Another [Launch Week](https://www.meilisearch.com/launch-week-q3-2025) in the books. We shipped five major updates this week, designed to solve real problems our users told us they were struggling with. Each one of them makes your search experience more powerful, more flexible, and easier to work with. 

Here's what we built:
##  Meilisearch Chat*: we brought conversational AI to Meilisearch

Search is becoming a conversation. Your users no longer want to scroll through results - they want to ask a question and get an answer. But building conversational AI has meant juggling LLM calls, vector databases, reranking logic, and weeks of infrastructure work. 

Our new `/chat` endpoint brings complete RAG capabilities directly to your Meilisearch index. One API call handles everything: understanding the query, retrieving relevant data, and generating accurate responses. 

Key features:

- **Conversational AI for search:** your users can ask natural-language questions and get precise, contextual answers directly from your Meilisearch index.
- **Built-in RAG capabilities:** retrieves relevant information, contextualizes it, and generates accurate responses - no separate vector database, reranker, or AI service required.
- **Native integration:** runs directly on your Meilisearch instance as a single API endpoint.

_*This feature is in open beta._

[See the demo → ](https://supportbot.meilisearch.com/)

[Explore the docs → ](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat)

[Read the full announcement and start building with `/chat` →](https://www.meilisearch.com/blog/meilisearch-chat)

##  Advanced GeoSearch

Location-based search is powerful, but until now, Meilisearch only supported [basic shapes](https://www.meilisearch.com/blog/powering-bildhistoria-discovery) and points on the map. If your use case involved complex shapes, delivery zones, city boundaries, or irregular service areas, you had to work around it.

We've added full GeoJSON support with polygon and custom shape filtering. It allows to define a complex geographic boundary and search within it with precision. It's built on the same fast, filterable foundation you're used to, just way more flexible.

Key features:

- **Advanced shapes search:** search within polygons, multi-points, and complex shapes
- **Fine-grained filtering:** mix geo queries with text relevance and filters
- **Faster performance:** optimized for real-time map and delivery use cases

[See the demo → ](https://chantiers-paris.vercel.app/)

[Explore geosearch with polygons in the docs →](https://docs.meilisearch.com/)

##  Enhanced multi-modal: integrated image search

Image search has been possible in Meilisearch, but it required you to generate embeddings externally and inject them manually. For a lot of teams, that friction was enough to shelve the idea entirely.

Now it's built in. Provide images in base64 format, select an embedder (like CLIP), and Meilisearch handles the rest-embedding, indexing, and search. Support text-to-image queries, image-to-image queries, or both. Whether you're building [product catalogs](https://www.meilisearch.com/blog/Agora), [media libraries](https://www.meilisearch.com/blog/motionelements), or visual discovery tools, you can ship image search without the setup headache.

Key features:

- **Integrated support:** image-to-image and text-to-image search now built directly into Meilisearch
- **Multi-modal queries:** mix image, text, and embedding inputs seamlessly
- **Simple setup:** everything runs through a single API and index with flexible configuration

[See the demo → ](https://multi-modal.meilisearch.com/)

[Explore how multi-modal search works in our docs →](https://www.meilisearch.com/docs/learn/ai_powered_search/image_search_with_multimodal_embeddings)

##  Search personalization: results that adapt to each user

Not everyone searches the same way. A user shopping for running shoes shouldn't see the same results as someone looking for dress boots - but most search engines treat every query identically.

Search personalization lets you rerank results based on user context: preferences, behavior, past interactions. Powered by Cohere Rerank 3.5, it works with a simple `personalize` parameter. You provide the user context, Meilisearch adjusts the ranking, and your users get results that actually match their intent.

Key features:

- **Personalization API:** individualize search results based on behavior
- **Cohere 3.5 embeddings:** accurate, privacy-safe personalization
- **Lightweight integration:** add personalization without reindexing

[See the demo →](https://personalization.meilisearch.com/) 

[Talk to our team to get access to personalized search →](https://meet.meilisearch.com/meetings/cloud/presentation)

##  Resource-based pricing: predictable costs that scale with you

Usage-based pricing works when your workload is unpredictable. But as you scale, you want to know exactly what you're paying for: compute, memory, and storage, ideally, without surprise bills tied to search volume.

We've introduced resource-based pricing alongside our existing usage-based model. Choose your dedicated resources (CPU and RAM) from predefined tiers, and your costs stay predictable as you grow. You can see real-time resource usage, upgrade when you need to, and only pay for what you allocate. 

Key features:

- **Resource-based Pricing:** transparent, usage-based costs
- **Predictable scaling:** pay as you grow with your data
- **Simple migration:** no changes needed to your setup

[Learn about resource-based pricing  →](https://www.meilisearch.com/blog/resource-based-pricing) 

[Get started now →](https://www.meilisearch.com/cloud)

---

## What's next

These releases aren't just features, they're the foundation for where we're taking Meilisearch. Conversational AI, multi-modal search, and personalization: it's all pointing toward search that understands context, not just keywords.

If you're building with Meilisearch or thinking about it, now's a good time to dig in. We're here if you need help.

That's a wrap: Meilisearch Launch Week, October 13-17th 2025

If pricing makes you nervous, you build less.

We’ve seen that first‑hand. When your bill swings with every spike in documents or searches, teams hesitate to ship, and finance can’t forecast.

Our philosophy towards building Meilisearch – from our [dual licensing model](https://www.meilisearch.com/blog/enterprise-license) to how we price – has always centered on giving our customers maximum flexibility.

That’s why we’re introducing resource-based pricing: a developer-first model that stabilizes spend, aligns pricing with actual infrastructure, and provides clear controls over your performance and infrastructure.

## Why we’re adding another pricing model

For a while, usage‑based pricing looked like the obvious path. It matched the serverless zeitgeist and mapped neatly to “documents” and “searches.” But reality didn’t fit the abstraction:

- **Unpredictability drove churn**. Teams with a large amount of data and relatively few searches were penalized. Bills rose in ways that were hard to anticipate.
- **Identical “units” did not have identical costs**. A million tiny product records and a hundred thousand large PDFs do not consume the same resources – yet usage pricing treats them the same.
- **We were over‑optimizing for plan limits**. Internally, we found ourselves hand‑tuning infrastructure to fit plan boxes rather than letting customers scale the resources they needed.

Customers (especially developers and CTOs) told us what they wanted instead: price stability, performance control, and transparency on what drives cost.

## What “resource‑based” means

With resource‑based pricing, you select dedicated compute for your project – and you pay **only** for those resources.

![Resource-based pricing](https://unable-actionable-car.media.strapiapp.com/resource_based_pricing_414c8e98aa.png)

- **Compute**: choose the CPU and RAM you want. Those resources are dedicated to your project via predefined tiers (e.g., XS through 4XL self‑serve; larger tiers via Sales).
- **Storage**: storage auto‑scales and isn’t customizable at the moment.
- **No caps on “documents” or “searches.”** Your resources define your limits and performance – scale them up or down as your needs change.

You can upgrade resources self‑serve. At the moment, downgrades are available via support, but very soon you’ll be able to do them from the Cloud interface.

Billing is at the team level. You can run mixed billing in the same invoice (usage‑based projects alongside resource‑based ones), and you can upgrade/downgrade resources as you go (downgrades via support for the moment, as mentioned above).

This mechanism is the same for both self-serve and enterprise. The Enterprise level then adds SLAs, SSO, support/SLOs, volume discounts, and optional add‑ons – not different machines.

Why this is better for teams needing to scale:

- **Predictability**: stabilize spend as you grow – no more surprise overages tied to abstract units.
- **Control**: developers can dial resources to match performance goals – whether that’s sub‑second search or faster indexing.
- **Transparency**: your bill tracks real cost drivers (CPU, RAM, storage), making budgeting (and explaining costs) far easier.

It also improves activation. If indexing or search feels slow, you can bump resources for a few cents per hour to validate your use case, then tune back once you’ve optimized.

## The developer experience: monitor, tune, repeat

Alongside the pricing model, we’re rolling out a new **Infrastructure** tab in the [Cloud UI](https://cloud.meilisearch.com/) so you can see what’s happening under the hood:

![The new Infrastructure tab allows you to monitor resources](https://unable-actionable-car.media.strapiapp.com/infrastructure_tab_278a604bfd.png)

The new interface allows you to monitor CPU, memory, and storage usage live and take action proactively to avoid performance issues.

Coming soon are also alerts and recommendations. You’ll be able to set up proactive alerts when resource usage reaches certain thresholds (e.g., 80/90/95%) to avoid outages and slow performance.

Wherever possible, we’ll suggest optimizations first (configuration and data‑model tips), then scaling guidance if you still need more headroom.

## How does this fit with usage‑based (we’re keeping both)

This isn’t an either/or ideology. It’s a toolbox:

- Start with usage‑based if you want near‑zero friction and the simplest mental model for a quick evaluation.
- Switch to resource‑based when predictable spend and performance control become more important than per‑unit simplicity.

We’re not forcing migrations. Existing plans are grandfathered. Going forward, when you create a project, you’ll be able to choose usage‑based or resource‑based in the flow – and mix them across a team as your portfolio grows.

## Add‑ons and future flexibility

Some capabilities sit above pricing mode:

- Team‑level add‑ons: SSO, SOC 2, analytics, merchandising
- AI services as first‑class add‑ons on a single Meilisearch bill: embeddings (by size), LLM tokens, re‑ranking models
- Horizontal scale via sharding (multiple machines per project) for advanced workloads

Bandwidth billing and alerting are planned future improvements.

## What to expect at launch

- Fully functional backend: applicable to any new or existing project; resource upgrades are self‑serve; downgrades via support
- Team‑level billing with mixed cycles: usage‑based and resource‑based projects on one invoice
- New Infrastructure tab in the dashboard to monitor CPU, memory, and storage
- Self‑serve and enterprise both supported; enterprise layers on support and governance – not different pricing mechanics

We are confident this new pricing model will lead to lower churn and faster activation because customers can plan with confidence and validate performance without friction.

## Choosing the right model (quick guide)

Choose usage‑based if:

- Business stakeholders lead the evaluation
- Traffic is modest and bursty
- You want the simplest on‑ramp

Choose resource‑based if:

- You’re a developer/CTO who needs predictable spend
- You want explicit control over performance via CPU/RAM
- Your data shape doesn’t map cleanly to “documents/searches”

## How to try it

- New projects: pick “Resource‑based” when creating a project.
- Existing projects: talk to our support team to switch to resource‑based pricing.
- Then, visit the Infrastructure tab to monitor resource usage and adjust CPU/RAM as needed. If you want help optimizing before scaling up, talk to us – we’re happy to review settings and tradeoffs with you.

<Callout intent="note">

Free trials are only offered for usage-based plans, if you want to start a resource-based project, you’ll need to purchase a subscription from the get-go. Of course, you can also start a trial on usage-based to verfy that Meilisearch is the right fit for you and then talk to Support to get migrated to a resource-based plan.

</Callout>

## Our goal

Pricing should reflect reality, not get in your way. Resource‑based pricing puts control back in developers’ hands and makes your bill as predictable as your architecture.

I’m excited to see what you build when pricing is finally one of the simplest aspects of running a search engine.

Scale with confidence: Introducing resource‑based pricing

We're kicking off [Launch Week](https://www.meilisearch.com/launch-week-q3-2025) with something we've been excited to share: Meilisearch Chat, a new way to make your data conversational.

For the first time, you can turn your existing Meilisearch index into an AI-powered chat experience – without complex infrastructure, separate vector databases, or months of custom development.

## Search is becoming a conversation

Your users and buyers don't want to search anymore – they want to ask. They expect apps, documentation, and internal tools to understand natural language and give them direct answers, not endless lists of results.

Until now, that's been really hard to deliver. Traditional conversational AI required juggling multiple LLM calls, vector databases, reranking pipelines, and endless tuning. The complexity alone made it expensive and slow, often taking months just to get a working prototype.

Meilisearch Chat changes all of that.

## One API call, complete conversational AI

The new `/chat` brings AI-native search to your Meilisearch data through a single API endpoint. It handles the entire Retrieval-Augmented Generation (RAG) workflow, from understanding the query to generating the response, automatically.

Here's what you get:

**Conversational AI for search** – Users ask natural-language questions and get precise, contextual answers from your Meilisearch index.

**Built-in RAG capabilities** – Retrieves relevant information, contextualizes it in your factual data, and generates accurate responses. No extra vector database, reranker, or separate AI service required.

**Native integration** – Runs directly on your Meilisearch instance with a single, OpenAI-compatible endpoint.

Less infrastructure. Faster implementation. No compromise on quality or accuracy.

## Why this matters for your business

**Get to market faster**

Meilisearch Chat turns months of AI integration work into days. You can prototype conversational experiences immediately, test them with real users, and iterate quickly without needing a dedicated AI team.

**Lower costs, less complexity**

We've streamlined the entire RAG pipeline, so you don't need separate LLM orchestration, reranking logic, or vector storage. Fewer moving parts means lower maintenance costs and less things that break.

**Better user experiences**

Whether it's customer support or internal knowledge bases, your users get direct, reliable answers in context – not just a list of links. That means faster resolution times, higher engagement, and happier users.

## Built for real-world use cases

**Retail & e-commerce** – Help shoppers find exactly what they need through natural conversation.

**Customer support** – Let users ask questions in plain language and get answers directly from your documentation or help center.

**Internal knowledge bases** – Turn your company documentation into a chat assistant that answers questions instantly.

**Developer docs** – Add conversational search to your API or SDK documentation so developers can find what they need faster.

## Simple to implement, easy to scale

If you're already using Meilisearch, adding conversational capabilities is straightforward. The `/chat` endpoint is [OpenAI-compatible](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat#configure-a-chat-completions-workspace), which means it works with your existing LLM setup and fits right into your current workflows.

You can start with your existing indexes – no need to rebuild or re-embed your data.

## A new approach to AI-powered search

With Meilisearch Chat, we're making it simpler to bring conversational AI to your data. Instead of managing multiple services and complex pipelines, you get a complete conversational search experience through one endpoint.

This isn't just another feature – it's a new foundation for more natural, human-like interactions with your data.

**Ready to make your search conversational? Explore the [docs](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat) for how to get started and [try /chat today](https://www.meilisearch.com/cloud)**

Introducing Meilisearch Chat: conversational search for your data

Here's everything we shipped this month at Meilisearch.

## Horizontal scaling with sharding

[![Horizontal scaling with sharding](https://unable-actionable-car.media.strapiapp.com/horizontal_scaling_with_sharding_792a7928b3.jpg)](https://www.meilisearch.com/blog/horizontal-scaling-with-sharding)

We introduced experimental support for sharding, enabling horizontal scaling. [Read more](https://www.meilisearch.com/blog/horizontal-scaling-with-sharding).

## SOC2 compliance

[![SOC 2 Type II announcement.jpg](https://unable-actionable-car.media.strapiapp.com/SOC_2_Type_II_announcement_fc6def3aea.jpg)](https://www.meilisearch.com/blog/soc2-typeii-compliance)

Meilisearch is now SOC2 Type II compliant. [Read more](https://www.meilisearch.com/blog/soc2-typeii-compliance).

## Multi-modal search

[![Example payload for a multi-modal request](https://unable-actionable-car.media.strapiapp.com/multi_modal_request_120e32cdc9.jpg)](https://www.meilisearch.com/blog/meilisearch-1-16#new-multi-modal-search-and-embeddings)

Leverage the new fragments API to get out-of-the-box multi-modal embeddings and search. [Read more](https://www.meilisearch.com/blog/meilisearch-1-16#new-multi-modal-search-and-embeddings).

## Product updates

New search features
- Stabilize the `STARTS_WITH` [filter](https://www.meilisearch.com/docs/learn/filtering_and_sorting/filter_expression_reference#starts-with) with improved performance
- Allow using the `EXIST` filter on `_vectors` fields

DX improvements
- Allow sorting on the [documents API](https://www.meilisearch.com/docs/reference/api/documents)
- Allow renaming indexes via the [index update](https://www.meilisearch.com/docs/reference/api/indexes#update-an-index) and [index swap](https://www.meilisearch.com/docs/reference/api/indexes#swap-indexes) APIs
- Search API responses can now [include query and documents vectors](https://www.meilisearch.com/docs/reference/api/search#display-vectors-in-response)
- New [webhooks API](https://www.meilisearch.com/docs/reference/api/webhooks) to trigger workflows after task processing
- Transfer documents between instances with the [export API](https://www.meilisearch.com/docs/reference/api/export)
- Improved hybrid search performance when using filters

Dashboard improvements
- Login: multiple OAuth providers can now be linked to the same account
- Tasks: allow tasks cancellation from the UI

## In the news

- [Introducing Meilisearch Enterprise Edition](https://www.meilisearch.com/blog/enterprise-license) Meilisearch is moving to a dual licensing model to serve both our community and enterprise users better.

- [What is RAG and how does it work?](https://www.meilisearch.com/blog/what-is-rag) Our complete guide to Retrieval-Augmented Generation.

- [Meilisearch voice search demo](https://voice.meilisearch.com/) Search for electronic products using your voice.

- `_geoPolygon` [filter prototype](https://chantiers-paris.vercel.app/) Check out this map of Paris construction sites built using the upcoming geo filter.

- [Flexible infrastructure pricing](https://www.meilisearch.com/pricing/platform) Customize the infrastructure for your Meilisearch deployment.

---

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

---

## Contributors shout-out

Thanks to [@curquiza](https://github.com/curquiza), [@dureuill](https://github.com/dureuill), [@irevoire](https://github.com/irevoire), [@Kerollmops](https://github.com/Kerollmops) [@LeSuisse](https://github.com/LeSuisse), [@ManyTheFish](https://github.com/ManyTheFish), [@martin-g](https://github.com/martin-g), [@Mubelotix](https://github.com/Mubelotix), and [@qdequele](https://github.com/qdequele) for their contribution to [Meilisearch](https://git.new/meilisearch) 🙌

## We’re hiring

Check out our open position:
- [Senior Software Engineer](https://www.meilisearch.com/careers)

---

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

August Changelog

We’re thrilled to announce the release of Meilisearch 1.16. In this article, we’ll highlight the key new features: multi-modal embeddings, exporting to other instances, and improvements to the documents API.

For an exhaustive listing of the changes, please consult the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.16.0) on GitHub.


<Callout intent="tip">

Meilisearch 1.16 is coming to [Meilisearch Cloud](https://www.meilisearch.com/cloud) on August 19th. Reach out if your team needs early access!

</Callout>

## New: Multi-modal search and embeddings

Meilisearch 1.16 introduces the new **fragments API**. This allows you to deconstruct documents and search queries into distinct semantic parts, or “fragments.“

Leveraging fragments, you can now create embeddings based on semantically different parts of the document and build multimodal search. For example, this allows users on [ecommerce sites and marketplaces](https://www.meilisearch.com/solutions/digital-commerce) to find products by describing them.

<Callout intent="tip">

Multi-modality is currently experimental. Learn how to [enable experimental features](https://www.meilisearch.com/docs/learn/resources/experimental_features_overview).

</Callout>

Let’s take the example of a movie document with the following structure:

```json
{
 "title": "Star Wars",
 "description": "A long time ago, in a galaxy...",
 "image_url": "https://images.com/star-wars.jpg"
}
```

We want to use [Voyage AI](https://www.meilisearch.com/docs/guides/embedders/voyage) to generate embeddings at indexing time as follows:
- At indexing time — Create a `text` embedding based on title and description as text, and create an `image` embedding based on the image URL
- At search time — Allow matching `text` based on a text query and `image` based on an inlined image

The payload below allows [updating embedder settings](https://www.meilisearch.com/docs/reference/api/settings#update-embedder-settings) accordingly:

```json
{
	"voyage": {
		"source": "rest",
		"url": "https://api.voyageai.com/v1/multimodalembeddings",
		"apiKey": "$VOYAGE_API_KEY",
		// describes how we index documents
		"indexingFragments": {
			"text": {
				"value": {
					"content": [
						{
							"type": "text",
							"text": "A movie titled {{doc.title}} whose description starts with {{doc.overview|truncateWords:20}}."
						}
					]
				}
			},
			"image": {
				"value": {
					"content": [
						{
							"type": "image_url",
							"image_url": "{{doc.poster}}"
						}
					]
				}
			}
		},
		// describes how to search in the index
		"searchFragments": {
			"image": {
				"value": {
					"content": [
						{
							"type": "image_base64",
							"image_base64": "data:{{media.image.mime}};base64,{{media.image.data}}"
						}
					]
				}
			},
			"text": {
				"value": {
					"content": [
						{
							"type": "text",
							// uses the `q` field from search queries
							"text": "{{q}}"
						}
					]
				}
			}
		},
		"request": {
			"inputs": [
				"{{fragment}}",
				"{{..}}"
			],
			"model": "voyage-multimodal-3"
		},
		"response": {
			"data": [
				{
					"embedding": "{{embedding}}"
				},
				"{{..}}"
			]
		}
	}
}
```

In the indexing fragments, the `value` object depends on the embedding API. In this example, it adheres to the [Voyage AI API](https://docs.voyageai.com/reference/multimodal-embeddings-api).

With these settings in effect, you can [make a search request](https://www.meilisearch.com/docs/reference/api/search) using image data as follows:

```json
{
 "media": {
 "image": {
 "mime": "image/jpeg",
 "data": "/9j/4AAQSkZJRgABAQAAAQABAAD/2w... images bytes encoded as base64"
 },
 "hybrid": {
 "embedder": "voyage"
 }
}
```

You can also perform a [hybrid search](https://www.meilisearch.com/docs/reference/api/search#hybrid-search) using a text query. Note that since the Voyage model we’re using here is multi-modal, Meilisearch will also compare the generated embeddings with the movie image. This means that describing the image could allow finding the movie.

```json
{
 "q": "A movie with lightsabers in space",
 "hybrid": {
 "embedder": "voyage",
 "semanticRatio": 0.5
 }
}
```

## New: Transferring documents between Meilisearch instances 

Meilisearch 1.16 introduces a new **exports API** to transfer documents from one instance to another. This new API does not require creating a dump or a snapshot. The goal is to ease the migration from a local environment to a production [Meilisearch Cloud](https://meilisearch.com/cloud) project and minimize deployment overhead.

Consult the documentation for more information about using the [exports API](https://www.meilisearch.com/docs/reference/api/export).

## New: Sorting on the documents API

Meilisearch 1.16 brings sorting to the documents API. The sorting behaves exactly like the sorting with the search API. This improves the administrative toolkit to manage your Meilisearch instance. Consult the documentation for more information about using the [documents API](https://www.meilisearch.com/docs/reference/api/documents).

## ICYMI: Conversational search

In Meilisearch 1.15.1, we introduced [conversational search](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat). This new feature allows you to get a functioning RAG pipeline by chatting with your Meilisearch instance via the [Chats API](https://www.meilisearch.com/docs/reference/api/chats). Check it out!

## Contributors shout-out

We want to give a massive thank you to the contributors who made this release possible. Special thanks [@martin-g](https://github.com/martin-g), [@lblack00](https://github.com/lblack00), [@mcmah309](https://github.com/mcmah309), [@nnethercott](https://github.com/nnethercott), [@arthurgousset](https://github.com/arthurgousset), [@Mubelotix](https://github.com/Mubelotix), [@diksipav](https://github.com/diksipav), [@Nymuxyzo](https://github.com/Nymuxyzo), [@kametsun](https://github.com/kametsun) for their contributions to [Meilisearch](https://github.com/meilisearch/meilisearch) and to [@nnethercott](nnethercott) for their contribution to [Arroy](https://github.com/meilisearch/arroy).

---

And that’s a wrap for v1.16! These release notes only highlight the most significant updates. For an exhaustive listing, refer to the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.16.0) on GitHub.

---

For more information, subscribe to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=release-v1-16&utm_source=blog), or join our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord?utm_campaign=release-v1-16&utm_source=blog).

Meilisearch 1.16

We're happy to announce the release of Meilisearch v1.15. In this article, we'll highlight the key changes and improvements in this release.

For a complete list of all updates and fixes, please visit the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.15.0) on GitHub.


<Callout intent="tip">

These powerful new features are available in [Meilisearch Cloud](https://www.meilisearch.com/cloud). Sign up now to be among the first to experience the latest improvements!

</Callout>

## New in 1.15.1: Conversational search 

Meilisearch 1.15.1 introduces [conversational search](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat). This new feature allows you to get a functioning RAG pipeline by chatting with your Meilisearch instance via the [Chats API](https://www.meilisearch.com/docs/reference/api/chats). Check it out! 

## New: Disable typo tolerance for numbers

With Meilisearch v1.15, you can turn off [typo tolerance](https://www.meilisearch.com/docs/learn/relevancy/typo_tolerance_settings) for numeric values—just like you already do for words and attributes.

A search for `2024` will match only `2024`, not `2025` or `2004`. This is especially helpful when searching for postal addresses or phone numbers.

Skipping number fuzziness can also speed up your indexing, especially for datasets that contain many unique numbers.

## New: Lexicographic string filters

You can now compare any string with `<`, `<=`, `>`, `>=` or an open range `TO`—just like numbers—using [lexicographic order](https://en.wikipedia.org/wiki/Lexicographic_order).

This is ideal for scenarios where you need to filter by human-readable values—such as dates (e.g., “2025-05-28”) instead of numeric timestamps.

The following filter finds every record released before July 17, 2023:

```
release_date < "2023-07-17" 
```

## Improvement: better support for Chinese

We’ve enhanced the Charabia tokenizer to provide more precise segmentation of Chinese text. **If your dataset contains Chinese-language content, you must reindex your database**—otherwise, Chinese queries and documents will not be processed correctly and may be ignored during search.

To reindex, you can use a [dump](https://www.meilisearch.com/docs/reference/api/dump) or—if you are using the [dumpless upgrade](https://www.meilisearch.com/docs/learn/update_and_migration/updating#dumpless-upgrade)—trigger a reindexing with one of the following methods:

1. **Add Chinese to your localized attributes**
Include the Chinese locale (e.g., cmn) in your [localizedAttributes](https://www.meilisearch.com/docs/reference/api/settings#localized-attributes).

2. **Update your stop-word list or dictionary**
Make any change to your [stop words](https://www.meilisearch.com/docs/reference/api/settings#update-stop-words) or custom [dictionary](https://www.meilisearch.com/docs/reference/api/settings#update-dictionary) entries. 

3. **Refresh the searchable attribute**
Temporarily remove an attribute from [searchable attributes](https://www.meilisearch.com/docs/reference/api/settings#update-searchable-attributes) list, then re-add it once the removal task has completed.

## Contributors shout-out

We want to give a massive thank you to the contributors who made this release possible. Special thanks to [CodeMan62](https://github.com/CodeMan62), [shaokeyibb](https://github.com/shaokeyibb), [HDT3213](https://github.com/HDT3213), [shu-kitamura](https://github.com/shu-kitamura), [DanasFi](https://github.com/DanasFi), [vuthanhtung2412](https://github.com/vuthanhtung2412), and [ZeroZ-lab](https://github.com/ZeroZ-lab) for their contributions to [Meilisearch](https://github.com/meilisearch/meilisearch); to [luflow](https://github.com/luflow), [mosuka](https://github.com/mosuka), and [HDT3213](https://github.com/HDT3213) for their work on [Charabia](https://github.com/meilisearch/charabia); and to [lucascool12](https://github.com/lucascool12) and [Dr-Emann](https://github.com/Dr-Emann) for their efforts on [RoaringBitmap](https://github.com/RoaringBitmap/roaring-rs).

---

And that’s a wrap for v1.15! These release notes only highlight the most significant updates. For an exhaustive listing, read the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.15.0) on GitHub.

---

For more information, subscribe to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=release-v1-15&utm_source=blog), or join our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=release-v1-15&utm_source=blog).

Meilisearch 1.15

We're excited to announce the release of Meilisearch v1.14. In this article, we'll highlight the key changes and improvements in this release.

For a complete list of all updates and fixes, please visit the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.14.0) on GitHub.


<Callout intent="tip">

These powerful new features are coming soon to [Meilisearch Cloud](https://cloud.meilisearch.com/?utm_campaign=release-v1-14&utm_source=blog). Sign up now to be among the first to experience the latest improvements!

</Callout>


## New: granular filterable attributes settings

With Meilisearch v1.14, you can configure filterable attributes using an [advanced object format](https://www.meilisearch.com/docs/reference/api/settings#filterable-attribute-object) that lets you specify exactly which filtering features to enable for each attribute pattern:

```json
{
	"filterableAttributes": [
	 {
	 "attributePatterns": ["genre", "artist"], 
	 "features": { "facetSearch": true, "filter": {"equality": true, "comparison": false } }
	 },
	 {
	 "attributePatterns": ["*rank"],
	 "features": { "facetSearch": false, "filter": {"equality": true, "comparison": true } }
	 },
	 {
	 "attributePatterns": ["albumId"],
	 "features": { "facetSearch": false, "filter": {"equality": true, "comparison": false } }
	 },
	]
}
```

* **String attributes** (e.g., `genre`, `artist`): enable facet search and equality operators (`=` / `!=`) for categorical filtering, while disabling comparison operators (`>`, `<`, `>=`, `<=`), which are unnecessary for string values.
* **Numeric attributes** (e.g., `*rank`): enable comparison operators (`>`, `<`, `>=`, `<=`) for range filtering, while disabling facet search, as it's not relevant for numerical data.
* **Unique identifiers** (e.g., `albumId`): enable equality operators for exact matching, while disabling unused features such as comparison operators and facet search.

This targeted configuration can notably boost indexing performance. 

## Experimental: composite embedders

Meilisearch 1.14 introduces a new experimental feature that lets you use [different embedders at search and indexing time](https://www.meilisearch.com/docs/reference/api/settings#indexingembedder-and-searchembedder), allowing you to optimize for both throughput and latency.
* Use a remote embedder for indexing in bulk, as remote embedders provide the highest bandwidth (embeddings/s)
* Use a local embedder for answering search queries, as local embedders provide the lowest latency (time to first embedding)

Configure your embedders using the new `composite` [source type](https://www.meilisearch.com/docs/reference/api/settings#source). For instance, you can combine a local Hugging Face model for fast search with a remote inference endpoint for efficient indexing:

```json
{
 "embedders": {
 "text": {
 "source": "composite",
 "searchEmbedder": {
 "source": "huggingFace", // locally computed embeddings using a model from the Hugging Face Hub
 "model": "baai/bge-base-en-v1.5",
 "revision": "a5beb1e3e68b9ab74eb54cfd186867f64f240e1a"
 },
 "indexingEmbedder": {
 "source": "rest", // remotely computed embeddings using Hugging Face inference endpoints
 "url": "https://URL.endpoints.huggingface.cloud",
 "apiKey": "hf_XXXXXXX",
 "documentTemplate": "Your {{doc.template}}",
 "request": {
 "inputs": [
 "{{text}}",
 "{{..}}"
 ]
 },
 "response": [
 "{{embedding}}",
 "{{..}}"
 ]
 }
 }
 }
}
```

Meilisearch automatically selects the appropriate embedder based on the current operation.

Activate this feature in your project overview by checking the "Composite embedders" box under "Experimental features". If you are self-hosting Meilisearch, [enable it](https://www.meilisearch.com/docs/reference/api/experimental_features) via the `/experimental-features` route. 

## Experimental: cache embeddings

Meilisearch 1.14 brings a new experimental feature that allows you to [cache search query embeddings](https://www.meilisearch.com/docs/learn/self_hosted/configure_meilisearch_at_launch#search-query-embedding-cache), significantly improving performance when the same query is run multiple times.

To enable the search query embedding cache, launch Meilisearch with either instance option specifying the maximum number of entries to store in the cache:
* the `--experimental-embedding-cache-entries=150` flag
* the `MEILI_EXPERIMENTAL_EMBEDDING_CACHE_ENTRIES=150` environment variable

When enabled, Meilisearch stores the embedding vectors for search queries, eliminating the need to repeatedly generate embeddings for identical queries. This is particularly valuable in scenarios where:

* The same queries are frequently repeated
* You're using multi-search across multiple indexes
* You've implemented local sharding where identical queries are sent to different indexes

## New: get documents by id

You can now [get a set of documents by their primary keys](https://www.meilisearch.com/docs/reference/api/documents#get-documents-with-post):

```json
// POST /indexes/INDEX_UID/documents/fetch
{
 "ids": [785084, 44214, 473],
}
```

The above query will return the corresponding documents:

```json
{
 "results": [
 {
 "id": 44214,
 "title": "Black Swan"
 },
 {
 "id": 473,
 "title": "Pi"
 },
 {
 "id": 785084,
 "title": "The Whale"
 }
 ],
 "offset": 0,
 "limit": 20,
 "total": 3
}
```

## Contributors shout-out

We want to give a massive thank you to the contributors who made this release possible. Special thanks to [@MichaScant](https://github.com/MichaScant) for their work on [Meilisearch](https://github.com/meilisearch/meilisearch), [@oXtxNt9U](https://github.com/oXtxNt9U) for contributions to [Heed](https://github.com/meilisearch/heed), [@ptondereau](https://github.com/ptondereau) for efforts on [Arroy](https://github.com/meilisearch/arroy), and both [@NarHakobyan](https://github.com/NarHakobyan) and [@mosuka](https://github.com/mosuka) for their contributions to [Charabia](https://github.com/meilisearch/charabia). We’re deeply grateful for your support and collaboration.

---

And that’s a wrap for v1.14! These release notes only highlight the most significant updates. For an exhaustive listing, read the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.14.0) on GitHub.

---

For more information, subscribe to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=release-v1-14&utm_source=blog), or join our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=release-v1-14&utm_source=blog).

Meilisearch 1.14

# Meilisearch AI launch week recap

As the first quarter of 2025 closes, we've completed our first [Meilisearch launch week](https://www.meilisearch.com/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://www.meilisearch.com/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://www.meilisearch.com/blog/Agora) as shoppers find products using natural language, while [app builders](https://www.meilisearch.com/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://www.meilisearch.com/blog/Agora) and [MotionElements](https://www.meilisearch.com/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://www.meilisearch.com/blog/how-to-build-rag?ref=dailydev) or [image search](https://www.meilisearch.com/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://www.meilisearch.com/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 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.org/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.org/docs/LinkTimeOptimization.html) yielding up to 12% improvements. We’re also considering advanced techniques like [profile-guided optimization (PGO)](https://doc.rust-lang.org/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://www.meilisearch.com/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

We're excited to introduce Meilisearch v1.13. In this article, we'll cover the most significant changes. For a full list of updates, visit the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.13.0) on GitHub.

<Callout intent="tip">
Meilisearch 1.13 is available on [Meilisearch Cloud](https://cloud.meilisearch.com/?utm_campaign=release-v1-13&utm_source=blog), too—upgrade now!
</Callout>

## AI-powered search

We are thrilled to announce that AI-powered search is now fully stable! 

First released in v1.3, our vector store has evolved to auto-embedding from common providers, semantic and hybrid search, always based on our community's invaluable feedback. This feature is now ready to leave the experimental phase. 

Therefore, you no longer need to activate it using the `/experimental-features` route.

v1.13 introduces the last breaking changes to the AI-powered search feature.

### Breaking: accepted Ollama URLs

Ollama URLs will only be accepted if they end with `/api/embed` and `/api/embeddings`, passing an unsupported endpoint will now trigger an error on Meilisearch

### Breaking: error code modifications
Prior to 1.13, if the embedder configuration was missing or wasn't a string, Meilisearch would return an `invalid_embedder`error for both search and similarity requests.

It has been split into:
 - `invalid_search_embedder`: for search requests
 - `invalid_similar_embedder`: for similar requests
 
When the `hybrid` search parameter is invalid, the error `invalid_search_hybrid_query` is now returned instead of `invalid_hybrid_search`.


## Experimental: remote federated search 

The new [network route](https://www.meilisearch.com/docs/reference/api/network) enables distributed search across multiple Meilisearch instances. Combined with federated search, this simplifies implementing horizontal database partitioning strategies like sharding.

[Activate](https://www.meilisearch.com/docs/reference/api/experimental_features) this feature via the `/experimental-features` route. If you are using Meilisearch Cloud, contact support to enable this feature in your projects.

Use the `remotes` object to define the Meilisearch instances that should participate in the search. 



```json
PATCH /network
{
 "remotes": {
 "ms-01": {
 "url": "http://ms-1.example.meilisearch.io",
 "searchApiKey": "SEARCH_KEY_1"
 },
 "ms-02": {
 "url": "http://ms-2.example.meilisearch.io",
 "searchApiKey": "SEARCH_KEY_2"
 }
 }
}
```

Set the same remotes on all set of Meilisearch instances, then configure each instance with its own identifier using `self`. The value of `self` must match the corresponding key in remotes:

```json
PATCH /network
{
 "self": "ms-01"
}
```

You can now perform a remote federated search:

```json
POST /multi-search
{
 "federation": { // enable federated mode
 "limit": 5
 },
 "queries": [
 { // #0
 "q": "Batman",
 "indexUid": "movies",
 "federationOptions": {
 "remote": "ms-01"
 }
 },
 { // #1
 "q": "Batman",
	 "indexUid": "movies",
	 "federationOptions": {
 "remote": "ms-02"
 }
 }
 ]
}
```

## Experimental: new dumpless upgrade

You can now upgrade from Meilisearch >=v1.12 to Meilisearch >=v1.13 without using a [dump](https://www.meilisearch.com/docs/reference/api/dump).

Simply install the latest version of Meilisearch and relaunch it with either [instance option](https://www.meilisearch.com/docs/learn/self_hosted/configure_meilisearch_at_launch#dumpless-upgrade):
- the `--experimental-dumpless-upgrade` flag
- or the `MEILI_EXPERIMENTAL_DUMPLESS_UPGRADE` environment variable

⚠️ As this is an experimental feature, please use [snapshots](https://www.meilisearch.com/docs/learn/advanced/snapshots) to back up all important data first.

## New stats 

Meilisearch 1.3 introduces 3 new fields to the [stats object](https://www.meilisearch.com/docs/reference/api/stats#stats-object):

- `numberOfEmbeddedDocuments`: total number of documents with at least one embedding
- `numberOfEmbeddings`: total number of embeddings in an index 
- `usedDatabaseSize`: storage space used by the database in bytes, excluding unused space claimed by LMDB. Unlike `databaseSize`, this value decreases when documents are removed.

Here is an example stats object returned (as of Meilisearch 1.13):

```json
GET /stats

{
 "databaseSize": 447819776,
 "usedDatabaseSize": 196608,
 "lastUpdate": "2019-11-15T11:15:22.092896Z",
 "indexes": {
 "movies": {
 "numberOfDocuments": 19654,
 "numberOfEmbeddedDocuments": 1,
 "numberOfEmbeddings": 1,
 "isIndexing": false,
 "fieldDistribution": {
 "poster": 19654,
 "overview": 19654,
 "title": 19654,
 "id": 19654,
 "release_date": 19654
 }
 },
 "books": {
 "numberOfDocuments": 5,
 "numberOfEmbeddedDocuments": 5,
 "numberOfEmbeddings": 10,
 "isIndexing": false,
 "fieldDistribution": {
 "id": 5,
 "title": 5,
 "author": 5,
 "price": 5, 
 "genres": 5
 }
 }
 }
}
```

## Contributors shout-out

We're incredibly grateful to the external contributors who helped make this release possible. Thanks to [@takaebato](https://github.com/takaebato), [@Sherlouk](https://github.com/Sherlouk), [@jameshiew](https://github.com/jameshiew), [@K-Kumar-01](https://github.com/K-Kumar-01), [@mhmoudr](https://github.com/mhmoudr), [@eltociear](https://github.com/eltociear), [@Gnosnay](https://github.com/Gnosnay), [@DerTimonius](https://github.com/DerTimonius), [@manojks1999](https://github.com/manojks1999), [@ellnix](https://github.com/ellnix), [@Guikingone](https://github.com/Guikingone), [@migueltarga](https://github.com/migueltarga) for their contributions to [Meilisearch](https://github.com/meilisearch/meilisearch).

---

And that’s a wrap for v1.13! These release notes only highlight the most significant updates. For an exhaustive listing, read the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.13.0) on GitHub.

---

For more information, subscribe to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=release-v1-13&utm_source=blog), check out the [roadmap](https://roadmap.meilisearch.com/?utm_campaign=release-v1-13&utm_source=blog), or join our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=release-v1-13&utm_source=blog).

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