This feature enhances your dashboard experience by offering a comprehensive view of Meilisearch's [asynchronous operations](https://www.meilisearch.com/docs/learn/async/asynchronous_operations?utm_campaign=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](__GHOST_URL__/v1-3-release/?utm_campaign=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

In this guide, we'll walk you through implementing promoted search results with Meilisearch. Our goal is to search results prioritizing specific documents when certain keywords match a user's query. These boosted documents should be returned at the top of the search results.

<Callout>
  This guide explains to implement promoted documents on the backend. For a frontend-first implementation, see [implementing promoted search results with React InstantSearch](__GHOST_URL__/promoted-search-results-with-react-instantsearch/).
</Callout>

Overview
--------

Here's a simplified breakdown of how to implement document boosting using a second index for “pinned documents” and the [multi-search feature](https://www.meilisearch.com/docs/reference/api/multi_search#perform-a-multi-search):

1.  **Create Indexes:** Set up two indexes: one for regular search and one for boosted results. The boosted index will have a special attribute, `keywords`, to trigger the boosting.
2.  **Populate the 'games' Index:** Populate a `games` index with your dataset using the provided JSON file. This index will serve as the source for our boosted documents.
3.  **Configure the 'pinned\_games' Index:** Configure the `pinned_games` index to display attributes without revealing keywords. Adjust the searchable and displayed attributes accordingly.
4.  **Boost Documents:** Identify documents you want to boost and assign relevant keywords to them. For instance, you can assign the keywords `fps` and `shooter` to the game `Counter-Strike`.
5.  **Implement the Multi-Search:** Utilize Meilisearch's multi-search feature to perform a search query across both the regular and boosted indexes. This way, boosted documents matching keywords will appear first.
6.  **Display Results:** Present the search results in a user-friendly format, highlighting boosted documents with a visual indicator.

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

### Installation

Before diving in, make sure you have Meilisearch up and running. If you haven't installed it yet, follow these steps:

1.  Launch a Meilisearch instance — you can run [Meilisearch in local](https://www.meilisearch.com/docs/learn/getting_started/installation) or via [Meilisearch Cloud](https://www.meilisearch.com/cloud).
2.  Ensure you have your favorite [language SDK](https://www.meilisearch.com/docs/learn/what_is_meilisearch/sdks) (or framework integration) installed.

<Callout>
  This guide uses the [Python SDK](https://python-sdk.meilisearch.com/), but it works the same with any other Meilisearch integration. 🎉
</Callout>

### Initializing indexes

For our example, we'll work with a dataset of Steam games. You can adapt this process to your own data:

1.  Download the `steam-games.json` and `settings.json` files for our [Steam games dataset](https://meilisearch.notion.site/Steam-6e6141755a854909b31ba8c8e9e74853)
2.  Load the dataset in your Meilisearch instance by [adding documents](https://www.meilisearch.com/docs/reference/api/documents#add-or-replace-documents) from the `steam-games.json` file.

#### `games` index

```python
import meilisearch
import json
from typing import Callable

client = meilisearch.Client(url="http://localhost:7700")
games = client.index("games")

# helper to wait for Meilisearch tasks
def wait_with_progress(client: meilisearch.Client, task_uid: int):
    while True:
        try:
            client.wait_for_task(task_uid, timeout_in_ms=1000)
            break
        except meilisearch.errors.MeilisearchTimeoutError:
            print(".", end="")
    task = client.get_task(task_uid)
    print(f" {task.status}")
    if task.error is not None:
        print(f"{task.error}")
            
print("Adding settings...", end="")
with open("settings.json") as settings_file:
    settings = json.load(settings_file)
    task = games.update_settings(settings)
    wait_with_progress(client, task.task_uid)


with open("steam-games.json") as documents_file:
    documents = json.load(documents_file)
    task = games.add_documents_json(documents)
    print("Adding documents...", end="")
    wait_with_progress(client, task.task_uid)
```

#### `pinned_games` index

This index will contain the promoted documents. The settings of the `pinned_games` index are the same as the `games` index, with the following differences:

*   the only `searchableAttributes` is the `keywords` attribute containing the words that trigger pinning that document.
*   the `displayedAttributes` are all the attributes of the documents, except for `keywords` (we don't want to show the keywords to end-users)

```python
pinned = client.index("pinned_games")

print("Adding settings...", end="")
with open("settings.json") as settings_file:
    settings = json.load(settings_file)
    settings["searchableAttributes"] = ["keywords"]
    # all but "keywords"
    settings["displayedAttributes"] = ["name", "description", "id", "price", "image", "releaseDate", "recommendationCount", "platforms", "players", "genres", "misc"]
    task = pinned.update_settings(settings)
    # see `wait_with_progress` implementation in previous code sample
    wait_with_progress(client, task.task_uid) 
```

### Updating the promoted documents index

We'll now populate the index with documents from the `games` index that we want to promote.

As an example, let's say we want to pin the game `"Counter-Strike"` to the `"fps"` and `"first", "person", "shooter"` keywords.

```python
counter_strike = games.get_document(document_id=10)
counter_strike.keywords = ["fps", "first", "person", "shooter"]

print("Adding pinned document...", end="")
task = pinned.add_documents(dict(counter_strike))
wait_with_progress(client, task.task_uid)
```

### Customizing search results

Now, let’s create a function to return the search results __with__ the pinned documents.

```python
from copy import deepcopy
from typing import Any, Dict, List
from dataclasses import dataclass

@dataclass
class SearchResults:
    pinned: List[Dict[str, Any]]
    regular: List[Dict[str, Any]]

def search_with_pinned(client: meilisearch.Client, query: Dict[str, Any]) -> SearchResults:
    pinned_query = deepcopy(query)
    pinned_query["indexUid"] = "pinned_games"
    regular_query = deepcopy(query)
    regular_query["indexUid"] = "games"
    results = client.multi_search([pinned_query, regular_query])
    # fetch the limit that was passed to each query so that we can respect that value when getting the results from each source
    limit = results["results"][0]["limit"]
    # fetch as many results from the pinned source as possible
    pinned_results = results["results"][0]["hits"]
    # only fetch results from the regular source up to limit
    regular_results = results["results"][1]["hits"][:(limit-len(pinned_results))]
    return SearchResults(pinned=pinned_results, regular=regular_results)
```

We can use this function to retrieve our search results with promoted documents:

```python
results = search_with_pinned(client, {"q": "first person shoot", "attributesToRetrieve": ["name"]})
```

The `results` object should look like:

```json
SearchResults(pinned=[{'name': 'Counter-Strike'}], regular=[{'name': 'Rogue Shooter: The FPS Roguelike'}, {'name': 'Rocket Shooter'}, {'name': 'Masked Shooters 2'}, {'name': 'Alpha Decay'}, {'name': 'Red Trigger'}, {'name': 'RAGE'}, {'name': 'BRINK'}, {'name': 'Voice of Pripyat'}, {'name': 'HAWKEN'}, {'name': 'Ziggurat'}, {'name': 'Dirty Bomb'}, {'name': 'Gunscape'}, {'name': 'Descent: Underground'}, {'name': 'Putrefaction'}, {'name': 'Killing Room'}, {'name': 'Hard Reset Redux'}, {'name': 'Bunny Hop League'}, {'name': 'Kimulator : Fight for your destiny'}, {'name': 'Intrude'}])
```

Tada 🎉 You now have a search results object that contains two arrays: the promoted results and the “regular” ones.

<Callout>
  Got stuck? Don’t hesitate to ask for help in our [Discord community](https://discord.meilisearch.com/).
</Callout>

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

This tutorial explored one approach for implementing promoted results. An alternative technique would be implementing documents pinning in the frontend; take a look at our [React implementation guide](__GHOST_URL__/promoted-search-results-with-react-instantsearch/). This different approach has the benefits of being [compatible with InstantSearch](https://github.com/meilisearch/meilisearch-js-plugins/tree/main/packages/instant-meilisearch).

Both techniques can achieve similar results. We also plan to integrate [promoted documents](https://portal.productboard.com/meilisearch/1-meilisearch/c/98-promoting-documents?utm_medium=social&utm_source=portal_share) in the Meilisearch engine. Give your feedback on the previous link to help us prioritize it.

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/).

Cheers!

How to implement document boosting with a second index

Today we're diving into a complex, yet essential, topic: utilizing Meilisearch in a multi-tenant application. By the end of this guide, advanced and intermediate developers will have a clear understanding of how to configure Meilisearch to manage multi-tenancy effectively.

Multi-tenancy: what is it?
--------------------------

In a multi-tenant architecture, a single instance of a software application serves multiple customers or "tenants". Each tenant's data is isolated and remains invisible to other tenants.

### Multi-tenancy use cases

A typical use case for [multi-tenancy](https://www.meilisearch.com/docs/learn/security/tenant_tokens#what-is-multitenancy) is in [Software as a Service (SaaS) applications](https://saas.meilisearch.com/), where multiple companies share the same database. Each company becomes a tenant with data that should not be accessible by others. In such scenarios, you're likely already implementing this specificity in your primary data store.

Multi-tenancy with Meilisearch: the ultimate approach
-----------------------------------------------------

While it's possible to [create one index](https://www.meilisearch.com/docs/learn/core_concepts/indexes#index-creation) per company with Meilisearch, it's not recommended due to [performance](https://www.meilisearch.com/docs/learn/advanced/indexing) reasons. Meilisearch processes [tasks](https://www.meilisearch.com/docs/learn/async/asynchronous_operations) one index after another and cannot maximize the indexation throughput, as cannot process tasks on different indexes in parallel.

Instead, the best way is to put all tenants in the same index and use [Meilisearch's powerful tenant tokens](https://www.meilisearch.com/docs/learn/security/tenant_tokens#tenant-token-payload-reference) to restrict access to a specific user's scope only. For this, stored documents should have not only an [ID](https://www.meilisearch.com/docs/learn/core_concepts/primary_key) but also a company identifier to separate users by their company.

<Callout>
  Experience multi-tenancy in action through our demos:  
\- [SaaS demo](https://saas.meilisearch.com/)  
\- [Tenant tokens demo](https://tenant-token.meilisearch.com/)
</Callout>

### Preparing Meilisearch for multi-tenancy

Let's illustrate this with an example akin to a Slack application. In this application, users belong to a company, and each user can create text messages in different threads. Our goal is to create an index containing all messages but allow only people from a specific company to access their company's messages. Subsequently, we might want to perform a search applying [filters](https://www.meilisearch.com/docs/learn/fine_tuning_results/filtering) on users and threads.

![Database diagram showing the relationships between companies, users, and messages.](https://unable-actionable-car.media.strapiapp.com/DB_diagram_8e2aa6bb68.png)

First, we need to [transform our data](https://www.meilisearch.com/docs/learn/core_concepts/documents#dataset-format) to make it optimal for indexing by Meilisearch. Here's what a sample document could look like:

```JSON
{
	"id": 48735873,
	"user":{
		"id": 5749,
		"name": "bob"
	},
	"company": {
		"id": 838,
		"name": "Acme Inc"
	},
	"thread": "meilisearch-is-awesome",
	"data": "Meilisearch is so easy to use! 🔥",
	"created_at": 1543230000
}
```
<Callout>
  Learn how to [update your settings](https://www.meilisearch.com/docs/reference/api/settings#update-settings) in the documentation.
</Callout>

Next, you'll need to [create a messages index](https://www.meilisearch.com/docs/learn/core_concepts/indexes#index-creation) and add the following settings:

```JSON
{
  "displayedAttributes": [
    "id",
		"user.name",
		"thread",
		"data",
		"created_at"
  ],
  "searchableAttributes": [
    "data"
  ],
  "filterableAttributes": [
		"user.name",
		"company.id",
		"thread"
	],
  "sortableAttributes": [
		"created_at"
	]
}
```

Here's what each setting does:

*   **`[displayedAttributes](https://www.meilisearch.com/docs/reference/api/settings#displayed-attributes)`**: These are the attributes that will appear in your application's search results. We're omitting **`user.id`** and **`company.id`** for security reasons, and **`company.name`** isn't necessary as the user is already aware of their company.
*   **`[searchableAttributes](https://www.meilisearch.com/docs/reference/api/settings#searchable-attributes)`**: These are the attributes that the search function will use. In our case, only the **`data`** (or message text) is searchable.
*   **`[filterableAttributes](https://www.meilisearch.com/docs/reference/api/settings#filterable-attributes)`**: These are the attributes that you can filter by. We've included **`company.id`** for the tenant tokens, **`user.name`**, and **`thread`** for on-the-fly filtering.
*   **`[sortableAttributes](https://www.meilisearch.com/docs/reference/api/settings#sortable-attributes)`**: These are the attributes you can sort by. We've only included **`created_at`** as you might want to sort messages chronologically.

### Creating tenant tokens

With your Meilisearch configuration in place, the next step is to create a new tenant token for each company. If you don't have particular security concerns, you can omit the expiration date to simplify your onboarding process.

<Callout>
  Learn to [generate tenant tokens with an SDK](https://www.meilisearch.com/docs/learn/security/tenant_tokens#generating-tenant-tokens-with-an-sdk).
</Callout>

The crucial part of the tenant tokens is the `searchRules`. In our example, you'll add the following rule:

```JSON
{
  "messages": {
    "filter": "company.id = 838"
  }
}
```

With the generated token, your search will restrict access based on `company.id`, so users will only see messages within their company.

Store this token in your primary data store (Postgres, MySQL, etc.). This mechanism is similar to those used in tools like Stripe.

Each time a user logs into your app, load this key into local storage and use it for the searches, ensuring that each user only has access to data relevant to their company.

Conclusion
----------

[Creating a multi-tenant application using Meilisearch](__GHOST_URL__/multi-tenancy/) is quite straightforward once you understand how to structure your data and configure your search attributes correctly. By following this guide, you can ensure that your application provides a secure, efficient, and user-friendly search experience. If you have any question, you can join us on [Discord](https://discord.gg/meilisearch/?ref=blog.meilisearch.com).

Boost productivity and streamline development with **[Meilisearch Cloud](https://www.meilisearch.com/cloud)**. No more server deployment or manual updates. Try it **free** for 14 days, no credit card needed.

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

_Happy coding!_

Multi-tenancy guide

Today we’ll learn how to promote results by pinning given documents at the top of search results. This can be used in ecommerce to give more visibility to featured products or in-app search to prioritize user-specific content. We’ll use Node.js and React to implement promoted documents.

<Callout>
  Need promoted documents inside the Meilisearch engine? [Give your feedback](https://portal.productboard.com/meilisearch/1-meilisearch/c/98-promoting-documents?utm_medium=social&utm_source=portal_share)!
</Callout>

This tutorial will use a sample movie dataset. Our objective will be to have “Finding Nemo” at the top of the results when typing “funny”, “children”, or “fish”.

Folder structure
----------------

In this guide, we’ll separate the frontend code from the backend code used to configure Meilisearch.

We’ll organize our project using the following folder structure:

```json
my-app/
├─ node_modules/
├─ data/ <-- Backend code goes there
│  ├─ setup.js
│  ├─ movies.json
│  ├─ promoted-movies.json
├─ src/ <-- React code will go there
│  ├─ App.jsx
│  ├─ main.js
│  ├─ ...
├─ package.json
```

First, create a directory for our application (eg. `my-app`) or use an existing one. In our application directory, let’s create a `data` folder for the backend code.

Download the JSON dataset and store them in our new `data` folder. Download the datasets here:

[

Movies

movies.json

15 MB

download-circle

](__GHOST_URL__/content/files/2023/07/movies.json "Download")

[

Promoted movies

promoted\_movies.json

1 KB

download-circle

](__GHOST_URL__/content/files/2023/07/promoted_movies.json "Download")

Alright. Let’s code!

Meilisearch setup
-----------------

First, we need to launch a Meilisearch instance. You can do so by following instructions in the [local installation guide](https://www.meilisearch.com/docs/learn/getting_started/installation#local-installation) or creating an account on [Meilisearch Cloud](https://www.meilisearch.com/pricing). We’ll write a setup script to configure our Meilisearch instance and seed the data.

<Callout>
  This setup script uses the JavaScript SDK, but you can use any of [Meilisearch SDKs](https://www.meilisearch.com/docs/learn/what_is_meilisearch/sdks).
</Callout>

In this tutorial, we're using [Node 18](https://nodejs.dev/en/download/). Let's install the JavaScript SDK:

```bash
npm install meilisearch
```

Then, let's create a `setup.js` file:

```javascript
// data/setup.js

import { MeiliSearch } from 'meilisearch'

// Load the datasets
import movies from './movies.json' assert { type: 'json' }
import promotedMovies from './promoted_movies.json' assert { type: 'json' }

// Load credentials from environment
const credentials = {
  host: 'your Meilisearch host',
  apiKey: 'your Meilisearch Admin API key' 
}

// Configuration
const INDEX_NAME = 'movies'
const PROMOTED_INDEX_NAME = `promoted-${INDEX_NAME}`

const searchableAttributes = ['title', 'overview', 'genre', 'release_date']
const displayedAttributes = ['id', 'title', 'overview', 'genre', 'poster', 'release_date']

const setup = async () => {
	console.log('🚀 Seeding your Meilisearch instance')
	
	const client = new MeiliSearch(credentials)
	
	// Adding searchable attributes to movies index
client.index(INDEX_NAME).updateSearchableAttributes(searchableAttributes)
	// In the promoted movies index, only `keywords` is searchable
client.index(PROMOTED_INDEX_NAME).updateSearchableAttributes(['keywords'])
	
	// Both indexes have the same visible attributes
	// `keywords` is hidden in the promoted movies index
	client
		.index(INDEX_NAME)
		.updateDisplayedAttributes(displayedAttributes)
	client
		.index(PROMOTED_INDEX_NAME)
		.updateDisplayedAttributes(displayedAttributes)
	
	// Adding documents
	await client.index(INDEX_NAME).addDocuments(movies)
	await client.index(PROMOTED_INDEX_NAME).addDocuments(promotedMovies)
	
	// Wait for tasks completion
	await watchTasks(client, INDEX_NAME)
	await watchTasks(client, PROMOTED_INDEX_NAME)
}

// Helper to watch tasks
const watchTasks = (client, uid) {
	console.log(`Watching tasks on index ${uid}`)
	const tasks = await client.index(uid).getTasks()
	console.log(`${uid} index: waiting for tasks`)
	await client.index(uid).waitForTasks(tasks)
	console.log(`${uid} index: tasks finished`)
}

await setup()
```

This script creates our two indexes. Let’s recap what it does:

*   It sets the same [displayed attributes](https://www.meilisearch.com/docs/learn/configuration/displayed_searchable_attributes#displayed-fields) for both `movies` and `promoted-movies`.
*   It sets the [searchable attributes](https://www.meilisearch.com/docs/learn/configuration/displayed_searchable_attributes#searchable-fields); only `keywords` is searchable in the `promoted-movies` index.
*   It adds documents to our Meilisearch indexes.

<Callout>
  To [optimize indexing speed](__GHOST_URL__/best-practices-for-faster-indexing/), always add documents _after_ configuring settings.
</Callout>

We can now run the script with Node.js:

```bash
node setup.js
```

Our Meilisearch instance is now configured and seeded. 🥳 It’s time to implement promoted results using React!

Displaying promoted results
---------------------------

First, we’ll navigate back to the root directory of our application (eg. my-app). We’ll use Vite to create the React app in this folder.

```bash
npm create vite@latest . --template react
```

If the CLI asks to remove existing files from the current directory, answer 'No'.

Then, let’s install the additional dependencies for [integrating InstantSearch with Meilisearch](__GHOST_URL__/instant-meilisearch/):

```bash
npm install @meilisearch/instant-meilisearch react-instantsearch-hooks-web
```

Let’s edit our App component to display search results. We want the promoted results to show up before the rest. Let’s replace the `App.jsx` boilerplate code with our own:

```javascript
// src/App.jsx

import { instantMeiliSearch } from '@meilisearch/instant-meilisearch'
import { InstantSearch, SearchBox, Hits, Index } from 'react-instantsearch-hooks-web';
   
const MEILI_HOST = 'your Meilisearch host'
const MEILI_SEARCH_KEY = 'your Meilisearch Search API key'

const meilisearchClient = instantMeiliSearch(MEILI_HOST, MEILI_SEARCH_KEY)

const App = () => (
	<div>
		<h1>Hello React</h1>
		<InstantSearch searchClient={meilisearchClient}>
			<SearchBox />
			<h2>Results</h2>
			<Index indexName='promoted-movies'>
				<Hits />
			</Index>
			<h2>More results</h2>
			<Index indexName='movies'>
				<Hits />
			</Index>
		</InstantSearch>
	</div>
);

export default App;

```

We can now run the development server using `npm run dev`. In the browser, our app should display something like this:

![HTML output of the code above](https://unable-actionable-car.media.strapiapp.com/Screenshot_2023_07_20_at_12_50_49_f0c190da8d.png)

Displaying promoted results before other results

Congrats. We’ve successfully displayed our promoted results before the rest of the results. 🎉

<Callout>
  Got stuck? Don’t hesitate to ask for help in our [Discord community](https://discord.meilisearch.com/).
</Callout>

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

This tutorial explored one approach for implementing promoted results. An alternative technique would be implementing document pinning in the backend—using one of [Meilisearch SDKs](https://www.meilisearch.com/docs/learn/what_is_meilisearch/sdks). This approach has the benefit of allowing to merge results in a single response (as if they were coming from a single index).

Both techniques can achieve similar results. We also plan to integrate [promoted documents](https://portal.productboard.com/meilisearch/1-meilisearch/c/98-promoting-documents?utm_medium=social&utm_source=portal_share) in the Meilisearch engine. Give your feedback on the previous link to help us prioritize it.

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/).

I’ll see you there.

Implementing promoted search results with React InstantSearch

Adding documents to Meilisearch is taking longer than expected? The following tips will help you speed up the indexing process.

<Callout>
  This is a very short guide. For a more comprehensive understanding, you can read our article on [indexing best practices](__GHOST_URL__/best-practices-for-faster-indexing/).
</Callout>

Define searchable attributes
----------------------------

To speed up indexing, review the [searchable attributes](https://www.meilisearch.com/docs/learn/configuration/displayed_searchable_attributes#searchable-fields) list and ensure it includes only the fields you want to be checked for query word matches. This improves relevance and search speed by eliminating irrelevant data and preventing unnecessary database growth.

Utilize filterable and sortable attributes
------------------------------------------

Some fields may not contain text but can still be vital for [filtering](https://www.meilisearch.com/docs/learn/fine_tuning_results/filtering) and [sorting](https://www.meilisearch.com/docs/learn/fine_tuning_results/sorting) results. Evaluate if numeric and boolean field values can be part of the filterable or sortable attributes lists.

Review ranking rules
--------------------

Modifying [ranking rules](https://www.meilisearch.com/docs/learn/core_concepts/relevancy#ranking-rules) may trigger a reindexing process. Consider the impact and plan accordingly.

Optimize document size
----------------------

Smaller documents are processed faster. Compress your data using methods such as `br`, `deflate`, or `gzip` supported by Meilisearch. Refer to the [documentation](https://www.meilisearch.com/docs/reference/api/overview#content-encoding) for more details.

Keep up with the latest stable version
--------------------------------------

Regularly update to the [latest Meilisearch release](https://github.com/meilisearch/meilisearch) to take advantage of performance improvements that can significantly enhance indexing speed.

Conclusion
----------

By following these recommendations, you can optimize the performance of Meilisearch and enhance the overall search experience for your users. If you have any question, you can join us on [Discord](https://discord.gg/meilisearch/?ref=blog.meilisearch.com).

Boost productivity and streamline development with [**Meilisearch Cloud**](https://www.meilisearch.com/pricing). No more server deployment or manual updates. Try it **free** for 14 days, no credit card needed.

_Happy coding!_

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

Indexing optimization guide

Vector search enables efficient retrieval of objects sharing similar characteristics. This AI-powered search technique uses embedding vectors. These vectors are mathematical representations of objects generated by machine learning models (like LLMs). Starting with the 1.3 release, Meilisearch supports storing and searching vectors.

<Callout>
  Meilisearch v1.3 is out! Read the [release notes](__GHOST_URL__/v1-3-release/?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement).
</Callout>

Powering the Future of Search
-----------------------------

Vector search might be the dawn of a new era for search. Its use cases are numerous. In [ecommerce](https://ecommerce.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement), it enables offering recommendations for similar products. It also allows building multi-modal search, like image or audio search. Building upon conversational AI technologies enables the creation of Q&A applications. Combining vector search with user-provided information like geolocation and search history can power even more contextual search experiences.

Vector search is also the foundation for [semantic search](https://en.wikipedia.org/wiki/Semantic_search), which aims to understand the query’s meaning. Conversely, traditional lexical search only matches keywords. With semantic search, a `warm clothes` query could give results like `gloves`, `coat`, and more results related to winter clothing.

Vector search unlocks a world of new capabilities for search. Take a look at what some users have already implemented:

*   [similar videos recommendations](https://github.com/adrienpoly/rubyvideo/pull/19) on a site listing Ruby conferences
*   a [documentation chatbot](https://blazy-chat.vercel.app/) proof of concept using [GPT3.5](https://platform.openai.com/docs/models/gpt-3-5) and [LangChain](https://github.com/hwchase17/langchain)

<Callout>
  Meilisearch is available as a [LangChain vector store](https://python.langchain.com/docs/integrations/vectorstores/meilisearch).
</Callout>

Getting Started with Meilisearch Vector Store
---------------------------------------------

  
Starting with v1.3, you can use Meilisearch as a vector store. Meilisearch allows you to store vector embeddings alongside your documents conveniently. You will need to create the vector embeddings using your third-party tool of choice ([Hugging Face](https://huggingface.co/), [OpenAI](https://openai.com/)).

First, spin up a Meilisearch instance. You can [install Meilisearch locally](https://www.meilisearch.com/docs/learn/getting_started/installation?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement#local-installation) or create a [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement) account.

Then, enable the _vector store_ experimental feature:

```Bash
curl \
  -X PATCH 'http://localhost:7700/experimental-features/' \
  -H 'Content-Type: application/json'  \
  --data-binary '{
    "vectorStore": true
  }'
```
<Callout>
  This guide uses \`curl\` to make HTTP requests to communicate with Meilisearch. In practice, we recommend using our [SDKs](https://github.com/meilisearch/integration-guides) instead.
</Callout>

Meilisearch now accepts a `_vector` field in your documents. Use it to store the vector embeddings corresponding to your document in this 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], "title": "And Your Bird Can Sing" }
     ]'
```

After storing your vectorized documents in Meilisearch, you can query them using the `search` or `multi-search` route. To do this, you need to compute the vector of your query (using a third-party tool) and send it in the `vector` field.

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

When using vector search, returned documents include a `semanticScore` field:

```Bash
{
  "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], "title": "And Your Bird Can Sing", "_semanticScore": 0.78 }
  ],
  "query": "",
  "vector": [0, 1, 2],
  "processingTimeMs": 0,
  "limit": 20,
  "offset": 0,
  "estimatedTotalHits": 2
}

```
<Callout>
  This API is experimental. You can help us improve it by sharing your feedback in [this Github discussion](https://github.com/meilisearch/product/discussions/677?utm_campaign=vector-search&utm_source=blog&utm_medium=textlink).
</Callout>

A First Step Toward Semantic Search
-----------------------------------

Vector search is our first step towards semantic search. But our long-term goal is to provide hybrid search—combining the benefits of full-text and semantic search to provide the most relevant search experience. Clément Renault, founder and CTO of Meilisearch, shared his thoughts on Github about [exploring semantic search](https://github.com/meilisearch/meilisearch/issues/3838?utm_campaign=vector-search&utm_source=blog&utm_medium=textlink) — read for a founder’s perspective. We can’t wait to share more with you!

Drop your email below to learn more about our progress with AI-powered search. We’ll keep you posted on all updates regarding vector search & semantic search.

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

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We’re excited to walk our first steps toward semantic search. We can’t wait to hear your thoughts on integrating Meilisearch as a vector store. You can give your feedback in [this Github discussion](https://github.com/meilisearch/product/discussions/677?utm_campaign=vector-search&utm_source=blog&utm_medium=textlink).

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

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

I’ll see you there.

Vector storage is coming to Meilisearch to empower search through AI

[Meilisearch v1.1](__GHOST_URL__/whats-new-in-v1-1/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search) introduces the `/multi-search` [endpoint](https://docs.meilisearch.com/reference/api/multi_search.html?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search#multi-search). With this endpoint, users can now send multiple search queries on one or more indexes by bundling them into a single HTTP request. Essentially, the `/multi-search` endpoint paves the way for [federated search](__GHOST_URL__/what-is-federated-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search).

<Callout>
  [Federated search](__GHOST_URL__/what-is-federated-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search) is available in [Meilisearch 1.10](__GHOST_URL__/release-1-10/?https://blog.meilisearch.com/what-is-federated-search/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search) and highger.
</Callout>

Multi-index search with Meilisearch
-----------------------------------

With 277 votes, federated search was one of the [most requested features](https://roadmap.meilisearch.com/c/74-multi-index-search?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search). We are glad to finally announce the first stage of the feature.

We have introduced a `/multi-search` route that allows searching in multiple indexes with a single HTTP request. Suppose we have two indexes: `movies` and `actors`. Using the `/multi-search` endpoint, we can search for the name of an actress and retrieve both her biography and movies in the search results.

Take, for instance, the following multi-search request:

```bash
curl \
-X POST 'http://localhost:7700/multi-search' \
-H 'Content-Type: application/json' \
--data-binary '{
  "queries": [
     {
       "indexUid": "movies",
       "q": "Kate Winslet",
       "limit": 1
     },
    {
       "indexUid": "actors",
       "q": "Kate Winslet",
       "limit": 1
    }
  ]
}'
```

The response will include a set of search results for each index searched:

```JSON
{
   "results": [
     {
       "indexUid": "movies",
       "hits": [
         {
           "title": "The Reader",
           "overview": "The story of Michael Berg, a German lawyer who, as a teenager in the late 1950s, had an affair with an older woman, Hanna, who then disappeared only to resurface years later as one of the defendants in a war crimes trial stemming from her actions as a concentration camp guard late in the war. He alone realizes that Hanna is illiterate and may be concealing that fact at the expense of her freedom.",
           "crew": [...],
           "cast": [
		...
             {
               "character": "Hanna Schmitz",
               "name": "Kate Winslet",
               "profile_path": "/e3tdop3WhseRnn8KwMVLAV25Ybv.jpg"
             },
		...
           ]
         }
       ],
// other search results fields: processingTimeMs, limit, ...
     },
     {
       "indexUid": "actors",
       "hits": [
     {
       "name": "Kate Winslet",
       "known_for": [
           "Titanic",
           "Eternal Sunshine of the Spotless Mind",
           "The Reader"
       ],
       "birthday": "1975-10-05",
       "deathday": null,
       "biography": "Kate Elizabeth Winslet (born 5 October 1975) is an English actress. Known for her work in independent films..."
     }
       ],
	// other search results fields: processingTimeMs, limit, ...
     }
   ]
 }

```

As you can see, in the results array, the indexes are sorted in the same order as in the query. The response contains the [usual fields](https://docs.meilisearch.com/reference/api/search?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search#response) that a conventional search returns along with the index UIDs. Note that, we restricted the number of documents returned to just one using the `limit` parameter in this example.

Multi-index search demo
-----------------------

![Searching for 'Kate Winslet' returns both her biography and movies in which she starred, while searching for 'Titanic' returns the movie itself as well as actors who starred in it.](https://unable-actionable-car.media.strapiapp.com/ezgif_com_optimize_1_da861cfac7.gif)

[Try it](https://federated-search.meilisearch.com/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search) yourself!

This demo uses two datasets: movies and actors. Each dataset is stored in its own index, has its own settings, and, as you can see in the example above, its own schema.

Since the two indexes have different schemas, they have different [searchable attributes](https://docs.meilisearch.com/learn/configuration/displayed_searchable_attributes?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search#the-searchableattributes-list). In the movies index, you can search for a movie by its title, characters, actors, producers, directors, or movie overview.

```JSON
//movies index 
searchableAttributes: [
   'title',
   'crew.name',
   'cast.name',
   'cast.character',
   'overview',
 ]
```

The actors index allows you to search for actors by name, as well as the movies or shows they are most known for, and by keywords found in their biographies.

```JSON
//actors index 
searchableAttributes: ['name', 'known_for', 'biography']
```

The full list of settings, dataset, and code are all available on [GitHub](https://github.com/meilisearch/demos/tree/main/src/federated-search/), so feel free to explore them on your own. Don't hesitate to play around with the settings to customize the behavior to your needs. If you have ideas for improving the demo, like adding facets, don't hesitate to submit a pull request. All contributions are welcome!

Conclusion
==========

We are excited to deliver this first iteration of federated search, and we’ll be working hard to push toward aggregated search results. We couldn't have achieved this without the valuable feedback and insights provided by our developer community. Your feedback has been instrumental in helping us prioritize and shape the feature to meet your needs.

If you're interested in keeping up with our progress, we encourage you to check out our [product roadmap](https://roadmap.meilisearch.com?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search), join our [discussions](https://github.com/meilisearch/product/discussions) on GitHub, or connect with us on [Discord](https://discord.meilisearch.com/?utm_campaign=federated-search&utm_source=blog&utm_medium=introducing-multi-search).

As always, we are committed to providing our users with the best possible search experience and appreciate your help in achieving this goal.

Introducing multi-index search

A step by step guide to adding site search to your Nuxt ecommerce

If you’ve just played around with Meilisearch using our quick start guide’s [movies dataset](https://www.meilisearch.com/movies.json), indexing your data may have taken Meilisearch only a few seconds. But if you worked with a larger dataset, it has probably taken a lot longer than that. In this article, we will review the best practices to help you index your data efficiently and speed up the indexing process.

Define your needs
-----------------

Meilisearch stores data in the form of discrete records, called [documents](https://docs.meilisearch.com/learn/core_concepts/documents.html), and each document must have a unique identifier—the [primary key](https://docs.meilisearch.com/learn/core_concepts/primary_key.html). Documents are grouped into collections, called [indexes](https://docs.meilisearch.com/learn/core_concepts/indexes.html).

To offer a search-as-you-type experience, Meilisearch needs to store and organize data in multiple manners so it can retrieve it in the most efficient way possible. Because of this, documents must undergo thorough processing before they are ready to be searched.

There are around twenty data structures per index in Meilisearch and constructing them is the most time-consuming part of the indexing process. Changing index settings may invalidate many of these data structures and require reindexing your data. Because of that, it’s usually a good idea to define the index settings before adding your documents.

### Searchable attributes

By default, all document fields added to Meilisearch are [searchable](https://docs.meilisearch.com/learn/configuration/displayed_searchable_attributes.html#the-searchableattributes-list). However, the words present in the fields listed in the [searchable attributes](https://docs.meilisearch.com/learn/configuration/displayed_searchable_attributes.html#searchable-fields) are the ones that require the most data structures–eleven, to be exact. A good way to speed up indexing is to make sure all the attributes in the searchable attributes list are the ones you really want to be checked for matching query words.

Let's take, for example, a document containing a field with an image URL. You probably want to display the image to the user, but I doubt that users are interested in being able to search for query terms in the URL. Don’t forget that not all [displayed fields](https://docs.meilisearch.com/learn/configuration/displayed_searchable_attributes.html#displayed-fields) need to be searchable!

It is not only important to specify in the list of searchable attributes the fields that are actually required, but it’s also essential to avoid having meaningless, random, or unique values in the searchable fields. Imagine all those databases out there full of ‘https’, ‘www’, ‘com’, or values like ‘I77lHE’—not exactly useful when trying to find a specific product or movie 😱

Speaking of which: 🤔unique values…does that ring a bell? 💡The primary key! That’s another field you can safely remove from the searchable attributes list.

#### Under the hood

To better understand the importance of customizing the searchable attributes index setting, let’s take a look at the largest data structures needed for the searchable attributes. Out of the eleven we mentioned, there are three that take the longest time to build: `WORD_DOCIDS`, `WORD_POSITION_DOCID`, and `WORD_PAIR_PROXIMITY_DOCIDS`.

To better understand how each data structure works, we’ll use the following set of documents as examples:

```JSON
{
    "id": 1,
    "description": "New York City is the most populous city in the USA"
},
{
    "id": 2,
    "description": "New York was named in honor of the Duke of York"
},
{
    "id": 3,
    "description": "Tel Aviv is the new most expensive city in the world"
}

```

In `WORD_DOCIDS`, each word is associated with the [primary key](https://docs.meilisearch.com/learn/core_concepts/primary_key.html) of the documents that contain it:

*   `“new” => [1, 2, 3]`
*   `“york” => [1, 2]`

In `WORD_POSITION_DOCID`, the keys are words together with their position in the document. The values associated with the keys are the documents where this word occupies the same position. In the documents above, `id` would be the attribute 0, and `description` the attribute 1:

*   `new(1,0) => [1, 2]` : in documents 1 and 2, the word “new” is located in attribute 1, position 0–it’s the first word of the attribute `description`
*   `new(1, 4) => [3]`: in document 3, “new” is located in attribute 1, position 4

Finally, in `WORD_PAIR_PROXIMITY_DOCIDS` Meilisearch keeps track of the distance between pairs of words across all documents in an index. Terms must be within 8 words of each other to be stored, as words further away are not considered part of the same context and, therefore, not relevant.

In the example documents above, Meilisearch would store the following pairs:

*   `newyork1 => [1, 2]`
*   `newcity2 => [1]`
*   `newcity3 => [3]`

The numbers attached to the word pairs represent the distance between them:

*   1 means the words are next to each other
*   2 means they are separated by one word
*   3 means they are separated by two words

As you can see, each new word represents extra rows in Meilisearch’s internal data structures. Values like unique ids or URL strings can make the databases grow tremendously–and most likely unnecessarily.

Refining which fields Meilisearch should search is critical to reduce indexing time. It can also lead to greater relevance and search speed, as the results won’t be contaminated with irrelevant data.

### Filterable and sortable attributes

Certain fields don’t contain any words, but might still be crucial to help your users find the results they need. This type of data might be a perfect match for filtering and sorting than regular text-based search.

[Filterable attributes](https://docs.meilisearch.com/learn/configuration/settings.html#filterable-attributes) are attributes that can be used as filters to refine search results. You can use them to restrict search results for a particular user or to create faceted search interfaces that allow the user to narrow down the list of results according to the criteria of their choice. Boolean-type values make great filters.

[Sortable attributes](https://docs.meilisearch.com/learn/configuration/settings.html#sortable-attributes) are attributes that can be used for sorting at search time, which allows users to decide which documents they want to see first. Numeric values are perfect for sorting.

As a rule of thumb, if your dataset contains documents with numeric and boolean field values, take the time to assess if they can be part of the filterable or sortable attributes lists.

### Ranking rules

Ranking rules are responsible for the relevancy of your search results. Meilisearch contains six built-in ranking rules:

```json
[
  "words",
  "typo",
  "proximity",
  "attribute",
  "sort",
  "exactness"
]
```

You can also add custom rules for ascending and descending sort to this list:

```json
[
  "words",
  "typo",
  "proximity",
  "attribute",
  "sort",
  "exactness",
  "price:asc"
]
```

Unlike sortable attributes, which are used to sort at search time, custom ranking rules are used to set a default order.

If you need this kind of default sorting, it is better to configure it beforehand. Adding a new rule after documents have already been indexed will trigger a reindexing and potentially your nerves!

Size down and batch up
----------------------

Index only what you really need. Are all the columns in your database necessary? The fewer columns in your dataset, the smaller the documents. The smaller the documents, the less they will weigh, and the faster they will reach Meilisearch, which will then be able to process them sooner.

Meilisearch combines consecutive document addition requests into a single batch and processes them together. This significantly speeds up the indexing process. Since the final size of each batch will depend on how much data Meilisearch received while it was processing the latest document addition request, it is therefore encouraged to send documents in groups rather than one by one.

For the same reason, consider compressing your data. Meilisearch supports the `br`, `deflate`, and `gzip` compression methods. You can find more information in our [documentation](https://docs.meilisearch.com/reference/api/overview.html#content-encoding).

Stay up to date and keep us updated
-----------------------------------

Use the latest [stable version of Meilisearch](https://github.com/meilisearch/meilisearch/releases). New releases often include performance improvements that can significantly increase indexing speed.

If you experience any indexing issues, please [report them](https://discord.gg/meilisearch)! It’s key to improving the product!

Don’t trust me, trust the data
------------------------------

While creating some benchmarks, [Many](https://github.com/ManyTheFish), software engineer of the Engine team, created the following graph. Please note that the indexing time is highly dependent on the size of the machine (CPUs, RAM) and the dataset: for a similar-sized dataset, you may obtain different results.

![Line graph that compares the indexing time with two different configurations (default and custom) in relation to the dataset size. In all cases, the speed is higher with a custom configuration.](https://lh4.googleusercontent.com/l8KRD6C64sT2wPL39Zx6EVzzqk1UOIbYUItL9uWSTT2hd1vfPMEp-j_yMCAXciebMOZQBm9IyVp5XnJHk7XWt3hVl4QUiqXHkRReBq3ERs-jZwVw6QQgU-_eircOWWxu0-VmCRGrQy3F8OTqvQOjU-Z-iCVi1ECgyqesCnTQhG4s-OWM-mz0zJQZ6oNfSw)

The horizontal axis represents the dataset's size, and the vertical axis represents the indexing time

The red line represents indexing time using default settings, and the blue line represents indexing time using a custom configuration. With the same machine and dataset, we observed a 36% gain in indexing speed when using custom settings.

We observed similar improvements in database size, which was considerably smaller when using custom settings:

![Bar graph of the database size where the database with custom settings is significantly smaller than the one with default settings.](https://lh3.googleusercontent.com/5whDEpcglxFIPRfTzHqmJudSWis4ig2BMfvSwJT5LrLVjp43PnsHXRBvJrsH9RTBkKYDjHuOPAcfu4ZWsUIuZnVNJGepYzRG_WD0zvkMoEMZyBGZcSuJcdFELNi96tzagnfe_H9Q8v7SG-kE7H8ysXfRxMTQyi-_Wmw5JPak5kwsQWdLo6QriHLyYN-n8Q)

Database size with custom settings in blue and default settings in red

* * *

Learn more about what Meilisearch can bring to your business

[Talk to a search expert](https://dub.sh/meili-sales)

* * *

And that’s all for now! We covered the best practices to speed up the indexing process. Did you know any of these tips? Have you noticed a difference in indexing speed after following them? Share your experience on our [Discord](https://discord.meilisearch.com/)!

Your feedback is key to helping us improve Meilisearch! I can't stress this enough and I never tire of repeating it, because it is the truth. Our community has built Meilisearch with us and continues to help us shape it.

[Issues](https://github.com/meilisearch/meilisearch/issues/new/choose) or [product discussions](https://github.com/meilisearch/product/discussions) on GitHub, our [roadmap](https://roadmap.meilisearch.com), our [Discord server](https://discord.meilisearch.com/)… No matter where, no matter how, we want to hear from you!

Meilisearch latest news and company updates