Turbopuffer vector store

The TurbopufferVector class provides vector search using Turbopuffer, 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

<PropertiesTable content={[ { name: 'apiKey', type: 'string', description: 'The API key to authenticate with Turbopuffer', }, { name: 'baseUrl', type: 'string', isOptional: true, defaultValue: 'https://api.turbopuffer.com', description: 'The base URL for the Turbopuffer API', }, { name: 'connectTimeout', type: 'number', isOptional: true, defaultValue: '10000', description: 'The timeout to establish a connection, in ms. Only applicable in Node and Deno.', }, { name: 'connectionIdleTimeout', type: 'number', isOptional: true, defaultValue: '60000', description: 'The socket idle timeout, in ms. Only applicable in Node and Deno.', }, { name: 'warmConnections', type: 'number', isOptional: true, defaultValue: '0', description: 'The number of connections to open initially when creating a new client.', }, { name: 'compression', type: 'boolean', isOptional: true, defaultValue: 'true', description: 'Whether to compress requests and accept compressed responses.', }, { name: 'schemaConfigForIndex', type: 'function', isOptional: true, description: '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()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to create', }, { name: 'dimension', type: 'number', description: 'Vector dimension (must match your embedding model)', }, { name: 'metric', type: "'cosine' | 'euclidean' | 'dotproduct'", isOptional: true, defaultValue: 'cosine', description: 'Distance metric for similarity search', }, ]} />

upsert()

<PropertiesTable content={[ { name: 'vectors', type: 'number[][]', description: 'Array of embedding vectors', }, { name: 'metadata', type: 'Record<string, any>[]', isOptional: true, description: 'Metadata for each vector', }, { name: 'ids', type: 'string[]', isOptional: true, description: 'Optional vector IDs (auto-generated if not provided)', }, ]} />

query()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to query', }, { name: 'queryVector', type: 'number[]', description: 'Query vector to find similar vectors', }, { name: 'topK', type: 'number', isOptional: true, defaultValue: '10', description: 'Number of results to return', }, { name: 'filter', type: 'Record<string, any>', isOptional: true, description: 'Metadata filters for the query', }, { name: 'includeVector', type: 'boolean', isOptional: true, defaultValue: 'false', description: 'Whether to include vectors in the results', }, ]} />

listIndexes()

Returns an array of index names as strings.

describeIndex()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to describe', }, ]} />

Returns:

interface IndexStats {
  dimension: number
  count: number
  metric: 'cosine' | 'euclidean' | 'dotproduct'
}

deleteIndex()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: '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.

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the vector', }, { name: 'id', type: 'string', isOptional: true, description: 'ID of the vector to update (mutually exclusive with filter)', }, { name: 'filter', type: 'Record<string, any>', isOptional: true, description: 'Metadata filter to identify vector(s) to update (mutually exclusive with id)', }, { name: 'update', type: '{ vector?: number[]; metadata?: Record<string, any>; }', description: 'Object containing the vector and/or metadata to update', }, ]} />

deleteVector()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the vector', }, { name: 'id', type: 'string', description: '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.

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the vectors to delete', }, { name: 'ids', type: 'string[]', isOptional: true, description: 'Array of vector IDs to delete (mutually exclusive with filter)', }, { name: 'filter', type: 'Record<string, any>', isOptional: true, description: 'Metadata filter to identify vectors to delete (mutually exclusive with ids)', }, ]} />

Response types

Query results are returned in this format:

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:

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:

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