📅 2025-01-28 — Session: Enhanced RAG Pipeline Integration
🕒 15:10–16:20
🏷️ Labels: RAG, Integration, Vectorstore, Query Processing, Python, Flask
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to enhance the integration of user query processing within a Retrieval-Augmented Generation (RAG) pipeline, focusing on improving the handling of dynamic inputs and ensuring robust fallback mechanisms.
Key Activities
- Improved AI model query integration within the vectorstore implementation, addressing potential fixes for direct queries and fallback scenarios.
- Streamlined the implementation of the RAG chain, including prompt template definition, context formatting, and query execution.
- Integrated flow into application code, emphasizing best practices.
- Updated backend and frontend code for dynamic user query processing in the RAG chain.
- Implemented the
query_or_fallbackfunction to ensure proper initialization of the vectorstore and fallback handling. - Enhanced logging for better traceability and debugging.
Achievements
- Successfully integrated user queries into the RAG pipeline, improving the query processing flow.
- Addressed initialization issues in the vectorstore with potential solutions.
- Improved code traceability with enhanced logging.
Pending Tasks
- Further assess the implementation quality focusing on modularity, maintainability, and performance improvements.
- Continue troubleshooting vectorstore initialization issues as needed.