📅 2025-02-01 — Session: Resolved Supabase ‘created_at’ Column Error and Enhanced RAG System
🕒 17:00–17:40
🏷️ Labels: Supabase, RAG, Embeddings, Error Handling, Vectorstores
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to resolve a ‘created_at’ column error in the Supabase ‘chunks’ table and to enhance the Retrieval-Augmented Generation (RAG) system setup.
Key Activities
- Supabase Error Fix: Followed a guide to troubleshoot and resolve the missing ‘created_at’ column error in the Supabase ‘chunks’ table, including checks for table structure, payload inspection, cache clearing, and debugging code.
- File Upload Logic: Improved a Python function for uploading file metadata to Supabase, focusing on graceful error handling.
- RAG System Setup: Implemented a dynamic RAG system using chunked data, focusing on data organization, dynamic querying, and semantic searches with tools like FAISS and Weaviate.
- Embeddings and Retrieval: Developed strategies for using embeddings in data retrieval and model training, emphasizing metadata for context reconstruction.
- Vectorstores Management: Managed vectorstores for RAG workflows, including creating, filtering, saving, and reusing vectorstores using libraries like FAISS and OpenAI tools.
Achievements
- Successfully resolved the Supabase ‘created_at’ column error.
- Enhanced the RAG system setup with improved data retrieval and organization strategies.
Pending Tasks
- Further fine-tuning of models with original text chunks versus embeddings.
- Continued exploration of metadata enrichment for semantic search.