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Google Drive to Pinecone Vector Storage Workflow

by Muhammad AsadullahUpdated: Last update 2 months agoSource: n8n.io

Getting Started

Document Chat Bot with Automated RAG System

This workflow creates a conversational assistant that can answer questions based on your Google Drive documents. It automatically processes various file types and uses Retrieval-Augmented Generation (RAG) to provide accurate answers based on your document content.

How It Works

  1. Monitors Google Drive for New Documents : Automatically detects when files are created or updated in designated folders
  2. Processes Multiple File Types : Handles PDFs, Excel spreadsheets, and Google Docs
  3. Builds a Knowledge Base : Converts documents into searchable vector embeddings stored in Supabase
  4. Provides Chat Interface : Users can ask questions about their documents through a web interface
  5. Retrieves Relevant Information : Uses advanced RAG techniques to find and present the most relevant information

Setup Steps (Estimated time: 25-30 minutes)

  1. API Credentials : Connect your OpenAI API key for text processing and embeddings
  2. Google Drive Integration : Set up Google Drive triggers to monitor specific folders
  3. Supabase Configuration : Configure Supabase vector database for document storage
  4. Chat Interface Setup : Deploy the web-based chat interface using the provided webhook

The workflow automatically chunks documents into manageable segments, generates embeddings, and stores them in a vector database for efficient retrieval. When users ask questions, the system finds the most relevant document sections and uses them to generate accurate, contextual responses.