mirror of
https://github.com/snailyp/gemini-balance.git
synced 2026-09-06 16:16:37 +08:00
refactor: 重构Gemini和OpenAI聊天服务以支持工具和安全设置
- 将 `_build_payload`、`_build_tools`、`_get_safety_settings` 和 `_has_image_parts` 函数从 `OpenAIChatService` 和 `GeminiChatService` 类中提取为独立的函数。 - 将 `_handle_stream_response` 和 `_handle_normal_response` 函数从 `GeminiResponseHandler` 和 `OpenAIResponseHandler` 类中提取为独立的函数。 - 将 `_extract_text` 函数从 `OpenAIResponseHandler` 类中提取为独立的函数, 并在 `GeminiResponseHandler` 中复用。 - 将 `_convert_image` 函数从 `OpenAIMessageConverter` 类中提取为独立的函数。 - 优化 `OpenAIChatService` 和 `GeminiChatService` 中的代码结构, 使其更清晰。 - 优化 `app/api/openai_routes.py` 和 `app/api/gemini_routes.py` 中的路由函数, 移除不必要的参数。
This commit is contained in:
yinpeng
@@ -4,24 +4,26 @@ from typing import Dict, Any, AsyncGenerator
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import httpx
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from abc import ABC, abstractmethod
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class ApiClient(ABC):
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"""API客户端基类"""
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@abstractmethod
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async def generate_content(self, payload: Dict[str, Any], model: str, api_key: str) -> Dict[str, Any]:
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pass
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@abstractmethod
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async def stream_generate_content(self, payload: Dict[str, Any], model: str, api_key: str) -> AsyncGenerator[str, None]:
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pass
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class GeminiApiClient(ApiClient):
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"""Gemini API客户端"""
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def __init__(self, base_url: str, timeout: int = 300):
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self.base_url = base_url
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self.timeout = timeout
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def generate_content(self, payload: Dict[str, Any], model: str, api_key: str) -> Dict[str, Any]:
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timeout = httpx.Timeout(self.timeout, read=self.timeout)
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if model.endswith("-search"):
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@@ -33,14 +35,14 @@ class GeminiApiClient(ApiClient):
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error_content = response.text
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raise Exception(f"API call failed with status code {response.status_code}, {error_content}")
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return response.json()
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async def stream_generate_content(self, payload: Dict[str, Any], model: str, api_key: str) -> AsyncGenerator[str, None]:
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timeout = httpx.Timeout(self.timeout, read=self.timeout)
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if model.endswith("-search"):
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model = model[:-7]
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async with httpx.AsyncClient(timeout=timeout) as client:
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url = f"{self.base_url}/models/{model}:streamGenerateContent?alt=sse&key={api_key}"
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async with client.stream("POST", url, json=payload) as response:
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async with client.stream(method="POST", url=url, json=payload) as response:
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if response.status_code != 200:
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error_content = await response.aread()
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error_msg = error_content.decode("utf-8")
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@@ -3,22 +3,39 @@
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from abc import ABC, abstractmethod
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from typing import List, Dict, Any
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class MessageConverter(ABC):
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"""消息转换器基类"""
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@abstractmethod
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def convert(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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pass
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def _convert_image(image_url: str) -> Dict[str, Any]:
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if image_url.startswith("data:image"):
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return {
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"inline_data": {
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"mime_type": "image/jpeg",
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"data": image_url.split(",")[1]
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}
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}
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return {
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"image_url": {
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"url": image_url
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}
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}
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class OpenAIMessageConverter(MessageConverter):
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"""OpenAI消息格式转换器"""
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def convert(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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converted_messages = []
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for msg in messages:
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role = "user" if msg["role"] == "user" else "model"
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parts = []
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if isinstance(msg["content"], str):
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parts.append({"text": msg["content"]})
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elif isinstance(msg["content"], list):
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@@ -29,22 +46,8 @@ class OpenAIMessageConverter(MessageConverter):
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if content["type"] == "text":
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parts.append({"text": content["text"]})
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elif content["type"] == "image_url":
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parts.append(self._convert_image(content["image_url"]["url"]))
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parts.append(_convert_image(content["image_url"]["url"]))
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converted_messages.append({"role": role, "parts": parts})
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return converted_messages
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def _convert_image(self, image_url: str) -> Dict[str, Any]:
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if image_url.startswith("data:image"):
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return {
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"inline_data": {
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"mime_type": "image/jpeg",
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"data": image_url.split(",")[1]
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}
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}
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return {
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"image_url": {
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"url": image_url
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}
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}
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@@ -6,331 +6,225 @@ import time
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import uuid
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from app.core.config import settings
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class ResponseHandler(ABC):
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"""响应处理器基类"""
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@abstractmethod
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def handle_response(self, response: Dict[str, Any], model: str, stream: bool = False) -> Dict[str, Any]:
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pass
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class GeminiResponseHandler(ResponseHandler):
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"""Gemini响应处理器"""
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def __init__(self):
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self.thinking_first = True
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self.thinking_status = False
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def handle_response(self, response: Dict[str, Any], model: str, stream: bool = False) -> Dict[str, Any]:
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if stream:
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return self._handle_stream_response(response, model, stream)
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return self._handle_normal_response(response, model, stream)
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def _handle_stream_response(self, response: Dict[str, Any], model: str, stream: bool) -> Dict[str, Any]:
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text = self._extract_text(response, model, stream=stream)
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content = {"parts": [{"text": text}],"role": "model"}
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response["candidates"][0]["content"] = content
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return response
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return _handle_gemini_stream_response(response, model, stream)
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return _handle_gemini_normal_response(response, model, stream)
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def _handle_normal_response(self, response: Dict[str, Any], model: str, stream: bool) -> Dict[str, Any]:
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text = self._extract_text(response, model, stream=stream)
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content = {"parts": [{"text": text}],"role": "model"}
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response["candidates"][0]["content"] = content
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return response
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def _extract_text(self, response: Dict[str, Any], model: str, stream: bool = False) -> str:
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text = ""
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if stream:
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if response.get("candidates"):
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candidate = response["candidates"][0]
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content = candidate.get("content", {})
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parts = content.get("parts", [])
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# if "thinking" in model:
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# if settings.SHOW_THINKING_PROCESS:
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# if len(parts) == 1:
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# if self.thinking_first:
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# self.thinking_first = False
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# self.thinking_status = True
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# text = "> thinking\n\n" + parts[0].get("text")
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# else:
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# text = parts[0].get("text")
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def _handle_openai_stream_response(response: Dict[str, Any], model: str, finish_reason: str) -> Dict[str, Any]:
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text = _extract_text(response, model, stream=True)
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return {
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"id": f"chatcmpl-{uuid.uuid4()}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": model,
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"choices": [{
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"index": 0,
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"delta": {"content": text} if text else {},
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"finish_reason": finish_reason
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}]
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}
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# if len(parts) == 2:
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# self.thinking_status = False
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# if self.thinking_first:
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# self.thinking_first = False
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# text = (
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# "> thinking\n\n"
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# + parts[0].get("text")
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# + "\n\n---\n> output\n\n"
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# + parts[1].get("text")
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# )
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# else:
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# text = (
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# parts[0].get("text")
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# + "\n\n---\n> output\n\n"
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# + parts[1].get("text")
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# )
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# else:
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# if len(parts) == 1:
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# if self.thinking_first:
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# self.thinking_first = False
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# self.thinking_status = True
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# text = ""
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# elif self.thinking_status:
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# text = ""
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# else:
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# text = parts[0].get("text")
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# if len(parts) == 2:
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# self.thinking_status = False
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# if self.thinking_first:
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# self.thinking_first = False
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# text = parts[1].get("text")
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# else:
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# text = parts[1].get("text")
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# else:
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# if "text" in parts[0]:
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# text = parts[0].get("text")
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# elif "executableCode" in parts[0]:
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# text = _format_code_block(parts[0]["executableCode"])
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# elif "codeExecution" in parts[0]:
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# text = _format_code_block(parts[0]["codeExecution"])
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# elif "executableCodeResult" in parts[0]:
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# text = _format_execution_result(
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# parts[0]["executableCodeResult"]
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# )
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# elif "codeExecutionResult" in parts[0]:
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# text = _format_execution_result(
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# parts[0]["codeExecutionResult"]
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# )
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# else:
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# text = ""
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if "text" in parts[0]:
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text = parts[0].get("text")
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elif "executableCode" in parts[0]:
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text = _format_code_block(parts[0]["executableCode"])
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elif "codeExecution" in parts[0]:
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text = _format_code_block(parts[0]["codeExecution"])
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elif "executableCodeResult" in parts[0]:
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text = _format_execution_result(
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parts[0]["executableCodeResult"]
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)
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elif "codeExecutionResult" in parts[0]:
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text = _format_execution_result(
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parts[0]["codeExecutionResult"]
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)
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else:
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text = ""
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text = _add_search_link_text(model, candidate, text)
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else:
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if response.get("candidates"):
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candidate = response["candidates"][0]
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if "thinking" in model:
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if settings.SHOW_THINKING_PROCESS:
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if len(candidate["content"]["parts"]) == 2:
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text = (
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"> thinking\n\n"
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+ candidate["content"]["parts"][0]["text"]
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+ "\n\n---\n> output\n\n"
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+ candidate["content"]["parts"][1]["text"]
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)
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else:
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text = candidate["content"]["parts"][0]["text"]
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else:
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if len(candidate["content"]["parts"]) == 2:
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text = candidate["content"]["parts"][1]["text"]
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else:
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text = candidate["content"]["parts"][0]["text"]
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else:
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text = candidate["content"]["parts"][0]["text"]
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text = _add_search_link_text(model, candidate, text)
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else:
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text = "暂无返回"
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return text
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def _handle_openai_normal_response(response: Dict[str, Any], model: str, finish_reason: str) -> Dict[str, Any]:
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text = _extract_text(response, model, stream=False)
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return {
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"id": f"chatcmpl-{uuid.uuid4()}",
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"object": "chat.completion",
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"created": int(time.time()),
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"model": model,
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"choices": [{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": text
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},
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"finish_reason": finish_reason
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}],
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"usage": {
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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}
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}
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class OpenAIResponseHandler(ResponseHandler):
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"""OpenAI响应处理器"""
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def __init__(self, config):
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self.config = config
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self.thinking_first = True
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self.thinking_status = False
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def handle_response(
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self,
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response: Dict[str, Any],
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model: str,
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stream: bool = False,
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finish_reason: str = None
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self,
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response: Dict[str, Any],
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model: str,
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stream: bool = False,
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finish_reason: str = None
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) -> Optional[Dict[str, Any]]:
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if stream:
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return self._handle_stream_response(response, model, finish_reason)
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return self._handle_normal_response(response, model, finish_reason)
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def _handle_stream_response(self, response: Dict[str, Any], model: str, finish_reason: str) -> Dict[str, Any]:
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text = self._extract_text(response, model, stream=True)
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return {
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"id": f"chatcmpl-{uuid.uuid4()}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
|
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"model": model,
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"choices": [{
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"index": 0,
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"delta": {"content": text} if text else {},
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"finish_reason": finish_reason
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}]
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}
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def _handle_normal_response(self, response: Dict[str, Any], model: str, finish_reason: str) -> Dict[str, Any]:
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text = self._extract_text(response, model, stream=False)
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return {
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"id": f"chatcmpl-{uuid.uuid4()}",
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"object": "chat.completion",
|
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"created": int(time.time()),
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"model": model,
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"choices": [{
|
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"index": 0,
|
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"message": {
|
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"role": "assistant",
|
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"content": text
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},
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"finish_reason": finish_reason
|
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}],
|
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"usage": {
|
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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}
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}
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def _extract_text(self, response: Dict[str, Any], model: str, stream: bool = False) -> str:
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text = ""
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if stream:
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if response.get("candidates"):
|
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candidate = response["candidates"][0]
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content = candidate.get("content", {})
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parts = content.get("parts", [])
|
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# if "thinking" in model:
|
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# if settings.SHOW_THINKING_PROCESS:
|
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# if len(parts) == 1:
|
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# if self.thinking_first:
|
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# self.thinking_first = False
|
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# self.thinking_status = True
|
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# text = "> thinking\n\n" + parts[0].get("text")
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# else:
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# text = parts[0].get("text")
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return _handle_openai_stream_response(response, model, finish_reason)
|
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return _handle_openai_normal_response(response, model, finish_reason)
|
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|
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# if len(parts) == 2:
|
||||
# self.thinking_status = False
|
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# if self.thinking_first:
|
||||
# self.thinking_first = False
|
||||
# text = (
|
||||
# "> thinking\n\n"
|
||||
# + parts[0].get("text")
|
||||
# + "\n\n---\n> output\n\n"
|
||||
# + parts[1].get("text")
|
||||
# )
|
||||
# else:
|
||||
# text = (
|
||||
# parts[0].get("text")
|
||||
# + "\n\n---\n> output\n\n"
|
||||
# + parts[1].get("text")
|
||||
# )
|
||||
# else:
|
||||
# if len(parts) == 1:
|
||||
# if self.thinking_first:
|
||||
# self.thinking_first = False
|
||||
# self.thinking_status = True
|
||||
# text = ""
|
||||
# elif self.thinking_status:
|
||||
# text = ""
|
||||
# else:
|
||||
# text = parts[0].get("text")
|
||||
|
||||
# if len(parts) == 2:
|
||||
# self.thinking_status = False
|
||||
# if self.thinking_first:
|
||||
# self.thinking_first = False
|
||||
# text = parts[1].get("text")
|
||||
# else:
|
||||
# text = parts[1].get("text")
|
||||
# else:
|
||||
# if "text" in parts[0]:
|
||||
# text = parts[0].get("text")
|
||||
# elif "executableCode" in parts[0]:
|
||||
# text = _format_code_block(parts[0]["executableCode"])
|
||||
# elif "codeExecution" in parts[0]:
|
||||
# text = _format_code_block(parts[0]["codeExecution"])
|
||||
# elif "executableCodeResult" in parts[0]:
|
||||
# text = _format_execution_result(
|
||||
# parts[0]["executableCodeResult"]
|
||||
# )
|
||||
# elif "codeExecutionResult" in parts[0]:
|
||||
# text = _format_execution_result(
|
||||
# parts[0]["codeExecutionResult"]
|
||||
# )
|
||||
# else:
|
||||
# text = ""
|
||||
# text = _add_search_link_text(model, candidate, text)
|
||||
if "text" in parts[0]:
|
||||
text = parts[0].get("text")
|
||||
elif "executableCode" in parts[0]:
|
||||
text = _format_code_block(parts[0]["executableCode"])
|
||||
elif "codeExecution" in parts[0]:
|
||||
text = _format_code_block(parts[0]["codeExecution"])
|
||||
elif "executableCodeResult" in parts[0]:
|
||||
text = _format_execution_result(
|
||||
parts[0]["executableCodeResult"]
|
||||
)
|
||||
elif "codeExecutionResult" in parts[0]:
|
||||
text = _format_execution_result(
|
||||
parts[0]["codeExecutionResult"]
|
||||
)
|
||||
else:
|
||||
text = ""
|
||||
text = _add_search_link_text(model, candidate, text)
|
||||
else:
|
||||
if response.get("candidates"):
|
||||
candidate = response["candidates"][0]
|
||||
if "thinking" in model:
|
||||
if settings.SHOW_THINKING_PROCESS:
|
||||
if len(candidate["content"]["parts"]) == 2:
|
||||
text = (
|
||||
def _extract_text(response: Dict[str, Any], model: str, stream: bool = False) -> str:
|
||||
text = ""
|
||||
if stream:
|
||||
if response.get("candidates"):
|
||||
candidate = response["candidates"][0]
|
||||
content = candidate.get("content", {})
|
||||
parts = content.get("parts", [])
|
||||
# if "thinking" in model:
|
||||
# if settings.SHOW_THINKING_PROCESS:
|
||||
# if len(parts) == 1:
|
||||
# if self.thinking_first:
|
||||
# self.thinking_first = False
|
||||
# self.thinking_status = True
|
||||
# text = "> thinking\n\n" + parts[0].get("text")
|
||||
# else:
|
||||
# text = parts[0].get("text")
|
||||
|
||||
# if len(parts) == 2:
|
||||
# self.thinking_status = False
|
||||
# if self.thinking_first:
|
||||
# self.thinking_first = False
|
||||
# text = (
|
||||
# "> thinking\n\n"
|
||||
# + parts[0].get("text")
|
||||
# + "\n\n---\n> output\n\n"
|
||||
# + parts[1].get("text")
|
||||
# )
|
||||
# else:
|
||||
# text = (
|
||||
# parts[0].get("text")
|
||||
# + "\n\n---\n> output\n\n"
|
||||
# + parts[1].get("text")
|
||||
# )
|
||||
# else:
|
||||
# if len(parts) == 1:
|
||||
# if self.thinking_first:
|
||||
# self.thinking_first = False
|
||||
# self.thinking_status = True
|
||||
# text = ""
|
||||
# elif self.thinking_status:
|
||||
# text = ""
|
||||
# else:
|
||||
# text = parts[0].get("text")
|
||||
|
||||
# if len(parts) == 2:
|
||||
# self.thinking_status = False
|
||||
# if self.thinking_first:
|
||||
# self.thinking_first = False
|
||||
# text = parts[1].get("text")
|
||||
# else:
|
||||
# text = parts[1].get("text")
|
||||
# else:
|
||||
# if "text" in parts[0]:
|
||||
# text = parts[0].get("text")
|
||||
# elif "executableCode" in parts[0]:
|
||||
# text = _format_code_block(parts[0]["executableCode"])
|
||||
# elif "codeExecution" in parts[0]:
|
||||
# text = _format_code_block(parts[0]["codeExecution"])
|
||||
# elif "executableCodeResult" in parts[0]:
|
||||
# text = _format_execution_result(
|
||||
# parts[0]["executableCodeResult"]
|
||||
# )
|
||||
# elif "codeExecutionResult" in parts[0]:
|
||||
# text = _format_execution_result(
|
||||
# parts[0]["codeExecutionResult"]
|
||||
# )
|
||||
# else:
|
||||
# text = ""
|
||||
if "text" in parts[0]:
|
||||
text = parts[0].get("text")
|
||||
elif "executableCode" in parts[0]:
|
||||
text = _format_code_block(parts[0]["executableCode"])
|
||||
elif "codeExecution" in parts[0]:
|
||||
text = _format_code_block(parts[0]["codeExecution"])
|
||||
elif "executableCodeResult" in parts[0]:
|
||||
text = _format_execution_result(
|
||||
parts[0]["executableCodeResult"]
|
||||
)
|
||||
elif "codeExecutionResult" in parts[0]:
|
||||
text = _format_execution_result(
|
||||
parts[0]["codeExecutionResult"]
|
||||
)
|
||||
else:
|
||||
text = ""
|
||||
text = _add_search_link_text(model, candidate, text)
|
||||
else:
|
||||
if response.get("candidates"):
|
||||
candidate = response["candidates"][0]
|
||||
if "thinking" in model:
|
||||
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"]
|
||||
)
|
||||
else:
|
||||
text = candidate["content"]["parts"][0]["text"]
|
||||
)
|
||||
else:
|
||||
if len(candidate["content"]["parts"]) == 2:
|
||||
text = candidate["content"]["parts"][1]["text"]
|
||||
else:
|
||||
text = candidate["content"]["parts"][0]["text"]
|
||||
text = candidate["content"]["parts"][0]["text"]
|
||||
else:
|
||||
text = candidate["content"]["parts"][0]["text"]
|
||||
text = _add_search_link_text(model, candidate, text)
|
||||
if len(candidate["content"]["parts"]) == 2:
|
||||
text = candidate["content"]["parts"][1]["text"]
|
||||
else:
|
||||
text = candidate["content"]["parts"][0]["text"]
|
||||
else:
|
||||
text = "暂无返回"
|
||||
return text
|
||||
text = candidate["content"]["parts"][0]["text"]
|
||||
text = _add_search_link_text(model, candidate, text)
|
||||
else:
|
||||
text = "暂无返回"
|
||||
return text
|
||||
|
||||
|
||||
def _handle_gemini_stream_response(response: Dict[str, Any], model: str, stream: bool) -> Dict[str, Any]:
|
||||
text = _extract_text(response, model, stream=stream)
|
||||
content = {"parts": [{"text": text}], "role": "model"}
|
||||
response["candidates"][0]["content"] = content
|
||||
return response
|
||||
|
||||
|
||||
def _handle_gemini_normal_response(response: Dict[str, Any], model: str, stream: bool) -> Dict[str, Any]:
|
||||
text = _extract_text(response, model, stream=stream)
|
||||
content = {"parts": [{"text": text}], "role": "model"}
|
||||
response["candidates"][0]["content"] = content
|
||||
return response
|
||||
|
||||
|
||||
|
||||
def _format_code_block(code_data: dict) -> str:
|
||||
"""格式化代码块输出"""
|
||||
language = code_data.get("language", "").lower()
|
||||
code = code_data.get("code", "").strip()
|
||||
return f"""\n\n---\n\n【代码执行】\n```{language}\n{code}\n```\n"""
|
||||
|
||||
|
||||
def _add_search_link_text(model:str, candidate:dict, text:str) -> 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"
|
||||
@@ -351,4 +245,4 @@ def _format_execution_result(result_data: dict) -> str:
|
||||
"""格式化执行结果输出"""
|
||||
outcome = result_data.get("outcome", "")
|
||||
output = result_data.get("output", "").strip()
|
||||
return f"""\n【执行结果】\n> outcome: {outcome}\n\n【输出结果】\n```plaintext\n{output}\n```\n\n---\n\n"""
|
||||
return f"""\n【执行结果】\n> outcome: {outcome}\n\n【输出结果】\n```plaintext\n{output}\n```\n\n---\n\n"""
|
||||
|
||||
@@ -8,26 +8,27 @@ from app.services.key_manager import KeyManager
|
||||
T = TypeVar('T')
|
||||
logger = get_retry_logger()
|
||||
|
||||
|
||||
class RetryHandler:
|
||||
"""重试处理装饰器"""
|
||||
|
||||
|
||||
def __init__(self, max_retries: int = 3, key_manager: KeyManager = None, key_arg: str = "api_key"):
|
||||
self.max_retries = max_retries
|
||||
self.key_manager = key_manager
|
||||
self.key_arg = key_arg
|
||||
|
||||
|
||||
def __call__(self, func: Callable[..., T]) -> Callable[..., T]:
|
||||
@wraps(func)
|
||||
async def wrapper(*args, **kwargs) -> T:
|
||||
last_exception = None
|
||||
|
||||
|
||||
for attempt in range(self.max_retries):
|
||||
try:
|
||||
return await func(*args, **kwargs)
|
||||
except Exception as e:
|
||||
last_exception = e
|
||||
logger.warning(f"API call failed with error: {str(e)}. Attempt {attempt + 1} of {self.max_retries}")
|
||||
|
||||
|
||||
if self.key_manager:
|
||||
old_key = kwargs.get(self.key_arg)
|
||||
new_key = await self.key_manager.handle_api_failure(old_key)
|
||||
@@ -36,5 +37,5 @@ class RetryHandler:
|
||||
|
||||
logger.error(f"All retry attempts failed, raising final exception: {str(last_exception)}")
|
||||
raise last_exception
|
||||
|
||||
return wrapper
|
||||
|
||||
return wrapper
|
||||
|
||||
Reference in New Issue
Block a user