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https://github.com/snailyp/gemini-balance.git
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2001bfdcd9
- 统一 add_error_log 的 request_time：优先使用 request_datetime， 否则使用 datetime.now()，去除 timezone.utc，避免与请求日志时区不一致 - 在 Gemini/OpenAI/Vertex/Embedding 等服务的异常处理处补充传入 request_datetime，使错误日志与请求日志可一一对应 - stats: 移除失败记录的错误日志时间窗匹配与 error_log_id 附带，降低查询开销 与误关联风险；建议通过统一时间戳（key + request_time）或独立错误日志 查询接口完成关联 - 调整部分导入顺序与长行换行等代码风格，无功能改动 BREAKING CHANGE: 统计详情接口不再返回 error_log_id 字段。需要关联错误日志的 客户端请改为基于 key 与 request_time 在错误日志接口中检索。
88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
import datetime
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import re
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import time
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from typing import List, Union
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import openai
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from openai import APIStatusError
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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.database.services import add_error_log, add_request_log
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from app.log.logger import get_embeddings_logger
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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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if isinstance(input_text, list):
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request_msg_log = {
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"input_truncated": [
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str(item)[:100] + "..." if len(str(item)) > 100 else str(item)
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for item in input_text[:5]
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]
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}
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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 = {
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"input_truncated": (
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input_text[:1000] + "..." if len(input_text) > 1000 else input_text
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)
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}
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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
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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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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
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raise e
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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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await add_error_log(
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gemini_key=api_key,
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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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request_datetime=request_datetime,
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)
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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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