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v1.7.0 🐇

11 Mar 09:02
@curquiza curquiza
v1.7.0
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v1.7.0 🐇

Meilisearch v1.7.0 focuses on improving v1.6.0 features, indexing speed and hybrid search.

🧰 All official Meilisearch integrations (including SDKs, clients, and other tools) are compatible with this Meilisearch release. Integration deployment happens between 4 to 48 hours after a new version becomes available.

Some SDKs might not include all new features—consult the project repository for detailed information. Is a feature you need missing from your chosen SDK? Create an issue letting us know you need it, or, for open-source karma points, open a PR implementing it (we'll love you for that ❤️).

New features and improvements 🔥

Improved AI-powered search — Experimental

To activate AI-powered search, set vectorStore to true in the /experimental-features route. Consult the Meilisearch documentation for more information.

🗣️ This is an experimental feature and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.

New OpenAI embedding models

When configuring OpenAI embedders), you can now specify two new models:

  • text-embedding-3-small with a default dimension of 1536.
  • text-embedding-3-large with a default dimension of 3072.

These new models are cheaper and improve search result relevancy.

Custom OpenAI model dimensions

You can configure dimensions for sources using the new OpenAI models: text-embedding-3-small and text-embedding-3-large. Dimensions must be bigger than 0 and smaller than the model size:

"embedders": {
  "new_model": {
    "source": "openAi",
    "model": "text-embedding-3-large",
    "dimensions": 512 // must be >0, must be <= 3072 for "text-embedding-3-large"
  },
  "legacy_model": {
    "source": "openAi",
    "model": "text-embedding-ada-002"
  }
}

You cannot customize dimensions for older OpenAI models such as text-embedding-ada-002. Setting dimensions to any value except the default size of these models will result in an error.

Done in #4375 by @Gosti.

GPU support when computing Hugging Face embeddings

Activate CUDA to use Nvidia GPUs when computing Hugging Face embeddings. This can significantly improve embedding generation speeds.

To enable GPU support through CUDA for HuggingFace embedding generation:

  1. Install CUDA dependencies
  2. Clone and compile Meilisearch with the cuda feature: cargo build --release --package meilisearch --features cuda
  3. Launch your freshly compiled Meilisearch binary
  4. Activate vector search
  5. Add a Hugging Face embedder

Done by @dureuill in #4304.

Improved indexing speed and reduced memory crashes

Stabilized showRankingScoreDetails

The showRankingScoreDetails search parameter, first introduce as an experimental feature in Meilisearch v1.3.0, is now a stable feature.

Use it with the /search endpoint to view detailed scores per ranking rule for each returned document:

curl \
  -X POST 'http://localhost:7700/indexes/movies/search' \
  -H 'Content-Type: application/json' \
  --data-binary '{ "q": "Batman Returns", "showRankingScoreDetails": true }'

When showRankingScoreDetails is set to true, returned documents include a _rankingScoreDetails field:

"_rankingScoreDetails": {
  "words": {
    "order": 0,
    "matchingWords": 1,
    "maxMatchingWords": 1,
    "score": 1.0
  },
  "typo": {
    "order": 1,
    "typoCount": 0,
    "maxTypoCount": 1,
    "score": 1.0
  },
  "proximity": {
    "order": 2,
    "score": 1.0
  },
  "attribute": {
    "order": 3,
    "attributes_ranking_order": 0.8,
    "attributes_query_word_order": 0.6363636363636364,
    "score": 0.7272727272727273
  },
  "exactness": {
    "order": 4,
    "matchType": "noExactMatch",
    "matchingWords": 0,
    "maxMatchingWords": 1,
    "score": 0.3333333333333333
  }
}

Done by @dureuill in #4389.

Improved logging

Done by @irevoire in #4391

Log output modified

Log messages now follow a different pattern:

# new format ✅
2024-02-06T14:54:11Z INFO actix_server::builder: 200: starting 10 workers
# old format ❌
[2024-02-06T14:54:11Z INFO  actix_server::builder] starting 10 workers

⚠️ This change may impact you if you have any automated tasks based on log output.

Log output format — Experimental

You can now configure Meilisearch to output logs in JSON.

Relaunch your instance passing json to the --experimental-logs-mode command-line option:

./meilisearch --experimental-logs-mode json

--experimental-logs-format accepts two values:

  • human: default human-readable output
  • json: JSON structured logs

🗣️ This feature is experimental and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.

⚠️ Experimental features may be incompatible between Meilisearch versions.

New /logs/stream and /logs/stderr routes — Experimental

Meilisearch v1.7 introduces 2 new experimental API routes: /logs/stream and /logs/stderr.

Use the /experimental-features route to activate both routes during runtime:

curl \
  -X PATCH 'http://localhost:7700/experimental-features/' \
  -H 'Content-Type: application/json'  \
--data-binary '{
    "logsRoute": true
  }'

🗣️ This feature is experimental, and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.

⚠️ Experimental features may be incompatible between Meilisearch versions.

/logs/stream

Use the POST endpoint to output logs in a stream. The following example disables actix logging and keeps all other logs at the DEBUG level:

curl \
  -X POST http://localhost:7700/logs/stream \
  -H 'Content-Type: application/json' \
  --data-binary '{
      "mode": "human",
      "target": "actix=off,debug"
    }'

This endpoint requires two paramaters:

  • target: defines the log level and on which part of the engine you want to apply it. Must be a string formatted as code_part=log_level. Omit code_part= to set a single log level for the whole strram. Valid values for log level are: trace, debug, info, warn, error, or off
  • mode: accepts fmt (basic) or profile (verbose trace)

Use the DELETE endpoint of /logs/stream to interrupt a stream:

curl -X DELETE http://localhost:7700/logs/stream

You may only have one listener at a time. Meilisearch log streams are not compatible with xh or httpie.

/logs/stderr

Use the POST endpoint to configure the default log output for non-stream logs:

curl \
  -X POST http://localhost:7700/logs/stream \
  -H 'Content-Type: application/json' \
  --data-binary '{
      "target": "debug"
    }'

/logs/stderr accepts one parameter:

  • target: defines the log level and on which part of the engine you want to apply it. Must be a string formatted as code_part=log_level. Omit code_part= to set a single log level for the whole strram. Valid values for log level are: trace, debug, info, warn, error, or off

Other improvements

Fixes 🐞

Misc

  • Dependencies upgrade
    • Bump rustls-webpki from 0.101.3 to 0.101.7 (#4263)
    • Bump h2 from 0.3.20 to 0.3.24 (#4345)
    • Update the dependencies (#4332) @Kerollmops
  • CIs and tests
Read more

Contributors

agourlay, timvisee, and 12 other contributors
Assets 8

v1.7.0-rc.2 🐇

06 Mar 15:44
@ManyTheFish ManyTheFish
v1.7.0-rc.2
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v1.7.0-rc.2 🐇 Pre-release
Pre-release

⚠️ Since this is a release candidate (RC), we do NOT recommend using it in a production environment. Is something not working as expected? We welcome bug reports and feedback about new features.

What's Changed since previous RC

Facet indexing

The facet incremental indexing has been optimized, and the threshold used to choose between bulk and incremental indexing has been changed to fit users' needs:

Semantic search

Benchmarks

HA

Fixes

Contributors

ManyTheFish, irevoire, and dureuill
Assets 7

v1.7.0-rc.1 🐇

15 Feb 15:50
@ManyTheFish ManyTheFish
v1.7.0-rc.1
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v1.7.0-rc.1 🐇 Pre-release
Pre-release

⚠️ Since this is a release candidate (RC), we do NOT recommend using it in a production environment. Is something not working as expected? We welcome bug reports and feedback about new features.

What's Changed since previous RC

Contributors

ManyTheFish and irevoire
Assets 7

v1.6.2 🦊

13 Feb 16:04
@dureuill dureuill
v1.6.2
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v1.6.2 🦊

Fixes 🪲

  • Disable incremental facet update as a stop-gap by @dureuill in #4408

Contributors

dureuill
Assets 8

v1.7.0-rc.0 🐇

12 Feb 11:38
@irevoire irevoire
v1.7.0-rc.0
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v1.7.0-rc.0 🐇 Pre-release
Pre-release

⚠️ Since this is a release candidate (RC), we do NOT recommend using it in a production environment. Is something not working as expected? We welcome bug reports and feedback about new features.

Meilisearch v1.7.0 mostly focuses on improving v1.6.0 features, indexing speed and hybrid search. GPU computing is now supported.

New features and improvements 🔥

Improve AI with Meilisearch (experimental feature)

🗣️ AI work is still experimental, and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.

To use it, you need to enable vectorSearch through the /experimental-features route.

💡 More documentation about AI search with Meilisearch here.

Add new OpenAI embedding models & ability to override their models dimensions

When using OpenAi as source in your embedders index settings (an example here), you can now specify two new models:

  • text-embedding-3-small with a default dimension of 1536.
  • text-embedding-3-large with a default dimension of 3072.

The new models:

  • are cheaper
  • produce more relevant results in standardized tests
  • allow to set up the dimensions of the embeddings to control the trade-off between accuracy and performance (including storage)

It means that it is now possible to pass the dimensions field when using the OpenAi source. This was previously only available for the userProvided source in the previous releases.

There are some rules, though, which we detail with these examples:

"embedders": {
  "large": {
    "source": "openAi",
    "model": "text-embedding-3-large",
    "dimensions": 512 // must be >0, must be <= 3072 for "text-embedding-3-large"
  },
  "small": {
    "source": "openAi",
    "model": "text-embedding-3-small",
    "dimensions": 1024 // must be >0, must be <= 1536 for "text-embedding-3-small"
  },
  "legacy": {
    "source": "openAi",
    "model": "text-embedding-ada-002",
    "dimensions": 1536 // must =1536  for "text-embedding-ada-002"
  },
  "omitted_dimensions": { // uses the default dimension
    "source": "openAi",
    "model": "text-embedding-ada-002",
  }
}

Done in #4375 by @Gosti.

Add GPU support to compute embeddings

Enabling the CUDA feature allows using an available GPU to compute embeddings with a huggingFace embedder.
On an AWS Graviton 2, this yields a x3 - x5 improvement on indexing time.

👇 How to enable GPU support through CUDA for HuggingFace embedding generation:

Prerequisites

  • Linux distribution with a compatible CUDA version
  • NVidia GPU with CUDA support
  • A recent Rust compiler to compile Meilisearch from source

Steps

  1. Follow the guide to install the CUDA dependencies
  2. Clone Meilisearch: git clone https://github.com/meilisearch/meilisearch.git
  3. Compile Meilisearch with the cuda feature: cargo build --release --package meilisearch --features cuda
  4. In the freshly compiled Meilisearch, enable the vector store experimental feature:
❯ curl \
  -X PATCH 'http://localhost:7700/experimental-features/' \
  -H 'Content-Type: application/json'  \
--data-binary '{ "vectorStore": true }'
  1. Add an HuggingFace embedder to the settings:
curl \
-X PATCH 'http://localhost:7700/indexes/your_index/settings/embedders' \
-H 'Content-Type: application/json' --data-binary \
'{ "default": { "source": "huggingFace" } }'

Done by @dureuill in #4304.

Improve indexing speed & reduce memory crashes

Stabilize scoreDetails feature

In v1.3.0, we introduced the experimental feature scoreDetails. We got enough positive feedback on the feature, and we now stabilize it, making this feature enabled by default.

View detailed scores per ranking rule for each document with the showRankingScoreDetails search parameter:

curl \
  -X POST 'http://localhost:7700/indexes/movies/search' \
  -H 'Content-Type: application/json' \
  --data-binary '{ "q": "Batman Returns", "showRankingScoreDetails": true }'

When showRankingScoreDetails is set to true, returned documents include a _rankingScoreDetails field. This field contains score values for each ranking rule.

"_rankingScoreDetails": {
  "words": {
    "order": 0,
    "matchingWords": 1,
    "maxMatchingWords": 1,
    "score": 1.0
  },
  "typo": {
    "order": 1,
    "typoCount": 0,
    "maxTypoCount": 1,
    "score": 1.0
  },
  "proximity": {
    "order": 2,
    "score": 1.0
  },
  "attribute": {
    "order": 3,
    "attributes_ranking_order": 0.8,
    "attributes_query_word_order": 0.6363636363636364,
    "score": 0.7272727272727273
  },
  "exactness": {
    "order": 4,
    "matchType": "noExactMatch",
    "matchingWords": 0,
    "maxMatchingWords": 1,
    "score": 0.3333333333333333
  }
}

Done by @dureuill in #4389.

Logs improvements

We made some changes regarding our logs to help with debugging and bug reporting.

Done by @irevoire in #4391

Log format change

⚠️ If you did any automation based on Meilisearch logs, be aware of the changes. More information here.

The default log format evolved slightly from this:

[2024-02-06T14:54:11Z INFO  actix_server::builder] starting 10 workers

To this:

2024-02-06T13:58:14.710803Z  INFO actix_server::builder: 200: starting 10 workers

Experimental: new routes to manage logs

This new version of Meilisearch introduces 3 new experimental routes

  • POST /logs/stream: streams the log happening in real-time. Requires two parameters:
    • target: selects what logs you’re interested in. It takes the form of code_part=log_level. For example, index_scheduler=info
    • mode: selects in what format of log you want. Two options are available: human (basic logs) or profile (a way more complex trace)
  • DELETE /logs/stream: stops the listener from the meilisearch perspective. Does not require any parameters.

💡 More information in the New experimental routes section of this file.

⚠️ Some remarks on this POST /logs/stream route:

  • You can have only one listener at a time
  • Listening to the route doesn’t seem to work with xh or httpie for the moment
  • When killing the listener, it may stay installed on Meilisearch for some time, and you will need to call the DELETE /logs/stream route to get rid of it.

🗣️ This feature is experimental, and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.

⚠️ Experimental features may be incompatible between Meilisearch versions.

Other improvements

Misc

❤️ Thanks again to our external contributors:

Contributors

Kerollmops, Gosti, and 6 other contributors
Assets 7

v1.6.1 🦊

31 Jan 15:45
@curquiza curquiza
v1.6.1
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v1.6.1 🦊

Fixes 🪲

Thanks @carlosbaraza for the report ❤️

Contributors

carlosbaraza, irevoire, and 2 other contributors
Assets 8

v1.6.0 🦊

15 Jan 10:43
@curquiza curquiza
v1.6.0
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v1.6.0 🦊

Meilisearch v1.6 focuses on improving indexing performance. This new release also adds hybrid search and simplifies the process of generating embeddings for semantic search.

🧰 All official Meilisearch integrations (including SDKs, clients, and other tools) are compatible with this Meilisearch release. Integration deployment happens between 4 to 48 hours after a new version becomes available.

Some SDKs might not include all new features—consult the project repository for detailed information. Is a feature you need missing from your chosen SDK? Create an issue letting us know you need it, or, for open-source karma points, open a PR implementing it (we'll love you for that ❤️).

New features and improvements 🔥

Experimental: Automated embeddings generation for vector search

With v1.6, you can configure Meilisearch so it automatically generates embeddings using either OpenAI or HuggingFace. If neither of these third-party options suit your application, you may provide your own embeddings manually:

  • openAI: Meilisearch uses the OpenAI API to auto-embed your documents. You must supply an OpenAPI key to use this embedder
  • huggingFace: Meilisearch automatically downloads the specified model from HuggingFace and generates embeddings locally. This will use your CPU and may impact indexing performance
  • userProvided: Compute embeddings manually and supply document vectors to Meilisearch. You may be familiar with this approach if you have used vector search in a previous Meilisearch release. Read further for details on breaking changes for user provided embeddings usage

Usage

Use the embedders index setting to configure embedders. You may set multiple embedders for an index. This example defines 3 embedders named default, image and translation:

curl \
  -X PATCH 'http://localhost:7700/indexes/movies/settings' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "embedders": {
      "default": {
        "source":  "openAi",
        "apiKey": "<your-OpenAI-API-key>",
        "model": "text-embedding-ada-002",
        "documentTemplate": "A movie titled \'{{doc.title}}\' whose description starts with {{doc.overview|truncatewords: 20}}"
      },
      "image": {
        "source": "userProvided",
        "dimensions": 512,
      },
      "translation": {
        "source": "huggingFace",
        "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
        "documentTemplate": "A movie titled \'{{doc.title}}\' whose description starts with {{doc.overview|truncatewords: 20}}"
      }
    }
  }'
  • documentTemplate is a view of your document that will serve as the base for computing the embedding. This field is a JSON string in the Liquid format
  • model is the model OpenAI or HuggingFace should use when generating document embeddings

Refer to the documentation for more vector search usage instructions.

⚠️ Vector search breaking changes

If you have used vector search between v1.3.0 and v1.5.0, API usage has changed with v1.6:

  • When providing both the q and vector parameters for a single query, you must provide the hybrid parameter

  • Define a model in your embedder settings is now mandatory:

"embedders": {
    "default": {
      "source": "userProvided",
      "dimensions": 512
    }
}
  • Vectors should be JSON objects instead of arrays:
"_vectors": { "image2text": [0.0, 0.1, …] } # ✅
"_vectors": [ [0.0, 0.1] ] # ❌

Done in #4226 by @dureuill, @irevoire, @Kerollmops and @ManyTheFish.

Experimental: Hybrid search

This release introduces hybrid search functionality. Hybrid search allows users to mix keyword and semantic search at search time.

Use the hybrid search parameter to perform a hybrid search:

curl \
  -X POST 'http://localhost:7700/indexes/movies/search' \
  -H 'Content-Type: application/json' \
  --data-binary '{
    "q": "Plumbers and dinosaurs",
    "hybrid": {
        "semanticRatio": 0.9,
        "embedder": "default"
    }
  }'
  • embedder is the embedder you choose to perform the search among the ones you defined in your settings
  • semanticRatio is a number between 0 and 1. The default value is 0.5. 1 corresponds to a full semantic search and 0 corresponds to keyword search

Tip

The new vector search functionality uses Arroy, a Rust library developed by the Meilisearch engine team. Check out @Kerollmops blog post describing the whole process.

Done in #4226 by @dureuill, @irevoire, @Kerollmops and @ManyTheFish.

Improve indexing speed

This version introduces significant indexing performance improvements. Meilisearch v1.6 has been optimized to:

  • store and pre-compute less data than in previous versions
  • re-index and delete only the necessary data when updating a document. For example, when you update one document field, Meilisearch will no longer re-index the whole document

On an e-commerce dataset of 2.5Gb of documents, these changes led to more than a 50% time reduction when adding documents for the first time. When updating documents frequently and partially, re-indexing performance hovers between 50% and 75%.

⚠️ Performance improvements depend on your dataset, your machine, and how you're indexing documents.

Done in #4090 by @ManyTheFish, @dureuill and @Kerollmops.

Disk space usage reduction

Meilisearch now stores less internal data. This leads to smaller database disk sizes.

With a ~15Mb dataset, the created database is 40% and 50% smaller. Additionally, the database size has become more stable and will display more modest growth with new document additions.

Proximity precision and performance

You can now customize the accuracy of the proximity ranking rule.

Computing this ranking rule uses a significant amount of resources and may lead to increased indexing times. Lowering its precision may lead to significant performance gains. In a minority of use cases, lower proximity precision may also impact relevancy for queries using multiple search terms.

Usage

curl \
  -X PATCH 'http://localhost:7700/indexes/books/settings/proximity-precision' \
  -H 'Content-Type: application/json'  \
  --data-binary '{
    "proximityPrecision": "byAttribute"
  }'

proximityPrecision accepts either byWord or byAttribute:

  • byWord calculates the exact distance between words. This is the default setting.
  • byAttribute only determines whether words are present in the same attribute. It is less accurate, but provides better performance.

Done in #4225 by @ManyTheFish.

Experimental: Limit the number of batched tasks

Meilisearch may occasionally batch too many tasks together, which may lead to system instability. Relaunch Meilisearch with the --experimental-max-number-of-batched-tasks configuration option to address this issue:

./meilisearch --experimental-max-number-of-batched-tasks 100

You may also configure --experimental-max-number-of-batched-tasks as an environment variable or directly in the config file with MEILI_EXPERIMENTAL_MAX_NUMBER_OF_BATCHED_TASKS.

Done in #4249 by @Kerollmops

Task queue webhook

This release introduces a configurable webhook url that will be called whenever Meilisearch finishes processing a task.

Relaunch Meilisearch using --task-webhook-url and --task-webhook-authorization-header to use the webhook:

./meilisearch \
  --task-webhook-url=https://example.com/example-webhook?foo=bar&number=8 \
  --task-webhook-authorization-header=Bearer aSampleAPISearchKey

You may also define the webhook URL and header with environment variables or in the configuration file with MEILI_TASK_WEBHOOK_URL and MEILI_TASK_WEBHOOK_AUTHORIZATION_HEADER.

Done by @irevoire in #4238

Fixes 🐞

  • Fix document formatting performances during search (#4313) @ManyTheFish
  • The dump tasks are now cancellable (#4208) @irevoire
  • Fix: the payload size limit is now also applied to all routes, not only routes to add and update documents (#4231) @Karribalu
  • Fix: typo tolerance is ineffective for attributes with similar content (related issue: #4256)
  • Fix: the geosort is no longer ignored after the first bucket of a preceding sort ranking rule (#4226)
  • Fix hang on /indexes and /stats routes (#4308) @dureuill
  • Limit the number of values returned by the facet search based on maxValuePerFacet setting (#4311) @Kerollmops

Misc

❤️ Thanks again to our external contributors: @Karribalu, and @vivek-26

Contributors

Kerollmops, ManyTheFish, and 6 other contributors
Assets 8
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v1.6.0-rc.8 🦊

11 Jan 15:50
@ManyTheFish ManyTheFish
v1.6.0-rc.8
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v1.6.0-rc.8 🦊 Pre-release
Pre-release

Fixes

Contributors

ManyTheFish
Assets 7

v1.6.0-rc.7 🦊

11 Jan 11:59
@Kerollmops Kerollmops
v1.6.0-rc.7
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v1.6.0-rc.7 🦊 Pre-release
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What's Changed

  • Limit the number of values returned by the facet search by @Kerollmops in #4311

Full Changelog: v1.6.0-rc.6...v1.6.0-rc.7

Contributors

Kerollmops
Assets 7

v1.6.0-rc.6 🦊

10 Jan 16:08
@curquiza curquiza
v1.6.0-rc.6
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v1.6.0-rc.6 🦊 Pre-release
Pre-release

Fix

Contributors

dureuill
Assets 7