mirror of
https://github.com/snailyp/gemini-balance.git
synced 2026-09-05 23:56:37 +08:00
snaily
f13a4fba5f
此更改将 `usage_metadata` 参数添加到了 `app/handler/response_handler.py` 和 `app/service/chat/openai_chat_service.py` 中的相关函数。 `usage_metadata`（通常包含 token 计数：prompt_tokens, completion_tokens, total_tokens）现在会从 OpenAI API 响应中提取，并用于填充标准化响应格式中的 `usage` 字段。 这样可以更准确地跟踪 OpenAI 聊天完成接口的 token 消耗。
500 lines
20 KiB
Python
500 lines
20 KiB
Python
# app/services/chat_service.py
|
|
|
|
import datetime
|
|
import json
|
|
import re
|
|
import time
|
|
from copy import deepcopy
|
|
from typing import Any, AsyncGenerator, Dict, List, Optional, Union
|
|
|
|
from app.config.config import settings
|
|
from app.core.constants import GEMINI_2_FLASH_EXP_SAFETY_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
|
|
from app.handler.stream_optimizer import openai_optimizer
|
|
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
|
|
|
|
logger = get_openai_logger()
|
|
|
|
|
|
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 content and "parts" in content and isinstance(content["parts"], list):
|
|
for part in content["parts"]:
|
|
if isinstance(part, dict) and "inline_data" in part:
|
|
return True
|
|
return False
|
|
|
|
|
|
def _build_tools(
|
|
request: ChatRequest, messages: List[Dict[str, Any]]
|
|
) -> List[Dict[str, Any]]:
|
|
"""构建工具"""
|
|
tool = dict()
|
|
model = request.model
|
|
|
|
if (
|
|
settings.TOOLS_CODE_EXECUTION_ENABLED
|
|
and not (
|
|
model.endswith("-search")
|
|
or "-thinking" in model
|
|
or model.endswith("-image")
|
|
or model.endswith("-image-generation")
|
|
)
|
|
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"] = {}
|
|
|
|
# 将 request 中的 tools 合并到 tools 中
|
|
if request.tools:
|
|
function_declarations = []
|
|
for item in request.tools:
|
|
if not item or not isinstance(item, dict):
|
|
continue
|
|
|
|
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", {}
|
|
):
|
|
function.pop("parameters", None)
|
|
|
|
function_declarations.append(function)
|
|
|
|
if function_declarations:
|
|
# 按照 function 的 name 去重
|
|
names, functions = set(), []
|
|
for fc in function_declarations:
|
|
if fc.get("name") not in names:
|
|
names.add(fc.get("name"))
|
|
functions.append(fc)
|
|
|
|
tool["functionDeclarations"] = functions
|
|
|
|
# 解决 "Tool use with function calling is unsupported" 问题
|
|
if tool.get("functionDeclarations"):
|
|
tool.pop("googleSearch", None)
|
|
tool.pop("codeExecution", None)
|
|
|
|
return [tool] if tool else []
|
|
|
|
|
|
def _get_safety_settings(model: str) -> List[Dict[str, str]]:
|
|
"""获取安全设置"""
|
|
# if (
|
|
# "2.0" in model
|
|
# and "gemini-2.0-flash-thinking-exp" not in model
|
|
# and "gemini-2.0-pro-exp" not in model
|
|
# ):
|
|
if model == "gemini-2.0-flash-exp":
|
|
return GEMINI_2_FLASH_EXP_SAFETY_SETTINGS
|
|
return settings.SAFETY_SETTINGS
|
|
|
|
|
|
def _build_payload(
|
|
request: ChatRequest,
|
|
messages: List[Dict[str, Any]],
|
|
instruction: Optional[Dict[str, Any]] = None,
|
|
) -> Dict[str, Any]:
|
|
"""构建请求payload"""
|
|
payload = {
|
|
"contents": messages,
|
|
"generationConfig": {
|
|
"temperature": request.temperature,
|
|
"stopSequences": request.stop,
|
|
"topP": request.top_p,
|
|
"topK": request.top_k,
|
|
},
|
|
"tools": _build_tools(request, messages),
|
|
"safetySettings": _get_safety_settings(request.model),
|
|
}
|
|
if request.max_tokens is not None:
|
|
payload["generationConfig"]["maxOutputTokens"] = request.max_tokens
|
|
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}
|
|
if request.model in settings.THINKING_BUDGET_MAP:
|
|
payload["generationConfig"]["thinkingConfig"] = {
|
|
"thinkingBudget": settings.THINKING_BUDGET_MAP.get(request.model, 1000)
|
|
}
|
|
|
|
if (
|
|
instruction
|
|
and isinstance(instruction, dict)
|
|
and instruction.get("role") == "system"
|
|
and instruction.get("parts")
|
|
and not request.model.endswith("-image")
|
|
and not request.model.endswith("-image-generation")
|
|
):
|
|
payload["systemInstruction"] = instruction
|
|
|
|
return payload
|
|
|
|
|
|
class OpenAIChatService:
|
|
"""聊天服务"""
|
|
|
|
def __init__(self, base_url: str, key_manager: KeyManager = None):
|
|
self.message_converter = OpenAIMessageConverter()
|
|
self.response_handler = OpenAIResponseHandler(config=None)
|
|
self.api_client = GeminiApiClient(base_url, settings.TIME_OUT)
|
|
self.key_manager = key_manager
|
|
self.image_create_service = ImageCreateService()
|
|
|
|
def _extract_text_from_openai_chunk(self, chunk: Dict[str, Any]) -> str:
|
|
"""从OpenAI响应块中提取文本内容"""
|
|
if not chunk.get("choices"):
|
|
return ""
|
|
|
|
choice = chunk["choices"][0]
|
|
if "delta" in choice and "content" in choice["delta"]:
|
|
return choice["delta"]["content"]
|
|
return ""
|
|
|
|
def _create_char_openai_chunk(
|
|
self, original_chunk: Dict[str, Any], text: str
|
|
) -> Dict[str, Any]:
|
|
"""创建包含指定文本的OpenAI响应块"""
|
|
chunk_copy = json.loads(json.dumps(original_chunk)) # 深拷贝
|
|
if chunk_copy.get("choices") and "delta" in chunk_copy["choices"][0]:
|
|
chunk_copy["choices"][0]["delta"]["content"] = text
|
|
return chunk_copy
|
|
|
|
async def create_chat_completion(
|
|
self,
|
|
request: ChatRequest,
|
|
api_key: str,
|
|
) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
|
|
"""创建聊天完成"""
|
|
# 转换消息格式
|
|
messages, instruction = self.message_converter.convert(request.messages)
|
|
|
|
# 构建请求payload
|
|
payload = _build_payload(request, messages, instruction)
|
|
|
|
if request.stream:
|
|
return self._handle_stream_completion(request.model, payload, api_key)
|
|
return await self._handle_normal_completion(request.model, payload, api_key)
|
|
|
|
async def _handle_normal_completion(
|
|
self, model: str, payload: Dict[str, Any], api_key: str
|
|
) -> Dict[str, Any]:
|
|
"""处理普通聊天完成"""
|
|
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(payload, model, api_key)
|
|
usage_metadata = response.get("usageMetadata", {})
|
|
is_success = True
|
|
status_code = 200
|
|
return self.response_handler.handle_response(
|
|
response, model, stream=False, finish_reason="stop", usage_metadata=usage_metadata
|
|
)
|
|
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-chat-non-stream",
|
|
error_log=error_log_msg,
|
|
error_code=status_code,
|
|
request_msg=payload,
|
|
)
|
|
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[str, Any], 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:
|
|
tool_call_flag = False
|
|
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(
|
|
chunk, model, stream=True, finish_reason=None
|
|
)
|
|
if openai_chunk:
|
|
# 提取文本内容
|
|
text = self._extract_text_from_openai_chunk(openai_chunk)
|
|
if text and settings.STREAM_OPTIMIZER_ENABLED:
|
|
# 使用流式输出优化器处理文本输出
|
|
async for (
|
|
optimized_chunk
|
|
) in openai_optimizer.optimize_stream_output(
|
|
text,
|
|
lambda t: self._create_char_openai_chunk(
|
|
openai_chunk, t
|
|
),
|
|
lambda c: f"data: {json.dumps(c)}\n\n",
|
|
):
|
|
yield optimized_chunk
|
|
else:
|
|
# 如果没有文本内容（如工具调用等），整块输出
|
|
if "tool_calls" in json.dumps(openai_chunk):
|
|
tool_call_flag = True
|
|
yield f"data: {json.dumps(openai_chunk)}\n\n"
|
|
if tool_call_flag:
|
|
yield f"data: {json.dumps(self.response_handler.handle_response({}, model, stream=True, finish_reason='tool_calls'))}\n\n"
|
|
else:
|
|
yield f"data: {json.dumps(self.response_handler.handle_response({}, model, stream=True, finish_reason='stop'))}\n\n"
|
|
yield "data: [DONE]\n\n"
|
|
logger.info("Streaming completed successfully")
|
|
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-chat-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"
|
|
|
|
async def create_image_chat_completion(
|
|
self, request: ChatRequest, api_key: str
|
|
) -> Union[Dict[str, Any], AsyncGenerator[str, None]]:
|
|
|
|
image_generate_request = ImageGenerationRequest()
|
|
image_generate_request.prompt = request.messages[-1]["content"]
|
|
image_res = self.image_create_service.generate_images_chat(
|
|
image_generate_request
|
|
)
|
|
|
|
if request.stream:
|
|
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
|
|
)
|
|
|
|
async def _handle_stream_image_completion(
|
|
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()
|
|
is_success = False
|
|
status_code = None
|
|
|
|
try:
|
|
if image_data:
|
|
openai_chunk = self.response_handler.handle_image_chat_response(
|
|
image_data, model, stream=True, finish_reason=None
|
|
)
|
|
if openai_chunk:
|
|
# 提取文本内容
|
|
text = self._extract_text_from_openai_chunk(openai_chunk)
|
|
if text:
|
|
# 使用流式输出优化器处理文本输出
|
|
async for (
|
|
optimized_chunk
|
|
) in openai_optimizer.optimize_stream_output(
|
|
text,
|
|
lambda t: self._create_char_openai_chunk(openai_chunk, t),
|
|
lambda c: f"data: {json.dumps(c)}\n\n",
|
|
):
|
|
yield optimized_chunk
|
|
else:
|
|
# 如果没有文本内容（如图片URL等），整块输出
|
|
yield f"data: {json.dumps(openai_chunk)}\n\n"
|
|
yield f"data: {json.dumps(self.response_handler.handle_response({}, model, stream=True, finish_reason='stop'))}\n\n"
|
|
logger.info(
|
|
f"Stream image completion finished successfully for model: {model}"
|
|
)
|
|
is_success = True
|
|
status_code = 200
|
|
yield "data: [DONE]\n\n"
|
|
except Exception as e:
|
|
is_success = False
|
|
error_log_msg = f"Stream image completion failed for model {model}: {e}"
|
|
logger.error(error_log_msg)
|
|
status_code = 500
|
|
await add_error_log(
|
|
gemini_key=api_key,
|
|
model_name=model,
|
|
error_type="openai-image-stream",
|
|
error_log=error_log_msg,
|
|
error_code=status_code,
|
|
request_msg={
|
|
"image_data_truncated": image_data[:1000]
|
|
},
|
|
)
|
|
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}"
|
|
)
|
|
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_normal_image_completion(
|
|
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()
|
|
is_success = False
|
|
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}"
|
|
)
|
|
is_success = True
|
|
status_code = 200
|
|
return result
|
|
except Exception as e:
|
|
is_success = False
|
|
error_log_msg = f"Normal image completion failed for model {model}: {e}"
|
|
logger.error(error_log_msg)
|
|
status_code = 500
|
|
await add_error_log(
|
|
gemini_key=api_key,
|
|
model_name=model,
|
|
error_type="openai-image-non-stream",
|
|
error_log=error_log_msg,
|
|
error_code=status_code,
|
|
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}"
|
|
)
|
|
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,
|
|
)
|