[Qogita](https://www.qogita.com/) is a leading global wholesale B2B platform that is trying to revolutionize how products are discovered and procured. On a mission to simplify B2B trade, Qogita helps its customers generate higher margins and turnover while saving time with the help of its innovative technology.

**By partnering with Meilisearch, Qogita is able to ensure high exposure for sellers and increased relevancy for buyers, resulting in an excellent buyer-seller interaction experience.**

> _"Meilisearch has empowered us to deliver lightning-fast and highly relevant search results tailored to our customers' needs. Its seamless integration with our systems has positively impacted developer experience, making local environment setup, testing, and CI/CD automation significantly more efficient. Additionally, Meilisearch has proven to be a cost-effective solution, resulting in substantial savings for Qogita."_  
> **_\- Ivo Silva, Engineering Director at Qogita_**

![](https://unable-actionable-car.media.strapiapp.com/ezgif_com_video_to_gif_87c9670025.gif)

### Challenge

The wholesale industry of today, due to its size and maturity, remains highly traditional in terms of business practices and procurement methods. This means suppliers are hard to find, hard to work with, and often resistant to even taking on new customers.

Qogita aims to address this challenge by making it incredibly easy for sellers and buyers to connect through its platform, all in one place. By offering innovative solutions such as AI-powered pricing and cost optimization, tailoring product search capabilities to users’ actual needs, and providing an excellent buyer-seller interaction via search.

### Why Qogita chose Meilisearch

Prior to Meilisearch, Qogita used Algolia as their search provider and was satisfied in terms of functionality and performance. However, Qogita's team strove for further optimization when it came to both technology and costs. An evaluation was launched to assess alternatives, and a thorough comparison between Meilisearch, Typesense, and Algolia was performed.

The evaluation primarily focused on relevancy, ensuring that the search results were not significantly disrupted with Algolia's results serving as the baseline for comparison. Key searches were conducted for popular products and categories to determine the sorting order and overall performance. Additionally, the evaluation considered the handling of typos and common misspellings, which frequently occur in user searches.

The other factors that contributed to the choice included:  

1.  ******Cost-effectiveness******  
    All the alternatives performed well in the key areas of evaluation, but Meilisearch emerged as the preferred choice due to its cost-effectiveness. Since Qogita has a relatively lean DevOps team, selecting Meilisearch Cloud for production offered a desirable combination of affordability and provided services, including confidence in terms of infrastructure reliability and the high availability assurance.  
    
2.  ******Developer experience******  
    Throughout the entire internal proof-of-concept (PoC) process, the developer experience was a key aspect for evaluation. Meilisearch proved to be advantageous for Qogita as it provided a convenient Python library which simplified the migration process. Setting up containers on Meilisearch Cloud was also a straightforward task. Additionally, Meilisearch's vibrant and engaged open source community served as a significant advantage, establishing trust within Qogita's team early on.  
    
3.  ******Product cadence and roadmap******  
    Qogita’s team was impressed by the speed with which Meilisearch was able to achieve feature parity with the alternatives, despite being one of the youngest search solutions on the market. Additionally, Meilisearch presented a solid [roadmap](https://roadmap.meilisearch.com/tabs/1-under-consideration) that included several features aligned with the value envisioned by Qogita's team for its search initiatives in the near future.

### Implementation and moving to Meilisearch Cloud

Qogita initially self-hosted to evaluate if Meilisearch fulfilled its requirements in terms of development experience. Following the evaluation, production was migrated to Meilisearch Cloud, providing Qogita's team with peace of mind regarding infrastructure outsourcing. The transition from Algolia and the rollout of Meilisearch production went seamlessly, maintaining a high level of internal developer satisfaction.

### Results and Vision

Qogita highlighted higher developer satisfaction as one of the main benefits of choosing Meilisearch Cloud as their search provider, with ease of integration and out-of-the-box relevancy being key contributing factors.

The engineering department's goal was to ensure accurate data and product delivery, as well as providing the best user experience in connecting buyers and sellers through the platform. Meilisearch empowered Qogita team to deliver lightning-fast and highly relevant search results, tailored to meet the specific needs of their customers.

In terms of their vision for search efforts, Qogita expressed that the upcoming facet value search feature (coming to Meilisearch in v1.3) would enhance the user search experience even further. Facets allow customers to refine search results by multiple categories simultaneously. For a company like Qogita, which carries tens of thousands of brands in its catalog, facet search will enable the display and exposure of a greater number of brands to users, with further improved relevancy.

Globally, AI innovation is gaining prominence, and Qogita's team envisions a future that involves contextual search, meaningful search suggestions, and personalization. Meilisearch's upcoming [vector search release](/blog/vector-search-announcement/ marks an exciting venture into the AI space, aiming to fulfill these requirements and provide fast and relevant search for AI-powered applications.

Qogita simplifies B2B trade with Meilisearch Cloud

Today, we’re very happy to be sitting down with another of our Japanese language experts whose help was absolutely invaluable to reach the point that we have in support of the language: Miiton!

Getting to know Miiton
----------------------

As usual, we wanted to start the interview by getting to know a bit more about Miiton:

> “Hello, I'm @miiton, and I live in Japan. While I don't have a specific area of expertise, I've been involved in a variety of fields from networking to application development. Before I got into the kind of development work I'm doing now, I spent a lot of time setting up and configuring server racks. Recently, my work has been centered around developing and maintaining an EC site, primarily for B2B transactions, using React and Go mainly.”

We were impressed with the variety of his background, which prompted us to ask how he got into the field in the first place:

> **“**I used to work setting up and configuring server racks, configuring network, server machines, storage, etc. However, with the advent of VMware and the growth of AWS and GCP in the background, I shifted to software engineering while learning programming on my own. I originally liked things like automating deployments with PowerShell, so the transition was smooth.”

Milton also shares that he initially found Meilisearch on GitHub while trying to add an Algolia-like search feature to his support documents.  
  
Is his experience similar to any of our other contributors or users? Do let us know if it’s the case!

A constant work for the improvement of Japanese support
-------------------------------------------------------

  
We then asked what, according to him, could be improved about Meilisearch at this point in time:  

> “Japanese language support and analysis functions. Japanese language support has [progressed quickly starting with PR](https://github.com/meilisearch/charabia/pull/70), but it is still not enough to handle Japanese. [](https://github.com/meilisearch/charabia/pull/70)I am impressed that @ManyTheFish actively listens to me in Discussions and on Twitter. I respect him because he understands Japanese even though he doesn't speak Japanese. [](https://github.com/meilisearch/product/discussions/532)The [analysis function](https://github.com/meilisearch/product/discussions/532) is an important indicator for us to know what users are looking for and what content we need to provide.”

  
Indeed, Many, search engine engineer at Meilisearch, has been doing his best work to tame verious languages! The least we can say about it is that it’s far from a walk in the park. If you’re curious and wish to read more on that topic, you can [find Many’s interview here](/blog/improving-meilisearchs-language-support/  
  
It is not the first time we heard that Japanese is a particularly tricky language to tackle, and we wondered if Miiton would be able to explain to us why it is the case:

> “This is because it is difficult to determine whether a "Kanji-only strings" is Japanese or Chinese. The current implementation of Meilisearch recognizes "Kanji-only strings" as Chinese, so 東京 is normalized to 东京, making it impossible for Japanese users to search for Tokyo. This has been worked around because @ManyTheFish private-release a prototype that forces Japanese, but I hope it will somehow be resolved and incorporated into the main branch.”

  
We can only imagine how bothersome the current situation is for our Japanese users, which is also why we’re so grateful for contributors such as Miiton or [Mosuka](/blog/meilistar-interview-minoru-osuka/ for their continued support in improving the language.

Milton then goes on to explain that Meilisearch is the only open-source project to which he contributed in recent years! Naturally, we were intrigued to learn what motivated him to take that first step and start contributing:

> “It made me think, "I want to use this." Because I was shocked that I could start it up without thinking, just throw in some JSON, and use it right away.”

We were genuinely curious to hear about his firsthand experience contributing to Meilisearch, especially considering it was his first time contributing to an open-source project in a very long time:

> “Contributing to Meilisearch has allowed me to reconsider the multitude of factors involved in implementing a search system. It also gave me the option of using Meilisearch for now. I am convinced that this experience will definitely be useful in my future work. It was also a good opportunity to get in touch with Rust ”

And what a surprise to learn Miiton wasn’t familiar with Rust before contributing to Meilisearch!

A further insight into Miiton’s projects  

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

Finally, we asked Miiton to share more about the projects he’s been using Meilisearch with:

![Image](https://lh4.googleusercontent.com/qEIsc8BYgC7K5i5TUuLlqU3oGP4tHgWNDOuw8iGtKE4oTJeApun7gutJBUaJ63q0lR0-i1H0hOR3LxzTT2ee_-O0RVYN5RcQZ_eaKLLqjNcYk-8ULyMGRCwpxO-gDpot79Igrz5pH0bHDHz_Nz6x-y4)

> “(I'll refrain from disclosing the specific site, as I can't.) An interesting recent effort is the integration into Shopify. Shopify has a standard & easy-to-use search function, but it only supports up to 1,000 products, and beyond that, you need to consider signing up for a third-party Shopify App. Furthermore, few of these apps are optimized for Japan, and even if they were, they were expensive. Using self-hosted Meilisearch has been a good fit so far. (I'm using the Prototype version with force Japanese language support, so this is not a implementation we can recommend to everyone at this time.) I'm operating a site with roughly 10,000 products on a 2GB memory instance, and I have plenty of resources to spare. I'm also using Prometheus' Metrics (experimental implementation) to create and monitor Grafana screens like this one.”

But that’s not all! As a hobby, Milton also created a public site where he showcases the search experience of Meilisearch in Japanese. You can find the website here, so don’t hesitate to give it a look and lots of love: [https://meilisearch-example-jp.miiton.dev/](https://meilisearch-example-jp.miiton.dev/).

And aside from language support, did any feature catch his attention in particular?

> “Of course response speed and UI development experience. I like that it is InstantSearch.js compatible because of the good development experience. It was very easy to create the above demo site. I don't have to tell you how fast the response time is.”

We couldn’t be more appreciative of Miiton’s time for this interview, and for all his continued support for the improvement of the Japanese language in Meilisearch.  
  
If like Miiton you’re also fluent in a non-european language and are interested in seeing your language improved in Meilisearch, our [Charabia repo is always open to you](https://github.com/meilisearch/charabia)!

Miiton: a master of Japanese has entered the fight!

Welcome to your bi-monthly dose of Meilisearch updates.

🤩 Meilisearch v1.2 is live
---------------------------

[![](https://unable-actionable-car.media.strapiapp.com/81602999_c47b_e299_27e9_41c0dee01e68_6a600f9d34.png)](/blog/v1-2-release/)

Meilisearch v1.2 is here everybody! Don’t forget to update your current version of Meilisearch to the latest one available to enjoy all the news features such as:

*   **Enhanced document management** via filters ⚒️
*   New IS NULL and IS EMPTY **filter operators** ✨
*   **Engine improvements 🚀**
*   And more!

Check out Laurent’s blog post for an in-depth look at [what is new in v1.2](/blog/v1-2-release/?utm_source=release-post&utm_id=v1-announcement&utm_content=whats-new-in-v1.2#new-update

* * *

🐾 What to expect from v1.3
---------------------------

With all that being said, we want to pump you up for the start of a new week! Here's the list of things we're working on for v1.3:

*   Search for **facet values**
*   Displaying **ranking details** at search
*   Sort **facet values** by alphanumerical or value count order
*   Customizing **text separators**
*   Define **fields to search on** at search-time

<Callout>
  The release candidate for v1.3 is out—[try it now](https://github.com/meilisearch/meilisearch/releases/tag/v1.3.0-rc.0)!
</Callout>

We hope to have caught your attention now…

* * *

🤖 Vector search for Meilisearch
--------------------------------

Last but not least, v1.3 will also bring **Vector Search**! We’ve started experimenting with it for the first time this year and are excited to take our first steps into AI. [Read the announcement](/blog/vector-search-announcement/  
  
If you’re as curious as us about this topic, we’re delighted to announce [we’ve released a prototype](https://github.com/meilisearch/product/discussions/621#discussioncomment-6183647) that allows Meilisearch to serve as a vector store. With this prototype, Meilisearch accepts vectors in the documents, stores them, and lets users retrieve the nearest documents using the search endpoint.  
  
We’d be grateful if you wanted to try our prototype and [share your feedback with us in this Discord conversation](https://discord.com/channels/1006923006964154428/1118827065890844733/1118827065890844733).

* * *

📰 In the news
--------------

  
**Community**

🎁 [June giveaway!](https://twitter.com/meilisearch/status/1666032750187429889)

This June, we’re hosting a giveaway! To join it, submit a PR in any of our repos, have it merged by a member of our team, and share the link to it in the ⁠[giveaway channel](https://discord.com/channels/1006923006964154428/1111273670657200198) of [our Discord](https://discord.gg/meilisearch). You’ll automatically be participating in the lottery for the chance to win some exclusive Meiliswag, similar to the one you can see below!

[![](https://unable-actionable-car.media.strapiapp.com/0f8251b0_f453_1509_8ed4_4f986132fc44_6510326af9.png)](https://twitter.com/meilisearch/status/1666032750187429889)

**Product**

☁️ [Search-as-a-service Meilisearch Cloud is open to the public](/blog/meilisearch-cloud-general-availability/?utm_source=release-post&utm_id=cloud-announcement&utm_content=earch-as-a-service#meilisearch-cloud)

Now any business can benefit from Meilisearch’s exceptional developer and search experience without dealing with server management!

🧠 [Squeezing millions of documents in 128 TB of virtual memory](/blog/dynamic-virtual-address-management/?utm_source=tech-post&utm_id=virtual-memory&utm_content=Squeezing-millions-of-documents-in-128-TB-of-virtual-memory)

Curious to know more about how dynamic management of virtual memory enabled us to remove limitations in Meilisearch indexing policy? Check out this blog post!

**Success stories**

👶 [Minipouce.fr provides a top-grade customer experience with Meilisearch's open-source offering](/blog/minipouce-top-grade-customer-experience/?utm_source=customer-experience&utm_id=minipouce-customer-experience&utm_content=Minipouce-fr-provides-a-top-grade-customer-experience-with-Meilisearchs-open-source-offering)

With Meilisearch, Minipouce.fr can maintain its commitment to delivering the best possible user experience. Read about Minipouce’s efforts to make parents’ lives easier.

📚 [Bookshop.org increases search-based purchases by 43% with Meilisearch](/blog/bookshop-increases-search-based-purchases/??utm_source=customer-experience&utm_id=bookshop-customer-experience&utm_content=Bookshop-org-increases-search-based-purchases-by-43%-with-Meilisearch)

How Meilisearch helped Bookshop.org boost their sales and how we made it happen.

* * *

🌾 Made with Meilisearch
------------------------

Our featured project of the month is [1jeune1solution.gouv.fr](https://www.1jeune1solution.gouv.fr/) submitted by our Meilistar **[LiquidIT](https://github.com/LiquidITGuy)**. This platform is an French government’s initiative to help young people get into the job market. Meilisearch allows users to search through a comprehensive list of jobs to quickly find their ideal offer.

[![](https://unable-actionable-car.media.strapiapp.com/0b6c7c06_926e_4a0f_bc3d_55cabc6dfc28_0a959bbdcf.gif)](https://www.1jeune1solution.gouv.fr/stages)

The website is built with **Next.js** and the repository is [available on Github](https://github.com/DNUM-SocialGouv/1j1s-front/). The codebase gives numerous examples of how to implement Meilisearch with **React InstantSearch Hooks** using facets, custom components, and URL synchronization.

We’re always looking to promote new projects from the community. Post yours in our [#built-with-meilisearch](https://discord.gg/meilisearch) Discord channel!

* * *

👯‍♂️ Meilisearch life
----------------------

The HR team presents a rundown of our best practices for a successful offsite with a remote-first team.

[![](https://unable-actionable-car.media.strapiapp.com/9347396d_ae70_588d_0d88_567674b7bec1_85da0966b1.jpg)](/blog/organizing-a-people-first-offsite/?utm_source=hr-post&utm_id=hr-organization&utm_content=organizing-a-people-first-offsite)

We are recruiting!

*   [Account Executive](https://jobs.lever.co/meili/92e2ebdc-3023-4fbe-8dd2-c3cfc9ecd7bf)

Wishing that you pass all your CI pipelines,

_The Meilisearch team_

Meilisearch July updates

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](/blog/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](/blog/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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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

This guide is intended for both advanced and medium-level developers and will provide a clear roadmap to managing data access efficiently in your application.

Understanding Role-Based Access Control
---------------------------------------

Role-Based Access Control (RBAC) is a method of regulating access to computer or network resources based on the roles of individual users within your enterprise. In the context of an application, it refers to controlling which users can access what information, ensuring data privacy and security.

Meilisearch provides an elegant solution to RBAC: [tenant tokens](https://www.meilisearch.com/docs/learn/security/tenant_tokens?ref=blog.meilisearch.com#what-are-tenant-tokens-and-how-are-they-different-from-api-keys-in-meilisearch). Tenant tokens allow you to easily limit a user's access to the specific data they are permitted to view.

Implementing RBAC in a Notion-like Application with Meilisearch
---------------------------------------------------------------

Let's illustrate this concept using an example inspired by Notion, the all-in-one workspace where you can write, plan, collaborate, and get organized.

In our simplified version of Notion, users belong to a workspace, and each workspace contains pages. Some users may have access to certain pages, while others may not.

A page could be represented as follows:

```json
    {
        "id": "477f110a-848a-4c23-969d-e7ff7a648c65",
        "workspace_id": "c3d3e966-60e2-4390-9d49-0a9d60f8f02d",
        "path": "/knowledge base/hello world",
        "title": "Hello World",
        "content": "#This is the first page",
        "can_view": [
            "ec6dfb90-2e89-4912-8e01-a25bb2a5a524",
            "b315c817-b3c4-4604-bd1f-122e5e452c91",
            "45c233cd-8160-4a38-8347-b13bc4709345",
            "ab58453d-6d56-496d-a5fa-ddb7c76b5a29",
            "d6de8463-5514-4bfe-90ba-471fbe9a23c6",
        ],
        "created_at": 1686512258,
        "updated_at": 1686512329,
    }
 ```

The `can_view` attribute is a list of all `user_id` that have access to this page.

<Callout intent="note">

Learn how to [update your settings](https://www.meilisearch.com/docs/reference/api/settings?ref=blog.meilisearch.com#update-settings) in the documentation.

</Callout>

Next, you'll need to create a `pages` index and add the following settings:

```json
    {
        "displayedAttributes": [
            "id",
            "path",
            "title",
            "updated_at"
        ],
        "searchableAttributes": [
            "title",
            "content"
        ],
        "filterableAttributes": [
            "workspace_id",
            "can_view"
        ],
        "sortableAttributes": [
            "created_at",
            "updated_at"
        ]
    }
```

Here's a brief explanation of these attributes:

*   `displayedAttributes`: The attributes shown in the search results of your application. In this case, we display the page `id`, `path`, `title`, and `updated_at`

*   `searchableAttributes`: The attributes that can be searched. For a Notion-like app, `title` and `content` of a page would be most relevant

*   `filterableAttributes`: These are the attributes you can filter by. We've included `workspace_id` and `can_view` to control access to pages based on the workspace and user permissions

*   `sortableAttributes`: These are the attributes by which you can sort your search results. We've included `created_at`  and `updated_at` to support sorting pages by creation and modification dates

Creating tenant tokens
----------------------

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

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

```json
    {
        "pages": {
            "filter": "workspace_id = c3d3e966-60e2-4390-9d49-0a9d60f8f02d && can_view = 45c233cd-8160-4a38-8347-b13bc4709345"
        }
    }
```

With the generated token, your search will restrict access based on `workspace_id` and `can_view` (user’s ID), so users will only see pages they have access to.

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

Load this key into local storage each time a user logs into your app, and use it for searches, thereby ensuring that each user only has access to the data they should.

<Callout intent="tip">

Seeking a detailed, step-by-step tutorial? Explore our in-depth [article on multi-tenancy](https://blog.meilisearch.com/multi-tenancy/)

</Callout>

Wrapping up
-----------

Implementing RBAC with Meilisearch allows you to create applications with fine-grained access controls, ensuring that users only have access to the data they are permitted to see. By following this guide, you can improve the data security and privacy of your application while still providing a seamless search experience to your users. If you have any question, you can join us on [Discord](https://discord.gg/meilisearch/?ref=blog.meilisearch.com).

_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).

How to implement RBAC with Meilisearch

Today, we’re joined by Gregory, or you may know him as GregoryCongad on Github, for yet again one of our Meilistars interviews!

Meet Greg!
----------

Let’s start by learning a little bit more about our latest Star: just last spring, Greg finished his undergrad in computer science at [WPI](https://www.wpi.edu/) and started contributing to Meilisearch at the end of 2022.  
He actually got introduced to Computer Science all the way back in 5th grade. That’s 10 years old for all non-Americans out there! And that’s all thanks to him stumbling across [Scratch, the famous MIT website](https://scratch.mit.edu/). It was love at first sight and Greg has been passionate about programming ever since.

But he didn’t stop there!

> “In middle school, I self-taught Python, C++, and Java by reading some books and by coding up some miscellaneous side projects. I've dabbled in many languages and projects over the years, but have finally picked up Rust after hearing so many good things about it (and the hype is there for a reason!)”

Truly a young prodigy! And that’s not even half of it, for the past two summers, Greg has been interning at Amazon under the Alexa project, focusing on internal tooling, creating products for internal consumption across the organisation. Not much more can be shared on this matter of course due to his NDA, but this sure looks like high profile work.

A serendipitous discovery
-------------------------

With all this experience and knowledge under his belt, we couldn’t help but wonder how he’d discovered Meilisearch. And why change a successful method? Just as with Scratch, Greg confesses he simply stumbled upon Meilisearch:  

> “I was looking for a full text search engine to embed in a Flutter library and was going to settle on TypeSense. TypeSense wasn’t exactly what I wanted though, and eventually I found Meilisearch through one of TypeSense’s comparison pages in their docs. I realized Meilisearch was perfect for my use case, especially since I saw it was written in Rust and I wanted to pick up Rust at the time.”

We really got lucky to have caught his attention! We wondered what had convinced him to give Meilisearch a try as opposed to TypeSense, and Greg shared two main reasons:  

> “1. Persistence on disk. Since I was embedding it, having persistence across application open/close was pretty important  
> 2.The license. Meilisearch is licensed under MIT, whereas TypeSense is GPL. GPL is pretty restrictive, so I try to stay away from it whenever possible”

Our curiosity got the best of us once again, and we couldn’t help being nosey by asking which features of Meilisearch he preferred, and which one could do with a bit more work:  

> “My favorite aspect is how well everything is architected—it’s super easy to embed Meilisearch in other Rust applications and only grab the features you need. Of course, the typo tolerance and relevance that are builtin/require zero configuration are probably my favorite aspects of the product! As far as what I’d like to see, I’d love to have the engine auto-update/have compatibility across versions. Currently, you need to make dumps between versions, but it’d be very nice for that process to be automated (and some work is being done toward that end, like updating how Settings are stored internally)”

A lot of food for thought for all, which we’re very grateful for since hearing our users’ feedback is the best way for us to stay relevant and truly offer you all the best possible search experience.

And what of Greg's personal projects?
-------------------------------------

  
Seeing Greg’s impressive experience despite his young age, we asked whether he’s had the chance to use Meilisearch in a professional context or for a personal project:  

> “I’ve used/embedded Meilisearch to create [Mimir](https://github.com/GregoryConrad/mimir), which is a Flutter library that gives developers an on-device NoSQL database with typo-tolerant & relevant full text search. I originally created Mimir with an application of my own in mind, but I’ve since put that application on hold. Now I just work on Mimir and contribute back to Meilisearch as a hobby project, since I enjoy working on both! The Meilisearch team is really welcoming and nice, which is a great change when compared to other open source projects. Really easy to land your first contribution!”

Go give [Mimir](https://github.com/GregoryConrad/mimir) some love if you have the chance! And we’re very touched to hear Greg’s first experience with us was a positive one, that’s definitely something we aim for!

Embedding Meilisearch to Mimir was actually how Greg first noticed that there were some points of improvement he could contribute to in order to make Meilisearch better! His [first contributions](https://github.com/meilisearch/milli/pull/706) were actually created as a way to solve the issues he ran into when embedding Meilisearch to Mimir! That PR was even featured in one of our release blog posts. Clearly, Greg and Meili were a match made in heaven.

A big thank you to Greg for his time and his answers, we were delighted to be able to spend a moment with him!

Gregory Conrad: POV of a raising young star

We knew it was only the beginning of our 4-day offsite when we boarded the train to Brittany. But little did we know it was only the beginning of our foray into strange and delightful vehicles.

Our hotel was on the coastal path, offering a beautiful sea view from the restaurant lounge. It could have been enough—but would it have been worth it had the offsite been too demanding on our team members? At Meili, we believe in prioritizing the well-being of our employees. As a [mainly remote company](/blog/remote-work-is-the-future-of-work/)he offsite is more than a break in our daily routine. It’s also the chance to meet all the team members who could make it in real life. However, we believe it’s our responsibility as a company to provide a safe environment to everyone, especially during offsites. Our core values are carefully reflected each step of the way, structurally and from workshops to team-building activities.

Upholding our values during an offsite
--------------------------------------

At Meilisearch, [we are committed to upholding our values](/blog/advice-on-answering-interview-questions-about-values/)e are constantly striving to improve on an individual and organizational level. An offsite is a perfect opportunity to educate ourselves collectively on skills that are important to us.

On our second day during this offsite, we attended a workshop about unconscious bias. We believe all companies must be watchful of these stereotypes; they can affect how we recruit, include, and support team members. The practical questions and situations were very relevant to see how quickly we can rely on stereotypes to make decisions and how we could mitigate these effects. Some of the actions are as easy as involving other people! We are more committed than ever to ensuring our decision process involves feedback from team members from different jobs at Meilisearch and different backgrounds.

During this 4-day week, we attended another workshop: the [Climate Fresk](https://climatefresk.org/). The goal was also to identify some actions we could take, individually and as a company in the tech industry. One of the first actions we have to take is to educate ourselves about the fundamental science behind climate change—that workshop was a beginning, and we now have to pursue our efforts, for example, with our servers.

Throughout the offsite, we cater to different preferences and needs. This means being comfortable with a loser schedule and allowing our team members time and space to go at their rhythm. For example, we choose to allow separate rooms for each person. We’ve found it makes a huge difference in our energy level, and our team members get to the end of the offsite with less mental fatigue.

We also give people free time each day to spend as they wish, whether exploring the local area, going to the hotel spa, chatting with people, or simply recharging their batteries.

![](https://unable-actionable-car.media.strapiapp.com/Bretagne2_80409eaefe.jpg)

Picture by Ingrid Hazan

One of our core values is to learn from feedback. The offsite is no exception: we ask questions after each offsite to see what we did well and what to work on for the following offsites. Measuring how we do is our way to ensure that everyone's needs are met. This is a plea to all hotels: we need more veggie and vegan qualitative options during meals!

Supporting a positive and safe team-building
--------------------------------------------

At Meili, we believe in the power of teamwork. Our [collaborative approach](/blog/how-meilisearch-built-upon-42-collaborative-approach/)s at the heart of our company. At the question “What was your favorite thing about the offsite?”, most people say it was chatting with other team members.

This is why we organize low-stakes team-building activities. A loose structure helps people to 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. As such, they can opt out of activities before the offsite.

We asked every team member to write a short anecdote about themselves before the offsite, printed them, and distributed them randomly to other team members. It served as a great ice-breaker and conversation starter on Tuesday evening!

On our first day, we went on a pedestrian treasure hunt in the Corsair city of Saint-Malo. Though we had to rush to make all the spots, it was a great way to explore the city with a squad of people we don’t usually work with. It’s a shame to go to a beautiful place only to spend all our time in a meeting room.

![Photo of rocks in the sea, with a tiny island in which there’s a house, surrounded by fortification walls.](https://unable-actionable-car.media.strapiapp.com/Bretagne3_3bb3447be8.jpg)

Picture by Ingrid Hazan

We had a blast building our cardboard karts on Wednesday afternoon. We had the blueprints, sheets, and cardboard tubes, and there we went! Each team did their best to build, decorate and name their cars. Each kart had its personality and backstory. The presentation speeches were a highlight of our offsite! We had a race to determine which was the fastest kart. I’m afraid I must report we suffered heavy losses and some cars didn’t make it—but they are always in our hearts!

![A white kart made all in cardboard on the road in front of trees. The kart sides are decorated with bone shapes.](https://unable-actionable-car.media.strapiapp.com/kart_379654d107.jpg)

Picture by Roxane Beaufils

Ultimately, we see ourselves as the vessel for a fantastic team to come together and create something special. Like the cars we build during our offsite activities, we provide the structure and the elements, but our team makes the jazz and soul of Meilisearch. We wish to thank the team of [Gloria](https://www.gloriaforbusiness.com/) with whom we worked on the workshops and the team of [Kymono Life](https://www.kymonolife.co/), with a special thanks to Anaïs who organized the offsite for us.

How to organize a people-first offsite

  
Hello, have you met Paul?
----------------------------

Today and for the third time, we’re very happy to share another one of our Meilistars interviews! The Meilistars are some of our top contributors, they all have a plethora of experience they kindly agree to share with us.

The Meilistar we’d like to shine our spotlight on in this article is Paul Sanders, or [sanders41](https://github.com/sanders41), as you may know him on GitHub! Paul has worked in various positions throughout his tech career, for instance he was a data manager as well as a software engineer.

Aside from that, Paul also enjoys spending his free time working on open-source software, both as a maintainer and contributor, which is how he got involved with Meilisearch.  
  
We were curious to know whether someone had mentioned the project to him or if he discovered us by himself:

> “I stumbled upon it. I believe it was Twitter where I saw it mentioned and it sounded like an interesting project. Looking through the repo on GitHub I saw some open issues that I knew how to fix and have been contributing ever since.”

We’re grateful he found his way to Meilisearch! We then wondered on the reasons that motivated him to keep on contributing all this time:  

> “The way Meilisearch does open source makes it a great project to contribute to, regardless of the repo. With a lot of other open source projects it's not unusual to have PRs sit for weeks before getting any review, then there are several weeks between each review. I've even had PRs sit for almost two years before getting reviewed on some projects. With Meilisearch the team is good about getting reviews done and seems to really appreciate the help. After contributing for so long the team there know me and I know them which also makes new contributions easier.”  

Upon first discovering us, Paul felt like Meilisearch and Elasticsearch were quite similar but that Meilisearch was easier to set up and maintain, which was interesting to him then.  
  
When asked about how Meilisearch today holds up in comparison to Meilisearch at the time, Paul shares:

> “The biggest difference I see from when I first found Meilisearch to now is all the work that has gone into indexing. Work done on indexing speed and batching of jobs have been big improvements. One difference that I haven't used myself, but I see a lot of talk about and seems to be a popular addition is the addition of geo search.”

We were delighted to hear his positive feedback, and we wondered if there was anything he’d noticed could be improved upon:

> “One thing that would be nice is some kind of configuration file for the settings. This is especially true for development when you are frequently starting fresh. What I have done for this so far is created a startup script that runs and updates the settings, but it would be nice to have a configuration file to do this.”

We’re very grateful for his feedback since Paul is such an experienced contributor.

A career in tech
----------------

Indeed, on top of contributing to Meilisearch on the regular, he worked, up until recently, for his former’s company’s open source [Fides projects](https://github.com/ethyca/fides). Fides is an open-source privacy engineering platform made to manage the fulfillment of data privacy requests and the enforcement of privacy regulations in code. And that’s without mentioning his personal projects:

> “ From my own projects the one that Meilisearch people may find interesting is Meilisearch [Python Async.](https://github.com/sanders41/meilisearch-python-async) As the name implies it is an SDK like Meilisearch Python, but for code bases using asyncio.”

  
During his time contributing to Meilisearch, Paul mentions he hasn’t had the chance to talk much with his fellow contributors outside of PRs. However, he would be keen to change that! Especially for anyone looking for a mentor:

> “Most of my time these days is spent in Python so if there is anyone that wants to get started with Meilisearch using Python, or contributing to the Python SDK and has any questions they can reach out to me and I'd be happy to help.”

With his extensive experience, we are sure he can provide valuable insights and guidance, so don't hesitate to reach out to him.  
  
Knowing that Paul works in tech, we wondered whether or not he’s had the chance to use Meilisearch in a professional environment:

> “I was building a system at a previous job where I was using Meilisearch, but ended up moving to a new job at a different company before it was finished. The idea in that system was in clinical trials there is a lot of free text data, audit trails and communications being a few examples. Because this was free text there was traditionally no good way to search this data (typos, misspellings, etc.) so people just had to know that the data existed and where to find it. With typo tolerance in Meilisearch I finally had a way to do it.”

  
And on his free time?
------------------------

Aside from his work, Paul also tells us a bit more about the way he uses Meilisearch for a pet project of his.

> “I am currently working on a personal project where I plan to use Meilisearch. My sister is a school teacher and she has been having me help her with some tracking data the school systems have to do on students. They are currently using Google Sheets to do this, and there are multiple issues with this in my view. For one it just wasn't made to do what they are trying to do, and for two each student is his/her own file so there is no way to search across students. How far this one goes depends on how interested the school system is in solving their pain point.”

As a final thought, Paul also shares with us an alternate way to look at Meilisearch:

>   
> “ I feel like a lot of times Meilisearch only gets looked at for it's ability to search through millions of records because that is impressive and has the "wow factor". I think there is a potential to bring a lot of value in using Meilisearch for much smaller datasets also. Manually searching through even hundreds of records is not something people are going to do, and with Meilisearch being so easy to setup I think this is a missed opportunity for people in these situations. ”

  
We’d like to extend a warm thank you to Paul for his time and candor during this interview. Don’t hesitate to check out more of our Meilistars interviews and the future ones as well!

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