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Hybrid Search Basics

Learn Hybrid Search Basics for free with explanations, exercises, and a quick test (for NLP Engineer).

Published: January 5, 2026 | Updated: January 5, 2026

Table of Contents

Why this matters

As an NLP Engineer, you will build systems that must find the right text quickly and reliably. Hybrid search blends keyword (lexical) search with embedding-based (dense) search to catch both exact terms and semantic meaning. This is essential for RAG chatbots, FAQ assistants, code/document search, and enterprise knowledge bases.

  • Power RAG to retrieve context even when users phrase queries differently from documents.
  • Improve e-commerce or help-center search where synonyms, abbreviations, and typos are common.
  • Boost recall without sacrificing precision by combining two complementary signals.

Concept explained simply

Hybrid search combines two retrieval styles:

  • Sparse (lexical): based on exact or near-exact token matches (e.g., BM25). Great for precise keywords, filters, and rare terms.
  • Dense (vector): based on semantic similarity of embeddings. Great for synonyms, paraphrases, or cross-lingual meaning.

Fusion strategies blend their results (e.g., weighted sum of normalized scores, Reciprocal Rank Fusion, or rank voting), optionally followed by a reranker.

Mental model

Think of two complementary “ears” listening to the query:

  • Ear 1 hears exact words: fast and precise for names, codes, or specific phrasing.
  • Ear 2 hears meaning: robust to wording changes and synonyms.

Hybrid search lets both ears vote, then optionally asks a careful judge (a reranker) to finalize the top results.

Key terms (quick reference)
  • BM25: classic lexical scoring based on term frequency and document length.
  • Embedding: numeric vector representing text meaning.
  • Cosine similarity: common metric for vector similarity.
  • Score normalization: scaling scores to make them comparable before fusion.
  • RRF (Reciprocal Rank Fusion): combines ranks rather than raw scores: score = sum(1/(k + rank)).
  • Reranker: a heavier model (often a cross-encoder) that re-scores top candidates.

The hybrid retrieval pipeline

  1. Indexing
    • Build a lexical index (inverted index) for BM25.
    • Build a vector index (store embeddings) for approximate nearest neighbor (ANN) search.
  2. Querying
    • Run the user query through both: BM25 and embedding search.
  3. Fusion
    • Normalize scores or use rank-based methods, then combine (weighted sum, RRF, etc.).
  4. Rerank (optional)
    • Feed the top N candidates into a reranker to improve precision.
  5. Return results
Design choices cheat sheet
  • Data is jargon-heavy or users search by codes? Increase lexical weight.
  • Users ask in natural language or multilingual? Increase dense weight.
  • Score scales are inconsistent? Normalize or use rank-based fusion like RRF.
  • High precision needed at top-5? Add a reranker.

Worked examples

Example 1 — Weighted sum (lexical + dense)

Query: "apple charging not working" over phone support docs.

  1. Lexical top candidates (BM25):
    • A: "iPhone won’t charge via cable" (high BM25)
    • B: "Diagnose MagSafe issues" (medium BM25)
    • C: "Battery health basics" (low BM25)
  2. Dense top candidates (cosine):
    • B: "Diagnose MagSafe issues" (high semantic match to "charging")
    • D: "Power adapter compatibility"
    • A: "iPhone won’t charge via cable"
  3. Normalize each method’s scores to [0,1], then fuse: fused = 0.6*lex + 0.4*dense
  4. Result: A and B rise to the top; D may appear if dense confidence is strong.
Why normalization matters

BM25 ranges differ from cosine similarities. Without normalization, one method could drown out the other. Min-max and z-score are common choices; rank-based fusion avoids direct score comparisons.

Example 2 — RRF (rank-based fusion)

Query: "cancel membership" while docs use "terminate subscription".

  1. Lex ranks: L1: Doc X (rank 1), Doc Y (rank 2), Doc Z (rank 3)
  2. Dense ranks: D1: Doc Y (rank 1), Doc Z (rank 2), Doc X (rank 10)
  3. RRF score(doc) = 1/(k + rank_lex) + 1/(k + rank_dense) with k=60
  4. Doc Y tends to win because it ranks high in both lists, even if not #1 everywhere.
RRF intuition

RRF rewards items that appear near the top across methods, improving robustness and reducing sensitivity to score scales.

Example 3 — Add a reranker

Query: "Paris local cuisine tips". Fusion returns candidate pages: A "French cuisine overview", B "Eat like a local in Paris", C "Where to find bistros".

  1. Initial hybrid retrieves A, B, C.
  2. Reranker reads (query, passage) pairs and scores precise relevance.
  3. Reranker promotes B and C above the generic A, improving top-1 precision.
When to rerank

Use reranking when you need high precision at small k (e.g., top-5). Keep the candidate set modest (e.g., 50–200) to control latency.

How to fuse scores (practical)

  • Weighted sum (after normalization): fused = α*lex + (1−α)*dense. Start with α=0.5; tune based on validation.
  • Normalization options:
    • Min-max: (x − min)/(max − min) per method.
    • Z-score: (x − mean)/std, then optionally rescale to [0,1].
    • Rank-based: avoid score scaling entirely (e.g., RRF).
  • Reranking: apply only to the top N fused candidates.
Small numeric demo

Suppose min-max normalize and α=0.6 (lex-heavy):
Doc D has lex=0.9 and dense=0.4 → fused=0.6*0.9 + 0.4*0.4=0.54+0.16=0.70

Evaluation and tuning

  • Build a small set of query–relevant document pairs from real traffic or annotated samples.
  • Metrics: Recall@k (coverage), MRR (how early the correct result appears), nDCG@k (graded relevance), Precision@k (focus on top results).
  • Procedure:
    1. Pick α (or choose RRF k).
    2. Measure metrics on your validation set.
    3. Iterate: adjust α, candidate pool sizes, or add a reranker.
Practical tips
  • Keep logs of queries and clicks to refine future labels.
  • Watch latency: ANN parameters and reranker batch size strongly affect responsiveness.
  • Use domain-specific embeddings when possible for a quality boost.

Who this is for

  • NLP Engineers building RAG, search, or QA systems.
  • Data scientists improving internal knowledge discovery.
  • Backend engineers integrating search APIs with ML models.

Prerequisites

  • Basics of embeddings and cosine similarity.
  • Understanding of lexical search (e.g., BM25) and inverted indexes.
  • Familiarity with Python and data structures for search.

Exercises

These mirror the practice task below. Do them now, then take the Quick Test. Progress is saved only for logged-in users; everyone can access the test.

Exercise 1: Manual score fusion

You ran both methods on a query and got these raw scores:

  • Lexical (BM25): D1=12, D2=7, D3=3, D4=0, D5=9
  • Dense (cosine): D1=0.22, D2=0.88, D3=0.66, D4=0.10, D5=0.44

Task:
1) Min-max normalize each method separately.
2) Fuse with α=0.5: fused = 0.5*lex + 0.5*dense.
3) Output the top-3 document IDs by fused score.

Write your top-3 in order.

Need a hint?
  • For lexical, min=0 and max=12.
  • For dense, min=0.10 and max=0.88.
  • Normalize each set first, then average.
  • [ ] I normalized each method separately.
  • [ ] I computed fused scores correctly with α=0.5.
  • [ ] I ranked documents by the fused score and selected top-3.

Common mistakes and self-check

  • Skipping normalization for weighted sum
    • Self-check: Are your fused scores dominated by one method’s scale?
  • Using too small candidate pools
    • Self-check: Does Recall@50 drop when you tighten ANN or BM25 thresholds?
  • Not tuning α or RRF k
    • Self-check: Did you validate several settings and pick the best on metrics?
  • Reranking too many docs
    • Self-check: Is latency acceptable? Try reranking fewer candidates or batching.

Practical projects

  • Build a hybrid FAQ search: BM25 + sentence embeddings, fuse via RRF, rerank top-50.
  • Domain support bot: create a small labeled set, tune α for best nDCG@10, add reranking.
  • Multilingual retrieval: use multilingual embeddings, compare dense-only vs hybrid on cross-language queries.

Mini challenge

Take your latest retrieval task and run three variants: BM25-only, dense-only, hybrid (α=0.5). Measure Recall@20 and nDCG@10 on 20 queries. Report which wins and why, then adjust α to beat your initial hybrid.

Learning path

  • Before this: Embeddings fundamentals, BM25 basics.
  • Now: Hybrid fusion and reranking.
  • Next: Index optimization, ANN tuning, cross-encoder reranking, and evaluation at scale.

Next steps

  • Try both weighted-sum and RRF on your data.
  • Add a reranker to the fused top-100 and measure precision gains.
  • Automate evaluation so you can tune and deploy confidently.

Note: The quick test is available to everyone; only logged-in users will have their progress saved.

Practice Exercises

1 exercises to complete

Instructions

You ran both methods on a query and got these raw scores:

  • Lexical (BM25): D1=12, D2=7, D3=3, D4=0, D5=9
  • Dense (cosine): D1=0.22, D2=0.88, D3=0.66, D4=0.10, D5=0.44

Tasks:

  1. Min-max normalize each method separately.
  2. Fuse with α=0.5: fused = 0.5*lex + 0.5*dense.
  3. Return the top-3 document IDs by fused score (in order).
Expected Output
Top-3 by fused score: D2, D5, D1

Hybrid Search Basics — Quick Test

Test your knowledge with 7 questions. Pass with 70% or higher.

7 questions70% to pass
Chunking StrategiesBuilding Retrieval Evaluation Sets

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