RAG-Powered Document Chatbot with OpenAI & Gemini

This workflow creates an advanced RAG-powered chatbot that processes and indexes various document formats like PDFs, Word docs, Excel spreadsheets, and text files. It intelligently chunks content, generates embeddings for storage in vector databases (e.g., Pinecone, Weaviate), and retrieves relevant sections to answer user queries accurately.

Key features include contextual understanding, human-like responses with source citations, multi-turn conversation history, and real-time processing. It integrates OpenAI and Gemini for embeddings and chat completion, making it versatile for dynamic document updates.

Ideal for finance professionals analyzing reports, contracts, or spreadsheets; legal teams reviewing docs; or any knowledge worker needing quick insights. Benefits include time savings on manual searches, improved accuracy over basic search, and scalable AI automation reducing reliance on experts.

Setup is straightforward: configure API keys, input sources (e.g., Google Drive), vector DB, and deploy the chatbot interface for instant use.