[Symfony](https://symfony.ac.cn/) is an open-source web development framework for PHP that enables users to create web applications by utilizing sets of reusable components. Originating from France, Symfony combines a web application framework, a philosophy, and a vibrant community.

**With Meilisearch, Symfony gained better control over parsing and indexing of documentation content to speed up and improve the quality of search results.**

> _Using Meilisearch has been a delight so far. It all starts with its quick and simple installation process, followed by its great and thorough documentation, and it ends with a powerful search engine with mind-blowing performance. Meilisearch feels fun, fresh and uncomplicated._ \- Javier Eguiluz, Symfony Evangelist.

Challenge
---------

Fast and efficient information retrieval from [Symfony’s documentation](https://symfony.ac.cn/doc/current/index.html) is essential for the success of its rapidly growing community. The Symfony Documentation, including code samples, comprises well **over 12 million words**, and throughout its history, Symfony has published **30 different version updates**, making smooth information retrieval from the documentation crucial. In fact, to ensure the search function is as accessible as possible for [symfony.com](https://symfony.ac.cn/) visitors, the team integrated multiple keyboard shortcuts to open the search input across the entire website.

In the past, Symfony’s team explored various products and services, including Apache Solr, Elasticsearch, Google Custom Search Engine, and Algolia Search. However, the team realized the need for better control over the parsing and indexing of search content in order to tweak the search rules and improve website navigation and the overall search experience as a result. While using a fully hosted service is convenient, the pre-existing [configuration via JSON files](https://github.com/algolia/docsearch-configs/blob/master/configs/symfony.json) proved cumbersome and did not provide Symfony with the flexibility it required.

Secondly, there was potential to improve the overall quality of the search results, with the Symfony team receiving some complaints about inaccurate or missing results from the documentation users. Unfortunately, due to the aforementioned lack of control over the parsing and indexing processes, significant improvements to these results were unattainable and the need for a better alternative became evident.

Why Symfony chose Meilisearch
-----------------------------

Determined to find a better alternative, Symfony’s team started research on search engines. From the very beginning, the preference was for open-source projects developed in Go, because there was existing internal prior expertise in that language. Although there were many search solutions on the markets that matched those requirements, many of them lacked active development, didn’t have an active community, or missed essential features such as support for synonyms.

Finally, the team looked at Meilisearch, which provides an open-source search engine developed in Rust, along with an option to self-host, and quickly found a fit. Meilisearch’s established reputation as an open-source project with a vibrant community played a pivotal role in influencing the final decision.

Implementation
--------------

Meilisearch offers integrations with a wide range of programming languages and frameworks. In addition to hosting Symfony's index data, the Meilisearch team provided valuable assistance during the migration process so that a single developer who had no prior experience with Meilisearch was able to handle an entire migration and the implementation process, using the [Meilisearch PHP integration](https://github.com/meilisearch/meilisearch-php) with the Symfony [HttpClient component](https://symfony.ac.cn/http-client).

Here’s a brief overview of the technical integration process:

1.  **Parsing Symfony Documentation:** For Symfony’s documentation, the process included splitting the entire page contents into multiple small documents in order to produce better search results, closely related to the query terms.
2.  **Indexing Documents** was the simplest part of the process and included configuring [stopwords](https://specs.meilisearch.com/specifications/text/0123-stop-words-setting-api.html#_3-functional-specification) (list of words to be ignored in search queries) and synonyms to produce better search results.

For a full walkthrough of how Symfony implemented parsing of their documentation, documents indexing, and search engine interface, we recommend taking a look at their blog post on [migrating symfony.com search engine to Meilisearch](https://symfony.ac.cn/blog/migrating-symfony-com-search-engine-to-meilisearch).

Results
-------

The Symfony team focuses primarily on two different aspects of performance when evaluating the results:

*   **Performance of indexing contents:** Meilisearch can index approximately 800 full documents per second, which includes the processing of hundreds of stopwords and tens of synonyms.
*   **Performance of queries:** Symfony, including all versions of Symfony Docs and documentation for bundles, parses 12,754 files and generates 85,146 Meilisearch documents. The total time taken to parse, create, and index all these documents is 17 seconds. The search index consumes approximately 1.2 GB of disk space, with most queries returning results in less than 10 milliseconds.

> Nicolas Grekas, Core team member at Symfony, summarizes it as follows: _“These numbers are quite impressive and offer an excellent experience when updating search results in real-time as you type."_

To complete the evaluation process, the Symfony team conducted a comprehensive comparison between the previous search engine results and the new Meilisearch engine. They tested typical queries, rare queries, long queries, and queries with typos. Below is a [comparison of the search results](https://symfony.ac.cn/blog/migrating-symfony-com-search-engine-to-meilisearch#evaluating-the-result) obtained before (left) and after implementing Meilisearch (right):

![](https://unable-actionable-car.media.strapiapp.com/lp_X3_F9zw6h_F_Ka_8_Wrm3h_W4_X2_O8_KL_8_OA_4hfb_C9igir_cs_F_Pwp_M9_NQA_Rs_A_Rm_Nqdb1_Sjozss6q4_L_Pcg_n5_Vp_Pj_Selc_J_Ms_Wk_Cn_Zrbm_C_eb917263f4.jpg)

![](https://unable-actionable-car.media.strapiapp.com/Yyk95_A_Bhdkqzb_E2nia_BG_Rth_F_Iq_C_yu_Ek4_N_Duoya_AGIL_4ld_Xvi_Nn_TNJ_2_Ybw_R7_A2_M_Xtij4g_Xi_Hemu5t_H4_HBWAMBL_Zmv_Z6hv_Eks_Gzv_459f570ba3.jpg)

Since the new search experience was deployed, the team hasn't received any negative feedback from the community, which normally served as an indicator of the quality of the search experience.

Vision
------

Currently, Symfony's team is satisfied with Meilisearch's feature offering, which offers all the essential features needed, including indexing arbitrary information, sorting, result weighting, handling stopwords, synonyms, facets, filtering, and more.

> Javier Eguiluz, Symfony Evangelist, advises: "_Before integrating it into your projects, be aware of some_ [_known limitations_](https://docs.meilisearch.com/learn/advanced/known_limitations.html)_. If your project requirements are complex (e.g., cross-faceting between multiple indexes, etc.), make sure to check if these advanced features are already supported. Additionally, it's a good idea to review the_ [_Meilisearch public roadmap_](https://roadmap.meilisearch.com/)_._"

For a full walkthrough of how Symfony implemented parsing of their documentation, documents indexing, and search engine interface, we recommend taking a look at their blog post on [**migrating symfony.com search engine to Meilisearch**](https://symfony.ac.cn/blog/migrating-symfony-com-search-engine-to-meilisearch)**.**  
  
Ready to optimize your documentation search? [Start a free trial](https://meilisearch.org.cn/cloud) with Meilisearch today.

Cracking the code: Symfony's search solution for 12 million words of documentation

**🚀 Introducing vector search**
--------------------------------

![Introducing Meilisearch's Vector search and Vector database to navigate the future of search](https://unable-actionable-car.media.strapiapp.com/introducing_vector_search_8e7f819b5e.png)

We’re excited to bring experimental support for vector search. This release introduces **semantic and hybrid search capabilities** to further enhance the search experience.

[Read the announcement](/blog/introducing-vector-search/)

**🤗 Hugging Face success story**
---------------------------------

![Hugging Face success story facilitating AI accessibility with Meilisearch Cloud](https://unable-actionable-car.media.strapiapp.com/huggingface_1d18e1c3b0.png)

Meilisearch powers the discovery of **300,000+ AI models, datasets, and demos** in the Hugging Face repository. Read to learn why Hugging Face chose Meilisearch Cloud to power their search.

[Read the success story](/blog/hugging-face-democratizes-ai/)

**📚 Introducing Documentation Crawler**
----------------------------------------

0:00

/

1×

We’re introducing the **Meilisearch Documentation Crawler** to enable to users integrate search into any docs site—paste your URL and let it do its magic. ✨ Check out the video or read the blog post below.

[Learn more about the Documentation Crawler](/blog/documentation-crawler-release/)

* * *

**📰 In the news**
------------------

### 🔄 [Syncing Meilisearch with your MySQL database](https://meilisearch.org.cn/docs/learn/cookbooks/meilisync_mysql#newsletter&utm_source=september-news-23)

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

### 🔝 [How to boost your search results with filter scoring](/blog/filter-scoring-guide/#newsletter&utm_source=september-news-23)

Assign weights to your filters and prioritize documents based on how well they match your criteria.

### 🦜🔗 [A step-by-step tutorial to building semantic search](/blog/langchain-semantic-search-tutorial/#newsletter&utm_source=september-news-23)

Learn how to use **LangChain** with Meilisearch to build semantic search using similarity search.

### 🔍 [Refining facets by ID with InstantSearch](/blog/id-based-facets-guide/#newsletter&utm_source=september-news-23)

Read this community article how to use Meilisearch with Pydantic.

### 🔼 [Vercel marketplace integration](https://vercel.com/integrations/meilisearch-cloud)

The Vercel marketplace now lists 1 search integration—Meilisearch.

### 🍿 [Prose linting with Vale](/blog/prose-linting-with-vale/#newsletter&utm_source=september-news-23)

Learn the importance of a style guide in technical writing and how Vale ensures your content complies with it.

* * *

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

Our project of the month is [notiondesk.so](https://notiondesk.so/). Notiondesk allows you to turn a Notion database into a help center for customers.

Notiondesk use Meilisearch to power search in the help centers. Take a look at the search in [this example](https://help.bazarafrique.com/fr).

[![The easiest way to build a help center with Notion](https://unable-actionable-car.media.strapiapp.com/Screenshot_2023_09_18_at_18_27_37_1f3e6deccc.png)](https://notiondesk.so/)

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

* * *

**🏡 Life at Meili**
--------------------

Our off-sites are designed to help people bond and get to know each other better without taking away the spontaneity of the connection. We want people to have fun without feeling pressured to perform.

[![Brittany, France](https://unable-actionable-car.media.strapiapp.com/Bretagne_1_5c86b3b085.jpg)](/blog/organizing-a-people-first-offsite/)

Brittany, France

[Read the blog post](/blog/organizing-a-people-first-offsite/)

* * *

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

See our open positions:

*   [Head of Finance](https://jobs.lever.co/meili/14970d91-fb2f-4130-b568-97872eb62b31)
*   [Founding 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 September Updates

  
In this guide, we’ll delve into the concept of faceting in [Meilisearch](https://meilisearch.org.cn/) and how to use it to display facet names while filtering by IDs.

**What is faceting?**
---------------------

[Faceting](https://meilisearch.org.cn/docs/learn/fine_tuning_results/faceted_search) is a technique used in search engines to classify search results into multiple categories or “facets”. These facets can be anything from categories, tags, price ranges, to even colors. This makes it easier for the user to navigate and filter through the results, providing a more refined and efficient search experience.

**Why use the ID as a facet filter?**
-------------------------------------

For most applications and users, filtering by facet names, such as the genre of movies, is often sufficient and intuitive. Facet names are human-readable and provide a clear idea of what the filter does. However, in some use cases, it is preferable to filter by ID for a couple of key reasons:

*   **Less prone to errors**: IDs are usually simpler and standardized, making them less susceptible to typos or inconsistencies that might be present in facet names.
*   **Unique identifiers**: In some databases, facet names might be repeated with slightly different characteristics, whereas IDs are always unique. This is especially useful in cases where you have items with similar or identical names but different properties.

By using IDs for filtering but displaying the corresponding facet names to the users, you can achieve the best of both worlds: efficiency and usability. This way, you are leveraging the strengths of both IDs and names, making your application robust and user-friendly.

**The ID-to-name challenge**
----------------------------

In Meilisearch, facets are a specialized use-case of filters. Essentially, you can use any attribute added to the [`filterableAttributes`](https://meilisearch.org.cn/docs/reference/api/settings#filterable-attributes) list as a facet. When you add a facet parameter to your search query, Meilisearch will return a `[facetDistribution](https://meilisearch.org.cn/docs/reference/api/search#facetdistribution)` object. This object provides the number of matching documents distributed among the values of the given facet.

```JSON
 "facetDistribution":{
    "genres":{
      "Classics":6,
      "Comedy":1,
      "Coming-of-Age":1,
      "Fantasy":2,
      "Fiction":8,
      …
    }
  }
```

However, if the field you’ve added to the filterableAttributes list is an ID, the `facetDistribution` object will return these IDs.

```JSON
 "facetDistribution":{
    "genres":{
      "5":6,
      "6":1,
      "8":1,
      "13":2,
      "16":8,
      …
    }
  }
```

While IDs are great for back-end operations, they are not necessarily user-friendly or meaningful on the front-end. This is why you might want to map these IDs back to their corresponding facet names when displaying them in your user interface.

**ID-to-name mapping in the frontend**
--------------------------------------

Suppose you have a dataset of movies following the structure below:

```JSON
{
    "id": 5,
    "title": "Four Rooms",
    "overview": "It's Ted the Bellhop's first night on the job....",
    "genres": [
        {
            "id": 7,
            "name": "Crime"
        },
        {
            "id": 6,
            "name": "Comedy"
        },
    ],
    "release_date": 818467200,
}
```

You want a faceted search based on the movies genres. So you add genres.id to the [filterable attributes](https://meilisearch.org.cn/docs/reference/api/settings#filterable-attributes) list. Of course, in the UI you want to display genres.name, so that the user will see “Crime” on the UI instead of “7”.

Let’s dive into how you can accomplish this with InstantSearch and instant-meilisearch.

### **Using InstantSearch and instant-meilisearch**

[InstantSearch](https://github.com/algolia/instantsearch) 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) is the go-to search client for integrating InstantSearch with Meilisearch.

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

**1\. Import the required modules, including instantMeiliSearch**

```JavaScript
import { instantMeiliSearch } from '@meilisearch/instant-meilisearch'
import instantsearch from 'instantsearch.js'
import { searchBox, infiniteHits,refinementList } from 'instantsearch.js/es/widgets'
```

  
**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
const searchIndex = instantsearch({
  indexName: 'movies',
  searchClient
})

```

In this guide, we’ll use InstantSearch’s `refinementList` widget to map IDs to user-friendly names.

This widget comes with an optional parameter called `transformItems`. This function receives the `items` (or facets) and allows you to transform them before they are displayed on the UI. It also includes the full results data in its parameters.

Each item—or facet—includes the following properties:

*   `count`: the number of occurrences of the facet in the result set
*   `value`: the value used for refining (in our case it would be genres.id)
*   `label`: the label to display
*   `highlighted`: the highlighted label. This value is displayed in the default template

As you can see `highlighted` is the label used by default in the `refinementlList` widget.

With this information, we can use the `transformItems` function to display the `genres.name` instead of `genres.id`.

```JavaScript
transformItems(items, { results }) {
    // The 'results' parameter contains the full results data
    return items.map(item => {
      // Initialize genreName with the existing highlighted label
      let genreName = item.highlighted;
      
      // Loop through results.hits to find a matching genre
      for (let hit of results.hits) {
        const matchedGenre = hit.genres.find(genre => genre.id.toString() === item.value);
        
        // Update genreName if a match is found
        if (matchedGenre) {
          genreName = matchedGenre.name;
          break;
        }
      }
      
      // Return the updated item with the new tagName as the highlighted value
      return {
        ...item,
        highlighted: genreName
      };
    });
  }
```

With this configuration, you’ll efficiently map IDs to more user-friendly names, enhancing the search experience for your users.

The full code should look like this:

```JavaScript
import { instantMeiliSearch } from '@meilisearch/instant-meilisearch'
import instantsearch from 'instantsearch.js'
import { searchBox, infiniteHits,refinementList } from 'instantsearch.js/es/widgets'

const searchClient = instantMeiliSearch(
  'https://ms-7053a8dd7c09-72.lon.meilisearch.io',
  'meilisearchApiKey'
)
  
const searchIndex = instantsearch({
  indexName: 'movies',
  searchClient
})

const searchBox = instantsearch.widgets.searchBox({
  // ...
});

const hits = instantsearch.widgets.hits({
  // ...
});

const refinementList = instantsearch.widgets.refinementList({
  container: '#facets',
  attribute: 'genres.id',
  transformItems(items, { results }) {
    return items.map(item => {
      let genreName = item.highlighted; 
      for (let hit of results.hits) {
        const matchedGenre = hit.genres.find(genre => genre.id.toString() === item.value);
        if (matchedGenre) {
          genreName = matchedGenre.name;
          break;
        }
      }
      return {
        ...item,
        highlighted: genreName
      };
    });
  }
})

searchIndex.addWidgets([searchBox, infiniteHits, refinementList]);

searchIndex.start()

```

This example shows how to implement ID-to-name mapping with [vanilla JavaScript](https://www.algolia.com/doc/api-reference/widgets/refinement-list/js/#widget-param-transformitems), but you can achieve similar results with your preferred frontend framework. Checkout the respective documentation for [React](https://www.algolia.com/doc/api-reference/widgets/refinement-list/react/#widget-param-transformitems) and [Vue](https://www.algolia.com/doc/api-reference/widgets/refinement-list/vue/#widget-param-transform-items).

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

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

Refining facets by ID with InstantSearch

How to boost your search results with filter scoring

Headquartered in New York and Paris, Hugging Face is an open-source provider of machine learning technologies, allowing users to train, deploy, and share AI models using Hugging Face open-source libraries and the Hub.

**By partnering with Meilisearch Cloud, Hugging Face actively fulfills its visionary commitment to advancing the democratization of AI.**

![](https://unable-actionable-car.media.strapiapp.com/ezgif_com_video_to_gif_1_e697b647e1.gif)

> _“Today, Meilisearch is used by Hugging Face to power the discoverability of 300,000+ AI models, datasets, and demos. It’s really important for AI democratization because if the knowledge is out there, but you can’t access it - what’s the point?” -_ Mishig Davaadorj, software engineer at Hugging Face

Challenge
---------

On its platform, Hugging Face enables users to upload their AI models, datasets, and demos. With a vibrant community of users constantly engaging with the [Hugging Face Hub](https://hugging-face.cn) - a platform for sharing and searching for Machine Learning artifacts - the question of discoverability takes center stage.

The Hugging Face Hub hosts over 220,000 AI [models](https://hugging-face.cn/models) catering to a variety of [machine-learning tasks](https://hugging-face.cn/tasks), all neatly stored in repositories. These model repositories are designed to make the exploration and utilization of models as seamless as possible. Each AI model in the Hugging Face repository is accompanied by a model card, a project file containing valuable metadata, which plays a crucial role in enhancing discoverability, reproducibility, and sharing. Model cards also provide important information about AI models’ biases and limitations, model descriptions, and training guidelines, and serve as comprehensive guides for users seeking models on the Hub or uploading their own.

Before the introduction of Meilsearch, Hugging Face relied on a simple filtering and keyword search solution. However, it became evident that there was a growing demand for a more flexible and typo-tolerant full-text search solution. This solution needed to ensure higher default relevancy for every search query and leverage the additional attributes and metadata stored within model cards.

Why Hugging Face chose Meilisearch Cloud
----------------------------------------

Before integrating Meilisearch into its ML model repository, Hugging Face had already been utilizing Meilisearch's free-of-charge open-source solution as a search engine for its [open-source libraries](https://hugging-face.cn/docs)’ documentation, including transformers & diffusers. This documentation comprised approximately 500 pages and had been in use for over a year. Because the team had already gained positive experiences from implementing and operating Meilisearch, when the need for enhanced discoverability in model card searches arose, there was no requirement for additional testing or proof of concept.  

**1\. Customizability of the ranking rules**

During the evaluation process, the Hugging Face team also considered Mongo Atlas Search. However, they weren't satisfied with the customizations available for ranking. Several factors were taken into account in order to enhance model card discoverability, such as project names, descriptions, and the number of likes or downloads on each card. Meilisearch has demonstrated greater flexibility and adaptability in accommodating these search criteria.

**2\. Transitioning to Meilisearh Cloud for its ease of use**

Having achieved successful implementation of Meilisearch in its documentation, the Hugging Face team smoothly transitioned to incorporating Meilisearch Cloud into their model cards repository, benefiting from out-of-the-box relevancy.

**3\. Quality of support and infrastructure outsourcing**

The Hugging Face team chose Meilisearch Cloud, in part due to its dedicated support capabilities. Given that achieving highly relevant search results within the repositories is of utmost importance to Hugging Face, the decision to opt for the Cloud version was further influenced by entrusting the infrastructure to the expertise of Meilisearch Cloud specialists. Outsourcing the infrastructure allows the Hugging Face team to enhance their development speed.

Implementation
--------------

With Meilisearch already implemented for Hugging Face documentation, the expansion of the search solution to other use cases was smooth, once the ranking and internal rules were in place.

Today, the Meilisearch engine powers the discovery of 220,000 model cards, 38,000 datasets, and 60,000 demos in the Hugging Face repository. The keyword filtering mechanism has already been implemented on the Hugging Face homepage for users who are already familiar with specific model names.

However, with the implementation of Meilisearch, users are given the option to perform full-text searches as well. This search functionality operates not only on the model names and IDs but also includes the entire content of model cards.

The additional button to “Try full-text search” was implemented in order to minimize changes in the search behavior, where users are free to choose the search experience that most fits their preference.

![](https://unable-actionable-car.media.strapiapp.com/ezgif_com_video_to_gif_bce9ae142e.gif)

Vision
------

With the ongoing evolution of AI, Hugging Face anticipates a shift in search behavior and the methods people use to find the projects they need. Hugging Face is looking forward to incorporating Semantic Search into its documentation and model card searches. Currently, the platform is actively testing Meilisearch [VectorDB](/blog/langchain-semantic-search-tutorial/ as one of the potential solutions.

Hugging Face also anticipates gaining more detailed usage insights, encompassing information about users' daily search patterns and the level of activity certain model card projects receive over time.

Hugging Face facilitates AI accessibility with Meilisearch

In this era of information overload, the efficiency of documentation is determined not just by its quality but also by how easily users can find what they're searching for. Enhancing your documentation with advanced search capabilities can significantly elevate user experience.

Meilisearch Cloud's **Documentation crawler** offers just that, bridging the gap between vast content and user-friendly searchability.

What is a crawler?
------------------

A web crawler, sometimes known as a 'spider' or 'bot', is a digital tool that roams the vast expanse of the internet, indexing and cataloging content for search engines. This ensures that the most relevant pieces of information are presented to users during their searches.

What is Meilisearch’s Documentation crawler?
--------------------------------------------

Meilisearch's Documentation crawler is not just a traditional crawler; it also functions as an efficient scraper. While it extracts vital data from web pages, it simultaneously honors the inherent structure of the documentation, ensuring precise and structured indexing.

This system allows users to integrate [search functionality](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#cloud-release&utm_source=blog&utm_medium=crawler-release#features) into their documentation, ensuring that their audiences can swiftly locate the information they need.

How to use the documentation crawler?
-------------------------------------

Using the Documentation Crawler is a breeze; all it requires is a Meilisearch Cloud project and your existing documentation's URL.

### Setting up your project

First of all, you need a running project in [Meilisearch Cloud](https://meilisearch.org.cn/cloud#cloud-release&utm_source=blog&utm_medium=crawler-release). Once you have created a project, navigate to your project's index list and create a new index.

![Step 1 of Create Index form: Enter the index name.](https://unable-actionable-car.media.strapiapp.com/create_an_index_57ca6f70f6.png)

### Crawling your content

When prompted to import your documents, choose 'Documentation Crawler.'

![Step 2 of Create Index form: Import documents via crawler or file upload.](https://unable-actionable-car.media.strapiapp.com/select_docs_crawler_e7c7fcfa52.png)

Then, input or paste your documentation's URL and click on ‘Next’.

![Step 2 of Create Index form: Import documents using a crawler by pasting the website URL](https://unable-actionable-car.media.strapiapp.com/paste_url_2f1c23072e.png)

Click on 'Run' to start crawling your documentation content and push it into a Meilisearch instance. This process may take several minutes.

![Setup interface for the Crawler with a 'Run' button.](https://unable-actionable-car.media.strapiapp.com/click_on_run_eeecb9b7eb.png)

### Post-crawling verification

Once the status updates to 'ready', you can return to your project's overview and access the 'search preview'. Click on the project’s URL or select the 'Preview search' option from the top menu.

![Project overview](https://unable-actionable-car.media.strapiapp.com/go_to_search_preview_255bd45a28.png)

Perform a few searches to ensure that your documentation has been indexed correctly. This step is especially useful for understanding the schema of your documents.

![Search preview displaying results for the query 'releva' with highlighted matches.](https://lh4.googleusercontent.com/BJ9jNV_RA4q42ONIw54vQXyCSEu41Q_6xKY5W6sMxK02XqufBV8iedNRSZZl6q2NtqKaZEjpOVDLEC7TFQmrj7nci5-FT87gFs8UnqfDa0M1PRrm-NLUD6odr2a4m1bbSIGgwlqxtyIqmEYQ3sI4YIc)

Adding a search bar to your documentation
-----------------------------------------

Once you've successfully indexed your documentation using Meilisearch's Documentation Crawler, the next step to enhancing user experience is integrating a search bar into your documentation.

Return to your project and navigate to your index settings. From there, select the 'tools' tab and then choose 'crawler'. This will reveal the crawler settings and associated resources.

![Setup interface for the Crawler with the 'UI starter kit: Doscsearch' option.](https://lh3.googleusercontent.com/woHfyr-bTDS7fbDTRnpq-GR61IwVq6fvfmCGoRDkiVORf7x6wSjSsXUgXRnmepr7YQ_E3HZHRBW3FScZSE1vS8GIJwvGs6GfUCIh0eJckapWsJViZyaZ-Bqa6Vdj_2EpXgHG4WLQTgUUN9ubYfXCQcA)

The [UI starter kit](https://github.com/tauri-apps/meilisearch-docsearch#cloud-release&utm_source=blog&utm_medium=crawler-release) helps you add a functional search bar to your documentation with just a few lines of code.

You are now fully equipped to add a seamless search experience to your documentation!

\_\_\_\_\_

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

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

Introducing Documentation Crawler — Meilisearch Cloud

This feature enhances your dashboard experience by offering a comprehensive view of Meilisearch's [asynchronous operations](https://meilisearch.org.cn/docs/learn/async/asynchronous_operations#cloud-release&utm_source=blog&utm_medium=tasks-view-release).

**Core features**
-----------------

*   **Detailed task list**: the Tasks View showcases a list of your tasks, detailing their UID, status, type, index UID, duration, and enqueued date. Moreover, you can sort the list based on these parameters.
*   **JSON panel**: alongside the list, there's a panel displaying the selected task's JSON, which can easily be copied.
*   **Filtering**: you can sift through tasks based on their status, type, and date.
*   **Refresh option**: whenever a task is enqueued, the `Show new tasks` button lets you decide when to view the latest task without disrupting your current view.
*   **Customization**: customize the view further by toggling the columns' visibility according to your preferences.

![Tasks view screenshot](https://storage.noticeable.io/projects/OQ6S6bocohVRzziV6Tlc/publications/BLDwc6IdPRkf5ZsdEOoa/01h821n7ed9mje0m5y9dagk9rt-image.png)

**Compatibility notes**
-----------------------

*   **Version requirements**: this feature is exclusively available for projects that run on Meilisearch v1.0.0 or higher.
*   **Latest version perks**: for those using the [Meilisearch v1.3.0](/blog/v1-3-release/#cloud-release&utm_source=blog&utm_medium=tasks-view-release) or above, the Tasks View also displays the count of fetched tasks.

We hope this addition enhances your project management efficiency. Enjoy exploring!

Introducing Tasks View — Meilisearch Cloud

At Meilisearch, we have long maintained our commitment to simplifying and streamlining the search process by prioritizing intuitive, user-friendly, and human-like search experiences.

In today's search technology landscape, _[keyword search](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/overview#vector-search&utm_source=blog&utm_medium=launch#features)_ continues to play a pivotal role, swiftly locating content via specific keywords. Leveraging advanced algorithms and robust indexing, it ensures users find relevant information across vast data volumes.

However, traditional keyword approaches have inherent limitations in understanding user intent and at times can be overly predictable and simplistic. This is why we are introducing Meilisearch's _[vector search](https://meilisearch.org.cn/docs/learn/experimental/vector_search#vector-search&utm_source=blog&utm_medium=launch),_ which aims to challenge and enhance the familiar keyword search paradigm.

Vector search for AI-enhanced future
------------------------------------

The market-wide introduction of _vector search_ marks the arrival of a new era for search technology, with countless versatile applications. In _e-commerce_, it offers product recommendations based on similarities and multi-modal search capabilities such as image, audio, and video search. The fusion of vector search with conversational AI technologies opens doors for intuitive Q&A applications. Adding user data like geolocation and search history further expands its potential.

New search technology enhances relevancy alongside traditional keywords with semantic understanding going beyond simple keyword matching. It is capable of comprehending intent, returning results like **\[gloves\]** from a **\[warm clothes\]** query. These applications rely on _vector embeddings_—a type of data representation with semantic information that enables AI to gain understanding and maintain long-term memory. Stored in _vector databases_, embeddings represent different data dimensions, essential for understanding patterns and relationships. AI-powered vector database then leverages this information when matching search queries to answers.

Democratizing search
--------------------

In a data-rich world, effective content discovery is not a luxury - it’s crucial. Meilisearch's vision balances ambition with accessibility, aiming to make cutting-edge search technology inclusive. Key Meilisearch partners, such as [Hugging Face](/blog/hugging-face-democratizes-ai/ are already actively testing Meilisearch’s new vector database and storage capabilities.

> **Quentin de Quelen, Meilisearch’s CEO and Co-founder, said:** _“Meilisearch's hybrid search will democratize advanced search technology, focusing on simplicity and open-source customizability.”_

Building the future of search with vector search
------------------------------------------------

This release brings semantic and hybrid search capabilities to enhance applications for image, video, and audio search, product recommendations for e-commerce, precise personalization, and intuitive Q&A interactions.

Here are a few examples of potential applications made possible with the introduction of vector search:

*   **Semantic understanding:** Enable your search engine to act as a human-like assistant, delivering natural, context-aware results in a more conversational manner
*   **Multiformat capabilities:** Cover images, audio, and video, ensuring versatility.
*   **Personalization**: Matches preferences for tailored results, with a deeper understanding of user behavior and preferences,
*   **Precise Recommendations:** Enhance the discovery experience by suggesting similar items that align with users' genuine interests.
*   **Improved accuracy:** Improve relevancy and search accuracy through a deep understanding of each search query's meaning

Search evolution culminates in a hybrid search
----------------------------------------------

Meilisearch's journey leads to _hybrid search_, which combines traditional full-text efficiency with the innovation of semantic search and the adaptability of vector database. The hybrid approach enables search solutions to cater to a broad spectrum of use cases driven by customer-generated data sources. By integrating models from AI solution providers like OpenAI, Cohere AI, or Hugging Face, Meilisearch empowers users to calibrate _vector embeddings_ and infuse their business logic into the search engine.

Getting started
---------------

<Callout>
  For more guidance on how to get started, explore our [step-by-step tutorial on building semantic search with LangChain](/blog/langchain-semantic-search-tutorial/#vector-search&utm_source=blog&utm_medium=launch)
</Callout>
```json
from langchain.vectorstores import Meilisearch
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import JSONLoader

# Load documents
loader = JSONLoader(
    file_path="./movies.json",
    jq_schema=".[] | {id: .id, title: .title, overview: .overview}",
    text_content=False,
)
documents = loader.load()

# Index documents
embeddings = OpenAIEmbeddings()
vector_store = Meilisearch.from_documents(documents=documents, embedding=embeddings)

# Search
query = "superhero fighting villains in a city corrupted by crime"
results = vector_store.similarity_search(
    query=query,
    k=3,
)

```

If you want to use Meilisearch vector search capabilities without LangChain, choose your favorite language and check out the [relevant SDK documentation here](https://meilisearch.org.cn/docs/learn/what_is_meilisearch/sdks#vector-search&utm_source=blog&utm_medium=launch).

Want the quality of open-source without the headache of managing your servers and service? [Sign up for a free trial](https://meilisearch.org.cn/cloud#vector-search&utm_source=blog&utm_medium=launch) with Meilisearch Cloud today.

Introducing Meilisearch's vector search and vector database to navigate the future of search

[HitPay](https://hitpayapp.com/) is a one-stop payment fintech platform from Southeast Asia on a mission to enable small and medium businesses to accept payments easily.

**HitPay chose [Meilisearch Cloud](https://meilisearch.org.cn/cloud) to power its customers' search securely and reliably, all while maintaining a lean engineering team and staying focused on its core offering.**

<Callout>
  ___"Using Meilisearch's open-source version for proof of concept, a single developer was able to quickly implement the complete flow, including UI and backend changes. This validated our use case and confirmed that Meilisearch was the right solution for our requirements. After that, transitioning to the Cloud was effortless."__ - Nitin Muthyala, Co-Founder and CTO at HitPay_
</Callout>

Challenge
---------

As an small and medium business-focused company, HitPay strives to simplify the learning curve for small business owners who lack time to learn and adapt complex payment tools.

Merchants utilizing HitPay often have sizable catalogs and a broad customer base, requiring efficient search functionality for e-commerce and in-store purchases. With search functionality not being within HitPay's core suite of solutions, it was decided to integrate a third-party tool. While searching for a suitable solution, the HitPay team came across Meilisearch.

Why Hit Pay chose Meilisearch
-----------------------------

### 1\. Easy to integrate

Historically, HitPay has been able to maintain a lean engineering team by actively seeking out nimble and adaptive tools to streamline its developers' work. When searching for a search solution to integrate into their existing back-end system built on PHP with the Laravel framework, HitPay prioritized simplicity and compatibility. They discovered that Meilisearch offered a driver for [Laravel Scout](https://laravel.net.cn/docs/master/scout) that perfectly matched their requirements. Additionally, HitPay’s team was well-acquainted with the open-source space, and adopting an open-source search solution seemed like a logical initial step.

Nitin Muthyala, Co-Founder and CTO of HitPay, emphasized: _'We always begin with the open-source version of a solution, which has proven successful for us thus far. After testing Meilisearch's open-source solution on the internal use case, we realized how powerful it is.”_

### 2\. Developer experience

During the initial evaluation, Meilisearch was integrated into HitPay's internal dashboard, which contains a database of all its customers. The dashboard serves HitPay’s own account managers who need to search through their accounts by business owner names, business names, and affiliated emails. This presented challenges with long and repetitive names that included variations in spelling. With the efforts of a single developer, supported by helpful documentation and Meilisearch’s [out-of-the-box relevancy](https://meilisearch.org.cn/docs/learn/core_concepts/relevancy), the pilot project didn’t require many tweaks and was successfully completed within a couple of days.

Implementation
--------------

After successfully testing the open-source version of Meilisearch, HitPay made the decision to switch to the Meilisearch Cloud. A highly relevant and reliable search solution was crucial for HitPay's customers as it directly impacted their sales. To avoid the additional burden of constantly monitoring the production environment, HitPay transitioned to the Cloud version. This decision provided peace of mind, knowing that the business-critical search activity would be in safe hands at all times.

Nitin Muthyala, Co-Founder and CTO of HitPay, explained: _"Search is a fundamental piece in our production process, and HitPay couldn't afford for it to ever go down. So we opted for Meilisearch Cloud."_

During the transition, other competitors were briefly considered but eliminated early on. Meilisearch Cloud stood out as the ideal choice due to its ease of integration and the ability to check all the boxes right from the start.

Since the implementation of Meilisearch, HitPay's customers have especially appreciated the ability to search for amounts within an order, typo recognition, and the **increase in search API response speed by at least 50%** for free-text searches.

![](https://unable-actionable-car.media.strapiapp.com/Transactions_Search_gif_d6e3b9c704.jpg)

Transactions search

Results
-------

Today, Meilisearch is a valuable asset for HitPay’s customers and is integrated into two of HitPay's commerce products. The first is an **[online eCommerce shop](https://www.hitpayapp.com/online-store)**, where businesses can create their own storefronts. The second is **[HitPay's POS](https://www.hitpayapp.com/pos-software)** (point of sale), the product that allows businesses to search, catalog and **sync products across online and offline sales channels, and manage unlimited locations** for brick-and-mortar stores, where sales assistants need to **locate specific products** quickly to ensure the overall customer experience.

![](https://unable-actionable-car.media.strapiapp.com/Products_Search_gif_9639f112fe.jpg)

Meilisearch in HitPay's Point of Sale

To always keep the search relevant, HitPay offers a feature where merchants can pin or favorite specific products, which are then prioritized in search results. For customers who opt-in, HitPay sets up a workflow to automatically update the index with the most recent SKUs (Stock-keeping units) or favorited products, ensuring highly relevant search results that are always up-to-date.

Vision
------

In the future, HitPay aims to provide customizable user-facing analytics that caters to their customer's specific needs. This is particularly important for small and medium businesses, whose analytics requirements vary due to seasonal business patterns. HitPay envisions offering customization options for running analytics on any software, allowing customers to identify their most sought-after products and analyze unsuccessful searches. This aligns with Meilisearch’s product vision and HitPay's mission of powering product search for online shops and simplifying catalog searches for sales assistants in physical stores.

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