Closed brunoocasali closed 1 year ago
Missing issues to be opened in the non-updated SDKs before closing this issue
Yes @curquiza that's correct!
I am closing this issue since all the missing features are now tracked into each SDK.
Thanks, team! 🦖 🎉
This issue gathers the changes related to the v1.3.0 of Meilisearch that will impact the integrations team.
📅 Release date: July 31th, 2023
The whole milestone of v1.3.0 is here!
⚠️ The SDKs, which should be done by the release date, are only
tier #1
. Some of the features described may be done fortier #2
but will not be ready for the release day. Check the features below to understand which tier will receive which feature.🖖 Click here to check the current tiers state of the integrations:
Understand everything about tiers here.
Integration | Tier | -------------|------| | Javascript | #1 | | PHP | #1 | | Instant Meilisearch | #1 | | Python | #2 | | Ruby | #2 | | Go | #2 | | Strapi | #2 | | .NET | #2 | | Rails | #2 | | Rust | #2 | | Symfony | #3 | | Java | #3 | | Firebase | #3 | | docs-searchbar.js | #3 | | Dart | #3 | | Swift | #3 | | Vuepress | #3 | | Gatsby | #3 |
⚠️ If no change in the public API is added during the bump pull requests, there is no need for a new release, according to the Integrations team's versioning policy guideline.
:warning: A disclaimer about labeled EXPERIMENTAL features:
[EXPERIMENTAL] Vector Store
Related to:
It allows the user to store dense vectors to be retrieved later during search time.
What needs to be changed:
vector
key during the search works:client.index('myindex').search('query', { vector: [0.1, ...] })
_vectors
special field in the documents:client.index('myindex').add_documents({ _vectors: [0.1, ...] })
vector
key similar to theq
key.:warning: This feature is enabled by querying
PATCH /experimental-features
with{ "vectorStore": true }
Extra: Add inline documentation for the method, explaining the availability of this feature only for Meilisearch v1.3 and newer. And that is also an experimental feature that needs to be opt-in manually using the
/experimental-features
https://github.com/meilisearch/meilisearch/issues/3857 endpoint.🤩 Affected integrations: All the integrations from
tier #1
andtier #2
(Python).Define fields to search on at search-time
Related to:
Add support for receiving a new key,
attributesToSearchOn
(which is an array of field names), that will enable running searches over a subset ofsearchableAttributes
without modifying Meilisearch’s index settings.🤩 Affected integrations: All the integrations from
tier #1
.Display hits' ranking scores
Related to:
This feature aims to return ranking details for each document to understand and tweak the score of the documents more efficiently.
Ensure the SDKs can handle the new search parameter
showRankingScore
. Also, ensure the SDK can handle the_rankingScore
attribute in the matchedhits
.🤩 Affected integrations: All the integrations from
tier #1
.[EXPERIMENTAL] Display ranking details at search
Related to:
This feature aims to return ranking details for each document to understand and tweak the score of the documents more easily.
Ensure the SDKs can handle the new search parameter
showRankingScoreDetails
. Also ensure the SDK can handle the_rankingScoreDetails
attributes in the matchedhits
.:warning: This feature is enabled by querying
PATCH /experimental-features
with{ "scoreDetails": true }
Extra: Add inline documentation for the method, explaining the availability of this feature only for Meilisearch v1.3 and newer. And that is also an experimental feature that must be manually opt-in using the
/experimental-features
https://github.com/meilisearch/meilisearch/issues/3857 endpoint.🤩 Affected integrations: All the integrations from
tier #1
.Sort facets value by alphanumerical or count order
Related to:
Adds the ability to sort facets by their value which could be by using
alpha
orcount
.Ensure the SDKs can handle the new index faceting configuration attribute
sortFacetValuesBy
. This enum could only takecount
oralpha
.The faceting configuration now have two attributes:
🤩 Affected integrations: All the integrations from
tier #1
.Search in facet values
Related to:
Users of bigger datasets could have difficulty interacting with vast lists of facets data since, until today, it was not possible to search on them.
To introduce this feature into the SDKs, we must:
Create a new method called
facetSearch(facetSearchQuery)
/facet_search(facetSearchQuery)
Besides all the other parameters that a regularSearchQuery
can take. The newFacetSearchQuery
can take afacetName
,facetQuery
,filter
,q
,matchingStrategy
.Params definition:
🤩 Affected integrations: All the integrations from
tier #1
.Total Tasks in task route
Related to:
Returns the total number of tasks matching the filters on the
GET /tasks
route.Ensure the SDKs can handle the new get tasks response field
total
.🤩 Affected integrations: All the integrations from
tier #1
.