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| title: Document Intelligence RAG | |
| emoji: 📚 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: docker | |
| sdk_version: "1.0" | |
| app_file: src/main.py | |
| pinned: false | |
| # Document Intelligence RAG System | |
| Production-grade Retrieval-Augmented Generation (RAG) system for analyzing research papers and documents with AI. | |
| Ask questions about your PDFs. Get answers grounded in your documents with source attribution. | |
| # Link | |
| Enjoy: https://huggingface.co/spaces/aankitdas/doc-intelligence-rag | |
| ## Features | |
| - PDF Ingestion: Extract text from PDFs using PDFProcessor | |
| - Document Chunking: Split documents into smaller chunks for better context | |
| - Embedding: Convert text chunks into vector embeddings using Ollama | |
| - Vector Storage: Store embeddings in ChromaDB for efficient retrieval | |
| - LLM Integration: Use Groq LLM for generating answers | |
| - Source Attribution: Track document origins for citation | |
| - FastAPI Integration: Build a REST API for easy access | |
| - Docker Support: Containerize the system for easy deployment | |
| - PDF Processing: Extract text from PDFs using PDFProcessor | |
| - Document Chunking: Split documents into smaller chunks for better context | |
| - Embedding: Convert text chunks into vector embeddings using Ollama | |
| - Vector Storage: Store embeddings in ChromaDB for efficient retrieval | |
| - LLM Integration: Use Groq LLM for generating answers | |
| - Source Attribution: Track document origins for citation | |
| - FastAPI Integration: Build a REST API for easy access | |
| - Docker Support: Containerize the system for easy deployment | |
| ## Quickstart | |
| ### Prerequisites | |
| - Python 3.12 | |
| - Ollama | |
| - Groq API Key | |
| - ChromaDB | |
| - FastAPI | |
| - Uvicorn | |
| - PDFProcessor | |
| - Embeddings | |
| - LLM | |
| - Vector Store | |
| 1. Setup environment variables | |
| ```bash | |
| # Clone repository | |
| git clone https://github.com/aankitdas/document-intelligence-rag.git | |
| cd document-intelligence-rag | |
| # Install Ollama (one-time setup) | |
| # Download from https://ollama.ai | |
| ollama pull nomic-embed-text | |
| # Start Ollama server (in background) | |
| ollama serve | |
| # Create Python environment | |
| uv venv | |
| source .venv/bin/activate # Windows: .venv\Scripts\activate | |
| # Install dependencies | |
| uv sync | |
| # Set API keys | |
| export GROQ_API_KEY="gsk_..." # Get from https://console.groq.com | |
| ``` | |
| 2. Prepare Documents | |
| ```bash | |
| # Create a folder for documents | |
| # Create papers folder | |
| mkdir papers | |
| # Add your PDFs to papers/ | |
| # Example: papers/research_paper.pdf | |
| ``` | |
| 3. Run API | |
| ```bash | |
| # Run API | |
| uvicorn src.api.main:app --reload | |
| ``` | |
| 4. Query API | |
| ```bash | |
| # Query API | |
| curl http://localhost:8000/ask -X POST -H "Content-Type: application/json" -d '{"question": "What is the main contribution of this paper?"}' | |
| ``` | |
| ## Tech Stack | |
| | Component | Technology | Why | | |
| |------------------|-------------------------------|---------------------------------------------------------------------| | |
| | Embeddings | Ollama (`nomic-embed-text`) | Local, free, 384-dimensional embeddings | | |
| | Vector Database | Chroma | Persistent storage, fast similarity search, completely free | | |
| | LLM | Groq (Llama 3.1) | Free API tier, very fast inference | | |
| | Backend | FastAPI | Production-grade, async, automatic API docs | | |
| | Frontend | HTML / CSS / JavaScript | Simple setup, no build tooling required | | |
| | Package Manager | UV | Fast dependency resolution, deterministic environments | | |
| #### Testing Github Actions sync to HF spaces |