LEARNING HUB
CURRENT TREND INSIGHT
Implementing hybrid search and reranking models for enterprise RAG Illustration

Implementing hybrid search and reranking models for enterprise RAG

Written by Nikhil Badjatya
Direct Summary:

Enterprise RAG systems typically combine two retrieval signals rather than relying on vector search alone: a sparse lexical search (BM25) that's strong on exact terms, part numbers, and rare keywords, and a dense vector search that's strong on semantic meaning. Merging both candidate sets and then running them through a cross-encoder reranker consistently outperforms either method alone — the reranker actually reads the query and each candidate together, rather than just comparing pre-computed embeddings.

"Discipline is the bridge between goals and accomplishment."

— Jim Rohn

Key Insights

  • BM25 catches what embeddings miss: exact SKUs, error codes, legal citations, and rare proper nouns are often where pure vector search underperforms — lexical search is still the right tool for these.
  • Reranking is a second pass, not a replacement: retrieve a wider candidate set (e.g. top 20-50) cheaply with vector + lexical search, then spend the more expensive cross-encoder pass only on narrowing that set down to the top 3-5.
  • The reranker adds real latency: a cross-encoder pass typically costs tens to low hundreds of milliseconds depending on candidate count and model size — budget for it rather than discovering it in production.

Enterprise document sets are exactly where pure vector search tends to underperform — part numbers, contract clause references, and compliance codes are precisely the kind of exact-match content dense embeddings are weakest at. Hybrid retrieval fixes this by running a lexical search alongside the vector search and merging the results, then using a reranker to sort the merged set by actual relevance rather than trusting either retrieval method's raw ranking.

Step-by-Step Implementation

1. Parse Input Documents: Extract text records and split them into chunks using smart recursive division rules.

2. Execute Index Queries: Search dense and sparse tables to collect candidate matches.

3. Apply Reranking Model: Sort results using a cross-encoder to select the most relevant chunks for the context window.

hybrid_rag_pipeline.py 
# Implementation of a hybrid retrieval and reranking loop
def hybrid_retrieve(query, lexical_db, vector_db, top_n=10):
    # 1. Retrieve lexical keyword hits (e.g. BM25)
    lexical_hits = lexical_db.search(query, limit=top_n)
    # 2. Retrieve dense vector hits
    vector_hits = vector_db.search(query, limit=top_n)
    
    # 3. Combine results and remove duplicates
    combined = {c.id: c for c in (lexical_hits + vector_hits)}.values()
    
    # 4. Rerank matches based on contextual similarity
    # Mock cross-encoder ranking scores
    ranked = sorted(combined, key=lambda x: x.score, reverse=True)
    return ranked[:3]
Retrieval System Accuracy Level Processing Overhead
Standard Vector Retrieval Moderate accuracy, struggles with exact terms Low search latency
Hybrid + Reranking Outstanding recall and semantic relevance Higher latency (~50-150ms cross-encoder step)

The two-stage design (cheap wide retrieval, then expensive narrow reranking) is what makes this practical at enterprise scale — running a cross-encoder over your entire document set for every query would be far too slow, but running it over a few dozen pre-filtered candidates is fast enough for production use.

Practical Challenge

Implement a sliding window chunker in Python that splits a sample essay into chunks of 100 words with a 20-word overlap.

Concept Check

Why is a reranker model valuable in a RAG pipeline?
Correct! Vector search returns candidates based on global cosine distance. Rerankers (cross-encoders) run a joint attention check on the query and the chunk, re-sorting them by exact relevance.
Incorrect. Try again! Hint: Vector search returns candidates based on global cosine distance. Rerankers (cross-encoders) run a joint attention check on the query and the chunk, re-sorting them by exact relevance.

Sources & Further Reading

Previous Guide Dashboard Next Guide