Build a PDF search system with Mistral OCR and Weaviate DB
# Build a PDF to Vector RAG System: Mistral OCR, Weaviate Database and MCP Server
A comprehensive RAG (Retrieval-Augmented Generation) workflow that transforms PDF documents into searchable vector embeddings using advanced AI technologies.
## 🚀 Features
- **PDF Document Processing**: Upload and extract text from PDF files using Mistral's OCR capabilities
- **Vector Database Storage**: Store document embeddings in Weaviate vector database for efficient retrieval
- **AI-Powered Search**: Search through documents using semantic similarity with Cohere embeddings
- **MCP Server Integration**: Expose the knowledge base as an AI tool through MCP (Model Context Protocol)
- **Document Metadata**: Basic document metadata including filename, content, source, and upload timestamp
- **Text Chunking**: Automatic text splitting for optimal vector storage and retrieval
## 🛠️ Technologies Used
- **Mistral AI**: OCR and text extraction from PDF documents
- **Weaviate**: Vector database for storing and retrieving document embeddings
- **Cohere**: Multilingual embeddings and reranking for improved search accuracy
- **MCP (Model Context Protocol)**: AI tool integration for external AI workflows
- **n8n**: Workflow automation and orchestration
## 📋 Prerequisites
Before using this template, you'll need to set up the following credentials:
1. **Mistral Cloud API**: For PDF text extraction
2. **Weaviate API**: For vector database operations
3. **Cohere API**: For embeddings and reranking
4. **HTTP Header Auth**: For MCP server authentication
## 🔧 Setup Instructions
1. **Import the template** into your n8n instance
2. **Configure credentials** for all required services
3. **Set up Weaviate collection** named "KnowledgeDocuments"
4. **Configure webhook paths** for the MCP server and form trigger
5. **Test the workflow** by uploading a PDF document
## 📊 Workflow Overview
```
PDF Upload → Text Extraction → Document Processing → Vector Storage → AI Search
↓ ↓ ↓ ↓ ↓
Form Trigger → Mistral OCR → Prepare Metadata → Weaviate DB → MCP Server
```
## 🎯 Use Cases
- **Knowledge Base Management**: Create searchable repositories of company documents
- **Research Documentation**: Process and search through research papers and reports
- **Legal Document Search**: Index and search through legal documents and contracts
- **Technical Documentation**: Make technical manuals and guides searchable
- **Academic Literature**: Process and search through academic papers and publications
## ⚠️ Important Notes
- **Model Consistency**: Use the same embedding model for both storage and retrieval
- **Collection Management**: Ensure your Weaviate collection is properly configured
- **API Limits**: Be aware of rate limits for Mistral, Cohere, and Weaviate APIs
- **Document Size**: Consider chunking large documents for optimal processing
## 🔗 Related Resources
- [n8n Documentation](https://docs.n8n.io/)
- [Weaviate Documentation](https://weaviate.io/developers/weaviate)
- [Mistral AI Documentation](https://docs.mistral.ai/)
- [Cohere Documentation](https://docs.cohere.com/)
- [MCP Protocol Documentation](https://modelcontextprotocol.io/)
## 📝 License
This template is provided as-is for educational and commercial use.
