Implemented RAG System with n8n and Supabase

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Implemented RAG System with n8n and Supabase

  • Day: 2025-01-30
  • Time: 14:10 to 17:45
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: N8N, RAG, Supabase, Workflow, Vectorization

Description

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.

Evidence

  • source_file=2025-01-30.sessions.jsonl, line_number=1, event_count=0, session_id=e0c40ccc211370e42fd1ff736dfcb5c5e6c96434fdfe8819b8cae9ff7c5aa2e3
  • event_ids: []

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