Overview

A sophisticated Retrieval-Augmented Generation (RAG) system built with n8n that enables intelligent document querying and contextual responses. This solution combines vector search, embeddings, and large language models to create a powerful knowledge retrieval system.

The Problem

Organizations struggle with:

  • Accessing relevant information across large document repositories
  • Maintaining context in conversations
  • Providing accurate, source-based responses
  • Scaling knowledge management efficiently

The Solution

We developed an advanced RAG system that:

  1. Processes and stores documents in a vector database (Supabase)
  2. Uses Mistral Cloud embeddings for semantic search
  3. Maintains conversation context with PostgreSQL
  4. Leverages LLMs for natural language understanding
  5. Provides source-based, contextual responses
Intelligent RAG Agent Workflow

Impact

  • Reduces information retrieval time by 90%
  • Ensures responses are grounded in actual documents
  • Maintains conversation context for better user experience
  • Scales knowledge management efficiently

Future Enhancements

  • Multi-modal document support
  • Advanced query optimization
  • Real-time document indexing
  • Enhanced security and access controls