---
title: What is Hybrid Search?
description: Hybrid search combines dense vector (semantic) search with sparse keyword (BM25-style) search, merging results from both to produce a single ranked list. It captures the strengths of each approach — semantic understanding from dense retrieval and exact-term precision from sparse retrieval — making it more accurate t...
canonical_url: https://superlinked.com/glossary/what-is-hybrid-search
last_updated: 2026-06-02
---

# What is Hybrid Search?

Hybrid search combines dense vector (semantic) search with sparse keyword (BM25-style) search, merging results from both to produce a single ranked list. It captures the strengths of each approach — semantic understanding from dense retrieval and exact-term precision from sparse retrieval — making it more accurate than either method alone for most production use cases.

---

## Why does hybrid search matter?

Neither dense nor sparse retrieval is universally better:

- **Dense retrieval** excels at understanding meaning, synonyms, and paraphrase — but struggles with rare terms, product codes, and proper nouns
- **Sparse retrieval** excels at exact keyword matching — but misses relevant documents that use different vocabulary

A user searching for "BGE-M3 throughput benchmark" needs both: semantic understanding of "throughput benchmark" and exact matching of the specific model name "BGE-M3". Hybrid search handles both simultaneously.

In practice, hybrid search consistently outperforms either approach alone on retrieval benchmarks across diverse query types.

---

## How does hybrid search work?

Hybrid search has two parallel retrieval paths that are then merged:

```
Query
  │
  ├──► [Embedding model] ──► dense vector ──► [Vector DB: ANN search] ──► dense results
  │
  └──► [Sparse encoder] ──► sparse vector ──► [Inverted index: BM25] ──► sparse results
                                                            │
                                              [Reciprocal Rank Fusion / weighted merge]
                                                            │
                                                    Hybrid ranked list
                                                            │
                                              [Optional: Reranker]
                                                            │
                                                      Final results
```

The merging step — often Reciprocal Rank Fusion (RRF) — combines the rank positions from both result sets into a single score without requiring score normalisation.

---

## What is Reciprocal Rank Fusion (RRF)?

RRF is a simple, robust algorithm for merging ranked lists. For each document, its RRF score is:

```
RRF(doc) = Σ 1 / (k + rank_in_list)
```

Where `k` is a constant (typically 60) and `rank_in_list` is the document's position in each retrieval result. Documents that rank highly in multiple lists get boosted; documents that only appear in one list are scored more conservatively.

RRF works well in practice because it's robust to score scale differences between dense and sparse systems.

---

## How do you implement hybrid search with SIE?

BGE-M3 produces both dense and sparse vectors from a single model, making it ideal for hybrid search with SIE:

```python
from sie_sdk import SIEClient
from sie_sdk.types import Item

client = SIEClient("http://localhost:8080")

# Encode documents with both dense and sparse vectors
results = client.encode(
    "BAAI/bge-m3",
    [Item(text=d) for d in documents],
    output_types=["dense", "sparse"],
)

dense_vectors = [r["dense"] for r in results]       # for ANN search
sparse_vectors = [r["sparse"] for r in results]     # for inverted index / BM25

# Most vector DBs (Qdrant, Weaviate) support hybrid search natively
# Pass both vector types and let the DB handle merging
```

Qdrant, Weaviate, and Chroma all support hybrid search with dense + sparse vectors. SIE integrates directly with each.

---

## Dense vs sparse vs hybrid: when to use each

| Scenario | Best approach |
|---|---|
| Natural language questions | Dense or hybrid |
| Product code / SKU lookup | Sparse or hybrid |
| Multilingual queries | Dense (BGE-M3) or hybrid |
| Mixed query types in production | **Hybrid** |
| Tight latency budget | Dense only |
| Domain-specific terminology | Hybrid or sparse |

For most production search and RAG systems, hybrid search is the recommended default.

---

## Hybrid search vs reranking: what's the difference?

These are complementary, not competing:

- **Hybrid search** improves the *retrieval* step — finding better candidates from the full corpus
- **Reranking** improves the *ranking* step — more precisely ordering a shortlist of candidates

The optimal pipeline for high-accuracy systems is: hybrid retrieval → reranking → LLM generation (for RAG).

---

## Frequently asked questions

**Does hybrid search require two separate models?**
Not with BGE-M3. It produces dense and sparse representations from a single model, reducing infrastructure complexity. SIE hosts BGE-M3 for both outputs.

**Is hybrid search significantly slower than dense-only search?**
The retrieval step adds minimal latency since both paths run in parallel. Encoding time is unchanged (same model, same call). The main overhead is the merging step, which is negligible.

**Which vector databases support hybrid search?**
Qdrant, Weaviate, and Milvus all support hybrid search natively. SIE has integration guides for each.

---

## Related resources

- [SIE + Qdrant hybrid search integration](/docs/integrations/qdrant)
- [SIE + Weaviate integration](/docs/integrations/weaviate)
- [What is BGE-M3?](/glossary/what-is-bge-m3)
- [What is semantic search?](/glossary/what-is-semantic-search)
- [What is a reranker?](/glossary/what-is-a-reranker)
- [What is RAG?](/glossary/what-is-rag)