mobile bluesky app made with flutter
lazurite.stormlightlabs.org/
mobile
bluesky
flutter
feat: vector search (data layer)
+547
-4
+4
-4
docs/tasks/phase-7.md
+4
-4
docs/tasks/phase-7.md
···
53
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#### Vector Search
55
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-
- [ ] `SemanticSearchRepository` - depends on `EmbeddingService`, `EmbeddingRepository`
57
-
- [ ] `search(query, accountDid, {source, maxResults})` - embed query, run `nearestNeighborsF32`, filter by `accountDid` and optional `source`, return `List<SemanticSearchResult>`
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- [ ] `SemanticSearchResult` model - `postUri`, `score` (cosine similarity as percentage), `source` (saved/liked)
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- [ ] Join results back to Drift `SavedPosts`/`LikedPosts` to hydrate full post JSON for display
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+
- [x] `SemanticSearchRepository` - depends on `EmbeddingService`, `EmbeddingRepository`
57
+
- [x] `search(query, accountDid, {source, maxResults})` - embed query, run `nearestNeighborsF32`, filter by `accountDid` and optional `source`, return `List<SemanticSearchResult>`
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- [x] `SemanticSearchResult` model - `postUri`, `score` (cosine similarity as percentage), `source` (saved/liked)
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- [x] Join results back to Drift `SavedPosts`/`LikedPosts` to hydrate full post JSON for display
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### Cubit
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+26
lib/features/search/data/embedding_repository.dart
+26
lib/features/search/data/embedding_repository.dart
···
1
+
import 'dart:typed_data';
2
+
1
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import 'package:lazurite/core/objectbox/embedded_post.dart';
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import 'package:lazurite/core/objectbox/objectbox_store.dart';
3
5
import 'package:lazurite/objectbox.g.dart';
···
37
39
}
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final query = builder.build();
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final results = query.find();
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query.close();
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return results;
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}
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+
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/// Return the nearest-neighbour [EmbeddedPost] entries for [accountDid],
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/// ordered by ascending cosine distance to [queryVector].
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///
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/// [source] optionally filters to 'saved' or 'liked'.
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/// [maxResults] caps the number of candidates returned by HNSW.
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List<ObjectWithScore<EmbeddedPost>> nearestNeighbors(
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Float32List queryVector,
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String accountDid, {
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String? source,
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int maxResults = 20,
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}) {
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final hnswCondition = EmbeddedPost_.embedding.nearestNeighborsF32(queryVector, maxResults);
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final accountCondition = EmbeddedPost_.accountDid.equals(accountDid);
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+
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final condition = source != null
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? hnswCondition.and(accountCondition).and(EmbeddedPost_.source.equals(source))
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: hnswCondition.and(accountCondition);
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final query = _box.query(condition).build();
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final results = query.findWithScores();
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query.close();
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return results;
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}
+76
lib/features/search/data/semantic_search_repository.dart
+76
lib/features/search/data/semantic_search_repository.dart
···
1
+
import 'package:lazurite/core/database/app_database.dart';
2
+
import 'package:lazurite/core/embedding/embedding_service.dart';
3
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import 'package:lazurite/features/search/data/embedding_repository.dart';
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import 'package:lazurite/features/search/data/semantic_search_result.dart';
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/// Performs on-device semantic (vector) search over a user's saved and liked posts.
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///
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/// Embeds the query using [EmbeddingService], runs an HNSW nearest-neighbour
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/// search via ObjectBox, then joins each result back to [AppDatabase] to
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/// hydrate the full post JSON for display.
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class SemanticSearchRepository {
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SemanticSearchRepository({
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required EmbeddingService embeddingService,
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required EmbeddingRepository embeddingRepository,
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required AppDatabase database,
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}) : _embeddingService = embeddingService,
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_embeddingRepository = embeddingRepository,
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_database = database;
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final EmbeddingService _embeddingService;
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final EmbeddingRepository _embeddingRepository;
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final AppDatabase _database;
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+
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/// Search for posts semantically similar to [query].
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///
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/// Returns an empty list when [EmbeddingService.isAvailable] is false or
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/// when [query] is blank.
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///
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/// [source] narrows results to 'saved', 'liked', or both when null.
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/// [maxResults] caps the number of results (default 20).
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Future<List<SemanticSearchResult>> search(
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String query,
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String accountDid, {
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String? source,
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int maxResults = 20,
36
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}) async {
37
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if (!_embeddingService.isAvailable) return const [];
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if (query.trim().isEmpty) return const [];
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+
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final queryVector = await _embeddingService.embed(query);
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+
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final rawResults = _embeddingRepository.nearestNeighbors(
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queryVector,
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accountDid,
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source: source,
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maxResults: maxResults,
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);
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+
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final results = <SemanticSearchResult>[];
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for (final result in rawResults) {
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final post = result.object;
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// Cosine distance is in [0, 2]; similarity = 1 - distance, clamped to [0, 1].
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final similarity = (1.0 - result.score).clamp(0.0, 1.0);
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final scorePercent = similarity * 100.0;
55
+
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final postJson = await _fetchPostJson(accountDid, post.postUri, post.source);
57
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if (postJson == null) continue;
58
+
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results.add(
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SemanticSearchResult(postUri: post.postUri, score: scorePercent, source: post.source, postJson: postJson),
61
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);
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+
}
63
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64
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return results;
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+
}
66
+
67
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Future<String?> _fetchPostJson(String accountDid, String postUri, String source) async {
68
+
if (source == 'saved') {
69
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final entry = await _database.getSavedPost(accountDid, postUri);
70
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return entry?.postJson;
71
+
} else {
72
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final entry = await _database.getLikedPost(accountDid, postUri);
73
+
return entry?.postJson;
74
+
}
75
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}
76
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}
+37
lib/features/search/data/semantic_search_result.dart
+37
lib/features/search/data/semantic_search_result.dart
···
1
+
/// A single result from a semantic (vector) search.
2
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class SemanticSearchResult {
3
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const SemanticSearchResult({
4
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required this.postUri,
5
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required this.score,
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required this.source,
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required this.postJson,
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});
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/// AT URI of the post (e.g. at://did:plc:xxx/app.bsky.feed.post/yyy).
11
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final String postUri;
12
+
13
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/// Cosine similarity expressed as a percentage in [0, 100].
14
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///
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/// Derived from the ObjectBox cosine distance: `(1 - distance) * 100`.
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final double score;
17
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18
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/// Whether this post came from the user's saves ('saved') or likes ('liked').
19
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final String source;
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/// Raw JSON for the post, fetched from the Drift [SavedPosts] or [LikedPosts]
22
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/// table for display.
23
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final String postJson;
24
+
25
+
@override
26
+
bool operator ==(Object other) =>
27
+
identical(this, other) ||
28
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other is SemanticSearchResult &&
29
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runtimeType == other.runtimeType &&
30
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postUri == other.postUri &&
31
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score == other.score &&
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source == other.source &&
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postJson == other.postJson;
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+
35
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@override
36
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int get hashCode => Object.hash(postUri, score, source, postJson);
37
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}
+95
test/features/search/data/embedding_repository_test.dart
+95
test/features/search/data/embedding_repository_test.dart
···
1
+
import 'dart:typed_data';
2
+
1
3
import 'package:flutter_test/flutter_test.dart';
2
4
import 'package:lazurite/core/objectbox/embedded_post.dart';
3
5
import 'package:lazurite/core/objectbox/objectbox_store.dart';
···
9
11
ObjectBoxStore _makeInMemoryStore() {
10
12
final store = Store(getObjectBoxModel(), directory: 'memory:test-${_storeCounter++}');
11
13
return ObjectBoxStore.forTesting(store);
14
+
}
15
+
16
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/// Fixed 384-dimensional unit vector.
17
+
Float32List _unitVector({double value = 1.0}) {
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final v = Float32List(384);
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for (var i = 0; i < 384; i++) {
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v[i] = value;
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}
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return v;
12
23
}
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14
25
EmbeddedPost _post({
···
133
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134
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expect(repo.countByAccount('did:plc:a'), equals(2));
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expect(repo.countByAccount('did:plc:b'), equals(1));
147
+
});
148
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});
149
+
150
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group('nearestNeighbors', () {
151
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test('returns empty list when no embeddings exist', () {
152
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final results = repo.nearestNeighbors(_unitVector(), 'did:plc:a');
153
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expect(results, isEmpty);
154
+
});
155
+
156
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test('returns posts for the matching account only', () {
157
+
repo.upsert(_post(postUri: 'at://did/post/1', accountDid: 'did:plc:a', embedding: _unitVector().toList()));
158
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repo.upsert(_post(postUri: 'at://did/post/2', accountDid: 'did:plc:b', embedding: _unitVector().toList()));
159
+
160
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final results = repo.nearestNeighbors(_unitVector(), 'did:plc:a');
161
+
162
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expect(results.length, equals(1));
163
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expect(results.first.object.accountDid, equals('did:plc:a'));
164
+
});
165
+
166
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test('filters by source when provided', () {
167
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repo.upsert(
168
+
_post(
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postUri: 'at://did/post/1',
170
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accountDid: 'did:plc:a',
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source: 'saved',
172
+
embedding: _unitVector().toList(),
173
+
),
174
+
);
175
+
repo.upsert(
176
+
_post(
177
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postUri: 'at://did/post/2',
178
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accountDid: 'did:plc:a',
179
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source: 'liked',
180
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embedding: _unitVector().toList(),
181
+
),
182
+
);
183
+
184
+
final savedOnly = repo.nearestNeighbors(_unitVector(), 'did:plc:a', source: 'saved');
185
+
expect(savedOnly.length, equals(1));
186
+
expect(savedOnly.first.object.source, equals('saved'));
187
+
188
+
final likedOnly = repo.nearestNeighbors(_unitVector(), 'did:plc:a', source: 'liked');
189
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expect(likedOnly.length, equals(1));
190
+
expect(likedOnly.first.object.source, equals('liked'));
191
+
});
192
+
193
+
test('returns all sources when source is not provided', () {
194
+
repo.upsert(
195
+
_post(
196
+
postUri: 'at://did/post/1',
197
+
accountDid: 'did:plc:a',
198
+
source: 'saved',
199
+
embedding: _unitVector().toList(),
200
+
),
201
+
);
202
+
repo.upsert(
203
+
_post(
204
+
postUri: 'at://did/post/2',
205
+
accountDid: 'did:plc:a',
206
+
source: 'liked',
207
+
embedding: _unitVector().toList(),
208
+
),
209
+
);
210
+
211
+
final results = repo.nearestNeighbors(_unitVector(), 'did:plc:a');
212
+
expect(results.length, equals(2));
213
+
});
214
+
215
+
test('respects maxResults cap', () {
216
+
for (var i = 1; i <= 5; i++) {
217
+
repo.upsert(_post(postUri: 'at://did/post/$i', accountDid: 'did:plc:a', embedding: _unitVector().toList()));
218
+
}
219
+
220
+
final results = repo.nearestNeighbors(_unitVector(), 'did:plc:a', maxResults: 3);
221
+
expect(results.length, lessThanOrEqualTo(3));
222
+
});
223
+
224
+
test('scores are non-negative (cosine distance in [0, 2])', () {
225
+
repo.upsert(_post(postUri: 'at://did/post/1', accountDid: 'did:plc:a', embedding: _unitVector().toList()));
226
+
227
+
final results = repo.nearestNeighbors(_unitVector(), 'did:plc:a');
228
+
expect(results, isNotEmpty);
229
+
expect(results.first.score, greaterThanOrEqualTo(0.0));
230
+
expect(results.first.score, lessThanOrEqualTo(2.0));
136
231
});
137
232
});
138
233
});
+309
test/features/search/data/semantic_search_repository_test.dart
+309
test/features/search/data/semantic_search_repository_test.dart
···
1
+
import 'dart:convert';
2
+
import 'dart:typed_data';
3
+
4
+
import 'package:drift/drift.dart' show Value;
5
+
import 'package:drift/native.dart';
6
+
import 'package:flutter_test/flutter_test.dart';
7
+
import 'package:lazurite/core/database/app_database.dart';
8
+
import 'package:lazurite/core/embedding/embedding_service.dart';
9
+
import 'package:lazurite/core/objectbox/embedded_post.dart';
10
+
import 'package:lazurite/core/objectbox/objectbox_store.dart';
11
+
import 'package:lazurite/features/search/data/embedding_repository.dart';
12
+
import 'package:lazurite/features/search/data/semantic_search_repository.dart';
13
+
import 'package:lazurite/objectbox.g.dart';
14
+
15
+
var _storeCounter = 0;
16
+
17
+
ObjectBoxStore _makeInMemoryStore() {
18
+
final store = Store(getObjectBoxModel(), directory: 'memory:search-repo-${_storeCounter++}');
19
+
return ObjectBoxStore.forTesting(store);
20
+
}
21
+
22
+
Float32List _unitVector() {
23
+
final v = Float32List(384);
24
+
const val = 1.0 / 384;
25
+
for (var i = 0; i < 384; i++) {
26
+
v[i] = val;
27
+
}
28
+
return v;
29
+
}
30
+
31
+
EmbeddingService _availableService() => EmbeddingService.forTesting((_) async => _unitVector());
32
+
33
+
EmbeddingService _unavailableService() => EmbeddingService();
34
+
35
+
String _savedPostJson(String uri, String text) => jsonEncode({
36
+
'uri': uri,
37
+
'cid': 'bafycid1',
38
+
'author': {'did': 'did:plc:author', 'handle': 'author.bsky.social'},
39
+
'record': {'\$type': 'app.bsky.feed.post', 'text': text, 'createdAt': '2024-01-01T00:00:00.000Z'},
40
+
'replyCount': 0,
41
+
'repostCount': 0,
42
+
'likeCount': 0,
43
+
'quoteCount': 0,
44
+
'indexedAt': '2024-01-01T00:00:00.000Z',
45
+
});
46
+
47
+
String _likedPostJson(String uri, String text) => jsonEncode({
48
+
'post': {
49
+
'uri': uri,
50
+
'cid': 'bafycid2',
51
+
'author': {'did': 'did:plc:author', 'handle': 'author.bsky.social'},
52
+
'record': {'\$type': 'app.bsky.feed.post', 'text': text, 'createdAt': '2024-01-01T00:00:00.000Z'},
53
+
'replyCount': 0,
54
+
'repostCount': 0,
55
+
'likeCount': 0,
56
+
'quoteCount': 0,
57
+
'indexedAt': '2024-01-01T00:00:00.000Z',
58
+
},
59
+
});
60
+
61
+
void main() {
62
+
late AppDatabase database;
63
+
late ObjectBoxStore objectBoxStore;
64
+
late EmbeddingRepository embeddingRepo;
65
+
late EmbeddingService embeddingService;
66
+
67
+
setUp(() async {
68
+
database = AppDatabase(executor: NativeDatabase.memory());
69
+
objectBoxStore = _makeInMemoryStore();
70
+
embeddingRepo = EmbeddingRepository(objectBoxStore);
71
+
embeddingService = _availableService();
72
+
await embeddingService.initialize();
73
+
});
74
+
75
+
tearDown(() async {
76
+
await database.close();
77
+
objectBoxStore.close();
78
+
});
79
+
80
+
SemanticSearchRepository makeRepo({EmbeddingService? service}) => SemanticSearchRepository(
81
+
embeddingService: service ?? embeddingService,
82
+
embeddingRepository: embeddingRepo,
83
+
database: database,
84
+
);
85
+
86
+
/// Inserts an EmbeddedPost in ObjectBox and the corresponding raw post JSON
87
+
/// in the Drift SavedPosts table.
88
+
Future<void> insertSavedPost(
89
+
String postUri,
90
+
String accountDid, {
91
+
String text = 'post text',
92
+
List<double>? embedding,
93
+
}) async {
94
+
final json = _savedPostJson(postUri, text);
95
+
embeddingRepo.upsert(
96
+
EmbeddedPost(
97
+
postUri: postUri,
98
+
accountDid: accountDid,
99
+
source: 'saved',
100
+
indexedText: text,
101
+
embedding: embedding ?? _unitVector().toList(),
102
+
embeddedAt: DateTime(2026, 1, 1),
103
+
),
104
+
);
105
+
await database.savePost(
106
+
SavedPostsCompanion(accountDid: Value(accountDid), postUri: Value(postUri), postJson: Value(json)),
107
+
);
108
+
}
109
+
110
+
/// Inserts an EmbeddedPost in ObjectBox and the corresponding raw post JSON
111
+
/// in the Drift LikedPosts table.
112
+
Future<void> insertLikedPost(
113
+
String postUri,
114
+
String accountDid, {
115
+
String text = 'post text',
116
+
List<double>? embedding,
117
+
}) async {
118
+
final json = _likedPostJson(postUri, text);
119
+
embeddingRepo.upsert(
120
+
EmbeddedPost(
121
+
postUri: postUri,
122
+
accountDid: accountDid,
123
+
source: 'liked',
124
+
indexedText: text,
125
+
embedding: embedding ?? _unitVector().toList(),
126
+
embeddedAt: DateTime(2026, 1, 1),
127
+
),
128
+
);
129
+
await database.upsertLikedPost(
130
+
LikedPostsCompanion(accountDid: Value(accountDid), postUri: Value(postUri), postJson: Value(json)),
131
+
);
132
+
}
133
+
134
+
group('SemanticSearchRepository', () {
135
+
group('search', () {
136
+
test('returns empty list when EmbeddingService is unavailable', () async {
137
+
await insertSavedPost('at://did/post/1', 'did:plc:user');
138
+
139
+
final repo = makeRepo(service: _unavailableService());
140
+
final results = await repo.search('hello', 'did:plc:user');
141
+
142
+
expect(results, isEmpty);
143
+
});
144
+
145
+
test('returns empty list when query is empty', () async {
146
+
await insertSavedPost('at://did/post/1', 'did:plc:user');
147
+
148
+
final repo = makeRepo();
149
+
final results = await repo.search('', 'did:plc:user');
150
+
151
+
expect(results, isEmpty);
152
+
});
153
+
154
+
test('returns empty list when query is whitespace only', () async {
155
+
await insertSavedPost('at://did/post/1', 'did:plc:user');
156
+
157
+
final repo = makeRepo();
158
+
final results = await repo.search(' ', 'did:plc:user');
159
+
160
+
expect(results, isEmpty);
161
+
});
162
+
163
+
test('returns empty list when no posts are indexed', () async {
164
+
final repo = makeRepo();
165
+
final results = await repo.search('query', 'did:plc:user');
166
+
167
+
expect(results, isEmpty);
168
+
});
169
+
170
+
test('returns SemanticSearchResult with correct postUri and source', () async {
171
+
await insertSavedPost('at://did/post/1', 'did:plc:user', text: 'interesting article');
172
+
173
+
final repo = makeRepo();
174
+
final results = await repo.search('interesting', 'did:plc:user');
175
+
176
+
expect(results, hasLength(1));
177
+
expect(results.first.postUri, equals('at://did/post/1'));
178
+
expect(results.first.source, equals('saved'));
179
+
});
180
+
181
+
test('result contains postJson from Drift SavedPosts', () async {
182
+
await insertSavedPost('at://did/post/1', 'did:plc:user', text: 'test post');
183
+
184
+
final repo = makeRepo();
185
+
final results = await repo.search('test', 'did:plc:user');
186
+
187
+
expect(results, hasLength(1));
188
+
final decoded = jsonDecode(results.first.postJson) as Map<String, dynamic>;
189
+
expect(decoded['uri'], equals('at://did/post/1'));
190
+
});
191
+
192
+
test('result contains postJson from Drift LikedPosts', () async {
193
+
await insertLikedPost('at://did/post/2', 'did:plc:user', text: 'liked content');
194
+
195
+
final repo = makeRepo();
196
+
final results = await repo.search('content', 'did:plc:user');
197
+
198
+
expect(results, hasLength(1));
199
+
expect(results.first.source, equals('liked'));
200
+
final decoded = jsonDecode(results.first.postJson) as Map<String, dynamic>;
201
+
// liked post JSON has a nested 'post' key
202
+
expect((decoded['post'] as Map<String, dynamic>)['uri'], equals('at://did/post/2'));
203
+
});
204
+
205
+
test('score is in the range [0, 100]', () async {
206
+
await insertSavedPost('at://did/post/1', 'did:plc:user');
207
+
208
+
final repo = makeRepo();
209
+
final results = await repo.search('hello', 'did:plc:user');
210
+
211
+
expect(results, isNotEmpty);
212
+
expect(results.first.score, greaterThanOrEqualTo(0.0));
213
+
expect(results.first.score, lessThanOrEqualTo(100.0));
214
+
});
215
+
216
+
test('identical-vector query produces near-100% score', () async {
217
+
await insertSavedPost('at://did/post/1', 'did:plc:user');
218
+
219
+
// Query with the same unit vector the post was indexed with.
220
+
final svc = EmbeddingService.forTesting((_) async => _unitVector());
221
+
await svc.initialize();
222
+
final repo = makeRepo(service: svc);
223
+
final results = await repo.search('hello', 'did:plc:user');
224
+
225
+
expect(results, isNotEmpty);
226
+
// Score should be very close to 100%.
227
+
expect(results.first.score, greaterThan(90.0));
228
+
});
229
+
230
+
test('filters results to the searched accountDid only', () async {
231
+
await insertSavedPost('at://did/post/1', 'did:plc:user-a');
232
+
await insertSavedPost('at://did/post/2', 'did:plc:user-b');
233
+
234
+
final repo = makeRepo();
235
+
final results = await repo.search('hello', 'did:plc:user-a');
236
+
237
+
expect(results.length, equals(1));
238
+
expect(results.first.postUri, equals('at://did/post/1'));
239
+
});
240
+
241
+
test('returns both saved and liked posts by default', () async {
242
+
await insertSavedPost('at://did/post/saved', 'did:plc:user');
243
+
await insertLikedPost('at://did/post/liked', 'did:plc:user');
244
+
245
+
final repo = makeRepo();
246
+
final results = await repo.search('hello', 'did:plc:user');
247
+
248
+
expect(results.length, equals(2));
249
+
final sources = results.map((r) => r.source).toSet();
250
+
expect(sources, containsAll(['saved', 'liked']));
251
+
});
252
+
253
+
test('filters to saved posts when source is "saved"', () async {
254
+
await insertSavedPost('at://did/post/saved', 'did:plc:user');
255
+
await insertLikedPost('at://did/post/liked', 'did:plc:user');
256
+
257
+
final repo = makeRepo();
258
+
final results = await repo.search('hello', 'did:plc:user', source: 'saved');
259
+
260
+
expect(results.length, equals(1));
261
+
expect(results.first.source, equals('saved'));
262
+
expect(results.first.postUri, equals('at://did/post/saved'));
263
+
});
264
+
265
+
test('filters to liked posts when source is "liked"', () async {
266
+
await insertSavedPost('at://did/post/saved', 'did:plc:user');
267
+
await insertLikedPost('at://did/post/liked', 'did:plc:user');
268
+
269
+
final repo = makeRepo();
270
+
final results = await repo.search('hello', 'did:plc:user', source: 'liked');
271
+
272
+
expect(results.length, equals(1));
273
+
expect(results.first.source, equals('liked'));
274
+
expect(results.first.postUri, equals('at://did/post/liked'));
275
+
});
276
+
277
+
test('skips results whose postJson cannot be found in Drift', () async {
278
+
// Insert embedding in ObjectBox but NOT in Drift.
279
+
embeddingRepo.upsert(
280
+
EmbeddedPost(
281
+
postUri: 'at://did/post/orphan',
282
+
accountDid: 'did:plc:user',
283
+
source: 'saved',
284
+
indexedText: 'orphaned post',
285
+
embedding: _unitVector().toList(),
286
+
embeddedAt: DateTime(2026, 1, 1),
287
+
),
288
+
);
289
+
290
+
final repo = makeRepo();
291
+
final results = await repo.search('hello', 'did:plc:user');
292
+
293
+
// The orphaned result is silently skipped.
294
+
expect(results, isEmpty);
295
+
});
296
+
297
+
test('respects maxResults limit', () async {
298
+
for (var i = 1; i <= 10; i++) {
299
+
await insertSavedPost('at://did/post/$i', 'did:plc:user');
300
+
}
301
+
302
+
final repo = makeRepo();
303
+
final results = await repo.search('hello', 'did:plc:user', maxResults: 3);
304
+
305
+
expect(results.length, lessThanOrEqualTo(3));
306
+
});
307
+
});
308
+
});
309
+
}