Cloudflare vector store

The CloudflareVector class provides vector search using Cloudflare Vectorize, a vector database service integrated with Cloudflare's edge network.

Constructor options

<PropertiesTable content={[ { name: 'accountId', type: 'string', description: 'Cloudflare account ID', }, { name: 'apiToken', type: 'string', description: 'Cloudflare API token with Vectorize permissions', }, ]} />

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 (dotproduct maps to dot-product)', }, ]} />

upsert()

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

createMetadataIndex()

Creates an index on a metadata field to enable filtering.

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the metadata field', }, { name: 'propertyName', type: 'string', description: 'Name of the metadata field to index', }, { name: 'indexType', type: "'string' | 'number' | 'boolean'", description: 'Type of the metadata field', }, ]} />

deleteMetadataIndex()

Removes an index from a metadata field.

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the metadata field', }, { name: 'propertyName', type: 'string', description: 'Name of the metadata field to remove indexing from', }, ]} />

listMetadataIndexes()

Lists all metadata field indexes for an index.

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

updateVector()

Updates a vector or metadata for a specific ID within an index.

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the ID to update', }, { name: 'id', type: 'string', description: 'Unique identifier of the vector or metadata to update', }, { name: 'update', type: '{ vector?: number[]; metadata?: Record<string, any>; }', description: 'Object containing the vector and/or metadata to update', }, ]} />

deleteVector()

Deletes a vector and its associated metadata for a specific ID within an index.

<PropertiesTable content={[ { name: 'indexName', type: 'string', description: 'Name of the index containing the ID to delete', }, { name: 'id', type: 'string', description: 'Unique identifier of the vector and metadata to delete', }, ]} />

Response types

Query results are returned in this format:

interface QueryResult {
  id: string
  score: number
  metadata: Record<string, any>
  vector?: number[]
}

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:

• CLOUDFLARE_ACCOUNT_ID: Your Cloudflare account ID
• CLOUDFLARE_API_TOKEN: Your Cloudflare API token with Vectorize permissions

Related

• Metadata Filters