Intelligent Document RAG with Mistral OCR and Gemini AI
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Transform your PDF documents into an interactive, AI-powered knowledge base. This workflow automates document processing with Mistral OCR and enables a Retrieval Augmented Generation (RAG) system using OpenAI embeddings and Google Gemini for intelligent Q&A.
About This Workflow
Unlock the insights hidden within your PDFs. This n8n workflow provides a comprehensive solution for building a dynamic RAG system. It starts by leveraging Mistral AI's cutting-edge OCR to extract text from your documents, preparing them for an AI-driven chat experience. The extracted content is then chunked, embedded using OpenAI, and stored in a Qdrant vector database. Finally, an interactive chatbot powered by Google Gemini (via a Langchain Q&A chain) allows users to ask questions and receive contextually accurate answers drawn directly from your uploaded documents, creating a powerful conversational AI agent for your data.
Key Features
- End-to-End RAG System: Seamlessly integrates OCR, embedding, vector storage, and AI response generation.
- Advanced Document OCR: Utilizes Mistral AI's
mistral-ocr-latestmodel for highly accurate text extraction from PDFs. - Intelligent Knowledge Retrieval: Stores and retrieves document chunks with OpenAI embeddings in a Qdrant vector database.
- Conversational AI: Powers interactive Q&A sessions using Google Gemini 1.5 Flash through a Langchain agent.
- Automated Document Ingestion: Streamlines the process of converting raw PDFs into an queryable knowledge base.
How To Use
To get started with this powerful workflow:
- Set up Credentials: Ensure you have the following n8n credentials configured:
Mistral Cloud accountfor Mistral AI API access.OpenAi accountfor OpenAI embeddings.Google Gemini(PaLM) Api accountfor Google Gemini LLM.- A Qdrant API credential (e.g.,
Qdrant API (Hetzner)/QdrantApi account (Hetzner)) for your vector database.
- Configure Qdrant URL: In the
Refresh collectionHTTP Request node, updatehttp://QDRANTURL/collections/COLLECTION/points/deletewith your actual Qdrant instance URL and collection name. Ensure the collection nameocr_mistral_test(used inQdrant Vector Store1nodes) is consistent. - Initiate Document Ingestion: Trigger the upper workflow path (starting with
When clicking ‘Test workflow’) to upload your PDF documents to Mistral AI for OCR processing, embedding, and storage in Qdrant. - Engage with the AI Chatbot: Once documents are ingested, use the
When chat message receivedtrigger to send queries. The chatbot will retrieve relevant information from your documents and provide answers via Google Gemini.
Apps Used
Workflow JSON
{
"id": "d0524e49-ff42-450f-8ea8-d1543c03e4da",
"name": "Intelligent Document RAG with Mistral OCR and Gemini AI",
"nodes": 22,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: d0524e49-ff42...
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