📅 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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)
*   [Founding Sales & Business Developer](https://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=august-24-updates&utm_content=senior-full-stack-software-engineer)
*   [Founding Sales & Business Developer](https://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/docs/learn/resources/comparison_to_alternatives?utm_campaign=federated-search&utm_source=blog&utm_medium=what-is-federated-search).

* * *

[Meilisearch](https://meilisearch.dev.org.tw/?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://meilisearch.dev.org.tw?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://meilisearch.dev.org.tw/cloud?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison) (i.e., the [Build plan](https://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/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://meilisearch.dev.org.tw/?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).

Choosing the best model for semantic search

Today we're announcing Meilisearch v1.10. In this article, we’ll review the most impactful changes. For an exhaustive listing, check out the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.10.0) on GitHub.

<Callout>
  Meilisearch 1.10 is available on [Meilisearch Cloud](https://cloud.meilisearch.com/?utm_campaign=release-v1-10&utm_source=blog), too—upgrade now!
</Callout>

New: federated search
---------------------

We’re excited to announce that [federated search](/blog/what-is-federated-search/?utm_campaign=release-v1-10&utm_source=blog is finally coming to Meilisearch. This feature extends the [multi-search API](https://meilisearch.dev.org.tw/docs/reference/api/multi_search?utm_campaign=release-v1-10&utm_source=blog#perform-a-multi-search) to allow you to merge results from multiple queries into a single, unified response. You can now search different indexes and get the most relevant results across the entire dataset, not only the most relevant for each index.

For SaaS platforms that need to search across various types of resources or ecommerce sites with diverse product categories, federated search is a game-changer.

Here is an example payload for making a [federated search request](https://meilisearch.dev.org.tw/docs/reference/api/multi_search?utm_campaign=release-v1-10&utm_source=blog#federated-multi-search) against the `movies` and `comics` indexes:

```json
{
  "federation": {
    "offset": 5,
    "limit": 10
  },
  "queries": [
    {
      "q": "Batman",
      "indexUid": "movies"
    },
    {
      "q": "Batman",
      "indexUid": "comics"
    }
  ]
}
```

In this example, the [federated search response](https://meilisearch.dev.org.tw/docs/reference/api/multi_search?utm_campaign=release-v1-10&utm_source=blog#response-200-ok-1) will look like this:

```json
{
  "hits": [
    {
      "id": 42,
      "title": "Batman returns",
      "overview": "..",
      "_federation": {
        "indexUid": "movies",
        "queriesPosition": 0
      }
    },
    {
      "comicsId": "batman-killing-joke",
      "description": "..",
      "title": "Batman: the killing joke",
      "_federation": {
        "indexUid": "comics",
        "queriesPosition": 1
      }
    },
 ],
  "processingTimeMs": 0,
  "limit": 2,
  "offset": 0,
  "estimatedTotalHits": 2,
  "semanticHitCount": 0
}
```

### Query weighting

Federated search isn't just about combining results; it's about making those results more meaningful and relevant. Thanks to [query weighting](https://meilisearch.dev.org.tw/docs/reference/api/multi_search?utm_campaign=release-v1-10&utm_source=blog#federationoptions), you can prioritize certain queries over others, ensuring that the most important data rises to the top.

The example payload below uses **query weighting** in a federated search to give more importance to documents where the `boosted` field is `true`:

```json
{
  "federation": {},
  "queries": [
    {
      "q": "iphone case",
      "indexUid": "products",
      "filter": "BOOSTED = true",
      "federationOptions": {
        "weight": 3.0
      }
    },
    {
      "q": "iphone case",
      "indexUid": "products"
      // federationOptions.weight defaults to 1
    }
  ]
}
```
<Callout>
  Learn more about [federated search and its use cases](/blog/what-is-federated-search/?utm_campaign=release-v1-10&utm_source=blog)
</Callout>

New: language settings for queries and documents
------------------------------------------------

By default, Meilisearch automatically detects the language used in documents and queries. This comes with some limitations for edge cases. Meilisearch 1.10 introduces new language settings that allow users to manually customize their language preferences.

There are two ways to state the language used in a search:

*   Set the language used in a query
*   Set the language used in a document field

### Setting the search query locale

You can define which language is used in a query in the [search parameters](https://meilisearch.dev.org.tw/docs/reference/api/search?utm_campaign=release-v1-10&utm_source=blog#query-locales).

**Example payload for searching with query `locales`:**

```json
{
   "q": "進撃の巨人",
   "locales": ["jpn"]
}
```

### Setting the document attributes’ locales

You can define which language is used in your dataset via the [index settings](https://meilisearch.dev.org.tw/docs/reference/api/settings?utm_campaign=release-v1-10&utm_source=blog#localized-attributes).

**Example payload for settings with `localizedAttributes`:**

```json
{
  "localizedAttributes": [
    {
      "locales": ["jpn"], 
      "attributePatterns": ["*_japanese"]
    }
  ]
}
```
<Callout>
  Meilisearch support all [ISO-639-2B](https://en.wikipedia.org/wiki/List_of_ISO_639-2_codes) locales.
</Callout>

Self-hosting: Ubuntu v20 is now required
----------------------------------------

If you’re self-hosting Meilisearch, take note: Meilisearch 1.10 now requires **Ubuntu version 20**. This update ensures compatibility with the latest features and maintains optimal performance and security. We advise reading the documentation on [how to upgrade your Ubuntu release](https://documentation.ubuntu.com/server/how-to/software/upgrade-your-release/).

<Callout>
  This change does not affect [Meilisearch Cloud](https://meilisearch.dev.org.tw/cloud?utm_campaign=release-v1-10&utm_source=blog) users.
</Callout>

Experimental: AI-powered search DX improvements
-----------------------------------------------

### New: custom headers to REST API-based embedders

Meilisearch 1.10 supports sending custom headers to your API-based embedding service. When using the `rest` source for your [embedder settings](https://meilisearch.dev.org.tw/docs/reference/api/settings?utm_campaign=release-v1-10&utm_source=blog#embedders-experimental), Meilisearch now accepts an optional `headers` object. These headers will be added to all requests made to the embedder API.

**Example payload for embedder settings:**

```json
{
  "source": "rest",
  "apiKey": "•••••••••••••••••",
  "headers": {
    "Custom-Header": "value"
  }
}
```

### New: quality of life improvements

Additionally, we added some minor changes to make working with Meilisearch AI-powered search API easier:

*   Embedder settings now accept a `url` parameter for OpenAI embedder to access OpenAI through a proxy
*   Embedder settings now accept a `dimensions` parameter for ollama embedder
*   Meilisearch now includes `_vectors` in the response for documents without vectors (only when `retrieveVectors` is enabled)

### ⚠️ Breaking: REST API-based embedders breaking changes

The API for [configuring an embedder based on a REST API](https://meilisearch.dev.org.tw/docs/learn/experimental/vector_search?utm_campaign=release-v1-10&utm_source=blog#generate-vector-embeddings) (e.g., OpenAI, Mistral, etc.) has quickly evolved and to avoid confusion, we have decided to reorganize the parameters in dedicated `request` and `response` fields.

In practice, this change:

*   removes `query` , `inputField`, `inputType`, `pathToEmbeddings` and `embeddingObject` fields
*   adds `request` and `response` fields

Take a look at the example below to learn how to migrate to the new syntax.

**Before** — With Meilisearch 1.9

```json
{
   "source": "rest",
   "url": "https://127.0.0.1:10006",
   "query": {
     "model": "minillm",
   },
   "inputField": ["prompt"],
   "inputType": "text",
   "embeddingObject": ["embedding"]
}
```

**After** — Starting with Meilisearch 1.10

```json
{
   "source": "rest",
   "url": "https://127.0.0.1:10006",
   "request": {
     "model": "minillm",
     "prompt": "{{text}}"
   },
   "response": {
     "embedding": "{{embedding}}"
   }
}
```

#### Upgrade procedure

This procedure is only necessary if you're using REST API-based embedder. To upgrade to Meilisearch 1.10, follow these steps:

1.  Remove embedders with source `"rest"`
2.  Update your [Meilisearch Cloud project](https://meilisearch.dev.org.tw/docs/learn/update_and_migration/updating?utm_campaign=release-v1-10&utm_source=blog#updating-meilisearch-cloud) or [self-hosted Meilisearch instance](https://meilisearch.dev.org.tw/docs/learn/update_and_migration/updating?utm_campaign=release-v1-10&utm_source=blog#updating-a-self-hosted-meilisearch-instance) as usual

Experimental: `CONTAINS` filter operator
----------------------------------------

Meilisearch 1.10 introduces the `CONTAINS` operator. This is still in the experimental stage due to some performance issues we’re working to resolve. This new [filter operator](https://meilisearch.dev.org.tw/docs/learn/filtering_and_sorting/filter_expression_reference?utm_campaign=release-v1-10&utm_source=blog#contains) allows to check if a substring is included in a larger string.

Given the document `{ id: 1, name: "Keffir" }`, you can use the filter as follows:

*   `name CONTAINS kef` — search will match the document
*   `name CONTAINS clifford` — search will not match the document

<Callout>
  Remember all filters used lowercase, normalized strings (all accents are removed).
</Callout><Callout>
  Share your feedback via this [Github discussion](https://github.com/orgs/meilisearch/discussions/763).
</Callout>

Experimental: update documents with a function
----------------------------------------------

Meilisearch 1.10 allows you to edit documents by executing [a Rhai function](https://rhai.rs/). This allows you to update only a part of your dataset using filters. Additionally, using a function allows you to implement dynamic logic based on your documents’ data.

The following payload updates the title of all documents with `id` above 3000:

```json
{
  "filter": "id > 3000",
  "function": "doc.title = `✨ ${doc.title.to_upper()} ✨`"
}
```

This also enables more complex use cases, like [implementing a decaying ranking strategy](https://felx.me/2021/08/29/improving-the-hacker-news-ranking-algorithm.html). Depending on your needs, you might implement a function such as the following that will be run regularly by a Cron job.

```json
{
  "context": { "now": 1715423249 },
  "function": "
    // `posted_at` and `now` are Unix Epoch timestamps in seconds
    // they must be converted to hours.
    let age_hours = (context.now - doc.posted_at) / 60 / 60;
    doc.ranking_score = doc.upvotes ** 0.8 / (age_hours + 2) ** 1.8;
  "
}
```
<Callout>
  Share your feedback via this [Github discussion](https://github.com/orgs/meilisearch/discussions/762).
</Callout>

* * *

And that’s a wrap for v1.10! These release notes only highlights the most significant updates. For an exhaustive listing, read the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.10.0) on Github.

Upgrade with ease using Meilisearch Cloud.

[Open Meilisearch Cloud](https://cloud.meilisearch.com/login?utm_campaign=release-v1-10&utm_source=blog)

* * *

Contributors shout-out
----------------------

This release wouldn’t be possible without [Meilisearch](https://github.com/meilisearch/meilisearch) and [Charabia](https://github.com/meilisearch/charabia) contributors [@Karribalu](https://github.com/Karribalu), [@hanbings](https://github.com/hanbings), [@junhochoi](https://github.com/hunhochoi), [@JWSong](https://github.com/JWSong), [@PeterDaveHello](https://github.com/PeterDaveHello), [@LukasKalbertodt](https://github.com/LukasKalbertodt), and [@phillitrOSU](https://github.com/phillitrOSU).

We also want to extend special thanks to our SDK maintainers working hard to make Meilisearch available across many [languages and frameworks](https://meilisearch.dev.org.tw/docs/learn/what_is_meilisearch/sdks). We’re excited for [@Sherlouk](https://github.com/Sherlouk) and [@Ja7ad](https://github.com/Ja7ad) to join our open-source maintainers. 💪

* * *

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=release-v1-10&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-v1-10&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-v1-10&utm_source=blog).

Meilisearch 1.10

This guide will walk you through setting up a Next.js storefront for your Shopify commerce. We will use the open-source [Enterprise Ecommerce](https://github.com/Blazity/enterprise-commerce) starter developed by [Blazity](https://blazity.com/) which comes with a CLI to get started in a snap.

Overview
--------

The diagram below gives an overview of the architecture of our application.

![Architecture of an application developed with Enterprise Ecommerce start](https://unable-actionable-car.media.strapiapp.com/blazity_commerce_architecture_0a30272f59.png)

The application stack is composed of 4 key components:

*   **Data sources** — Shopify admin, personalization tools, etc.
*   **Search engine** — Meilisearch
*   **Full-stack app** — Next.js application
*   **Third-party integrations** — Analytics, CRM, etc.

In this architecture, the Meilisearch database aggregates data from multiple sources to make it accessible by the Next.js application.

Our final application will look like this: [live preview](https://commerce.blazity.com/) ✨

Prerequisites
-------------

This guide requires:

*   [Node.js](https://node.dev.org.tw/en/download/package-manager) ≥ 20.x
*   [Yarn](https://yarn.dev.org.tw/getting-started/install) ≥ 4.x
*   [Meilisearch](https://meilisearch.dev.org.tw/cloud?utm_campaign=blazity&utm_source=blog&utm_medium=shopify-nextjs-guide) ≥ 1.8
*   A [Shopify](https://www.shopify.com/) store

Setup
-----

This setup phase provides you with the necessary credentials for your application to interact with Meilisearch and your Shopify store.

These credentials match an environment variable that will be needed during the installation phase. For clarity, this guide will indicate with a "✅" emoji each of these variables.

<Callout>
  Thanks to Blazity for the instructions. If you’re running into issues, please consult the [Blazity documentation](https://docs.commerce.blazity.com/).
</Callout>

### Creating a Shopify store

If you don’t have one already, go to [Shopify](https://www.shopify.com/) and create a new store. Shopify associates a unique ID with each store named the _store domain_.

To find your Shopify store domain, go to _Settings_ > _Domains_ . Your store domain looks like this: `your-domain.myshopify.com`.

✅ `Shopify store domain`

### Installing Shopify Headless

In the Shopify Marketplace, find the [Headless application](https://apps.shopify.com/headless) and install it. After installation, click on _Create storefront_.

Then, we will update the API access. In the _Manage API access_ section, click on _Manage_ next to _Storefront API_.

Edit the Storefront API permissions to add the following:

*   `unauthenticated_read_product_inventory`
*   `unauthenticated_read_customer_tags`

After saving the permissions, you can copy the **private access token**.

✅ `Shopify app Storefront API Token`

### Creating a Shopify App

Now, we will create a Shopify App for our store. Shopify Apps allow you to build custom integrations for your store. This app will allow us to get API keys to read and manage data in our Shopify store.

To create a Shopify app:

*   Navigate to your store’s Shopify admin dashboard
*   Open the store’s _Settings_ and navigate to the _Apps and sales channels_ tab
*   Click on _Develop apps_
*   If requested, follow the steps to allow custom app development for your store
*   Create an app and enter a name, e.g., “Next.js App”

After creating your app, navigate to the API credentials tab to find your Shopify app’s API key (not the API secret key!)

✅ `Shopify app API key`

### Configuring the Shopify Admin API key

Shopify apps work with a set of two keys:

*   An **admin API key** — to manage the Shopify admin, e.g., to edit your product catalog
*   A **storefront API key** — to read the product catalog, create carts, etc.

We can configure the access scopes for these API keys in your Shopify app’s _Configuration_ tab. In this section, we will enable the required scopes for our Admin API key.

To configure the scopes for the Admin API key, follow these steps:

*   In the _Admin API integration_ section, click on _Configure_
*   Enable the following scopes:
    *   `write_product_listings`
    *   `read_product_listings`
    *   `read_products`
    *   `write_products`
*   Under _Webhook subscriptions_, select the 2024-01 API version
*   Save :)

<Callout>
  The API version might have changed since this guide was published. We recommend checking out the [Blazity documentation](https://docs.commerce.blazity.com/providers#3-private-admin-app).
</Callout>

With our tokens API scopes defined, we can return to the _API credentials_ tab and install our app. Click _Install app_ to generate your API access tokens.

After installing your Shopify app, you will have access to your Admin API access token. For security reasons, this token will only be revealed to you _once_—**make sure to save it**.

✅ `Shopify app Admin API token`

### Creating your Meilisearch database

Meilisearch is an open-source search engine that provides full-text, vector, and hybrid search capabilities. Our Shopify products data will be synced with Meilisearch, enabling the front-end app to get fast, relevant search results for end users.

If you don’t have one, create a [Meilisearch Cloud](https://meilisearch.dev.org.tw/cloud?utm_campaign=blazity&utm_source=blog&utm_medium=shopify-nextjs-guide) account. If necessary, you can first learn how to [get started with Meilisearch Cloud](https://meilisearch.dev.org.tw/docs/learn/getting_started/cloud_quick_start?utm_campaign=blazity&utm_source=blog&utm_medium=shopify-nextjs-guide).

<Callout>
  Prefer self-hosting? Learn how to [install Meilisearch locally](https://meilisearch.dev.org.tw/docs/learn/getting_started/installation?utm_campaign=blazity&utm_source=blog&utm_medium=shopify-nextjs-guide).
</Callout>

First, create a new _Project_, e.g., Shopify store. This will create a new Meilisearch database and should take a few seconds to complete.

Then, you can navigate to your project’s Settings page to find your _Database URL_ (your host) and _Default Admin API Key_.

✅ `Meilisearch host URL`

✅ `Meilisearch API token`

<Callout>
  We recommend enabling [analytics & monitoring](https://meilisearch.dev.org.tw/docs/learn/analytics/analytics?utm_campaign=blazity&utm_source=blog&utm_medium=shopify-nextjs-guide) at this step, so you don’t need to change host later.
</Callout>

### Configuring your Meilisearch

After creating your database, we will create the indexes. You can choose any names for them, but you’ll have to configure your environment variables accordingly. Let’s create two indexes:

*   An index for our products, e.g., `products`
*   An index for our product categories, e.g., `categories`

✅ `Meilisearch Products Index Name`

✅ `Meilisearch Categories Index Name`

With our indexes created, we can now configure them. Meilisearch defaults will work well for most settings. For each index, we will only configure the filterable attributes and sortable attributes.

**Product index** — Filterable attributes

```json
[
	"collections",
	"collections.handle",
	"collections.id",
	"flatOptions",
	"handle",
	"minPrice",
	"tags",
	"variants.availableForSale",
	"vendor",
	"hierarchicalCategories",
	"hierarchicalCategories.lvl0",
	"hierarchicalCategories.lvl1",
	"hierarchicalCategories.lvl2",
	"avgRating"
]
```

**Product index** — Sortable attributes

```json
[
	"minPrice",
	"updatedAtTimestamp",
	"avgRating"
]
```

**Categories index** — Filterable attributes

```json
[
	"handle",
	"id"
]
```

You can leave all other attribute settings to their default value.

Installation
------------

With all credentials in hand, we’re ready to launch the installation wizard. Let’s create our project using Blazity’s `commerce` CLI.

```bash
# with NPM
npm create commerce

# with Yarn
yarn create commerce
```

The first prompt will ask you to select the services to enable. For this guide, we’ll only enable the defaults: Shopify and Meilisearch.

Then, the CLI will ask for a series of credentials. If you followed the Setup section, you should already have all the necessary credentials. After providing all the credentials, the CLI will scaffold your project in a new folder.

After creation, navigate to your project directory. Your `apps/web/.env.local` should look like this:

```bash
# apps/web/.env.local

SHOPIFY_STOREFRONT_ACCESS_TOKEN=shpat_a-very-long-token
SHOPIFY_ADMIN_ACCESS_TOKEN=shpat_another-very-long-token
SHOPIFY_STORE_DOMAIN=your-domain.myshopify.com
SHOPIFY_APP_API_SECRET_KEY=a-32-characters-long-api-key
SHOPIFY_HIERARCHICAL_NAV_HANDLE=hierarchical-categories
MEILISEARCH_PRODUCTS_INDEX=products
MEILISEARCH_CATEGORIES_INDEX=categories
MEILISEARCH_ADMIN_KEY=your-meilisearch-admin-api-key
MEILISEARCH_HOST=https://edge.meilisearch.com
LIVE_URL=https://your-domain.myshopify.com
FLAGS_SECRET=randomly-generated-secret
CRON_SECRET=another-randomly-generated-secret
```

Syncing Meilisearch and Shopify
-------------------------------

Blazity provides a CLI for synchronizing the data between your Shopify and Meilisearch. In this section, we will see how to achieve that using `commerce-cli`.

To sync your Shopify data with your Meilisearch, navigate to your project folder and run:

```bash
npx commerce-cli@latest sync
```

This command will pull all the necessary data from your Shopify store and sync it into your Meilisearch database.

<Callout>
  Additionally, the [`commerce-cli` package](https://docs.commerce.blazity.com/features/cli) can assist you in configuring webhooks to keep your Meilisearch in sync at all times.
</Callout>

Starting your app
-----------------

After initializing your application and syncing the data between Shopify and Meilisearch, you are ready to start your application.

To start your application in development mode, run:

```bash
# with NPM
npm run dev

# with Yarn
yarn dev
```

And that’s it! You can visit your application at `https://127.0.0.1:3000` 🎉

You can also consult a live example here: [https://commerce.blazity.com/](https://commerce.blazity.com/?ref=blog.meilisearch.com)

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

This guide doesn’t cover all available features in the Blazity template. For more advanced configurations, you can learn how to set up hierarchical categories, implement highly scalable redirects, and more on [Blazity documentation](https://docs.commerce.blazity.com/introduction).

Additionally, here are resources to help you integrate your new Shopify store with Meilisearch and Next.js:

*   [Meilisearch documentation](https://meilisearch.dev.org.tw/docs?utm_campaign=blazity&utm_source=blog&utm_medium=shopify-nextjs-guide)
*   [Next.js documentation](https://nextjs.dev.org.tw/docs)
*   [Integrate Meilisearch Cloud with Vercel](https://meilisearch.dev.org.tw/docs/guides/integrations/vercel?utm_campaign=blazity&utm_source=blog&utm_medium=shopify-nextjs-guide)

* * *

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

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord/?utm_campaign=blazity&utm_source=blog&utm_medium=shopify-nextjs-guide).

Build your Next.js Shopify storefront with Blazity

🦎 Releasing Meilisearch 1.9
----------------------------

[![](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_9_d1239d3d00.jpg)](/blog/meilisearch-1-9/?utm_campaign=newsletter&utm_source=blog&utm_medium=july-24-updates)

Meilisearch 1.9 brings **similar documents**, **ranking score threshold**, **grouping by attribute**, and **improved AI-powered search**.

[Discover updates](/blog/meilisearch-1-9/?utm_campaign=newsletter&utm_source=blog&utm_medium=july-24-updates)

⚖️ Algolia comparison
---------------------

![](https://unable-actionable-car.media.strapiapp.com/meilisearch_vs_algolia_cover_5c7691f971.jpg)

We published a **comparison of the key features** between Meilisearch and Algolia to help you choose search engine that fits your needs.

[Read comparison](/blog/meilisearch-vs-algolia/?utm_campaign=newsletter&utm_source=blog&utm_medium=july-24-updates)

📌 Community picks
------------------

### 📢 [RFC: Database synchronization tooling](https://github.com/orgs/meilisearch/discussions/765)

We're collecting feedback on tooling to synchronize Meilisearch with your database.

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

We want to make testing your search easier, and we need your feedback!

### 🎮 [A better way to build Meilisearch filters in TypeScript](https://levelup.gitconnected.com/a-better-way-to-build-meilisearch-filters-d88846c280d1)

Check out the meilisearch-helper package to build filters with a fluent API.

### ⚡️ [Lightning-fast Laravel search with Meilisearch](https://www.youtube.com/watch?v=FY_x9_eX1AQ)

Watch this video to learn how to build full-text search for Laravel with Meilisearch.

### 📽️ [Product live event](/blog/heres-what-you-missed-q2-2024/?utm_campaign=newsletter&utm_source=blog&utm_medium=july-updates-24#check-out-the-recording-of-the-event-for-more-details)

Watch the replay to learn more about our recent releases and what's coming next.

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

A community member built [RemoteJobs.org](https://remotejobs.org) and shared it with the Discord community. It's a remote jobs board focussing on quality offers. It is, of course,  powered by Meilisearch. 🎉

[![](https://unable-actionable-car.media.strapiapp.com/Screenshot_2024_08_01_at_18_55_39_59f37af3bf.png)](https://remotejobs.org)

_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:

*   [Growth Marketing Manager](https://meilisearch.dev.org.tw/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=july-updates-24)
*   [Senior Full-Stack Software Engineer](https://meilisearch.dev.org.tw/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=july-updates-24)
*   [Founding Sales & Business Developer](https://meilisearch.dev.org.tw/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=july-updates-24)

* * *

Get these monthly updates straight to your inbox by subscribing to our [newsletter](https://dub.sh/meili-newsletter?utm_campaign=newsletter&utm_source=blog&utm_medium=july-24-updates). 💌

For more things Meilisearch, join our developers community on [Discord](https://discord.meilisearch.com/?ref=blog.meilisearch.com). You can learn more about the product by checking out the [roadmap](https://roadmap.meilisearch.com/?ref=blog.meilisearch.com) and participating in [product discussions](https://github.com/meilisearch/product/discussions?ref=blog.meilisearch.com).

Until next time!

Meilisearch 最新消息和公司更新

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