Process documents & build semantic search with OpenAI, Gemini & Qdrant

🎯 Overview

This n8n workflow automates the process of ingesting documents from multiple sources (Google Drive and web forms) into a Qdrant vector database for semantic search capabilities. It handles batch processing, document analysis, embedding generation, and vector storage - all while maintaining proper error handling and execution tracking.

🚀 Key Features

- Dual Input Sources: Accepts files from both Google Drive folders and web form uploads - Batch Processing: Processes files one at a time to prevent memory issues and ensure reliability - AI-Powered Analysis: Uses Google Gemini to extract metadata and understand document context - Vector Embeddings: Generates OpenAI embeddings for semantic search capabilities - Automated Cleanup: Optionally deletes processed files from Google Drive (configurable) - Loop Processing: Handles multiple files efficiently with Split In Batches nodes - Interactive Chat Interface: Built-in chatbot for testing semantic search queries against indexed documents

📋 Use Cases

- Knowledge Base Creation: Build searchable document repositories for organizations - Document Compliance: Process and index legal/regulatory documents (like Fair Work documents) - Content Management: Automatically categorize and store uploaded documents - Research Libraries: Create semantic search capabilities for research papers or reports - Customer Support: Enable instant answers to policy and documentation questions via chat interface

🔧 Workflow Components

Input Methods