Google Drive to Pinecone Vector Storage Workflow

Description

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

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

Setup Steps (Estimated time: 25-30 minutes)

API Credentials: Connect your OpenAI API key for text processing and embeddings
Google Drive Integration: Set up Google Drive triggers to monitor specific folders
Supabase Configuration: Configure Supabase vector database for document storage
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.