants import (
+    AUDIO_FORMAT_TO_MIMETYPE,
+    DATA_URL_PATTERN,
+    IMAGE_URL_PATTERN,
+    MAX_AUDIO_SIZE_BYTES,
+    MAX_VIDEO_SIZE_BYTES,
+    SUPPORTED_AUDIO_FORMATS,
+    SUPPORTED_ROLES,
+    SUPPORTED_VIDEO_FORMATS,
+    VIDEO_FORMAT_TO_MIMETYPE,
+)
+from app.log.logger import get_message_converter_logger
+
+logger = get_message_converter_logger()
 
 
 class MessageConverter(ABC):
     """消息转换器基类"""
 
     @abstractmethod
-    def convert(self, messages: List[Dict[str, Any]]) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
+    def convert(
+        self, messages: List[Dict[str, Any]]
+    ) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
         pass
 
+
 def _get_mime_type_and_data(base64_string):
     """
     从 base64 字符串中提取 MIME 类型和数据。
-    
+
     参数:
         base64_string (str): 可能包含 MIME 类型信息的 base64 字符串
-        
+
     返回:
         tuple: (mime_type, encoded_data)
     """
     # 检查字符串是否以 "data:" 格式开始
-    if base64_string.startswith('data:'):
+    if base64_string.startswith("data:"):
         # 提取 MIME 类型和数据
         pattern = DATA_URL_PATTERN
         match = re.match(pattern, base64_string)
         if match:
-            mime_type = "image/jpeg" if match.group(1) == "image/jpg" else match.group(1)
+            mime_type = (
+                "image/jpeg" if match.group(1) == "image/jpg" else match.group(1)
+            )
             encoded_data = match.group(2)
             return mime_type, encoded_data
-    
+
     # 如果不是预期格式，假定它只是数据部分
     return None, base64_string
 
+
 def _convert_image(image_url: str) -> Dict[str, Any]:
     if image_url.startswith("data:image"):
         mime_type, encoded_data = _get_mime_type_and_data(image_url)
-        return {
-            "inline_data": {
-                "mime_type": mime_type,
-                "data": encoded_data
-            }
-        }
+        return {"inline_data": {"mime_type": mime_type, "data": encoded_data}}
     else:
         encoded_data = _convert_image_to_base64(image_url)
-        return {
-            "inline_data": {
-                "mime_type": "image/png",
-                "data": encoded_data
-            }
-        }
+        return {"inline_data": {"mime_type": "image/png", "data": encoded_data}}
 
 
 def _convert_image_to_base64(url: str) -> str:
@@ -69,7 +78,7 @@ def _convert_image_to_base64(url: str) -> str:
     response = requests.get(url)
     if response.status_code == 200:
         # 将图片内容转换为base64
-        img_data = base64.b64encode(response.content).decode('utf-8')
+        img_data = base64.b64encode(response.content).decode("utf-8")
         return img_data
     else:
         raise Exception(f"Failed to fetch image: {response.status_code}")
@@ -93,12 +102,9 @@ def _process_text_with_image(text: str) -> List[Dict[str, Any]]:
         # 将URL对应的图片转换为base64
         try:
             base64_data = _convert_image_to_base64(img_url)
-            parts.append({
-                "inlineData": {
-                    "mimeType": "image/png",
-                    "data": base64_data
-                }
-            })
+            parts.append(
+                {"inline_data": {"mimeType": "image/png", "data": base64_data}}
+            )
         except Exception:
             # 如果转换失败，回退到文本模式
             parts.append({"text": text})
@@ -111,42 +117,215 @@ def _process_text_with_image(text: str) -> List[Dict[str, Any]]:
 class OpenAIMessageConverter(MessageConverter):
     """OpenAI消息格式转换器"""
 
-    def convert(self, messages: List[Dict[str, Any]]) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
+    def _validate_media_data(
+        self, format: str, data: str, supported_formats: List[str], max_size: int
+    ) -> tuple[Optional[str], Optional[str]]:
+        """Validates format and size of Base64 media data."""
+        if format.lower() not in supported_formats:
+            logger.error(
+                f"Unsupported media format: {format}. Supported: {supported_formats}"
+            )
+            raise ValueError(f"Unsupported media format: {format}")
+
+        try:
+            # Decode Base64 to check size
+            # Be careful with memory usage for very large files
+            # Consider streaming decoding or checking length heuristic first if memory is a concern
+            decoded_data = base64.b64decode(
+                data, validate=True
+            )  # Use validate=True for stricter check
+            if len(decoded_data) > max_size:
+                logger.error(
+                    f"Media data size ({len(decoded_data)} bytes) exceeds limit ({max_size} bytes)."
+                )
+                raise ValueError(
+                    f"Media data size exceeds limit of {max_size // 1024 // 1024}MB"
+                )
+            # No need to return decoded_data, just the original base64 if valid
+            return data
+        except base64.binascii.Error as e:
+            logger.error(f"Invalid Base64 data provided: {e}")
+            raise ValueError("Invalid Base64 data")
+        except Exception as e:
+            logger.error(f"Error validating media data: {e}")
+            raise
+
+    def convert(
+        self, messages: List[Dict[str, Any]]
+    ) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
         converted_messages = []
         system_instruction_parts = []
 
         for idx, msg in enumerate(messages):
             role = msg.get("role", "")
-            
             parts = []
-            # 特别处理最后一个assistant的消息，按\n\n分割
-            if "content" in msg and isinstance(msg["content"], str) and msg["content"] and role == "assistant" and idx == len(messages) - 2:
-                # 按\n\n分割消息
-                content_parts = msg["content"].split("\n\n")
-                for part in content_parts:
-                    if not part.strip():  # 跳过空内容
+
+            if "content" in msg and isinstance(msg["content"], list):
+                for content_item in msg["content"]:
+                    if not isinstance(content_item, dict):
+                        # Skip non-dict items if any unexpected format appears
+                        logger.warning(
+                            f"Skipping unexpected content item format: {type(content_item)}"
+                        )
                         continue
-                    # 处理可能包含图片的文本
-                    parts.extend(_process_text_with_image(part))
-            elif "content" in msg and 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
+
+                    content_type = content_item.get("type")
+
+                    if content_type == "text" and content_item.get("text"):
+                        parts.append({"text": content_item["text"]})
+                    elif content_type == "image_url" and content_item.get(
+                        "image_url", {}
+                    ).get("url"):
+                        try:
+                            parts.append(
+                                _convert_image(content_item["image_url"]["url"])
+                            )
+                        except Exception as e:
+                            logger.error(
+                                f"Failed to convert image URL {content_item['image_url']['url']}: {e}"
+                            )
+                            # Decide how to handle: skip part, add error text, etc.
+                            parts.append(
+                                {
+                                    "text": f"[Error processing image: {content_item['image_url']['url']}]"
+                                }
+                            )
+                    # --- Add handling for input_audio ---
+                    elif content_type == "input_audio" and content_item.get(
+                        "input_audio"
+                    ):
+                        audio_info = content_item["input_audio"]
+                        audio_data = audio_info.get("data")
+                        audio_format = audio_info.get("format", "").lower()
+
+                        if not audio_data or not audio_format:
+                            logger.warning(
+                                "Skipping audio part due to missing data or format."
+                            )
+                            continue
+
+                        try:
+                            # Validate size and format
+                            validated_data = self._validate_media_data(
+                                audio_format,
+                                audio_data,
+                                SUPPORTED_AUDIO_FORMATS,
+                                MAX_AUDIO_SIZE_BYTES,
+                            )
+
+                            # Get MIME type
+                            mime_type = AUDIO_FORMAT_TO_MIMETYPE.get(audio_format)
+                            if not mime_type:
+                                # Should not happen if format validation passed, but double-check
+                                logger.error(
+                                    f"Could not find MIME type for supported format: {audio_format}"
+                                )
+                                raise ValueError(
+                                    f"Internal error: MIME type mapping missing for {audio_format}"
+                                )
+
+                            parts.append(
+                                {
+                                    "inline_data": {
+                                        "mimeType": mime_type,
+                                        "data": validated_data,  # Use the validated Base64 data
+                                    }
+                                }
+                            )
+                            logger.debug(
+                                f"Successfully added audio part (format: {audio_format})"
+                            )
+
+                        except ValueError as e:
+                            logger.error(
+                                f"Skipping audio part due to validation error: {e}"
+                            )
+                            parts.append({"text": f"[Error processing audio: {e}]"})
+                        except Exception:
+                            logger.exception("Unexpected error processing audio part.")
+                            parts.append(
+                                {"text": "[Unexpected error processing audio]"}
+                            )
+
+                    elif content_type == "input_video" and content_item.get(
+                        "input_video"
+                    ):
+                        video_info = content_item["input_video"]
+                        video_data = video_info.get("data")
+                        video_format = video_info.get("format", "").lower()
+
+                        if not video_data or not video_format:
+                            logger.warning(
+                                "Skipping video part due to missing data or format."
+                            )
+                            continue
+
+                        try:
+                            validated_data = self._validate_media_data(
+                                video_format,
+                                video_data,
+                                SUPPORTED_VIDEO_FORMATS,
+                                MAX_VIDEO_SIZE_BYTES,
+                            )
+                            mime_type = VIDEO_FORMAT_TO_MIMETYPE.get(video_format)
+                            if not mime_type:
+                                raise ValueError(
+                                    f"Internal error: MIME type mapping missing for {video_format}"
+                                )
+
+                            parts.append(
+                                {
+                                    "inline_data": {
+                                        "mimeType": mime_type,
+                                        "data": validated_data,
+                                    }
+                                }
+                            )
+                            logger.debug(
+                                f"Successfully added video part (format: {video_format})"
+                            )
+
+                        except ValueError as e:
+                            logger.error(
+                                f"Skipping video part due to validation error: {e}"
+                            )
+                            parts.append({"text": f"[Error processing video: {e}]"})
+                        except Exception:
+                            logger.exception("Unexpected error processing video part.")
+                            parts.append(
+                                {"text": "[Unexpected error processing video]"}
+                            )
+
+                    else:
+                        # Log unrecognized but present types
+                        if content_type:
+                            logger.warning(
+                                f"Unsupported content type or missing data in structured content: {content_type}"
+                            )
+
+            elif (
+                "content" in msg and isinstance(msg["content"], str) and msg["content"]
+            ):
                 parts.extend(_process_text_with_image(msg["content"]))
-            elif "content" in msg and isinstance(msg["content"], list):
-                for content in msg["content"]:
-                    if isinstance(content, str) and content:
-                        parts.append({"text": content})
-                    elif isinstance(content, dict):
-                        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"]))
             elif "tool_calls" in msg and isinstance(msg["tool_calls"], list):
+                # Keep existing tool call processing
                 for tool_call in msg["tool_calls"]:
-                    function_call = tool_call.get("function",{})
-                    function_call["args"] = json.loads(function_call.get("arguments","{}"))
-                    del function_call["arguments"]
+                    function_call = tool_call.get("function", {})
+                    # Sanitize arguments loading
+                    arguments_str = function_call.get("arguments", "{}")
+                    try:
+                        function_call["args"] = json.loads(arguments_str)
+                    except json.JSONDecodeError:
+                        logger.warning(
+                            f"Failed to decode tool call arguments: {arguments_str}"
+                        )
+                        function_call["args"] = {}
+                    if "arguments" in function_call:
+                        if "arguments" in function_call:
+                            del function_call["arguments"]
+
                     parts.append({"functionCall": function_call})
-            
+
             if role not in SUPPORTED_ROLES:
                 if role == "tool":
                     role = "user"
@@ -158,7 +337,14 @@ class OpenAIMessageConverter(MessageConverter):
                         role = "model"
             if parts:
                 if role == "system":
-                    system_instruction_parts.extend(parts)
+                    text_only_parts = [p for p in parts if "text" in p]
+                    if len(text_only_parts) != len(parts):
+                        logger.warning(
+                            "Non-text parts found in system message; discarding them."
+                        )
+                    if text_only_parts:
+                        system_instruction_parts.extend(text_only_parts)
+
                 else:
                     converted_messages.append({"role": role, "parts": parts})
 
@@ -170,4 +356,4 @@ class OpenAIMessageConverter(MessageConverter):
                 "parts": system_instruction_parts,
             }
         )
-        return converted_messages, system_instruction
+        return converted_messages, system_instruction

app/handler/response_handler.py

@@ -1,12 +1,12 @@
-
 import base64
 import json
 import random
 import string
-from abc import ABC, abstractmethod
-from typing import Dict, Any, List, Optional
 import time
 import uuid
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional
+
 from app.config.config import settings
 from app.utils.uploader import ImageUploaderFactory
 
@@ -15,7 +15,9 @@ class ResponseHandler(ABC):
     """响应处理器基类"""
 
     @abstractmethod
-    def handle_response(self, response: Dict[str, Any], model: str, stream: bool = False) -> Dict[str, Any]:
+    def handle_response(
+        self, response: Dict[str, Any], model: str, stream: bool = False
+    ) -> Dict[str, Any]:
         pass
 
 
@@ -26,14 +28,20 @@ class GeminiResponseHandler(ResponseHandler):
         self.thinking_first = True
         self.thinking_status = False
 
-    def handle_response(self, response: Dict[str, Any], model: str, stream: bool = False) -> Dict[str, Any]:
+    def handle_response(
+        self, response: Dict[str, Any], model: str, stream: bool = False
+    ) -> Dict[str, Any]:
         if stream:
             return _handle_gemini_stream_response(response, model, stream)
         return _handle_gemini_normal_response(response, model, stream)
 
 
-def _handle_openai_stream_response(response: Dict[str, Any], model: str, finish_reason: str) -> Dict[str, Any]:
-    text, tool_calls = _extract_result(response, model, stream=True, gemini_format=False)
+def _handle_openai_stream_response(
+    response: Dict[str, Any], model: str, finish_reason: str
+) -> Dict[str, Any]:
+    text, tool_calls = _extract_result(
+        response, model, stream=True, gemini_format=False
+    )
     if not text and not tool_calls:
         delta = {}
     else:
@@ -50,8 +58,12 @@ def _handle_openai_stream_response(response: Dict[str, Any], model: str, finish_
     }
 
 
-def _handle_openai_normal_response(response: Dict[str, Any], model: str, finish_reason: str) -> Dict[str, Any]:
-    text, tool_calls = _extract_result(response, model, stream=False, gemini_format=False)
+def _handle_openai_normal_response(
+    response: Dict[str, Any], model: str, finish_reason: str
+) -> Dict[str, Any]:
+    text, tool_calls = _extract_result(
+        response, model, stream=False, gemini_format=False
+    )
     return {
         "id": f"chatcmpl-{uuid.uuid4()}",
         "object": "chat.completion",
@@ -60,7 +72,11 @@ def _handle_openai_normal_response(response: Dict[str, Any], model: str, finish_
         "choices": [
             {
                 "index": 0,
-                "message": {"role": "assistant", "content": text, "tool_calls": tool_calls},
+                "message": {
+                    "role": "assistant",
+                    "content": text,
+                    "tool_calls": tool_calls,
+                },
                 "finish_reason": finish_reason,
             }
         ],
@@ -77,59 +93,67 @@ class OpenAIResponseHandler(ResponseHandler):
         self.thinking_status = False
 
     def handle_response(
-            self,
-            response: Dict[str, Any],
-            model: str,
-            stream: bool = False,
-            finish_reason: str = None
+        self,
+        response: Dict[str, Any],
+        model: str,
+        stream: bool = False,
+        finish_reason: str = None,
     ) -> Optional[Dict[str, Any]]:
         if stream:
             return _handle_openai_stream_response(response, model, finish_reason)
         return _handle_openai_normal_response(response, model, finish_reason)
-    
-    def handle_image_chat_response(self, image_str: str, model: str, stream=False, finish_reason="stop"):
+
+    def handle_image_chat_response(
+        self, image_str: str, model: str, stream=False, finish_reason="stop"
+    ):
         if stream:
-            return _handle_openai_stream_image_response(image_str,model,finish_reason)
-        return _handle_openai_normal_image_response(image_str,model,finish_reason)
-       
-            
-def _handle_openai_stream_image_response(image_str: str,model: str,finish_reason: str) -> Dict[str, Any]:
+            return _handle_openai_stream_image_response(image_str, model, finish_reason)
+        return _handle_openai_normal_image_response(image_str, model, finish_reason)
+
+
+def _handle_openai_stream_image_response(
+    image_str: str, model: str, finish_reason: str
+) -> Dict[str, Any]:
     return {
         "id": f"chatcmpl-{uuid.uuid4()}",
         "object": "chat.completion.chunk",
         "created": int(time.time()),
         "model": model,
-        "choices": [{
-            "index": 0,
-            "delta": {"content": image_str} if image_str else {},
-            "finish_reason": finish_reason
-        }]
+        "choices": [
+            {
+                "index": 0,
+                "delta": {"content": image_str} if image_str else {},
+                "finish_reason": finish_reason,
+            }
+        ],
     }
 
 
-def _handle_openai_normal_image_response(image_str: str,model: str,finish_reason: str) -> Dict[str, Any]:
+def _handle_openai_normal_image_response(
+    image_str: str, model: str, finish_reason: str
+) -> Dict[str, Any]:
     return {
         "id": f"chatcmpl-{uuid.uuid4()}",
         "object": "chat.completion",
         "created": int(time.time()),
         "model": model,
-        "choices": [{
-            "index": 0,
-            "message": {
-                "role": "assistant",
-                "content": image_str
-            },
-            "finish_reason": finish_reason
-        }],
-        "usage": {
-            "prompt_tokens": 0,
-            "completion_tokens": 0,
-            "total_tokens": 0
-        }
+        "choices": [
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": image_str},
+                "finish_reason": finish_reason,
+            }
+        ],
+        "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
     }
 
 
-def _extract_result(response: Dict[str, Any], model: str, stream: bool = False, gemini_format: bool = False) -> tuple[str, List[Dict[str, Any]]]:
+def _extract_result(
+    response: Dict[str, Any],
+    model: str,
+    stream: bool = False,
+    gemini_format: bool = False,
+) -> tuple[str, List[Dict[str, Any]]]:
     text, tool_calls = "", []
     if stream:
         if response.get("candidates"):
@@ -145,13 +169,9 @@ def _extract_result(response: Dict[str, Any], model: str, stream: bool = False,
             elif "codeExecution" in parts[0]:
                 text = _format_code_block(parts[0]["codeExecution"])
             elif "executableCodeResult" in parts[0]:
-                text = _format_execution_result(
-                    parts[0]["executableCodeResult"]
-                )
+                text = _format_execution_result(parts[0]["executableCodeResult"])
             elif "codeExecutionResult" in parts[0]:
-                text = _format_execution_result(
-                    parts[0]["codeExecutionResult"]
-                )
+                text = _format_execution_result(parts[0]["codeExecutionResult"])
             elif "inlineData" in parts[0]:
                 text = _extract_image_data(parts[0])
             else:
@@ -165,10 +185,10 @@ def _extract_result(response: Dict[str, Any], model: str, stream: bool = False,
                 if settings.SHOW_THINKING_PROCESS:
                     if len(candidate["content"]["parts"]) == 2:
                         text = (
-                                "> thinking\n\n"
-                                + candidate["content"]["parts"][0]["text"]
-                                + "\n\n---\n> output\n\n"
-                                + candidate["content"]["parts"][1]["text"]
+                            "> thinking\n\n"
+                            + candidate["content"]["parts"][0]["text"]
+                            + "\n\n---\n> output\n\n"
+                            + candidate["content"]["parts"][1]["text"]
                         )
                     else:
                         text = candidate["content"]["parts"][0]["text"]
@@ -186,34 +206,47 @@ def _extract_result(response: Dict[str, Any], model: str, stream: bool = False,
                         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)
+            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)
+        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)
+        image_uploader = ImageUploaderFactory.create(
+            provider=settings.UPLOAD_PROVIDER, api_key=settings.PICGO_API_KEY
+        )
     elif settings.UPLOAD_PROVIDER == "cloudflare_imgbed":
-        image_uploader = ImageUploaderFactory.create(provider=settings.UPLOAD_PROVIDER,base_url=settings.CLOUDFLARE_IMGBED_URL,auth_code=settings.CLOUDFLARE_IMGBED_AUTH_CODE)
+        image_uploader = ImageUploaderFactory.create(
+            provider=settings.UPLOAD_PROVIDER,
+            base_url=settings.CLOUDFLARE_IMGBED_URL,
+            auth_code=settings.CLOUDFLARE_IMGBED_AUTH_CODE,
+        )
     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
+    # 将base64_data转成bytes数组
     bytes_data = base64.b64decode(base64_data)
-    upload_response = image_uploader.upload(bytes_data,filename)
+    upload_response = image_uploader.upload(bytes_data, filename)
     if upload_response.success:
         text = f"\n\n![image]({upload_response.data.url})\n\n"
     else:
         text = ""
     return text
-    
-def _extract_tool_calls(parts: List[Dict[str, Any]], gemini_format: bool) -> List[Dict[str, Any]]:
+
+
+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 []
@@ -249,8 +282,12 @@ def _extract_tool_calls(parts: List[Dict[str, Any]], gemini_format: bool) -> Lis
     return tool_calls
 
 
-def _handle_gemini_stream_response(response: Dict[str, Any], model: str, stream: bool) -> Dict[str, Any]:
-    text, tool_calls = _extract_result(response, model, stream=stream, gemini_format=True)
+def _handle_gemini_stream_response(
+    response: Dict[str, Any], model: str, stream: bool
+) -> Dict[str, Any]:
+    text, tool_calls = _extract_result(
+        response, model, stream=stream, gemini_format=True
+    )
     if tool_calls:
         content = {"parts": tool_calls, "role": "model"}
     else:
@@ -259,8 +296,12 @@ def _handle_gemini_stream_response(response: Dict[str, Any], model: str, stream:
     return response
 
 
-def _handle_gemini_normal_response(response: Dict[str, Any], model: str, stream: bool) -> Dict[str, Any]:
-    text, tool_calls = _extract_result(response, model, stream=stream, gemini_format=True)
+def _handle_gemini_normal_response(
+    response: Dict[str, Any], model: str, stream: bool
+) -> Dict[str, Any]:
+    text, tool_calls = _extract_result(
+        response, model, stream=stream, gemini_format=True
+    )
     if tool_calls:
         content = {"parts": tool_calls, "role": "model"}
     else:
@@ -278,10 +319,10 @@ def _format_code_block(code_data: dict) -> str:
 
 def _add_search_link_text(model: str, candidate: dict, text: str) -> str:
     if (
-            settings.SHOW_SEARCH_LINK
-            and model.endswith("-search")
-            and "groundingMetadata" in candidate
-            and "groundingChunks" in candidate["groundingMetadata"]
+        settings.SHOW_SEARCH_LINK
+        and model.endswith("-search")
+        and "groundingMetadata" in candidate
+        and "groundingChunks" in candidate["groundingMetadata"]
     ):
         grounding_chunks = candidate["groundingMetadata"]["groundingChunks"]
         text += "\n\n---\n\n"

app/log/logger.py

@@ -206,3 +206,15 @@ def get_update_logger():
 
 def get_scheduler_routes():
     return Logger.setup_logger("scheduler_routes")
+
+
+def get_message_converter_logger():
+    return Logger.setup_logger("message_converter")
+
+
+def get_api_client_logger():
+    return Logger.setup_logger("api_client")
+
+
+def get_openai_compatible_logger():
+    return Logger.setup_logger("openai_compatible")

app/middleware/middleware.py

@@ -30,6 +30,8 @@ class AuthMiddleware(BaseHTTPMiddleware):
             and not request.url.path.startswith(f"/{API_VERSION}")
             and not request.url.path.startswith("/health")
             and not request.url.path.startswith("/hf")
+            and not request.url.path.startswith("/openai")
+            and not request.url.path.startswith("/api/version/check")
         ):
 
             auth_token = request.cookies.get("auth_token")

app/router/gemini_routes.py

@@ -1,15 +1,16 @@
 from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import StreamingResponse, JSONResponse
 from copy import deepcopy
+import asyncio
 from app.config.config import settings
 from app.log.logger import get_gemini_logger
 from app.core.security import SecurityService
-import asyncio # 导入 asyncio
 from app.domain.gemini_models import GeminiContent, GeminiRequest, ResetSelectedKeysRequest, VerifySelectedKeysRequest # 添加导入
 from app.service.chat.gemini_chat_service import GeminiChatService
 from app.service.key.key_manager import KeyManager, get_key_manager_instance
 from app.service.model.model_service import ModelService
 from app.handler.retry_handler import RetryHandler
+from app.handler.error_handler import handle_route_errors
 from app.core.constants import API_VERSION
 
 # 路由设置
@@ -43,62 +44,57 @@ async def list_models(
     _=Depends(security_service.verify_key_or_goog_api_key),
     key_manager: KeyManager = Depends(get_key_manager)
 ):
-    """获取可用的Gemini模型列表"""
-    logger.info("-" * 50 + "list_gemini_models" + "-" * 50)
+    """获取可用的 Gemini 模型列表，并根据配置添加衍生模型（搜索、图像、非思考）。"""
+    operation_name = "list_gemini_models"
+    logger.info("-" * 50 + operation_name + "-" * 50)
     logger.info("Handling Gemini models list request")
-    
-    api_key = await key_manager.get_first_valid_key()
-    logger.info(f"Using API key: {api_key}")
-    
-    models_json = model_service.get_gemini_models(api_key)
-    model_mapping = {x.get("name", "").split("/", maxsplit=1)[1]: x for x in models_json["models"]}
-    
-    # 添加搜索模型
-    if settings.SEARCH_MODELS:
-        for name in settings.SEARCH_MODELS:
-            model = model_mapping.get(name)
+
+    try:
+        api_key = await key_manager.get_first_valid_key()
+        if not api_key:
+             raise HTTPException(status_code=503, detail="No valid API keys available to fetch models.")
+        logger.info(f"Using API key: {api_key}")
+
+        models_data =await model_service.get_gemini_models(api_key)
+        if not models_data or "models" not in models_data:
+             raise HTTPException(status_code=500, detail="Failed to fetch base models list.")
+
+        models_json = deepcopy(models_data) # 操作副本以防修改原始缓存
+        model_mapping = {x.get("name", "").split("/", maxsplit=1)[-1]: x for x in models_json.get("models", [])}
+
+        def add_derived_model(base_name, suffix, display_suffix):
+            model = model_mapping.get(base_name)
             if not model:
-                continue
-                
+                logger.warning(f"Base model '{base_name}' not found for derived model '{suffix}'.")
+                return
             item = deepcopy(model)
-            item["name"] = f"models/{name}-search"
-            display_name = f'{item.get("displayName")} For Search'
+            item["name"] = f"models/{base_name}{suffix}"
+            display_name = f'{item.get("displayName", base_name)}{display_suffix}'
             item["displayName"] = display_name
             item["description"] = display_name
-            
             models_json["models"].append(item)
-    
-    # 添加图像生成模型
-    if settings.IMAGE_MODELS:
-        for name in settings.IMAGE_MODELS:
-            model = model_mapping.get(name)
-            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)
-            
-    # 添加思考模型的非思考版本
-    if settings.THINKING_MODELS:
-        for name in settings.THINKING_MODELS:
-            model = model_mapping.get(name)
-            if not model:
-                continue
-                
-            item = deepcopy(model)
-            item["name"] = f"models/{name}-non-thinking"
-            display_name = f'{item.get("displayName")} Non Thinking'
-            item["displayName"] = display_name
-            item["description"] = display_name
-            
-            models_json["models"].append(item)
-            
-    return models_json
+
+        # 添加衍生模型
+        if settings.SEARCH_MODELS:
+            for name in settings.SEARCH_MODELS:
+                add_derived_model(name, "-search", " For Search")
+        if settings.IMAGE_MODELS:
+            for name in settings.IMAGE_MODELS:
+                 add_derived_model(name, "-image", " For Image")
+        if settings.THINKING_MODELS:
+            for name in settings.THINKING_MODELS:
+                add_derived_model(name, "-non-thinking", " Non Thinking")
+
+        logger.info("Gemini models list request successful")
+        return models_json
+    except HTTPException as http_exc:
+        # 重新抛出已知的 HTTP 异常
+        raise http_exc
+    except Exception as e:
+        logger.error(f"Error getting Gemini models list: {str(e)}")
+        raise HTTPException(
+            status_code=500, detail="Internal server error while fetching Gemini models list"
+        ) from e
 
 
 @router.post("/models/{model_name}:generateContent")
@@ -112,25 +108,22 @@ async def generate_content(
     key_manager: KeyManager = Depends(get_key_manager),
     chat_service: GeminiChatService = Depends(get_chat_service)
 ):
-    """非流式生成内容"""
-    logger.info("-" * 50 + "gemini_generate_content" + "-" * 50)
-    logger.info(f"Handling Gemini content generation request for model: {model_name}")
-    logger.debug(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:
+    """处理 Gemini 非流式内容生成请求。"""
+    operation_name = "gemini_generate_content"
+    async with handle_route_errors(logger, operation_name, failure_message="Content generation failed"):
+        logger.info(f"Handling Gemini content generation request for model: {model_name}")
+        logger.debug(f"Request: \n{request.model_dump_json(indent=2)}")
+        logger.info(f"Using API key: {api_key}")
+
+        if not await model_service.check_model_support(model_name):
+            raise HTTPException(status_code=400, detail=f"Model {model_name} is not supported")
+
         response = await chat_service.generate_content(
             model=model_name,
             request=request,
             api_key=api_key
         )
         return response
-    except Exception as e:
-        logger.error(f"Chat completion failed after retries: {str(e)}")
-        raise HTTPException(status_code=500, detail="Chat completion failed") from e
 
 
 @router.post("/models/{model_name}:streamGenerateContent")
@@ -144,25 +137,24 @@ async def stream_generate_content(
     key_manager: KeyManager = Depends(get_key_manager),
     chat_service: GeminiChatService = Depends(get_chat_service)
 ):
-    """流式生成内容"""
-    logger.info("-" * 50 + "gemini_stream_generate_content" + "-" * 50)
-    logger.info(f"Handling Gemini streaming content generation for model: {model_name}")
-    logger.debug(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:
+    """处理 Gemini 流式内容生成请求。"""
+    operation_name = "gemini_stream_generate_content"
+    # 流式请求的成功/失败日志在流处理中更复杂，这里仅用上下文管理器处理启动错误
+    async with handle_route_errors(logger, operation_name, failure_message="Streaming request initiation failed"):
+        logger.info(f"Handling Gemini streaming content generation for model: {model_name}")
+        logger.debug(f"Request: \n{request.model_dump_json(indent=2)}")
+        logger.info(f"Using API key: {api_key}")
+
+        if not await model_service.check_model_support(model_name):
+            raise HTTPException(status_code=400, detail=f"Model {model_name} is not supported")
+
         response_stream = chat_service.stream_generate_content(
             model=model_name,
             request=request,
             api_key=api_key
         )
+        # 注意：流本身的错误需要在服务层或流迭代中处理，这里只返回流响应
         return StreamingResponse(response_stream, media_type="text/event-stream")
-    except Exception as e:
-        logger.error(f"Streaming request failed: {str(e)}")
-        raise HTTPException(status_code=500, detail="Streaming request failed") from e
 
 @router.post("/reset-all-fail-counts")
 async def reset_all_key_fail_counts(key_type: str = None, key_manager: KeyManager = Depends(get_key_manager)):

app/router/openai_compatiable_routes.py

@@ -0,0 +1,121 @@
+from fastapi import APIRouter, Depends
+from fastapi.responses import StreamingResponse
+
+from app.config.config import settings
+from app.core.security import SecurityService
+from app.domain.openai_models import (
+    ChatRequest,
+    EmbeddingRequest,
+    ImageGenerationRequest,
+)
+from app.handler.retry_handler import RetryHandler
+from app.handler.error_handler import handle_route_errors
+from app.log.logger import get_openai_compatible_logger
+from app.service.key.key_manager import KeyManager, get_key_manager_instance
+from app.service.openai_compatiable.openai_compatiable_service import OpenAICompatiableService
+
+
+router = APIRouter()
+logger = get_openai_compatible_logger()
+
+# 初始化服务
+security_service = SecurityService()
+
+async def get_key_manager():
+    return await get_key_manager_instance()
+
+
+async def get_next_working_key_wrapper(
+    key_manager: KeyManager = Depends(get_key_manager),
+):
+    return await key_manager.get_next_working_key()
+
+
+async def get_openai_service(key_manager: KeyManager = Depends(get_key_manager)):
+    """获取OpenAI聊天服务实例"""
+    return OpenAICompatiableService(settings.BASE_URL, key_manager)
+
+
+@router.get("/openai/v1/models")
+async def list_models(
+    _=Depends(security_service.verify_authorization),
+    key_manager: KeyManager = Depends(get_key_manager),
+    openai_service: OpenAICompatiableService = Depends(get_openai_service),
+):
+    """获取可用模型列表。"""
+    operation_name = "list_models"
+    async with handle_route_errors(logger, operation_name):
+        logger.info("Handling models list request")
+        api_key = await key_manager.get_first_valid_key()
+        logger.info(f"Using API key: {api_key}")
+        return await openai_service.get_models(api_key)
+
+
+@router.post("/openai/v1/chat/completions")
+@RetryHandler(max_retries=settings.MAX_RETRIES, key_arg="api_key")
+async def chat_completion(
+    request: ChatRequest,
+    _=Depends(security_service.verify_authorization),
+    api_key: str = Depends(get_next_working_key_wrapper),
+    key_manager: KeyManager = Depends(get_key_manager),
+    openai_service: OpenAICompatiableService = Depends(get_openai_service),
+):
+    """处理聊天补全请求，支持流式响应和特定模型切换。"""
+    operation_name = "chat_completion"
+    # 检查是否为图像生成相关的聊天模型，如果是，则使用付费密钥
+    is_image_chat = request.model == f"{settings.CREATE_IMAGE_MODEL}-chat"
+    current_api_key = api_key # 保存原始key（可能是普通key）
+    if is_image_chat:
+        current_api_key = await key_manager.get_paid_key() # 获取付费密钥
+
+    async with handle_route_errors(logger, operation_name):
+        logger.info(f"Handling chat completion request for model: {request.model}")
+        logger.debug(f"Request: \n{request.model_dump_json(indent=2)}")
+        logger.info(f"Using API key: {current_api_key}") # 使用 current_api_key
+
+        if is_image_chat:
+            # 图像生成聊天，调用特定服务，不处理流式
+            response = await openai_service.create_image_chat_completion(request, current_api_key)
+            return response # 直接返回结果
+        else:
+            # 普通聊天补全
+            response = await openai_service.create_chat_completion(request, current_api_key)
+            # 处理流式响应
+            if request.stream:
+                 # 假设 openai_service.create_chat_completion 在流式时返回异步生成器
+                return StreamingResponse(response, media_type="text/event-stream")
+            # 非流式直接返回结果
+            return response
+
+
+@router.post("/openai/v1/images/generations")
+async def generate_image(
+    request: ImageGenerationRequest,
+    _=Depends(security_service.verify_authorization),
+    openai_service: OpenAICompatiableService = Depends(get_openai_service),
+):
+    """处理图像生成请求。"""
+    operation_name = "generate_image"
+    async with handle_route_errors(logger, operation_name):
+        logger.info(f"Handling image generation request for prompt: {request.prompt}")
+        # 强制使用配置的模型，确保请求中包含正确的模型信息
+        request.model = settings.CREATE_IMAGE_MODEL
+        return await openai_service.generate_images(request)
+
+
+@router.post("/openai/v1/embeddings")
+async def embedding(
+    request: EmbeddingRequest,
+    _=Depends(security_service.verify_authorization),
+    key_manager: KeyManager = Depends(get_key_manager),
+    openai_service: OpenAICompatiableService = Depends(get_openai_service),
+):
+    """处理文本嵌入请求。"""
+    operation_name = "embedding"
+    async with handle_route_errors(logger, operation_name):
+        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}")
+        return await openai_service.create_embeddings(
+            input_text=request.input, model=request.model, api_key=api_key
+        )

app/router/openai_routes.py

@@ -9,6 +9,7 @@ from app.domain.openai_models import (
     ImageGenerationRequest,
 )
 from app.handler.retry_handler import RetryHandler
+from app.handler.error_handler import handle_route_errors # 导入共享错误处理器
 from app.log.logger import get_openai_logger
 from app.service.chat.openai_chat_service import OpenAIChatService
 from app.service.embedding.embedding_service import EmbeddingService
@@ -47,17 +48,13 @@ async def list_models(
     _=Depends(security_service.verify_authorization),
     key_manager: KeyManager = Depends(get_key_manager),
 ):
-    logger.info("-" * 50 + "list_models" + "-" * 50)
-    logger.info("Handling models list request")
-    api_key = await key_manager.get_first_valid_key()
-    logger.info(f"Using API key: {api_key}")
-    try:
-        return model_service.get_gemini_openai_models(api_key)
-    except Exception as e:
-        logger.error(f"Error getting models list: {str(e)}")
-        raise HTTPException(
-            status_code=500, detail="Internal server error while fetching models list"
-        ) from e
+    """获取可用的 OpenAI 模型列表 (兼容 Gemini 和 OpenAI)。"""
+    operation_name = "list_models"
+    async with handle_route_errors(logger, operation_name):
+        logger.info("Handling models list request")
+        api_key = await key_manager.get_first_valid_key()
+        logger.info(f"Using API key: {api_key}")
+        return await model_service.get_gemini_openai_models(api_key)
 
 
 @router.post("/v1/chat/completions")
@@ -70,33 +67,38 @@ async def chat_completion(
     key_manager: KeyManager = Depends(get_key_manager), # 保留 key_manager 用于获取 paid_key
     chat_service: OpenAIChatService = Depends(get_openai_chat_service),
 ):
-    # 如果model是imagen3,使用paid_key
-    if request.model == f"{settings.CREATE_IMAGE_MODEL}-chat":
-        api_key = await key_manager.get_paid_key()
-    logger.info("-" * 50 + "chat_completion" + "-" * 50)
-    logger.info(f"Handling chat completion request for model: {request.model}")
-    logger.debug(f"Request: \n{request.model_dump_json(indent=2)}")
-    logger.info(f"Using API key: {api_key}")
+    """处理 OpenAI 聊天补全请求，支持流式响应和特定模型切换。"""
+    operation_name = "chat_completion"
+    # 检查是否为图像生成相关的聊天模型
+    is_image_chat = request.model == f"{settings.CREATE_IMAGE_MODEL}-chat"
+    current_api_key = api_key # 保存原始 key
+    if is_image_chat:
+        current_api_key = await key_manager.get_paid_key() # 获取付费密钥
 
-    if not model_service.check_model_support(request.model):
-        raise HTTPException(
-            status_code=400, detail=f"Model {request.model} is not supported"
-        )
+    async with handle_route_errors(logger, operation_name):
+        logger.info(f"Handling chat completion request for model: {request.model}")
+        logger.debug(f"Request: \n{request.model_dump_json(indent=2)}")
+        logger.info(f"Using API key: {current_api_key}")
 
-    try:
-        # 如果model是imagen3,使用paid_key
-        if request.model == f"{settings.CREATE_IMAGE_MODEL}-chat":
-            response = await chat_service.create_image_chat_completion(request, api_key)
+        # 检查模型支持性应在错误处理块内，以便捕获并记录错误
+        if not await model_service.check_model_support(request.model):
+            # 使用 HTTPException，会被 handle_route_errors 捕获并记录
+            raise HTTPException(
+                status_code=400, detail=f"Model {request.model} is not supported"
+            )
+
+        if is_image_chat:
+            # 图像生成聊天
+            response = await chat_service.create_image_chat_completion(request, current_api_key)
+            return response # 直接返回，不处理流式
         else:
-            response = await chat_service.create_chat_completion(request, api_key)
-        # 处理流式响应
-        if request.stream:
-            return StreamingResponse(response, media_type="text/event-stream")
-        logger.info("Chat completion request successful")
-        return response
-    except Exception as e:
-        logger.error(f"Chat completion failed after retries: {str(e)}")
-        raise HTTPException(status_code=500, detail="Chat completion failed") from e
+            # 普通聊天补全
+            response = await chat_service.create_chat_completion(request, current_api_key)
+            # 处理流式响应
+            if request.stream:
+                return StreamingResponse(response, media_type="text/event-stream")
+            # 非流式直接返回结果
+            return response
 
 
 @router.post("/v1/images/generations")
@@ -105,18 +107,14 @@ async def generate_image(
     request: ImageGenerationRequest,
     _=Depends(security_service.verify_authorization),
 ):
-    logger.info("-" * 50 + "generate_image" + "-" * 50)
-    logger.info(f"Handling image generation request for prompt: {request.prompt}")
-
-    try:
+    """处理 OpenAI 图像生成请求。"""
+    operation_name = "generate_image"
+    async with handle_route_errors(logger, operation_name):
+        logger.info(f"Handling image generation request for prompt: {request.prompt}")
+        # 注意：这里假设 image_create_service.generate_images 是同步函数
+        # 如果它是异步的，需要 await
         response = image_create_service.generate_images(request)
-        logger.info("Image generation request successful")
         return response
-    except Exception as e:
-        logger.error(f"Image generation request failed: {str(e)}")
-        raise HTTPException(
-            status_code=500, detail="Image generation request failed"
-        ) from e
 
 
 @router.post("/v1/embeddings")
@@ -126,19 +124,16 @@ async def embedding(
     _=Depends(security_service.verify_authorization),
     key_manager: KeyManager = Depends(get_key_manager),
 ):
-    logger.info("-" * 50 + "embedding" + "-" * 50)
-    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:
+    """处理 OpenAI 文本嵌入请求。"""
+    operation_name = "embedding"
+    async with handle_route_errors(logger, operation_name):
+        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}")
         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 HTTPException(status_code=500, detail="Embedding request failed") from e
 
 
 @router.get("/v1/keys/list")
@@ -147,10 +142,10 @@ async def get_keys_list(
     _=Depends(security_service.verify_auth_token),
     key_manager: KeyManager = Depends(get_key_manager),
 ):
-    """获取有效和无效的API key列表"""
-    logger.info("-" * 50 + "get_keys_list" + "-" * 50)
-    logger.info("Handling keys list request")
-    try:
+    """获取有效和无效的API key列表 (需要管理 Token 认证)。"""
+    operation_name = "get_keys_list"
+    async with handle_route_errors(logger, operation_name):
+        logger.info("Handling keys list request")
         keys_status = await key_manager.get_keys_by_status()
         return {
             "status": "success",
@@ -160,8 +155,3 @@ async def get_keys_list(
             },
             "total": len(keys_status["valid_keys"]) + len(keys_status["invalid_keys"]),
         }
-    except Exception as e:
-        logger.error(f"Error getting keys list: {str(e)}")
-        raise HTTPException(
-            status_code=500, detail="Internal server error while fetching keys list"
-        ) from e

app/router/routes.py

@@ -8,9 +8,9 @@ from fastapi.templating import Jinja2Templates
 
 from app.core.security import verify_auth_token
 from app.log.logger import get_routes_logger
-from app.router import error_log_routes, gemini_routes, openai_routes, config_routes, scheduler_routes, stats_routes, version_routes # 新增导入 version_routes
+from app.router import error_log_routes, gemini_routes, openai_routes, config_routes, scheduler_routes, stats_routes, version_routes, openai_compatiable_routes
 from app.service.key.key_manager import get_key_manager_instance
-from app.service.stats_service import StatsService
+from app.service.stats.stats_service import StatsService
 
 logger = get_routes_logger()
 
@@ -31,9 +31,10 @@ def setup_routers(app: FastAPI) -> None:
     app.include_router(gemini_routes.router_v1beta)
     app.include_router(config_routes.router)
     app.include_router(error_log_routes.router)
-    app.include_router(scheduler_routes.router) # 新增包含 scheduler 路由
-    app.include_router(stats_routes.router) # 包含 stats API 路由
-    app.include_router(version_routes.router) # 包含 version API 路由
+    app.include_router(scheduler_routes.router)
+    app.include_router(stats_routes.router)
+    app.include_router(version_routes.router)
+    app.include_router(openai_compatiable_routes.router)
 
     # 添加页面路由
     setup_page_routes(app)

app/router/stats_routes.py

@@ -1,7 +1,7 @@
 from fastapi import APIRouter, Depends, HTTPException, Request
 from starlette import status
 from app.core.security import verify_auth_token
-from app.service.stats_service import StatsService
+from app.service.stats.stats_service import StatsService
 from app.log.logger import get_stats_logger
 
 logger = get_stats_logger()

app/service/chat/gemini_chat_service.py

@@ -81,10 +81,10 @@ def _get_safety_settings(model: str) -> List[Dict[str, str]]:
             {"category": "HARM_CATEGORY_CIVIC_INTEGRITY", "threshold": "OFF"},
         ]
     return [
-        {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
-        {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
-        {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
-        {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
+        {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "OFF"},
+        {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "OFF"},
+        {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "OFF"},
+        {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "OFF"},
         {"category": "HARM_CATEGORY_CIVIC_INTEGRITY", "threshold": "BLOCK_NONE"},
     ]
 

app/service/chat/openai_chat_service.py

@@ -1,13 +1,17 @@
 # app/services/chat_service.py
 
+import datetime
 import json
 import re
-import datetime # Add datetime import
-import time # Add time import
+import time
 from copy import deepcopy
 from typing import Any, AsyncGenerator, Dict, List, Optional, Union
 
 from app.config.config import settings
+from app.database.services import (
+    add_error_log,
+    add_request_log,
+)
 from app.domain.openai_models import ChatRequest, ImageGenerationRequest
 from app.handler.message_converter import OpenAIMessageConverter
 from app.handler.response_handler import OpenAIResponseHandler
@@ -16,17 +20,16 @@ from app.log.logger import get_openai_logger
 from app.service.client.api_client import GeminiApiClient
 from app.service.image.image_create_service import ImageCreateService
 from app.service.key.key_manager import KeyManager
-from app.database.services import add_error_log, add_request_log # Import add_request_log
 
 logger = get_openai_logger()
 
 
-def _has_image_parts(contents: List[Dict[str, Any]]) -> bool:
-    """判断消息是否包含图片部分"""
+def _has_media_parts(contents: List[Dict[str, Any]]) -> bool:
+    """判断消息是否包含图片、音频或视频部分 (inline_data)"""
     for content in contents:
-        if "parts" in content:
+        if content and "parts" in content and isinstance(content["parts"], list):
             for part in content["parts"]:
-                if "image_url" in part or "inline_data" in part:
+                if isinstance(part, dict) and "inline_data" in part:
                     return True
     return False
 
@@ -46,9 +49,13 @@ def _build_tools(
             or model.endswith("-image")
             or model.endswith("-image-generation")
         )
-        and not _has_image_parts(messages)
+        and not _has_media_parts(messages)  # Use the updated check
     ):
         tool["codeExecution"] = {}
+        logger.debug("Code execution tool enabled.")
+    elif _has_media_parts(messages):
+        logger.debug("Code execution tool disabled due to media parts presence.")
+
     if model.endswith("-search"):
         tool["googleSearch"] = {}
 
@@ -62,7 +69,9 @@ def _build_tools(
             if item.get("type", "") == "function" and item.get("function"):
                 function = deepcopy(item.get("function"))
                 parameters = function.get("parameters", {})
-                if parameters.get("type") == "object" and not parameters.get("properties", {}):
+                if parameters.get("type") == "object" and not parameters.get(
+                    "properties", {}
+                ):
                     function.pop("parameters", None)
 
                 function_declarations.append(function)
@@ -93,20 +102,8 @@ def _get_safety_settings(model: str) -> List[Dict[str, str]]:
     #     and "gemini-2.0-pro-exp" not in model
     # ):
     if model == "gemini-2.0-flash-exp":
-        return [
-            {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "OFF"},
-            {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "OFF"},
-            {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "OFF"},
-            {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "OFF"},
-            {"category": "HARM_CATEGORY_CIVIC_INTEGRITY", "threshold": "OFF"},
-        ]
-    return [
-        {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
-        {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
-        {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
-        {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
-        {"category": "HARM_CATEGORY_CIVIC_INTEGRITY", "threshold": "BLOCK_NONE"},
-    ]
+        return settings.GEMINI_2_FLASH_EXP_SAFETY_SETTINGS
+    return settings.SAFETY_SETTINGS
 
 
 def _build_payload(
@@ -131,9 +128,11 @@ def _build_payload(
     if request.model.endswith("-image") or request.model.endswith("-image-generation"):
         payload["generationConfig"]["responseModalities"] = ["Text", "Image"]
     if request.model.endswith("-non-thinking"):
-        payload["generationConfig"]["thinkingConfig"] = {"thinkingBudget": 0} 
+        payload["generationConfig"]["thinkingConfig"] = {"thinkingBudget": 0}
     if request.model in settings.THINKING_BUDGET_MAP:
-        payload["generationConfig"]["thinkingConfig"] = {"thinkingBudget": settings.THINKING_BUDGET_MAP.get(request.model,1000)}
+        payload["generationConfig"]["thinkingConfig"] = {
+            "thinkingBudget": settings.THINKING_BUDGET_MAP.get(request.model, 1000)
+        }
 
     if (
         instruction
@@ -205,7 +204,7 @@ class OpenAIChatService:
         try:
             response = await self.api_client.generate_content(payload, model, api_key)
             is_success = True
-            status_code = 200 # Assume 200 on success
+            status_code = 200
             return self.response_handler.handle_response(
                 response, model, stream=False, finish_reason="stop"
             )
@@ -218,17 +217,17 @@ class OpenAIChatService:
             if match:
                 status_code = int(match.group(1))
             else:
-                status_code = 500 # Default if parsing fails
+                status_code = 500
 
             await add_error_log(
-                gemini_key=api_key, # Note: Parameter name is gemini_key in add_error_log
+                gemini_key=api_key,
                 model_name=model,
                 error_type="openai-chat-non-stream",
                 error_log=error_log_msg,
                 error_code=status_code,
-                request_msg=payload
+                request_msg=payload,
             )
-            raise e # Re-throw exception
+            raise e
         finally:
             end_time = time.perf_counter()
             latency_ms = int((end_time - start_time) * 1000)
@@ -238,7 +237,7 @@ class OpenAIChatService:
                 is_success=is_success,
                 status_code=status_code,
                 latency_ms=latency_ms,
-                request_time=request_datetime
+                request_time=request_datetime,
             )
 
     async def _handle_stream_completion(
@@ -261,6 +260,7 @@ class OpenAIChatService:
                 async for line in self.api_client.stream_generate_content(
                     payload, model, current_attempt_key
                 ):
+                    # print(line)
                     if line.startswith("data:"):
                         chunk = json.loads(line[6:])
                         openai_chunk = self.response_handler.handle_response(
@@ -293,7 +293,7 @@ class OpenAIChatService:
                 yield "data: [DONE]\n\n"
                 logger.info("Streaming completed successfully")
                 is_success = True
-                status_code = 200 # Assume 200 on success
+                status_code = 200
                 break  # 成功后退出循环
             except Exception as e:
                 retries += 1
@@ -307,7 +307,7 @@ class OpenAIChatService:
                 if match:
                     status_code = int(match.group(1))
                 else:
-                    status_code = 500 # Default if parsing fails
+                    status_code = 500
 
                 # Log error to error log table
                 await add_error_log(
@@ -316,38 +316,40 @@ class OpenAIChatService:
                     error_type="openai-chat-stream",
                     error_log=error_log_msg,
                     error_code=status_code,
-                    request_msg=payload
+                    request_msg=payload,
                 )
 
                 # Attempt to switch API Key
                 # Ensure key_manager is available (might need adjustment if not always passed)
                 if self.key_manager:
-                    api_key = await self.key_manager.handle_api_failure(current_attempt_key, retries)
+                    api_key = await self.key_manager.handle_api_failure(
+                        current_attempt_key, retries
+                    )
                     if api_key:
                         logger.info(f"Switched to new API key: {api_key}")
                     else:
-                        logger.error(f"No valid API key available after {retries} retries.")
-                        break # Exit loop if no key available
+                        logger.error(
+                            f"No valid API key available after {retries} retries."
+                        )
+                        break 
                 else:
-                        logger.error("KeyManager not available for retry logic.")
-                        break # Exit loop if key manager is missing
+                    logger.error("KeyManager not available for retry logic.")
+                    break 
 
                 if retries >= max_retries:
-                    logger.error(
-                        f"Max retries ({max_retries}) reached for streaming."
-                    )
-                    break # Exit loop after max retries
+                    logger.error(f"Max retries ({max_retries}) reached for streaming.")
+                    break
             finally:
                 # Log the final outcome of the streaming request
                 end_time = time.perf_counter()
                 latency_ms = int((end_time - start_time) * 1000)
                 await add_request_log(
                     model_name=model,
-                    api_key=final_api_key, # Log the last key used
-                    is_success=is_success, # Log the final success status
-                    status_code=status_code, # Log the last known status code
-                    latency_ms=latency_ms, # Log total time including retries
-                    request_time=request_datetime
+                    api_key=final_api_key,
+                    is_success=is_success,
+                    status_code=status_code,
+                    latency_ms=latency_ms,
+                    request_time=request_datetime,
                 )
                 # If the loop finished due to failure, yield error and DONE
                 if not is_success and retries >= max_retries:
@@ -355,9 +357,7 @@ class OpenAIChatService:
                     yield "data: [DONE]\n\n"
 
     async def create_image_chat_completion(
-        self,
-        request: ChatRequest,
-        api_key: str
+        self, request: ChatRequest, api_key: str
     ) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
 
         image_generate_request = ImageGenerationRequest()
@@ -367,18 +367,22 @@ class OpenAIChatService:
         )
 
         if request.stream:
-            return self._handle_stream_image_completion(request.model, image_res, api_key)
+            return self._handle_stream_image_completion(
+                request.model, image_res, api_key
+            )
         else:
-            return await self._handle_normal_image_completion(request.model, image_res, api_key)
+            return await self._handle_normal_image_completion(
+                request.model, image_res, api_key
+            )
 
     async def _handle_stream_image_completion(
-        self, model: str, image_data: str, api_key:str
+        self, model: str, image_data: str, api_key: str
     ) -> AsyncGenerator[str, None]:
         logger.info(f"Starting stream image completion for model: {model}")
         start_time = time.perf_counter()
-        request_datetime = datetime.datetime.now() # Although not used for DB log here
+        request_datetime = datetime.datetime.now()
         is_success = False
-        status_code = None # Although not used for DB log here
+        status_code = None 
 
         try:
             if image_data:
@@ -402,7 +406,9 @@ class OpenAIChatService:
                         # 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"
-            logger.info(f"Stream image completion finished successfully for model: {model}")
+            logger.info(
+                f"Stream image completion finished successfully for model: {model}"
+            )
             is_success = True
             status_code = 200
             yield "data: [DONE]\n\n"
@@ -410,46 +416,51 @@ class OpenAIChatService:
             is_success = False
             error_log_msg = f"Stream image completion failed for model {model}: {e}"
             logger.error(error_log_msg)
-            status_code = 500 # Default error code
+            status_code = 500 
             await add_error_log(
                 gemini_key=api_key,
                 model_name=model,
-                error_type="openai-image-stream", # Specific error type
+                error_type="openai-image-stream",
                 error_log=error_log_msg,
                 error_code=status_code,
-                request_msg={"image_data_truncated": image_data[:1000]} # Log truncated data
+                request_msg={
+                    "image_data_truncated": image_data[:1000]
+                }, 
             )
-            yield f"data: {json.dumps({'error': error_log_msg})}\n\n" # Send error to client
-            yield "data: [DONE]\n\n" # Still need DONE message
-            # Re-raising might break the stream, decide if needed
+            yield f"data: {json.dumps({'error': error_log_msg})}\n\n"
+            yield "data: [DONE]\n\n"
         finally:
             end_time = time.perf_counter()
             latency_ms = int((end_time - start_time) * 1000)
-            logger.info(f"Stream image completion for model {model} took {latency_ms} ms. Success: {is_success}")
+            logger.info(
+                f"Stream image completion for model {model} took {latency_ms} ms. Success: {is_success}"
+            )
             await add_request_log(
                 model_name=model,
                 api_key=api_key,
                 is_success=is_success,
                 status_code=status_code,
                 latency_ms=latency_ms,
-                request_time=request_datetime
+                request_time=request_datetime,
             )
 
     async def _handle_normal_image_completion(
-        self, model: str, image_data: str, api_key: str # Add api_key parameter
+        self, model: str, image_data: str, api_key: str 
     ) -> Dict[str, Any]:
         logger.info(f"Starting normal image completion for model: {model}")
         start_time = time.perf_counter()
-        request_datetime = datetime.datetime.now() # Although not used for DB log here
+        request_datetime = datetime.datetime.now() 
         is_success = False
-        status_code = None # Although not used for DB log here
+        status_code = None 
         result = None
 
         try:
             result = self.response_handler.handle_image_chat_response(
                 image_data, model, stream=False, finish_reason="stop"
             )
-            logger.info(f"Normal image completion finished successfully for model: {model}")
+            logger.info(
+                f"Normal image completion finished successfully for model: {model}"
+            )
             is_success = True
             status_code = 200
             return result
@@ -457,26 +468,30 @@ class OpenAIChatService:
             is_success = False
             error_log_msg = f"Normal image completion failed for model {model}: {e}"
             logger.error(error_log_msg)
-            status_code = 500 # Default error code
+            status_code = 500
             await add_error_log(
                 gemini_key=api_key,
                 model_name=model,
-                error_type="openai-image-non-stream", # Specific error type
+                error_type="openai-image-non-stream",
                 error_log=error_log_msg,
                 error_code=status_code,
-                request_msg={"image_data_truncated": image_data[:1000]} # Log truncated data
+                request_msg={
+                    "image_data_truncated": image_data[:1000]
+                },
             )
             # Re-raise the exception so the caller knows about the failure
             raise e
         finally:
             end_time = time.perf_counter()
             latency_ms = int((end_time - start_time) * 1000)
-            logger.info(f"Normal image completion for model {model} took {latency_ms} ms. Success: {is_success}")
+            logger.info(
+                f"Normal image completion for model {model} took {latency_ms} ms. Success: {is_success}"
+            )
             await add_request_log(
                 model_name=model,
                 api_key=api_key,
                 is_success=is_success,
                 status_code=status_code,
                 latency_ms=latency_ms,
-                request_time=request_datetime
+                request_time=request_datetime,
             )

app/service/client/api_client.py

@@ -1,11 +1,14 @@
 # app/services/chat/api_client.py
 
-from typing import Dict, Any, AsyncGenerator
+from typing import Dict, Any, AsyncGenerator, Optional
 import httpx
+import random
 from abc import ABC, abstractmethod
-
+from app.config.config import settings
+from app.log.logger import get_api_client_logger
 from app.core.constants import DEFAULT_TIMEOUT
 
+logger = get_api_client_logger()
 
 class ApiClient(ABC):
     """API客户端基类"""
@@ -37,11 +40,41 @@ class GeminiApiClient(ApiClient):
             model = model[:-20]
         return model
 
+    async def get_models(self, api_key: str) -> Optional[Dict[str, Any]]:
+        """获取可用的 Gemini 模型列表"""
+        timeout = httpx.Timeout(timeout=5)
+        
+        proxy_to_use = None
+        if settings.PROXIES:
+            proxy_to_use = random.choice(settings.PROXIES)
+            logger.info(f"Using proxy for getting models: {proxy_to_use}")
+
+        async with httpx.AsyncClient(timeout=timeout, proxy=proxy_to_use) as client:
+            url = f"{self.base_url}/models?key={api_key}"
+            try:
+                response = await client.get(url)
+                response.raise_for_status()  # 如果状态码不是 2xx，则引发 HTTPStatusError
+                return response.json()
+            except httpx.HTTPStatusError as e:
+                logger.error(f"获取模型列表失败: {e.response.status_code}")
+                logger.error(e.response.text)
+                # 返回 None 而不是抛出异常，以便上层处理
+                return None
+            except httpx.RequestError as e:
+                logger.error(f"请求模型列表失败: {e}")
+                # 返回 None 而不是抛出异常
+                return None
+            
     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:
+        proxy_to_use = None
+        if settings.PROXIES:
+            proxy_to_use = random.choice(settings.PROXIES)
+            logger.info(f"Using proxy: {proxy_to_use}")
+            
+        async with httpx.AsyncClient(timeout=timeout, proxy=proxy_to_use) as client:
             url = f"{self.base_url}/models/{model}:generateContent?key={api_key}"
             response = await client.post(url, json=payload)
             if response.status_code != 200:
@@ -53,7 +86,12 @@ class GeminiApiClient(ApiClient):
         timeout = httpx.Timeout(self.timeout, read=self.timeout)
         model = self._get_real_model(model)
         
-        async with httpx.AsyncClient(timeout=timeout) as client:
+        proxy_to_use = None
+        if settings.PROXIES:
+            proxy_to_use = random.choice(settings.PROXIES)
+            logger.info(f"Using proxy: {proxy_to_use}")
+
+        async with httpx.AsyncClient(timeout=timeout, proxy=proxy_to_use) 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:
                 if response.status_code != 200:
@@ -62,3 +100,96 @@ class GeminiApiClient(ApiClient):
                     raise Exception(f"API call failed with status code {response.status_code}, {error_msg}")
                 async for line in response.aiter_lines():
                     yield line
+
+
+class OpenaiApiClient(ApiClient):
+    """OpenAI API客户端"""
+
+    def __init__(self, base_url: str, timeout: int = DEFAULT_TIMEOUT):
+        self.base_url = base_url
+        self.timeout = timeout
+        
+    async def get_models(self, api_key: str) -> Dict[str, Any]:
+        timeout = httpx.Timeout(self.timeout, read=self.timeout)
+        async with httpx.AsyncClient(timeout=timeout) as client:
+            url = f"{self.base_url}/openai/models"
+            headers = {"Authorization": f"Bearer {api_key}"}
+            response = await client.get(url, headers=headers)
+            if response.status_code != 200:
+                error_content = response.text
+                raise Exception(f"API call failed with status code {response.status_code}, {error_content}")
+            return response.json()
+
+    async def generate_content(self, payload: Dict[str, Any], api_key: str) -> Dict[str, Any]:
+        timeout = httpx.Timeout(self.timeout, read=self.timeout)
+        
+        proxy_to_use = None
+        if settings.PROXIES:
+            proxy_to_use = random.choice(settings.PROXIES)
+            logger.info(f"Using proxy: {proxy_to_use}")
+
+        async with httpx.AsyncClient(timeout=timeout, proxy=proxy_to_use) as client:
+            url = f"{self.base_url}/openai/chat/completions"
+            headers = {"Authorization": f"Bearer {api_key}"}
+            response = await client.post(url, json=payload, headers=headers)
+            if response.status_code != 200:
+                error_content = response.text
+                raise Exception(f"API call failed with status code {response.status_code}, {error_content}")
+            return response.json()
+
+    async def stream_generate_content(self, payload: Dict[str, Any], api_key: str) -> AsyncGenerator[str, None]:
+        timeout = httpx.Timeout(self.timeout, read=self.timeout)
+        
+        proxy_to_use = None
+        if settings.PROXIES:
+            proxy_to_use = random.choice(settings.PROXIES)
+            logger.info(f"Using proxy: {proxy_to_use}")
+
+        async with httpx.AsyncClient(timeout=timeout, proxy=proxy_to_use) as client:
+            url = f"{self.base_url}/openai/chat/completions"
+            headers = {"Authorization": f"Bearer {api_key}"}
+            async with client.stream(method="POST", url=url, json=payload, headers=headers) as response:
+                if response.status_code != 200:
+                    error_content = await response.aread()
+                    error_msg = error_content.decode("utf-8")
+                    raise Exception(f"API call failed with status code {response.status_code}, {error_msg}")
+                async for line in response.aiter_lines():
+                    yield line
+    
+    async def create_embeddings(self, input: str, model: str, api_key: str) -> Dict[str, Any]:
+        timeout = httpx.Timeout(self.timeout, read=self.timeout)
+        
+        proxy_to_use = None
+        if settings.PROXIES:
+            proxy_to_use = random.choice(settings.PROXIES)
+            logger.info(f"Using proxy: {proxy_to_use}")
+
+        async with httpx.AsyncClient(timeout=timeout, proxy=proxy_to_use) as client:
+            url = f"{self.base_url}/openai/embeddings"
+            headers = {"Authorization": f"Bearer {api_key}"}
+            payload = {
+                "input": input,
+                "model": model,
+            }
+            response = await client.post(url, json=payload, headers=headers)
+            if response.status_code != 200:
+                error_content = response.text
+                raise Exception(f"API call failed with status code {response.status_code}, {error_content}")
+            return response.json()
+                    
+    async def generate_images(self, payload: Dict[str, Any], api_key: str) -> Dict[str, Any]:
+        timeout = httpx.Timeout(self.timeout, read=self.timeout)
+        
+        proxy_to_use = None
+        if settings.PROXIES:
+            proxy_to_use = random.choice(settings.PROXIES)
+            logger.info(f"Using proxy: {proxy_to_use}")
+
+        async with httpx.AsyncClient(timeout=timeout, proxy=proxy_to_use) as client:
+            url = f"{self.base_url}/openai/images/generations"
+            headers = {"Authorization": f"Bearer {api_key}"}
+            response = await client.post(url, json=payload, headers=headers)
+            if response.status_code != 200:
+                error_content = response.text
+                raise Exception(f"API call failed with status code {response.status_code}, {error_content}")
+            return response.json()

app/service/model/model_service.py

@@ -1,50 +1,47 @@
 from datetime import datetime, timezone
 from typing import Any, Dict, Optional
 
-import requests
-
 from app.config.config import settings
 from app.log.logger import get_model_logger
+from app.service.client.api_client import GeminiApiClient
 
 logger = get_model_logger()
 
 
 class ModelService:
-    def get_gemini_models(self, api_key: str) -> Optional[Dict[str, Any]]:
-        url = f"{settings.BASE_URL}/models?key={api_key}"
+    async def get_gemini_models(self, api_key: str) -> Optional[Dict[str, Any]]:
+        """使用 GeminiApiClient 获取并过滤模型列表"""
+        api_client = GeminiApiClient(base_url=settings.BASE_URL) # 实例化客户端
+        gemini_models = await api_client.get_models(api_key)
 
-        try:
-            response = requests.get(url)
-            if response.status_code == 200:
-                gemini_models = response.json()
-
-                filtered_models_list = []
-                for model in gemini_models.get("models", []):
-                    model_id = model["name"].split("/")[-1]
-                    if model_id not in settings.FILTERED_MODELS:
-                        filtered_models_list.append(model)
-                    else:
-                        logger.debug(f"Filtered out model: {model_id}")
-
-                gemini_models["models"] = filtered_models_list
-                return 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}")
+        if gemini_models is None:
+            logger.error("从 API 客户端获取模型列表失败。")
             return None
 
-    def get_gemini_openai_models(self, api_key: str) -> Optional[Dict[str, Any]]:
         try:
-            gemini_models = self.get_gemini_models(api_key)
-            return self.convert_to_openai_models_format(gemini_models)
-        except requests.RequestException as e:
-            logger.error(f"Request failed: {e}")
+            filtered_models_list = []
+            for model in gemini_models.get("models", []):
+                model_id = model["name"].split("/")[-1]
+                if model_id not in settings.FILTERED_MODELS:
+                    filtered_models_list.append(model)
+                else:
+                    logger.debug(f"Filtered out model: {model_id}")
+
+            gemini_models["models"] = filtered_models_list
+            return gemini_models
+        except Exception as e:
+            logger.error(f"处理模型列表时出错: {e}")
             return None
 
-    def convert_to_openai_models_format(
+    async def get_gemini_openai_models(self, api_key: str) -> Optional[Dict[str, Any]]:
+        """获取 Gemini 模型并转换为 OpenAI 格式"""
+        gemini_models = await self.get_gemini_models(api_key)
+        if gemini_models is None:
+            return None
+        
+        return await self.convert_to_openai_models_format(gemini_models)
+
+    async def convert_to_openai_models_format(
         self, gemini_models: Dict[str, Any]
     ) -> Dict[str, Any]:
         openai_format = {"object": "list", "data": [], "success": True}
@@ -81,7 +78,7 @@ class ModelService:
             openai_format["data"].append(image_model)
         return openai_format
 
-    def check_model_support(self, model: str) -> bool:
+    async def check_model_support(self, model: str) -> bool:
         if not model or not isinstance(model, str):
             return False
 

app/service/openai_compatiable/openai_compatiable_service.py

@@ -0,0 +1,197 @@
+
+import datetime
+import json
+import re
+import time
+from typing import Any, AsyncGenerator, Dict, Union
+
+from app.config.config import settings
+from app.database.services import (
+    add_error_log,
+    add_request_log,
+)
+from app.domain.openai_models import ChatRequest, ImageGenerationRequest
+from app.service.client.api_client import OpenaiApiClient
+from app.service.key.key_manager import KeyManager
+from app.log.logger import get_openai_compatible_logger
+
+logger = get_openai_compatible_logger()
+
+class OpenAICompatiableService:
+
+    def __init__(self, base_url: str, key_manager: KeyManager = None):
+        self.key_manager = key_manager
+        self.base_url = base_url
+        self.api_client = OpenaiApiClient(base_url, settings.TIME_OUT)
+        
+    async def get_models(self, api_key: str) -> Dict[str, Any]:
+        return await self.api_client.get_models(api_key)
+
+    async def create_chat_completion(
+        self,
+        request: ChatRequest,
+        api_key: str,
+    ) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
+        """创建聊天完成"""
+        request_dict = request.model_dump()
+        # 移除值为null的
+        request_dict = {k: v for k, v in request_dict.items() if v is not None}
+        del request_dict["top_k"] # 删除top_k参数，目前不支持该参数
+        if request.stream:
+            return self._handle_stream_completion(request.model, request_dict, api_key)
+        return await self._handle_normal_completion(request.model, request_dict, api_key)
+
+    async def generate_images(
+        self,
+        request: ImageGenerationRequest,
+    ) -> Dict[str, Any]:
+        """生成图片"""
+        request_dict = request.model_dump()
+        # 移除值为null的
+        request_dict = {k: v for k, v in request_dict.items() if v is not None}
+        api_key = settings.PAID_KEY
+        return await self.api_client.generate_images(request_dict, api_key)
+
+    async def create_embeddings(
+        self,
+        input_text: str,
+        model: str,
+        api_key: str,
+    ) -> Dict[str, Any]:
+        """创建嵌入"""
+        return await self.api_client.create_embeddings(input_text, model, api_key)
+
+    async def _handle_normal_completion(
+        self, model: str, request: dict, api_key: str
+    ) -> Dict[str, Any]:
+        """处理普通聊天完成"""
+        start_time = time.perf_counter()
+        request_datetime = datetime.datetime.now()
+        is_success = False
+        status_code = None
+        response = None
+        try:
+            response = await self.api_client.generate_content(request, api_key)
+            is_success = True
+            status_code = 200
+            return response
+        except Exception as e:
+            is_success = False
+            error_log_msg = str(e)
+            logger.error(f"Normal API call failed with error: {error_log_msg}")
+            # Try to parse status code from exception
+            match = re.search(r"status code (\d+)", error_log_msg)
+            if match:
+                status_code = int(match.group(1))
+            else:
+                status_code = 500
+
+            await add_error_log(
+                gemini_key=api_key,
+                model_name=model,
+                error_type="openai-compatiable-non-stream",
+                error_log=error_log_msg,
+                error_code=status_code,
+                request_msg=request,
+            )
+            raise e
+        finally:
+            end_time = time.perf_counter()
+            latency_ms = int((end_time - start_time) * 1000)
+            await add_request_log(
+                model_name=model,
+                api_key=api_key,
+                is_success=is_success,
+                status_code=status_code,
+                latency_ms=latency_ms,
+                request_time=request_datetime,
+            )
+
+    async def _handle_stream_completion(
+        self, model: str, payload: dict, api_key: str
+    ) -> AsyncGenerator[str, None]:
+        """处理流式聊天完成，添加重试逻辑"""
+        retries = 0
+        max_retries = settings.MAX_RETRIES
+        is_success = False
+        status_code = None
+        final_api_key = api_key
+
+        while retries < max_retries:
+            start_time = time.perf_counter()
+            request_datetime = datetime.datetime.now()
+            current_attempt_key = api_key
+            final_api_key = current_attempt_key
+            try:
+                async for line in self.api_client.stream_generate_content(
+                    payload, current_attempt_key
+                ):
+                    if line.startswith("data:"):
+                        # print(line)
+                        yield line + "\n\n"
+                logger.info("Streaming completed successfully")
+                is_success = True
+                status_code = 200
+                break  # 成功后退出循环
+            except Exception as e:
+                retries += 1
+                is_success = False
+                error_log_msg = str(e)
+                logger.warning(
+                    f"Streaming API call failed with error: {error_log_msg}. Attempt {retries} of {max_retries}"
+                )
+                # Parse error code for logging
+                match = re.search(r"status code (\d+)", error_log_msg)
+                if match:
+                    status_code = int(match.group(1))
+                else:
+                    status_code = 500
+
+                # Log error to error log table
+                await add_error_log(
+                    gemini_key=current_attempt_key,
+                    model_name=model,
+                    error_type="openai-compatiable-stream",
+                    error_log=error_log_msg,
+                    error_code=status_code,
+                    request_msg=payload,
+                )
+
+                # Attempt to switch API Key
+                # Ensure key_manager is available (might need adjustment if not always passed)
+                if self.key_manager:
+                    api_key = await self.key_manager.handle_api_failure(
+                        current_attempt_key, retries
+                    )
+                    if api_key:
+                        logger.info(f"Switched to new API key: {api_key}")
+                    else:
+                        logger.error(
+                            f"No valid API key available after {retries} retries."
+                        )
+                        break 
+                else:
+                    logger.error("KeyManager not available for retry logic.")
+                    break 
+
+                if retries >= max_retries:
+                    logger.error(f"Max retries ({max_retries}) reached for streaming.")
+                    break
+            finally:
+                # Log the final outcome of the streaming request
+                end_time = time.perf_counter()
+                latency_ms = int((end_time - start_time) * 1000)
+                await add_request_log(
+                    model_name=model,
+                    api_key=final_api_key,
+                    is_success=is_success,
+                    status_code=status_code,
+                    latency_ms=latency_ms,
+                    request_time=request_datetime,
+                )
+                # If the loop finished due to failure, yield error and DONE
+                if not is_success and retries >= max_retries:
+                    yield f"data: {json.dumps({'error': 'Streaming failed after retries'})}\n\n"
+                    yield "data: [DONE]\n\n"
+                    
+    

app/static/js/config_editor.js

@@ -1,3 +1,7 @@
+// 将需要在外部函数访问的 DOM 元素移到外部
+const safetySettingsContainer = document.getElementById('SAFETY_SETTINGS_container');
+const thinkingModelsContainer = document.getElementById('THINKING_MODELS_container');
+
 document.addEventListener('DOMContentLoaded', function() {
     // 初始化配置
     initConfig();
@@ -63,13 +67,29 @@ document.addEventListener('DOMContentLoaded', function() {
     const cancelBulkDeleteApiKeyBtn = document.getElementById('cancelBulkDeleteApiKeyBtn'); // 新增
     const confirmBulkDeleteApiKeyBtn = document.getElementById('confirmBulkDeleteApiKeyBtn'); // 新增
     const bulkDeleteApiKeyInput = document.getElementById('bulkDeleteApiKeyInput'); // 新增
-
+ 
+    // --- 新增：Proxy 模态框相关 ---
+    const proxyModal = document.getElementById('proxyModal');
+    const addProxyBtn = document.getElementById('addProxyBtn'); // Changed from bulkAddProxyBtn
+    const closeProxyModalBtn = document.getElementById('closeProxyModalBtn');
+    const cancelAddProxyBtn = document.getElementById('cancelAddProxyBtn');
+    const confirmAddProxyBtn = document.getElementById('confirmAddProxyBtn');
+    const proxyBulkInput = document.getElementById('proxyBulkInput');
+    const bulkDeleteProxyBtn = document.getElementById('bulkDeleteProxyBtn'); // 新增
+    const bulkDeleteProxyModal = document.getElementById('bulkDeleteProxyModal'); // 新增
+    const closeBulkDeleteProxyModalBtn = document.getElementById('closeBulkDeleteProxyModalBtn'); // 新增
+    const cancelBulkDeleteProxyBtn = document.getElementById('cancelBulkDeleteProxyBtn'); // 新增
+    const confirmBulkDeleteProxyBtn = document.getElementById('confirmBulkDeleteProxyBtn'); // 新增
+    const bulkDeleteProxyInput = document.getElementById('bulkDeleteProxyInput'); // 新增
+    // --- 结束：Proxy 模态框相关 ---
+ 
     // --- 新增：重置确认模态框相关 ---
     const resetConfirmModal = document.getElementById('resetConfirmModal');
     const closeResetModalBtn = document.getElementById('closeResetModalBtn');
     const cancelResetBtn = document.getElementById('cancelResetBtn');
     const confirmResetBtn = document.getElementById('confirmResetBtn');
     // --- 结束：新增 ---
+    // const safetySettingsContainer = document.getElementById('SAFETY_SETTINGS_container'); // Moved outside
 
 
     // 打开模态框
@@ -111,8 +131,14 @@ document.addEventListener('DOMContentLoaded', function() {
         if (event.target == bulkDeleteApiKeyModal) { // 新增对批量删除模态框的处理
             bulkDeleteApiKeyModal.classList.remove('show');
         }
+        if (event.target == proxyModal) { // 新增对代理模态框的处理
+            proxyModal.classList.remove('show');
+        }
+        if (event.target == bulkDeleteProxyModal) { // 新增对批量删除代理模态框的处理
+            bulkDeleteProxyModal.classList.remove('show');
+        }
     });
-
+ 
     // 确认添加 API Key
     if (confirmAddApiKeyBtn) {
         confirmAddApiKeyBtn.addEventListener('click', handleBulkAddApiKeys);
@@ -158,7 +184,77 @@ document.addEventListener('DOMContentLoaded', function() {
     }
     // --- 结束：批量删除 API Key 相关 ---
     // --- 结束：API Key 相关 ---
+ 
+    // --- 新增：Proxy 模态框事件 ---
+    // 打开模态框 (Changed event listener to addProxyBtn)
+    if (addProxyBtn) {
+        addProxyBtn.addEventListener('click', () => {
+            if (proxyModal) {
+                proxyModal.classList.add('show');
+            }
+            if (proxyBulkInput) proxyBulkInput.value = ''; // 清空输入框
+        });
+    }
+ 
+    // 关闭模态框 (X 按钮)
+    if (closeProxyModalBtn) {
+        closeProxyModalBtn.addEventListener('click', () => {
+            if (proxyModal) {
+                proxyModal.classList.remove('show');
+            }
+        });
+    }
+ 
+    // 关闭模态框 (取消按钮)
+    if (cancelAddProxyBtn) {
+        cancelAddProxyBtn.addEventListener('click', () => {
+            if (proxyModal) {
+                proxyModal.classList.remove('show');
+            }
+        });
+    }
+ 
+    // 确认添加 Proxy
+    if (confirmAddProxyBtn) {
+        confirmAddProxyBtn.addEventListener('click', handleBulkAddProxies);
+    }
+    // --- 结束：Proxy 模态框事件 ---
 
+    // --- 新增：批量删除 Proxy 相关事件 ---
+    // 打开批量删除模态框
+    if (bulkDeleteProxyBtn) {
+        bulkDeleteProxyBtn.addEventListener('click', () => {
+            if (bulkDeleteProxyModal) {
+                bulkDeleteProxyModal.classList.add('show');
+            }
+            if (bulkDeleteProxyInput) bulkDeleteProxyInput.value = ''; // 清空输入框
+        });
+    }
+
+    // 关闭批量删除模态框 (X 按钮)
+    if (closeBulkDeleteProxyModalBtn) {
+        closeBulkDeleteProxyModalBtn.addEventListener('click', () => {
+            if (bulkDeleteProxyModal) {
+                bulkDeleteProxyModal.classList.remove('show');
+            }
+        });
+    }
+
+    // 关闭批量删除模态框 (取消按钮)
+    if (cancelBulkDeleteProxyBtn) {
+        cancelBulkDeleteProxyBtn.addEventListener('click', () => {
+            if (bulkDeleteProxyModal) {
+                bulkDeleteProxyModal.classList.remove('show');
+            }
+        });
+    }
+
+    // 确认批量删除 Proxy
+    if (confirmBulkDeleteProxyBtn) {
+        confirmBulkDeleteProxyBtn.addEventListener('click', handleBulkDeleteProxies);
+    }
+    // --- 结束：批量删除 Proxy 相关 ---
+ 
     // --- 新增：重置确认模态框事件监听 (移到 DOMContentLoaded 内部) ---
     if (closeResetModalBtn) {
         closeResetModalBtn.addEventListener('click', () => {
@@ -206,7 +302,7 @@ document.addEventListener('DOMContentLoaded', function() {
     // --- 结束：思考模型预算映射相关 ---
 
     // 添加事件委托，处理动态添加的 THINKING_MODELS 输入框的 input 事件
-    const thinkingModelsContainer = document.getElementById('THINKING_MODELS_container');
+    // const thinkingModelsContainer = document.getElementById('THINKING_MODELS_container'); // Moved outside
     if (thinkingModelsContainer) {
         thinkingModelsContainer.addEventListener('input', function(event) {
             if (event.target && event.target.classList.contains('array-input') && event.target.closest('.array-item[data-model-id]')) {
@@ -220,6 +316,12 @@ document.addEventListener('DOMContentLoaded', function() {
         });
     }
 
+    // --- 新增：安全设置相关 ---
+    const addSafetySettingBtn = document.getElementById('addSafetySettingBtn');
+    if (addSafetySettingBtn) {
+        addSafetySettingBtn.addEventListener('click', () => addSafetySettingItem());
+    }
+    // --- 结束：安全设置相关 ---
 
 }); // <-- DOMContentLoaded 结束括号
 
@@ -265,6 +367,10 @@ async function initConfig() {
         if (!config.FILTERED_MODELS || !Array.isArray(config.FILTERED_MODELS) || config.FILTERED_MODELS.length === 0) {
             config.FILTERED_MODELS = ['gemini-1.0-pro-latest'];
         }
+        // --- 新增：处理 PROXIES 默认值 ---
+        if (!config.PROXIES || !Array.isArray(config.PROXIES)) {
+            config.PROXIES = []; // 默认为空数组
+        }
         // --- 新增：处理新字段的默认值 ---
         if (!config.THINKING_MODELS || !Array.isArray(config.THINKING_MODELS)) {
             config.THINKING_MODELS = []; // 默认为空数组
@@ -272,7 +378,11 @@ async function initConfig() {
         if (!config.THINKING_BUDGET_MAP || typeof config.THINKING_BUDGET_MAP !== 'object' || config.THINKING_BUDGET_MAP === null) {
             config.THINKING_BUDGET_MAP = {}; // 默认为空对象
         }
-        // --- 结束：处理新字段的默认值 ---
+        // --- 新增：处理 SAFETY_SETTINGS 默认值 ---
+        if (!config.SAFETY_SETTINGS || !Array.isArray(config.SAFETY_SETTINGS)) {
+            config.SAFETY_SETTINGS = []; // 默认为空数组
+        }
+        // --- 结束：处理 SAFETY_SETTINGS 默认值 ---
 
         populateForm(config);
 
@@ -296,6 +406,7 @@ async function initConfig() {
             SEARCH_MODELS: ['gemini-1.5-flash-latest'],
             FILTERED_MODELS: ['gemini-1.0-pro-latest'],
             UPLOAD_PROVIDER: 'smms',
+            PROXIES: [], // 添加默认值
             THINKING_MODELS: [],
             THINKING_BUDGET_MAP: {}
         };
@@ -410,6 +521,24 @@ function populateForm(config) {
     if (uploadProvider) {
         toggleProviderConfig(uploadProvider.value);
     }
+
+    // --- 新增：填充 SAFETY_SETTINGS ---
+    let safetyItemsAdded = false;
+    if (safetySettingsContainer && Array.isArray(config.SAFETY_SETTINGS)) {
+        config.SAFETY_SETTINGS.forEach(setting => {
+            if (setting && typeof setting === 'object' && setting.category && setting.threshold) {
+                addSafetySettingItem(setting.category, setting.threshold);
+                safetyItemsAdded = true;
+            } else {
+                console.warn("Invalid safety setting item found:", setting);
+            }
+        });
+    }
+    // 如果没有添加任何安全设置项，则显示占位符
+    if (safetySettingsContainer && !safetyItemsAdded) {
+         safetySettingsContainer.innerHTML = '<div class="text-gray-500 text-sm italic">定义模型的安全过滤阈值。</div>';
+    }
+    // --- 结束：填充 SAFETY_SETTINGS ---
 }
 
 // --- 新增：处理批量添加 API Key 的逻辑 ---
@@ -521,6 +650,92 @@ function handleBulkDeleteApiKeys() {
     bulkDeleteTextarea.value = '';
 }
 
+// --- 新增：处理批量添加 Proxy 的逻辑 ---
+function handleBulkAddProxies() {
+    const proxyBulkInput = document.getElementById('proxyBulkInput');
+    const proxyContainer = document.getElementById('PROXIES_container');
+    const proxyModal = document.getElementById('proxyModal');
+
+    if (!proxyBulkInput || !proxyContainer || !proxyModal) return;
+
+    const bulkText = proxyBulkInput.value;
+    // 匹配 http(s):// 或 socks5:// 格式的代理，允许包含用户名密码
+    const proxyRegex = /(?:https?|socks5):\/\/(?:[^:@\/]+(?::[^@\/]+)?@)?(?:[^:\/\s]+)(?::\d+)?/g;
+    const extractedProxies = bulkText.match(proxyRegex) || [];
+
+    // 获取当前已有的 proxies
+    const currentProxyInputs = proxyContainer.querySelectorAll('.array-input');
+    const currentProxies = Array.from(currentProxyInputs).map(input => input.value).filter(proxy => proxy.trim() !== '');
+
+    // 合并并去重
+    const combinedProxies = new Set([...currentProxies, ...extractedProxies]);
+    const uniqueProxies = Array.from(combinedProxies);
+
+    // 清空现有列表显示
+    const existingItems = proxyContainer.querySelectorAll('.array-item');
+    existingItems.forEach(item => item.remove());
+
+    // 重新填充列表
+    uniqueProxies.forEach(proxy => {
+        addArrayItemWithValue('PROXIES', proxy);
+    });
+
+    // 关闭模态框
+    proxyModal.classList.remove('show');
+    showNotification(`添加/更新了 ${uniqueProxies.length} 个唯一代理`, 'success');
+}
+// --- 结束：处理批量添加 Proxy 的逻辑 ---
+
+// --- 新增：处理批量删除 Proxy 的逻辑 ---
+function handleBulkDeleteProxies() {
+    const bulkDeleteTextarea = document.getElementById('bulkDeleteProxyInput');
+    const proxyContainer = document.getElementById('PROXIES_container');
+    const bulkDeleteModal = document.getElementById('bulkDeleteProxyModal');
+
+    if (!bulkDeleteTextarea || !proxyContainer || !bulkDeleteModal) return;
+
+    const bulkText = bulkDeleteTextarea.value;
+    if (!bulkText.trim()) {
+        showNotification('请粘贴需要删除的代理地址', 'warning');
+        return;
+    }
+
+    // 使用与添加时相同的正则表达式来提取要删除的代理
+    const proxyRegex = /(?:https?|socks5):\/\/(?:[^:@\/]+(?::[^@\/]+)?@)?(?:[^:\/\s]+)(?::\d+)?/g;
+    const proxiesToDelete = new Set(bulkText.match(proxyRegex) || []); // 使用 Set 进行高效查找
+
+    if (proxiesToDelete.size === 0) {
+        showNotification('未在输入内容中提取到有效的代理地址格式', 'warning');
+        return;
+    }
+
+    const proxyItems = proxyContainer.querySelectorAll('.array-item');
+    let deleteCount = 0;
+
+    proxyItems.forEach(item => {
+        const input = item.querySelector('.array-input');
+        // 检查输入框是否存在及其值是否在要删除的集合中
+        if (input && proxiesToDelete.has(input.value)) {
+            item.remove(); // 删除整个数组项元素
+            deleteCount++;
+        }
+    });
+
+    // 关闭模态框
+    bulkDeleteModal.classList.remove('show');
+
+    // 提供反馈
+    if (deleteCount > 0) {
+        showNotification(`成功删除了 ${deleteCount} 个匹配的代理`, 'success');
+    } else {
+        showNotification('列表中未找到您输入的任何代理进行删除', 'info');
+    }
+
+    // 处理后清空文本区域
+    bulkDeleteTextarea.value = '';
+}
+// --- 结束：处理批量删除 Proxy 的逻辑 ---
+
 // 切换标签
 function switchTab(tabId) {
     // 更新标签按钮状态
@@ -781,6 +996,23 @@ function collectFormData() {
     }
     // --- 结束：处理 THINKING_BUDGET_MAP ---
 
+    // --- 新增：处理 SAFETY_SETTINGS ---
+    if (safetySettingsContainer) {
+        formData['SAFETY_SETTINGS'] = [];
+        const settingItems = safetySettingsContainer.querySelectorAll('.safety-setting-item');
+        settingItems.forEach(item => {
+            const categorySelect = item.querySelector('.safety-category-select');
+            const thresholdSelect = item.querySelector('.safety-threshold-select');
+            if (categorySelect && thresholdSelect && categorySelect.value && thresholdSelect.value) {
+                formData['SAFETY_SETTINGS'].push({
+                    category: categorySelect.value,
+                    threshold: thresholdSelect.value
+                });
+            }
+        });
+    }
+    // --- 结束：处理 SAFETY_SETTINGS ---
+
     return formData;
 }
 
@@ -975,10 +1207,6 @@ function generateRandomToken() {
 }
 // --- 结束：生成随机令牌函数 ---
 
-// --- 修改：添加思考模型预算映射项 (现在由添加思考模型触发) ---
-// function addBudgetMapItem() {
-//    // 不再需要手动添加
-// }
 
 // Deprecated: This function is now effectively replaced by createAndAppendBudgetMapItem
 // for the initial population logic. It delegates to the new function if called.
@@ -988,3 +1216,86 @@ function addBudgetMapItemWithValue(mapKey, mapValue, modelId) {
     createAndAppendBudgetMapItem(mapKey, mapValue, modelId);
 }
 /* --- 结束：(addBudgetMapItemWithValue 已弃用) --- */
+
+
+// --- 新增：添加安全设置项的函数 ---
+function addSafetySettingItem(category = '', threshold = '') {
+    const container = document.getElementById('SAFETY_SETTINGS_container');
+    if (!container) {
+        console.error("Cannot add safety setting: SAFETY_SETTINGS_container not found!");
+        return;
+    }
+
+    // 如果容器当前只有占位符，则清除它
+    const placeholder = container.querySelector('.text-gray-500.italic');
+    if (placeholder && container.children.length === 1 && container.firstChild === placeholder) {
+        container.innerHTML = '';
+    }
+
+    const harmCategories = [
+        "HARM_CATEGORY_HARASSMENT",
+        "HARM_CATEGORY_HATE_SPEECH",
+        "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+        "HARM_CATEGORY_DANGEROUS_CONTENT",
+        "HARM_CATEGORY_CIVIC_INTEGRITY" // 根据需要添加或移除
+    ];
+    const harmThresholds = [
+        "BLOCK_NONE",
+        "BLOCK_LOW_AND_ABOVE",
+        "BLOCK_MEDIUM_AND_ABOVE",
+        "BLOCK_ONLY_HIGH",
+        "OFF" // 根据 Google API 文档添加或移除
+    ];
+
+    const settingItem = document.createElement('div');
+    settingItem.className = 'safety-setting-item flex items-center mb-2 gap-2';
+
+    // Category Select
+    const categorySelect = document.createElement('select');
+    categorySelect.className = 'safety-category-select flex-grow px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:border-primary-500 focus:ring focus:ring-primary-200 focus:ring-opacity-50 bg-white';
+    harmCategories.forEach(cat => {
+        const option = document.createElement('option');
+        option.value = cat;
+        option.textContent = cat.replace('HARM_CATEGORY_', ''); // 显示更友好的名称
+        if (cat === category) {
+            option.selected = true;
+        }
+        categorySelect.appendChild(option);
+    });
+
+    // Threshold Select
+    const thresholdSelect = document.createElement('select');
+    thresholdSelect.className = 'safety-threshold-select w-48 px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:border-primary-500 focus:ring focus:ring-primary-200 focus:ring-opacity-50 bg-white';
+    harmThresholds.forEach(thr => {
+        const option = document.createElement('option');
+        option.value = thr;
+        option.textContent = thr.replace('BLOCK_', '').replace('_AND_ABOVE', '+'); // 简化显示
+        if (thr === threshold) {
+            option.selected = true;
+        }
+        thresholdSelect.appendChild(option);
+    });
+
+    // Remove Button
+    const removeBtn = document.createElement('button');
+    removeBtn.type = 'button';
+    removeBtn.className = 'remove-btn text-gray-400 hover:text-red-500 focus:outline-none transition-colors duration-150';
+    removeBtn.innerHTML = '<i class="fas fa-trash-alt"></i>';
+    removeBtn.title = '删除此设置';
+    removeBtn.addEventListener('click', function() {
+        const currentItem = this.closest('.safety-setting-item');
+        currentItem.remove();
+        // 检查容器是否为空，如果是，则添加回占位符
+        if (container.children.length === 0) {
+            container.innerHTML = '<div class="text-gray-500 text-sm italic">定义模型的安全过滤阈值。</div>';
+        }
+    });
+
+    settingItem.appendChild(categorySelect);
+    settingItem.appendChild(thresholdSelect);
+    settingItem.appendChild(removeBtn);
+
+    container.appendChild(settingItem);
+}
+// --- 结束：添加安全设置项的函数 ---
+

app/static/service-worker.js

@@ -17,13 +17,27 @@ self.addEventListener('install', event => {
 
 self.addEventListener('fetch', event => {
   event.respondWith(
-    caches.match(event.request)
-      .then(response => {
-        if (response) {
-          return response;
-        }
-        return fetch(event.request);
-      })
+    caches.open(CACHE_NAME).then(cache => {
+      // 1. 尝试从缓存获取
+      return cache.match(event.request).then(responseFromCache => {
+        // 2. 同时从网络获取 (后台进行)
+        const fetchPromise = fetch(event.request).then(responseFromNetwork => {
+          // 3. 网络请求成功，更新缓存
+          cache.put(event.request, responseFromNetwork.clone());
+          return responseFromNetwork;
+        }).catch(err => {
+          // 网络请求失败时，可以选择记录错误或不执行任何操作
+          console.error('Network fetch failed:', err);
+          // 确保即使网络失败，如果缓存存在，我们仍然返回缓存
+          // 如果缓存也不存在，则此 Promise 会 reject
+          throw err;
+        });
+
+        // 4. 如果缓存存在，立即返回缓存；否则等待网络响应
+        //    后台的网络请求仍在进行，用于更新缓存
+        return responseFromCache || fetchPromise;
+      });
+    })
   );
 });
 

app/templates/config_editor.html

@@ -182,6 +182,22 @@
                         <input type="number" id="MAX_RETRIES" name="MAX_RETRIES" min="0" max="10" class="w-full px-4 py-3 rounded-lg border border-gray-300 focus:border-primary-500 focus:ring focus:ring-primary-200 focus:ring-opacity-50">
                         <small class="text-gray-500 mt-1 block">API请求失败后的最大重试次数</small>
                     </div>
+                    <!-- 代理服务器列表 -->
+                    <div class="mb-6">
+                        <label for="PROXIES" class="block font-semibold mb-2 text-gray-700">代理服务器列表</label>
+                        <div class="array-container bg-white rounded-lg border border-gray-200 p-4 mb-2" id="PROXIES_container">
+                            <!-- 代理项将在这里动态添加 -->
+                        </div>
+                        <div class="flex justify-end gap-2">
+                            <button type="button" class="bg-danger-600 hover:bg-danger-700 text-white px-4 py-2 rounded-lg font-medium transition-all duration-200 flex items-center gap-2" id="bulkDeleteProxyBtn">
+                                <i class="fas fa-trash-alt"></i> 删除代理
+                            </button>
+                            <button type="button" class="bg-primary-600 hover:bg-primary-700 text-white px-4 py-2 rounded-lg font-medium transition-all duration-200 flex items-center gap-2" id="addProxyBtn">
+                                <i class="fas fa-plus"></i> 添加代理
+                            </button>
+                        </div>
+                        <small class="text-gray-500 mt-1 block">代理服务器列表，支持 http 和 socks5 格式，例如: http://user:pass@host:port 或 socks5://host:port。点击按钮可批量添加或删除。</small>
+                    </div>
                 </div>
                 
                 <!-- 模型相关配置 -->
@@ -295,6 +311,20 @@
                         </div> -->
                         <small class="text-gray-500 mt-1 block">为每个思考模型设置预算（整数，最大值 24576），此项与上方模型列表自动关联。</small>
                     </div>
+<!-- 安全设置 -->
+                    <div class="mb-6">
+                        <label for="SAFETY_SETTINGS" class="block font-semibold mb-2 text-gray-700">安全设置 (Safety Settings)</label>
+                        <div class="bg-white rounded-lg border border-gray-200 p-4 mb-2 space-y-3" id="SAFETY_SETTINGS_container">
+                            <!-- 安全设置项将在这里动态添加 -->
+                            <div class="text-gray-500 text-sm italic">定义模型的安全过滤阈值。</div>
+                        </div>
+                        <div class="flex justify-end">
+                            <button type="button" class="bg-primary-600 hover:bg-primary-700 text-white px-4 py-2 rounded-lg font-medium transition-all duration-200 flex items-center gap-2" id="addSafetySettingBtn">
+                                <i class="fas fa-plus"></i> 添加安全设置
+                            </button>
+                        </div>
+                        <small class="text-gray-500 mt-1 block">配置模型的安全过滤级别，例如 HARM_CATEGORY_HARASSMENT: BLOCK_NONE。</small>
+                    </div>
                 </div>
 
                 <!-- 图像生成相关配置 -->
@@ -511,6 +541,43 @@
             </div>
         </div>
     </div>
+
+    <!-- Proxy Add Modal -->
+    <div id="proxyModal" class="modal">
+        <div class="w-full max-w-lg mx-auto bg-white rounded-2xl shadow-2xl overflow-hidden animate-fade-in">
+            <div class="p-6">
+                <div class="flex justify-between items-center mb-4">
+                    <h2 class="text-xl font-bold text-gray-800">批量添加代理服务器</h2>
+                    <button id="closeProxyModalBtn" class="text-gray-400 hover:text-gray-600 text-xl">&times;</button>
+                </div>
+                <p class="text-gray-600 mb-4">每行粘贴一个或多个代理地址，将自动提取有效地址并去重。</p>
+                <textarea id="proxyBulkInput" rows="10" placeholder="在此处粘贴代理地址 (例如 http://user:pass@host:port 或 socks5://host:port)..." class="w-full px-4 py-3 rounded-lg border border-gray-300 focus:border-primary-500 focus:ring focus:ring-primary-200 focus:ring-opacity-50 font-mono text-sm"></textarea>
+                <div class="flex justify-end gap-3 mt-6">
+                    <button type="button" id="confirmAddProxyBtn" class="bg-primary-600 hover:bg-primary-700 text-white px-6 py-2 rounded-lg font-medium transition">确认添加</button>
+                    <button type="button" id="cancelAddProxyBtn" class="bg-gray-200 hover:bg-gray-300 text-gray-700 px-6 py-2 rounded-lg font-medium transition">取消</button>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Bulk Delete Proxy Modal -->
+    <div id="bulkDeleteProxyModal" class="modal">
+        <div class="w-full max-w-lg mx-auto bg-white rounded-2xl shadow-2xl overflow-hidden animate-fade-in">
+            <div class="p-6">
+                <div class="flex justify-between items-center mb-4">
+                    <h2 class="text-xl font-bold text-gray-800">批量删除代理服务器</h2>
+                    <button id="closeBulkDeleteProxyModalBtn" class="text-gray-400 hover:text-gray-600 text-xl">&times;</button>
+                </div>
+                <p class="text-gray-600 mb-4">每行粘贴一个或多个代理地址，将自动提取有效地址并从列表中删除。</p>
+                <textarea id="bulkDeleteProxyInput" rows="10" placeholder="在此处粘贴要删除的代理地址..." class="w-full px-4 py-3 rounded-lg border border-gray-300 focus:border-danger-500 focus:ring focus:ring-danger-200 focus:ring-opacity-50 font-mono text-sm"></textarea>
+                <div class="flex justify-end gap-3 mt-6">
+                    <button type="button" id="confirmBulkDeleteProxyBtn" class="bg-danger-600 hover:bg-danger-700 text-white px-6 py-2 rounded-lg font-medium transition">确认删除</button>
+                    <button type="button" id="cancelBulkDeleteProxyBtn" class="bg-gray-200 hover:bg-gray-300 text-gray-700 px-6 py-2 rounded-lg font-medium transition">取消</button>
+                </div>
+            </div>
+        </div>
+    </div>
+
     <!-- Reset Confirmation Modal -->
     <div id="resetConfirmModal" class="modal">
         <div class="w-full max-w-md mx-auto bg-white rounded-2xl shadow-2xl overflow-hidden animate-fade-in">

requirements.txt

@@ -1,5 +1,5 @@
 fastapi
-httpx
+httpx[socks]
 openai
 pydantic
 pydantic_settings
@@ -16,6 +16,5 @@ sqlalchemy
 aiomysql
 databases
 python-dotenv
-apscheduler # 添加定时任务库
-
+apscheduler
 packaging