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Ask anything. Get answers from your own documents.

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⚡ What is this?

A production-ready Hybrid RAG system that combines the best of two retrieval worlds — semantic understanding + keyword precision — to answer questions from your documents with pinpoint accuracy.

No hallucinations. No generic answers. Just your data, intelligently retrieved.

Your PDF  ──▶  Dense Search (meaning)  ──┐
                                          ├──▶  RRF Fusion  ──▶  LLaMA 3.3  ──▶  Answer ✓
               Sparse Search (keywords) ──┘

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🧠 Why Hybrid RAG?

	Dense Only	Sparse Only	Hybrid RAG
Semantic understanding	✅	❌	✅
Exact keyword match	❌	✅	✅
Handles typos	✅	❌	✅
Technical terms	❌	✅	✅
Overall accuracy	🟡 Good	🟡 Good	🟢 Best

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🏗️ Architecture

┌─────────────────────────────────────────────────────┐
│                   INGESTION PIPELINE                 │
│                                                      │
│  PDF/TXT ──▶ Chunker ──▶ BGE Embeddings ──▶ ChromaDB│
│                    │                                 │
│                    └──────────────▶ BM25 Index       │
└─────────────────────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────┐
│                   QUERY PIPELINE                     │
│                                                      │
│  Query ──▶ Dense Retrieval (top-5)  ──┐              │
│       └──▶ Sparse Retrieval (top-5) ──┴▶ RRF ──▶ LLM│
└─────────────────────────────────────────────────────┘

Stack (zero LangChain):

• 🔍 ChromaDB — persistent vector store
• 🧮 BAAI/bge-base-en-v1.5 — sentence embeddings
• 📊 BM25Okapi — keyword retrieval
• 🔀 RRF — Reciprocal Rank Fusion (custom implementation)
• ⚡ Groq API — blazing fast LLM inference
• 🦙 LLaMA 3.3 70B — answer generation
• 🖥️ Streamlit — interactive UI

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🚀 Quick Start

# 1. Clone & setup
git clone https://github.com/SHALINISAURAV/hybrid-rag
cd hybrid-rag
python -m venv venv && source venv/bin/activate

# 2. Install
pip install -r requirements.txt

# 3. Add your Groq API key
echo "GROQ_API_KEY=your_key_here" > .env

# 4. Drop PDFs in docs/ folder, then index
python ingest.py

# 5. Launch
streamlit run app.py

🔑 Get your free Groq API key at console.groq.com

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📁 Project Structure

hybrid_rag/
│
├── 📂 docs/              ← drop your PDFs here
├── 📂 chroma_db/         ← auto-generated vector store
│
├── 📄 ingest.py          ← document ingestion pipeline
├── 📄 retriever.py       ← hybrid retrieval + RRF fusion
├── 📄 app.py             ← streamlit UI + RAG chain
├── 📄 .env               ← GROQ_API_KEY
└── 📄 requirements.txt

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✨ Features

• 📤 Upload PDFs directly from the UI — no manual file moving
• 🔀 True Hybrid Search — dense + sparse with RRF fusion
• ⚡ Groq-powered — sub-second LLM responses
• 📍 Source citations — see exactly which page answered your question
• 💬 Chat memory — multi-turn conversation within a session
• 🚫 No LangChain — lightweight, no dependency hell

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🛠️ How RRF Works

Reciprocal Rank Fusion merges two ranked lists into one superior list:

# For each document in either list:
rrf_score = 1/(60 + rank_in_dense) + 1/(60 + rank_in_sparse)

# Higher score = more relevant
# Documents appearing in BOTH lists get a big boost

This means a document that ranks #3 in dense AND #2 in sparse will beat a document that ranks #1 in only one list.

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📊 Performance

• ⚡ Query response time: ~1-2 seconds
• 📄 Handles documents up to 500+ pages
• 🎯 Chunk size: 512 tokens with 50-token overlap

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Built with ❤️ — no hallucinations guaranteed