# 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.

```typescript
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.
