- Updated `chains.py` to streamline imports and improve error handling for LLM initialization.
- Modified `models.py` to enhance the `AnalysisFlags` model with field aliases and added datetime import.
- Deleted outdated prompt files (`jira_analysis_v1.0.0.txt`, `jira_analysis_v1.1.0.txt`, `jira_analysis_v1.2.0.txt`) to clean up the repository.
- Introduced a new prompt file `jira_analysis_v1.2.0.txt` with updated instructions for analysis.
- Removed `logging_config.py` and test files to simplify the codebase.
- Updated webhook handler to improve error handling and logging.
- Added a new shared store for managing processing requests in a thread-safe manner.
- 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.
- Added FastAPI application to handle Jira webhooks.
- Created Pydantic models for Jira payload and LLM output.
- Integrated LangChain with OpenAI and Ollama for LLM processing.
- Set up Langfuse for tracing and monitoring.
- Implemented analysis logic for Jira tickets, including sentiment analysis and label suggestions.
- Added test endpoint for LLM integration.
- Updated requirements.txt to include necessary dependencies and versions.
