AnswersHybrid Search & Ranking

When Do You Need Hybrid Search?

When do queries need keyword and vector search combined, and how do you tell from your own failed queries?

2 min readJuly 2026

The short answer

You need hybrid search when your queries mix natural language with exact tokens: product codes, names, error strings, domain jargon. Vector search alone blurs exact tokens into their semantic neighborhood, while keyword search alone misses synonyms and paraphrases.

If your failed queries show both patterns, run both retrievals and combine them. Pure prose queries can live on semantic search alone, and pure identifier lookups can live on keyword search alone, but most real query streams are neither.

Why does vector search miss exact matches?

An embedding is a summary of meaning, and rare tokens carry almost none of it. Search for SKU A17-B and the embedding dissolves it into a rough neighborhood of product-code-ish text, so the exact match is gone before scoring even starts.

A keyword index does the opposite: BM25 scoring on term frequency, term rarity, and document length (Robertson & Zaragoza, 2009) matches those tokens exactly, which is why keyword search still anchors most production systems.

Why does keyword search miss paraphrases?

To a keyword index, "car" and "automobile" are unrelated strings. Any query phrased differently from the document scores zero, no matter how close the meaning. Your user asked a perfectly good question, phrased it differently from the document, and got nothing. Embeddings exist precisely to close that gap.

Two opposite blind spots: hybrid runs both, so each covers the other's.

Vector only

Query: SKU A17-B

Blurred into a semantic neighborhood

Exact token lost

Misses identifiers

Keyword only

Query: automobile

No token overlap with car

Paraphrase scores zero

Misses meaning

How do the two get combined?

This is the real design decision, and you have three options: merge two ranked lists by position (RRF), normalize and weight the raw scores (score fusion), or score every signal inside one query (true hybrid). Each one trades tuning effort for control over the final ranking. We compare all three in RRF vs score fusion vs true hybrid.

How do you know you actually need it?

Look at your failed queries. If they cluster around exact identifiers, you're missing keyword search; if they cluster around rephrasings, you're missing vectors. When you see both patterns, you need hybrid, and most real query streams get there fast.

TopK runs keyword, vector, and multi-vector scoring in a single query with one ranking expression, so you never stitch together a second engine or a fusion layer. In TopK's BEIR case study (July 2025), that single-query approach scored 4.58% higher nDCG@10 on average than fusing two result lists with RRF. The whole thing is one query:

from topk_sdk.query import select, field, fn, match


docs = client.collection("articles").query(
    select(
        "title",
        semantic=fn.semantic_similarity("content", "climate change policies"),
        keyword=fn.bm25_score(),
    )
    .filter(match("carbon") | match("renewable energy"))
    .sort(field("semantic") * 0.6 + field("keyword") * 0.4, asc=False)
    .limit(10)
)

The true hybrid search guide covers the full API.

Keep reading

RRF vs Score Fusion vs True Hybrid: Which Should You Use?RRF merges lists by position, score fusion tunes weighted scores, and true hybrid scores every signal in one query. Start with RRF; move up as you measure.What Is Multi-Vector Retrieval?Multi-vector retrieval stores one embedding per token and scores documents with MaxSim, so precise queries match exact passages instead of a lossy document summary.Do You Still Need a Reranker?Add a reranker when the right documents reach your top 100 but not your top 5. With a multi-vector first stage, measure first: it may be redundant.