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, optimizing for either performance or richness. --- ### **How It Works** 1. **PDF Processing with Mistral OCR**: - The workflow starts by uploading a PDF file to Mistral's API, which performs OCR to extract text and metadata. - The extracted content is split into manageable chunks (e.g., pages or sections) for further processing. 2. **Vector Storage in Qdrant**: - The extracted text is converted into embeddings using OpenAI's embedding model. - These embeddings are stored in a Qdrant vector database, enabling efficient similarity searches for RAG. 3. **Question-Answering with RAG**: - When a user submits a question via a chat interface, the workflow retrieves relevant text chunks from Qdrant using vector similarity. - A language model (Google Gemini) generates answers based on the retrieved context, providing accurate and context-aware responses. 4. **Optional Summarization**: - The workflow includes an optional summarization step using Google Gemini to condense the extracted text for faster processing or lighter RAG usage. --- ### **Set Up Steps** To deploy this workflow in n8n, follow these steps: 1. **Configure Qdrant Database**: - Replace `QDRANT_URL` and `COLLECTION` in the Create collection and Refresh collection nodes with your Qdrant instance details. - Ensure the Qdrant collection is configured with the correct vector size (e.g., 1536 for OpenAI embeddings) and distance metric (e.g., Cosine). 2. **Set Up Credentials**: - Add credentials for: - **Mistral Cloud API** (for OCR processing). - **OpenAI API** (for embeddings). - **Google Gemini API** (for chat and summarization). - **Google Drive** (if sourcing PDFs from Drive). - **Qdrant API** (for vector storage). 3. **PDF Source Configuration**: - If using Google Drive, specify the folder ID in the Search PDFs node. - Alternatively, modify the workflow to accept PDFs from other sources (e.g., direct uploads or external APIs). 4. **Customize Text Processing**: - Adjust chunk size and overlap in the Token Splitter node to optimize for your document type. - Choose between raw text or summarized content for RAG by toggling between the Set page and Summarization Chain nodes. 5. **Test the RAG**: - Trigger the workflow manually or via a chat message to verify OCR, embedding, and Qdrant storage. - Use the Question and Answer Chain node to test query responses. 6. **Optional Sub-Workflows**: - The workflow supports execution as a sub-workflow for batch processing (e.g., handling multiple PDFs). --- ### **Need help customizing?** [Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/).

Platform

n8n

Category

AI & Machine Learning

Price

Free

Creator

Davide

• set
• code
• wait
• stickyNote
• googleDrive
• httpRequest
• manualTrigger
• splitInBatches
• executeWorkflow
• chatTrigger