##  🐿️ Meilisearch 1.11 release

![Release notes - Meilisearch 1.11](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_11_59ea013828.jpg)

Meilisearch 1.11 introduces key **improvements** in **AI-powered search**, including **binary quantization**. This release also **enhances federated search** functionality based on user feedback and introduces a **new filter operator**.

[Discover updates](https://www.meilisearch.com/blog/meilisearch-1-11)

***


## 🌎 TutKit's journey with Meilisearch 

[![Meilisearch powers multilingual learning at scale for Tutkit](https://unable-actionable-car.media.strapiapp.com/Tut_Kit_516315e429.png)](https://www.meilisearch.com/blog/tutkit-multilingual-learning-at-scale)

Discover how Tutkit.com scaled its learning platform to handle search across **15,000+ resources in 26 languages**.

[Read case study](https://www.meilisearch.com/blog/tutkit-multilingual-learning-at-scale)

***

## 🎥 Webinar recording
[![October 9th.png](https://unable-actionable-car.media.strapiapp.com/October_9th_b09a084f13.png)](https://www.youtube.com/watch?v=CvutT64u0fE)


Missed our October event? No worries—we've got you covered with the full recording!

 

Discover Meilisearch's **roadmap** and latest **updates**, learn more about **federated search and its benefits** and see a live demo of the **new Cloud functionality**.

[Watch it now!](https://www.youtube.com/watch?v=CvutT64u0fE)

***

## 📰 In the news
### ⚛️  [Add AI-powered search to React](https://www.meilisearch.com/blog/add-ai-powered-search-to-react)
Build a React movie search and recommendation app with Meilisearch's AI-powered search.

### 🔍 [Federated search guide](https://www.meilisearch.com/docs/learn/multi_search/performing_federated_search)

Learn how to create separate indexes for different CRM data types and query them simultaneously for a unified results list.

### ✨ [Semantic search with Cloudflare embeddings](https://www.meilisearch.com/docs/guides/ai/cloudflare)

Learn how to set up Meilisearch with Cloudflare Worker AI embeddings to enable semantic search capabilities.

### 🛍️ [Shopify storefront guide for Next.js](https://www.meilisearch.com/blog/next-js-shopify-storefront-with-blazity)

Learn to build a Shopify storefront with Next.js and the [Blazity commerce starter](https://github.com/Blazity/enterprise-commerce), using Meilisearch hybrid search to serve all front-end data.

### 🔒 [Laravel multitenancy guide](https://www.meilisearch.com/docs/guides/security/laravel_multitenancy)

How to implement search in a multitenant Laravel application.

***

## 🤗 Made with Meilisearch

A community member built [Firebits.io](https://firebits.io) and shared it with the Discord community. Firebits is a modern online Second Brain that helps you organize and share your online learning resources. Its search is, of course, powered by Meilisearch 🎉 

![firebits-2.gif](https://unable-actionable-car.media.strapiapp.com/firebits_2_869f9caaa0.gif)

We’re always looking to promote new projects from the community. Post yours in the [#built-with-meilisearch](https://discord.meilisearch.com/) channel!

***

## 🦸‍♀️ We’re hiring!

See our open positions:

- [Chief of Staff](https://jobs.lever.co/meili/4abb7650-a5ca-4aac-9f35-e2fe5144b78d)

***

## 🤓 Did you know?

The fastest way to try Meilisearch is our [Cloud free trial](https://www.meilisearch.com/cloud).

[Get started!](https://www.meilisearch.com/cloud)

***

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=newsletter&utm_source=blog&utm_medium=october-24-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/).



Meilisearch October updates

[TutKit](https://www.tutkit.com/en) is a comprehensive multilingual learning and content platform that offers training materials, marketing resources, and creative assets to its subscribers worldwide. Built by German agency [4eck Media](https://4eck-media.de/), the platform stands as a testament to the power of effective search functionality in educational platforms. 

**TutKit self-hosts Meilisearch to power high-performance search functionality across their Laravel-based learning platform, managing 15,000+ multilingual content pieces with millisecond response times.**

![TutKit.gif](https://unable-actionable-car.media.strapiapp.com/Tut_Kit_f4b038e4a6.gif)


>"With Meilisearch, we have chosen the right company for the perfect search." - Matthias Petri, founder of TutKit.com and managing director of [4eck Media](https://4eck-media.de/).

The challenge: scaling search for multilingual content
---------

TutKit.com's initial search functionality, implemented by an external agency using MySQL, suffered from severe performance issues due to inefficient programming. With the platform hosting tens of thousands of content pieces, users endured unacceptable loading times of up to 10 seconds for search results—a critical flaw for a content platform. The system also lacked proper multilingual support, making it impossible to effectively filter and display results across different languages.

Frustrated with these limitations, the TutKit team decided to take matters into their own hands. They began a comprehensive refactoring project, bringing the development in-house with a clear goal: to create an ultra-fast search system that could handle multiple languages.


The solution: implementing Meilisearch
---------
The search for a solution that would provide complete control over search results led Tutkit to Meilisearch. While the platform's modern UX and compelling website initially caught their attention, the final decision was driven by several additional factors:
- **Lightning-fast search performance**: while the platform's modern UX and compelling website initially caught their attention, it was Meilisearch's exceptional speed that stood out—presenting a dramatic improvement over their existing system
- **Robust multilingual support.** Meilisearch powers a fast search performance across 26  languages on TutKit platform, coupled with straightforward management capabilities.
- **Regular platform updates and improvements.** Meilisearch’s regular monthly updates continue to bring valuable improvements, enhancing the experience for both the internal TutKit team and the website visitors.

Technical implementation
---------
The TutKit development team executed a comprehensive implementation strategy:

 **1. System setup:** the implementation of Meilisearch began with a straightforward server installation from the official website. To ensure reliable operation, the team configured a system to manage the Meilisearch instance, including automatic startup and failure recovery. This configuration guarantees continuous service availability, even after server restarts.
 
**2. Security:** the instance was implemented through a dual-key system: 
- a master key for comprehensive backend access, which allows full access to the Meilisearch instance (including creation and index management), is used exclusively in the backend. 
- A custom API key with limited permissions for front-end operations. 
This separation creates a robust security layer, protecting backend operations while maintaining efficient front-end interactions. The Laravel application connects to Meilisearch using the API key, effectively minimizing the risk of unauthorized access and potential data breaches.

**3. Optimized data flow:** built on the Meilisearch PHP library, TutKit's custom Laravel integration ensures only necessary changes reach the search index. This selective approach minimizes data processing overhead while maintaining complete control over the implementation, resulting in streamlined index updates and improved search performance.

**4. Smart batch processing:** A custom Laravel console command handles large-scale updates and initial indexing processes. By targeting only relevant records and executing selectively for major changes, this tool significantly reduces server load and maintains system reliability through efficient resource utilization.

**5. Real-time synchronization:** The system automatically detects and processes changes in Laravel models, updating Meilisearch indices instantly without full reindexing. This targeted approach keeps search data synchronized with minimal resource usage, ensuring fast, responsive search functionality for users.

Impact and results
---------
The implementation of Meilisearch has delivered significant benefits for TutKit:
**Performance improvements:** thanks to Meilisearch's continuously improving indexing speed, TutKit can process new data into their search index with remarkable efficiency. The platform's lightning-fast search capabilities significantly reduce server load by eliminating the need for resource-intensive MySQL queries. 

**Real-time filtering capabilities** further enhance the system's responsiveness, allowing users to instantly refine and view their search results.

**Cost benefits:** adoption of Meilisearch as an open-source solution has delivered cost advantages for TutKit. Beyond eliminating ongoing licensing fees typically associated with paid providers, the platform's intuitive nature allowed TutKit's in-house development team to handle implementation and maintenance independently, avoiding external developer costs.

**User experience impact:** improved search functionality has directly enhanced the visitor experience on TutKit's platform. Fast, accurate search results have eliminated user frustration caused by long wait times and irrelevant results. This enhanced user experience has proven valuable for business outcomes, as satisfied users are more likely to convert into paying customers.

**Scalability success:** despite managing hundreds of thousands of content pieces across multiple languages, the platform maintains its exceptional performance. Meilisearch consistently delivers precise, relevant results while handling TutKit's growing content library and language variants. This robust scalability gives the team confidence in their ability to expand the platform's capabilities over the long term.

Looking forward
---------

TutKit continues to scale its platform, adding more content and language variants with confidence in Meilisearch's ability to handle growing demands. The team looks forward to the further development of [AI-powered features](https://www.meilisearch.com/solutions/hybrid-search) that could further enhance search intelligence and simplify the user interface by reducing the need for extensive filtering options.

Matthias Petri, founder of TutKit.com and managing director of [4eck Media](https://4eck-media.de/) said: "A good search is like a stage for large content platforms, where exactly what the user wants to see is always played. Without Meilisearch, our platform would be just one of many - with mediocre search results and without the clear added value that we would like to offer our customers."

For TutKit, Meilisearch has proven to be more than just a search solution – it's become a cornerstone of their platform's success, enabling them to deliver on their promise of effective learning and productive application for users across the globe.


TutKit's journey with Meilisearch: powering multilingual learning at scale.

We're excited to introduce Meilisearch v1.11. In this article, we'll cover the most significant changes. For a full list of updates, visit the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.11.0) on GitHub.

<Callout>
  Meilisearch 1.11 is available on [Meilisearch Cloud](https://cloud.meilisearch.com/?utm_campaign=release-v1-11&utm_source=blog), too—upgrade now!
</Callout>

## Experimental: vector database indexing performance improvements

Meilisearch v1.11 introduces a [new embedder option](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=release-v1-11&utm_source=blog#binaryquantized): `binaryQuantized`.

Binary quantization compresses vectors by encoding each dimension with 1-bit values, significantly boosting performance, especially in datasets with over 1M documents and models with more than 1024 dimensions.

Smaller embeddings (under 1024 dimensions) tend to give worse results. With fewer dimensions, the binary vector doesn't retain enough information for good relevancy.


<Callout>
Enabling this feature permanently updates all vectors to contain only 1s and -1s, which can greatly affect search relevancy. This change is irreversible.
</Callout>

Before enabling this feature, we recommend testing it in a smaller or duplicate index in a development environment to evaluate its impact on your use case.

## Experimental: AI-powered search improvements

### Breaking changes

This release introduces several breaking changes for feature stabilization. 

####  `embedder` parameter becomes mandatory

In previous versions, the default embedder was automatically used for search and similar requests when no embedder was specified. Starting with v1.11, it’s now mandatory to specify the `embedder` parameter when performing:
- Semantic or hybrid search via [`GET`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#search-in-an-index-with-get) and [`POST`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#search-in-an-index-with-post) where the embedder must be included as part of the `hybrid` object.
- Similarity search via [`GET`](https://www.meilisearch.com/docs/reference/api/similar?utm_campaign=release-v1-11&utm_source=blog#get-similar-documents-with-get) and [`POST`](https://www.meilisearch.com/docs/reference/api/similar?utm_campaign=release-v1-11&utm_source=blog#get-similar-documents-with-post), where the `embedder` is required as a top-level parameter.


```javascript
// Semantic and hybrid search
client.index('INDEX_NAME').search(
  query, {
    hybrid: {
      embedder: 'EMBEDDER_NAME' // mandatory as of v1.11
   }
  }
)

// Similarity search
client.index('INDEX_NAME').searchSimilarDocuments({ 
  id: 'TARGET_DOCUMENT_ID',
  embedder: 'EMBEDDER_NAME' // mandatory as of v1.11
})
```

Additionally, even for full-vector searches (when using `vector` without `q`), you must include the `hybrid` object with the `embedder`.

```javascript
client.index('INDEX_NAME').search({
  vector: [0, 1, 2],
  hybrid: {
      embedder: 'EMBEDDER_NAME'// mandatory as of v1.11
   }
})
```

#### Non-zero `semanticRatio` now ignored when passing `vector` without `q`

Before Meilisearch v1.11, when performing a search with the [`vector`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#vector-experimental) and [`hybrid`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#hybrid-search-experimental) parameters but without [`q`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#query-q), a [placeholder keyword search](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#placeholder-search) occurred unless `hybrid.semanticRatio` was set to `1.0`. In v1.11, a semantic search is always performed unless `hybrid.semanticRatio` is set to `0.0`.

### Additions and improvements

#### Default OpenAI model update

The default [OpenAI model](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=release-v1-11&utm_source=blog#model) has been updated from `text-embedding-ada-002` to the new `text-embedding-3-small`. This new model is both more cost-effective and more accurate than the legacy `ada` model, offering better performance at a lower cost.

#### New embedder parameter

Meilisearch 1.11's new `documentTemplateMaxBytes` [parameter](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=release-v1-11&utm_source=blog#documenttemplatemaxbytes) allows truncating the document's template text when it exceeds the specified byte limit. It is set, by default, to 400 bytes. 

This change is backward compatible. Existing embedders will retain the 400-byte default, with no need to recompute embeddings during the upgrade. The new limit will only apply when a document is modified and the template is re-rendered.

#### Improved document template

Fields in `documentTemplate` now include the `field.is_searchable` property. The default [document template](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=release-v1-11&utm_source=blog#documenttemplate) will filter out both empty fields and fields not in the searchable attributes list. The new default template only applies to embedders created starting with v1.11.


```liquid
// v1.11 default document template
{% for field in fields %}
  {% if field.is_searchable and not field.value == nil %}
    {{ field.name }}: {{ field.value }}
  {% endif %}
{% endfor %}

// v1.10 default document template
{% for field in fields %}
  {{ field.name }}: {{ field.value }}
{% endfor %}
```

## Federated search improvements: facet distribution and stats

Meilisearch v1.11 introduces two new options for federated searches: `facetsByIndex` and `mergeFacets`. These options allow you to request facet [distribution](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#facetdistribution) and [stats](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#facetstats) for federated search results.

### Facet Information by index

To retrieve facet distribution and stats for each individual index, use the `facetsByIndex` [option](https://www.meilisearch.com/docs/reference/api/multi_search?utm_campaign=release-v1-11&utm_source=blog#facetsbyindex). 


```json
{
  "federation": {
    "facetsByIndex": {
      "movies": ["title", "id"],
      "comics": ["title"]
    }
  },
  "queries": [
    { "q": "Batman", "indexUid": "movies" },
    { "q": "Batman", "indexUid": "comics" }
  ]
}
```

The response will include facet distribution and stats for each index:


```json
{
  "facetsByIndex": {
    "movies": {
      // Facet distribution for queries on the `movies` index
      "distribution": {
        "title": {
          "Batman returns": 1
        },
        "id": {
          "42": 1
        }
      },
      // Facet stats for queries on the `movies` index
      "stats": {
        "id": {
          "min": 42,
          "max": 42
        }
      }
    },
    "comics": {
      // Facet distribution for queries on the `comics` index
      "distribution": {
        "title": {
          "Batman: the killing joke": 1
        }
      },
      // Facet stats for queries on the `comics` index (empty because only numeric values are considered)
      "stats": {}
    }
  }
}
```

### Merged facet information

To get facet distribution and stats from all indexes in a single result, use both [`facetsByIndex`](https://www.meilisearch.com/docs/reference/api/multi_search?utm_campaign=release-v1-11&utm_source=blog#facetsbyindex) and [`mergeFacets`](https://www.meilisearch.com/docs/reference/api/multi_search?utm_campaign=release-v1-11&utm_source=blog#mergefacets):


```json
POST /multi-search
{
  "federation": {
    "facetsByIndex": {
      "movies": ["title", "id"],
      "comics": ["title"]
    },
    "mergeFacets": { "maxValuesPerFacet": 10 }
  },
  "queries": [
    { "q": "Batman", "indexUid": "movies" },
    { "q": "Batman", "indexUid": "comics" }
  ]
}
```

The response will include merged facet data:


```json
{
  "facetDistribution": {
    "title": {
      "Batman returns": 1,
      "Batman: the killing joke": 1
    },
    "id": {
      "42": 1
    }
  },
  "facetStats": {
    "id": {
      "min": 42,
      "max": 42
    }
  }
}
```

<Callout>
Learn more about [federated search and its use cases](https://www.meilisearch.com/blog/what-is-federated-search?utm_campaign=release-v1-11&utm_source=blog).
</Callout>

## Experimental: new `STARTS_WITH` filter operator

The new `STARTS_WITH` [filter operator](https://www.meilisearch.com/docs/learn/filtering_and_sorting/filter_expression_reference?utm_campaign=release-v1-11&utm_source=blog#starts-with) matches any field values that start with the specified string pattern:


```json
// returns all heroes whose name starts with 'super'
"filter": "hero STARTS_WITH super"
```

This is an experimental feature. To enable it, use the [experimental features](https://www.meilisearch.com/docs/reference/api/experimental_features?utm_campaign=release-v1-11&utm_source=blog) endpoint and set the `containsFilter` option to `true`. Note that this will also activate the `CONTAINS` filter operator.

<Callout>
Share your feedback via this [GitHub discussion](https://github.com/orgs/meilisearch/discussions/763)
</Callout>

## Contributors shout-out

We want to give a massive thank you to the contributors who made this release possible: [@iornstein](https://github.com/iornstein) for their help with [Meilisearch](https://github.com/meilisearch/meilisearch), and [@luflow](https://github.com/luflow), [@tkhshtsh0917](https://github.com/tkhshtsh0917) for their work on [charabia](https://github.com/meilisearch/charabia).

We also want to extend special thanks to our SDK maintainers working hard to make Meilisearch available across many [languages and frameworks](https://www.meilisearch.com/docs/learn/what_is_meilisearch/sdks?utm_campaign=release-v1-11&utm_source=blog).

* * *

And that’s a wrap for v1.11! These release notes only highlights the most significant updates. For an exhaustive listing, read the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.11.0) on Github.

Upgrade with ease using Meilisearch Cloud.

[Open Meilisearch Cloud](https://cloud.meilisearch.com/login?utm_campaign=release-v1-11&utm_source=blog)

* * *

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=release-v1-11&utm_source=blog). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/?utm_campaign=release-v1-11&utm_source=blog) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=release-v1-11&utm_source=blog).

Meilisearch 1.11

_Bildhistoria chose Meilisearch to power the search through its quickly growing database of historical photographs and affiliated data, driving the search from multiple sources of metadata and mapsearch._

> _“Bildhistoria's large volumes of historical photos, persons, places and organizations require a fast and easy to maintain search engine, like Meilisearch. Meilisearch is used on so many places around Bildhistoria, enabling our users to explore photos and learn more about ancestors and local history. This makes Meilisearch instrumental in the success of our community.”_ - Per Rohdin, board member of the community, and project manager at Bildhistoria.

[**Bildhistoria**](https://bildhistoria.se/)**, a Swedish non-profit, operates the country's largest collection of copyright-free historical photographs. It focuses on collecting and preserving historical photographs from museums and private collections. While the project has not yet reached full general availability, Bildhistoria already boasts an impressive archive of around 90,000 images dating from the 1950s and earlier.**

The project aims to create a dynamic and collaborative platform where the public can interact with history, and where professional and amateur genealogists alike can contribute by enriching the photographs with additional data.

**Challenge: Bildhistoria tackles data fragmentation and community engagement**
-------------------------------------------------------------------------------

From the start, Bildhistoria faced three main challenges in bringing their vision to life:

**Challenge: searching across multiple types of data:** Bildhistoria's main challenge with search is effectively searching across the different data types. The platform features not only photographs but also information about people’s names, organizations, and locations associated with each image. The team needed a search solution that could seamlessly navigate through these different data types. To address this, they have implemented Meilisearch’s "[multi-index search](https://www.meilisearch.com/docs/reference/api/multi_search)" that returns results from all the different indexes, allowing users to find relevant information regardless of whether they are searching for a person, place, or organization.

**Challenge: data scattered across multiple sources:** Historical photographs were scattered across various sources, including museums, archives, and private collections. There was no centralized platform in Sweden to consolidate these copyright-free images, making it difficult for researchers and the public to access this wealth of visual history.

![](https://unable-actionable-car.media.strapiapp.com/AD_4n_Xf_R_Ormjkncn9_Uj8i_Un6vaqs_Wi_I_Uln1hutwuz0h_KI_wm_GIA_P_Wry5j_Rj5_R_Altplg_VV_5_Jy_ANKPL_2_Hayt3_Jv_Ku_A_Gw9ou_P8l0va_9f26862acc.jpg)

**Challenge: Community accessibility and engagement:** Unlike traditional digital archives, Bildhistoria is an interactive platform where users can contribute additional historical facts and enrich the metadata associated with the photographs. The community of genealogists is large and diverse, spanning professional organizations to amateur genealogists and the general public. Bildhistoria’s team knew from the beginning that the only way to make the project work was to make it easily accessible and easy to use for everyone.

**Solution: Bildhistoria chose Meilisearch for simplified operations, team-wide accessibility, and rapid data synchronization**
-------------------------------------------------------------------------------------------------------------------------------

After careful consideration, Bildhistoria chose Meilisearch as their search solution for several key reasons:

**Less operational overhead:** Meilisearch proved much easier to set up and connect to existing indexes compared to alternatives like Elasticsearch. This was crucial for a non-profit organization with limited resources.

**Accessibility for all team members:** Meilisearch's intuitive admin interface allowed even non-technical staff to understand and configure search settings. This democratization of the search configuration process was particularly valuable for fine-tuning Bildhistoria’s search experience.

**Fast synchronization:** Bildhistoria required efficient update mechanisms to keep Meilisearch indexes in sync with their database changes. Meilisearch's ability to handle rapid updates and re-indexing was crucial for maintaining an up-to-date and responsive search experience.

**Implementation**
------------------

Bildhistoria's implementation of Meilisearch involved several key steps:

1.  The team initially deployed the open-source version on Azure but faced challenges with the hosting environment.
2.  Bildhistoria team then switched to Meilisearch Cloud, which provided better management and support, allowing them to focus on their core mission rather than infrastructure concerns.
3.  Meilisearch was integrated throughout the platform, powering the main search functionality and an innovative map search feature.
4.  Finally, Bildhistoria invested significant effort in creating an efficient data synchronization pipeline between its database and Meilisearch to handle the constant flow of new information and user contributions.

**Results**
-----------

The implementation of Meilisearch has yielded impressive results for Bildhistoria:

**Multi-index search:** Users can now perform searches that capture results across various types of data—photographs, persons, organizations, and places. This holistic approach has dramatically improved the user experience, allowing for a more comprehensive exploration of historical connections.

**Improved user experience:** The search functionality provides alternative search suggestions for similar queries and provides rich context for each image.

**Interactive map search:** Meilisearch’s [Geosearch](https://www.meilisearch.com/docs/learn/filtering_and_sorting/geosearch) allows users to explore photographs associated with specific locations in Sweden. As users interact with the map, new searches are performed in real-time, updating the displayed photographs.

![](https://unable-actionable-car.media.strapiapp.com/Bildhistoria_Mapseaechdemogif_ezgif_com_video_to_gif_converter_a70a28b422.gif)

Overall, the team noticed a significant improvement in engagement and user experience since the implementation of Meilisearch.

**Conclusion: Meilisearch is a key part of Bildhistoria's vision for the future**
---------------------------------------------------------------------------------

Looking to the future, Bildhistoria has ambitious plans to further leverage Meilisearch to expand its platform's capabilities to become Sweden's leading platform for historical photographs. While initially focused on the Swedish market, Bildhistoria is open to European collaborations to scale the concept internationally. The next steps for improving the user experience on the platform would include:

*   Integrate AI-driven facial recognition across photographs to link images of the same individuals across different time periods and locations.
*   Add a _houses and buildings_ index, enabling users to trace the lifecycle of specific structures through photographs.
*   Implement features encouraging community collaboration, such as allowing premium users to follow and receive notifications about new edits and contributions.
*   Scale operations to manage a vast archive of 1-3 million photographs by collaborating with more museums and institutions.

As the project progresses and reaches full production mode, Bildhistoria continues to assemble the right technical team to establish a sustainable and long-term setup for the community, laying the groundwork for its future growth and impact.

Meilisearch powers the discovery of history for Bildhistoria

📅 Save the date: October 9th
-----------------------------

[![What's new in Meilisearch: roadmap, v1.10, federated search, and Cloud news!](https://unable-actionable-car.media.strapiapp.com/October_9th_2_f41b190f00.png)](https://us06web.zoom.us/meeting/register/tZUpcu2gqT4pHtcB0Wh8P-aIUd3e4LF-T-UX#/registration)

Join us on October 9th for a **Meilisearch webinar** featuring:

*   A **live demo** of Meilisearch Cloud's latest capabilities
*   A sneak peek at our **product roadmap** and vision
*   Insights on **federated search**
*   **Q&A session** with our experts

We look forward to seeing you there!

<Callout>
  Wednesday, October 9th  
4PM CEST | 3PM BST | 10AM EDT
</Callout>

[Register now](https://us06web.zoom.us/meeting/register/tZUpcu2gqT4pHtcB0Wh8P-aIUd3e4LF-T-UX#/registration)

👀 Meilisearch 1.11 pre-release
-------------------------------

[![Release candidate - Meilisearch 1.11](https://unable-actionable-car.media.strapiapp.com/146_1_12d5d22c9f.jpg)](https://github.com/meilisearch/meilisearch/releases/tag/v1.11.0-rc.0)

Meilisearch 1.11 RC: Major improvements to **hybrid search performance** and usability, plus enhanced **federated search**

❗️Not production-ready. Your feedback is crucial!

[Preview updates](https://github.com/meilisearch/meilisearch/releases/tag/v1.11.0-rc.0)

🤔 Choosing the best model for semantic search
----------------------------------------------

[![](https://unable-actionable-car.media.strapiapp.com/best_sematic_model_8490bd88d2.png)](/blog/choosing-the-best-model-for-semantic-search/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

A **comparison** of model performance, cost, and relevancy.

[Read guide](/blog/choosing-the-best-model-for-semantic-search/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

🎉 Join Meilisearch's referral program
--------------------------------------

[![New referral program](https://unable-actionable-car.media.strapiapp.com/Referral_program_804eb40f12.png)](https://help.meilisearch.com/en/article/join-meilisearch-referral-program-and-earn-rewards-t3m5rl/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

Earn **rewards** while helping grow the community! 🤝 It's a **limited-time** program—don't miss it!

[Learn more](https://help.meilisearch.com/en/article/join-meilisearch-referral-program-and-earn-rewards-t3m5rl/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

📰 In the news
--------------

💡 [What is federated search?](/blog/what-is-federated-search/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

Learn how it surpasses multi-index search, and elevates both user experience and developer efficiency.

🎮 [Meilisearch playground](https://playground.meilisearch.com/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

Explore and compare Meilisearch with other search engines. Test multiple search modes and AI models and evaluate their performance!

📣 [RFC: Similar Documents API](https://github.com/meilisearch/meilisearch/issues/4736)

Join the discussion to help us shape the future of the similar  documents API in Meilisearch.

📣 [RFC: Improving the search preview](https://github.com/orgs/meilisearch/discussions/766)

We want your feedback: how can we make it easier to test your search settings?

✨ [Semantic search with Mistral embeddings](https://www.meilisearch.com/docs/guides/ai/mistral?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

Learn how to set up Meilisearch with Mistral embeddings to enhance your search experience and retrieve more relevant results.

🤗 Made with Meilisearch
------------------------

Discover how [Bookshop.org](https://bookshop.org/) increased search-bases purchases by 43% after switching to Meilisearch. 

[![Bookshop.org success story, +43% search-based purchases with Meilisearch](https://unable-actionable-car.media.strapiapp.com/59_fc6540f28f.png)](/blog/bookshop-increases-search-based-purchases/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

[Read case study](/blog/bookshop-increases-search-based-purchases/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

_We’re always looking to promote new projects from the community. Post yours in the_ [_#built-with-meilisearch_](https://discord.gg/uB94t8PGV4) _channel!_

🦸 We're hiring!
----------------

See our open positions:

*   [Senior Full-Stack Software Engineer](https://www.meilisearch.com/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)
*   [Founding Sales & Business Developer](https://www.meilisearch.com/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)  
    

* * *

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions?ref=blog.meilisearch.com).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates).

Meilisearch September Updates

In today's interconnected world, many applications need to handle content in multiple languages. Meilisearch offers powerful capabilities for multilingual search, combining intelligent language handling with specialized models for optimal relevance. This guide will walk you through setting up multilingual search with Meilisearch, covering both full-text and semantic search approaches.

Why multilingual search matters
===============================

Before we dive into the technical details, let's consider why multilingual search is crucial:

1.  Global reach: Expand your audience by catering to users in their native languages.
2.  Improved user experience: Provide relevant results regardless of the language used in queries or content.
3.  Increased engagement: Users are more likely to interact with content they can easily find and understand.

Now, let's explore how Meilisearch makes multilingual search a breeze.

Full-text search: intelligent language handling
-----------------------------------------------

At its core, Meilisearch excels at handling diverse languages out of the box. Our [advanced tokenizer](https://www.meilisearch.com/docs/learn/indexing/tokenization?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search) is designed to work with:

*   Languages that use whitespace to separate words (like English, Spanish, and French)
*   Languages with different writing systems (such as Chinese, Japanese, and Thai)

### Automatic language detection

When you add documents to your Meilisearch index, the engine automatically detects the language of each field. This means you don't need to specify languages manually in most cases - Meilisearch will handle it for you.

For example, if your documents contain a mix of English and Spanish content:

```json
[
  {
    "id": 1,
    "title": "The quick brown fox",
    "description": "El zorro marrón rápido salta sobre el perro perezoso"
  },
  {
    "id": 2,
    "title": "La luna y las estrellas",
    "description": "A beautiful night sky filled with twinkling stars"
  }
]
```

Meilisearch will automatically detect and properly handle the different languages in each field, ensuring accurate search results across your multilingual content.

### Specifying languages for specific fields

While automatic detection works well in most cases, you might want more control over language handling for certain fields. Meilisearch allows you to [specify languages](https://www.meilisearch.com/docs/reference/api/settings#localized-attributes?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search) for particular attributes using the `localizedAttributes` setting.

Here's how you can set it up:

```json
{
  "localizedAttributes": [
    {
      "locales": ["eng"],
      "attributePatterns": ["title_en", "*_english"]
    },
    {
      "locales": ["spa"],
      "attributePatterns": ["description_es", "*_spanish"]
    }
  ]
}
```

This configuration tells Meilisearch to treat fields ending with `_en` or `_english` as English, and fields ending with `_es` or `_spanish` as Spanish. This approach is particularly useful when you have a consistent naming convention for your multilingual fields or when you want to ensure specific treatment for certain attributes.

Semantic search: Enhancing multilingual relevance
=================================================

While full-text search works well for many multilingual scenarios, semantic search can provide even better results, especially for complex queries or when dealing with nuanced meanings across languages.

Meilisearch supports various embedding models for semantic search, offering powerful auto-embedding capabilities. This means you can easily set up semantic search without having to manage the embedding process yourself. Meilisearch handles the generation and storage of embeddings automatically, seamlessly integrating them into your search experience.

For multilingual applications, we recommend using specialized multilingual models rather than relying on general-purpose models. These models are designed to understand and represent meaning across multiple languages, leading to more relevant search results in multilingual contexts.

One excellent option for multilingual semantic search is the [Cohere Multilingual embedding model](https://docs.cohere.com/docs/multilingual-language-models). It offers a great balance of speed and relevance across a wide range of languages. Cohere's model is particularly adept at understanding context and nuance in different languages, making it an ideal choice for diverse, multilingual content. Follow [the guide](https://www.meilisearch.com/docs/guides/ai/cohere?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search) if you want to do it now.

To explore different models and their performance, you can use the [Meilisearch playground](https://playground.meilisearch.com). This interactive tool allows you to test various embedding models and see how they perform with your specific multilingual queries and content.

Conclusion
----------

We encourage you to experiment with different settings and models to find the optimal configuration for your specific use case. And remember, the Meilisearch community is always here to help - join us on [Discord](https://discord.meilisearch.com/) or check out our documentation for more detailed information and support.

Ready to get started with multilingual search? Try Meilisearch today [with a 14 day trial](https://cloud.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search)!

Searching across multiple languages

How to add AI-powered search to a React app

🦩 Meilisearch 1.10 release
---------------------------

[![Meilisearch 1.10 release notes](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_10_2b7000d5bd.jpg)](/blog/meilisearch-1-10/?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates)

Meilisearch 1.10 introduces federated search and new language settings, and paves the way for AI-powered search stabilization.

[Read release notes](/blog/meilisearch-1-10/?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates)

🚀 Meilisearch Next.js project starter
--------------------------------------

[![Developer Terminal with code: npx create-next-app -e with-meilisearch my-app](https://unable-actionable-car.media.strapiapp.com/GV_6_J_Ld_W_Ws_A_Ar6_Cd_3723955f2a.jpeg)](https://github.com/vercel/next.js/tree/canary/examples/with-meilisearch)

Integrating Meilisearch in a Next.js application just got much easier. Use the create-next-app CLI to scaffold a Meilisearch example in your new Next.js application.

[Learn more](https://github.com/vercel/next.js/tree/canary/examples/with-meilisearch?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates)

🛍️ Shopify storefront guide for Next.js
----------------------------------------

[![Build your Shopify storefront with Next.js and Blazity](https://unable-actionable-car.media.strapiapp.com/blazity_guide_14d74d3924.jpg)](/blog/next-js-shopify-storefront-with-blazity/utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates)

Learn to build a Shopify storefront with Next.js and the Blazity commerce starter, using Meilisearch hybrid search to serve all front-end data.

[Read guide](/blog/next-js-shopify-storefront-with-blazity/utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates)

📰 In the news
--------------

🎮 [Federated search demo](https://saas.meilisearch.com/deals?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates)

See how federated search can power app-wide search in our example CRM.

🐇 [Integrating Meilisearch Indexing with Directus](https://docs.directus.io/blog/integrating-meilisearch-indexing-with-directus.html)

Learn to build a custom Directus hook extension to index your data in Meilisearch.

🍿 [From Hackathon to Success: The Meilisearch Story](https://thebootstrappedfounder.com/kerollmops-from-hackathon-to-success-the-meilisearch-story/)

Listen to Arvid Kahl's interview of Meilisearch's founder and lead developer, Kero.

📢 [RFC: Query Suggestions](https://github.com/orgs/meilisearch/discussions/740)

Join the discussion to help us shape the future of query suggestions in Meilisearch.

📢 [RFC: Improving the search preview](https://github.com/orgs/meilisearch/discussions/766)

We want your feedback: how can we make it easier to test your search settings?

🤗 Made with Meilisearch
------------------------

Discover how Symfony implemented search in their documentation with Meilisearch PHP. This enables developers to search for guides across 30 different versions—more than 12 million words.

[![Symfony's search solution for 12M words of documentation with Meilisearch](https://unable-actionable-car.media.strapiapp.com/image_67e9f20ca0.png)](/blog/symfonys-solution-for-documentation-search/?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates)

[Read case study](/blog/symfonys-solution-for-documentation-search/?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates)

_We’re always looking to promote new projects from the community. Post yours in the_ [_#built-with-meilisearch_](https://discord.com/invite/hTD5hU2yWV) _channel!_

🦸‍♀️ We’re hiring!
-------------------

See our open positions:

*   [Senior Full-Stack Software Engineer](https://www.meilisearch.com/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates&utm_content=senior-full-stack-software-engineer)
*   [Founding Sales & Business Developer](https://www.meilisearch.com/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates&utm_content=founding-sales-and-business-developer)

* * *

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions?ref=blog.meilisearch.com).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates).

Meilisearch August Updates

Searching across disparate data sources can be a real headache. But aggregating information from various databases or indices enables the creation of more relevant search experiences for users. In this guide, we'll explore what federated search is and the use cases it unlocks.

What is federated search?
-------------------------

Federated search empowers you to conduct searches across multiple systems simultaneously. In the context of a search engine, federated search allows querying numerous indices and retrieving the results in a single, consolidated list.

This differs from having a monolithic index that stores various types of resources where documents could be filtered by type. Leveraging multiple indices enables the implementation of specific ranking rules for each index.

Federated search extends the capabilities of [multi-index search](/blog/introducing-multi-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search merging results into a unified, cohesive response. This allows surfacing the most relevant results across the entire dataset, rather than just the top hits for each individual index.

Federated search is a crucial feature for SaaS applications, where users frequently need to search through all available resources regardless of type. An example you might be familiar with is searching in Slack—a single search bar to find users, messages, and shared documents.

What are the benefits of federated search?
------------------------------------------

Integrating federated search into your application can facilitate more convenient search experiences. By supporting searches across multiple sources, you can build one search bar to query all the data. This eliminates the need for multiple menus or lists of results, resulting in a more intuitive and user-friendly interface.

The search experience is further enhanced by the increased relevance of the results. The comprehensiveness of the returned resources will match items that the user may not have initially considered including in their search (e.g., discovering a prospect's email in meeting notes instead of just contact details).

Uncovering the most pertinent result can be transformative for your users and your business. In an internal tool, relevant results enable users to make more informed decisions. However, in customer-facing applications, it can be the pivotal factor between converting a sale or not.

Locating information effortlessly within your application or e-commerce platform leads to higher customer satisfaction and improved retention. This allows users to extract more value from the service, ultimately elevating the overall experience quality.

How federated search improves developer experience
--------------------------------------------------

Implementing federated search leads to a more streamlined search implementation process. With federated search, results are delivered in a single, unified list, simplifying front-end development. This goes beyond [multi-index search](/blog/introducing-multi-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) because it comes without the added complexity of implementing front-end logic to merge, sort, and paginate the results.

In this setup, the relevance of documents can be further fine-tuned thanks to granular, per-index configuration. This allows tailoring the [relevancy settings](https://www.meilisearch.com/docs/learn/relevancy/relevancy?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) to specific data types (a specific index) rather than the entire dataset (all indices). As a federated search request comes, the search engine can ensure that the most important results rise to the top.

This also simplifies extending the search functionality if new types of documents need to be included in the future. You'll only need to focus on configuring the relevancy settings for the new document type, instead of revisiting the relevancy for the entire dataset.

Conclusion
----------

Federated search enables performing searches against multiple indices of a search engine and returns a single, consolidated list of results. With a search engine that supports federated search, developers can build more relevant user search experiences without incurring additional complexity.

Federated search is available in [Meilisearch 1.10](/blog/meilisearch-1-10/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) and higher. Learn more about implementing federated search with Meilisearch by [reading the documentation](https://www.meilisearch.com/docs/reference/api/multi_search?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search#federated-multi-search). Check out which search engines support federated search and other key features via our [comparison of search engines](https://www.meilisearch.com/docs/learn/resources/comparison_to_alternatives?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search).

* * *

[Meilisearch](https://meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=full-text-vs-vector) is an open-source AI-powered search engine enabling developers to build state-of-the-art experiences while enjoying simple, intuitive DX.

For more things Meilisearch, you can join the community on [Discord](https://discord.meilisearch.com/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) or subscribe to the [newsletter](https://dub.sh/meili-newsletter?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search). You can learn more about Meilisearch by looking at the [roadmap](https://roadmap.meilisearch.com/?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search) and participating in [product discussions](https://github.com/orgs/meilisearch/discussions?ref=blog.meilisearch.com).

What is federated search?

  
[Semantic search](/blog/what-is-a-vector-database/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#what-is-similarity-search) is transforming search technology by providing more accurate and relevant results. However, with many embedding models available, choosing the right one can be challenging. This guide will help you understand the key factors to consider when selecting a model to build semantic search.

Overview
--------

In this guide, we will use the open-source search engine [Meilisearch](https://www.meilisearch.com?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) to perform the semantic searches. For the purpose of the tests, we're using the entry tier of [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) (i.e., the [Build plan](https://www.meilisearch.com/pricing?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) .)

This guide will cover the following models:

| 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://mistral.ai/product/embeddings/) | 1024 | 8192 |
| [VoyageAI voyage-2](https://voyageai.com/) | 1024 | 4000 |
| [VoyageAI voyage-large-2](https://voyageai.com/) | 1536 | 16000 |
| [VoyageAI voyage-multilingual-2](https://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/Cloudflare/bge-small-en-v1.5) | 1024 | 512 |
| [OSS bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | 1536 | 512 |

Factors to consider
-------------------

### 1\. Results relevancy

[Relevancy](https://www.meilisearch.com/docs/learn/relevancy/relevancy?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) is crucial for effective search, as it ensures that users find the most pertinent results quickly. In the realm of semantic search, achieving a balance between relevancy and speed is essential to provide a seamless user experience. It's important to consider the tradeoffs of [vector search vs full-text search](/blog/full-text-search-vs-vector-search/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison)

When selecting a model, consider your specific use case, such as the need for multilingual support, handling multi-modal data, or addressing domain-specific requirements. If you have a highly specialized use case or need to support a particular language, it may be beneficial to explore models that can be trained on your data or opt for multilingual models.

The performance difference between a very small model and a large model is not always substantial. **Smaller models are generally less expensive and faster**, making them a practical choice in many scenarios. Therefore, it is often worth considering smaller models for their cost-effectiveness and speed.

Additionally, you should always consider the context you're providing to the model. In Meilisearch, this comes in the form of a [document template](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#embedders-object). The more accurately the template describes the data, the better the search results will be, leading to a more satisfying user experience.

### 2\. Search performance

You read everywhere now. Time is money. And the web is no different. Nowadays, [search-as-you-type](https://www.meilisearch.com/docs/learn/getting_started/what_is_meilisearch?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#features) is the baseline for customer-facing applications. Saving users time greatly enhances their satisfaction and keeps them engaged with your platform.

To achieve lightning-fast search performance, **consider using a local model** to minimize latency by eliminating the need for round trips to the embedding service. If you need to use a remote model, then hosting your search service (e.g., your Meilisearch database) in close proximity to the embedding service can significantly reduce latency.

The table below showcases latency benchmarks for various local embedding models and embedding APIs. All requests are originated from a Meilisearch instance hosted on AWS (London datacenter.)

| Model/Service | Latency |
| --- | --- |
| [Cloudflare bge-small-en-v1.5](https://huggingface.co/Cloudflare/bge-small-en-v1.5) | ±800ms |
| [Cloudflare bge-large-en-v1.5](https://huggingface.co/Cloudflare/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/sentence-transformers/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/Cloudflare/bge-small-en-v1.5) | ±20ms |
| [Local bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | ±60ms |
| [Mistral](https://mistral.ai/product/embeddings/) | ±200ms |
| [Jina colbert](https://huggingface.co/jinac/cobert-v1) | ±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://voyageai.com/) | ±350ms |
| [VoyageAI voyage-large-2](https://voyageai.com/) | ±400ms |

Here you can see that there are some clear winners in terms of latency. Unfortunately, latency is not the same as throughput, so we also need to take a close look at the indexing time.

### 3\. Indexing performance

Indexing performance is another critical aspect when [comparing search solutions](https://www.meilisearch.com/docs/learn/resources/comparison_to_alternatives?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison). The embedding model performance will directly impact the indexing speed of your search solution. And the speed at which your data can be indexed directly impacts the overall efficiency and scalability of your search solution.

Local **models without GPUs may have slower indexing** due to limited processing power. In contrast, third-party services offer varying speeds and limitations based on their infrastructure and service agreements. It is essential to evaluate these factors to ensure that your chosen model and service can meet your requirements effectively.

Several factors come into play when optimizing indexing. Again, the latency plays a big role: reducing the time taken for data to travel between your application and the model is always going to improve your experience. Additionally, the **maximum size of API calls** the API accepts, the provider's **rate limiting**, and the model's supported **number of dimensions** can all influence the efficiency and scalability of the indexing process.

The benchmark below compares the indexing of a 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/Cloudflare/bge-small-en-v1.5) | 152s |
| [Cloudflare bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | 159s |
| [Jina Colbert V2](https://huggingface.co/jinac/cobert-v1) | 375s |
| [VoyageAI voyage-large-2](https://voyageai.com/) | 409s |
| [Mistral](https://mistral.ai/product/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/Cloudflare/bge-small-en-v1.5) | 3379s |
| [Local bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | 9132s |

### 4\. Pricing

While local embedders are free, most services charge per million of tokens. Here's a breakdown of the pricing for each platform:

| Provider      | Price                                      |
|---------------|--------------------------------------------|
| Cohere        | $0.10 per million tokens                   |
| OpenAI        | $0.13 per million tokens for text-embedding-3-large  |
|               | $0.02 per million tokens for text-embedding-3-small  |
| Cloudflare    | $0.011 per 1,000 Neurons                   |
| Jina          | $0.18 per million tokens                   |
| Mistral       | $0.10 per million tokens                   |
| VoyageAI      | $0.10 per million tokens for voyage-2      |
|               | $0.12 per million tokens for voyage-large-2 |
|               | $0.12 per million tokens for voyage-multilingual-2 |
| Local model   | Free                                       |


As your search needs grow and scale, it may become more cost-effective to invest in your own GPU machine. By having your own hardware, you can have greater control over the performance and scalability of your search solution and potentially reduce costs in the long run.

It is often best to start with a well-known model from the list provided. They are generally easy to setup and you will easily find community resources to help you.. As the need arises, you can consider migrating the model to a cloud provider like AWS. Many services offer this option, allowing you to leverage their infrastructure for improved performance and scalability.

Alternatively, you can choose an equivalent open-source model to self-host, giving you even more flexibility and control over your search solution in the long term. Please note that optimizing local models for performance or high volume may require to scale your infrastructure accordingly.

* * *

Ready to elevate your search experience?

[Talk to a search expert](https://dub.sh/meili-sales?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison)

* * *

Going further
-------------

While this article provides a comprehensive overview, we did not delve deeply into optimization techniques. There are several additional optimizations that can be explored to further enhance the performance of semantic search.

Here is a list of additional areas to investigate when choosing a model for your search experience:

*   Experiment with different presets (query vs. document) for models that offer this option to potentially improve relevancy
*   Evaluate specialized models for specific applications to assess their performance and suitability for your use case
*   Explore models that provide a reranking function to further refine search results
*   Test higher-tier accounts on each platform to check for improved performance and reduced rate limiting
*   Investigate parameters for receiving quantized data directly from the API to optimize data transfer and processing

Conclusion
----------

| 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://mistral.ai/product/embeddings/) | 1024 | 8192 | ±200ms | 409s | $0.10 |
| [VoyageAI voyage-2](https://voyageai.com/) | 1024 | 4000 | ±350ms | 330s | $0.10 |
| [VoyageAI voyage-large-2](https://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/Cloudflare/bge-small-en-v1.5) | 1024 | 512 | ±20ms | 3379s | Free |
| [OSS bge-large-en-v1.5](https://huggingface.co/Cloudflare/bge-large-en-v1.5) | 1536 | 512 | ±60ms | 9132s | Free |

Choosing the right model and service for semantic search involves carefully balancing several key factors: relevancy, search performance, indexation performance, and cost.

Each option presents its own set of trade-offs:

*   Cloud-based services like Cohere and OpenAI offer excellent relevancy and reasonable latency, with Cohere's embed-english-light-v3.0 standing out for its balance of speed and performance.
*   Local models provide the fastest search latency but may struggle with indexation speed on limited hardware.
*   Emerging services like Mistral and VoyageAI show promise with competitive pricing and performance.
*   Open-source models offer cost-effective solutions for those willing to manage their own infrastructure.

Ultimately, the best choice depends on your specific use case, budget, and performance requirements. For many applications, starting with a cloud-based service like Cohere or OpenAI provides a good balance of ease of use, performance, and cost. As your needs grow, consider exploring local or specialized models, or contact Meilisearch's sales team for tailored solutions.

* * *

[Meilisearch](https://www.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) is an open-source search engine enabling developers to build state-of-the-art experiences while enjoying simple, intuitive DX.

For more things Meilisearch, you can join the community on [Discord](https://discord.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) or subscribe to the [newsletter](https://dub.sh/meili-newsletter?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison). You can learn more about the product by checking out its [roadmap](https://roadmap.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) and participating in [product discussions](https://github.com/orgs/meilisearch/discussions).

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