Rerankers & Hybrid Search (BM25 + Embeddings)

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🔁 Rerankers & Hybrid Search

Getting the Best of Both Worlds: Keywords + Meaning

In GenAI search systems, retrieving relevant information is just as important as generating great answers.

While vector search (embeddings) finds results based on meaning, it can sometimes miss important exact keywords. On the other hand, keyword search (like BM25) is precise — but lacks deep understanding.

Solution: 👉 Combine both with Hybrid Search 👉 Improve precision with Rerankers

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🧠 What Is Hybrid Search?

Hybrid Search blends:

• BM25 (keyword-based search)

• Embedding-based search (semantic search)

This lets you:

• Retrieve results that match exact terms

• Also include results that are contextually relevant

Example:

Query: “refund for enterprise customers”

• BM25 might find documents with "refund"

• Embeddings might surface docs with "money-back policy" → Hybrid brings both into the top results.

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🧠 What Is a Reranker?

A reranker is an LLM or ML model that takes the top N retrieved chunks (from vector + BM25 search), and reorders them by relevance to the query.

It’s like a final filter that says:

“Out of these 10 results, which 3 are truly best?”

Rerankers boost RAG performance by feeding only the most relevant context into the LLM.

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🔍 Pipeline Overview

[User Query] 
    ↓
[Hybrid Retrieval: BM25 + Embedding Search]
    ↓
[Top 20 Results]
    ↓
[Reranker reorders results]
    ↓
[Top 3 → LLM Prompt]
    ↓
[Generated Answer]

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🔑 Popular Tools for Hybrid Search & Reranking

Tool/Feature	Description
BM25 (via Elasticsearch, Weaviate, Qdrant)	Fast keyword search engine
Embedding Search (FAISS, Pinecone, etc.)	Finds contextually similar vectors
Rerankers	Models like Cohere’s rerank, BAAI bge-reranker, or Hugging Face cross-encoders
LangChain / LlamaIndex	Built-in support for hybrid + rerank pipelines
Cohere Rerank API	Drop-in hosted reranking model (semantic + fast)

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🧪 Example Use Case

You’re building a legal document assistant.

• BM25 finds sections with exact matches (e.g., “termination clause”)

• Embeddings find paraphrased versions (e.g., “how a contract can end”)

• Reranker sorts and refines to pick the best 2–3 snippets for the LLM to answer accurately

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📊 Summary

• Hybrid search = keyword + semantic = better recall

• Rerankers = smarter filtering = better precision

• Both dramatically improve the quality of GenAI answers, especially in RAG systems

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