📅 2025-01-23 — Session: Enhanced Flask App with Vectorstore Integration
🕒 22:45–23:40
🏷️ Labels: Flask, Vectorstore, Python, Troubleshooting, Logging, Raptor Pipeline
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to enhance a Flask application by integrating vectorstore management and improving error handling and logging.
Key Activities
- Troubleshooting VS Code Issues: Addressed red underline errors related to
langchain_openaiin VS Code by resolving virtual environment and import issues. - Flask App Modifications: Adjusted the Flask app to use hardcoded file paths for pre-chunked files, eliminating the need for dynamic uploads.
- Environment Variable Troubleshooting: Resolved issues with the
OPENAI_API_KEYnot being recognized in Python scripts. - Auto-Initialization of Vectorstore: Implemented automatic initialization of vectorstore on app startup to ensure readiness for queries.
- Verbose Logging Enhancements: Added detailed logging to improve debugging and tracking of processes in the Flask app.
- Debugging Vectorstore Initialization: Systematically troubleshot and fixed initialization issues within the application.
- OpenAI API Key Fixes: Resolved invalid API key issues and refactored code for better error handling.
- Vectorstore Management Delegation: Integrated vectorstore management within the Raptor Pipeline for enhanced embedding and summarization capabilities.
- Refactoring for Vectorstore Independence: Planned a strategy to make vectorstore optional, allowing fallback processing of documents.
Achievements
- Successfully integrated vectorstore management into the Flask app.
- Improved error handling and logging for better debugging.
Pending Tasks
- Finalize the refactoring of app logic for vectorstore independence.
- Conduct further testing to ensure robustness of the new features.