build(deps): bump objectbox, objectbox_generator and objectbox_flutter_libs

[dependabot]

Bumps objectbox, objectbox_generator and objectbox_flutter_libs. These dependencies needed to be updated together.
Updates objectbox from 2.5.1 to 4.0.2

Release notes

Sourced from objectbox's releases.

v4.0.2

• Sync: support option to enable shared global IDs.

Note: this release includes the same versions of the Android library and ObjectBox pod as release 4.0.0. See update instructions there.

v4.0.1

• Export ObjectWithScore and IdWithScore used by the new find with score Query 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 apps dart 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.

... (truncated)

Commits

• 27b29f8 Prepare release 4.0.2
• 23f0c23 Merge branch '117-sync-shared-global-ids' into 'main'
• 05e170b Sync: note combined annotation and configuration class
• 130edc9 Sync global IDs: add and detect annotation parameter, set entity flag
• f07afeb Merge branch 'readme-update-examples' into 'main'
• 0791184 READMEs: update code examples to match website, more complex query
• 2bb12c3 Merge branch 'relation-docs-updates' into 'main'
• 772c1f1 ToOne: update get, set and set ID methods, note important details
• 4377c1c ToMany: document add, remove and get, note important details
• 6ab66b4 ToOne/ToMany: improve class descriptions, notable things first
• Additional commits viewable in compare view

Updates objectbox_generator from 2.5.1 to 4.0.2

Release notes

Sourced from objectbox_generator's releases.

v4.0.2

• Sync: support option to enable shared global IDs.

Note: this release includes the same versions of the Android library and ObjectBox pod as release 4.0.0. See update instructions there.

v4.0.1

• Export ObjectWithScore and IdWithScore used by the new find with score Query 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 apps dart 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.

... (truncated)

Commits

• 27b29f8 Prepare release 4.0.2
• 23f0c23 Merge branch '117-sync-shared-global-ids' into 'main'
• 05e170b Sync: note combined annotation and configuration class
• 130edc9 Sync global IDs: add and detect annotation parameter, set entity flag
• f07afeb Merge branch 'readme-update-examples' into 'main'
• 0791184 READMEs: update code examples to match website, more complex query
• 2bb12c3 Merge branch 'relation-docs-updates' into 'main'
• 772c1f1 ToOne: update get, set and set ID methods, note important details
• 4377c1c ToMany: document add, remove and get, note important details
• 6ab66b4 ToOne/ToMany: improve class descriptions, notable things first
• Additional commits viewable in compare view

Updates objectbox_flutter_libs from 2.5.1 to 4.0.2

Release notes

Sourced from objectbox_flutter_libs's releases.

v4.0.2

• Sync: support option to enable shared global IDs.

Note: this release includes the same versions of the Android library and ObjectBox pod as release 4.0.0. See update instructions there.

v4.0.1

• Export ObjectWithScore and IdWithScore used by the new find with score Query 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 apps dart 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.

... (truncated)

Commits

• 27b29f8 Prepare release 4.0.2
• 23f0c23 Merge branch '117-sync-shared-global-ids' into 'main'
• 05e170b Sync: note combined annotation and configuration class
• 130edc9 Sync global IDs: add and detect annotation parameter, set entity flag
• f07afeb Merge branch 'readme-update-examples' into 'main'
• 0791184 READMEs: update code examples to match website, more complex query
• 2bb12c3 Merge branch 'relation-docs-updates' into 'main'
• 772c1f1 ToOne: update get, set and set ID methods, note important details
• 4377c1c ToMany: document add, remove and get, note important details
• 6ab66b4 ToOne/ToMany: improve class descriptions, notable things first
• Additional commits viewable in compare view

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