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queryVectorItem

codebolt.vectordb.queryVectorItem(key: string): Promise<QueryVectorItemResponse>
Queries a vector item from the vector database based on the provided key.

Parameters

NameTypeDescription
keystringThe key of the vector to query the item from.

Returns:

 Promise<QueryVectorItemResponse>
A promise that resolves with the queried vector item.

Response Structure

// Single item query response
{
type: 'qeryVectorItemResponse';
item: Array<{
item: any; // The vector item data
score: number; // Similarity score (0-1)
}>;
}

// Multiple items query response
{
type: 'qeryVectorItemsResponse';
items: Array<{
icon: string; // Query text
retrieved: any[]; // Retrieved items array
}>;
}

Simple Example

// Query a single vector item
const queryResult = await codebolt.vectordb.queryVectorItem('test document vector');
console.log('✅ Vector query result:', queryResult);

Detailed Example

// Query vector item with error handling
try {
const queryResult = await codebolt.vectordb.queryVectorItem('test document vector');
console.log('✅ Vector query result:', queryResult);
console.log(' - Type:', queryResult?.type);
console.log(' - Results count:', queryResult?.item?.length || 0);

// Display similarity scores
if (queryResult?.item) {
queryResult.item.forEach((result, index) => {
console.log(` - Result ${index + 1}: Score ${result.score}`);
});
}
} catch (error) {
console.log('⚠️ Vector query failed:', error.message);
}

Multiple Items Query

// Query multiple vector items
const queryItems = [
'test document',
'vector database',
'machine learning',
'artificial intelligence'
];
const dbPath = './vector_db';

try {
const multiQueryResult = await codebolt.vectordb.queryVectorItems(queryItems, dbPath);
console.log('✅ Multiple vector query result:', multiQueryResult);
console.log(' - Type:', multiQueryResult?.type);
console.log(' - Query items count:', queryItems.length);
} catch (error) {
console.log('⚠️ Multiple vector query failed:', error.message);
}