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## The Problem Nobody Talks About When you upload documents to an AI service, you're placing a LOT of trust in that company: - Trust that they'll keep your documents secure - Trust that they won't use them to train their models - Trust that they won't end up exposed in a data breach For everyday documents, that's fine. But for **sensitive documents** - legal, medical, financial, client docs - that's a much bigger ask. **For these, you need full control.** --- ## 🎯 The Solution: Fully Local Multimodal RAG Today I'm sharing the complete stack we use for processing private documents with AI - **no external APIs, fully airgapped, available to your local network**. ### What is Multimodal RAG? Retrieval across a knowledge base with **multiple data types**: - Text documents - PDFs with embedded images/tables - Audio files (meeting transcripts) - Videos The magic? When you process a PDF with an embedded diagram, that diagram can be **retrieved and displayed** in your chat. Most AI agents only return text - we're going beyond that. --- ## 🛠️ The Tech Stack | Component | Purpose | Where it Runs | |-----------|---------|---------------| | **Docling** | Document processing (IBM open-source) | Local Docker / Modal GPU | | **Qdrant** | Vector database | Local Docker | | **Static File Server** | Serve extracted images | Local Docker (nginx) | | **Ollama** | Local LLM & VLM | Local Docker (GPU) | | **N8N** | Orchestration | Local Docker | | **Modal** | Scalable GPU processing | Cloud (when needed) | --- ## 📄 Why Docling is a Game-Changer Docling is IBM's open-source document processing library. Feed it: - PDFs - Word docs - PowerPoint presentations - Images - Audio files And it outputs **clean structured markdown or JSON** your agent can search over. ### This isn't basic text extraction: ✅ Recognizes headers and document structure ✅ Extracts tables accurately ✅ Pulls out diagrams as images ✅ Text in diagrams is searchable ✅ Maintains semantic structure (bullet points, lists)