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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 |