Here's a recap of everything that happened this month!

## 💬 Introducing conversational search

[![Conversational search](https://unable-actionable-car.media.strapiapp.com/conversational_search_cb1af6a9e5.jpg)](https://meilisearch.com.cn/docs/guides/ai/getting_started_with_chat)

Learn to build conversational search using Meilisearch's chat completions. 

[Read the guide](https://meilisearch.com.cn/docs/guides/ai/getting_started_with_chat)

## 📰 In the news

### 🫣 Meilisearch 1.16 preview

Get a sneak peek at what's coming in v1.16—releasing August 4th.

[Read the changelog](https://github.com/meilisearch/meilisearch/releases)

### 💳 Same Meilisearch, new pricing

ICYMI, you can get early access to our resource-based model. Check it out!

[Discover new pricing](https://meilisearch.com.cn/pricing/platform)

### 💥 Most personalized search experiences are a lie

How to implement search personalization the simple, effective way.

[Read the blog post](https://meilisearch.com.cn/blog/personalized-search-experiences)

### 🔍 Mastering RAG: unleashing precision and recall

Enhance LLM accuracy using RAG with Meilisearch's hybrid search capabilities.

[Read the blog post](https://meilisearch.com.cn/blog/mastering-rag)

### 🔥 How to implement search in Firebase

A short step-by-step tutorial to get you started with Firebase and Meilisearch.

[Read the tutorial](https://meilisearch.com.cn/blog/firebase-search)

---

_We’re always looking to promote new projects from our [Discord community](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=july-25-updates). Share yours!_

---

Receive this updates in your inbox by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter#newsletter&utm_source=blog&utm_medium=july-25-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#newsletter&utm_source=blog&utm_medium=july-25-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=july-25-updates).

Meilisearch July updates

Here's a recap of everything that happened this month!

## 🔔 Reminder to upgrade

[![Release notes Meilisearch 1.15](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_15_e1b23d5c0c.png)](https://meilisearch.com.cn/blog/meilisearch-1-15)

Don't forget to upgrade to Meilisearch 1.15 to benefit from improved AI search with composite embedders and embedding cache. v1.15 is coming soon!

[Read the release notes](https://meilisearch.com.cn/blog/meilisearch-1-15)

## 📰 In the news

### 🔍 Why traditional hybrid search falls short (and how we fixed it)

How Meilisearch's innovative scoring system revolutionizes hybrid search and delivers more relevant results than traditional rank fusion methods.

[Read the blog post](https://meilisearch.com.cn/blog/fixing-hybrid-search)

### 🔍 What 1,000 Contributors Taught Me About Open-Source

Our Head of Engineering, Clémentine, reflects on the lessons she’s learned from leading an open-source project—and what it takes to foster a strong, engaged community.

[Read the blog post](https://blog.curqui.com/what-1000-contributors-taught-me-about-open-source)

### 🗄️ Get early access to our new resource-based pricing

Previously reserved for Enterprise plans, our resource-based model is finally open to all.

[Get early access](https://eu1.hubs.ly/H0lf50s0#newsletter&utm_source=blog&utm_medium=june-25-updates)

### 🚀 Help us shape search personalisation — book a call!

We're exploring search personalization in Meilisearch and would love to hear about your needs and use case.

[Book a call](https://cal.com/team/meilisearch/search-personalization-discovery-call#blog&utm_medium=june-25-updates)

## 🤗 Made with Meilisearch

One of our community members has built [Meilisearch extension for Magento 2](https://github.com/walkwizus/magento2-module-meilisearch)!

This integration lets you replace Magento's default search engine with Meilisearch, bringing lightning-fast and relevant search results to your online store.

They’re looking for feedback on the integration—give it a try and let them know what you think!

You can also check out the [live demo](https://demo-meilisearch.walkwizus.com/) to see it in action.

[![meili-magento2.png](https://unable-actionable-car.media.strapiapp.com/meili_magento2_e38319eba7.png)](https://github.com/walkwizus/magento2-module-meilisearch)

_We’re always looking to promote new projects from the community. Post yours in the [#built-with-meilisearch](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=june-25-updates) Discord channel!_

---

Receive this updates in your inbox by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter#newsletter&utm_source=blog&utm_medium=june-25-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#newsletter&utm_source=blog&utm_medium=june-25-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=june-25-updates).

Meilisearch June updates

Here's a recap of everything that happened this month!

## 🔔 Reminder to upgrade

[![meilisearch-1.14.png](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_14_166a7ff64e.png)](https://meilisearch.com.cn/blog/meilisearch-1-14)

Don't forget to upgrade to Meilisearch 1.14 to benefit from improved AI search with composite embedders and embedding cache. v1.15 is coming soon!

[Read the release notes](https://meilisearch.com.cn/blog/meilisearch-1-14)

## 📰 In the news

### 💬 Talk to Meilisearch with Claude desktop

Connect your LLM to Meilisearch using our MCP server. 

[Read the guide](https://meilisearch.com.cn/docs/guides/ai/mcp)

### 🚀 How we're making AI work at Meilisearch

How we turned scattered AI usage into systematic success for our teams.

[Read the blog post](https://meilisearch.com.cn/blog/making-ai-work)

### 🌱 Six years working with the open-source community

Our Head of Engineering Clémentine reflects on our experience working in OSS.

[Read the blog post](https://blog.curqui.com/six-years-working-with-the-open-source-community)

### 📣 How are CTOs spending their AI budgets?

You can help us understand you better by taking this survey!

[Take the survey](https://s.surveyplanet.com/befuwdt7)

### 📣 How long does it take to integrate search?

You can help us understand you better by taking this survey!

[Take the survey](https://s.surveyplanet.com/sjyv6prl)

## 🤗 Made with Meilisearch

HuggingFace's [Daily Papers](https://hugging-face.cn/papers) uses Meilisearch hybrid search to search across hand-picked machine learning papers published over the last two years.

[![huggingface-papers.gif](https://unable-actionable-car.media.strapiapp.com/huggingface_papers_fceeaf233b.gif)](https://hugging-face.cn/papers)

_We’re always looking to promote new projects from the community. Post yours in the [#built-with-meilisearch](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=may-25-updates) Discord channel!_

---

Receive this updates in your inbox by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter#newsletter&utm_source=blog&utm_medium=may-25-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#newsletter&utm_source=blog&utm_medium=may-25-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=may-25-updates).

Meilisearch May updates

Hey, here's a recap of everything that happened at Meilisearch during April.

## 🚀 Meilisearch 1.14

[![meilisearch-1.14.png](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_14_166a7ff64e.png)](https://meilisearch.com.cn/blog/meilisearch-1-14)

Meilisearch 1.14 improves AI search with composite embedders and embedding cache. It also adds granular filterable attributes and batch document retrieval by ID.

[Read the release notes](https://meilisearch.com.cn/blog/meilisearch-1-14)

## 📰 In the news

### 🔢 Why you shouldn't use a vector database for RAG

The contrarian take on building better retrieval augmented generation systems.

[Read the blog](https://meilisearch.com.cn/blog/vector-dbs-rag)

### ⚙️ What is full-text search, and how does it work?

Learn the inner workings of a full-text search engine.

[Read the blog](https://meilisearch.com.cn/blog/how-full-text-search-engines-work)

### 🎙️ Coruzant podcast with Quentin de Quelen

Listen to our CEO discuss his entrepreneur journey and all things Meilisearch.

[Listen to the podcast](https://coruzant.com/profiles/quentin-de-quelen/)

### 🔝 Top 10 alternatives to Elasticsearch

Discover their key features, pricing, pros, and cons based on users feedback.

[Read the blog](https://meilisearch.com.cn/blog/elasticsearch-alternatives)

### 🔍 What is federated search?

Discover the different types of federated search, and their benefits and challenges.

[Read the blog](https://meilisearch.com.cn/blog/what-is-federated-search)


## 🤗 Made with Meilisearch

[Agora](https://meilisearch.com.cn/blog/Agora) uses Meilisearch hybrid search to provide instant search results across various data sources to build a fast and intuitive ecommerce experience.

[![Agora gif.gif](https://unable-actionable-car.media.strapiapp.com/Agora_gif_eca98f7605.gif)](https://meilisearch.com.cn/blog/Agora)

_We’re always looking to promote new projects from the community. Post yours in the [#built-with-meilisearch](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=april-25-updates) Discord channel!_

---

Receive this updates in your inbox by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter#newsletter&utm_source=blog&utm_medium=april-25-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#newsletter&utm_source=blog&utm_medium=april-25-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=april-25-updates).

Meilisearch April updates

Our Product Hunt launch made it to the monthly Top 5 in the UX category 🔥

[![Meilisearch is #5 Product of the Month in User Experience](https://unable-actionable-car.media.strapiapp.com/PH_top_5_1_864dcb16f7.png)](https://www.producthunt.com/posts/meilisearch-ai)

## 🚀 Meilisearch AI launch week

[![Meilisearch AI: semantic and hybrid search in GA!](https://unable-actionable-car.media.strapiapp.com/Day_0_0c60c1ece6.png)](https://meilisearch.com.cn/blog/launch-week-1)

We organized a Launch Week to celebrate the stabilization of AI-powered search. Read the day-by-day recap of our releases.

[Read the recap](https://meilisearch.com.cn/blog/launch-week-1)

## 🔮 Building the future of search

[![Building the future of search with Meilisearch AI](https://unable-actionable-car.media.strapiapp.com/Building_the_future_of_search_with_Meilisearch_AI_e75f1ae673.png)](https://meilisearch.com.cn/blog/introducing-meilisearch-ai)

We're transforming how developers build search with Meilisearch AI. No more complex infrastructure—just powerful, intelligent search that works out of the box.

[Learn more](https://meilisearch.com.cn/blog/introducing-meilisearch-ai)

## 📰 In the news

### 🔢 What are vector embeddings?

We updated our guide about everything you need to know about using embeddings.

[Read the guide](https://meilisearch.com.cn/blog/what-are-vector-embeddings)

### 🛍️ Scaling to billions: how Agora elevates e-commerce search with AI search

Learn how Agora leverages hybrid search across millions of ecommerce products.

[Read the case study](https://meilisearch.com.cn/blog/Agora)

### 📐 Rayon chose Meilisearch to help facilitate seamless search UX

Leveraging semantic search to build a platform for architecture and interior design.

[Read the case study](https://meilisearch.com.cn/blog/Rayon)

### 🔍 MotionElements: AI-powered discovery for video creators

Read how Meilisearch improved search performance, accuracy, and engagement.

[Read the case study](https://meilisearch.com.cn/blog/motionelements)

### 📊 How to choose the best model for semantic search

Compare model performance, cost, and relevancy to build semantic search.

[Read the guide](https://meilisearch.com.cn/blog/choosing-the-best-model-for-semantic-search)

## 🤗 Made with Meilisearch

Meilisearch [ecommerce demo](https://ecommerce.meilisearch.com/#newsletter&utm_source=blog&utm_medium=march-25-updates) is an open-source Nuxt 3 application implementing filters, sorting, facet search, and more to power search across 45k documents.

[![Scrolling on the ecommerce demo](https://unable-actionable-car.media.strapiapp.com/ecommerce_demo_scroll_bc09f703ae.gif)](https://ecommerce.meilisearch.com/#newsletter&utm_source=blog&utm_medium=march-25-updates)

_We’re always looking to promote new projects from the community. Post yours in the [#built-with-meilisearch](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=march-25-updates) Discord channel!_

---

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter#newsletter&utm_source=blog&utm_medium=march-25-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#newsletter&utm_source=blog&utm_medium=march-25-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=march-25-updates).

Meilisearch March updates

# Meilisearch AI launch week recap

As the first quarter of 2025 closes, we've completed our first [Meilisearch launch week](https://meilisearch.com.cn/launch-week), marking our most significant product evolution since introducing Meilisearch Cloud. This update brings comprehensive AI-powered search capabilities to all users.

Traditional keyword search has limits. It matches characters, not intent—often returning results that miss the mark. [AI-powered search changes that](https://meilisearch.com.cn/blog/introducing-meilisearch-ai). By understanding meaning, not just words, it delivers smarter, more intuitive results.

## What we announced: Day by day

#### Day 1: Semantic and hybrid search now GA

We kicked off the week by announcing that our AI capabilities, including semantic search and hybrid search, are now generally available:

- **Semantic search**: AI-powered understanding delivers more relevant results beyond simple keyword matching
- **Hybrid search**: Combines traditional full-text reliability with semantic intelligence
- **Performance optimization**: All this intelligence with sub-50ms response time

We launched semantic and hybrid search as generally available on the Meilisearch platform. Now your search results understand context, personalize experiences, and power better recommendations, whether you're building an e-commerce site, a knowledge base, a media and news archive, or a SaaS app.

These capabilities significantly reduce failed searches and "no results" responses. [E-commerce platforms report higher conversion rates](https://meilisearch.com.cn/blog/Agora) as shoppers find products using natural language, while [app builders](https://meilisearch.com.cn/blog) see increased engagement as users discover precisely what they need, even with imprecise queries.

**📹 [Watch our Head of Product, Guillaume, explain the release](https://www.youtube.com/watch?v=3JaDBEQNLZc&t).**


#### Day 2: Multiple embedder options and vector storage

On day two, we made vector search more flexible with multiple embedder choices—eliminating the need for separate vector database infrastructure:

- **Choose your embedder**: OpenAI, Hugging Face, Ollama, or bring your own
- **documentTemplate feature**: Better prompting for improved results
- **Binary quantization**: Optimize large databases for speed while maintaining result quality

Our flexible embedder options address critical infrastructure and cost concerns. Organizations can choose solutions that balance budget constraints, performance requirements, and proprietary needs. This approach eliminates the expense of maintaining separate vector databases and reduces operational complexity.

**📹 [Watch the walkthrough – Our Sr. Product Manager, María, explains embedders and best practices.](https://youtu.be/0tPsBrmtf3o?si=6KAyuREv0DYVfCZD).**


#### Day 3: Multi-modal search capabilities

The middle of the week brought multi-modal search, allowing searches beyond just text. This opens new possibilities for industries with diverse content types:

- **Flexible input types**: Search across text, images, and more
- **AI-powered semantics**: Smarter results across content types
- **Real-world implementations**: learn more about how early adopters like [Agora](https://meilisearch.com.cn/blog/Agora) and [MotionElements](https://meilisearch.com.cn/blog/motionelements) already using these capabilities
    
    ![MotionElements_multimodal.gif](https://unable-actionable-car.media.strapiapp.com/Motion_Elements_multimodal_6cfa386d8d.gif)

#### Day 4: Personalized recommendations

Thursday's focus was on helping users discover relevant content faster:

- **/SIMILAR documents**: Find content similar to what you're viewing
- **Engagement improvements**: Smart, similarity-based product and document recommendations
- **Seamless user experience**: Help users find what they need (and didn't know about)
    
    ![day 4_recommendations.gif](https://unable-actionable-car.media.strapiapp.com/day_4_recommendations_e67bc9c528.gif)

#### Day 5: Wrapping up and looking ahead

https://youtu.be/I42NbytO0hQ

We closed the week with a recap and vision for the future with our CEO, Quentin.

At Meilisearch, our mission is to make the best search experience accessible to everyone. We want to deliver the best technologies and combine them with an amazing user experience. This week, we’ve taken a big step forward and are closer to this vision, helping search results understand context, offer personalized experiences, and enable smarter recommendations.

In the coming months and quarters, you'll see exciting new features—from better search analytics and more insights for taking action on your business, to more customization capabilities for tailored experiences, but also we’ll continue advancing on exclusive capabilities like [built-in RAG](https://meilisearch.com.cn/blog/how-to-build-rag#dailydev) or [image search](https://meilisearch.com.cn/docs/learn/ai_powered_search/image_search_with_user_provided_embeddings) to make them work out of the box, with little to no configuration required.

**📹 [Watch Meilisearch AI launch week wrap up with CEO](https://youtu.be/0tPsBrmtf3o?si=6KAyuREv0DYVfCZD).**

**📖 Read Quentin’s [blog](https://meilisearch.com.cn/blog/introducing-meilisearch-ai) on building the future of search with Meilisearch AI.**
## Join us on this journey

As we celebrate reaching **50,000 GitHub stars,** we're just getting started on our mission to redefine search. Whether you're new to Meilisearch or already part of our community, these new capabilities unlock more intuitive, personalized search experiences.

To mark the occasion, we’re giving new subscribers an exclusive gift: use the code **`MEILIAI`** and **get [2 months of Meilisearch](https://help.meilisearch.com/en/article/meilisearchs-promo-discount-code-program-1ykitx7/), including AI-powered searches, completely free**. It’s the perfect time to start building the search experience you’ve always wanted.

Meilisearch AI launch week recap

When [Bookshop.org](http://Bookshop.org)'s small engineering team struggled to push past a 14% search conversion rate, they faced a familiar dilemma. Despite having Elasticsearch—a powerful but complex search solution—their eight-person team was spending more time maintaining search infrastructure than improving their core platform. They needed something different: a solution that could deliver better results with less overhead.

Their challenge reflects a broader reality in modern software development. Every click, every search query, and every page load directly impact business metrics, yet the tools meant to optimize these experiences often become bottlenecks themselves. For engineering teams, especially smaller ones, the complexity of search infrastructure can become a _hidden tax_ on innovation and growth.

This is where Meilisearch comes in. Built to work seamlessly out of the box, it's the solution that lets developers focus on building rather than maintaining. Today, we're taking that mission further with [the launch of Meilisearch AI](https://meilisearch.com.cn/launch-week)—our next step in making it radically easier for developers to build world-class search experiences.

In this article, we'll explore how modern search has evolved beyond simple queries, examine the technical challenges facing development teams, and introduce Meilisearch's innovative solutions—including our new AI capabilities. You'll learn how our approach to developer experience is transforming search implementation, and discover our roadmap for making advanced search features accessible to teams of all sizes.

## Redefining modern search

Modern search experiences extend far beyond the traditional search bar, encompassing two key facets: search and discovery. 

Search can take the appearance of inputs, menus, filters, or the trending UX of conversational search (i.e. chatbots). Discovery, on the other hand, plays a key role in navigating content and suggesting recommendations.

Across industries, we see search and discovery most prevalent as follows:

- **Traditional full-text search & semantic search:** standard in all industries ranging from SaaS to ecommerce, marketplaces, and content platforms
- **Conversational search:** prominent in RAG (Retrieval-Augmented Generation) and AI-enhanced search interfaces like Perplexity
- **Discovery for navigation:** common in social media and content platforms where a feed is the primary navigation
- **Recommendations:** tie in more critically with business value in ecommerce and content platforms

For early-stage startups and SMBs, implementing these search capabilities presents significant technical challenges. The complexity multiplies when adding AI integration and personalization layers. Addressing these pain points diverts the workforce from building core product value. Meilisearch is here to relieve you from this burden, allowing you to focus on what really matters. 

We want Meilisearch built-ins to handle modern use cases like [document embedding](https://meilisearch.com.cn/docs/learn/ai_powered_search/document_template_best_practices). Complex setups—such as managing [heavyweight solutions like Elasticsearch](https://meilisearch.com.cn/blog/meilisearch-vs-elasticsearch), [tweaking a Postgres instance](https://meilisearch.com.cn/blog/postgres-full-text-search-limitations), or handling hybrid search—are additional integration burdens. This ignores the security consideration related to exposing front-end accessible APIs or the analytics necessary to collect business KPIs—things that don’t come packaged with custom-made solutions.

![bookshop_meilisearch.gif](https://unable-actionable-car.media.strapiapp.com/bookshop_meilisearch_9498082583.gif)

These DX considerations impact business metrics down the road as engineering bottlenecks lead to longer time-to-market, hindering growth for companies that want to move fast. In ecommerce, for example, search accuracy directly translates to conversion rate, as shown by how we helped [Bookshop increase search-based purchases by 43%](https://meilisearch.com.cn/blog/bookshop-increases-search-based-purchases). On SaaS and content platforms, discovery enables engagement and adoption growth, both of which are key to keeping churn in check. 

## Leveraging DX to move mountains

Meilisearch is a no-configuration, plug-and-play solution that you can install and forget. At the heart of any search tool—and often the biggest challenge for developers—is ensuring relevant results. **CascadeRank** is our answer to [relevancy fine-tuning](https://meilisearch.com.cn/docs/learn/relevancy/relevancy). Our algorithm uses human-readable ranking rules that enable configuration and easy customization. With Meilisearch, simplicity is key, allowing you to set up and focus on your core tasks. 

Though customization is easy, Meilisearch comes with strong defaults to handle common requirements out of the box—things like being typo-tolerant and prioritizing word proximity. Indeed, we’ve seen [Minipouce get better relevancy with Meilisearch’s default configuration than their previous Typesense setup](https://meilisearch.com.cn/blog/minipouce-top-grade-customer-experience), leading to a 5x search volume increase. 

Scalability is a concern for small teams and enterprises alike. Performance is the name of the game for user-facing search—both for search latency and indexing time. As demonstrated by [Podscan.fm](http://Podscan.fm) [searching across 600GB of data](https://x.com/arvidkahl/status/1807889668748275802), search remains fast against large datasets. Our [next-generation indexer boasts 4x faster updates](https://meilisearch.com.cn/blog/introducing-indexer-2024), making Meilisearch suitable at scale for write-intensive workflows, too.

[![arvid-kahl-tweet.png](https://unable-actionable-car.media.strapiapp.com/arvid_kahl_tweet_0c41da4824.png)
](https://x.com/arvidkahl/status/1807889668748275802)

When it comes to security, no compromise is possible. So Meilisearch provides fundamental security features to handle simple and complex use cases. Meilisearch facilitates [API key management](https://meilisearch.com.cn/docs/learn/security/basic_security) to facilitate multi-environments. The built-in [multi-tenancy features](https://meilisearch.com.cn/docs/learn/security/multitenancy_tenant_tokens) also enable more advanced security patterns, allowing developers to customize their access rules.

Using our expertise in search requirements across various customers, we built [Meilisearch Cloud](https://meilisearch.com.cn/cloud). It’s the quickest way to get started. In addition to freeing teams from managing infrastructure, it integrates analytics and monitoring, making it [the preferred production platform for companies like HitPay](https://meilisearch.com.cn/blog/hitpay-streamlines-search). Open-source Meilisearch can be self-hosted via Docker and a plethora of hosting platforms. 

This feature set positions Meilisearch well as a simple solution to get the best of modern search experience. This includes benefiting from an easy LLM integration to leverage AI-powered solutions.

## Future-proofing your search strategy

With native [hybrid and vector search](https://meilisearch.com.cn/docs/learn/ai_powered_search/getting_started_with_ai_search) integration, Meilisearch makes it simple to implement AI-powered search capabilities. But we’re not stopping there—our 2025 roadmap is packed with features enabling businesses to uncover more value from AI while sustaining performance and scalability.

### Improved conversions with personalization

[Our research shows that personalization significantly improves conversion rates](https://meilisearch.com.cn/blog/personalization-recommendations) across diverse business types. We aim to release a personalization framework enabling the collection of end-user context on each request. This comprehensive context management system allows developers to leverage user preferences, recent interactions, and behavioral patterns to further customize the relevancy of results on a per-user basis. 

### Retrieval augmented generation without overhead 

Although Meilisearch already enables [building retrieval-augmented generation (RAG)](https://meilisearch.com.cn/blog/how-to-build-rag), we want to integrate built-in RAG capabilities in the engine, seamlessly combining full-text search, vector search, and LLM interaction. Then, we want to tackle inherent problems in state-of-the-art RAG applications by tackling the orchestration of filtering, ranking, and results merging in RAG scenarios. Of course, we want this to come with strong defaults, making it easy for developers to get started.

### Advanced ranking integrations

Our goal is to provide a native way for users to leverage AI reranking models to improve the relevancy of results. This provides a more flexible approach to configuring result relevancy by allowing real-time results reranking based on user context and behavior. We also want to enable organizations to integrate with their own custom ranking models to allow for further dynamic adjustment of results based on hand-picked factors.

### Reliability as you scale

We also plan to improve Meilisearch’s infrastructure to handle additional use cases better. In particular, we want to continue improving the distributed system capabilities for better scaling and high availability by building upon our sharding experiments with [federated remote search](https://meilisearch.com.cn/docs/learn/multi_search/implement_sharding). Another layer of performance is integrating more sophisticated caching mechanisms to improve out-of-the-box performance. Monitoring and observability are also continuously improved based on user feedback.

## Meilisearch AI, refreshingly simple search DX

The release of Meilisearch AI marks a new age for Meilisearch users, fully integrating with the modern landscape of AI-powered applications. But it’s also a continuous fulfillment of our core value—making good search UX easy to build. We believe focusing on developer experience enables engineers to achieve faster time-to-value and maintain focus on their core business.

Meilisearch ships frequently to keep packaging built-in features, enabling fast-shipping companies to do what they do best. For more information on the Meilisearch AI release, consult our [launch week page](https://meilisearch.com.cn/launch-week). 

Start with a [14-day free trial of Meilisearch Cloud](https://cloud.meilisearch.com/) or [request a demo](https://dub.sh/meili-sales) to discuss your needs with our search experts.

Building the future of search with Meilisearch AI

When you're handling millions of documents in a search engine, every millisecond counts. That's why we've completely reimagined how Meilisearch processes data with our new indexer in v1.12. We've rewritten our indexer—the core component that processes your documents and builds search structures—from the ground up. The result? A faster, more efficient, and more scalable system.

This complete redesign fundamentally changes how our search engine handles data. In this article, we'll show you how we've achieved up to 4x faster document updates and 30% smaller database sizes, and what this means for your applications.

## A foundation for the future

We've named this release `Indexer edition 2024`, reflecting our vision to build a foundation that will power Meilisearch's performance for years to come. Inspired by [Rust's edition model](https://doc.rust-lang.net.cn/edition-guide/editions/index.html), this isn't just an update—it's a new chapter in Meilisearch's evolution, setting the stage for future innovations in search technology.

(Also like Rust editions, we took the year it was developed rather than the year it was released 😁)

## Why would you write a new indexer?

![the-doctor-meme.jpg](https://unable-actionable-car.media.strapiapp.com/the_doctor_meme_1f1323b0c6.jpg "The Doctor would not rewrite an indexer")

_The Doctor would not rewrite an indexer_

You need a _very_ strong reason to re-write an indexer. And we had one.

As our CTO Kero put it bluntly in his blog post: [Meilisearch is too slow](https://blog.kerollmops.com/meilisearch-is-too-slow). The new indexer is our answer to that challenge—a ground-up rewrite to dramatically improve speed.

The main goal of the new indexer is to improve the indexing speed of larger indexes with dozens of millions of documents, to relieve some of the stress on our infrastructure, to better exploit the resources of the machine, and to address some long-running pain points in a structured way.

## The crux of the matter: performance improvements

![Meme from the Jurassic Park movie with a t-rex (10m document updates) chasing a Jeep (Meilisearch)](https://unable-actionable-car.media.strapiapp.com/sometimes_more_speed_e32b6e5390.jpg "Sometimes you really gotta go faster")

_Sometimes you really gotta go faster_

In various scenarios, the new indexer is faster, sometimes multiple times faster, than the already optimized v1.11 indexer.

On machines with multiple cores, good IO and a lot of RAM, performance is vastly increased. Inserting new documents is twice as fast, while incrementally updating documents in a large database is x4 faster!

While the goal was to improve performance on bigger machines, we were successful in keeping similar or better performance for more modest machines with fewer cores and humble RAM (good IO remains vital to a database, though).

Additionally, for Cloud customers, we’re exploring optimizing our build by using native CPU instruction and [link-time-optimization (LTO),](https://llvm.net.cn/docs/LinkTimeOptimization.html) yielding up to 12% improvements. We’re also considering advanced techniques like [profile-guided optimization (PGO)](https://doc.rust-lang.net.cn/rustc/profile-guided-optimization.html), which so far has yielded an additional 12% improvement over native instructions and LTO.

## How it’s done: more parallelism, fewer I/O operations, pipelining writes

Meilisearch used to split a payload of documents into multiple chunks (roughly one per indexing thread), and would then work each chunk throughout the entire extraction process. Once a chunk was finished, it would then be persisted in the database.

This approach had a couple of drawbacks, performance-wise:

- As a lot of temporary data was extracted at the same time, it had to be written to disk and then read back to be persisted in the database, causing a lot of I/O operations.

- As the chunks were built independently of each other, they could result in us writing the same key to the database for each chunk, again causing unnecessary writes.

- As the chunks would finish computing at the same time on average, the indexing process would intensively use all the CPUs while computing the chunks, but then be solely single-threaded while writing everything to the database.

![old-process.png](https://unable-actionable-car.media.strapiapp.com/old_process_75e046f6ab.png "Simplified representation of the previous indexing process")

_Simplified representation of the previous indexing process_

In the new indexer, Meilisearch iterates in parallel over documents, performing a single extraction operation at a time. The result of that extraction is then merged in parallel, to compute the list of keys and values to send to the database. These keys and values are sent to the writer thread in a channel that acts as a pipeline, reducing the time where the merger threads wait for the writer thread.

![waits-faster.jpg](https://unable-actionable-car.media.strapiapp.com/waits_faster_36160c6cfb.jpg "The merger threads wait less thanks to pipelining")

_The merger threads wait less thanks to pipelining_

We successfully made the merger step parallel thanks to a key insight: while the extraction steps split documents between multiple threads, the merger step has to split database keys between multiple threads. For instance, to build the reverse index that matches a word with the list of documents that contain that word, each thread reads some of the documents to extract the words it contain, then all the documents containing a word are merged into a single list.

To make this possible, all threads during the extraction step deterministically hashes each word it encounters. Then during the merger step, words to handle are partitioned between threads depending on their hash value.

![new-process.png](https://unable-actionable-car.media.strapiapp.com/new_process_a34768ffec.png "Simplified representation of indexing process edition 2024")

_Simplified representation of indexing process edition 2024_

The drawback to this new approach is that some CPU-intensive operations, such as document tokenization, have to be duplicated across all steps. Still, merging in parallel utilizes a machine's multiple cores much more, reducing the time Meilisearch spends single-threaded.

Furthermore, by not duplicating key writes, the database size has been reduced by more than 30%.

## Better RAM control and usage

Meilisearch uses [bumpalo](https://docs.rs/bumpalo/latest/bumpalo/) to improve RAM control. Bumpalo is an arena allocator that allows to bulk deallocate memory and to query how much memory was allocated in an arena.

Arena allocators are often used in video games for objects that have to be recreated on each game frame, because allocating in an arena is very cheap (just a pointer bump), and deallocating can happen in bulk (yet another pointer bump) as long as no destructor has to be run.

In some regards, indexing documents with Meilisearch also has “frames”. In the new indexing approach, we can even detect two kinds of such frames: A very short-lived frame that corresponds to the work done on each document. And a longer-lived frame that starts at the beginning of each extraction step, and ends right after the merger is done sending everything to the writer. The “document” arena contains all allocations related to reading and tokenizing that document, while the “extraction” arena contains all allocations related to the data being extracted that will then be merged and written to the database.

![new-process(1).png](https://unable-actionable-car.media.strapiapp.com/new_process_1_ffaf674cd9.png)

During this process, Meilisearch queries the size of the “extraction” arena to detect when its RAM usage is above the threshold. When this happens, Meilisearch spills excess data to disk, so that the RAM usage stays in control.

If the extracted data fits in RAM, it never hits the disk, yielding fewer I/O operations.

## Faster and more reliable task cancellation

Historically, in the previous indexer, chunks of documents used to be very small, so it made sense to allow cancellation only after a chunk was done processing. Unfortunately, small chunks were inefficient when writing temporary data to disk, so we decided to switch to a “one chunk per core” model a few versions ago. This had an adverse effect on cancellation, as it would be processed almost at the end of the indexing process after all threads finished processing their chunk.

As the new Meilisearch iterates on documents, and further split the process by each extraction step, task cancellation is faster than ever, routinely being processed under 1s!

![cancel-all-the-tasks.jpg](https://unable-actionable-car.media.strapiapp.com/cancel_all_the_tasks_16b21cbbec.jpg "For all of you who regret sending all these documents at once")

_For all of you who regret sending all these documents at once_

## Better error messages

The new architecture exposes the document id to all extractors. This means that error messages can be enriched with this information as was done in [this PR](https://github.com/meilisearch/meilisearch/pull/5153): knowing which document caused the error allows for more effective troubleshooting.

## Better observability with progress

Lastly, the new indexer brings better observability to the table: because we know both which step we’re at and how many documents have been handled for this step, we can expose this information in the newly added `batches` [](https://www.notion.so/Enhance-visibility-on-batched-tasks-1194b06b651f810b8fe0fab5d72846a8?pvs=21)[route](https://www.notion.so/Enhance-visibility-on-batched-tasks-1194b06b651f810b8fe0fab5d72846a8?pvs=21) as a `progress` object.

```
{
  "uid": 160,
  "progress": {
    "steps": [
      {
        "currentStep": "processing tasks",
        "finished": 0,
        "total": 2
      },
      {
        "currentStep": "indexing",
        "finished": 2,
        "total": 3
      },
      {
        "currentStep": "extracting words",
        "finished": 3,
        "total": 13
      },
      {
        "currentStep": "document",
        "finished": 12300,
        "total": 19546
      }
    ],
    "percentage": 37.986263
  },
  "details": {
    "receivedDocuments": 19547,
    "indexedDocuments": null
  },
  "stats": {
    "totalNbTasks": 1,
    "status": {
      "processing": 1
    },
    "types": {
      "documentAdditionOrUpdate": 1
    },
    "indexUids": {
      "mieli": 1
    }
  },
  "duration": null,
  "startedAt": "2024-12-12T09:44:34.124726733Z",
  "finishedAt": null
}
```

In the above, we know that we finished extracting the words of 12300 out of 19546 documents. There are a total of 13 indexing steps, and we finished 3 of them already, for a global completion of 37.986263%.

Note that the `progress` object should only be used for display purposes, we do not guarantee that the step names or number remain the same from one version to the next.

Adding the `progress` object is a boon for the engine's observability, which already scored us concrete wins. We identified the bottleneck in a customer’s workload just by repeatedly calling the `batches` route and looking at which step was frequently seen.

## The best is yet to come!

The new indexer reaches new heights in maintainability with its structure that decouples the “business” extractors from its support functions (cancel, progress, reading a document), allowing us to quickly iterate on features, such as new settings to fine-tune the indexing behavior and the [AI stabilization](https://meilisearch.com.cn/blog/meilisearch-1-13). In the future, we plan on adding progress to the settings tasks, enriching finished batches with the timing of each step. Feel free to share your ideas about what you’d like to see in Meilisearch on the [product discussion](https://github.com/orgs/meilisearch/discussions). If you’re new to Meilisearch, you can also try it for free on our Cloud.

Introducing Meilisearch's next-generation indexer: 4x faster updates, 30% less storage

## 🇯🇵 New Meilisearch Cloud region

[
![New Meilisearch Cloud region: Japan](https://unable-actionable-car.media.strapiapp.com/Ggs8g_MXXMA_Ar_GY_8_4134d2969a.jpeg)](https://cloud.meilisearch.com/#newsletter&utm_source=blog&utm_medium=january-25-updates)

Meilisearch Cloud is expanding! You can now **deploy Meilisearch in Japan**. Get faster, localized search today.

[Get started](https://cloud.meilisearch.com/#newsletter&utm_source=blog&utm_medium=january-25-updates)

## 🔔 Upgrade to v1.12

[![Meilisearch 1.12 release notes](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_12_7139b280d2.jpg)](https://meilisearch.com.cn/blog/meilisearch-1-12)

Last month, we shipped v1.12 which brings 2x faster document insertion, 4x faster incremental updates, and 1.5x faster embedding generation. Update now!

[Learn more](https://meilisearch.com.cn/blog/meilisearch-1-12)

## 📰 In the news

### 💬 Building a RAG system with Meilisearch

Learn the best practices for building a retrieval-augmented generation system.

[Read the guide](https://meilisearch.com.cn/blog/how-to-build-rag)

### 🔍 Semantic search with Mistral embeddings

Build semantic search with the latest Mistral Small 3 model from Anthropic.

[Read the guide](https://meilisearch.com.cn/docs/guides/ai/mistral)

### ✨ How personalization and recommendations enhance search and discovery

A case study of how Amazon, Reddit, Notion, and more build engaging search UX.

[Read the case study](https://meilisearch.com.cn/blog/personalization-recommendations)

### ⚛️ How to add AI-powered search to a React app

Learn to build a React movie search and recommendation app with Meilisearch.

[Read the guide](https://meilisearch.com.cn/blog/add-ai-powered-search-to-react)

### 🚀 Building docs search with Docs Crawler

Integrate search in your documentation website using the documentation crawler.

[Read the guide](https://meilisearch.com.cn/blog/documentation-crawler-release)

## 🤗 Made with Meilisearch

[LibreChat](https://www.librechat.ai/) is an open-source AI Conversation app compatible with all AI providers. Use a single, customizable UI to interact with any LLM models. LibreChat uses Meilisearch to search through conversation history.

[![LibreChat](https://unable-actionable-car.media.strapiapp.com/demo_light_70288907aa.webp)](https://www.librechat.ai/)

_We’re always looking to promote new projects from the community. Post yours in the [#built-with-meilisearch](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=january-25-updates) channel!_


## 😏 Did you know?

The fastest way to get started is with Meilisearch Cloud.

[Start free trial](https://meilisearch.com.cn/cloud)

---

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter#newsletter&utm_source=blog&utm_medium=january-25-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#newsletter&utm_source=blog&utm_medium=january-25-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=january-25-updates).

Meilisearch January Updates

## 🚀 Meilisearch 1.12 release

![Meilisearch 1.12 release notes](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_12_7139b280d2.jpg)

Meilisearch 1.12 release notes
Meilisearch 1.12 significantly **improves indexing speed** and introduces new APIs to **disable prefix search and facet search** and retrieve **task batches**.

[Learn more](https://meilisearch.com.cn/blog/meilisearch-1-12)

## 💫 Recent releases wrap up 

![Group 143726039 (2).png](https://unable-actionable-car.media.strapiapp.com/Group_143726039_2_a316baf38d.png)

Catch up on everything we shipped recently—faster search, revamped search preview, new index settings UI, and more.

[Learn more](https://meilisearch.com.cn/blog/q4-2024-release-wrap-up)

## 📰 In the news

### ⚡ Indexing vector embeddings 7x faster

How integrating binary quantization allowed Meilisearch to be even faster.

[Read the blog post](https://blog.kerollmops.com/meilisearch-indexes-embeddings-7x-faster-with-binary-quantization)

### 🔍 Semantic search with OpenAI embeddings

Learn to use OpenAI embeddings to build semantic search.

[Read the guide](https://meilisearch.com.cn/docs/guides/ai/openai)

### 🛍️ Building ecommerce search with Nuxt

Learn how to integrate real time sorting, filtering, and faceted search with Nuxt 3.

[Read the guide](https://meilisearch.com.cn/blog/nuxt-ecommerce-search-guide)

### 🎓 A complete guide to predictive search

Key concepts, optimization techniques, and real-world examples to enhance UX.

[Read the blog post](https://meilisearch.com.cn/blog/predictive-search)

## 🤗 Made with Meilisearch

[Fintable](https://fintable.io/) enables syncing bank transactions to spreadsheets. On their [API Coverage page](https://fintable.io/coverage), they use Meilisearch to allow searching through more than 15,000+ bank API connections.

![Searching on Fintable](https://unable-actionable-car.media.strapiapp.com/fintable_d4d4483e90.gif)

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


## 🤓 Did you know?

The fastest way to get started is with Meilisearch Cloud. [Start a free trial](https://meilisearch.com.cn/cloud).

---

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter#newsletter&utm_source=blog&utm_medium=december-24-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/#newsletter&utm_source=blog&utm_medium=december-24-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord#newsletter&utm_source=blog&utm_medium=december-24-updates).

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