Releases: meilisearch/meilisearch
v1.8.0-rc.2 🪼
What's Changed
- Remove useless analytics by @irevoire in #4578
- Stop crashing when panic occurs in thread pool by @Kerollmops in #4593
- Fix embedders api by @ManyTheFish in #4600
- Fix embeddings settings update by @ManyTheFish in #4597
Contributors
Assets 7
v1.8.0-rc.1 🪼
What's Changed
- Avoid clearing db in transform by @ManyTheFish in #4504
- Update the search logs by @irevoire in #4580
- Always show facet numbers in alpha order in the facet distribution by @Kerollmops in #4581
- increase the default search time budget from 150ms to 1.5s by @irevoire in #4576
- Update charabia v0.8.9 by @ManyTheFish in #4583
- Remove pinyin normalization
\tis now part of the default separators
Contributors
Assets 7
v1.8.0-rc.0 🪼
Meilisearch v1.8 introduces new changes and optimizations related to the Hybrid search with the addition of new models and embedders like REST embedders and the Ollama model. This version also focuses on stability by adding more security around the search requests. Finally, we introduce the negative operator to exclude specific terms from a search query.
New features and improvements 🔥
Hybrid search improvements
Full description of hybrid search changes here.
🗣️ This is an experimental feature and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.
Done by @dureuill and @jakobklemm in #4456, #4537, #4509, #4548, #4549.
⚠️ Breaking changes of hybrid search usage
- To ease the search answer speed and bandwidth, Meilisearch no longer returns the query vector in the search response. The
vectorfield will not be displayed. _semanticScoreis no longer returned in the search response. The_rankingScorefield has the same value as the_semanticScore, and should be used in its place. To get the_rankingScorevalue, add"showRankingScore": trueto the search query.- When adding
"showRankingScoreDetails": trueto a semantic search query, the vector and itsvalueare no longer displayed to improve the search speed and bandwidth use.
New embedders: generic REST embedder and Ollama model
New embedder sources have been added
ollamasourcerestsource
REST embedder
Meilisearch now supports any REST embedder. You can set them up with the following configuration:
"default": {
"source": "rest", // 👈 Use the REST source
"url": "http://localhost:12345/api/v1/embed",
// ☝️ Mandatory, full URL to the embedding endpoint
"apiKey": "187HFLDH97CNHN",
// ☝️ optional, will be passed as Bearer in the Authorization header
"dimensions": 512,
// ☝️ optional, inferred with a dummy request if missing
"documentTemplate": "blabla",
"inputField": ["data", "text"],
// ☝️ inject texts in data.text in the query
// Optional, defaults to []
"inputType": "text", // text or textArray
// ☝️ inject a single text
// Optional, defaults to text
"query": {
// A JSON object describing other fields to send in a query
// for example
"model": "name-of-your-model",
"dimensions": 512
},
// ☝️ A JSON object describing other fields to send in a query
// Optional, defaults to {}
"pathToEmbeddings": ["data"],
// ☝️ look at embeddings in "data" in the response
// Optional, defaults to []
"embeddingObject": ["embedding"]
// ☝️ look at the embedding inside of "embedding"
// Optional, defaults to []
}Here is an example of setting OpenAI embedder with the rest source:
{
"source": "rest",
"apiKey": "<your-openai-api-key>",
"dimensions": 1536,
"url": "https://api.openai.com/v1/embeddings",
"query": {
"model": "text-embedding-ada-002"
},
"inputField": ["input"],
"inputType": "textArray",
"pathToEmbeddings": ["data"],
"embeddingObject": ["embedding"]
}Ollama model
Here is how to set up the Ollama model:
"default": {
"source": "ollama", // 👈 Use the Ollama source
"url": "http://localhost:11434/api/embeddings",
// ☝️ optional, fetched from MEILI_OLLAMA_URL environment variable if missing
"apiKey": "<foobarbaz>",
// ☝️ optional
"model": "nomic-embed-text",
"documentTemplate": "blabla" // like for openAI and huggingFace sources
}Expose the distribution shift setting
When setting an embedder, you can now set the distribution shift.
"default": {
"source": "huggingFace", // supported for any source
"model": "some/model",
"distribution": { // describes the natural distribution of results
"mean": 0.7, // mean value
"sigma": 0.3 // variance
}
}The “distribution shift” is an affine transformation applied to the _rankingScore of a semantic search result with the aim of making the comparison to the _rankingScore of a keyword search result more meaningful.
Other hybrid search improvements
- Hide the API key in settings and task queue (#4533) @dureuill
- Return the keyword search results even in case of a failure of the embedding (#4548) @dureuill
- For hybrid or semantic search requests, add a
semanticHitCountfield at the top of the search response indicating the number of hits originating from the semantic search (#4548) @dureuill
Support negative keyword when searching
Search queries can now contain a negative keyword to exclude terms from the search. Use the - operator in front of a word or a phrase to make sure no document that contains those words are shown in the results.
-escapereturns a placeholder search without any document contains theescapeword.-escape roomreturns only documents containing theroomword but not theescapeone.-"on demand"returns a placeholder search but without any document containing the"on demand"phrase.
Done by @Kerollmops in #4535.
Search robustness improvements
Add a search cutoff
To avoid any crash and performance issues, Meilisearch now stops search requests lasting more than 150ms.
If you want to customize this value, you can update the searchCutoffMs settings (value in ms):
curl \
-X PATCH 'http://localhost:7700/indexes/movies/settings' \
-H 'Content-Type: application/json' \
--data-binary '{
"searchCutoffMs": 50
}'The default value of the searchCutoffMs setting is null and corresponds to 150ms.
Limit concurrent search requests
Meilisearch now limits the number of search requests waiting to be processed to avoid consuming an unbounded amount of RAM and crashing. So a queue of search requests waiting to be processed has been introduced.
👉 This change does NOT impact the search performance, but only the number of enqueued search requests to prevent from any security issues.
The default number of requests in the queue is 1000.
To change this limit, use the experimental CLI flag:
./meilisearch --experimental-search-queue-size 100🗣️ This is an experimental flag and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.
Other improvements
- The
sortFacetValuesBysetting now impacts the/facet-searchroute (#4476) @Kerollmops - Related to Prometheus experimental feature: add
statuscode label to the HTTP request counter (#4373) @rohankmr414 - Tokenizer improvements by bumping charabia to 0.8.8 (#4511) @6543
- Support markdown formatted code blocks
- Improve Korean segmentation to correctly use the context ID registered in the dictionary
Fixes 🐞
- Related to Prometheus experimental feature: fix the HTTP request duration histogram bucket boundaries to follow the OpenTelemetry spec (#4530) @rohankmr414
- Related to Hybrid search experimental feature: fix an error on Windows when generating embeddings (#4549) @dureuill
Misc
- Dependencies upgrade
- CIs and tests
- Add automation to create openAPI issue (#4520) @curquiza
- Add tests when the field limit is reached (#4463) @irevoire
- Allow running benchmarks without sending results to the dashboard (#4475) @dureuill
- Create automation when creating Milestone to create update-version issue (#4416) @curquiza
- Fix reason param when benches are triggered from a comment (#4483) @dureuill
- Documentation
- Fix milli link in contributing doc (#4499) @mohsen-alizadeh
- Fix some typos in comments (#4546) @redistay
- Remove repetitive words in Benchmark docs (#4526) @availhang
- Remove repetitive words in code-base comments (#4491) @shuangcui
- Update sprint_issue.md (#4516) @curquiza
- Add documentation for benchmarks (#4477) @dureuill
- Fix typos (#4542) @brunoocasali
- Misc
❤️ Thanks again to our external contributors:
Contributors
Assets 7
v1.7.6 🐇
v1.7.5 🐇
After a security flaw has been discovered in the implementation of http2, it’s possible for an attacker to slow down your instance; see this link for more information.
This PR updates our web stack to the latest version containing a fix against this attack.
What's Changed
Full Changelog: v1.7.4...v1.7.5
Contributors
Assets 8
v1.7.4 🐇
v1.7.3 🐇
This new release doesn’t contain any fixes or features.
We make it only because the release-v1.7.2 had an issue and didn’t contain all the required assets (Linux, macOS, and Windows x86 binaries were missing).
What's Changed
- Update version for the next release (v1.7.3) in Cargo.toml by @meili-bot in #4519
Full Changelog: v1.7.2...v1.7.3
Contributors
Assets 8
v1.7.2 🐇
f2f1367What's Changed
- Adds a timeout to the webhook in #4508
Some assets weren’t built correctly, but the docker image is available.
Full Changelog: v1.7.1...v1.7.2
v1.7.1 🐇
Indexing Speed Improvement 🏇
- Skip reindexing when modifying unknown faceted fields by @Kerollmops in #4479
Contributors
Assets 8
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-smallwith a default dimension of 1536.text-embedding-3-largewith 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.
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:
- Install CUDA dependencies
- Clone and compile Meilisearch with the
cudafeature:cargo build --release --package meilisearch --features cuda - Launch your freshly compiled Meilisearch binary
- Activate vector search
- Add a Hugging Face embedder
Improved indexing speed and reduced memory crashes
- Auto-batch task deletion to reduce indexing time (#4316) @irevoire
- Improved indexing speed for vector store (Hybrid search experimental feature indexing time more than 10 times faster) (#4332) @Kerollmops @irevoire
- Capped the maximum memory of grenad sorters to reduce memory usage (#4388) @Kerollmops
- Added multiple technical and internal indexing improvements (#4350) @ManyTheFish
- Enhance facet incremental indexing (#4433) @ManyTheFish
- Change the threshold triggering incremental indexing (#4462) @ManyTheFish
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
}
}Improved logging
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 workersLog 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 outputjson: JSON structured logs
🗣️ This feature is experimental and we need your help to improve it! Share your thoughts and feedback on this GitHub discussion.
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.
/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 ascode_part=log_level. Omitcode_part=to set a single log level for the whole strram. Valid values for log level are:trace,debug,info,warn,error, oroffmode: acceptsfmt(basic) orprofile(verbose trace)
Use the DELETE endpoint of /logs/stream to interrupt a stream:
curl -X DELETE http://localhost:7700/logs/streamYou 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 ascode_part=log_level. Omitcode_part=to set a single log level for the whole strram. Valid values for log level are:trace,debug,info,warn,error, oroff
Other improvements
- Prometheus experimental feature: add job variable to Grafana dashboard (#4330) @capJavert
- Multiple language support improvements, including expanded Vietnamese normalization (Ð and Đ into d). Now uses Charabia v0.8.7. (#4365) @agourlay, @choznerol, @ngdbao, @timvisee, @xshadowlegendx, and @ManyTheFish
- New experimental feature: change the behavior of Meilisearch in a few ways to run meilisearch in a cluster by externalizing the task queue.
- Add the content type to the webhook (#4450) @irevoire
Fixes 🐞
- Make update file deletion atomic (#4435) @irevoire
- Do not omit vectors when importing a dump (#4446) @dureuill
- Put a bound on OpenAI timeout (#4459) @dureuill