# Top Considerations When Choosing a Hybrid Search Solution

[MongoDB](https://yomu.fyi/company/mongodb) · Jose Parra · Sep 30, 2025

**Type:** Explainer

## Summary

Natural language queries and generative AI workflows like retrieval-augmented generation have exposed the limitations of standalone vector search, particularly for tokens outside an embedding model's training corpus. Hybrid search addresses these gaps by merging traditional lexical precision with dense vector context. Platforms combine these modalities using techniques such as reciprocal rank fusion and relative score fusion, often complemented by downstream cross-encoders or learning-to-rank models. Architecture choices center on the trade-offs between lexical-first systems using inverted indexes and vector-first platforms employing sparse vectors. MongoDB has integrated vector search indexes alongside its existing lexical indexes, releasing native hybrid search functions across Atlas, Community Edition, and Enterprise Server to unify operational data and search retrieval.

## Context

Vector embeddings alone cannot reliably capture tokens outside an embedding model's training corpus, creating a need in generative AI and retrieval-augmented generation workloads to combine vector search with lexical search precision.

## Approach / What changed

Platforms implement hybrid search via lexical-first or vector-first architectures, fusing results using techniques like reciprocal rank fusion (RRF) and relative score fusion (RSF), with MongoDB natively integrating vector search indexes alongside traditional lexical search indexes.

## Takeaways

- Reciprocal rank fusion (RRF) combines search modalities by ranking position across retrieval methods, whereas relative score fusion (RSF) normalizes raw scores directly to minimize outliers.
- Vector-first search platforms frequently adopt sparse vectors to incorporate keyword search capabilities without incurring the architectural overhead of traditional inverted indexes.
- Using separate indexes provides granular tuning and independent scaling at the cost of pipeline complexity, while a combined index simplifies management but binds the scaling of both modalities together.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [LLMs](https://yomu.fyi/topic/llm), [Search](https://yomu.fyi/topic/search)

[Read original post](https://www.mongodb.com/company/blog/technical/top-considerations-when-choosing-hybrid-search-solution)