| title | SimplyRAG |
|---|---|
| emoji | 📚 |
| colorFrom | blue |
| colorTo | green |
| sdk | gradio |
| app_file | src/app.py |
| pinned | false |
A production-grade RAG system that ingests PDF documents and answers questions using hybrid search, cross-encoder reranking, agentic query routing, citation grounding, conversation memory, and streaming responses.
Live Demo on Hugging Face Spaces | Architecture | Benchmarks | Quick Start
- Hybrid Search: Combines BM25 keyword matching with dense vector retrieval using Reciprocal Rank Fusion
- Cross-Encoder Reranking: Re-scores retrieved passages with
cross-encoder/ms-marco-MiniLM-L-6-v2for precision ordering - Citation Grounding: Every claim includes inline citations verified against source passages
- Agentic Query Routing: LangGraph state graph routes questions to specialized handlers (Retrieval, Summarization, Comparison, Table Analysis)
- Conversation Memory: Maintains context across multi-turn interactions with sliding window
- Streaming Responses: Token-by-token output with real-time status updates
- Multimodal PDF Understanding: Extracts and reasons over images and tables from documents
- RAGAS Evaluation: Quantified metrics (Faithfulness, Context Precision, Answer Relevancy, Citation Accuracy)
| Component | Technology |
|---|---|
| LLM | Groq (Llama 3.3 70B Versatile) |
| Embeddings | sentence-transformers (all-MiniLM-L6-v2, 384-dim) |
| Vector Store | ChromaDB (persistent, local) |
| Keyword Search | rank-bm25 |
| Reranker | cross-encoder/ms-marco-MiniLM-L-6-v2 |
| Agent Framework | LangGraph (StateGraph, conditional edges) |
| Evaluation | RAGAS (Faithfulness, Context Precision, Relevancy) |
| Backend | FastAPI (REST + WebSocket) |
| Frontend | Gradio (ChatInterface, streaming) |
| PDF Parsing | PyMuPDF (text, images, tables) |
| Deployment | Hugging Face Spaces (Docker SDK) |
# Clone the repository
git clone https://github.com/Swayamjimmy/rag-system.git
cd rag-system
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Add your GROQ_API_KEY to .env
# Run the application
python -m src.appThe Gradio interface will open at http://localhost:7860.
rag-system/
├── src/
│ ├── ingest.py # PDF loading and text chunking
│ ├── embeddings.py # Sentence-transformer embedding generation
│ ├── retriever.py # ChromaDB vector retriever
│ ├── bm25_retriever.py # BM25 keyword search index
│ ├── hybrid_retriever.py # EnsembleRetriever with RRF
│ ├── reranker.py # Cross-encoder reranking pipeline
│ ├── citations.py # Citation extraction and verification
│ ├── pipeline.py # End-to-end retrieval pipeline
│ ├── agent.py # LangGraph agentic state graph
│ ├── multimodal.py # Image/table extraction from PDFs
│ ├── api.py # FastAPI REST + WebSocket endpoints
│ └── app.py # Gradio chat interface
├── evals/
│ └── benchmark_report.md # RAGAS evaluation results
├── docs/
│ ├── architecture.png # System architecture diagram
│ ├── retrieval_flow.png # Retrieval pipeline flow
│ └── routing_diagram.png # Intent routing paths
├── data/ # PDF documents for testing
├── tests/ # Unit and integration tests
├── requirements.txt
├── Dockerfile
├── .env.example
└── README.md
The system processes documents through three stages:
-
Ingestion: PDFs are loaded with PyMuPDF, split into overlapping chunks (512 chars, 50 overlap), and embedded with all-MiniLM-L6-v2 into ChromaDB. A parallel BM25 index is built for keyword search.
-
Retrieval: User questions pass through an intent router that classifies them into one of four paths. Hybrid search (BM25 + vector) retrieves candidate passages, then a cross-encoder reranks them for precision.
-
Generation: The LangGraph agent grades retrieved documents for relevance, rewrites the query if needed, and generates a cited response. Every factual claim includes an inline citation verified against the source passage.
| Pipeline | Faithfulness | Context Precision | Answer Relevancy | Citation Accuracy |
|---|---|---|---|---|
| Basic RAG | 0.72 | 0.65 | 0.78 | - |
| Hybrid Search | 0.79 | 0.81 | 0.82 | - |
| Hybrid + Reranking | 0.85 | 0.89 | 0.86 | 0.94 |
| Agentic (Full System) | 0.88 | 0.91 | 0.89 | 0.96 |
Key Improvements:
- Context Precision: +40% (Basic → Agentic)
- Faithfulness: +22% (Basic → Agentic)
- Citation Accuracy: 96% of inline citations verified as grounded in source text
| Pipeline | Avg Response Time |
|---|---|
| Basic RAG | ~1.2s |
| Hybrid Search | ~1.8s |
| Hybrid + Reranking | ~2.4s |
| Agentic (Full System) | ~3.1s |
Measured on Groq API with llama-3.3-70b-versatile. Streaming provides first-token latency of ~0.3s.
Contributions are welcome! Please:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License. See LICENSE for details.