What is full-text search and how does it work?

AI-powered search: What you need to know [2025]

The 10 best AI enterprise search tools and platforms [2025]

Top 10 Elasticsearch alternatives and competitors in 2025

Spending hours digging through data, only to find irrelevant or incomplete information, is a productivity nightmare.

**Intelligent search is revolutionizing how we navigate digital information**. It transforms frustrating searches into fast, precise discoveries. Intelligent search understands not just your words, but your intent.

By leveraging artificial intelligence and advanced machine learning, intelligent search is more than a technological upgrade. **It's a solution that turns information overload into instant, meaningful insights**.

## Understanding intelligent search

The way we find information online is changing significantly. **Search engines no longer simply match keywords to deliver results.** Today's intelligent search systems use artificial intelligence to understand the deeper meaning behind our queries. They deliver precisely what we need, even when we're not sure how to ask for it.

### What is intelligent search?

Intelligent search is a major advancement over conventional search methods. Traditional search engines rely on exact keyword matching. For example, they find "red shoes" only when those exact words appear. **Intelligent search, however, understands context and intent.**

It can recognize that someone searching for "comfortable footwear for running" might be interested in athletic shoes, even if they don't use those specific terms. This contextual understanding makes intelligent search more intuitive. It also makes it more human-like in its ability to interpret and respond to queries.

### Key components of intelligent search

Intelligent search relies on three fundamental technologies working together:

- **Natural Language Processing (NLP):** Helps systems understand human language in all its complexity, including colloquialisms, misspellings, and context.
- **Machine learning algorithms:** Continuously improve search accuracy by learning from user interactions and feedback.
- [**Semantic analysis**](https://meilisearch.com/blog/semantic-search)**:** Goes beyond surface-level word matching to understand the relationships between concepts. This ensures that searches return relevant results even when query terms don't exactly match the content.

### How intelligent search improves user intent understanding

Understanding user intent is perhaps the most powerful aspect of intelligent search.

When someone types "best pizza near me open now," the system doesn't just look for those exact words. **It understands that the user wants currently open restaurants serving pizza in their geographic area, likely with good reviews.**

This intent recognition extends to professional contexts too. For instance, when an employee searches for "quarterly reports," the system might prioritize recent financial documents while considering the user's role and access permissions.

This contextual awareness makes intelligent search particularly valuable for both consumer applications and enterprise knowledge management.

Modern intelligent search bridges the gap between what users say and what they actually need. By combining advanced technologies with an understanding of human behavior, these systems are making information retrieval more natural and efficient than ever before.

## How intelligent search works

Intelligent search now uses AI and machine learning to understand information like humans do, transforming raw data into meaningful insights.

### Ingesting and connecting data sources for unified search

Creating a unified search experience starts with [connecting multiple data sources](https://meilisearch.com/blog/what-is-federated-search). These sources can include structured databases, unstructured documents, emails, and multimedia content.

Think of it as building a digital library. **Every piece of information, regardless of its format or location, becomes easily accessible through a single search interface.**

For example, imagine a sales manager searching for the most effective sales strategies in the past quarter. The system could analyze sales reports, CRM data, and even transcripts of successful sales calls to identify winning patterns. This gives the manager actionable insights to share with their team.

![enterprise search](https://unable-actionable-car.media.strapiapp.com/enterprise_search_0a1a2e0746.avif)

### Indexing and enriching content for better search outcomes

The real power of intelligent search happens during the indexing phase. This is where raw content transforms into searchable information. This process goes far beyond simple keyword cataloging.

**Modern systems analyze documents for context, extract entities, identify relationships, and create semantic connections between different pieces of content.**

For instance, imagine indexing a technical article about "python programming". The system automatically recognizes that this content relates to both computer programming and the Python language. It creates connections to related topics like "software development," "coding tutorials," or "debugging techniques".

This enrichment process makes search results more relevant and comprehensive. It helps users discover related information they might not have explicitly searched for.

<Callout intent="note">

Curious about how intelligent search systems handle complex relationships in data? Dive deeper into the role of [vector embeddings](https://meilisearch.com/blog/what-are-vector-embeddings)—the backbone of modern semantic search. It explains how these numerical representations help capture the true meaning and context of data, making search systems smarter and more intuitive.
	
</Callout>

### Leveraging NLP to understand user intent and context

NLP serves as the bridge between human communication and machine understanding. When someone types "how do I reset my password," the system doesn't just look for those exact words. **It understands the intent behind the query and recognizes this as a how-to request related to account security.** It can then provide relevant solutions, even if they're worded differently in the knowledge base.

This capability becomes particularly powerful when users don't know the exact terminology for what they're seeking. For example, a customer searching for "phone won't turn on" might receive results about "device power cycling," "battery replacement," and "hardware troubleshooting." These are all relevant solutions that might not share the exact keywords but address the underlying problem.

### Delivering personalized and relevant results

The final piece of the intelligent search puzzle involves delivering results that are not just accurate but [personally relevant](https://www.meilisearch.com/blog/personalization-recommendations) to each user. This process combines several factors:

- User context (location, department, role, previous searches)
- Behavioral data (which results similar users found helpful)
- Content popularity and freshness
- Access permissions and security constraints

For example, imagine two different employees searching for "quarterly reports." A sales manager might see financial performance metrics first. A marketing director might see campaign performance data. **This personalization happens in real-time, creating a more efficient and satisfying search experience.**

## Benefits of intelligent search

[AI-powered search](https://meilisearch.com/blog/ai-powered-search) has transformed information interaction, offering significant advantages over keyword-based systems. Its value is increasingly clear for businesses managing growing digital environments.

### Faster and more accurate information retrieval

Gone are the days of scrolling through endless pages of irrelevant results. Modern intelligent search systems cut through the noise, understanding context and intent to deliver precisely what users need.

![Encrypt emails search results](https://unable-actionable-car.media.strapiapp.com/Encrypt_emails_search_results_c261bc76d1.avif "Example of the Symfony search when they moved to Meilisearch")

For example, when an employee searches for "quarterly marketing budget template 2025", the system recognizes not just the keywords. **It understands they likely want the most recent template, not historical versions or related documents.**

### Enhanced decision-making through better insights

Intelligent search doesn't just find information. **It connects dots across disparate data sources to surface meaningful insights.**

When a sales representative searches for information about a client, the system pulls together recent interactions, contract details, support tickets, and market intelligence into a coherent narrative. This comprehensive view enables faster, more informed decisions.

### Delivering personalized customer experiences at scale

Perhaps the most visible benefit of intelligent search is its ability to transform customer experiences. Ecommerce platforms using intelligent search have seen conversion rates increase significantly. For instance, Bookshop.org, an online bookstore, [**increased overall conversion by 43%**](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases) by delivering relevant search results.

The real power of intelligent search comes from its ability — and from the people responsible of it — to learn and improve over time.

<Callout intent="tip">

Ready to supercharge your search experience? Deliver lightning-fast search results that will keep your users engaged and boost your conversion rates.
[Explore Meilisearch Cloud](https://meilisearch.com/cloud)

</Callout>

## Intelligent search use cases and applications

Intelligent search is now widely adopted across industries due to its versatility. It transforms how organizations and users interact with information, streamlining operations, enhancing customer experiences, and revolutionizing data retrieval and decision-making.

### Enterprise knowledge management and operational efficiency

Organizations often struggle with scattered information across multiple systems. This makes it challenging for employees to quickly find crucial documents. **Intelligent [enterprise search](https://meilisearch.com/blog/enterprise-search) unifies data sources to deliver relevant information instantly.**

### Ecommerce and personalized customer support

In ecommerce, intelligent search has become a crucial differentiator for businesses seeking to provide seamless shopping experiences. Modern search solutions can process natural language queries like "comfortable running shoes under $100".

They understand not just the price constraint but also the implied comfort requirement. This capability extends to customer support, where AI-powered search helps service representatives quickly access relevant information.

### SaaS platform efficiency

SaaS companies also benefit significantly from intelligent search. By integrating AI-powered search into their platforms, SaaS providers enable users to quickly find relevant features, documentation, or support resources within the application. This reduces user frustration, improves onboarding, and increases overall user satisfaction.

![Example Intelligent Search Huggingface](https://unable-actionable-car.media.strapiapp.com/Example_Intelligent_Search_Huggingface_bad47d2ffb.gif)

[Hugging Face uses Meilisearch](https://meilisearch.com/blog/hugging-face-democratizes-ai) to improve AI model discovery. This integration allows developers and researchers to effectively search over 300,000 AI models, datasets, and demos. By leveraging model card metadata, the enhanced search delivers relevant results even with typos or incomplete queries, increasing AI accessibility.

### Social and visual search: adapting to new user behaviors

The rise of social media has fundamentally changed how people search for information. [45% of Gen Z users](https://nypost.com/2024/09/12/tech/gen-z-ditches-google-for-this-next-most-popular-search-engine/) now turn to platforms like TikTok or Instagram as their primary search engines, particularly for discovery-oriented queries.

This shift has prompted businesses to adapt their search strategies, incorporating visual elements and social proof into their search experiences. Visual search capabilities now allow users to find products by uploading images. Social signals help rank results based on user engagement and relevance.

## Implementing intelligent search: a practical guide

Implementing intelligent search requires careful planning and execution. This ensures it delivers value while minimizing disruption. **Whether you're a large enterprise or a growing business, the key is to approach implementation systematically while keeping your specific needs and resources in mind.**

### Choosing the right search solution

The success of your intelligent search implementation hinges on choosing the right solution for your specific needs. While [Elasticsearch](https://meilisearch.com/blog/meilisearch-vs-elasticsearch) offers comprehensive capabilities for complex data analysis and distributed search, Meilisearch provides lightning-fast search with excellent relevancy out of the box. **Consider your data volume, search complexity, and technical expertise when making this decision.**

Key factors to evaluate include:

- Search relevancy requirements and ranking capabilities
- Integration complexity and API flexibility
- Scalability needs and performance expectations
- Security features and compliance requirements
- Total cost of ownership, including maintenance and hosting

<Callout intent="tip">
	
If you're running an ecommerce site handling thousands of product searches daily, you'll need robust typo tolerance and filtering capabilities. However, if you're implementing search for an internal knowledge base, features like document parsing and metadata extraction might be more critical.
	
</Callout>

### Measuring return on investment

To justify the investment in intelligent search, establish clear metrics aligned with your business objectives. **Track both quantitative and qualitative measures to get a complete picture of the implementation's impact.**

Key metrics to monitor:

- Average time to find information
- Search success rate and abandonment rate
- User satisfaction scores
- Support ticket reduction
- Employee productivity gains
- Revenue impact for customer-facing search

For instance, a manufacturing company implementing intelligent search might track how quickly employees can locate technical documentation. An online retailer might focus on conversion rates from search results to purchases.

### Cost-effective adoption strategies

Small businesses can implement intelligent search without overspending by taking a strategic approach. Start with essential features and scale up as needed. **Open-source solutions like MeiliSearch offer a cost-effective entry point with the flexibility to grow.**

<Callout intent="note">
	
You can easily deploy MeiliSearch on your own infrastructure, even on your local computer, to experiment and test its capabilities before committing to a full-scale implementation.
		
</Callout>

### Success metrics and optimization

To maintain consistently effective search functionality, continuous monitoring and optimization are paramount. Regularly analyzing search patterns and user behavior allows us to pinpoint areas ripe for enhancement. Key metrics to track in this process include:

- Query patterns and popular search terms
- Zero-result searches and failed queries
- Click-through rates on search results
- Time spent on search results pages
- User feedback and satisfaction scores

Use these insights to refine your search algorithms, update relevance settings, and enhance the user experience.

For example, if users frequently search for synonyms of common terms, update your [synonym lists](https://www.meilisearch.com/docs/learn/relevancy/synonyms) accordingly. If certain searches consistently yield no results, consider adding content to address these gaps or adjusting your ranking parameters. Use an [analytics solution](https://www.meilisearch.com/docs/learn/analytics/analytics) to make sure to capture all this data.

## The future of finding: intelligent search as a transformative technology

Intelligent search is more than just a technological upgrade; it's a fundamental **change in how we discover and interact with information**.

These advanced search systems seamlessly blend AI, NLP, and ML. This is revolutionizing user experiences across industries, from ecommerce and SaaS to enterprise knowledge management.

As technology evolves, **intelligent search will be essential in making information retrieval more intuitive, personalized, and efficient**, helping users and organizations easily find information.

Intelligent search: the future of finding relevant results

10 Best Typesense alternatives: Full comparison [2025]

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.




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