In today’s data-driven world, the ability to retrieve contextually relevant information quickly is critical—especially in Retrieval-Augmented Generation (RAG) systems. Traditional retrieval methods often fall short, either focusing too much on keywords or relying entirely on semantic similarity. That’s where a hybrid retrieval system, combining FAISS and BM25, comes into play.

Why Combine FAISS and BM25?

• FAISS (Facebook AI Similarity Search) is excellent for vector-based searches. It quickly finds semantically similar documents using embeddings.
   

• BM25, a proven keyword-based algorithm, ranks documents based on term frequency and relevance.
   

Each has its strengths—FAISS excels in understanding meaning, while BM25 is great at pinpointing keyword relevance. By combining them, you get the best of both worlds: speed, accuracy, and contextual precision.

Building a Hybrid Retrieval System: Key Steps

1. Load and Process Documents
   Begin by extracting text from PDFs and converting them into vector embeddings using models like TF-IDF or more advanced alternatives.
    

2. Implement FAISS for Vector Search
   FAISS indexes your documents and enables lightning-fast similarity searches based on user queries.
    

3. Compute BM25 Scores for Keyword Matching
   Using the BM25 algorithm, compute scores that reflect how relevant documents are based on keyword occurrence.
    

4. Merge Results for Optimal Relevance
   Combine the top results from both FAISS and BM25 to deliver the most contextually accurate and relevant documents.
    

Looking to Build Smarter Search Systems?
Let ProsperaSoft help you unlock the full potential of hybrid retrieval. Contact us today to explore how we can tailor intelligent AI solutions for your document workflows.