📅 2025-01-30 — Session: Implemented RAG System with n8n and Supabase
🕒 14:10–17:45
🏷️ Labels: N8N, RAG, Supabase, Workflow, Vectorization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop a Retrieval-Augmented Generation (RAG) system using n8n and Supabase, focusing on workflow setup, document processing, and vectorization.
Key Activities
- Set up the RAG system workflow using n8n and integrated it with Supabase for document processing.
- Developed a SQL function for similarity-based document retrieval using vector embeddings.
- Evaluated the use of n8n versus Python for chunkization and vector storage.
- Implemented dynamic vectorstore management, enabling on-the-fly updates without restarting the application.
- Enhanced the UI for directory selection, including checkbox features for multi-directory processing.
- Resolved technical issues with Chroma’s
persist()in LangChain. - Migrated to a better storage structure for RAG processing.
Achievements
- Successfully integrated n8n with Supabase for a functional RAG system.
- Created a robust document retrieval function using vector embeddings.
- Improved UI/UX for directory management and processing.
Pending Tasks
- Further evaluate the performance implications of using n8n versus Python for specific tasks.
- Continue refining the RAG app’s architecture for better performance and usability.