_This is part 1 of a series of blog posts_ [_originally published on my personal blog_](https://blog.kerollmops.com/spotify-inspired-elevating-meilisearch-with-hybrid-search-and-rust)_._

Uniting keyword and semantic search
-----------------------------------

At Meilisearch [we are working hard on hybrid search](https://github.com/meilisearch/meilisearch/issues/3838), mixing the widespread keyword search algorithm with the not-so-common semantic search. The former is very good at exact matching, while the latter finds stuff you can _describe_ better.

I will explain why we even talk about [Meilisearch/arroy](https://github.com/meilisearch/arroy) and why it is important to us. The semantic search was something new to us. The principle is quite simple: documents are associated with vectors (lists of floats), and the closer they are to each other, the closer they are semantically. When a user queries the engine, you compute the embeddings (vectors) of the query and do an approximate nearest neighbor search to get the nth closest items to the user query. Fun fact: after all this time, Annoy [is still among the top competitors](https://ann-benchmarks.com/gist-960-euclidean_10_euclidean.html), so imagine when we will finally release Arroy.

Seems simple, right? Do you **really** think this series of blog posts will talk about a dead simple subject? Join us on a journey as [@irevoire](https://github.com/irevoire) and I ([@Kerollmops](https://github.com/Kerollmops)), with the assistance of [@dureuill](https://github.com/dureuill), work on porting this cutting-edge library to Rust.

Optimizing storage and search for high-dimension embeddings
-----------------------------------------------------------

The embeddings are something between 768 and 1536 dimensions. We have customers at Meilisearch who want to store over 100M of them. Embeddings that were not modified by any dimension reduction algorithm use 32-bit floats. This means storing this data will take between 286 GiB and 572 GiB, depending on the dimensions.

[Yes, it fits in RAM](https://yourdatafitsinram.net/), but at what cost? Storing on disk is much cheaper. Ho! And it is only the raw vectors. I assure you that doing an approximate neighbors search among all vectors in `O(n)` will be quite slow. So we decided to store them on disk. We have looked at many different solutions before choosing Spotify/Annoy. Initially, we used the HNSW from instant-distance Rust crate, another data structure, to do an ANNs (approximate nearest neighbors) search. However, it is not disk-based; everything lives in memory, which is unpractical.

Spotify's hyperplan trees for efficient ANNs
--------------------------------------------

Spotify worked on a cool C++ library to search in enormous vector sets. The algorithm generates multiple trees that refer to the vectors. The tree nodes are random hyperplanes that split subsets of vectors in half. The root node splits the whole set of vectors in half, and the left and right branches split the subsets of vectors recursively again until the subset is small enough. When we perform an ANNs search, we go through all the root trees and decide whether to go on the hyperplanes' left or right, depending on the haystack provided. The advantage is that every node and vector is directly stored in a memory-mapped file.

![Hyperplanes mapped to binary trees](https://unable-actionable-car.media.strapiapp.com/blog_arroy_1_76462ae24c.png)

The way Annoy and Arroy generate hyperplanes to split the vector space

At the end of the tree, we can see the descendants' nodes. Those define the final list of leaf vectors that fit on this side of the recursive split nodes above. It is a list of unsigned integers the user provides. Annoy represents them as slices of `u32`s, but we decided to go [with RoaringBitmaps](https://roaringbitmap.org/about/) to reduce their size. Annoy cannot compress them because they have a fun constraint: All nodes, user leaf vectors, split nodes, and descendants must be the same size to be accessible using offsets on disk.

The above image shows the way a split plane can be represented. A hyperplane splits a subset of nodes here in two dimensions, but imagine that with 768 to 1536 dimensions. Each hyperplane creates two subsets of points recursively split by another hyperplane. Once the number of points to split is small enough, we create a descendants node containing the item IDs corresponding to these points. Furthermore, the embeddings of the points are never duplicated in memory; we refer to them by their IDs.

Adapting Annoy to LMDB
----------------------

So, if it works so well, why must we port it to Rust? One, because I follow the _Rewrite it in Rust_ dogma 😛, two because this is a C++ library with a lot of horrifying hacks and undefined behaviors, and three because Meilisearch is based on LMDB, an atomic, transaction-based, memory-mapped key-value store. Moreover, [since 2015, they have wanted to use LMDB](https://github.com/spotify/annoy/issues/96), but they have yet to achieve it; it requires a lot of time to change the data structures accordingly.

LMDB uses a BTree to order the entries, and it takes more space for the intermediate data structures than annoy, which directly identifies vectors using the offset in the file. Another advantage of using this key-value store is managing incremental updates. It is easier to insert and remove vectors. Suppose you insert a vector identified by a high 32-bit integer in Annoy. In that case, it will allocate a lot of memory to store it as the dedicated offset, increasing the file size if necessary.

In Meilisearch and Arroy, [we use heed, a typed LMDB Rust wrapper](https://github.com/meilisearch/heed), to avoid undefined behavior, bugs, and stuff related to C/C++ libraries. We, therefore, use `&mut` and `&` extensively to be sure that we are not modifying the key-value store entries while keeping references inside it, and we make sure that the read-write transactions can't be referenced or sent between threads. But that will be for another story.

We first thought using this key-value store would make Arroy slower than Annoy. However, LMDB writes its pages in memory before writing them to disk, which seems much faster than directly writing into mutable memory-mapped areas. On the other hand, LMDB doesn't guarantee the memory alignment of the values that Annoy's file format permits; we'll talk about that later.

Optimizing vector handling with SIMD
------------------------------------

The most CPU-intensive task that Arroy was supposed to do was to try to split clouds of vectors in half. This requires computing a massive number of distances in hot loops. But we read the embeddings from a memory-mapped disk. What could go wrong?

```rust
fn align_floats(unaligned_bytes: &[u8]) -> Vec<f32> {
    let count = unaligned_bytes.len() / mem::size_of::<f32>();
    let mut floats = vec![f32::NAN; count];
    cast_slice_mut(&mut floats).copy_from_slice(unaligned_bytes);
    floats
}
```

We used Instrument on macOS to profile our program and discovered a lot of time was spent moving stuff around, i.e., `_platform_memmove`. The reason is that reading unaligned floats from disk is undefined behavior, so we were copying our floats into an aligned memory area as shown above. Cost: An allocation by reading plus a calling `memmove`.

![Profiling showing a big memmove usage](https://unable-actionable-car.media.strapiapp.com/blog_arroy_2_c34c821b3c.png)

Profiling shows a lot of memory copies to align bytes

While porting the distance function from C++ to Rust, we used the Qdrant SIMD-optimized distance functions without modifying them beforehand. Although, [being aware of the performance cost of reading unaligned memory](https://dwrensha.github.io/capnproto-rust/2020/01/11/unaligned-memory-access), we decided to execute the distance functions on unaligned floats, making sure not to represent it as a `&[f32]` as it would be undefined behavior. The functions take raw slices of bytes and deal with them using pointers and SIMD functions. We unlocked performances. The distances functions were slower, but it compensated the `memmove`s and allocations!

![Profiling showing no memmove usage](https://unable-actionable-car.media.strapiapp.com/blog_arroy_3_519276495b.png)

Directly dealing with unaligned memory removed the copies

Similarly to the `memmove` calls, we can see that the `_platform_memcmp` function is taking a lot of space here. The reason is that LMDB uses this standard function to compare key bytes and decide whether a key is lexicographically located above or below another key in the tree. It is used whenever we read or write in LMDB. [@irevoire](https://github.com/irevoire) drastically reduced the size of the keys and saw an important gain in performance. We further tried using the `MDB_INTEGERKEY`, which makes LMDB compare memory using the computer endianness, but it was complex to use and not showing significant performance gains.

Upcoming challenges
-------------------

While porting this cool algorithm to Rust and LMDB, we were missing one of the most important features: Multithreading the tree building. The main reason for this missing feature is LMDB, which does not support concurrent writes to disk. It is part of the beauty of this library; writing is deterministic. We already know LMDB very well, and we will explain how we leveraged the power of LMDB in part 2 of this series and how we beat the Spotify library.

In addition to equalizing the current Annoy features, we needed more for Meilisearch. We implemented [Microsoft's Filtered-DiskANN feature](https://harsha-simhadri.org/pubs/Filtered-DiskANN23.pdf) in Arroy. By providing the subset of item IDs we want to retrieve, we avoid searching the whole trees to fetch the approximate nearest neighbors. We will talk about this in a soon-to-be-released article.

In Meilisearch v1.6, we've optimized performance for updating only document parts, as with vote counts or view numbers. The described single-threaded version of Arroy constructs tree nodes anew for each vector adjustment. [@irevoire](https://github.com/irevoire) will explore Arroy's incremental indexing in his next article, a capability not offered by Annoy.

You can comment about this article on [Lobste.rs](https://lobste.rs/s/4pw9kf/spotify_inspired_elevating_meilisearch), [Hacker News](https://news.ycombinator.com/), [the Rust Subreddit](https://old.reddit.com/r/rust/comments/18dq493/spotifyinspired_elevating_meilisearch_with_hybrid), or [X (formerly Twitter)](https://twitter.com/Kerollmops/status/1733158652444369286).

[Part 2](/blog/refining-ann-performance-with-rust/ and [part 3](/blog/arroy-filtered-disk-ann/ of this series are now available, exploring further advancements in ANN algorithms with Rust.

* * *

[Meilisearch](https://meilisearch.org.cn/#blog.meilisearch.com) is an open-source search engine that not only provides state-of-the-art experiences for end users but also a simple and intuitive developer experience.

For more things Meilisearch, you can join the community on [Discord](https://discord.gg/meilisearch/#blog.meilisearch.com) or subscribe to the [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&ref=blog.meilisearch.com). You can learn more about the product by checking out the [roadmap](https://roadmap.meilisearch.com/#blog.meilisearch.com) and participating in [product discussions](https://github.com/orgs/meilisearch/discussions).

Spotify-Inspired: Elevating Meilisearch with Hybrid Search and Rust

⚡ Meilisearch 1.5
-----------------

[![Meilisearch 1.5 release notes](https://unable-actionable-car.media.strapiapp.com/124_1_50202b66d0.jpg)](/blog/meilisearch-1-5/#newsletter&utm_source=november-news-23&utm_medium=blog)

We just shipped Meilisearch 1.5, bringing up to **25% faster indexing**, **on-demand snapshots**, and **Puffin reports** exports. Read the release notes to get the full picture.

[Read release notes](/blog/meilisearch-1-5/#newsletter&utm_source=november-news-23&utm_medium=blog)

💼 Meet our customers
---------------------

[![Meilisearch customers page](https://unable-actionable-car.media.strapiapp.com/Screenshot_2023_11_28_at_18_25_33_6d4a7777c7.png)](https://meilisearch.org.cn/customers#newsletter&utm_source=november-news-23&utm_medium=blog)

We’ve built a cosy page to host all our customers. All our customer stories —like the [HuggingFace](/blog/hugging-face-democratizes-ai/#newsletter&utm_source=november-news-23&utm_medium=blog and [Symfony](/blog/symfonys-solution-for-documentation-search/#newsletter&utm_source=november-news-23&utm_medium=blog success stories— now live there as well.

[View customers](https://meilisearch.org.cn/customers#newsletter&utm_source=november-news-23&utm_medium=blog)

📰 In the news
--------------

### 🔍 [A complete guide to full-text search with Laravel](/blog/laravel-full-text-search/#newsletter&utm_source=november-news-23&utm_medium=blog)

Everything you need to know to build full-text search with Laravel 10.

### 📈 [Introducing Cloud search analytics](/blog/search-analytics-release/#newsletter&utm_source=november-news-23&utm_medium=blog)

Get immediate actionable insight to optimize your search.

### ✈️ [Migrating to Meilisearch Cloud](https://meilisearch.org.cn/docs/learn/update_and_migration/migrating_cloud#newsletter&utm_source=november-news-23&utm_medium=blog)

Migrating from self-hosted? Here’s how to handle the migration.

### 🎓 [Navigating search results: pagination vs infinite scroll vs load more](/blog/pagination-vs-infinite-scroll-vs-load-more/#newsletter&utm_source=november-news-23&utm_medium=blog)

Learn the pros & cons of the different ways to display search results.

### 🦜🔗 [Building semantic search with LangChain tutorial](/blog/langchain-semantic-search-tutorial/#newsletter&utm_source=november-news-23&utm_medium=blog)

Learn how to build semantic search using similarity search.

🥰 Made with Meilisearch
------------------------

[Minipouce.fr](/blog/minipouce-top-grade-customer-experience/#newsletter&utm_source=november-news-23&utm_medium=blog) is an online platform that enables future parents to create a baby registry list—a list of items to receive as gifts. They integrated Meilisearch to power **search in their marketplace**.

[![Minipouce.fr marketplace](https://unable-actionable-car.media.strapiapp.com/gif_minipouce_aef574247b.gif)](blog/minipouce-top-grade-customer-experience/#newsletter&utm_source=november-news-23&utm_medium=blog)

Minipouce.fr marketplace

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

🏡 Life at Meili
----------------

Company culture and benefits are a huge plus. But a decent and fair salary is the number one thing we can do to ensure we take care of our employees. Here’s our **approach to salaries at Meilisearch**.

[![Waving lucky cat](https://unable-actionable-car.media.strapiapp.com/billsbillsbills_9a52817e21.jpeg)](/blog/our-approach-to-salaries/#newsletter&utm_source=november-news-23&utm_medium=blog)

[Read blog post](/blog/our-approach-to-salaries/#newsletter&utm_source=november-news-23&utm_medium=blog)

🦸 We're hiring!
----------------

See our open positions:

*   [Head of Developer Marketing](https://jobs.lever.co/meili/6c9637b0-399a-4300-9f03-1e6f4c450efa)
*   [Senior Account Executive](https://jobs.lever.co/meili/92e2ebdc-3023-4fbe-8dd2-c3cfc9ecd7bf)
*   [People Ops](https://jobs.lever.co/meili/e85fbbd1-0da5-48e1-b65c-35df12f97d76)

* * *

Get these monthly updates straight to your inbox by subscribing to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&ref=blog.meilisearch.com).

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

Until next time.

Meilisearch November Updates

In this guide, we will see how to use the search functionality in Laravel 10. We'll start with by introducing the benefits of full-text search. Then, we'll walk you through setting up full-text search in your Laravel application.

Why use full-text search?
-------------------------

In traditional SQL or NoSQL databases, queries find results exactly matching given criteria. Conversely, full-text search queries can match some _or_ all of a text query with the database’s content. So essentially, full-text search can provide results even in case of partial matches.

When building user-facing search interfaces, full-text search is empowering for users. Tolerance to typos, prefix search, and synonyms help them get results more quickly. It improves discoverability when users do not know _what_ they're looking for.

<Callout>
  See all [full-text search features](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#features#laravel&utm_source=blog&utm_medium=full-text-search-guide) of Meilisearch search engine.
</Callout>

How to use search functionality in Laravel 10?
----------------------------------------------

### Installing Laravel Scout

Laravel comes with out-of-the-box full-text search capabilities via Laravel Scout.

To enable it, navigate to your Laravel application directory and install Scout via the [Composer](https://getcomposer.org.cn/) package manager:

```sh
composer require laravel/scout
```

After installing Scout, you should publish the Scout configuration file. You can do this by running the following artisan command:

```sh
php artisan vendor:publish --provider="Laravel\Scout\ScoutServiceProvider"
```

This command should create a new configuration file in your application directory: `config/scout.php`.

### Configuring the Laravel Scout driver

Let’s configure Laravel Scout to use the Meilisearch driver. Meilisearch is an open-source search engine built in Rust. This will allow to get the best full-text search performance. Indeed, the database driver comes with [limitations inherent to SQL databases](/blog/postgres-full-text-search-limitations/#laravel&utm_source=blog&utm_medium=full-text-search-guide

First, install the dependencies required to use Scout with Meilisearch via Composer:

```sh
composer require meilisearch/meilisearch-php http-interop/http-factory-guzzle
```

Then, update the environment variables in your `.env` file:

```sh
SCOUT_DRIVER=meilisearch
# Use the host below if you're running Meilisearch via Laravel Sail
MEILISEARCH_HOST=http://meilisearch:7700
MEILISEARCH_KEY=masterKey
```

Laravel’s official Docker development environment, [Laravel Sail](https://laravel.net.cn/docs/master/sail), comes with a Meilisearch service out-of-the-box. Please note that when running Meilisearch via Sail, Meilisearch’s host is `http://meilisearch:7700`.

<Callout>
  This comes from Docker [Bridge network driver](https://docs.dockerd.com.cn/network/drivers/bridge/#differences-between-user-defined-bridges-and-the-default-bridge) behavior.
</Callout>

For production use cases, we recommend using a managed Meilisearch via [Meilisearch Cloud](https://meilisearch.org.cn/cloud#laravel&utm_source=blog&utm_medium=full-text-search-guide). On Meilisearch Cloud, you can find your host URL in your project settings.

<Callout>
  Learn [how to get started with Meilisearch Cloud](https://meilisearch.org.cn/docs/learn/getting_started/cloud_quick_start#laravel&utm_source=blog&utm_medium=full-text-search-guide).
</Callout>

### Making Eloquent models searchable

With Scout installed and configured, just add the `Laravel\Scout\Searchable` trait to your Eloquent models to make them searchable. This trait will use Laravel’s model observers to keep the data in your model in sync with Meilisearch.

Here’s an example model:

```php
<?php

namespace App\Models;

use Illuminate\Database\Eloquent\Model;
use Laravel\Scout\Searchable;

class Contact extends Model
{
	use Searchable;
}
```

You can use the `toSearchableArray` method to configure which fields to store in Meilisearch. This notably enables storing a model and its relationships’ data in the same document.

The example below shows how to store a model's relationships data in Meilisearch:

```php
<?php

namespace App\Models;

use App\Models\Company;
use Laravel\Scout\Searchable;
use Illuminate\Database\Eloquent\Model;
use Illuminate\Database\Eloquent\Relations\BelongsTo;

class Contact extends Model
{
    use Searchable;

    public function company(): BelongsTo
    {
        return $this->belongsTo(Company::class);
    }

    public function toSearchableArray(): array
    {
	    // All model attributes are made searchable
        $array = $this->toArray();

		// Then we add some additional fields
        $array['organization_id'] = $this->company->organization->id;
        $array['company_name'] = $this->company->name;
        $array['company_url'] = $this->company->url;

        return $array;
    }
}

```

### Configuring filterable and sortable attributes

Meilisearch allows you to perform advanced [filtering](https://meilisearch.org.cn/docs/learn/fine_tuning_results/filtering#laravel&utm_source=blog&utm_medium=full-text-search-guide) and [sorting](https://meilisearch.org.cn/docs/learn/fine_tuning_results/sorting#laravel&utm_source=blog&utm_medium=full-text-search-guide) on your search results. Choose which attributes are filterable and sortable via your Meilisearch index settings.

Configure your Meilisearch index settings via the `config/scout.php` file:

```php
<?php

use App\Models\Contact;

return [
	// additional configuration...
	
    'meilisearch' => [
        'host' => env('MEILISEARCH_HOST', 'http://localhost:7700'),
        'key' => env('MEILISEARCH_KEY'),
        'index-settings' => [
            Contact::class => [
                'filterableAttributes' => ['organization_id'],
                'sortableAttributes' => ['name', 'company_name']
            ],
        ],
    ],
```

The example above updates Meilisearch index settings for the Contact model:

*   it makes the `organization_id` field filterable
*   it makes the `name` and `company_name` fields sortable

Update your Meilisearch index settings by running the following Artisan command:

```sh
php artisan scout:sync-index-settings
```

Laravel full-text search example
--------------------------------

We built a demo application to give you a feel of what full-text search looks like in a Laravel application. This demo showcases an app-wide search in a CRM (Customer Relationship Management) application.

[![CRM demo application build with Laravel](https://unable-actionable-car.media.strapiapp.com/saas_demo_screenshot_e72232c181.png)](https://saas.meilisearch.com/#laravel&utm_source=blog&utm_medium=full-text-search-guide)

[Laravel SaaS search demo](https://saas.meilisearch.com/#laravel&utm_source=blog&utm_medium=full-text-search-guide)

This demo application uses the following search features:

*   [Multi-search](https://meilisearch.org.cn/docs/reference/api/multi_search#laravel&utm_source=blog&utm_medium=full-text-search-guide) (search across multiple indexes)
*   [Multi-tenancy](https://meilisearch.org.cn/docs/learn/security/tenant_tokens#laravel&utm_source=blog&utm_medium=full-text-search-guide)
*   [Filtering](https://meilisearch.org.cn/docs/learn/fine_tuning_results/filtering#laravel&utm_source=blog&utm_medium=full-text-search-guide)
*   [Sorting](https://meilisearch.org.cn/docs/learn/fine_tuning_results/sorting#laravel&utm_source=blog&utm_medium=full-text-search-guide)

The code is open-sourced on Github. 🎉

👉 Check out the repository: [https://github.com/meilisearch/saas-demo](https://github.com/meilisearch/saas-demo)

* * *

We hope this guide helped to understand the importance of full-text search and how to implement it with Laravel. For more information, read the [Laravel Scout](https://laravel.net.cn/docs/10.x/scout) and [Meilisearch](https://meilisearch.org.cn/docs#laravel&utm_source=blog&utm_medium=full-text-search-guide) docs.

[Meilisearch](https://meilisearch.org.cn/#blog.meilisearch.com) is an open-source search engine with intuitive developer experience to build user-facing search. You can [self-host](https://meilisearch.org.cn/docs/learn/getting_started/quick_start#laravel&utm_source=blog&utm_medium=full-text-search-guide) it or get a premium experience with [Meilisearch Cloud](https://meilisearch.org.cn/cloud#laravel&utm_source=blog&utm_medium=full-text-search-guide).

For more things Meilisearch, you can join the community on [Discord](https://discord.gg/meilisearch/#blog.meilisearch.com) or subscribe to the [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&ref=blog.meilisearch.com). You can learn more about the product by checking out the [roadmap](https://roadmap.meilisearch.com/#blog.meilisearch.com) and participating in [product discussions](https://github.com/meilisearch/product/discussions#blog.meilisearch.com).

A complete guide to full-text search with Laravel

We’re announcing the release of Meilisearch 1.5. Let’s dive into some of the most important changes.

<Callout>
  Meilisearch 1.5 is also available on [Meilisearch Cloud](https://cloud.meilisearch.com/login#release-v1-5&utm_source=blog)—upgrade without any downtime now.
</Callout>

Indexing speed improvements
---------------------------

We’re excited to release the first of a series of improvements to Meilisearch’s indexing speed. 🎉

We’ve noticed **up to 25% faster indexing** depending on the use case. The performance gain is more significant on datasets containing a lot of words like wikis or ebooks.

We’ve achieved this by tweaking the settings for multiple-words queries. If you see any impact on your search relevancy, [let us know](https://github.com/orgs/meilisearch/discussions)!

<Callout>
  Take a look at the [indexing speed benchmarks](https://x.com/StriftCodes/status/1726539789401546880).
</Callout>

There’s more regarding indexing speed in version 1.6—but I don’t want to spoil anything 🤫

New feature: on-demand snapshots
--------------------------------

A [snapshot](https://meilisearch.org.cn/docs/learn/advanced/snapshots#release-v1-5&utm_source=blog) is a copy of your database. Snapshot files are useful to backup your database content and settings. Meilisearch 1.5 introduces the ability to create snapshots on demand.

To create a snapshot, use the [relevant SDK](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/sdks#release-v1-5&utm_source=blog) method or the [REST API](https://meilisearch.org.cn/docs/reference/api/snapshots#release-v1-5&utm_source=blog). In JavaScript, create a snapshot like this:

```javascript
const client = new MeiliSearch({ /* credentials */ })
const task = await client.createSnapshot()
```
<Callout>
  You can also [schedule automatic snapshots](https://meilisearch.org.cn/docs/learn/advanced/snapshots#release-v1-5&utm_source=blog#creating-snapshots) on regular intervals.
</Callout>

Experimental feature: Puffin reports
------------------------------------

Puffin reports allow to get information about what’s happening in the search engine. Enabling this feature will make Meilisearch automatically export .puffin file reports. If you’re encountering performance issues, make sure to share your reports with the Meilisearch team.

This experimental feature can be enabled via the [REST API](https://meilisearch.org.cn/docs/reference/api/experimental_features#release-v1-5&utm_source=blog#configure-experimental-features):

```sh
curl \
  -X PATCH 'http://localhost:7700/experimental-features/' \
  -H 'Content-Type: application/json'  \
--data-binary '{
    "exportPuffinReports": true
  }'
```
<Callout>
  Give feedback regarding Puffin reports in this [Github discussion](https://github.com/orgs/meilisearch/discussions/693).
</Callout>

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

We’re grateful for all the community members who participated to this release. We would like to thank [@braddotcoffee](https://github.com/braddotcoffee), [@harshau007](https://github.com/harshau007), and [@vivek-26](https://github.com/vivek-26). Also, thanks to [@choznerol](https://github.com/choznerol) and [@xshadowlegendx](https://github.com/xshadowlegendx) for contributing to [Charabia](https://github.com/meilisearch/charabia), Meilisearch’s tokenizer. Finally, we extend special thanks to our SDKs maintainers. 🫶

<Callout>
  Learn how you can [contribute to Meilisearch](https://github.com/meilisearch/meilisearch/blob/main/CONTRIBUTING.md).
</Callout>

* * *

And that’s a wrap for v1.5! This release post highlights the most significant updates. For an exhaustive listing, read the changelog on Github.

Stay in the loop of everything Meilisearch by subscribing to [the newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/) and come participate in our [Product Discussions](https://github.com/meilisearch/product/discussions).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/#release-v1-5&utm_source=blog). I’ll see you there 👋

Meilisearch v1.5

You asked for it, and it's finally here! Search analytics has officially landed in the Meilisearch Cloud! We've received numerous [requests](https://roadmap.meilisearch.com/tabs/1-under-consideration) for a way to track key analytics in the Meilisearch Cloud, and we've been hard at work developing a solution that not only empowers you to make data-driven decisions, but also enhances the overall user experience.

So, what's all the buzz about? 🤔
---------------------------------

The new dashboard incorporates a set of metrics providing insights into your users' search behaviors. Its purpose is to give you a quick and comprehensible overview of user search queries and highlight what they might be missing. The implications are vast:

*   Improve recommendations and popular search sections, bolstering e-commerce through cross-selling.
*   Create intelligence reports for your customers.
*   Fine-tune settings and ranking rules to optimize search experiences and inform business decisions.

![](https://unable-actionable-car.media.strapiapp.com/search_analytics_gif_093e60c664.jpg)

What metrics can you expect?
----------------------------

The first iteration of cloud analytics covers the following metrics:

*   Daily searches chart
*   Most searched queries
*   Searches without results
*   Daily search latency
*   Total searches
*   Total users
*   No result rate
*   Click through rate\*
*   Average click position
*   Daily searches per index
*   Conversion\*

\*_The CTR (click-through rate) and the conversion rate_ [_must be configured_](https://meilisearch.org.cn/docs/learn/cloud/analytics#analytics-v1&utm_source=blog) _to fit a specific use case or need._

The default view combines the data from the indexes per project, but it’s also possible to filter out the insights for a specific project of your choice. The dashboard also allows you to select a custom range using a date picker:

![](https://unable-actionable-car.media.strapiapp.com/sa_index_data_picker_gif_56b1687bfd.jpg)

For a detailed walkthrough, check out the [live demo recording of search analytics](https://www.youtube.com/watch?v=yCQWha562M0) on our YouTube channel.

The benefits of search analytics insights
-----------------------------------------

There are numerous advantages to gaining deeper insights into how your users interact with search. With our brand-new dashboard, you can:

*   **Gain rapid, actionable insights into your search performance** at a glance.
*   **Enhance your users' experience** by continuously optimizing result relevancy.
*   **Make more informed business decisions** by understanding your users and their needs.
*   For eCommerce and marketplaces: **develop cross-selling and upselling strategies to boost conversions.**

This marks just the beginning of our optimization and insights journey. Stay tuned for v2, where we will introduce advanced analytics capabilities and subviews.

### Availability and plans

The new Cloud search analytics are available for all Cloud users across all pricing tiers. Each tier has access to the same analytics and chart functionalities of the search analytics dashboard, with the distinction occurring in the _length of data retention_ provided to each tier: 7 days for the Build plan, 30 days for the Grow plan, and 90 days for both Pro and Enterprise plans.

[Try it in Meilisearch Cloud](https://cloud.meilisearch.com/#analytics-v1&utm_source=blog)

Learn how to [get started with Meilisearch Cloud](https://meilisearch.org.cn/docs/learn/getting_started/cloud_quick_start#analytics-v1&utm_source=blog) and read the [Analytics documentation](https://meilisearch.org.cn/docs/learn/cloud/analytics#analytics-v1&utm_source=blog).

* * *

Stay in the loop by subscribing to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&utm_campaign=teams-release&utm_source=blog). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#teams-release&utm_source=blog) and participate in our [Product Discussions](https://github.com/meilisearch/product/discussions/#teams-release&utm_source=blog).

For anything else, join our developer community on [Discord](https://discord.meilisearch.com/#teams-release&utm_source=blog).

Search analytics have arrived!

[Meilisearch Cloud](https://meilisearch.org.cn/cloud#teams-release&utm_source=blog) introduces the all-new Teams feature. Now you can host and manage your projects in collaboration with your teammates.

Keep reading to learn how to use our Teams feature.

**Creating a team**
-------------------

From your main dashboard, click on the dropdown menu next to the Meilisearch logo on the top navbar, select _Create a team_. By clicking it and assigning a name to your team, your collaborative space is instantly set up.

![Create a team](https://unable-actionable-car.media.strapiapp.com/teams_1_cb16300cee.png)

**Inviting team members**
-------------------------

To invite your colleagues, navigate to the team settings, click on _Invite new member_, and enter their email address.

![Invite new member](https://unable-actionable-car.media.strapiapp.com/team_2_03149ccabf.png)

When you invite someone to a team, they receive an invitation link via email, and upon acceptance, they can start collaborating immediately. If they already have an account, a notification pops up on their dashboard too.

Collaborating
-------------

Every team member can create their own projects, ensuring everyone has a say in the collaboration.

Managing a team
---------------

For team owners have extensive control. They can modify project plans and update billing information.

From the _Team settings_ section, team owners can rename their team, manage user invitations and removals, and even transfer ownership.

![Team settings](https://unable-actionable-car.media.strapiapp.com/teams_4_468f5be5d0.png)

We hope this new release will improve collaboration with your team! Now start sending those invites 😜

[Open Meilisearch Cloud](https://cloud.meilisearch.com/#teams-release&utm_source=blog)

Learn how to [get started with Meilisearch Cloud](https://meilisearch.org.cn/docs/learn/getting_started/cloud_quick_start#teams-release&utm_source=blog).

* * *

Stay in the loop by subscribing to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&utm_campaign=teams-release&utm_source=blog). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#teams-release&utm_source=blog) and come participate in our [Product Discussions](https://github.com/meilisearch/product/discussions/#teams-release&utm_source=blog).

For anything else, join our developer community on [Discord](https://discord.meilisearch.com/#teams-release&utm_source=blog).

Introducing Teams — Meilisearch Cloud

🤩 Meilisearch v1.4 is live
---------------------------

0:00

/0:59

1×

Meilisearch v1.4 introduces the ability to configure **custom text separators** and **user-defined dictionaries**. Check out these shiny new features in action in our [video](https://www.youtube.com/watch?si=a8RiA4ZidNkC0Bgd&v=730tYmlFG9I&feature=youtu.be).  For more details, you can also read the [release blog post](/blog/v1-4-release/#newsletter&utm_source=october-news-23

[Read release blog](/blog/v1-4-release/#newsletter&utm_source=october-news-23)

🚀 Symfony success story
------------------------

![Symfony's search solution for 12M words of documentation with Meilisearch](https://unable-actionable-car.media.strapiapp.com/symfony_4179adf8bf.png)

With Meilisearch, Symfony enhanced the search functionality across its **30 versions and 12M words of documentation**, significantly **boosting the speed and quality** of search results.

[Read sucess story](/blog/symfonys-solution-for-documentation-search/)

🗓 Save the date: upcoming live demo
------------------------------------

![Getting started with Meilisearch Cloud. Featuring live demo, product release updates, and Q&A. Thursday, Novemeber 2nd. 4PM CET, 3PM GMT, 11AM EDT](https://unable-actionable-car.media.strapiapp.com/unnamed_0a521c383b.png)

Join us on **November 2nd** to see a **live demo** of **Meilisearch Cloud** and its key capabilities. Get a first sneak peek of our newest **Search Analytics product release**. Bring your questions for the **live Q&A session** with our experts: **Thomas, Meilisearch’s cofounder**; and **María from the Product team**.

👉 Secure your participation [here](https://us06web.zoom.us/meeting/register/tZMlc-mqrjIsH912-HTRe-AaT-pp41bDe81a#/registration).  
  
We look forward to seeing you there!

[Register now](https://us06web.zoom.us/meeting/register/tZMlc-mqrjIsH912-HTRe-AaT-pp41bDe81a#/registration)

* * *

📰 In the news
--------------

### 🔁 [Syncing Meilisearch with your PostgreSQL database](https://meilisearch.org.cn/docs/learn/cookbooks/meilisync_postgresql#newsletter&utm_source=october-news-23)

Learn how to keep Meilisearch in sync with your PostgreSQL database.

### 🔻 [Guide to hierarchical faceted search](/blog/nested-hierarchical-facets-guide/#newsletter&utm_source=october-news-23)

Structure data and use Meilisearch to navigate through nested hierarchical facets from general to specific categories.

### 🌥 [Getting started with Meilisearch Cloud](https://meilisearch.org.cn/docs/learn/getting_started/cloud_quick_start#newsletter&utm_source=october-news-23)

Our latest guide helps you get the best of Meilisearch without the hassle of self-hosting.

### 💾 [Squeezing millions of documents in 128 TB of virtual memory](/blog/dynamic-virtual-address-management/#newsletter&utm_source=october-news-23)

How dynamic management of virtual memory enabled us to remove limitations in Meilisearch indexing policy.

### 🤓 [Are you indexing the smart way?](/blog/best-practices-for-faster-indexing/#newsletter&utm_source=october-news-23)

Review the best practices to index your data efficiently and speed up the indexing process

* * *

🥰 Made with Meilisearch
------------------------

[Cardmapr](https://cardmapr.nl/) is back on the spotlight! Originally featured in our [newsletter #9](https://mailchi.mp/meilisearch/newsletter-5970105) for helping users discover businesses and their accepted credit cards, we're bringing it to the limelight again.

Why? They've  integrated a **WhatsApp service** into their platform. Now, users can send their location through WhatsApp, and thanks to **Meilisearch's [**geosearch**](https://meilisearch.org.cn/docs/learn/fine_tuning_results/geosearch)** capability, instantly receive nearby hotspots.

![nimated GIF showing a mobile phone screen displaying a messaging app. A message is received from 'Location' with the headline 'Locaties in Zwolle.' A brief summary mentions 157 locations found near the user's location in Zwolle, including 23 restaurants, 6 hotels, 20 fashion stores, and 13 supermarkets. There's also a link provided to view all these locations. ](https://unable-actionable-car.media.strapiapp.com/cardmapr_8a2b570a4e.gif)

<Callout>
  __We’re always looking to promote new projects from the community. Post yours in the__ [__#built-with-meilisearch__](https://discord.meilisearch.com/#blog.meilisearch.com) __Discord channel or share it on Twitter/X with the #MadeWithMeilisearch hashtag!__)
 
</Callout>

* * *

🏡 Life at Meili
----------------

Discover the rationale, implementation, and outcomes of our shift to a four-day workweek, reflecting our commitment to work-life balance while maintaining operational excellence.

![A pair of hands holding up a large red alarm clock with a white frame, set against a bright turquoise background. The clock displays 4:22 time. The person's nails are painted in a vibrant red to match the clock's face.](https://unable-actionable-car.media.strapiapp.com/malvestida_magazine_Ffb_VFLA_Vscw_unsplash_f041fe4532.jpeg)

[Read blog post](/blog/how-we-implemented-the-4-day-work-week/#newsletter&utm_source=october-news-23)

* * *

🦸 We are hiring!
-----------------

See our open positions:

*   [People Ops](https://jobs.lever.co/meili/e85fbbd1-0da5-48e1-b65c-35df12f97d76)
*   [Senior Account Executive](https://jobs.lever.co/meili/92e2ebdc-3023-4fbe-8dd2-c3cfc9ecd7bf)

* * *

Get these monthly updates straight to your inbox by subscribing to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&ref=blog.meilisearch.com).

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

Until next time.

Meilisearch October updates

This guide breaks down the concept of nested hierarchical facets and offers practical tips for implementation with [Meilisearch](https://meilisearch.org.cn/).

* * *

Table of contents
-----------------

[What is faceted search?](#what-is-faceted-search?)  
[Nested hierarchical facets](#nested-hierarchical-facets)  
[Data structuring for hierarchical faceted search](#data-structuring-for-hierarchical-faceted-search)  
[Creating a hierarchical faceted search experience with Meilisearch](#creating-a-hierarchical-faceted-search-experience-with-meilisearch)  
[Using InstantSearch and instant-meilisearch](#using-instantsearch-and-instant-meilisearch)

* * *

What is faceted search?
-----------------------

Faceted search lets users refine search results via broad categories or facets. It's an intuitive way to sift through content, enabling users to find results matching their exact requirements.

Take ecommerce websites as a case in point. Users, when searching for products, encounter both a result list and a range of facets to help refinement. These facets can typically be found on the sidebar.

Example from our [ecommerce demo](https://ecommerce.meilisearch.com/):

![](https://lh6.googleusercontent.com/vYLVdlN0R66skuGADtqg1mO_yYVuNs-kaNUKE0FRAS55tGHj5_08OEZ4-0rLMoyx9NpBm5z-AIV6ySCJThl1jjoh5GtDFFzAH1B-Lzk38N9ksnGMFD7zzMCn0zL2zb-3fiYFQPBUlAQwi9S5BKXC6n0)

Nested hierarchical facets
--------------------------

Nested facets provide a multi-level structure that helps users navigate through data by applying successive filters, often displayed in a hierarchical manner.

**Nested hierarchical facets** are particularly useful for navigating and searching large datasets or collections. Users can navigate up and down through various levels, thereby improving discoverability.

Picture an online book shopping experience. Users can:

1.  Begin with the overarching "Books" category
2.  Dive deeper by selecting a "Genre"
3.  Focus on a sub-genre
4.  And further refine by attributes like "Author", "Publication Date", or "Rating"

The foundation for such a search lies in the structuring of data.

### Data structuring for hierarchical faceted search

When implementing a hierarchical faceted search, data documents need a hierarchical categorization too—broad categories leading to finer ones.

Take a product dataset as an example:

```JSON
{
    "id": 1,
    "name": "Seagate BarraCuda 1 To (ST1000DM014)",
    "hierarchicalCategories": {
        "lvl0": "Electronics",
        "lvl1": "Electronics > Computer and Tablets",
        "lvl2": "Electronics > Computer and Tablets > Computer Parts",
        "lvl3": "Electronics > Computer and Tablets > Computer Parts > Storage"
    } 
}

```

Each product is linked to hierarchical categories, flowing from broad to specific, to establish a clear pathway through the data. This organized categorization is crucial in implementing faceted search, steering users from general categories like "Electronics" to more precise ones, such as "Storage" within "Computer Parts"

### Creating a hierarchical faceted search experience with Meilisearch

In Meilisearch, facets are a specific use-case of [filters](https://meilisearch.org.cn/docs/learn/fine_tuning_results/filtering). Whether something is a filter or a facet depends above all on UX and UI design. To be able to refine results based on facets, you need to add the attributes you want to use as facets to the [filterableAttributes list](https://meilisearch.org.cn/docs/reference/api/settings#filterable-attributes). From our product example, you should set  `hierarchicalCategories` as filterable.

Hierarchical facets typically use the [`AND` logical operator](https://meilisearch.org.cn/docs/learn/fine_tuning_results/filtering#and), as each level of the hierarchy acts conjunctively with the others to narrow down the search progressively.  

Example:

*   Category: Electronics
    *   Subcategory: Computers
        *   Brand: Apple

The results would show items that are in the Electronics category AND the Computers subcategory AND of the Apple brand.

When creating a faceted search interface, it is helpful to display the distribution of results across the different facets. Showing users the number of results in different categories as they refine their search can enhance their search experience.

Meilisearch supports this functionality through the [`facets` search parameter](https://meilisearch.org.cn/docs/reference/api/search#facets). When you add the `facets` parameter to your search query, Meilisearch returns a [facetDistribution](https://meilisearch.org.cn/docs/reference/api/search#blog.meilisearch.com#facetdistribution) object. This object provides the number of matching documents distributed among the values of the given facet.

```JSON
  "facetDistribution":{
    "categories":{
      "Electronics":20,
      "Electronics > Computer and Tablets":7,
      …
      "Electronics > Computer and Tablets > Computer Parts > Storage":3
    }
```

Leveraging the facets search parameter, you can present a neatly organized and intuitive search interface, allowing users to filter through hierarchical categories effectively. Meilisearch hands you the keys to build your own faceted search UI, yet for a quick and easy setup, employing InstantSearch with instant-meilisearch is a highly efficient alternative.

### Using InstantSearch and instant-meilisearch

[InstantSearch](https://github.com/algolia/instantsearch#blog.meilisearch.com) is an open-source front-end library to build search UIs. [Instant-meilisearch](https://github.com/meilisearch/meilisearch-js-plugins/tree/main/packages/instant-meilisearch#blog.meilisearch.com) is the go-to search client for integrating InstantSearch with Meilisearch.

In this guide, we’ll be using React code samples, you can find more examples in InstantSearch’s documentation

To use InstantSearch with instant-meilisearch, you need to:

#### 1\. Import the required modules

```JavaScript
import React from 'react';
import { InstantSearch, SearchBox, InfiniteHits, HierarchicalMenu } from 'react-instantsearch';
import { instantMeiliSearch } from '@meilisearch/instant-meilisearch';
```

####   
2\. Establish a Meilisearch client with your Meilisearch host and search API key

```JavaScript
const searchClient = instantMeiliSearch( 'https://ms-7053a8dd7c09-72.lon.meilisearch.io', 'meilisearchApiKey' )
```

#### 3\. Set up instantsearch with your Meilisearch index name and the search client  

```Javascript
function App() {
  return (
    <InstantSearch indexName="products" searchClient={searchClient}>
      {/* Widgets */}
    </InstantSearch>
  );
}

```

To have a better understanding of how InstantSearch and instant-meilisearch work together, you can refer to our [dedicated blog post](/blog/instant-meilisearch/

In this guide we’ll use InstantSearch’s `HierarchicalMenu` widget to display categories in a hierarchical fashion.

This widget has a required prop called `attributes`. It takes an array of string values. Each string identifies a specific attribute from your data structure, intended to be used in generating the hierarchical menu.

```JavaScript
<HierarchicalMenu
    attributes={[
        "hierarchicalCategories.lvl0",
        "hierarchicalCategories.lvl1",
        "hierarchicalCategories.lvl2",
        "hierarchicalCategories.lvl3"
    ]}
/>
```

With this widget you can create a menu akin to the example below:

![Screenshot of a hierarchical faceted search interface under 'Categories'. 'Electronics' is the main category with 2874 items, under which 'Image & Sound' is a subcategory with 1047 items. Further nested, 'Television' is highlighted with 689 items, which breaks down into an additional level showing 'TV' with 689 items.](https://lh5.googleusercontent.com/sv4j33IY1r0fpIqWnFRZpT_ntj1ccjqPKnzQuI40BvzH2Ko26pTfsYDCbal8llJsoc2y3RuQACarskaoOJrtMJOrV0Tr6bf8yLwVFeb-s_tEudtr7UIdXpufpoR7SWMGjRHhc40fpMJ-Teb2-Gwe5dE)

* * *

This guide equips you with the fundamental understanding and steps needed to implement a hierarchical faceted search using Meilisearch and InstantSearch, enhancing user navigation through layered, intuitive categorization.

For more things Meilisearch, you can subscribe to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&ref=blog.meilisearch.com). You can learn more about our product by checking out the [roadmap](https://roadmap.meilisearch.com/#blog.meilisearch.com) and participating in our [product discussions](https://github.com/meilisearch/product/discussions#blog.meilisearch.com).

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

Guide to hierarchical faceted search

Let's take a look at some of the most significant changes in Meilisearch's latest update. We’ll go over the main changes in this article, but you can also view the [full changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.4.0) on GitHub.

<Callout>
  v1.4 is now available on [Meilisearch Cloud](https://cloud.meilisearch.com/#release-v1-4&utm_source=blog). Upgrade your Meilisearch instance in one click without downtime.
</Callout>

New feature: custom text separators  

--------------------------------------

To make string data searchable, Meilisearch relies on [separators](https://meilisearch.org.cn/docs/learn/advanced/datatypes#release-v1-4&utm_source=blog#separators), as they serve to divide a string into tokens or words. Examples of separators include whitespaces, full-stops, or number signs (#). They play a crucial role in helping Meilisearch segment text effectively and enhance search relevancy.

Meilisearch comes with a predefined list of separators. However, these separators do not suit all use cases. For example, in a hashtag search, the number sign should not be considered a separator, but part of the word.

Starting with v1.4, Meilisearch allows you to customize the separators list to suit your specific needs.

### Using an SDK or the API

<Callout>
  This section shows code samples in JavaScript. Check out our [official SDKs](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/sdks#release-v1-4&utm_source=blog) or the [API reference](https://meilisearch.org.cn/docs/reference/api/settings#release-v1-4&utm_source=blog#separator-tokens) for more examples.
</Callout>

#### Adding characters to the separators list  

To include a character in the separators list, update your index’s _Separators Tokens_ settings:

```JavaScript
client.index('myIndex').updateSeparatorTokens(['&sect;'])
```

This request adds the encoded version of the section sign character (\`§\`) to the separator tokens list.

#### Removing characters from the separators list  

To remove one or more characters from the separators list, update your index’s _Non Separator Tokens_ settings:

```JavaScript
client.index('myIndex').updateNonSeparatorTokens(['@', '#', '&'])
```

### Using Meilisearch Cloud

On [Meilisearch Cloud](https://cloud.meilisearch.com/#release-v1-4&utm_source=blog), you can configure _Separator Tokens_ from your index’s settings. Both fields accept JSON format. To include separators, add them to the 'separators' field. To exclude certain characters as separators, list them under 'non-separators'.

![Screenshot of Meilisearch Cloud UI showcasing the 'separator settings' section. It features two fields: one for listing separators and another for listing non-separators. ](https://unable-actionable-car.media.strapiapp.com/0dfq_LJ_Qj_G_L_Rt9_X_r_F6d6_RKYCTU_Kt_K_ib_Q9_E_Ye3_A8_Cq_V_Ea20_Mv_q3_Pgo_Yrdkbcy_WC_8_QSZN_6f_Bn_D_Wu_QN_87_H_Izf_1_Zyk_FWKKD_Lv_b64487b283.jpg)

New feature: custom dictionary
------------------------------

<Callout>
  This section shows code samples in JavaScript. Check out our [official SDKs](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/sdks#release-v1-4&utm_source=blog) or the [API reference](https://meilisearch.org.cn/docs/reference/api/settings#release-v1-4&utm_source=blog#dictionary) for more examples.
</Callout>

Now, you can add a custom word dictionary to improve Meilisearch's segmentation of specific words. This is particularly useful when working with domain-specific terminology like “Node.js” or proper nouns like “E. E. Cummings”.

To add words to the dictionary, update your index’s _Dictionary_ settings:

```JavaScript
client.index('myIndex').updateDictionary(['e. e.', 'e.e.', 'e e'])
```

On [Meilisearch Cloud](https://cloud.meilisearch.com/#release-v1-4&utm_source=blog), you can configure a _Custom Dictionary_ by providing a JSON array in your index’s settings. You can also upload a JSON file as dictionary.

### Using custom dictionary with stop words and synonyms

The introduction of a custom dictionary acts as a powerful adjunct to existing features like [stopWords](https://meilisearch.org.cn/docs/reference/api/settings#release-v1-4&utm_source=blog#stop-words) and [synonyms](https://meilisearch.org.cn/docs/learn/configuration/synonyms#release-v1-4&utm_source=blog). Together, they synergize to improve the relevancy of the search results.

Let’s consider a literary database where an author's name may appear in various forms or abbreviations. This leads to fragmented search results, making it challenging for users to find works by that specific author. For example, take the different ways one might search for works by E. E. Cummings. Using the custom dictionary feature along synonyms can standardize these name variations, thereby enhancing the relevancy of search results.

Using synonyms and custom dictionary together, here’s an example of index settings that address this scenario:

```JSON
{
"dictionary": ["E. E.", "E.E.", "E E"],
"synonyms": {
    "E. E.": [ "E.E.", "E E", "Edward Estlin"],
    "E.E.": ["E. E.", "E E", "Edward Estlin"],
    "E E": ["E. E.", "E.E.", "Edward Estlin"],
    "Edward Estlin": ["E. E.", "E.E.", "E E"]
}
```

Breaking bug fix: improved filtering with backslashes
-----------------------------------------------------

In v1.4, we've addressed a longstanding bug that users faced when using backslashes (\\) at the end of the \`filter\` search parameter expression.

Let’s consider the following documents:

```JSON
[
  {
    "id": 1,
    "path": "my\\test\\path"
  },
  {
    "id": 2,
    "path": "my\\test\\path\\"
  }
]

```

**Note**: The double backslashes in the example are for JSON escaping.

Before v1.4.0, trying to filter on the second document using either of the filters `path = "my\\test\\path\\"` or `path = "my\\test\\path\\\"` would lead to errors.

Now, you can use any filter expression with backslashes. Just make sure to escape each `\` character in your filter.  

Using our example, to filter on the second document successfully, the filter should be written as: `path = "my\\\\test\\\\path\\\\"`.  

⚠️ **Warning:** If you're upgrading from v1.3.X or earlier and have previously used backslashes in filters, be aware that in v1.4.0, the correct filter for the first document should be `path = "my\\\\test\\\\path"`.  

Two layers of escaping are applied: : first, escape for JSON, then for the Meilisearch filter. Meilisearch turns `\\\\` back to `\\`, and JSON parsing will result in a single `\`.  

<Callout>
  Consider using built-in methods of your programming language to handle backslashes:- PHP: [addslashes() function](https://php.ac.cn/manual/en/function.addslashes.php)\- JavaScript: While JS doesn't have a specific method for adding slashes. You can use the [replace method](https://mdn.org.cn/en-US/docs/Web/JavaScript/Reference/Global_Objects/String/replace), as suggested on [StackOverflow](https://stackoverflow.com/questions/7744912/making-a-javascript-string-sql-friendly)
</Callout>

Contributors
------------

We are really grateful for all the community members who participated in this release. We would like to thank: [@dogukanakkaya](https://github.com/dogukanakkaya), [@JannisK89](https://github.com/JannisK89), and [@vivek-26](https://github.com/vivek-26) for their help with Meilisearch. We want to send a special shout out to [mmachatschek](https://github.com/mmachatschek/) for his help and involvement with the backlash bug.

Conclusion
----------

And that’s it for v1.4! Remember to check the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.4.0) for the full release notes, and see you next time!  

You can stay in the loop by subscribing to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/) and come participate in our [Product Discussions](https://github.com/meilisearch/product/discussions).  

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

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