Build a Hybrid RAG System with FAISS, BM25, LangGraph and Claude Sonnet Model

2026-06-21 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

This article details the construction of a Hybrid Retrieval-Augmented Generation (RAG) system, integrating FAISS for dense vector search and BM25 for keyword search. The system employs Reciprocal Rank Fusion (RRF) with a smoothing constant k=60 to merge results from both retrievers, leveraging their respective strengths in semantic understanding and exact matching. Orchestrated using LangGraph and powered by the Claude Sonnet 4.6 API, the solution includes a Streamlit UI for interactive document querying and transparent inspection of retrieved chunks, scores, and token usage. The implementation uses "all-MiniLM-L6-v2" for embeddings and processes PDFs into 200-token chunks with 50-token overlap. It demonstrates how hybrid RAG effectively overcomes the limitations of single-mode retrieval, which often fails on specific query types, at a near-zero additional computational cost.

Key takeaway

For AI Engineers building robust RAG systems, you should integrate hybrid retrieval to overcome the limitations of single-mode approaches. By combining FAISS for semantic understanding and BM25 for exact matches, fused with Reciprocal Rank Fusion, your system will handle diverse query types more effectively. This approach significantly improves retrieval accuracy without substantial performance overhead, ensuring more reliable answers from your LLM. Consider implementing a transparent UI to debug retrieval failures.

Key insights

Hybrid RAG combines semantic and keyword search via Reciprocal Rank Fusion to enhance retrieval accuracy for diverse query types.

Principles

• Neither dense vector search nor keyword search is universally superior.
• Reciprocal Rank Fusion (RRF) effectively merges ranked lists from disparate retrievers.
• RRF is score-scale agnostic and robust to single-retriever outliers.

Method

Build FAISS and BM25 indexes from chunked PDF text. Retrieve results from both, then fuse using Reciprocal Rank Fusion. Orchestrate with LangGraph and generate answers via LLM.

In practice

• Use FAISS for semantic queries and BM25 for exact matches.
• Implement RRF with k=60 for robust rank merging.
• Share embedding models to optimize memory and load time.

Topics

• Hybrid RAG
• Reciprocal Rank Fusion
• FAISS
• BM25
• LangGraph
• Claude Sonnet
• Streamlit UI

Code references

• alphaiterations/agentic-ai-usecases

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.