We’re announcing the release of Meilisearch 1.6. Let’s dive into some of the most important changes. You can also view the [full changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.6.0) on GitHub.

<Callout>
  Meilisearch 1.6 is also available on [Meilisearch Cloud](https://cloud.meilisearch.com/login?utm_campaign=release-v1-6&utm_source=blog), including all experimental features.
</Callout>

Experimental feature: hybrid search
-----------------------------------

Meilisearch introduces hybrid search. It combines full-text and semantic search to enhance the accuracy and comprehensiveness of search results. Picture a movie app like [where2watch](https://where2watch.meilisearch.com/?utm_campaign=release-v1-6&utm_source=blog). Now, your users will be able to find those movies they can't quite name but remember the story.

Furthermore, Meilisearch now streamlines the creation of vector embeddings. Choose your preferred embedders and Meilisearch will handle all interactions with external tools for you.

### Configuring embedders

You can [configure the embedders](https://meilisearch.org.cn/docs/learn/experimental/vector_search?utm_campaign=release-v1-6&utm_source=blog#generate-vector-embeddings) in your index settings. Select from three types of embedders for your needs:

`openAI`:

*   Uses the [OpenAI](https://openai.com/) API for computing embeddings
*   Requires an OpenAI API key for operation

`huggingFace`:

*   Enables local computation of embeddings by downloading models from the [HuggingFace Hub](https://hugging-face.cn/models)
*   Operates on your CPU–not your GPU–which may impact indexing performance

`userProvided`:

*   Functions similarly to Meilisearch v1.3, with a key difference: you must define a specific embedder
*   Allows you to add pre-computed embeddings into your documents. You perform searches using vectors instead of text.

To use hybrid search, define at least one embedder in the index settings:

```JSON
{
  "embedders": {
    "default": {
      "source":  "openAi",
      "apiKey": "<your-OpenAI-API-key>",
      "model": "text-embedding-ada-002",
      "documentTemplate": "A movie titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}"
    },
    "image": {
      "source": "userProvided",
      "dimensions": 512
    },
    "translation": {
      "source": "huggingFace",
      "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
      "documentTemplate": "A movie titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}"
    }
  }
}

```

The `documentTemplate` field acts as a blueprint for creating your document's embedding. It uses the [Liquid template language](https://shopify.dev/docs/api/liquid). While its inclusion is optional, it is highly recommended, especially since embedding models are optimized for concise texts. It keeps only the necessary content, excluding non-essential data like `id`, and helps in adding context to increase relevancy.

### **Hybrid search**

To [perform a hybrid search](https://meilisearch.org.cn/docs/learn/experimental/vector_search?utm_campaign=release-v1-6&utm_source=blog#vector-search-with-auto-embeddings), use the hybrid field within the `POST /index/:index_uid/search` route.

```JSON
{
    "q": "Plumbers and dinosaurs",
    "hybrid": {
        "semanticRatio": 0.9,
        "embedder": "default"
    }
}

```

`embedder`: an embedder from the options configured in your index settings.

`semanticRatio`: a floating value ranging from `0` to `1`; `1` is a  fully semantic search; `0` is an exact, match-focused full-text search; the default is `0.5` which mixes both methods.

Your control over the semantic ratio directly influences how search results are ranked. A higher semantic ratio shifts the focus towards the context and meaning behind your query, ranking results that are more semantically relevant higher. 

On the other hand, a lower semantic ratio increases the weight given to keyword accuracy in the ranking process, bringing results that closely match your specific search terms to the forefront.

### **Breaking changes in the experimental vector search API**

Meilisearch v1.6 introduces some breaking changes in the vector search API.

Previously, you could send vectors without specifying a model. Now, you must define a model in the settings:

```JSON
"embedders": {
    "default": {
      "source": "userProvided",
      "dimensions": 512
    }
}

```

Because Meilisearch now supports multiple embedders, it has updated the vector submission format from arrays to JSON objects.

*   Previews format: `“_vectors”: [[0.0, 0.1]]`
*   New format: `“_vectors”: {“image2text”: [0.0, 0.1, …]}`

For detailed information on these updates, refer to the [documentation](https://meilisearch.org.cn/docs/learn/experimental/vector_search?utm_campaign=release-v1-6&utm_source=blog).

For in-depth technical information, explore the [series of articles](/blog/spotify-inspired-hybrid-search-and-rust/?utm_campaign=release-v1-6&utm_source=blog on [Arroy](https://github.com/meilisearch/arroy), an open-source repository based on [Spotify’s Annoy](https://github.com/spotify/annoy) and developed in Rust. This library, created and maintained by the Meilisearch engine team, specializes in searching for vectors in a space that are near a specified query vector.

**Performance optimization**
----------------------------

### Improved indexing speed

We're thrilled to share a major enhancement in Meilisearch's indexing performance. Our recent tests, including scenarios with frequent and partial document updates, have shown impressive results: **a reduction in indexing time by up to 50%**, and in some cases, **even as much as 75%**. 

Thanks to our latest optimizations, Meilisearch now **stores and pre-computes less data**. Additionally, during document updates, it **re-indexes or deletes only the necessary data**. For instance, in an e-commerce dataset, updating the stock level of a product results in re-indexing just the 'stock' field, rather than the entire product document.

### Disk space usage reduction

Meilisearch reduces internal data storage, leading to a more **compact database size** on your disk. With a dataset of approximately 15Mb, we observed a **40% to 50% reduction in database size**.

This enhancement not only reduces the database size, but also **improves its stability**, making the space savings more evident as the number of documents increases.

**New feature: customize proximity precision**
----------------------------------------------

To further reduce indexing speed, Meilisearch now allows you to tailor the accuracy of the [proximity ranking rule](https://meilisearch.org.cn/docs/learn/core_concepts/relevancy?utm_campaign=release-v1-6&utm_source=blog#3-proximity) to your specific needs.

The proximity ranking rule is computationally demanding and may lead to longer indexing times. Reducing its accuracy can greatly enhance performance, and in most scenarios, it will not substantially affect the relevancy of the results.

To adjust its impact [configure the `proximityPrecision` setting](https://meilisearch.org.cn/docs/reference/api/settings?utm_campaign=release-v1-6&utm_source=blog#proximity-precision):

```JSON
curl \
  -X PATCH 'https://127.0.0.1:7700/indexes/books/settings/proximity-precision' \
  -H 'Content-Type: application/json'  \
  --data-binary '{
    "proximityPrecision": "byAttribute"
  }'
```

The default `proximityPrecision` setting is `byWord`, which calculates proximity based on exact word distances. 

The `byAttribute` setting considers words in the same attribute as proximate, regardless of their exact distance.

Using `byAttribute` can boost the indexing speed, but it might slightly change how relevant the results are. This becomes more noticeable in searches where it's important for words to be close to each other.

For example, when you're looking through song lyrics or long articles, like trying to find 'world war' in a bunch of Wikipedia pages, you might end up with results that contain these words but not necessarily close together or in the desired order. This is also true for [phrase searches](https://meilisearch.org.cn/docs/reference/api/search?utm_campaign=release-v1-6&utm_source=blog#phrase-search) and for searches involving [multi-word synonyms](https://meilisearch.org.cn/docs/learn/configuration/synonyms?utm_campaign=release-v1-6&utm_source=blog#multi-word-synonyms), where the specific combination of words is crucial.

**New feature: task queue webhook**
-----------------------------------

Meilisearch now offers a webhook feature to notify a custom URL when an asynchronous task finishes (either succeeds, fails, or gets canceled).

This feature is particularly useful for streamlining workflows, saving you from polling the tasks route.

Set up your webhook at launch using these environment variables:

```bash
MEILI_TASK_WEBHOOK_URL=https://mywebsite.com/my-super-webhook?user=1234&number=8

MEILI_TASK_WEBHOOK_AUTHORIZATION_HEADER='Bearer 12340987546wowowlolol'
```

You can also use the respective command-line options.

Once set up, the webhook sends out a payload in [JSON Lines (ndjson) format](https://meilisearch.org.cn/docs/learn/core_concepts/documents?utm_campaign=release-v1-6&utm_source=blog#ndjson) to your specified URL, containing the list of finished tasks:

```json
//POST HTTP request to https://myproject.com/mywebhook?common=people

{"uid":4,"indexUid":"movie","status":"failed","type":"indexDeletion","canceledBy":null,"details.deletedDocuments":0,"error.message":"Index `movie` not found.","error.code":"index_not_found","error.type":"invalid_request","error.link":"https://docs.meilisearch.com/errors#index_not_found","duration":"PT0.001192S","enqueuedAt":"2022-08-04T12:28:15.159167Z","startedAt":"2022-08-04T12:28:15.161996Z","finishedAt":"2022-08-04T12:28:15.163188Z"}
{"uid":5,"indexUid":"movie","status":"failed","type":"indexDeletion","canceledBy":null,"details.deletedDocuments":0,"error.message":"Index `movie` not found.","error.code":"index_not_found","error.type":"invalid_request","error.link":"https://docs.meilisearch.com/errors#index_not_found","duration":"PT0.001192S","enqueuedAt":"2022-08-04T12:28:15.159167Z","startedAt":"2022-08-04T12:28:15.161996Z","finishedAt":"2022-08-04T12:28:15.163188Z"}
```

**Experimental feature: limit the number of batched tasks**
-----------------------------------------------------------

To speed up the indexing process, Meilisearch processes similar [tasks](https://meilisearch.org.cn/docs/learn/async/asynchronous_operations?utm_campaign=release-v1-6&utm_source=blog#tasks-and-asynchronous-operations) in large batches. However, excessive [queued tasks](https://meilisearch.org.cn/docs/learn/async/asynchronous_operations?utm_campaign=release-v1-6&utm_source=blog#task-queue) can occasionally cause crashes or stalls.

To control the number of batched tasks, set the limit at launch using either the command-line argument `--experimental-max-number-of-batched-tasks`, the `MEILI_EXPERIMENTAL_MAX_NUMBER_OF_BATCHED_TASKS` environment variable, or the [configuration file](https://meilisearch.org.cn/docs/learn/configuration/instance_options?utm_campaign=release-v1-6&utm_source=blog#configuration-file).

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

We are really grateful for all the community members who participated in this release.We would like to thank [@Karribalu](https://github.com/Karribalu) and [@vivek-26](https://github.com/vivek-26) for their help with [Meilisearch](https://github.com/meilisearch/meilisearch). We also want to send a special shout-out to our SDKs maintainers 🦸

* * *

And that’s it for v1.6! This release post highlights the most significant updates. For an exhaustive listing, read the [changelog on Github](https://github.com/meilisearch/meilisearch/releases/tag/v1.6.0).

Stay in the loop of everything Meilisearch by subscribing to [the newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&ref=blog.meilisearch.com). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/?utm_campaign=release-v1-6&utm_source=blog) and come participate in our [Product Discussions](https://github.com/meilisearch/product/discussions?ref=blog.meilisearch.com).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=release-v1-6&utm_source=blog).

Meilisearch v1.6

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?utm_campaign=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?utm_campaign=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?utm_campaign=release-v1-5&utm_source=blog) method or the [REST API](https://meilisearch.org.cn/docs/reference/api/snapshots?utm_campaign=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?utm_campaign=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?utm_campaign=release-v1-5&utm_source=blog#configure-experimental-features):

```sh
curl \
  -X PATCH 'https://127.0.0.1:7700/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/?utm_campaign=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?utm_campaign=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/?utm_campaign=analytics-v1&utm_source=blog)

Learn how to [get started with Meilisearch Cloud](https://meilisearch.org.cn/docs/learn/getting_started/cloud_quick_start?utm_campaign=analytics-v1&utm_source=blog) and read the [Analytics documentation](https://meilisearch.org.cn/docs/learn/cloud/analytics?utm_campaign=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/?utm_campaign=teams-release&utm_source=blog) and participate in our [Product Discussions](https://github.com/meilisearch/product/discussions/?utm_campaign=teams-release&utm_source=blog).

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

Search analytics have arrived!

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/?utm_campaign=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?utm_campaign=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?utm_campaign=release-v1-4&utm_source=blog) or the [API reference](https://meilisearch.org.cn/docs/reference/api/settings?utm_campaign=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/?utm_campaign=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?utm_campaign=release-v1-4&utm_source=blog) or the [API reference](https://meilisearch.org.cn/docs/reference/api/settings?utm_campaign=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/?utm_campaign=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?utm_campaign=release-v1-4&utm_source=blog#stop-words) and [synonyms](https://meilisearch.org.cn/docs/learn/configuration/synonyms?utm_campaign=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).

What's new in v1.4?

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/v1.3.0) on GitHub.

v1.3 is now available on [Meilisearch Cloud](https://meilisearch.org.cn/cloud), including all experimental features. Upgrade your Meilisearch instance in one click without downtime.

Experimental feature: vector search
-----------------------------------

<Callout>
  Using LangChain? You can now use the [Meilisearch vector store](https://python.langchain.ac.cn/docs/integrations/vectorstores/meilisearch) to benefit from powerful search features!
</Callout>

We are excited to introduce [vector storage](https://meilisearch.org.cn/docs/learn/experimental/vector_search)! Now, you can index and search documents using vector embeddings—numerical representations of data. For the first iteration of this feature, you need to use an external tool such as [Hugging Face](https://hugging-face.cn/), [Cohere](https://cohere.com/), or [OpenAI](https://openai.com/), to create such embeddings. Check out the [announcement post](/blog/vector-search-announcement/ to discover the extensive possibilities and potential applications of this feature.

<Callout>
  This feature is experimental, you can enable it using the new [experimental-features endpoint](https://meilisearch.org.cn/docs/reference/api/experimental_features).
</Callout>

To search documents based on their vector proximity, make sure they contain a `_vectors` field:

```bash
curl -X POST -H 'content-type: application/json' \
  'localhost:7700/indexes/songs/documents' \
  --data-binary '[
      { "id": 0, "_vectors": [0, 0.8, -0.2], "title": "Across The Universe" },
      { "id": 1, "_vectors": [1, -0.2, 0], "title": "All Things Must Pass" },
      { "id": 2, "_vectors": [[0.5, 3, 1], [-0.2, 4, 6]], "title": "And Your Bird Can Sing" }
  ]'
```

Once stored, you can search your vectorized documents by using the new `vector` search parameter in combination with the [search](https://meilisearch.org.cn/docs/reference/api/search) and [multi-search](https://meilisearch.org.cn/docs/reference/api/multi_search) routes.

```bash
curl -X POST -H 'content-type: application/json' \
'localhost:7700/indexes/songs/search' \
--data-binary '{ "vector": [0, 1, 2] }'
```

👉 Note that you need to generate the vector in your query using a third-party tool.

The returned documents include a `_semanticScore` field indicating the semantic similarity—or relevancy—of each document with the query.

```JSON
{
  "hits": [
    { "id": 0, "_vectors": [0, 0.8, -0.2], "title": "Across The Universe", "_semanticScore": 0.6754 },
    { "id": 1, "_vectors": [1, -0.2, 0], "title": "All Things Must Pass", "_semanticScore": 0.7546 },
    { "id": 2, "_vectors": [[0.5, 3, 1], [-0.2, 4, 6]], "title": "And Your Bird Can Sing", "_semanticScore": 0.78 }
  ],
  "query": "",
  "vector": [0, 1, 2],
  "processingTimeMs": 0,
  "limit": 20,
  "offset": 0,
  "estimatedTotalHits": 2
}
```
<Callout>
  This feature is experimental and we need your help to improve it! Share feedback on [this GitHub discussion](https://github.com/meilisearch/product/discussions/677).
</Callout>

New feature: display ranking scores at search
---------------------------------------------

Use the new `showRankingScore` search parameter to see the [ranking score](https://meilisearch.org.cn/docs/reference/api/search#ranking-score) of relevancy of each document:

```bash
curl \
  -X POST 'https://127.0.0.1:7700/indexes/movies/search' \
  -H 'Content-Type: application/json' \
  --data-binary '{ "q": "Batman Returns", "showRankingScore": true }'
```

Each document in the response will include a `_rankingScore` property, which represents a score between 0 and 1:

```JSON
"_rankingScore": 0.8575757575757575,
```

The higher the `_rankingScore`, the more relevant the document is.

Experimental feature: ranking score details
-------------------------------------------

For every document returned, Meilisearch provides a [detail of the ranking score](https://meilisearch.org.cn/docs/learn/experimental/ranking_rule_score_details) of each ranking rule. This feature is a perfect example of [how community engagement can enrich the design process](/blog/from-ranking-to-scoring/ at Meilisearch.

<Callout>
  This feature is experimental, you can enable it using the new [experimental-features endpoint](https://meilisearch.org.cn/docs/reference/api/experimental_features).
</Callout>

Set the `showRankingScoreDetails` search parameter to `true` to view the ranking score details:

```bash
curl \
  -X POST 'https://127.0.0.1:7700/indexes/movies/search' \
  -H 'Content-Type: application/json' \
  --data-binary '{ "q": "Batman Returns", "showRankingScoreDetails": true }'
```

  
The response should include the relevancy score of each rule:

```JSON
"_rankingScoreDetails": {
  "words": {
    "order": 0,
    "matchingWords": 1,
    "maxMatchingWords": 1,
    "score": 1.0
  },
  "typo": {
    "order": 1,
    "typoCount": 0,
    "maxTypoCount": 1,
    "score": 1.0
  },
  "proximity": {
    "order": 2,
    "score": 1.0
  },
  "attribute": {
    "order": 3,
    "attributesRankingOrder": 0.8,
    "attributesQueryWordOrder": 0.6363636363636364,
    "score": 0.7272727272727273
  },
  "exactness": {
    "order": 4,
    "matchType": "noExactMatch",
    "score": 0.3333333333333333
  }
}
```
<Callout>
  This feature is experimental. You can help us improve it by sharing your feedback on [this GitHub discussion](https://github.com/meilisearch/product/discussions/674).
</Callout>

New feature: search for facet values
------------------------------------

Once you have defined your [filterable attributes](https://meilisearch.org.cn/docs/reference/api/settings#filterable-attributes), you can use the new endpoint `POST /indexes/{index}/facet-search` to [search for facet values](https://meilisearch.org.cn/docs/learn/fine_tuning_results/faceted_search#searching-facet-values).

```bash
 curl \
  -X POST 'https://127.0.0.1:7700/indexes/movies/facet-search' \
  -H 'Content-Type: application/json'  \
  --data-binary '{
    "facetName": "genres",
    "facetQuery": "a"
  }'
```

  
As facet search is typo tolerant and supports prefix search, the above query will return de following results:

```JSON
{
    "facetHits": [
        {
            "value": "Action",
            "count": 5403
        },
        {
            "value": "Adventure",
            "count": 3020
        },
        {
            "value": "Animation",
            "count": 1969
        }
    ],
    "facetQuery": "a",
    "processingTimeMs": 0
}
```

To test this feature, check out our updated [ecommerce demo](https://ecommerce.meilisearch.com/).

  

![](https://lh5.googleusercontent.com/AvwqKTkbB0RfTYrWL4xeBUZEdzRwrEjgL0yKllbM5AZu0aXkpmO-peqP7y-5SC-O30TsAdaiXEMdOck4hiS44UvF8UFV-MwsM-mvtdRmxEeKtPowTUXQThTlqlP5yqz2p_5WohLCIgOtydEvK33TdAY)

  

New feature: sort facet values by count
---------------------------------------

By default, facets are sorted in ascending alphanumeric order. Now, you can sort facet values in descending order based on the number of matched documents containing each facet value.

To order facets by count, you can use the new `sortFacetValuesBy` property in the  [faceting index settings](https://meilisearch.org.cn/docs/reference/api/settings#faceting).

To modify the order for all facets, you can use the asterisk (\*) wildcard:

```bash
 curl \
  -X PATCH 'https://127.0.0.1:7700/indexes/movies/settings/faceting \
  -H 'Content-Type: application/json'  \
  --data-binary '{
    "sortFacetValuesBy": {"*": "count"}
  }'
```

Alternatively, you have the flexibility to order a single facet by count while maintaining alphanumeric ordering for other attributes:

```json
 curl \
  -X PATCH 'https://127.0.0.1:7700/indexes/movies/settings/faceting \
  -H 'Content-Type: application/json'  \
--data-binary '{
    "sortFacetValuesBy": {"*": "alpha", "genre": "count"}
  }'
```

You can also try it in the [ecommerce demo](https://ecommerce.meilisearch.com/).

![](https://lh4.googleusercontent.com/s7qTls-ayHQpaIASqAQjNX0AnGUfc5TkZu6r83XvZBJyyyxU54qpeAXvjgofCgEbiMs72wwmVQF5m-LnLNkiQVkQFJULEvqgSYdtVh_dxGufHr8FC7zxag9PMsSXb0YbsjysSqBQuRRiSSFWcqNqyKc)

New feature: searchable attributes at query time
------------------------------------------------

With the new `attributesToSearchOn` search parameter, you can now [restrict the search](https://meilisearch.org.cn/docs/reference/api/search#customize-attributes-to-search-on-at-search-time) to a subset of the [searchable attributes](https://meilisearch.org.cn/docs/learn/configuration/displayed_searchable_attributes#the-searchableattributes-list) at query time. It accepts an array of strings indicating one or more document attributes:

```JSON
{
  "q": "adventure",
  "attributesToSearchOn": ["genre"]
}
```

Given the following documents, the above query will only return document with id 1.

```JSON
{
    "id": 0,
    "name": "An Adventure in Space and Time",
    "genre": ["drama"]
},
{
    "id": 1,
    "name": "A Strange and Stubborn Endurance",
    "genre": ["adventure"]
}
```

Relevancy change: attribute ranking rule
----------------------------------------

With v1.3, the [attribute ranking rule](https://meilisearch.org.cn/docs/learn/core_concepts/relevancy#4-attribute) assigns higher relevancy to documents where the query words appear closer to their position in the query, compared to documents where the query words are farther from their position in the query.

Before, this rule measured relevancy based on the position of the words in the attribute rather than on their position in the search query. Documents with attributes containing the query words at the beginning of the attribute were considered more relevant.

Consider the following query: "Batman the dark knight returns" and the corresponding documents:

```JSON
{ 
    "id": 0, 
    "title": "Batman the dark knight returns" 
},
{ 
    "id": 1, 
    "title": "Dark the Batman knight returns" 
},
{ 
    "id": 2, 
    "title": "Batman returns: a retrospective", 
    "description": "The Dark knight is analyzed in this new Batman documentary" 
}
```

Prior to version 1.3, the attribute ranking rule placed document 2 at the top of the list, followed by documents 0 and 1, ranked in a tie.

Since version 1.3, the order is strictly: document 0, document 1, document 2, which provides a more natural ranking.

Other improvements
------------------

*   The `/tasks` route now shows the total number of tasks in the queue using the `total` property. It also displays the total number of tasks based on specific filters
*   Enhanced Japanese support and segmentation
*   Improvements in the Prometheus `/metrics` experimental feature

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

We are really grateful for all the community members who participated in this release. We would like to thank: [@vvv](https://github.com/vvv), [@jirutka](https://github.com/jirutka), [@gentcys](https://github.com/gentcys), [@cuishuang](https://github.com/cuishuang), [@0xflotus](https://github.com/0xflotus), and [@roy9495](https://github.com/roy9495) for their help with [Meilisearch](https://github.com/meilisearch/meilisearch?ref=blog.meilisearch.com).

We also want to send a special shout-out to our Meilistar, [@mosuka](https://github.com/mosuka), for his continuous Japanese improvements on [Charabia](https://github.com/meilisearch/charabia?ref=meilisearch-blog).

Conclusion
----------

And that’s it for v1.3! Remember to check the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.3.0) 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&ref=blog.meilisearch.com). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/?ref=blog.meilisearch.com) and come participate in our [Product Discussions](https://github.com/meilisearch/product/discussions?ref=blog.meilisearch.com).

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

What’s new in v1.3?

We’re thrilled to announce the release of Meilisearch v1.2. This minor version comprises new features for filters and engine improvements across the board. Let’s dig into the most noteworthy changes!

New feature: enhanced document management with filters
------------------------------------------------------

We’re excited to improve Meilisearch developers' experience by introducing filters for fetching and deleting documents. 🥳

<Callout>
  As always with filters, document fields must first be set as [filterable attributes](https://meilisearch.org.cn/docs/learn/core_concepts/indexes#filterable-attributes).
</Callout>

### Retrieving documents

Filters bring new capabilities to fetching documents. For example, we could check how many documents match some filters before performing a deletion.

So we’re introducing a new endpoint to retrieve documents:

`**POST** /indexes/{indexUid}/documents/fetch`

Let’s consider an index that contains movies. We will write a request to retrieve 10 movies from the _Horror_ or _Mystery_ genre. Using the [array syntax](https://meilisearch.org.cn/docs/learn/advanced/filtering#creating-filter-expressions-with-arrays), we can express the filters in the request body as following:

```json
{
  "filter": [
    "genre = Horror",
    "genre = Mystery"
  ],
  "limit": 10
}
```

This new endpoint saves you from having to make a search request to explore your data. Read more about fetching documents using filters in the [dedicated docs](https://meilisearch.org.cn/docs/reference/api/documents#get-documents-with-post).

### Deleting documents

You can now delete documents matching a list of filters. This feature comes with a dedicated endpoint:

`**POST** /indexes/{indexUid}/documents/delete`

Taking the example of an index that contains movies, let’s write a request to delete all _Horror_ or _Comedy_ movies, as well as movies directed by _James Cameron_. This can expressed with the following request body:

```json
{
  "filter": [
    [ "genre = Horror", "genre = Comedy" ],
    "director = \"James Cameron\""
  ]
}
```

This requests launches a task to delete matching documents. Meilisearch will respond with a summarized task object:

```json
{
  "taskUid": 42,
  "indexUid": "movies",
  "status": "enqueued",
  "type": "documentDeletion",
  "enqueuedAt": "2023-06-05T11:01:58.721841Z"
}
```

This task represents the asynchronous deletion operation. Don’t forget to [check the task status](https://meilisearch.org.cn/docs/reference/api/tasks#get-one-task) to ensure that documents have been deleted before proceeding. Read more about deleting documents in the [dedicated docs](https://meilisearch.org.cn/docs/reference/api/documents#delete-documents-by-filter).

New feature: `IS EMPTY` and `IS NULL` filter operators
------------------------------------------------------

This release gives new powers to filters. So we thought, how can we go further? Maybe if we could filter in even more ways. I mean, don’t you want some more filters with your filtering?

So, we’re introducing two new filter operators: `IS EMPTY` and `IS NULL`. `IS EMPTY` matches existing attributes with empty value while `IS NULL`  matches fields with a null value.

Here’s an example considering the following documents:

```json
[
  {
    "id": 0,
    "color": []
  },
  {
    "id": 1,
    "color": null
  },
  {
    "id": 2,
  }
]
```

The new filter operators work like this:

*   `color IS EMPTY`: matches document `0`
*   `color IS NULL`: matches document `1`

Note that neither `IS EMPTY` nor `IS NULL` matches documents missing the `color` field. Both operators can also be used in conjunction with `NOT`. Read more in the [related documentation](https://meilisearch.org.cn/docs/learn/advanced/filtering#filter-operators).

Experimental feature: reducing memory usage
-------------------------------------------

We’re introducing a new experimental flag to decrease the RAM usage of Meilisearch. Try it by launching Meilisearch with the `--experimental-reduce-indexing-memory-usage` CLI flag or the `MEILI_EXPERIMENTAL_REDUCE_INDEXING_MEMORY_USAGE` environment variable.

You can give feedback on this [Github discussion](https://github.com/meilisearch/product/discussions/652).

Improvement: relevancy
----------------------

We’ve enhanced [typo tolerance](https://meilisearch.org.cn/docs/learn/configuration/typo_tolerance). Split words are now considered a typo, meaning `whit ehorse` may now match documents containing `white horse`. However, [n-grams](https://en.wikipedia.org/wiki/N-gram) and split words will now rank lower. This means that `sun flower` will now return a document containing `Sun Flower` before one with `Sunflower`.

Improvement: automated task deletion
------------------------------------

Meilisearch handles operations like adding documents to an index [asynchronously](https://meilisearch.org.cn/docs/learn/advanced/asynchronous_operations). This means they are not handled immediately. Instead, Meilisearch places them in a tasks queue and processes them in the order they were received. Starting with v1.2, the maximum capacity of the task queue is 1 million.

Once the queue is full, Meilisearch will try to delete the oldest 100,000 tasks. Meilisearch will not remove tasks that are _not_ finished (enqueued or processing). It will simply delete as many finished tasks as possible among the 100,000 oldest.

<Callout>
  When automatic deletion fails for a task, Meilisearch will issue logs with \`warning_\`_ levels.
</Callout>

Other changes
-------------

*   Updated how Meilisearch handles ranking rules to improve search consistency and latency
*   [Geosearch](https://meilisearch.org.cn/docs/learn/advanced/geosearch) sorting is now more performant for both descending and ascending sort
*   Enhanced language support for Latin languages and Arabic thanks to [charabia](https://github.com/meilisearch/charabia/releases/tag/v0.7.2) update

Conclusion
----------

This wraps up the biggest changes in Meilisearch v1.2. For an exhaustive review, including security updates and bug fixes, consult the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.2.0).

We’re already preparing version 1.3 of Meilisearch. We already have three prototypes ready for you to play with: [search for facet values](https://github.com/meilisearch/product/discussions/515#discussioncomment-5806759), [sorting facet values](https://github.com/meilisearch/product/discussions/519#discussioncomment-6030480), and the  [CONTAINS,  STARTS WITH, and ENDS WITH filter operators](https://github.com/meilisearch/product/discussions/544#discussioncomment-5918077). We’re eager to hear from you to nail the finishing touches.

This release wouldn’t be possible without the contributions of [@cymruu](https://github.com/cymruu), [@GregoryConrad](https://github.com/GregoryConrad), [@inductor](https://github.com/inductor), [@jirutka](https://github.com/jirutka), [@jlucktay](https://github.com/jlucktay), [@roy9495](https://github.com/roy9495), and [@Sufflope](https://github.com/Sufflope) to [meilisearch](https://github.com/meilisearch/meilisearch). This also includes the work of [@akeamc](https://github.com/akeamc), [@DrAliRagab](https://github.com/DrAliRagab), [@goodhoko](https://github.com/goodhoko), and [@mosuka](https://github.com/mosuka) who contributed to the last release of [charabia](https://github.com/meilisearch/charabia) to keep improving language support. Massive thanks to our awesome contributors. 🫶

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).

**Join the discussion on [/r/rust](https://www.reddit.com/r/rust/comments/1418k4v/opensource_search_engine_meilisearch_releases_v12/).**

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

I’ll see you there.

What's new in v1.2?

Meilisearch, one of the fastest-growing open-source search engines on GitHub, has reached general availability with its cloud-based Search-as-a-Service product, [Meilisearch Cloud](https://cloud.meilisearch.com/register?utm_campaign=cloud-ga&utm_source=blog).

What is Meilisearch Cloud?
--------------------------

Meilisearch Cloud is a **cloud-based SaaS platform** that enables developers to implement search functionality quickly and easily. It is built on top of the popular [Meilisearch open-source search engine](https://github.com/meilisearch/meilisearch), which has gained a reputation for being lightning-fast, easy to use, and incredibly flexible. Meilisearch Cloud takes these attributes to the next level, offering a managed search service that is scalable, reliable, and designed to meet the needs of all companies.

With Meilisearch Cloud, we handle **all server management**, including

*   monitoring server health
*   installing and updating Meilisearch
*   server upgrades and patches

Our service is designed to **scale automatically** as your usage increases, so you don't have to worry about any sudden surge in traffic affecting your search performance.

Additionally, our costs scale with your usage, so there are no hidden fees to worry about. You only **pay for what you use**, which makes our platform an affordable and efficient solution for businesses of all sizes.

### Save time and resources

Meilisearch Cloud simplifies the process of implementing search, saving time and resources. It is made to be so user-friendly that a front-end developer with no prior search experience can build a world-class search experience in no time. Our intuitive interface streamlines the whole search process — no server management or DevOps experience is required!

We handle the grunt work. Once your Meilisearch project is up and running, we’ll ensure it stays that way so **you can focus on building your products**.

### Meilisearch Cloud has a great track-record

We’ve spent the last year gradually rolling out Meilisearch Cloud for selected users.

> _______Initial value came from the fact that Bookshop started using the Meilisearch open source solution, hosting the infrastructure locally. However, working with it in staging environments and test environments was a harder task for a small team like ours that can't focus on search year-round. We did eventually switch to Meilisearch Cloud because it was better for us to outsource._______

__\- Mason Stewart, CTO at Bookshop.org__

Our platform's commitment to customer satisfaction is reflected in our top-of-the-line support, which is always available to help customers with any issues they encounter. Overall, Meilisearch Cloud's customer-focused approach makes it a top choice for businesses looking to enhance their search capabilities.

Why businesses love Meilisearch Cloud?
--------------------------------------

**1\. Lightning-fast relevant search results**  
Meilisearch Cloud is designed from the ground up for performance and delivers search results in milliseconds. It does not only improve the user experience, but it also leads to increased engagement and conversion rates.

![](https://unable-actionable-car.media.strapiapp.com/where2watch_f91f072ead.gif)

[See Meilisearch Cloud in action here](https://where2watch.meilisearch.com/?utm_campaign=cloud-ga&utm_source=blog)

**2\. Reliable and secure**  
Meilisearch Cloud is built on top of a highly secure and reliable infrastructure, leveraging the industry's best practices to ensure your search solution is always available and secure. You can focus on building the best possible search experience for your users without worrying about servers going down in the middle of the night.

**3\. Easy to use**  
Meilisearch Cloud is designed to be easy to use, even for developers without extensive experience working with search engines. Its intuitive interface makes creating and managing search indexes easy, allowing developers to quickly create highly relevant search experiences tailored to their specific needs.

![](https://unable-actionable-car.media.strapiapp.com/attributes_1_eb989f4eec.png)

![](https://unable-actionable-car.media.strapiapp.com/ranking_rules_3035b01c74.png)

Meilisearch Cloud upcoming interface

**4\. Highly scalable**  
Meilisearch Cloud is designed to be highly scalable, making it ideal for businesses of all sizes. Whether you are a small startup or a large enterprise, Meilisearch Cloud can easily scale to meet your needs.

### Meilisearch Cloud will adapt to your business

Meilisearch provides a range of plans to meet the needs of your business. The _Pro plan_ includes enough search units to accommodate most large businesses. It becomes the most cost-effective plan available, starting from 5 million searches per month. But until you reach 10M searches, it won't cost another penny. Many online merchants will be able to handle flash sales: even if the number of requests double, the price won't.

### Usage-based pricing

Meilisearch Cloud scales with your success. You will never be surprised by [our pricing](https://meilisearch.org.cn/pricing?utm_campaign=cloud-ga&utm_source=blog): **we’ll only bill when your users search**. And when they do, they find more and make your business more successful.

This usage-based pricing works for any project: from a developer's personal project to a super-scaled company. And our free tier plan includes enough credits to accommodate most individual or small projects, so you’ll only start paying when it makes sense.

### Bookshop.org, an e-commerce platform, sees a +43% surge in search-based sales

[For the Bookshop.org team](/blog/bookshop-increases-search-based-purchases?utm_campaign=cloud-ga&utm_source=blog the advantage of Meilisearch lies in its simplicity and ease of use, making it possible for decision-makers and non-developers to be very close to the ranking rules. It was necessary to accommodate the ever-changing book search criteria during the busy holiday period and keep the search results relevant at all times.

> ______The fact that we can iterate back and forth quickly and have speedy re-indexing times when we change our rules was a new way of working with search. Previously, only our engineers had the expertise to tweak the rules for book search.______

__\- Mason Stewart, CTO at Bookshop.org__

Future of Meilisearch Cloud
---------------------------

### Upcoming Meilisearch features

In upcoming versions of Meilisearch, a range of new features will cater specifically to the e-commerce industry:

*   Searching for facet values and sorting them
*   Defining searchable fields at search time
*   Defining ranking rules at search time

Additionally, we will introduce scalability improvements enabling search engines to handle more queries and data more efficiently.

### Enterprise-ready improvements

The Cloud development team will soon release new features for production purposes:

*   geo-replication to provide fast search results to end users
*   specific e-commerce analytics
*   an even simpler interface

![](https://unable-actionable-car.media.strapiapp.com/Cloud_UI_1_7e3ac5c3c0.png)

![](https://unable-actionable-car.media.strapiapp.com/Cloud_UI_2_ae1bc3536b.png)

![](https://unable-actionable-car.media.strapiapp.com/Cloud_UI_3_90c8c9fa3c.png)

The upcoming interface of Meilisearch Cloud

We are currently in the process of obtaining a SOC 2 security certification, an auditing procedure that ensures we are securely managing sensitive and private data.

Conclusion
----------

With Meilisearch Cloud, you can be confident that your search is in safe hands. We offer a comprehensive and reliable search engine solution that can help you improve the performance and functionality of your search.

[Register for a Meilisearch Cloud account](https://cloud.meilisearch.com/register?utm_campaign=cloud-ga&utm_source=blog) today.

Search-as-a-service Meilisearch Cloud is open to the public

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/v1.1.0) on GitHub.

New feature: multi-search API endpoint
--------------------------------------

With the introduction of the `/multi-search` API endpoint, Meilisearch allows you to send multiple search queries using a single HTTP request. This will allow you to accomplish a number of different things, like federated search and multi-select facets!

### Federated search

Suppose you have two distinct datasets: “brands” and “products”. Wouldn’t it be convenient if you could search for both at once while keeping them separate in their respective indexes? You can perform a federated search, also known as multi-index search, by querying multiple indexes:

```bash
curl \
-X POST 'https://127.0.0.1:7700/multi-search' \
-H 'Content-Type: application/json' \
--data-binary '{
  "queries": [
     {
       "indexUid": "products",
       "q": "Nike",
       "limit": 1
     },
    {
       "indexUid": "brands",
       "q": "Nike",
       "limit": 1
    }
  ]
}'

```

The response is a `results` array containing one object for each queried index:

```JSON
{
 "results": [
   {
     "indexUid": "products",
     "hits": […],
     "query": "Nike",
     "processingTimeMs": 1,
     "limit": 1,
     "offset": 0,
     "estimatedTotalHits": 17
   },
   {
     "indexUid": "brands",
     "hits": […],
     "query": "Nike",
     "processingTimeMs": 0,
     "limit": 1,
     "offset": 0,
     "estimatedTotalHits": 7
   }
 ]
}

```

### Multi-select facets

The multi-search requests API is particularly useful when it comes to implementing multi-select facets behavior in a faceted search interface.

Multi-select facets, also called disjunctive facets, allow users to view items that meet multiple criteria at the same time. This type of interface also shows the number of items available for each option, even if these items have not been selected. This allows for fast and efficient exploration of items that meet your users’ specific criteria, ultimately saving time and effort.

![](https://lh5.googleusercontent.com/HMSHnrtzKaXnDTE1Wh2AH3KupbhGTEM7-xIEid8X7oKjgHF30bQfb07gkpMBc9vz0Bb07NfanDCUHWfBhq37sxP_L_KMPem-sUKuhZ2ZNfJQ-z_vmXJPIuVEDB6978G0kYTy76Vvcrqvd6Xp4tvW560)

Since multi-select facets operate according to the inclusive `OR` logic, computing them requires sending several queries to Meilisearch. Sending these queries separately can lead to reduced performance and potentially higher hosting service costs.

Using the `/multi-search` endpoint, you can send multiple queries to the same index in a single request, thereby optimizing performance and reducing hosting costs.

<Callout>
  Prior to v1.1, achieving disjunctive facets behavior with [Instant Meilisearch](https://github.com/meilisearch/meilisearch-js-plugins/tree/main/packages/instant-meilisearch) was possible but required making multiple requests to the API. This workaround is no longer necessary since Instant Meilisearch now uses the `/multi-search` endpoint.
</Callout>

Check our documentation to learn more about [multi-search requests](https://docs.meilisearch.com/reference/api/multi_search) and [faceted search](https://docs.meilisearch.com/learn/advanced/faceted_search).

New feature: \_geoBoundingBox
-----------------------------

A bounding box is a rectangular box that completely encloses an area. With the new `_geoBoundingBox` filter expression, you can search for results contained within a rectangular geographic area.

![](https://lh4.googleusercontent.com/YuchesiNXoIqJBYTcBTjVRUkuNiwTTn9xUG0ac7nvq8KDVIJH9sMmsBexIzyfbqfxjxUewijKk5XXIuUjEQPi7uozvu48lrF8XpYg_CC8VR7TU5qzdT2e8S3o06ahx05_THxQR4d0307Qevq9RQLoIQ)

It takes two parameters corresponding to the coordinates of the top right (northeast) and bottom left (southwest) corners of the desired area: `_geoBoundingBox([lat, lng], [lat, lng])`

```bash
curl \
-X POST 'https://127.0.0.1:7700/indexes/restaurants/search' \
-H 'Content-type:application/json' \
--data-binary '{ "filter": "_geoBoundingBox([45.472735, 9.184019],[45.473711, 9.185613] )" }'
```

Check our [guide](https://docs.meilisearch.com/learn/advanced/geosearch#filtering-results-with-georadius-and-geoboundingbox) on filtering results based on geographic coordinates.

New feature: introducing facetStats
-----------------------------------

When using the `facets` parameter in a search query, the search results object will now include a `facetStats` object that provides minimum and maximum values for each numerical facet. Any facets without numerical values will be omitted.

Given an index of products, suppose that you want to know the highest and the lowest price to implement a range slider component:

```bash
curl \
 -X POST 'https://127.0.0.1:7700/indexes/products/search' \
 -H 'Content-Type: application/json' \
 --data-binary '{
 "facets": ["price"]
 }'

```

You would get the following response with all necessary information ready to use:

```JSON
{
   "hits": […],
   "facetDistribution": {
     "price": {…}
   },
   "facetStats": {
     "price": {
       "min": 1.99,
       "max": 1898.28
     }
   }
 }

```

Experimental feature: systems monitoring with Prometheus
--------------------------------------------------------

This release introduces a new `/metrics` endpoint that provides data compatible with [Prometheus](https://prometheus.ac.cn/), an open-source systems monitoring and alerting toolkit. This experimental endpoint gives valuable insights into the behavior and performance of Meilisearch.

To enable it, use the `--experimental-enable-metrics` flag when launching Meilisearch.

Are you planning to use this feature? Tell us about your experience in the [GitHub discussion](https://github.com/meilisearch/product/discussions/625).

Improvement: removing limits on index size and count
----------------------------------------------------

Meilisearch has lifted restrictions on the number and size of indexes per instance, so you can create as many indexes as you need 🥳

The only limit now is the memory address space allocated to a single process by your operating system. Read more about this in the [documentation](https://docs.meilisearch.com/learn/advanced/known_limitations#maximum-number-of-indexes-in-an-instance).

Other improvements
------------------

*   You can now use wildcards (`*`) at the end of index names when managing API keys and tenant tokens. For example, `"medical*"` will match the `medical_records` and `medical_equipement` indexes
*   You can override the default CSV delimiter–a single comma (`,`)– using the `csvDelimiter` parameter of the [add or update documents](https://docs.meilisearch.com/reference/api/documents#add-or-update-documents) or [add or replace documents](https://docs.meilisearch.com/reference/api/documents#add-or-replace-documents) endpoints
*   Interleaved document addition and deletion tasks are now automatically batched together, speeding up the indexing process
*   Our fantastic community has helped us enhance support for Greek and Arabic languages
*   Language detection now happens at indexing time, reducing erroneous language recognition during search and thus improving relevancy

We are really grateful for all the community members who participated in this release. We would like to thank: [@akhildevelops](https://github.com/akhildevelops), [@AymanHamdoun](https://github.com/AymanHamdoun), [@cymruu](https://github.com/cymruu), [@FrancisMurillo](https://github.com/FrancisMurillo) [@GregoryConrad](https://github.com/GregoryConrad), [@gregsadetsky](https://github.com/gregsadetsky), [@james-2001](https://github.com/james-2001), [@MixusMinimax](https://github.com/MixusMinimax), [@waveywaves](https://github.com/waveywaves), and [@ztkmkoo](https://github.com/ztkmkoo) for their help with [Meilisearch](https://github.com/meilisearch/meilisearch).

We also want to send a special shout-out to [@choznerol](https://github.com/choznerol), [@cymruu](https://github.com/cymruu), and [@james-2001](https://github.com/james-2001) for their work on [Charabia](https://github.com/meilisearch/charabia?ref=meilisearch-blog).

And that’s it for v1.1! Remember to check the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.1.0) for the full release notes. If you have any questions or would like to stay updated, you can join us on [Discord](https://discord.gg/meilisearch) or subscribe to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e). You can also check out our [roadmap](https://roadmap.meilisearch.com/) and participate in our [Product Discussions](https://github.com/meilisearch/product/discussions) to help us improve.

What’s new in v1.1

We are thrilled to announce that the first major release of Meilisearch is here! It's the result of the combined efforts of many people. We are proud to release a completely stable version of Meilisearch, which will guarantee compatibility with future versions of Meilisearch. We are very grateful to our community, which has supported us and helped us to get this far.

Let's take a look at some of the most significant changes. You can read the [full changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.0.0) on GitHub, but we’ll go over the main ones in this article.

New feature: Korean support
---------------------------

Meilisearch is multilingual and features optimized support for several languages. Thanks to our amazing contributor [qbx2](https://github.com/qbx2), we now support Korean as well.

Improvement: language support
-----------------------------

Thanks to the engagement, feedback, suggestions, and PRs of our global community, we improved language support all around with notable enhancements for Chinese:

*   Transliteration of Chinese characters into [Pinyin](https://en.wikipedia.org/wiki/Pinyin) to improve relevancy
*   Updates to [our Chinese segmenter](https://crates.io/crates/jieba-rs) to remove [inconsistencies](https://github.com/meilisearch/charabia/issues/136) when splitting text into words
*   Unification of [Chinese character variants](https://www.unicode.org/reports/tr38/#N10211) to enhance Chinese normalization

We are always looking to improve, join the [discussion](https://github.com/meilisearch/product/discussions/503) about Chinese support on GitHub!

We have also implemented a new, more efficient [normalizer](https://github.com/meilisearch/charabia/blob/main/CONTRIBUTING.md#implement-a-normalizer) to remove [accents and other non-spacing marks](https://www.compart.com/en/unicode/category/Mn), which refines the support of Hebrew, Thai, Arabic, and Latin languages.

Improvement: one-step upgrade
-----------------------------

Users of Meilisearch v0.20 or below had to perform a two-step migration to be able to use their database with any version above 0.24. Until now.

v1.0 is compatible with dumps from all previous versions.

We have simplified the process so that anyone can smoothly migrate to our first major version! Check the [updating guide](https://meilisearch.org.cn/docs/learn/update_and_migration/updating) in our documentation.

Improvement: indexing and search speed
--------------------------------------

*   We have reduced memory usage of search requests containing multiple long words.
*   We have also reduced the computational complexity of the exactness ranking rule and have implemented a caching system, resulting in a significant improvement in its performance, especially for search queries containing many words.
*   When interpreting a search query, multi-word synonyms are no longer treated as regular search query terms, but as phrases. Suppose you set San Francisco and SF as synonyms. The following search request:  
    `I am going to SF soon`  
    will now be interpreted as:  
    `I am going to "San Francisco" soon`  
    This change prevents requests containing multi-word synonyms from exceeding the word length limit and degrading the search performance. At the same time, it removes a source of denial-of-service attacks, and improves the relevancy of the search results.
*   We have tweaked the behavior of the proximity rule for search requests ending with prefixes or very short words to achieve substantial speed improvements.
*   Before v1.0, if the settings object contained searchable attributes, updating the settings always triggered a reindex. Now, Meilisearch checks if any changes were made to avoid unnecessary reindexing. Many thanks to [GregoryConrad](https://github.com/GregoryConrad) for implementing this!

Breaking changes
----------------

You will be happy to learn that the following are the last breaking changes we are going to introduce until v2.0 🥳 You can relax for a while, v2.0 is not yet planned ☺️

Check our [new versioning policy](https://github.com/meilisearch/engine-team/blob/main/resources/versioning-policy.md) to learn more about breaking changes.

### New master key security requirement

‌‌To strengthen security, Meilisearch now requires master keys to be at least 16 bytes when the [server environment](https://docs.meilisearch.com/learn/configuration/instance_options.html#environment) is set to `production`. If none is provided or if it doesn’t meet the criteria, Meilisearch will suggest a secure autogenerated master key you can use when restarting your instance.

### Primary key inference revamp

When creating an index or adding documents to Meilisearch, you can set the primary key explicitly or let Meilisearch infer it from your dataset.

To guess the primary key, also known as inference, Meilisearch used to look in your first document for an attribute **containing** the string `id`. Now, it looks for an attribute **ending** with the string `id`.

Furthermore, if multiple attributes ending with `id` are detected, Meilisearch will now throw an error asking you to specify the primary key manually using the [update index endpoint](https://docs.meilisearch.com/reference/api/indexes.html#update-an-index).

### New CLI behavior

`--max-index-size` and `--max-task-db` have been removed as they were not actually limiting the disk space taken by Meilisearch. This change has two consequences:

1.  Due to operating system limits on the amount of virtual memory allocatable by a single process, the number of indexes that can exist simultaneously in a Meilisearch database are:

*   around 200 for Linux/MacOS
*   around 20 for Windows

2\.  The size of an index is limited to 500GiB

While we plan on lifting the limitations described above in v1.1, if any of these changes affect you, it would be of great help to have [your input](https://github.com/meilisearch/product/discussions/567#discussioncomment-4634432). You can find more details in this [GitHub discussion](https://github.com/meilisearch/product/discussions/567).

Since its introduction in v0.26, the auto-batching feature has proven its worth. The `disable-auto-batching` CLI option and the associated environment variable `MEILI_DISABLE_AUTO_BATCHING`, which had been kept for debugging purposes, are no longer needed and have been removed.

‌‌We’re also removing the `--snapshot-interval-sec` flag to make scheduling snapshots simpler.

Before:‌‌

```bash
meilisearch --schedule-snapshot --snapshot-interval-sec 3600
```

After:

```bash
meilisearch --schedule-snapshot 3600
# If no value is provided, Meilisearch will take a new snapshot every 24 hours.
```

‌‌‌‌Finally, for the sake of consistency, the `--dumps-dir` CLI option has been renamed to `--dump-dir`, which is more idiomatic and in conformity with other existing options.

### Error handling improvements

To provide greater clarity and predictability in the use of the API, we have added granular error codes and a new `system` error type. You can consult the detailed list in the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.0.0).

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

A big thank you to all our contributors! We couldn't have reached this milestone without your support. You're really, truly great and we can't thank you enough for all the effort, suggestions, comments, and time you dedicate to Meilisearch. We are so grateful to have such an amazing community.

This month, we want to send a special shout-out to [@amab8901](https://github.com/amab8901), [@colbsmcdolbs](https://github.com/colbsmcdolbs), [@elbertronnie](https://github.com/elbertronnie), [@funilrys](https://github.com/funilrys), [@jiangbo212](https://github.com/jiangbo212), [@mohitsaxenaknoldus](https://github.com/mohitsaxenaknoldus), and [@shivaylamba](https://github.com/shivaylamba) for their help with [Meilisearch](https://github.com/meilisearch/meilisearch), and to [@amab8901](https://github.com/amab8901), [@GregoryConrad](https://github.com/GregoryConrad), and [@pnhatminh](https://github.com/pnhatminh) for their contributions to [Milli](https://github.com/meilisearch/milli). Likewise, we would like to thank [@choznerol](https://github.com/choznerol), [@crudiedo](https://github.com/crudiedo), [@daniel-shuy](https://github.com/daniel-shuy), [@harshalkhachane](https://github.com/harshalkhachane), [@mosuka](https://github.com/mosuka), [@qbx2](https://github.com/qbx2), [@Roms1383](https://github.com/Roms1383), [@Sokom141](https://github.com/Sokom141), and [@yenwel](https://github.com/yenwel) for their precious support on [Charabia](https://github.com/meilisearch/charabia).

Meilisearch 最新新闻和公司更新

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