PDF RAG System: Mistral OCR, Qdrant & Gemini AI

This workflow processes PDF documents using Mistral's OCR to extract text and metadata, splits content into chunks, generates embeddings with OpenAI, and stores them in a Qdrant vector database for efficient similarity searches. It supports both light (summarized) and full indexing modes for optimized performance or detail. A chat interface allows natural language queries, retrieving relevant chunks via RAG for accurate, context-grounded responses from Gemini AI.

Key benefits include end-to-end automation with no manual intervention, scalability through subflows and batching, and multi-model leverage: Mistral for precise OCR, OpenAI for robust embeddings, and Gemini for intelligent answering. It saves significant time on document ingestion and analysis, enabling real-time Q&A on large PDFs.

Ideal use cases: Knowledge bases for enterprises, legal document review, research paper analysis, customer support with internal docs, or any scenario needing searchable PDF intelligence. Easily customizable for production-scale RAG applications.