Search has evolved. Today, natural language queries have largely replaced simple keyword searches when addressing our information needs. Instead of typing “Peru travel guide” into a search engine, we now ask a large language model (LLM) “Where should I visit in Peru in December during a 10-day trip? Create a travel guide.”

Is keyword search no longer useful? While the rise of LLMs and vector search may suggest that traditional keyword search is becoming less prevalent, the future of search actually relies on effectively combining both methods. This is where hybrid search plays a crucial role, blending the precision of traditional text search with the powerful contextual understanding of vector search. Despite advances in vector technology, keyword search still has a lot to contribute and remains essential to meeting current user expectations.

The rise of hybrid search

By late 2022 and particularly throughout 2023, as vector search saw a surge in popularity (see image 1 below), it quickly became clear that vector embeddings alone were not enough. Even as embedding models continue to improve at retrieval tasks, full-text search will always remain useful for identifying tokens outside the training corpus of an embedding model. That is why users soon began to combine vector search with lexical search, exploring ways to leverage both precision and context-aware retrieval. This shift was driven in large part by the rise of generative AI use cases like retrieval-augmented generation (RAG), where high-quality retrieval is essential.

Figure 1. Number of vector search vendors per year and type.


As hybrid search matured beyond basic score combination, the main fusion techniques emerged - reciprocal rank fusion (RRF) and relative score fusion (RSF). They offer ways to combine results that do not rely on directly comparable score scales. RRF focuses on ranking position, rewarding documents that consistently appear near the top across different retrieval methods. RSF, on the other hand, works directly with raw scores from different sources of relevance, using normalization to minimize outliers and align modalities effectively at a more granular level than rank alone can provide. Both approaches quickly gained traction and have become standard techniques in the market.

How did the market react?

The industry realized the need to introduce hybrid search capabilities, which brought different challenges for different types of players.

For lexical-first search platforms, the main challenge was to add vector search features and implement the bridging logic with their existing keyword search infrastructure. These vendors understood that the true value of hybrid search emerges when both modalities are independently strong, customizable, and tightly integrated.

On the other hand, vector-first search platforms faced the challenge of adding lexical search. Implementing lexical search through traditional inverted indexes was often too costly due to storage differences, increased query complexity, and architectural overhead. Many adopted sparse vectors, which represent keyword importance in a way similar to traditional term-frequency methods used in lexical search. Sparse vectors were key for vector-first databases in enabling a fast integration of lexical capabilities without overhauling the core architecture.

Hybrid search soon became table stakes and the industry focus shifted toward improving developer efficiency and simplifying integration. This led to a growing trend of vendors building native hybrid search functions directly into their platforms. By offering out-of-the-box support to combine and manage both search types, the delivery of powerful search experiences was accelerated.

As hybrid search became the new baseline, more sophisticated re-ranking approaches emerged. Techniques like cross-encoders, learning-to-rank models, and dynamic scoring profiles began to play a larger role, providing systems with additional alternatives to capture nuanced user intent. These methods complement hybrid search by refining the result order based on deeper semantic understanding.

What to choose? Lexical-first or vector-first solutions? Top considerations when choosing a hybrid search solution

When choosing how to implement hybrid search, your existing infrastructure plays a major role in the decision. For users working within a vector-first database, leveraging their lexical capabilities without rethinking the architecture is often enough. However, if the lexical search requirements are advanced, commonly the optimal solution is served with a traditional lexical search solution coupled with vector search, like MongoDB. Traditional lexical - or lexical-first - search offers greater flexibility and customization for keyword search, and when combined with vectors, provides a more powerful and accurate hybrid search experience.

Figure 2. Vector-first vs Lexical-first systems: Hybrid search evaluation.


Indexing strategy is another factor to consider. When setting up hybrid search, users can either keep keyword and vector data in separate indexes or combine them into one. Separate indexes give more freedom to tweak each search type, scale them differently, and experiment with scoring. The compromise is higher complexity, with two pipelines to manage and the need to normalize scores. On the other hand, a combined index is easier to manage, avoids duplicate pipelines, and can be faster since both searches run in a single pass. However, it limits flexibility to what the search engine supports and ties the scaling of keyword and vector search together. The decision is mainly a trade-off between control and simplicity.

Lexical-first solutions were built around inverted indexes for keyword retrieval, with vector search added later as a separate component. This often results in hybrid setups that use separate indexes. Vector-first platforms were designed for dense vector search from the start, with keyword search added as a supporting feature. These tend to use a single index for both approaches, making them simpler to manage but sometimes offering less mature keyword capabilities.

Lastly, a key aspect to take into account is the implementation style. Solutions with hybrid search functions handle the combination of lexical and vector search natively, removing the need for developers to manually implement it. This reduces development complexity, minimizes potential errors, and ensures that result merging and ranking are optimized by default. Built-in function support streamlines the entire implementation, allowing teams to focus on building features rather than managing infrastructure.

In general, lexical-first systems tend to offer stronger keyword capabilities and more flexibility in tuning each search type, while vector-first systems provide a simpler, more unified hybrid experience. The right choice depends on whether you prioritize control and mature lexical features or streamlined management with lower operational overhead.

How does MongoDB do it?

When vector search emerged, MongoDB added vector search indexes to the existing traditional lexical search indexes. With that, MongoDB evolved into a competitive vector database by providing developers with a unified architecture for building modern applications. The result is an enterprise-ready platform that integrates traditional lexical search indexes and vector search indexes into the core database.

MongoDB recently released native hybrid search functions to MongoDB Atlas and as part of a public preview for use with MongoDB Community Edition and MongoDB Enterprise Server deployments. This feature is part of MongoDB’s integrated ecosystem, where developers get an out-of-the-box hybrid search experience to enhance the accuracy of application search and RAG use cases.

As a result, instead of managing separate systems for different workloads, MongoDB users benefit from a single platform designed to support both operational and AI-driven use cases. As generative AI and modern applications advance, MongoDB gives organizations a flexible, AI-ready foundation that grows with them.