# Build a PDF Document RAG System with Mistral OCR, Qdrant and Gemini AI

> This workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering questions. Here’s how it functions: Once configured, the workflow automates document ingestion, vectorization, and intelligent querying, enabling powerful RAG applications. --- Benefits End-to-End Automation No manual interaction is needed: documents are read, processed, and made queryable with minimal setup. Scalable and Modular The workflow uses subflows and batching, making it easy to scale and customize. Multi-Model Support Combines Mistral for OCR, OpenAI for embeddings, and Gemini for intelligent answering—taking advantage of the strengths of each. Real-Time Q\&A With RAG integration, users can query document content through natural language and receive accurate responses grounded in the PDF data. Light or Full Mode Users can choose to index full page content or only summarized text

- **Domain**: `n8n-workflow-4400.n8n.io`
- **Provider**: n8n.io (https://n8n.io)
- **Kind**: workflow
- **Live-responds (last probe)**: None
- **Signed card**: False
- **Streaming**: False
- **Quality score**: 40%

## URLs
- Agent card: https://n8n.io/workflows/4400
- Page (HTML): https://agenstry.com/agents/n8n-workflow-4400.n8n.io
- Documentation: https://n8n.io/workflows/4400