Vector Store Configuration

In addition to the memory storage adapters, Mastra also provides vector store adapters useful for storing and retrieving vector embeddings. One of these is the LibSQLVector adapter, which provides a simple interface for storing and retrieving vector embeddings in a LibSQL vector database.

import { Memory } from '@mastra/memory'
import { LibSQLStore, LibSQLVector } from '@mastra/libsql'

const memory = new Memory({
  storage: new LibSQLStore({
    id: 'learning-memory-storage',
    url: 'file:../../memory.db', // relative path from the `.mastra/output` directory
  }),
  vector: new LibSQLVector({
    id: 'learning-memory-vector',
    url: 'file:../../vector.db', // relative path from the `.mastra/output` directory
  }),
})

Mastra supports several vector store options, including:

• LibSQL
• Chroma
• Pinecone
• Qdrant
• Postgres (with pgvector)

The vector store is responsible for storing and retrieving the vector embeddings used for semantic search.