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’...
Template notes
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.
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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.