Pinecone vector store

The PineconeVector class provides an interface to Pinecone's vector database. It provides real-time vector search, with features like hybrid search, metadata filtering, and namespace management.

Constructor options

The constructor accepts all Pinecone configuration options plus Mastra-specific fields.

<PropertiesTable content={[ { name: 'id', type: 'string', description: 'Unique identifier for this vector store instance', }, { name: 'apiKey', type: 'string', description: 'Pinecone API key', }, { name: 'controllerHostUrl', type: 'string', isOptional: true, description: 'Custom Pinecone controller host URL', }, { name: 'additionalHeaders', type: 'Record<string, string>', isOptional: true, description: 'Additional HTTP headers to include in requests', }, { name: 'sourceTag', type: 'string', isOptional: true, description: 'Source tag for request tracking', }, { name: 'cloud', type: "'aws' | 'gcp' | 'azure'", isOptional: true, defaultValue: 'aws', description: 'Cloud provider for new index creation', }, { name: 'region', type: 'string', isOptional: true, defaultValue: 'us-east-1', description: 'Region for new index creation', }, ]} />

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. Use 'dotproduct' if you plan to use hybrid search.", }, ]} />

upsert()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of your Pinecone index', }, { name: 'vectors', type: 'number[][]', description: 'Array of dense embedding vectors', }, { name: 'sparseVectors', type: '{ indices: number[], values: number[] }[]', isOptional: true, description: 'Array of sparse vectors for hybrid search. Each vector must have matching indices and values arrays.', }, { 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)', }, { name: 'namespace', type: 'string', isOptional: true, description: 'Optional namespace to store vectors in. Vectors in different namespaces are isolated from each other.', }, ]} />

query()

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index to query', }, { name: 'queryVector', type: 'number[]', description: 'Dense query vector to find similar vectors', }, { name: 'sparseVector', type: '{ indices: number[], values: number[] }', isOptional: true, description: 'Optional sparse vector for hybrid search. Must have matching indices and values arrays.', }, { 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 the vector in the result', }, { name: 'namespace', type: 'string', isOptional: true, description: 'Optional namespace to query vectors from. Only returns results from the specified namespace.', }, ]} />

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: 'namespace', type: 'string', isOptional: true, description: 'Optional namespace for the update operation', }, { name: 'update', type: 'object', description: 'Update parameters', }, { name: 'update.vector', type: 'number[]', isOptional: true, description: 'New vector values to update', }, { name: 'update.metadata', type: 'Record<string, any>', isOptional: true, description: 'New 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)', }, { name: 'namespace', type: 'string', isOptional: true, description: 'Optional namespace for the deletion operation', }, ]} />

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
}

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
  }
}

Environment variables

Required environment variables:

• PINECONE_API_KEY: Your Pinecone API key

Hybrid search

Pinecone supports hybrid search by combining dense and sparse vectors. To use hybrid search:

1. Create an index with metric: 'dotproduct'
2. During upsert, provide sparse vectors using the sparseVectors parameter
3. During query, provide a sparse vector using the sparseVector parameter

Related

• Metadata Filters