Qdrant Has a Free Vector Database API for AI and Semantic Search

Qdrant is a vector database built for AI applications — semantic search, recommendation systems, and RAG pipelines. REST and gRPC APIs with filtering and payload storage.

Setup

docker run -p 6333:6333 qdrant/qdrant

Create Collection

const response = await fetch('http://localhost:6333/collections/products', {
  method: 'PUT',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    vectors: { size: 384, distance: 'Cosine' }
  })
});

Index Documents with Embeddings

import { QdrantClient } from '@qdrant/js-client-rest';

const client = new QdrantClient({ url: 'http://localhost:6333' });

// Upsert points with vectors and payload
await client.upsert('products', {
  points: [
    {
      id: 1,
      vector: await getEmbedding('Wireless bluetooth headphones with noise cancellation'),
      payload: { name: 'Headphones', price: 89.99, category: 'audio' }
    },
    {
      id: 2,
      vector: await getEmbedding('Mechanical keyboard with Cherry MX switches'),
      payload: { name: 'Keyboard', price: 129.99, category: 'peripherals' }
    }
  ]
});

Semantic Search

const queryVector = await getEmbedding('good headphones for music');

const results = await client.search('products', {
  vector: queryVector,
  limit: 5,
  filter: {
    must: [{ key: 'price', range: { lte: 100 } }]
  },
  with_payload: true
});
// Returns headphones (semantically similar) under $100

Recommendation API

const recommendations = await client.recommend('products', {
  positive: [1, 5],   // IDs user liked
  negative: [3],       // IDs user disliked
  limit: 10,
  filter: { must: [{ key: 'category', match: { value: 'audio' } }] }
});

REST API

# Search
curl -X POST http://localhost:6333/collections/products/points/search \
  -d '{"vector":[0.1,0.2,...],"limit":5,"filter":{"must":[{"key":"category","match":{"value":"audio"}}]}}'

# Get collection info
curl http://localhost:6333/collections/products

# Scroll through points
curl -X POST http://localhost:6333/collections/products/points/scroll \
  -d '{"limit":10,"with_payload":true}'

Why This Matters

• Built for AI: First-class support for embeddings and RAG
• Filtering: Combine vector search with exact filters
• Payload storage: Store metadata alongside vectors
• Fast: Written in Rust, optimized for billion-scale

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