# Turbopuffer Vector Store

The TurbopufferVector class provides vector search using [Turbopuffer](https://turbopuffer.com/), a high-performance vector database optimized for RAG applications. Turbopuffer offers fast vector similarity search with advanced filtering capabilities and efficient storage management.

## Constructor Options

**apiKey:** (`string`): The API key to authenticate with Turbopuffer

**baseUrl?:** (`string`): The base URL for the Turbopuffer API (Default: `https://api.turbopuffer.com`)

**connectTimeout?:** (`number`): The timeout to establish a connection, in ms. Only applicable in Node and Deno. (Default: `10000`)

**connectionIdleTimeout?:** (`number`): The socket idle timeout, in ms. Only applicable in Node and Deno. (Default: `60000`)

**warmConnections?:** (`number`): The number of connections to open initially when creating a new client. (Default: `0`)

**compression?:** (`boolean`): Whether to compress requests and accept compressed responses. (Default: `true`)

**schemaConfigForIndex?:** (`function`): A callback function that takes an index name and returns a config object for that index. This allows you to define explicit schemas per index.

## Methods

### createIndex()

**indexName:** (`string`): Name of the index to create

**dimension:** (`number`): Vector dimension (must match your embedding model)

**metric?:** (`'cosine' | 'euclidean' | 'dotproduct'`): Distance metric for similarity search (Default: `cosine`)

### upsert()

**vectors:** (`number[][]`): Array of embedding vectors

**metadata?:** (`Record<string, any>[]`): Metadata for each vector

**ids?:** (`string[]`): Optional vector IDs (auto-generated if not provided)

### query()

**indexName:** (`string`): Name of the index to query

**queryVector:** (`number[]`): Query vector to find similar vectors

**topK?:** (`number`): Number of results to return (Default: `10`)

**filter?:** (`Record<string, any>`): Metadata filters for the query

**includeVector?:** (`boolean`): Whether to include vectors in the results (Default: `false`)

### listIndexes()

Returns an array of index names as strings.

### describeIndex()

**indexName:** (`string`): Name of the index to describe

Returns:

```typescript
interface IndexStats {
  dimension: number;
  count: number;
  metric: "cosine" | "euclidean" | "dotproduct";
}
```

### deleteIndex()

**indexName:** (`string`): Name of the index to delete

### updateVector()

Update a single vector by ID or by metadata filter. Either `id` or `filter` must be provided, but not both.

**indexName:** (`string`): Name of the index containing the vector

**id?:** (`string`): ID of the vector to update (mutually exclusive with filter)

**filter?:** (`Record<string, any>`): Metadata filter to identify vector(s) to update (mutually exclusive with id)

**update:** (`{ vector?: number[]; metadata?: Record<string, any>; }`): Object containing the vector and/or metadata to update

### deleteVector()

**indexName:** (`string`): Name of the index containing the vector

**id:** (`string`): ID of the vector to delete

### deleteVectors()

Delete multiple vectors by IDs or by metadata filter. Either `ids` or `filter` must be provided, but not both.

**indexName:** (`string`): Name of the index containing the vectors to delete

**ids?:** (`string[]`): Array of vector IDs to delete (mutually exclusive with filter)

**filter?:** (`Record<string, any>`): Metadata filter to identify vectors to delete (mutually exclusive with ids)

## Response Types

Query results are returned in this format:

```typescript
interface QueryResult {
  id: string;
  score: number;
  metadata: Record<string, any>;
  vector?: number[]; // Only included if includeVector is true
}
```

## Schema Configuration

The `schemaConfigForIndex` option allows you to define explicit schemas for different indexes:

```typescript
schemaConfigForIndex: (indexName: string) => {
  // Mastra's default embedding model and index for memory messages:
  if (indexName === "memory_messages_384") {
    return {
      dimensions: 384,
      schema: {
        thread_id: {
          type: "string",
          filterable: true,
        },
      },
    };
  } else {
    throw new Error(`TODO: add schema for index: ${indexName}`);
  }
};
```

## Error Handling

The store throws typed errors that can be caught:

```typescript
try {
  await store.query({
    indexName: "index_name",
    queryVector: queryVector,
  });
} catch (error) {
  if (error instanceof VectorStoreError) {
    console.log(error.code); // 'connection_failed' | 'invalid_dimension' | etc
    console.log(error.details); // Additional error context
  }
}
```

## Related

- [Metadata Filters](https://mastra.ai/reference/rag/metadata-filters)