Imagine searching billions of images, documents, or products and finding exactly what you need in milliseconds. **Similarity search makes this possible, transforming how we interact with massive, complex datasets.**

It doesn't rely on matching exact words. Instead, it understands the deeper meaning behind your query.

This technology translates abstract concepts into mathematical representations. Computers can then instantly compare and retrieve these representations. From recommendation engines to medical research, this technique is reshaping how machines understand and navigate information with unprecedented precision and speed.

## Understanding similarity search

When you search for "black leather boots" on an e-commerce website or look for "songs that sound like Taylor Swift" on a music platform, **you're using similarity search without even knowing it.** At its core, similarity search helps find items that are alike.

### What is similarity search and why is it important?

Think of similarity search as a smart librarian. This librarian doesn't just look at book titles, but understands what each book is about. When you ask for "books like Harry Potter," this librarian knows to recommend other fantasy novels.

These novels would have coming-of-age stories and magical schools. **This is exactly what similarity search does – it understands the essence of what you're looking for and finds items that match that essence.**

### How similarity search works with vector embeddings

To understand how computers can find similar items, imagine turning everything into a list of numbers. When you take a photo, write a sentence, or record a song, similarity search converts it into a special list of numbers. This list is called a [vector embedding](https://meilisearch.com/blog/what-are-vector-embeddings).

**These numbers capture the important features of the item.** For a photo, it might include information about colors, shapes, and objects present.

Think of these vectors like coordinates on a map. You can find nearby cities by looking at their location on a map. **Similarity search finds similar items by looking at how close their vectors are to each other.** Items that are similar will have vectors that are close together in this mathematical space.

### Key differences between similarity search and nearest neighbor search

While these terms are often used interchangeably, they serve slightly different purposes. Nearest neighbor search is like using a measuring tape to find the closest points to where you're standing. **It's precise but can be slow if you have to measure the distance to every single point.**

Similarity search, on the other hand, is more like asking for directions. It might not give you the absolute closest match, but it's faster and usually good enough.

### What is the role of similarity search in AI-driven applications?

AI applications rely heavily on similarity search to make sense of vast amounts of information. When you use a chatbot that answers your questions, it's likely using similarity search to find relevant information in its knowledge base.

<Callout intent="note">
	
Curious about how to implement similarity search in a real application? Learn [how to build a RAG system with similarity search](https://www.meilisearch.com/blog/how-to-build-rag) to enhance your AI applications.
	
</Callout>

## Real-world applications of similarity search

Similarity search has changed how we interact with digital content. It powers many features we use daily. Let's explore some practical applications that showcase its impact across different domains.

### Content-based retrieval in multimedia systems

When you upload a photo to Google Images and ask "find similar images", you're experiencing content-based retrieval.

Pinterest, for instance, uses this technology to help users discover visually similar pins. If you find a cozy living room design you like, the platform can instantly show you dozens of similar interior designs by comparing their visual embeddings.

![Green kitchen cabinets interior design](https://unable-actionable-car.media.strapiapp.com/Green_kitchen_cabinets_interior_design_980c857e9b.avif)

These systems break down images and videos into vector embeddings that capture visual elements like colors, shapes, and patterns.

### Making recommendations more personal and accurate

Recommendation systems have come a long way from simple "users who bought X also bought Y" suggestions. **Modern platforms use similarity search to create rich, personalized experiences.**

Take Spotify's Discover Weekly playlist. It combines your listening history, favorite genres, and even the acoustic properties of songs you love to recommend new music you're likely to enjoy.

![Spotify Discover Weekly ad](https://unable-actionable-car.media.strapiapp.com/Spotify_Discover_Weekly_ad_46d6a0219c.avif)

E-commerce sites like Amazon use multi-modal similarity, combining different types of data to improve recommendations. They might consider:

- Product descriptions and reviews (text data)
- Product images (visual data)
- Purchase patterns (behavioral data)
- Price ranges and categories (numerical and categorical data)

By analyzing all these dimensions together, they can suggest products that truly match what you're looking for, rather than just showing popular items in the same category. **This creates a better experience for the user.**

### Real success stories from the field

Similarity search in healthcare, beyond imaging, aids diagnosis. Hospital systems use it to match patients with similar symptoms and histories by vectorizing patient data.

[Research shows its utility in predictive analytics](https://pmc.ncbi.nlm.nih.gov/articles/PMC5357318/), improving accuracy in areas like diabetes prediction. This supports personalized treatment and outcome prediction for more effective care.

### Powering modern AI applications

The rise of LLM and retrieval-augmented generation (RAG) systems has created new applications for similarity search. When you chat with an AI assistant that needs to pull relevant information from a knowledge base, similarity search is working behind the scenes to find the most relevant content to inform its responses. **This is a game changer for AI applications.**

![RAG workflow diagram explanation](https://unable-actionable-car.media.strapiapp.com/RAG_workflow_diagram_explanation_53f23eb06c.avif)

For example, when a customer service chatbot needs to answer a specific question about a product, it uses similarity search to find the most relevant product documentation, support tickets, and FAQ entries. This helps the bot provide accurate, contextual responses rather than generic answers.

## Distance metrics: the heart of similarity search

When searching for similar items, we need a way to measure how close or far apart vectors are from each other. This is like using a ruler to measure physical distances.

### Understanding the main distance metrics

The choice of distance metric can make or break your similarity search. Think of it like choosing the right tool for the job. You wouldn't use a hammer to cut wood, right? **The three most popular distance metrics each have their sweet spots.**

![Euclidean distance diagram](https://unable-actionable-car.media.strapiapp.com/Euclidean_distance_diagram_e711c166fc.avif)

Euclidean distance works like a straight line between two points. It's the distance "as the crow flies". This metric shines when working with physical measurements or when the magnitude of your vectors matters. For example, if you're building a system to find similar house prices, Euclidean distance would be a great choice because the actual numerical differences matter.

![Cosines distance illustration](https://unable-actionable-car.media.strapiapp.com/Cosines_distance_illustration_f59696183b.avif)

Cosine similarity, on the other hand, cares about the angle between vectors, not their length. It's perfect for text search because it can tell if two documents are about the same topic even if one is much longer than the other. It helps deliver more relevant results regardless of text length.

![Manhattan distance L1 distance](https://unable-actionable-car.media.strapiapp.com/Manhattan_distance_L1_distance_828cda7a2a.avif)

Manhattan distance (also called L1 distance) measures distance as if you're walking through city blocks. You can only move horizontally or vertically. It's particularly useful when dealing with grid-like data or when you want to give equal weight to all differences between vectors.

### Choosing the right metric for your data

| Metric | Use Case | Key Benefit |
| --- | --- | --- |
| Cosine Similarity | Text Search | Handles varying document lengths; focuses on meaning. |
| Euclidean Distance | Image Search, Sensor Data | Measures numerical differences; scale is meaningful. |
| Manhattan Distance | Categorical Data (User Preferences, etc.) | Treats each dimension independently; robust to outliers in features. |

Here's a practical way to think about it.

For text search, cosine similarity is usually your best bet. It handles documents of different lengths well and focuses on the meaning rather than the size. This is especially important when searching through articles, product descriptions, or user queries.

For image search, Euclidean distance often works better because the actual numerical differences between pixel values or image features matter. The same goes for sensor data or any numerical measurements where the scale is meaningful.

For categorical data (like user preferences or product attributes), Manhattan distance can be more appropriate. It treats each dimension independently and doesn't get thrown off by large differences in individual features.

### Handling mixed data types

Real-world applications often deal with multiple types of data at once. For instance, an e-commerce search might need to consider both product descriptions (text) and product images. **In these cases, you can use hybrid approaches.**

1. Calculate similarities separately using appropriate metrics for each data type
2. Combine the results using weighted averages
3. Normalize the scores to ensure fair comparison

This flexible approach allows you to fine-tune the importance of different features. You might want product images to carry more weight than text descriptions when searching for clothing items.

### Impact on search performance

The choice of distance metric doesn't just affect accuracy. **It can significantly impact search speed too.** Euclidean and Manhattan distances are generally faster to compute than cosine similarity, but modern search engines like Meilisearch optimize these calculations so you rarely need to worry about performance differences.

What matters more is choosing a metric that matches your data and use case. A faster metric that gives less relevant results isn't a good trade-off. Focus first on what makes sense for your users and their search needs, then optimize for performance if necessary.

## Algorithms that power similarity search

Now we understand how distance metrics help measure similarity between vectors. Let's then explore the algorithms that make searching through these vectors efficient and scalable. They are specifically designed to handle vector-based searches while balancing speed and accuracy requirements.

Different search algorithms handle distance calculations in various ways - some compare against every vector for perfect accuracy, while others use clever shortcuts to speed up the process. The choice of algorithm often depends on your dataset size, the dimensionality of your vectors, and whether you need exact or approximate results.

Let's examine the main approaches to similarity search, from basic exact matching to sophisticated approximate methods.

### Exact vs. approximate: finding the best match

![](https://unable-actionable-car.media.strapiapp.com/knn_algorithm_illustration_4f027a5683.avif)

The k-Nearest Neighbors (k-NN) algorithm finds the *exact* closest matches. It compares your search to every single item. While accurate, this is slow with large datasets. **k-NN isn't practical when speed is important.** Imagine comparing one book to millions!

![ANN algorithm illustration](https://unable-actionable-car.media.strapiapp.com/ANN_algorithm_illustration_ad3c4df68e.avif)

Approximate Nearest Neighbors (ANN) algorithms are a faster alternative. They make educated guesses to find good matches quickly. **ANN might miss the *absolute* best match sometimes, but it's much faster and accurate enough for most uses.**

### Space partitioning and graph navigation

Space partitioning methods, like KD-trees and Voronoi diagrams, organize data for faster searching. KD-trees divide the search space into smaller regions. Voronoi diagrams divide space based on proximity to certain points. **These are good for simpler data, but less useful with complex, high-dimensional data.**

[Hierarchical Navigable Small World](https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world) (HNSW) is a cutting-edge algorithm for similarity search. It creates a network of connections between data points. **HNSW is great for complex data used in modern AI.** It can search millions of items quickly while providing relevant results, ideal for things like [semantic search](https://meilisearch.com/blog/semantic-search) and [recommendation systems](https://meilisearch.com/blog/personalization-recommendations).

## The future of search: embracing semantic intelligence

Similarity search is transforming information retrieval by connecting traditional methods with advanced semantic understanding.

**This technology uses vector embeddings and sophisticated algorithms for more intelligent, context-aware search experiences.** As AI evolves, similarity search will be essential in making search more intuitive, precise, and meaningful across many domains and applications.

Similarity search: a guide to vector-based retrieval  

When [Bookshop.org](http://Bookshop.org)'s small engineering team struggled to push past a 14% search conversion rate, they faced a familiar dilemma. Despite having Elasticsearch—a powerful but complex search solution—their eight-person team was spending more time maintaining search infrastructure than improving their core platform. They needed something different: a solution that could deliver better results with less overhead.

Their challenge reflects a broader reality in modern software development. Every click, every search query, and every page load directly impact business metrics, yet the tools meant to optimize these experiences often become bottlenecks themselves. For engineering teams, especially smaller ones, the complexity of search infrastructure can become a _hidden tax_ on innovation and growth.

This is where Meilisearch comes in. Built to work seamlessly out of the box, it's the solution that lets developers focus on building rather than maintaining. Today, we're taking that mission further with [the launch of Meilisearch AI](https://www.meilisearch.com/launch-week)—our next step in making it radically easier for developers to build world-class search experiences.

In this article, we'll explore how modern search has evolved beyond simple queries, examine the technical challenges facing development teams, and introduce Meilisearch's innovative solutions—including our new AI capabilities. You'll learn how our approach to developer experience is transforming search implementation, and discover our roadmap for making advanced search features accessible to teams of all sizes.

## Redefining modern search

Modern search experiences extend far beyond the traditional search bar, encompassing two key facets: search and discovery. 

Search can take the appearance of inputs, menus, filters, or the trending UX of conversational search (i.e. chatbots). Discovery, on the other hand, plays a key role in navigating content and suggesting recommendations.

Across industries, we see search and discovery most prevalent as follows:

- **Traditional full-text search & semantic search:** standard in all industries ranging from SaaS to ecommerce, marketplaces, and content platforms
- **Conversational search:** prominent in RAG (Retrieval-Augmented Generation) and AI-enhanced search interfaces like Perplexity
- **Discovery for navigation:** common in social media and content platforms where a feed is the primary navigation
- **Recommendations:** tie in more critically with business value in ecommerce and content platforms

For early-stage startups and SMBs, implementing these search capabilities presents significant technical challenges. The complexity multiplies when adding AI integration and personalization layers. Addressing these pain points diverts the workforce from building core product value. Meilisearch is here to relieve you from this burden, allowing you to focus on what really matters. 

We want Meilisearch built-ins to handle modern use cases like [document embedding](https://www.meilisearch.com/docs/learn/ai_powered_search/document_template_best_practices). Complex setups—such as managing [heavyweight solutions like Elasticsearch](https://www.meilisearch.com/blog/meilisearch-vs-elasticsearch), [tweaking a Postgres instance](https://www.meilisearch.com/blog/postgres-full-text-search-limitations), or handling hybrid search—are additional integration burdens. This ignores the security consideration related to exposing front-end accessible APIs or the analytics necessary to collect business KPIs—things that don’t come packaged with custom-made solutions.

![bookshop_meilisearch.gif](https://unable-actionable-car.media.strapiapp.com/bookshop_meilisearch_9498082583.gif)

These DX considerations impact business metrics down the road as engineering bottlenecks lead to longer time-to-market, hindering growth for companies that want to move fast. In ecommerce, for example, search accuracy directly translates to conversion rate, as shown by how we helped [Bookshop increase search-based purchases by 43%](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases). On SaaS and content platforms, discovery enables engagement and adoption growth, both of which are key to keeping churn in check. 

## Leveraging DX to move mountains

Meilisearch is a no-configuration, plug-and-play solution that you can install and forget. At the heart of any search tool—and often the biggest challenge for developers—is ensuring relevant results. **CascadeRank** is our answer to [relevancy fine-tuning](https://www.meilisearch.com/docs/learn/relevancy/relevancy). Our algorithm uses human-readable ranking rules that enable configuration and easy customization. With Meilisearch, simplicity is key, allowing you to set up and focus on your core tasks. 

Though customization is easy, Meilisearch comes with strong defaults to handle common requirements out of the box—things like being typo-tolerant and prioritizing word proximity. Indeed, we’ve seen [Minipouce get better relevancy with Meilisearch’s default configuration than their previous Typesense setup](https://www.meilisearch.com/blog/minipouce-top-grade-customer-experience), leading to a 5x search volume increase. 

Scalability is a concern for small teams and enterprises alike. Performance is the name of the game for user-facing search—both for search latency and indexing time. As demonstrated by [Podscan.fm](http://Podscan.fm) [searching across 600GB of data](https://x.com/arvidkahl/status/1807889668748275802), search remains fast against large datasets. Our [next-generation indexer boasts 4x faster updates](https://www.meilisearch.com/blog/introducing-indexer-2024), making Meilisearch suitable at scale for write-intensive workflows, too.

[![arvid-kahl-tweet.png](https://unable-actionable-car.media.strapiapp.com/arvid_kahl_tweet_0c41da4824.png)
](https://x.com/arvidkahl/status/1807889668748275802)

When it comes to security, no compromise is possible. So Meilisearch provides fundamental security features to handle simple and complex use cases. Meilisearch facilitates [API key management](https://www.meilisearch.com/docs/learn/security/basic_security) to facilitate multi-environments. The built-in [multi-tenancy features](https://www.meilisearch.com/docs/learn/security/multitenancy_tenant_tokens) also enable more advanced security patterns, allowing developers to customize their access rules.

Using our expertise in search requirements across various customers, we built [Meilisearch Cloud](https://www.meilisearch.com/cloud). It’s the quickest way to get started. In addition to freeing teams from managing infrastructure, it integrates analytics and monitoring, making it [the preferred production platform for companies like HitPay](https://www.meilisearch.com/blog/hitpay-streamlines-search). Open-source Meilisearch can be self-hosted via Docker and a plethora of hosting platforms. 

This feature set positions Meilisearch well as a simple solution to get the best of modern search experience. This includes benefiting from an easy LLM integration to leverage AI-powered solutions.

## Future-proofing your search strategy

With native [hybrid and vector search](https://www.meilisearch.com/docs/learn/ai_powered_search/getting_started_with_ai_search) integration, Meilisearch makes it simple to implement AI-powered search capabilities. But we’re not stopping there—our 2025 roadmap is packed with features enabling businesses to uncover more value from AI while sustaining performance and scalability.

### Improved conversions with personalization

[Our research shows that personalization significantly improves conversion rates](https://www.meilisearch.com/blog/personalization-recommendations) across diverse business types. We aim to release a personalization framework enabling the collection of end-user context on each request. This comprehensive context management system allows developers to leverage user preferences, recent interactions, and behavioral patterns to further customize the relevancy of results on a per-user basis. 

### Retrieval augmented generation without overhead 

Although Meilisearch already enables [building retrieval-augmented generation (RAG)](https://www.meilisearch.com/blog/how-to-build-rag), we want to integrate built-in RAG capabilities in the engine, seamlessly combining full-text search, vector search, and LLM interaction. Then, we want to tackle inherent problems in state-of-the-art RAG applications by tackling the orchestration of filtering, ranking, and results merging in RAG scenarios. Of course, we want this to come with strong defaults, making it easy for developers to get started.

### Advanced ranking integrations

Our goal is to provide a native way for users to leverage AI reranking models to improve the relevancy of results. This provides a more flexible approach to configuring result relevancy by allowing real-time results reranking based on user context and behavior. We also want to enable organizations to integrate with their own custom ranking models to allow for further dynamic adjustment of results based on hand-picked factors.

### Reliability as you scale

We also plan to improve Meilisearch’s infrastructure to handle additional use cases better. In particular, we want to continue improving the distributed system capabilities for better scaling and high availability by building upon our sharding experiments with [federated remote search](https://www.meilisearch.com/docs/learn/multi_search/implement_sharding). Another layer of performance is integrating more sophisticated caching mechanisms to improve out-of-the-box performance. Monitoring and observability are also continuously improved based on user feedback.

## Meilisearch AI, refreshingly simple search DX

The release of Meilisearch AI marks a new age for Meilisearch users, fully integrating with the modern landscape of AI-powered applications. But it’s also a continuous fulfillment of our core value—making good search UX easy to build. We believe focusing on developer experience enables engineers to achieve faster time-to-value and maintain focus on their core business.

Meilisearch ships frequently to keep packaging built-in features, enabling fast-shipping companies to do what they do best. For more information on the Meilisearch AI release, consult our [launch week page](https://www.meilisearch.com/launch-week). 

Start with a [14-day free trial of Meilisearch Cloud](https://cloud.meilisearch.com/) or [request a demo](https://dub.sh/meili-sales) to discuss your needs with our search experts.

Building the future of search with Meilisearch AI

What are vector embeddings? A complete guide [2025]

Semantic search transforms how we find information online by focusing on understanding the meaning behind the words and phrases in user searches rather than relying solely on word-for-word matching.

Using machine learning (ML) and natural language processing (NLP), it decodes intent, context, and relationships between words, thus allowing for more conversational queries. This makes it a vital tool for businesses needing fast, accurate, and relevant results from large or complex data sets.

Embedding models are ML techniques that render words and phrases into complex numerical representations, which are then classified based on their context and relationships. However, not all models are the same. Accuracy, speed, indexing efficiency, and pricing can vary significantly between them.

Choosing the right semantic search model is crucial for delivering precise, efficient, and scalable search experiences for specific tasks.

Understanding the trade-offs—whether for multilingual support, search latency, or computational efficiency—will help you implement a high-performing semantic search system for your brand.

## What is semantic search?

Semantic search is an advanced AI-powered approach to information retrieval that focuses on NLP, ML, and knowledge representation to deduce the intent and contextual meaning of user queries.

Traditional keyword-based search engines match exact strings of words, but that’s not always enough. Semantic search goes further by incorporating **named entity recognition**, **relationships between words**, and **contextual disambiguation** to deliver more relevant results.

Semantic search engines can understand synonyms, paraphrased queries, and even infer implicit meanings exclusively because they use deep learning models such as transformers.

Given its ability to enhance the search criteria, [AI-powered search](https://www.meilisearch.com/blog/ai-powered-search) is crucial for applications requiring faster, broader, and even less structured [data retrieval](https://www.meilisearch.com/blog/how-to-cache-semantic-search).

## Why is semantic search important?
Semantic search is important because it improves search capability and relevance, thus helping users find information faster and with fewer irrelevant results. It also reduces uncertainty by interpreting natural language queries more clearly.

Like similarity search, it allows users to phrase their questions conversationally and intuitively rather than using specific—sometimes even long-tail—keywords. Think of it like this: if you can ask a question from your neighbor on a bus, then you can ask it with a quick semantic search.

Additionally, businesses benefit from semantic search by **enhancing customer experiences**, **improving product discovery**, and **optimizing knowledge management systems**.

For example, e-commerce stores like Bookshop.org can [boost search-based sales by up to 43%](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases) by implementing semantic search tools like Meilisearch.

A core component of semantic search is embedding models through which similarity computations are made possible.

## What are embedding models?

Embedding models are machine learning models that transform words, phrases, or documents into dense numerical representations called embeddings.

These vector representations encode word relationships, which allow search engines to compare semantic meaning and context rather than rely solely on exact similarity between words.

For instance, embeddings generated for words like “**phone**,” “**mobile**,” and “**cell**” will be more closely aligned in vector space than, say, “**cell**” and “**bell**,” even though the latter two are closer in letter-by-letter similarity.

This allows a search engine to retrieve relevant results even if the exact keyword is absent, underpinning various NLP and LLM (large language model) applications.

### What’s the difference between semantic and search embeddings?

Semantic and search embeddings serve distinct purposes in NLP applications, particularly in semantic search.

**Semantic embeddings** capture the meaning and relationships between words, which is helpful for tasks like document classification, recommendation systems, language translation, and sentiment analysis. They do this by reflecting the words’ conceptual similarities, even if they are not used in the same context.

On the other hand, **search embeddings** are specifically optimized for retrieval tasks, thus ensuring queries and indexed documents align effectively in vector space to maximize search relevance.

Unlike generic semantic embeddings, search embeddings often incorporate domain- and background-specific optimizations to fine-tune the effectiveness of the specific retrieval activity.

For example, a semantic embedding model might learn that "mac" is related to pasta, the makeup company, and the Apple laptop computer. However, a _search embedding model_ trained on computers and hardware would prioritize the third meaning when processing search queries in that domain.

Embedding models serve as the backbone of semantic search by enabling similarity-based retrieval mechanisms that align with user intent.

## What is the role of embedding models in semantic search?

Embedding models power semantic search by converting text into a structured format that can be efficiently indexed and retrieved.

Instead of relying on exact keyword matches, these models compare **query** embeddings with **indexed** document embeddings. This enables nearest-neighbor searches and significantly improves retrieval accuracy over traditional algorithms that depend on keyword matching.

These models often use transformer-based architectures such as BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and their derivative neural networks to capture context-aware representations.

That’s how they can handle nuanced queries at scale. For instance, even though “cat” and “bat” sound (and look) similar, only one of them may be used in context as a conventional house pet.

Several fine-tuned and pre-trained models continue to emerge as industry standards for semantic search. Each improves precision, relevance, efficiency, and/or scalability in its own way.

## What are the most commonly used models for semantic search?

Different embedding models vary in terms of vector dimensionality, context length, and certain other performance characteristics.

In the context of LLMs, dimensionality points to the number of components in a vector, with each corresponding to an attribute of the encoded variable. Additionally, context length relates to the amount of text—usually measured in tokens—a model can “remember” and refer to at any given time.

To assess these variations, a series of benchmark tests were conducted using [Meilisearch](https://www.meilisearch.com), evaluating each model's effectiveness in real-world search scenarios that you will encounter.

These tests measured factors such as **retrieval accuracy**, **indexing speed**, and **query latency**, which is how they evaluated each model’s performance under specific search conditions.

| **Model/Service** | **Dimensions** | **Context Length** |
| --- | --- | --- |
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | 384 | 512 |
| [Cohere embed-multilingual-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 |
| [Cohere embed-multilingual-light-v3.0](https://docs.cohere.com/docs/embeddings) | 384 | 512 |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | 3072 | 8192 |
| [Mistral](https://docs.mistral.ai/capabilities/embeddings/) | 1024 | 8192 |
| [VoyageAI voyage-2](https://www.voyageai.com/) | 1024 | 4000 |
| [VoyageAI voyage-large-2](https://www.voyageai.com/) | 1536 | 16000 |
| [VoyageAI voyage-multilingual-2](https://www.voyageai.com/) | 1024 | 32000 |
| [Jina Colbert v2](https://jina.ai/news/jina-colbert-v2-multilingual-late-interaction-retriever-for-embedding-and-reranking/) | 128, 96, or 64 | 8192 |
| [OSS all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 |
| [OSS bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 1024 | 512 |
| [OSS bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1536 | 512 |

Your choice of a model for semantic search will depend on a variety of factors, including, but not limited to, its **accuracy**, **computational efficiency**, and **cost**.

## What factors should be considered when choosing the best model for semantic search?

### 1. Results relevancy
Relevance is the cornerstone of effective semantic search, especially in achieving optimal user experience. The right model should balance precision, recall, and speed to ensure users receive highly relevant results without excessive noise.

This balance becomes especially important when comparing hybrid approaches like vector and full-text search. When selecting an embedding model, think about the following:

- multilingual support;
- handling multi-modal data;
- domain-specific performance.

In this context, bigger might not always mean better. While larger models often provide better accuracy, smaller models can offer competitive results at lower computational costs.

Moreover, effectively structuring data, such as with optimized document templates in Meilisearch, can improve search quality.

### 2. Search performance
Search latency is a critical factor in user experience. Search-as-you-type has become the standard for customer-facing applications as fast, responsive search results improve user engagement and retention. 

Local embedding models are ideal to achieve lightning-fast performance, as they eliminate the need for round trips to external services and reduce latency. If you must rely on remote models, hosting your search service close to the embedding service can minimize delays and improve user experience.

The table below showcases latency benchmarks for various local embedding models and embedding APIs. All requests originate from a Meilisearch instance hosted in the AWS London data center.

| **Model/Service** | **Latency** |
| --- | --- | 
| [Cloudflare bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | ±800ms |
| [Cloudflare bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | ±500ms |
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | ±170ms |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | ±160ms |
| [Local gte-small](https://huggingface.co/models?search=gte-small) | ±20ms |
| [Local all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | ±10ms |
| [Local bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | ±20ms |
| [Local bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | ±60ms |
| [Mistral](https://docs.mistral.ai/capabilities/embeddings/) | ±200ms |
| [Jina colbert](https://huggingface.co/jinaai/jina-colbert-v2) | ±400ms |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | ±460ms |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | ±750ms |
| [VoyageAI voyage-2](https://www.voyageai.com/) | ±350ms |
| [VoyageAI voyage-large-2](https://www.voyageai.com/) | ±400ms |

Benchmarks conducted in Meilisearch highlight the stark differences in latency across various models, with local models achieving response times as low as 10ms while some cloud-based services reach 800ms.

### 3. Indexing performance

Efficient indexing is another critical factor for the scalability of your search solution. As expected, the time required to process and store embeddings varies widely among models. Notable metrics influencing processing time are API rate limits, batch processing capabilities, and model dimensions.

Local models without GPUs may experience slower indexing due to limited processing power, while third-party services vary in speed based on their infrastructure and agreements.

As mentioned earlier, minimizing data travel time between your application and the model can reduce latency and optimize indexing. Evaluating these factors ensures your chosen model and service can effectively meet your needs.

The benchmark below compares the indexing performance of a collection of 10k e-commerce documents (with [automatic embedding generation](https://www.meilisearch.com/docs/learn/experimental/vector_search?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#generate-auto-embeddings)).

| **Model/Service** | **Indexation time** | 
| --- | --- | 
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 43s |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | 16s |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | 95s |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | 151s |
| [Cloudflare bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 152s |
| [Cloudflare bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 159s |
| [Jina Colbert V2](https://huggingface.co/jinaai/jina-colbert-v2) | 375s |
| [VoyageAI voyage-large-2](https://www.voyageai.com/) | 409s |
| [Mistral](https://docs.mistral.ai/capabilities/embeddings/) | 409s |
| [Local all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 880s |
| [Local bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)| 3379s |
| [Local bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 9132s |

Meilisearch benchmarks indicate that indexing times range from under a minute for optimized cloud-based solutions to several hours for certain local models without GPU acceleration.

These should be important points to ponder when you weigh the frequency and volume of data updates in your applications. This is because they directly impact how quickly your system can process frequent or large data updates, which is crucial for maintaining the performance and responsiveness of your search solution.

### 4. Pricing
Embedding model costs vary depending on the provider and usage model. While local models are free to run, they require computational resources, potentially necessitating GPU investment.

On the other hand, cloud-based services charge per million tokens (or thousand neurons for Cloudflare), with costs ranging from $0.02 to $0.18 per million tokens.

| **Provider** | **Price** |
| --- | --- | 
| Cohere | $0.10 per million tokens for Embed 3 |
| OpenAI | $0.02 per million tokens for text-embedding-3-small |
| | $0.10 per million tokens for text-embedding-ada-002 |
| | $0.13 per million tokens for text-embedding-3-large | 
| Cloudflare | $0.011 per 1,000 Neurons |
| Jina | $0.18 per million tokens |
| Mistral | $0.10 per million tokens |
| VoyageAI |$0.02 per million tokens for voyage-3-lite |
| | $0.06 per million tokens for voyage-3|
| | $0.12 per million tokens for voyage-multimodal-3 |
| | $0.18 per million tokens for voyage-code-3| 
| | $0.18 per million tokens for voyage-3-large |
| Local model | Free |

As such, analyze cost-effectiveness against search demand and performance requirements. It's often a good idea to start with a well-known model that's easy to set up and has robust community support. You can migrate to a cloud provider like AWS for improved performance when necessary. 

Alternatively, you can choose an open-source model to self-host, giving you more flexibility. Just be aware that optimizing local models for high volume may require scaling your infrastructure.

### 5. Other optimization techniques

To maximize search performance, [hybrid search approaches](https://www.meilisearch.com/blog/full-text-search-vs-vector-search) combining full-text and vector search can yield the best results. To refine semantic search performance, consider the following optimizations:

- Experiment with model presets, as some models allow tuning for query vs. document embedding, which can improve relevancy.
- Assess specialized models, especially those employing retrieval-augmented generation, as domain-specific models may offer superior results for particular use cases.
- Explore models that provide reranking functions that further enhance search precision.
- Test higher-tier accounts as premium tiers may provide faster processing and reduced rate limiting.
- Optimize data transfer with quantization options to reduce API response sizes and improve efficiency.

Carefully evaluating these factors will help you select the best semantic search model to meet your needs.

## The bottom line
Now that we’ve taken this journey together, let’s round up what we’ve discovered.

| **Model/Service** | **Dimensions** | **Context Length** | **Latency** | **Indexation time** | **Pricing (per million tokens)** |
| --- | --- | --- | --- | --- | --- | 
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 | ±170ms | 43s | $0.10|
|[Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings)|384|512|±160ms|16s|$0.10|
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) |1536| 8192| ±460ms| 95s|$0.02|
|[OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings)| 3072|8192|±750ms|151s|$0.13|
|[Mistral](https://docs.mistral.ai/capabilities/embeddings/)|1024|8192|±200ms|409s|$0.10|
|[VoyageAI voyage-2](https://www.voyageai.com/)|1024|4000|±350ms|330s|$0.10|
| [VoyageAI voyage-large-2](https://www.voyageai.com/)|1536|16000|±400ms|409s|$0.12|
|[Jina Colbert v2](https://jina.ai/news/jina-colbert-v2-multilingual-late-interaction-retriever-for-embedding-and-reranking/)|128, 96, or 64|8192|±400ms|375s|$0.18|
|[OSS all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)|384|512|±10ms|880s|Free|
|[OSS bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)|1024|512|±20ms|3379s|Free|
|[OSS bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)|1536|512|±60ms|9132s|Free|

Choosing the best model for semantic search depends on what (and how) you aim to achieve with your specific use case, budget, and performance requirements. For most cases, cloud-based solutions such as those provided by Cohere or OpenAI may be optimal.

As the needs of your organization grow, it might be worth the cost and effort to upgrade to a local or self-hosted solution. Understanding your own needs is paramount to making a well-informed decision.

If you’re unsure which model is best for you or if you’re looking for a tailored solution, contact a search expert at Meilisearch.

## Frequently asked questions (FAQs)

### How does semantic search differ from keyword-based search?
Semantic search focuses on understanding meaning, while keyword-based search relies on exact word matches.
### What are the key features of a good semantic search model?
A good model should offer high accuracy, low latency, efficient indexing, and cost-effectiveness.
### How do transformer-based models improve semantic search?
Transformers process text in context, capturing contextual relationships between words to enhance search relevance while reducing downtime.
### How do vector databases enhance semantic search?
Vector databases work to efficiently store embeddings, enabling fast and scalable search operations.
### What are some open-source models for semantic search?
Popular open-source semantic search models include all-MiniLM-L6-v2 and Universal Sentence Encoder.




How to choose the best model for semantic search

What is a vector database? What you need to know [2025]

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](https://www.meilisearch.com/blog/what-are-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:

![What is hybrid search.png](https://unable-actionable-car.media.strapiapp.com/What_is_hybrid_search_111362249c.png)

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.

![Prepocessing.png](https://unable-actionable-car.media.strapiapp.com/Prepocessing_80a5f32b48.png)

### 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:

```json
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:

```json
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. 

![How does hybrid search work.png](https://unable-actionable-car.media.strapiapp.com/How_does_hybrid_search_work_3224ecf2f4.png)

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](https://www.meilisearch.com/solutions/hybrid-search).

## 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](https://cloud.google.com/vertex-ai/docs/start/install-sdk) 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](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/hybrid-search.ipynb).

As of January 2025, Google Search maintains an [89.79% market share](https://gs.statcounter.com/search-engine-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](https://www.meilisearch.com/customers?usecase=Hybrid+search): "_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](https://opinly.ai/), a company that allows you to monitor your competitors' websites, was able to [increase the search speed](https://carbon.ai/customers/opinly) 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](https://www.collection.nfsa.gov.au/) 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](https://www.collection.nfsa.gov.au/), 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.
 
![image9.png](https://unable-actionable-car.media.strapiapp.com/image9_3b6346806d.png)

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](https://cloud.meilisearch.com/login?ref=navbar).

## 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](https://www.meilisearch.com/blog/introducing-hybrid-search) 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](https://www.meilisearch.com/cloud). You can register for free and enjoy a 14-day trial.

![image6.png](https://unable-actionable-car.media.strapiapp.com/image6_9891a63d13.png)

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.

![image8.png](https://unable-actionable-car.media.strapiapp.com/image8_78aeaabd0b.png)

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

![image1.png](https://unable-actionable-car.media.strapiapp.com/image1_a6dc1af35b.png)

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:

```json
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:

```json
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:

```json
{"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](https://www.meilisearch.com/cloud). Seamlessly integrate the hybrid search capabilities into your infrastructure using [Python](https://www.meilisearch.com/docs/reference/api/search#example-22) 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:

| **Hybrid search** | **Vector search** | **Semantic search** | **Keyword search** | **Neural search** |
| --- | --- | --- | --- | --- |
| Combines dense and sparse vector representations to enhance search accuracy and contextual relevance. | Uses dense vector embeddings and algorithms like ANN to retrieve semantically relevant results. | A broader term for search technologies that use dense vectors to obtain contextual or semantic outputs. | Uses technologies like BM25 and SPLADE to create sparsed vectors that are used for accurate lexical matching. | This technology uses Deep Neural Networks (DNNs) for making dense vectors and supports different data 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](https://www.meilisearch.com/cloud) 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.

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

Neural search is redefining the future of information retrieval, pushing beyond traditional keyword and vector search approaches. While vector search applies machine learning (ML) to map semantic relationships between user queries and data embeddings, neural search leverages deep neural networks (DNNs) to optimize the entire search pipeline, from indexing and semantic analysis to ranking mechanisms.

This advancement enables neural search systems to continuously learn and refine results, outperforming conventional search methods in accuracy and adaptability. More importantly, they support multimodal data retrieval, allowing seamless search across text, images, audio, and even complex 3D datasets, critical for AI-driven applications and next-gen user experiences.

However, adoption comes with challenges. Neural search models often function as ‘black boxes,’ raising concerns about interpretability, and their high computational requirements can drive up infrastructure costs.

This guide explains the fundamentals of neural search, how it differs from existing search systems, and its strategic advantages. It also explores practical applications and potential trade-offs, helping you assess whether neural search aligns with your company’s AI roadmap.

## What is neural search?

Neural search, or neural information retrieval, leverages DNNs to interpret the contextual meaning behind search queries, enabling more accurate results than traditional keyword-matching systems and vector search. Instead of relying on exact terms, it transforms the user’s input and data into dense vector embeddings—numerical representations that capture semantic relationships. This process is similar to vector search systems, but instead of using ML models like approximate nearest neighbor (ANN) and a ranking algorithm, the neural search relies entirely on DNNs for the complete process.

A practical example involves querying a film database. A search like ‘Recommend a drama exploring modern parenting challenges’ would return a ranked list of movies aligned with the query’s intent, prioritizing contextual relevance over isolated keywords. The ranking is refined by the DNN itself, which needs to understand concepts like family dynamics and contemporary parenting struggles.

Here’s an example from Meilisearch: 

![image1.png](https://unable-actionable-car.media.strapiapp.com/image1_4431fc4739.png)

You can also try this for yourself by [checking out our demo](https://wheretowatch.meilisearch.com/). 

A commonly misunderstood term is often confused with neural search. In the next chapter, we’ll look into the key distinctions between neural search and neural architecture search (NAS).

## How does neural search differ from neural architecture search?

Neural search and NAS are often confused but serve different purposes. Neural search enhances information retrieval by understanding semantic meaning, while NAS focuses on automating the design of neural network architecture to optimize model performance for specific tasks.

NAS leverages techniques like recurrent neural networks and reinforcement learning (RL) to generate new language models, visual models, and overall neural architecture, eliminating manual experimentation and accelerating model development. In contrast, neural search applies deep learning to improve search relevance across text, images, sound, and other structured data types, transforming how systems retrieve and rank results.

Despite their differences, these technologies can intersect. For instance, you could use NAS to create a highly specialized artificial neural network tailored to your neural search. Let’s get a deeper understanding of how the latter works in the next chapter.

## How does neural search work?

Neural search enhances the search experience with DNNs using two main mechanisms: indexing and embedding. These are integrated in the following steps:

- **Data encoding and indexing**: The DNN analyzes documents (e.g., movie descriptions) to generate vector embeddings. These dense numerical representations capture semantic contexts, such as themes, relationships, and emotions, and are properly indexed in a vector database.
- **Query Encoding**: The user’s input (e.g., ‘Recommend a drama about modern parenting’) is converted into an embedding using the same DNN. This ensures the query and data share a unified semantic framework.
- **Semantic matching & ranking**: Unlike vector search systems that rely on external algorithms (ANN, cosine similarity) the DNN directly compares query and document embeddings. It evaluates learned relationships (e.g., linking ‘parenting challenges’ to films about complex family dynamics) to rank results by contextual alignment.

Neural searches can process various data inputs, depending on their types and activation functions, including text, images, audio, and 3D information. The embedding model adapts based on the input type. For instance, a convolutional neural network (CNN) is used for images, while large language models like BERT, are employed for text.

## What are some examples of neural search use?

Neural search can be applied to a wide range of use cases that require the processing of different types of data inputs and constant learning and optimization. Here are two simple examples of its potential applications:

1. **Optimize ranking results**: Jina AI offers a superior ranking solution based on neural search. It takes the initial outputs from a search query and reevaluates them to ensure they align more closely with the user's intent. [Jina’s AI Reranker](https://jina.ai/reranker) natural language processing model is available as open-source and can be accessed through Jina’s API.
2. **Search tools with images**: The German company [PROFISHOP](https://www.profishop.de/) uses a search engine powered by CNNs, allowing users to submit an image as input and fetch related content from a large industrial, durable, and consumer goods database.
3. **Retrieve ML models**: [Hugging Face](https://huggingface.co/chat/) has a large repository of models, including image-to-image, image-to-text, text-to-text, and more. Finding the appropriate model was initially challenging. However, the platform has since implemented a [hybrid search system](https://www.meilisearch.com/blog/hugging-face-democratizes-ai) that combines keyword matching with contextual understanding to enhance model discovery.
48. **Improve e-commerce**: [Google Shopping](https://shopping.google.com/) has integrated artificial intelligence (AI) to enhance its search capabilities, providing users with more relevant and personalized product recommendations. In addition, Google uses generative AI to summarize key factors during the search.

The neural search's ability to integrate RL for constant learning, accept different data types, and allow GenAI integration makes this technology extremely versatile and applicable to countless applications while ensuring accurate, personalized, and evolving results.

## What are the benefits of neural search?

Neural search systems offer several key benefits. Let’s take a look at some of them:

- **Increased engagement & reduced bounce rates**: Users expect almost real-time, precise, and relevant search results. When they don’t find what they need, [bounce rates surge](https://www.allaboutai.com/ai-seo/average-bounce-rate-guide/), and potential customers may never return. An optimal bounce rate is usually below 50%.
- **Enhanced productivity & efficiency**: Instead of manually searching through vast documentation or using inefficient keyword searches, neural search enables employees to find exact information instantly.
- **Multimodal search capabilities**: Neural search processes various data formats, including text, images, and audio. This versatility allows new use cases, particularly in e-commerce. According to [Think With Google](https://www.thinkwithgoogle.com/marketing-strategies/search/online-shopping-image-statistics/), 50% of online shoppers say images directly influence purchasing decisions.
- **Revenue growth**: Search is an important yet undervalued feature in e-commerce. [Bloomreach](https://www.bloomreach.com/) reports that while only 15% of B2C customers use site search, this small group drives 45% of online revenue.
- **Personalization & continuous learning**: Neural search systems can adapt by learning from user interactions and refining results based on behavior, clicks, and historical preferences. This dynamic personalization enhances user experience, improves retention, and drives long-term customer loyalty.

While the applications and benefits seem tempting, this is still a new field, so there is plenty of room for optimization. In the next section, we’ll explore some of this technology's challenges and limitations.

## What are the drawbacks of neural search?

The key drawbacks of neural search are listed below:

- **High computational cost and complexity**: DNNs require substantial computational resources for training and operation. This led to the development of GPUs with enhanced parallel processing power, which can be expensive. For instance, the newest model from NVIDIA (which has a [90% share](https://www.ft.com/content/03f66c13-3531-4ff2-b348-f17dac588c93) of the GPU market) is currently priced at [$2,529](https://www.tomshardware.com/news/lowest-gpu-prices).
- **Data dependency**: Neural search systems use high-quality, labeled training data to function properly. Without it, the system risks producing biased or skewed results. Designing the required metrics for the system before data ingestion is key to delivering better search functionality.
- **Lack of interpretability**: DNNs often function as 'black boxes,' learning complex patterns that can be difficult to understand. Due to this lack of transparency, debugging unexpected or odd results is challenging. This is why logs and [monitoring tools](https://www.meilisearch.com/blog/monitoring-metrics-release) are important once the search system is deployed. 
- **Model maintenance and engineering**: To stay effective, DNNs require frequent retraining to adapt to language and user behavior changes, increasing operational overhead. Additionally, the infrastructure and expertise required to maintain neural systems can be too expensive for smaller organizations. AI engineers are among the most in-demand jobs according to [LinkedIn’s 2025 report](https://www.linkedin.com/pulse/linkedin-jobs-rise-2025-25-fastest-growing-us-linkedin-news-gryie/) with an average annual salary of [$120,000 globally](https://artsmart.ai/blog/how-much-do-ai-engineers-make/), which is 30% higher than other engineering roles.

While technological advances are gradually reducing the computational demands of these models, these challenges must still be carefully considered when deciding whether to implement a neural search system.

## How does neural search compare to other types of search?

Neural search is a form of semantic search, similar to vector search, but it uses DNNs to handle indexing, encoding, and ranking. Other search types include keyword search and hybrid search. Here’s how they differ:

| **Neural search** | **Vector search** | **Keyword search** | **Hybrid search** |
| --- | --- | --- | --- |
| Leverages DNNs to embed and get the most relevant results based on semantic relationships. | Uses ML algorithms such as ANN to find semantic relationships. | Outputs content based on the keywords used in the query and doesn’t understand the context. | Combines both keyword search and neural search. |

### Neural search vs. vector search

Both [vector search](https://www.meilisearch.com/solutions/vector-search) and neural search retrieve data by matching query vectors to embeddings, making them forms of semantic search. Although neural search might be interpreted as a type of vector search, the latter typically refers to a method that leverages ML models like ANN for contextual retrieval and uses cosine similarity for ranking. In contrast, neural search bypasses these steps, relying entirely on DNNs throughout the search process.

### Neural search vs. keyword search

Keyword search relies solely on matching literal terms in search queries to those in a dataset, ignoring context or meaning. For example, a keyword search for 'best smartphone' won’t return results mentioning 'top mobile devices' unless those exact words are present, whereas neural search recognizes their semantic equivalence.

### Neural search vs. hybrid search

[Hybrid search](https://www.meilisearch.com/solutions/hybrid-search) combines the contextual intelligence of DNNs with the speed and simplicity of keyword matching, optimizing for precision and efficiency. It uses keyword search to rapidly filter exact term matches while leveraging neural techniques for semantic understanding, ensuring relevance even when queries or data require deeper interpretation. [Meilisearch](https://www.meilisearch.com/) gives you control over your hybrid search engine, enabling you to adjust the semantic context as needed.

## What is the future of neural search?

The fast evolution of AI, specifically in DNNs and NAS, will bring tremendous advances to neural search and its use-case scenarios. NAS, which automates the design of DNN architecture, enables the creation of highly specialized models that optimize accuracy, speed, and resource efficiency for specific tasks. These new and more powerful models have transformative applications in different industries:

- **Reshaping e-commerce**: Neural search engines can retrieve the most relevant products based on the context provided by the customer’s query. Already, giants like Google are using this technology to enhance searches — [Google Shopping](https://www.voguebusiness.com/story/technology/google-shakes-up-shopping-with-generative-ai) now includes an AI-generated brief containing what it thinks are the top three choices and why. The AI brief also includes tips on what to look for when shopping for a particular product, which is sourced from the web and summarized by AI. 
- **Education & skill development**: Learning platforms can use neural search to deliver materials tailored to an individual’s progress, learning style, and career goals. Imagine an AI tutor that dynamically assembles courses from videos, papers, and exercises. [Three in five](https://www.aiprm.com/ai-in-education-statistics/) (60%) teachers claim to have integrated AI into their teaching habits.
- **Healthcare transformation**: Neural search engines can potentially analyze patient histories, symptoms, and genomic data to identify personalized treatment options or clinical trials. This could significantly reduce the time patients wait for an accurate diagnosis and treatment. [Deep6 AI](https://deep6.ai/) is an example of this, as it uses neural search techniques to sift through massive amounts of data to match patients to clinical trials. 

Another emerging technology is Generative AI, which can go hand-in-hand with neural searches and be applied in different formats (text, image, sound, video):

- **Design & creativity**: Architects, for example, could use search engines by providing sketches and verbal descriptions (e.g., 'sustainable office building with natural light'), prompting the system to generate 3D models that integrate principles from existing designs and environmental data.
- **Dynamic content creation**: Media platforms could leverage generative AI to auto-generate videos, articles, or music playlists by retrieving and delivering content aligned with the user’s preferences, location, or mood.

These technologies will enhance information retrieval and actively influence human decision-making, creativity, and problem-solving as they evolve.

## Unlock next-generation neural search with Meilisearch

Neural search systems are state-of-the-art in delivering contextual results, but they may not be the ideal choice for every situation. This is why a hybrid search system can be handy, especially when it offers control over the semantic ratio and ranking outcomes. 



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