Hybrid Search 101: how it works and why It's important

Hybrid search combines keyword and semantic search, giving users the best of both worlds and control over the level of contextual depth they need.

This type of information retrieval (IR) is especially critical in enterprise search, e-commerce, and knowledge management systems, where some inputs require deep contextual understanding while others demand precise keyword matching.

A clear advantage of hybrid search is its ability to handle data with less computational costs.

This is thanks to the lexical matching system, which uses considerably less power than semantic algorithms that may rely on Large Language Models (LLMs), Convolutional Neural Networks (CNNs), and other energy-consuming models.

Therefore, these systems can be tweaked for higher performance while being a cost-effective alternative to pure semantic search systems.

However, implementing hybrid search requires strategic planning. Users unfamiliar with semantic weighting may find it confusing, leading to frustration or disengagement.

In the following piece, we will take a deep look into the importance of hybrid search, how to implement it, and in which situations it is the preferred solution.

What is hybrid search?

Hybrid search systems combine keyword-based retrieval (sparse vector methodology) with semantic search systems (dense vector embeddings) to optimize precision and contextual relevance.

For a visual explanation of the terms and technologies used in the hybrid search, let’s take a look at the schema below:

Semantic search relies on dense vectors, requiring both the search query and target data to be embedded using Machine Learning (ML) models. Some methods, like neural search, leverage Deep Neural Networks (DNNs) to generate rich contextual insights for embedding, retrieval, and ranking. Vector search is another type of semantic search that uses embedding models to create dense vectors, ML algorithms such as Approximate Nearest Neighbors (ANN) for information retrieval, and cosine similarity search for ranking.

On the other hand, keyword search relies on sparse vectors generated through algorithms like BM25 that use Term Frequency-Inverse Document Frequency (TF-IDF). A sparse vector is focused on tokens, where each value corresponds to a specific keyword within a large vocabulary. When a search query is entered, the system preprocesses the input by extracting individual words and matching them against the sparse vector values of the documents. These are then ranked based on keyword relevance.

Dense vectors

The dense vectors are extensive and can have hundreds or thousands of floats (numerical representations) to identify a single document. They represent the similarity of objects in a vector space and can have the following shape:

dense_vector = [0.8, 0.4, 0.2, 0.7, 0.9, 0.1, … ]

Dense vectors are usually multidimensional and don’t have zero values because they are created continuously to capture the complete information of the document or query.

Sparse vectors

The sparse vectors are much shorter than dense vectors, and only a few numerical representations target specific keywords:

sparse_vector = [{213: 0.3}, {543: 0.8}]

Unlike dense vectors, these mostly consist of zeros, with some float values used to map the keywords. Hybrid search combines the strengths of dense and sparse vector retrieval to enhance search scores. It first gathers matches from both methods and then refines the final output using techniques like Reciprocal Rank Fusion (RRF).

In the next chapter, we’ll explore each step of the hybrid search process.

How does hybrid search work?

Hybrid search works by leveraging the semantic capabilities of dense vectors and the exact matching and accuracy of sparse vectors. The outputs retrieved from these vectors are re-ranked using techniques like RRF.

By looking at the schematic above, we can structure the hybrid search workflow into distinct steps, allowing both semantic and keyword searches to operate in parallel:

Data cleansing & preprocessing

• Keyword Search: Requires robust data cleaning (e.g., using NLP tools to remove stopwords) to ensure accurate term matching.
• Semantic Search: Benefits from noise reduction and strategic text segmentation (chunking) to improve the quality of document embeddings.

Embedding: dense and sparse representations

• Semantic Embeddings: Models like Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors (GloVe) transform documents into dense vectors, capturing nuanced contextual meanings.
• Keyword Embeddings: While BM25 functions primarily as a scoring algorithm based on term frequencies, SPLADE leverages neural networks to generate sparse embeddings.

Retrieval mechanisms of dense and sparse vectors

• Semantic Retrieval: Utilizes algorithms like Approximate Nearest Neighbor (ANN) and K-Nearest Neighbor (KNN) to search within the dense vector space efficiently.
• Keyword Retrieval: Directly matches query terms with document vectors.

Ensemble retrieval

• Hybrid search re-ranking: The final step involves re-ranking results from both retrieval methods using the Reciprocal Rank Fusion (RRF) algorithm.

The hybrid search process can be tweaked to assign importance to one type of result over another. If contextual meaning outweighs lexical matching, the system prioritizes outputs from the semantic search. Otherwise, it prioritizes keyword matching. This feature is available and easily controlled in Meilisearch’s hybrid search setup.

What is a hybrid search engine example?

Companies have adopted hybrid search engines to improve the accuracy and relevance of search results. One of the most advanced hybrid search systems is Google Search, which combines multiple search techniques and algorithms to deliver precise and contextually relevant results.

Google integrates both keyword-based search and machine learning models to interpret user queries, rank web pages, and present the most relevant information. Currently, they utilize Vertex AI Embedding models to generate dense vectors that capture semantic meaning while simultaneously employing BM25 and SPLADE to create sparse vectors for keyword-based retrieval.

To configure search results, Google merges output from semantic and keyword-based searches using Reciprocal Rank Fusion (RRF), as detailed in their official notebook.

As of January 2025, Google Search maintains an 89.79% market share, continuing to dominate the search landscape. While AI-driven chatbot search features have begun gaining traction, they still fall short of Google's accuracy and real-time information retrieval capabilities.

What are the benefits of hybrid search?

Hybrid search provides several advantages over standalone keyword-based or semantic search methods. Some of these benefits include:

1. Enhanced search accuracy and relevance: The re-ranking mechanism used in hybrid search produces the best outputs from exact matches and meaning. This level of accuracy ultimately retains users and reduces the bounce rate.
2. Improved user experience: The system can deliver meaningful content even if users enter inaccurate terms or vague keywords. This ease of retrieving information allows designers to create engaging search elements. Just ask CarbonGraph: "We migrated to Meilisearch from Pinecone to consolidate our search service [...] The setup of the OpenAI embedder was very straightforward, and we love that embeddings are created automatically using the contents of a search document."
3. Cost-effective implementation: Lexical matching in hybrid search reduces memory usage compared to pure semantic search engines. This is crucial for lowering cloud costs related to storage and computational demand, especially since keyword search algorithms do not depend on GPUs.
4. Increased search speed: Opinly, a company that allows you to monitor your competitors' websites, was able to increase the search speed and the relevance of results by adopting hybrid search technologies.
5. Personalization and adaptability: Hybrid search systems can be configured to dynamically adjust the weight of keywords and semantic relevance or provide the user with control over it. The NFSA collection has this option available in their search engine.

Hybrid search provides significant advantages across various business domains, offering speed, robustness, and efficiency. However, it is not always the optimal solution for every search application. The next chapter will explore its limitations and when alternative approaches may be more suitable.

What are the drawbacks of hybrid search?

While hybrid search offers the best of both keyword-based and semantic search, it also comes with challenges that can impact implementation, performance, and user experience. Below are some key drawbacks to consider when adopting a hybrid search approach:

1. Increased complexity in implementation: Hybrid search requires integrating multiple search algorithms (e.g., keyword matching using BM25 and semantic search using dense embeddings). This integration can be technically complex and requires deep technical understanding.
2. Difficulty in balancing keyword precision and context: Over-relying on one method may diminish the benefits of the other (ex., more semantic power than keyword precision). If a good balance is not achieved, this could lead to a bad user experience and increased bounce rates.
3. Bad user experience: If users can adjust semantic weight, the interface should be intuitive or designed for an audience familiar with the terminology. Otherwise, it may lead to confusion and increase the risk of user drop-off. According to this Toptotal report, 88% of users are less likely to return after a bad user experience.

Despite these challenges, hybrid search remains a powerful tool when applied correctly.

When should you use hybrid search?

Hybrid search isn't the optimal solution in all scenarios. In cases where data is highly structured — such as product inventories or specific academic research — precision is key, and similar-sounding terms with distinct meanings must be strictly differentiated. Hybrid search truly excels in the following examples:

1. E-commerce platforms: Online retailers like Amazon implement hybrid search to enhance product discovery. When customers input vague queries, the system leverages keyword matching and semantic analysis to present relevant products. You can see it in action below; the user searched for “bottle that keeps drinks cold” and received results about thermoses.

2. Enterprise knowledge bases: Organizations often maintain extensive repositories of documents, manuals, and communications. Hybrid search enables employees to retrieve pertinent information efficiently and increase productivity.
3. Streaming services: Platforms such as Netflix utilize hybrid search to help users find content, whether they search by specific titles or describe themes.
4. Marketplaces: Hybrid search in e-commerce can improve search accuracy, handle complex queries, and increase product discovery, leading to increased sales.

Now let’s see how you can seamlessly introduce hybrid search systems in your project or workflow with Meilisearch.

How can you implement hybrid search?

Implementing a hybrid search requires a vector store solution. Several languages and AI frameworks can be used for implementation, but Python with Langchain is often a good stack to start building efficiently.

The AI-enhanced hybrid search from Meilisearch allows third-party embedding models and control over the semantic weight of the outputs, allowing a deeper semantic understanding of the user inputs.

To start using Meilisearch's hybrid search features, you must create an account and gain access to the API keys and cloud platform. You can register for free and enjoy a 14-day trial.

After registering, you can create a new project and use the vector store to add and index documents, run queries, monitor analytics, and more.

In the settings tab, you'll find an option called Embedders, where you can enhance your hybrid search capabilities by integrating any embedding model of your choice. Below is an example of the OpenAI embedding model added to the list of embedders.

After adding a model, you can jump to the search preview tab and control the semantic weight directly there — you’re using hybrid search!

To integrate the search engine into your workflow, use Meilisearch’s API, available on the main page of the cloud dashboard. Here’s a Python script to query and retrieve results:

import meilisearch


client = meilisearch.Client(
    '<meilisearch_server_url>',
    '<master_token>')
query = "Give me a book about a post-apocalyptic world"
results = client.index('books').search(query, opt_params={
  'hybrid': {
    'semanticRatio': 0.7,
    'embedder': 'openai'
  },
  'limit':4
})


for result in results['hits']:
    print(result['metadata']['text'])

To be able to run the code without issues, you first need to have the Meilisearch package installed:

pip install meilisearch

Next, you need an index — a collection of documents you've added to Meilisearch Cloud ( 'books' in this example). Additionally, you’ll need an embedding model.

The results of the Python script are the following:

{"id": 15, "title": "The Road", "description": "A father and his young son journey through post-apocalyptic America, fighting for survival while holding onto their humanity.", "genre": "Post-Apocalyptic"}
{"id": 6, "title": "1984", "description": "A dystopian social science fiction novel that follows Winston Smith and his rebellion against the totalitarian government that controls their society.", "genre": "Dystopian Fiction"}
{"id": 18, "title": "The Handmaid's Tale", "description": "In a dystopian future, a woman is forced to live as a concubine under a fundamentalist theocratic dictatorship.", "genre": "Dystopian Fiction"}
{"id": 19, "title": "Snow Crash", "description": "A pizza delivery driver and hacker investigates a dangerous computer virus that can affect human minds in both virtual and real worlds.", "genre": "Cyberpunk"}

Start building today by effortlessly uploading your documents to Meilisearch Cloud. Seamlessly integrate the hybrid search capabilities into your infrastructure using Python and other supported languages, ensuring scalability and fast results.

How does hybrid search compare to other search types?

Hybrid search combines two key approaches: semantic search and keyword search. However, semantic search is a broader term for methods that retrieve contextual or semantic meaning, including vector search and neural search. Let's explore the differences between all these search types:

Now, let's examine how hybrid search differs from each of the other search methods individually.

What is the difference between hybrid search and vector search?

Hybrid search enhances vector search by incorporating keyword matching for improved accuracy. As a form of semantic search, vector search relies on an embedder to generate dense vectors and retrieval algorithms like ANN and KNN to identify relevant results. By merging these with sparse vector outputs, hybrid search optimizes retrieval using techniques such as RFF.

What is the difference between semantic search and hybrid search?

Hybrid search is a combination of semantic and keyword searches. The quality of the hybrid search system response highly depends on the embedder used for the semantic search. The better the embedder and the retrieval algorithm applied on the dense vectors, the better the semantical or contextual response of the hybrid search. In addition, the hybrid search uses keyword matching for lexical accuracy.

What is the difference between keyword search and hybrid search?

Hybrid search utilizes keyword search to deliver precise lexical results. Keyword search relies on algorithms like BM25 or SPLADE to generate sparse vectors from queries and documents, enabling fast and accurate retrieval. However, it lacks semantic understanding, so hybrid search integrates semantic search technologies to enhance search relevance and context.

What is the difference between hybrid search and neural search?

Neural search, a type of semantic search, can be integrated into a hybrid search system. It leverages deep neural networks (DNNs) to deliver highly contextual results and supports various data input types. In a hybrid setup, neural search enhances typo tolerance while maintaining accuracy through lexical matching when users enter precise terms.

Hybrid search gives you the best of both worlds

Hybrid search delivers exceptional accuracy and contextual relevance, but implementing it can be complex. It involves selecting the right vector database, choosing optimal embedding models, and fine-tuning outputs from both dense and sparse vectors.

Meilisearch simplifies this process by providing an intuitive platform backed by insightful tutorials. On this platform, you can effortlessly upload datasets, experiment with embeddings, fine-tune semantic relevance, and access advanced metrics like analytics and monetization.