30 lines
1.0 KiB
Markdown
30 lines
1.0 KiB
Markdown
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# Vector Store Configuration
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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.
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```typescript
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import { Memory } from "@mastra/memory";
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import { LibSQLStore, LibSQLVector } from "@mastra/libsql";
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const memory = new Memory({
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storage: new LibSQLStore({
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id: "learning-memory-storage",
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url: "file:../../memory.db", // relative path from the `.mastra/output` directory
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}),
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vector: new LibSQLVector({
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id: "learning-memory-vector",
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connectionUrl: "file:../../vector.db", // relative path from the `.mastra/output` directory
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}),
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});
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```
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Mastra supports several vector store options, including:
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- LibSQL
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- Chroma
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- Pinecone
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- Qdrant
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- Postgres (with pgvector)
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The vector store is responsible for storing and retrieving the vector embeddings used for semantic search.
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