title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned
Document Intelligence RAG	📚	blue	purple	docker	1.0	src/main.py	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

# 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

# Create a folder for documents
# Create papers folder
mkdir papers

# Add your PDFs to papers/
# Example: papers/research_paper.pdf

3. Run API

# Run API
uvicorn src.api.main:app --reload

4. Query API

# 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

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