RAG-Powered Document Chatbot with OpenAI & Gemini for Multi-Format Documents

Overview

This intelligent chatbot workflow enables natural language conversations with your documents, supporting multiple file formats including PDFs, Word documents, Excel spreadsheets, and text files. Built with advanced RAG (Retrieval-Augmented Generation) technology, this chatbot can understand, analyze, and answer questions about your document content with contextual accuracy and intelligent responses.

How It Works

Intelligent Document Processing & Conversation Pipeline: Multi-Format Document Ingestion: Automatically processes and indexes various document formats (PDF, DOCX, XLSX, TXT, etc.) Smart Content Chunking: Breaks down documents into meaningful segments while preserving context and relationships Vector Database Storage: Creates searchable embeddings for fast and accurate information retrieval Contextual Conversation Engine: Uses AI to understand user queries and retrieve relevant document sections Natural Language Responses: Generates human-like responses with citations and source references Multi-Turn Conversations: Maintains conversation history and context across multiple interactions Real-Time Processing: Instant responses with live document updates and dynamic content refresh

Setup Instructions

Estimated Setup Time: 15-20 minutes

Prerequisites n8n instance (v0.200.0 or higher recommended) OpenAI/Gemini API key for embeddings and chat completion Vector database service (optional: Pinecone, Weaviate, or Qdrant) File storage service (optional: Google Drive, Dropbox, AWS S3) Web server for chatbot interface (optional)

Configuration Steps