Automated RAG System with Google Drive, OpenAI GPT-4o, and Pinecone Integration

Overview

This workflow automates the creation of a Retrieval-Augmented Generation (RAG) system by integrating Google Drive, OpenAI GPT-4o, and Pinecone. It streamlines the process of ingesting documents, generating embeddings, and enabling intelligent chat-based queries over stored content.

Key Features

• Automated Document Ingestion: Monitors Google Drive for new or updated files and loads them automatically.
• Text Processing: Utilizes recursive character text splitting for optimal chunking of documents.
• AI Embeddings: Generates vector embeddings using OpenAI's GPT-4o-mini model for semantic search.
• Vector Storage: Stores and manages embeddings in Pinecone for fast, scalable retrieval.
• Chat Integration: Enables chat-triggered queries, allowing users to interact with the document knowledge base via natural language.

Benefits

• Time Savings: Eliminates manual document processing and indexing.
• Enhanced Knowledge Access: Provides instant, AI-powered answers from company documents.
• Scalability: Handles large volumes of documents and queries efficiently.

Use Cases

• Internal knowledge management
• Automated customer support
• Research and compliance document search

Integrations

• Google Drive: For document storage and triggers
• OpenAI: For advanced language understanding and embeddings
• Pinecone: For high-performance vector search and retrieval