Vector API

Embedding and similarity search

Complete reference for KiteDB's vector search capabilities.

Defining Vector Properties

typescript

import { vector } from '@kitedb/core';

// Define with dimensions
embedding: vector('embedding', 1536)

Similarity Search Methods

typescript

import { createVectorIndex } from '@kitedb/core';

const index = createVectorIndex({ dimensions: 1536 });

// Add vectors
index.set(nodeId, embedding);

// Search
const hits = index.search(queryVector, {
  k: 10,          // Max results
  threshold: 0.8, // Min similarity score (cosine)
  nProbe: 10,     // IVF probe count (optional)
});

Vector Indexing

typescript

const index = createVectorIndex({ dimensions: 1536 });

// Build/rebuild IVF index for faster search
index.buildIndex();

./edit --remote