conning_tower
conning_tower copied to clipboard
chore(deps): bump objectbox_flutter_libs, objectbox and objectbox_generator
Bumps objectbox_flutter_libs, objectbox and objectbox_generator. These dependencies needed to be updated together.
Updates objectbox_flutter_libs
from 2.5.1 to 4.0.1
Release notes
Sourced from objectbox_flutter_libs's releases.
v4.0.1
- Export
ObjectWithScore
andIdWithScore
used by the new find with scoreQuery
methods. #637- Add simple
vectorsearch_cities
Dart Native example application.Note: this release includes the same versions of the Android library and ObjectBox pod as release 4.0.0. See update instructions there.
V4.0 Vector Search
To upgrade to this major release run
flutter pub upgrade objectbox --major-versions
(or for Dart Native appsdart pub upgrade objectbox --major-versions
).ObjectBox now supports Vector Search to enable efficient similarity searches.
This is particularly useful for AI/ML/RAG applications, e.g. image, audio, or text similarity. Other use cases include semantic search or recommendation engines.
Create a Vector (HNSW) index for a floating point vector property. For example, a
City
with a location vector:@Entity() class City {
@HnswIndex
(dimensions: 2)@Property
(type: PropertyType.floatVector) List<double>? location;}
Perform a nearest neighbor search using the new
nearestNeighborsF32(queryVector, maxResultCount)
query condition and the new "find with scores" query methods (the score is the distance to the query vector). For example, find the 2 closest cities:final madrid = [40.416775, -3.703790]; final query = box .query(City_.location.nearestNeighborsF32(madrid, 2)) .build(); final closest = query.findWithScores()[0].object;
For an introduction to Vector Search, more details and other supported languages see the Vector Search documentation.
- The generator correctly errors when using an unsupported index on a vector type.
- Flutter for Linux/Windows, Dart Native: update to objectbox-c 4.0.0.
- Flutter for Android: update to objectbox-android 4.0.0. If you are using Admin, make sure to update to
io.objectbox:objectbox-android-objectbrowser:4.0.0
inandroid/app/build.gradle
.- Flutter for iOS/macOS: update to objectbox-swift 2.0.0. Existing projects may have to run
pod repo update
andpod update ObjectBox
.
Commits
a4c88f2
Prepare release 4.0.193efa5f
READMEs: update vector search docs link, hint at on-device564b3dc
Merge branch '106-vector-search-example' into 'main'aceaa52
Cities: add first basic Vector Search example using Cities61a04a7
Merge branch 'gh-637-export-with-score-wrappers' into 'main'fbbd4e4
Regression: export with score wrapper classes GH#637045460f
Prepare for next release6a54675
Prepare release 4.0.052e9f9d
CHANGELOG: clarify generator errors on unsupported indexes on vectors210a139
README: fix line breaks- Additional commits viewable in compare view
Updates objectbox
from 2.5.1 to 4.0.1
Release notes
Sourced from objectbox's releases.
v4.0.1
- Export
ObjectWithScore
andIdWithScore
used by the new find with scoreQuery
methods. #637- Add simple
vectorsearch_cities
Dart Native example application.Note: this release includes the same versions of the Android library and ObjectBox pod as release 4.0.0. See update instructions there.
V4.0 Vector Search
To upgrade to this major release run
flutter pub upgrade objectbox --major-versions
(or for Dart Native appsdart pub upgrade objectbox --major-versions
).ObjectBox now supports Vector Search to enable efficient similarity searches.
This is particularly useful for AI/ML/RAG applications, e.g. image, audio, or text similarity. Other use cases include semantic search or recommendation engines.
Create a Vector (HNSW) index for a floating point vector property. For example, a
City
with a location vector:@Entity() class City {
@HnswIndex
(dimensions: 2)@Property
(type: PropertyType.floatVector) List<double>? location;}
Perform a nearest neighbor search using the new
nearestNeighborsF32(queryVector, maxResultCount)
query condition and the new "find with scores" query methods (the score is the distance to the query vector). For example, find the 2 closest cities:final madrid = [40.416775, -3.703790]; final query = box .query(City_.location.nearestNeighborsF32(madrid, 2)) .build(); final closest = query.findWithScores()[0].object;
For an introduction to Vector Search, more details and other supported languages see the Vector Search documentation.
- The generator correctly errors when using an unsupported index on a vector type.
- Flutter for Linux/Windows, Dart Native: update to objectbox-c 4.0.0.
- Flutter for Android: update to objectbox-android 4.0.0. If you are using Admin, make sure to update to
io.objectbox:objectbox-android-objectbrowser:4.0.0
inandroid/app/build.gradle
.- Flutter for iOS/macOS: update to objectbox-swift 2.0.0. Existing projects may have to run
pod repo update
andpod update ObjectBox
.
Commits
a4c88f2
Prepare release 4.0.193efa5f
READMEs: update vector search docs link, hint at on-device564b3dc
Merge branch '106-vector-search-example' into 'main'aceaa52
Cities: add first basic Vector Search example using Cities61a04a7
Merge branch 'gh-637-export-with-score-wrappers' into 'main'fbbd4e4
Regression: export with score wrapper classes GH#637045460f
Prepare for next release6a54675
Prepare release 4.0.052e9f9d
CHANGELOG: clarify generator errors on unsupported indexes on vectors210a139
README: fix line breaks- Additional commits viewable in compare view
Updates objectbox_generator
from 2.5.1 to 4.0.1
Release notes
Sourced from objectbox_generator's releases.
v4.0.1
- Export
ObjectWithScore
andIdWithScore
used by the new find with scoreQuery
methods. #637- Add simple
vectorsearch_cities
Dart Native example application.Note: this release includes the same versions of the Android library and ObjectBox pod as release 4.0.0. See update instructions there.
V4.0 Vector Search
To upgrade to this major release run
flutter pub upgrade objectbox --major-versions
(or for Dart Native appsdart pub upgrade objectbox --major-versions
).ObjectBox now supports Vector Search to enable efficient similarity searches.
This is particularly useful for AI/ML/RAG applications, e.g. image, audio, or text similarity. Other use cases include semantic search or recommendation engines.
Create a Vector (HNSW) index for a floating point vector property. For example, a
City
with a location vector:@Entity() class City {
@HnswIndex
(dimensions: 2)@Property
(type: PropertyType.floatVector) List<double>? location;}
Perform a nearest neighbor search using the new
nearestNeighborsF32(queryVector, maxResultCount)
query condition and the new "find with scores" query methods (the score is the distance to the query vector). For example, find the 2 closest cities:final madrid = [40.416775, -3.703790]; final query = box .query(City_.location.nearestNeighborsF32(madrid, 2)) .build(); final closest = query.findWithScores()[0].object;
For an introduction to Vector Search, more details and other supported languages see the Vector Search documentation.
- The generator correctly errors when using an unsupported index on a vector type.
- Flutter for Linux/Windows, Dart Native: update to objectbox-c 4.0.0.
- Flutter for Android: update to objectbox-android 4.0.0. If you are using Admin, make sure to update to
io.objectbox:objectbox-android-objectbrowser:4.0.0
inandroid/app/build.gradle
.- Flutter for iOS/macOS: update to objectbox-swift 2.0.0. Existing projects may have to run
pod repo update
andpod update ObjectBox
.
Commits
a4c88f2
Prepare release 4.0.193efa5f
READMEs: update vector search docs link, hint at on-device564b3dc
Merge branch '106-vector-search-example' into 'main'aceaa52
Cities: add first basic Vector Search example using Cities61a04a7
Merge branch 'gh-637-export-with-score-wrappers' into 'main'fbbd4e4
Regression: export with score wrapper classes GH#637045460f
Prepare for next release6a54675
Prepare release 4.0.052e9f9d
CHANGELOG: clarify generator errors on unsupported indexes on vectors210a139
README: fix line breaks- Additional commits viewable in compare view
You can trigger a rebase of this PR by commenting @dependabot rebase
.
Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR:
-
@dependabot rebase
will rebase this PR -
@dependabot recreate
will recreate this PR, overwriting any edits that have been made to it -
@dependabot merge
will merge this PR after your CI passes on it -
@dependabot squash and merge
will squash and merge this PR after your CI passes on it -
@dependabot cancel merge
will cancel a previously requested merge and block automerging -
@dependabot reopen
will reopen this PR if it is closed -
@dependabot close
will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually -
@dependabot show <dependency name> ignore conditions
will show all of the ignore conditions of the specified dependency -
@dependabot ignore this major version
will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) -
@dependabot ignore this minor version
will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) -
@dependabot ignore this dependency
will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
Note Automatic rebases have been disabled on this pull request as it has been open for over 30 days.