BM25, SPLADE, and Reciprocal Rank Fusion: The Building Blocks of Production Hybrid Search
BM25, SPLADE, and reciprocal rank fusion each solve a different retrieval problem. Here's how the three combine into a production hybrid search system.
Hybrid Search
Hybrid search combines two ways of finding documents: dense vector search, which matches by meaning, and sparse keyword search like BM25, which matches by exact words.
Used together, they cover each other's weaknesses — vectors handle paraphrases and concepts, keywords handle names, codes, and rare terms. In production RAG systems, this combination consistently retrieves more relevant results than either method alone. Also known as: Hybrid Retrieval.
What this topic covers
- Foundations — Hybrid search isn't just two retrievers running in parallel — the real engineering challenge lives in how their scores get fused.
- Implementation — The build guides walk through wiring BM25 and vector search together, picking a fusion strategy, and tuning the weights without overfitting to a benchmark.
- What's changing — Hybrid search is shifting from a hand-rolled pattern to a first-class feature in vector databases, with new fusion algorithms and query APIs landing fast.
- Risks & limits — Hybrid search looks like a neutral combination of two methods, but it inherits the biases of both — including how poorly keyword matching handles morphologically rich languages.
This topic is curated by our AI council — see how it works.
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Understand the Fundamentals
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
What Is Hybrid Search and How BM25 Plus Dense Vectors Beat Either Alone in RAG
Hybrid search fuses BM25 keyword retrieval with dense vector search using reciprocal rank fusion. Why two ranked lists beat either alone in RAG pipelines.
Score Mismatch, Tuning Hell: The Hard Limits of Hybrid Search Fusion
Hybrid search merges BM25 and vector results, but the fusion step has hard limits. Score mismatch, RRF blindness, and tuning hell — explained.
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Build with Hybrid Search
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
How to Build a Hybrid Search Pipeline with Weaviate, Qdrant, and SPLADE in 2026
Build a hybrid search pipeline by decomposing it into sparse, dense, and fusion specs. Covers Weaviate, Qdrant, and SPLADE-v3 for enterprise RAG.
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What's Changing in 2026
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated April 2026
Notion, Perplexity, and Glean: How Hybrid Search Powers Production RAG at Scale
Hybrid search is now the production RAG default. How Perplexity, Glean, and Notion combine lexical and semantic retrieval at scale, and what it signals.
Weaviate BlockMax WAND, Qdrant Query API: The 2026 Hybrid Search Race
Hybrid search is no longer a vendor differentiator. Weaviate's BlockMax WAND, Qdrant's Query API, and Postgres extensions are converging on one shape.
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Risks and Considerations
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Hybrid Search Looks Neutral but Isn't: Lexical Bias and the Languages BM25 Leaves Behind
Hybrid search looks neutral. But BM25's tokenizer favors English, and the languages it leaves behind reveal what fairness asks of retrieval systems.