Insert Embedding in LibSQL

After generating embeddings, you need to store them in a vector database for similarity search. The LibSQLVector class provides methods to create collections and insert embeddings into LibSQL, a fork of SQLite with vector extensions. This example shows how to store embeddings in LibSQL for later retrieval.

import { openai } from "@ai-sdk/openai";
import { LibSQLVector } from "@mastra/core/vector/libsql";
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"),
});
 
const libsql = new LibSQLVector({
  connectionUrl: process.env.DATABASE_URL,
  authToken: process.env.DATABASE_AUTH_TOKEN, // Optional: for Turso cloud databases
});
 
await libsql.createIndex({
  indexName: "test_collection",
  dimension: 1536,
});
 
await libsql.upsert({
  indexName: "test_collection",
  vectors: embeddings,
  metadata: chunks?.map((chunk) => ({ text: chunk.text })),
});

View Example on GitHub

Insert Embedding in Vectorize Insert Embedding in Chroma