Ireneusz Bachanowicz
2763b40b60
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- Simplified the FastAPI application structure and improved error handling with middleware. - Introduced a retry decorator for asynchronous functions to enhance reliability. - Modularized the LLM initialization and prompt loading into separate functions for better maintainability. - Updated Pydantic models for Jira webhook payload and analysis flags to ensure proper validation and structure. - Implemented a structured logging configuration for better traceability and debugging. - Added comprehensive unit tests for prompt loading, response validation, and webhook handling. - Established a CI/CD pipeline with GitHub Actions for automated testing and coverage reporting. - Enhanced the prompt template for LLM analysis to include specific instructions for handling escalations.
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
import os
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import sys
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from typing import Optional
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from pydantic_settings import BaseSettings
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from pydantic import validator, ConfigDict
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from loguru import logger
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from watchfiles import watch, Change
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from threading import Thread
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class LogConfig(BaseSettings):
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level: str = 'INFO'
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model_config = ConfigDict(
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env_prefix='LOG_',
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extra='ignore'
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)
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class LLMConfig(BaseSettings):
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mode: str = 'ollama'
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# OpenAI settings
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openai_api_key: Optional[str] = None
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openai_api_base_url: Optional[str] = None
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openai_model: Optional[str] = None
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# Ollama settings
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ollama_base_url: Optional[str] = None
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ollama_model: Optional[str] = None
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@validator('mode')
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def validate_mode(cls, v):
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if v not in ['openai', 'ollama']:
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raise ValueError("LLM mode must be either 'openai' or 'ollama'")
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return v
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model_config = ConfigDict(
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env_prefix='LLM_',
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env_file='.env',
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env_file_encoding='utf-8',
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extra='ignore'
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)
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class Settings:
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def __init__(self):
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self.log = LogConfig()
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self.llm = LLMConfig()
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self._validate()
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self._start_watcher()
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def _validate(self):
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logger.info(f"LLM mode set to: '{self.llm.mode}'")
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if self.llm.mode == 'openai':
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if not self.llm.openai_api_key:
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raise ValueError("LLM mode is 'openai', but OPENAI_API_KEY is not set.")
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if not self.llm.openai_api_base_url:
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raise ValueError("LLM mode is 'openai', but OPENAI_API_BASE_URL is not set.")
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if not self.llm.openai_model:
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raise ValueError("LLM mode is 'openai', but OPENAI_MODEL is not set.")
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elif self.llm.mode == 'ollama':
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if not self.llm.ollama_base_url:
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raise ValueError("LLM mode is 'ollama', but OLLAMA_BASE_URL is not set.")
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if not self.llm.ollama_model:
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raise ValueError("LLM mode is 'ollama', but OLLAMA_MODEL is not set.")
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logger.info("Configuration validated successfully.")
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def _start_watcher(self):
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def watch_config():
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for changes in watch('config/application.yml'):
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for change in changes:
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if change[0] == Change.modified:
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logger.info("Configuration file modified, reloading settings...")
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try:
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self.llm = LLMConfig()
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self._validate()
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logger.info("Configuration reloaded successfully")
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except Exception as e:
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logger.error(f"Error reloading configuration: {e}")
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Thread(target=watch_config, daemon=True).start()
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# Create a single, validated instance of the settings to be imported by other modules.
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try:
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settings = Settings()
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except ValueError as e:
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logger.error(f"FATAL: {e}")
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logger.error("Application shutting down due to configuration error.")
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sys.exit(1) |