Insert Embedding in Qdrant

After generating embeddings, you need to store them in a vector database for similarity search. The QdrantVector class provides methods to create collections and insert embeddings into Qdrant, a high-performance vector database. This example shows how to store embeddings in Qdrant for later retrieval.

import { openai } from '@ai-sdk/openai';
import { QdrantVector } from '@mastra/qdrant';
import { MDocument } from '@mastra/rag';
import { embedMany } from 'ai';
 
const doc = MDocument.fromText('Your text content...');
 
const chunks = await doc.chunk();
 
const { embeddings } = await embedMany({
  values: chunks.map(chunk => chunk.text),
  model: openai.embedding('text-embedding-3-small'),
  maxRetries: 3,
});
 
const qdrant = new QdrantVector(
  process.env.QDRANT_URL,
  process.env.QDRANT_API_KEY,
);
 
await qdrant.createIndex({
  indexName: 'test_collection',
  dimension: 1536,
});
 
await qdrant.upsert({
  indexName: 'test_collection',
  vectors: embeddings,
  metadata: chunks?.map(chunk => ({ text: chunk.text })),
});

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