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.

id:

string

Unique identifier for this vector store instance

apiKey:

string

Pinecone API key

controllerHostUrl?:

string

Custom Pinecone controller host URL

additionalHeaders?:

Record<string, string>

Additional HTTP headers to include in requests

sourceTag?:

string

Source tag for request tracking

cloud?:

'aws' | 'gcp' | 'azure'

= aws

Cloud provider for new index creation

region?:

string

= us-east-1

Region for new index creation

Methods

createIndex()

indexName:

string

Name of the index to create

dimension:

number

Vector dimension (must match your embedding model)

metric?:

'cosine' | 'euclidean' | 'dotproduct'

= cosine

Distance metric for similarity search. Use 'dotproduct' if you plan to use hybrid search.

upsert()

indexName:

string

Name of your Pinecone index

vectors:

number[][]

Array of dense embedding vectors

sparseVectors?:

{ indices: number[], values: number[] }[]

Array of sparse vectors for hybrid search. Each vector must have matching indices and values arrays.

metadata?:

Record<string, any>[]

Metadata for each vector

ids?:

string[]

Optional vector IDs (auto-generated if not provided)

namespace?:

string

Optional namespace to store vectors in. Vectors in different namespaces are isolated from each other.

query()

indexName:

string

Name of the index to query

queryVector:

number[]

Dense query vector to find similar vectors

sparseVector?:

{ indices: number[], values: number[] }

Optional sparse vector for hybrid search. Must have matching indices and values arrays.

topK?:

number

= 10

Number of results to return

filter?:

Record<string, any>

Metadata filters for the query

includeVector?:

boolean

= false

Whether to include the vector in the result

namespace?:

string

Optional namespace to query vectors from. Only returns results from the specified namespace.

listIndexes()

Returns an array of index names as strings.

describeIndex()

indexName:

string

Name of the index to describe

Returns:

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)

namespace?:

string

Optional namespace for the update operation

update:

object

Update parameters

update.vector?:

number[]

New vector values to update

update.metadata?:

Record<string, any>

New 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)

namespace?:

string

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 MastraError instances from @mastra/core/error:

import { MastraError } from '@mastra/core/error'

try {
  await store.query({
    indexName: 'index_name',
    queryVector: queryVector,
  })
} catch (error) {
  if (error instanceof MastraError) {
    console.log(error.id) // 'MASTRA_VECTOR_PINECONE_QUERY_FAILED'
    console.log(error.domain) // 'STORAGE'
    console.log(error.category) // 'USER' | 'THIRD_PARTY'
    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