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https://github.com/snailyp/gemini-balance.git
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56f6f5e198
- 为 OpenAI 兼容路由的图像生成聊天添加流式支持。 - 重构 `gemini-2.0-flash-exp` 安全设置，使用常量统一管理。 - 更改图像生成默认响应格式为 `url`。 - 启用 `.env.example` 中的 `AUTH_TOKEN`。 - 清理部分代码注释。
83 lines
3.4 KiB
Python
83 lines
3.4 KiB
Python
import datetime
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import time
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import re # For potential status code parsing from generic errors
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from typing import List, Union
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import openai
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from openai import APIStatusError # Import specific error type
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from openai.types import CreateEmbeddingResponse
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from app.config.config import settings
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from app.log.logger import get_embeddings_logger
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from app.database.services import add_error_log, add_request_log # Import DB logging functions
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logger = get_embeddings_logger()
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class EmbeddingService:
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async def create_embedding(
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self, input_text: Union[str, List[str]], model: str, api_key: str
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) -> CreateEmbeddingResponse:
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"""Create embeddings using OpenAI API with database logging"""
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start_time = time.perf_counter()
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request_datetime = datetime.datetime.now()
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is_success = False
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status_code = None
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response = None
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error_log_msg = ""
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# Prepare request message for logging (truncate if list or long string)
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if isinstance(input_text, list):
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request_msg_log = {"input_truncated": [str(item)[:100] + "..." if len(str(item)) > 100 else str(item) for item in input_text[:5]]}
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if len(input_text) > 5:
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request_msg_log["input_truncated"].append("...")
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else:
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request_msg_log = {"input_truncated": input_text[:1000] + "..." if len(input_text) > 1000 else input_text}
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try:
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client = openai.OpenAI(api_key=api_key, base_url=settings.BASE_URL)
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response = client.embeddings.create(input=input_text, model=model)
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is_success = True
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status_code = 200
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return response
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except APIStatusError as e:
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is_success = False
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status_code = e.status_code
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error_log_msg = f"OpenAI API error: {e}"
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logger.error(f"Error creating embedding (APIStatusError): {error_log_msg}")
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raise e # Re-raise the specific error
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except Exception as e:
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is_success = False
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error_log_msg = f"Generic error: {e}"
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logger.error(f"Error creating embedding (Exception): {error_log_msg}")
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# Try to parse status code from generic error (less reliable)
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match = re.search(r"status code (\d+)", str(e))
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if match:
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status_code = int(match.group(1))
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else:
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status_code = 500 # Default if parsing fails
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raise e # Re-raise the generic error
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finally:
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end_time = time.perf_counter()
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latency_ms = int((end_time - start_time) * 1000)
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if not is_success:
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# Log error to database if it failed
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await add_error_log(
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gemini_key=api_key, # Using gemini_key parameter name for consistency
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model_name=model,
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error_type="openai-embedding",
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error_log=error_log_msg,
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error_code=status_code,
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request_msg=request_msg_log
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)
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# Log request outcome to database regardless of success/failure
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await add_request_log(
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model_name=model,
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api_key=api_key,
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is_success=is_success,
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status_code=status_code,
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latency_ms=latency_ms,
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request_time=request_datetime
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)
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