mirror of
https://github.com/snailyp/gemini-balance.git
synced 2026-05-12 02:19:59 +08:00
a6cfc12443
- 修改了 response_handler.py 中的 _handle_openai_stream_response 和 _handle_openai_normal_response 方法，增加了对推理内容 (reasoning_content) 的支持。 - 更新了 _extract_result 方法的返回值，确保能够提取推理内容。 - 在 gemini_chat_service.py 和 openai_chat_service.py 中，调整了生成配置以包含思考过程的选项。 - 在 vertex_express_chat_service.py 中，增强了对客户端思考配置的处理逻辑，确保优先使用客户端提供的配置。
353 lines
12 KiB
Python
353 lines
12 KiB
Python
import base64
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import json
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import random
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import string
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import time
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import uuid
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional
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from app.config.config import settings
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from app.utils.uploader import ImageUploaderFactory
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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(
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self, response: Dict[str, Any], model: str, stream: bool = False
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) -> 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(
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self, response: Dict[str, Any], model: str, stream: bool = False, usage_metadata: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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if stream:
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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_openai_stream_response(
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response: Dict[str, Any], model: str, finish_reason: str, usage_metadata: Optional[Dict[str, Any]]
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) -> Dict[str, Any]:
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text, reasoning_content, tool_calls, _ = _extract_result(
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response, model, stream=True, gemini_format=False
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)
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if not text and not tool_calls and not reasoning_content:
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delta = {}
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else:
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delta = {"content": text, "reasoning_content": reasoning_content, "role": "assistant"}
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if tool_calls:
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delta["tool_calls"] = tool_calls
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template_chunk = {
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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": [{"index": 0, "delta": delta, "finish_reason": finish_reason}],
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}
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if usage_metadata:
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template_chunk["usage"] = {"prompt_tokens": usage_metadata.get("promptTokenCount", 0), "completion_tokens": usage_metadata.get("candidatesTokenCount",0), "total_tokens": usage_metadata.get("totalTokenCount", 0)}
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return template_chunk
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def _handle_openai_normal_response(
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response: Dict[str, Any], model: str, finish_reason: str, usage_metadata: Optional[Dict[str, Any]]
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) -> Dict[str, Any]:
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text, reasoning_content, tool_calls, _ = _extract_result(
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response, model, stream=False, gemini_format=False
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)
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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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{
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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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"reasoning_content": reasoning_content,
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"tool_calls": tool_calls,
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},
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"finish_reason": finish_reason,
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}
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],
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"usage": {"prompt_tokens": usage_metadata.get("promptTokenCount", 0), "completion_tokens": usage_metadata.get("candidatesTokenCount",0), "total_tokens": usage_metadata.get("totalTokenCount", 0)},
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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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usage_metadata: Optional[Dict[str, Any]] = None,
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) -> Optional[Dict[str, Any]]:
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if stream:
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return _handle_openai_stream_response(response, model, finish_reason, usage_metadata)
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return _handle_openai_normal_response(response, model, finish_reason, usage_metadata)
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def handle_image_chat_response(
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self, image_str: str, model: str, stream=False, finish_reason="stop"
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):
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if stream:
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return _handle_openai_stream_image_response(image_str, model, finish_reason)
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return _handle_openai_normal_image_response(image_str, model, finish_reason)
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def _handle_openai_stream_image_response(
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image_str: str, model: str, finish_reason: str
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) -> Dict[str, Any]:
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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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{
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"index": 0,
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"delta": {"content": image_str} if image_str else {},
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"finish_reason": finish_reason,
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}
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],
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}
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def _handle_openai_normal_image_response(
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image_str: str, model: str, finish_reason: str
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) -> Dict[str, Any]:
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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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{
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"index": 0,
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"message": {"role": "assistant", "content": image_str},
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"finish_reason": finish_reason,
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}
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],
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"usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
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}
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def _extract_result(
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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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gemini_format: bool = False,
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) -> tuple[str, Optional[str], List[Dict[str, Any]], Optional[bool]]:
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text, reasoning_content, tool_calls, thought = "", "", [], None
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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 not parts:
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return "", None, [], None
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if "text" in parts[0]:
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text = parts[0].get("text")
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if "thought" in parts[0]:
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if not gemini_format and settings.SHOW_THINKING_PROCESS:
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reasoning_content = text
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text = ""
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thought = parts[0].get("thought")
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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(parts[0]["executableCodeResult"])
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elif "codeExecutionResult" in parts[0]:
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text = _format_execution_result(parts[0]["codeExecutionResult"])
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elif "inlineData" in parts[0]:
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text = _extract_image_data(parts[0])
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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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tool_calls = _extract_tool_calls(parts, gemini_format)
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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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text, reasoning_content = "", ""
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if "parts" in candidate["content"]:
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for part in candidate["content"]["parts"]:
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if "text" in part:
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if "thought" in part and settings.SHOW_THINKING_PROCESS:
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reasoning_content += part["text"]
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else:
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text += part["text"]
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if "thought" in part and thought is None:
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thought = part.get("thought")
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elif "inlineData" in part:
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text += _extract_image_data(part)
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text = _add_search_link_text(model, candidate, text)
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tool_calls = _extract_tool_calls(
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candidate["content"]["parts"], gemini_format
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)
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else:
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text = "暂无返回"
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return text, reasoning_content, tool_calls, thought
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def _extract_image_data(part: dict) -> str:
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image_uploader = None
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if settings.UPLOAD_PROVIDER == "smms":
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image_uploader = ImageUploaderFactory.create(
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provider=settings.UPLOAD_PROVIDER, api_key=settings.SMMS_SECRET_TOKEN
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)
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elif settings.UPLOAD_PROVIDER == "picgo":
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image_uploader = ImageUploaderFactory.create(
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provider=settings.UPLOAD_PROVIDER, api_key=settings.PICGO_API_KEY
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)
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elif settings.UPLOAD_PROVIDER == "cloudflare_imgbed":
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image_uploader = ImageUploaderFactory.create(
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provider=settings.UPLOAD_PROVIDER,
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base_url=settings.CLOUDFLARE_IMGBED_URL,
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auth_code=settings.CLOUDFLARE_IMGBED_AUTH_CODE,
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upload_folder=settings.CLOUDFLARE_IMGBED_UPLOAD_FOLDER,
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)
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current_date = time.strftime("%Y/%m/%d")
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filename = f"{current_date}/{uuid.uuid4().hex[:8]}.png"
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base64_data = part["inlineData"]["data"]
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# 将base64_data转成bytes数组
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bytes_data = base64.b64decode(base64_data)
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upload_response = image_uploader.upload(bytes_data, filename)
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if upload_response.success:
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text = f"\n\n\n\n"
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else:
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text = ""
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return text
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def _extract_tool_calls(
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parts: List[Dict[str, Any]], gemini_format: bool
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) -> List[Dict[str, Any]]:
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"""提取工具调用信息"""
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if not parts or not isinstance(parts, list):
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return []
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letters = string.ascii_lowercase + string.digits
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tool_calls = list()
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for i in range(len(parts)):
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part = parts[i]
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if not part or not isinstance(part, dict):
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continue
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item = part.get("functionCall", {})
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if not item or not isinstance(item, dict):
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continue
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if gemini_format:
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tool_calls.append(part)
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else:
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id = f"call_{''.join(random.sample(letters, 32))}"
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name = item.get("name", "")
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arguments = json.dumps(item.get("args", None) or {})
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tool_calls.append(
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{
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"index": i,
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"id": id,
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"type": "function",
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"function": {"name": name, "arguments": arguments},
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}
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)
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return tool_calls
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def _handle_gemini_stream_response(
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response: Dict[str, Any], model: str, stream: bool
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) -> Dict[str, Any]:
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text, reasoning_content, tool_calls, thought = _extract_result(
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response, model, stream=stream, gemini_format=True
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)
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if tool_calls:
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content = {"parts": tool_calls, "role": "model"}
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else:
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part = {"text": text}
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if thought is not None:
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part["thought"] = thought
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content = {"parts": [part], "role": "model"}
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response["candidates"][0]["content"] = content
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return response
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def _handle_gemini_normal_response(
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response: Dict[str, Any], model: str, stream: bool
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) -> Dict[str, Any]:
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text, reasoning_content, tool_calls, thought = _extract_result(
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response, model, stream=stream, gemini_format=True
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)
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parts = []
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if tool_calls:
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parts = tool_calls
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else:
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if thought is not None:
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parts.append({"text": reasoning_content,"thought": thought})
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part = {"text": text}
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parts.append(part)
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content = {"parts": parts, "role": "model"}
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response["candidates"][0]["content"] = content
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return response
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def _format_code_block(code_data: dict) -> str:
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"""格式化代码块输出"""
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language = code_data.get("language", "").lower()
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code = code_data.get("code", "").strip()
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return f"""\n\n---\n\n【代码执行】\n```{language}\n{code}\n```\n"""
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def _add_search_link_text(model: str, candidate: dict, text: str) -> str:
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if (
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settings.SHOW_SEARCH_LINK
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and model.endswith("-search")
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and "groundingMetadata" in candidate
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and "groundingChunks" in candidate["groundingMetadata"]
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):
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grounding_chunks = candidate["groundingMetadata"]["groundingChunks"]
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text += "\n\n---\n\n"
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text += "**【引用来源】**\n\n"
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for _, grounding_chunk in enumerate(grounding_chunks, 1):
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if "web" in grounding_chunk:
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text += _create_search_link(grounding_chunk["web"])
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return text
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else:
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return text
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def _create_search_link(grounding_chunk: dict) -> str:
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return f'\n- [{grounding_chunk["title"]}]({grounding_chunk["uri"]})'
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def _format_execution_result(result_data: dict) -> str:
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"""格式化执行结果输出"""
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outcome = result_data.get("outcome", "")
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output = result_data.get("output", "").strip()
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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"""
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