Documentation

import { VectorsDB } from '@aetherfy/vectorsdb'

const client = new VectorsDB({
  apiKey: process.env.VECTORSDB_API_KEY
})

// Create collection for agent memories
await client.createCollection('agent-memory', {
  vector_size: 1536,
  distance: 'cosine'
})

// Store agent memory
await client.upsert('agent-memory', {
  id: 'memory-001',
  vector: embeddings,
  metadata: {
    agent_id: 'assistant',
    content: 'User prefers morning meetings',
    timestamp: Date.now()
  }
})

// Search memories
const results = await client.search('agent-memory', {
  vector: queryEmbedding,
  limit: 5,
  filter: { agent_id: 'assistant' }
}) // Returns in <100ms globally