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

apiKey:

string

Pinecone API key

environment?:

string

Pinecone environment controller host URL

cloud?:

'aws' | 'gcp' | 'azure'

= aws

Cloud provider for Pinecone serverless

region?:

string

= us-east-1

Region for Pinecone serverless

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

vector:

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()

indexName:

string

Name of the index containing the vector

id:

string

ID of the vector to update

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

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
• PINECONE_ENVIRONMENT: Pinecone environment (e.g., 'us-west1-gcp')

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