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DOC-11959: Vector Search Code Snippets - iOS #850

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merged 13 commits into from
Mar 25, 2024
Merged

DOC-11959: Vector Search Code Snippets - iOS #850

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f75b672
Vector Search Code Snippets - iOS
velicuvlad Mar 20, 2024
229bea4
typo
velicuvlad Mar 20, 2024
b71b8d4
re arrange
velicuvlad Mar 20, 2024
4154757
feedback
velicuvlad Mar 21, 2024
98dad2e
cleaned up and addressed feedback
velicuvlad Mar 22, 2024
6926829
clean obj-c project
velicuvlad Mar 22, 2024
d6e244e
remove original placement
velicuvlad Mar 22, 2024
c912b94
update actions
velicuvlad Mar 22, 2024
e1e24c7
actions on PR instead of push
velicuvlad Mar 22, 2024
b2f0797
pick up the correct simulator in Actions
velicuvlad Mar 22, 2024
0560479
removed printing default values
velicuvlad Mar 25, 2024
afafe5c
removed workflow - will be moved on Jenkins
velicuvlad Mar 25, 2024
eef8627
remove empty lines
velicuvlad Mar 25, 2024
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17 changes: 0 additions & 17 deletions .github/workflows/xcodebuild.yml
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57 changes: 20 additions & 37 deletions modules/objc/examples/code_snippets.xcodeproj/project.pbxproj
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Original file line number Diff line number Diff line change
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Expand Down
142 changes: 142 additions & 0 deletions modules/objc/examples/code_snippets/VectorSeach.m
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@@ -0,0 +1,142 @@
//
// VectorSeach.m
// CouchbaseLite
//
// Copyright © 2024 couchbase. All rights reserved.
//


#import <Foundation/Foundation.h>
#import <CouchbaseLite/CouchbaseLite.h>
#import <NaturalLanguage/NLEmbedding.h>

CBLDatabase* database;
CBLCollection* collection;
NLEmbedding* model;
// placeholder
NSArray* vectorArray;

@interface VectorSearchSnippets : NSObject

@end

@implementation VectorSearchSnippets

// MARK: Configuring a project to use Vector Search.

/*/
Download Couchbase Lite iOS Obj-C and Couchbase Lite Vector Search libraries
Extract the xcframeworks and copy them into the App Project Directory

Add both libraries to the *Frameworks, Libraries and Embedded Content* of your desired target
*/

- (void) createDefaultIndex {

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// MARK: Create a default Vector Index Configuration
CBLVectorIndexConfiguration* config = [[CBLVectorIndexConfiguration alloc] initWithExpression: @"vector" dimensions: 300 centroids: 8];

// MARK: Set custom settings
config.encoding = [CBLVectorEncoding none];
config.metric = kCBLDistanceMetricCosine;
config.minTrainingSize = 50;
config.maxTrainingSize = 300;
}

- (CBLQueryResultSet*) vectorIndexEmbedding {

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// MARK: Create Vector Index with Embedding
CBLVectorIndexConfiguration* config = [[CBLVectorIndexConfiguration alloc] initWithExpression: @"vector" dimensions: 300 centroids: 8];

NSError* error;
[collection createIndexWithName: @"vector_index" config: config error: &error];

NSArray<NSNumber*>* vectorArray = [model vectorForString: @"word"];

NSString* sql = @"select meta().id, word from _default.words where vector_match(vector_index, $vector, 20)";
CBLQuery* query = [database createQuery: sql error: &error];

CBLQueryParameters* parameters = [[CBLQueryParameters alloc] init];
[parameters setValue: vectorArray forName: @"vector"];
[query setParameters: parameters];

return [query execute: &error];
}

- (CBLQueryResultSet*) useVectorMatch {

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// MARK: Use vector_match
CBLVectorIndexConfiguration* config = [[CBLVectorIndexConfiguration alloc] initWithExpression: @"vector" dimensions: 300 centroids: 8];

NSError* error;
[collection createIndexWithName: @"vector_index" config: config error: &error];

NSString* sql = @"select meta().id, word from _default.words where vector_match(vector_index, $vector, 20)";
CBLQuery* query = [database createQuery: sql error: &error];

CBLQueryParameters* parameters = [[CBLQueryParameters alloc] init];
[parameters setValue: vectorArray forName: @"vector"];
[query setParameters: parameters];

return [query execute: &error];
}

- (CBLQueryResultSet*) useVectorDistance {

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// MARK: Use vector_distance
CBLVectorIndexConfiguration* config = [[CBLVectorIndexConfiguration alloc] initWithExpression: @"vector" dimensions: 300 centroids: 8];

NSError* error;
[collection createIndexWithName: @"vector_index" config: config error: &error];

NSString* sql = @"select meta().id, word, vector_distance(vector_index) from _default.words where vector_match(vector_index, $vector, 20)";
CBLQuery* q = [database createQuery: sql error: &error];

CBLQueryParameters* parameters = [[CBLQueryParameters alloc] init];
[parameters setValue: vectorArray forName: @"vector"];
[q setParameters: parameters];

return [q execute: &error];
}

@end


// MARK: Create Vector Index with Predictive Model

@interface WordModel : NSObject <CBLPredictiveModel>
@end

@implementation WordModel


- (CBLDictionary*) predict: (CBLDictionary*)input {
model = [NLEmbedding wordEmbeddingForLanguage: @"english"];

NSString* word = [input stringForKey: @"word"];
if (!word) {
NSLog(@"No word found !!!");
return nil;
}

NSArray* vector = [model vectorForString: @"word"];
CBLMutableDictionary* output = [[CBLMutableDictionary alloc] init];
[output setValue: vector forKey: @"vector"];

return output;
}

- (void) createVectorIndex {
WordModel* model = [[WordModel alloc] init];
[[CBLDatabase prediction] registerModel: model withName: @"WordEmbedding"];

NSString* expression = @"prediction(WordEmbedding,{\"word\": word}).vector";
CBLVectorIndexConfiguration* config = [[CBLVectorIndexConfiguration alloc] initWithExpression: expression dimensions: 300 centroids: 8];

NSError* error;
[collection createIndexWithName: @"vector_pred_index" config: config error: &error];

[[CBLDatabase prediction] unregisterModelWithName: @"WordEmbedding"];
}

@end
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