VectorBucketScope

import { VectorBucketScope } from "https://esm.sh/@supabase/storage-js@2.89.0/dist/index.d.mts";

class VectorBucketScope extends VectorIndexApi {

constructor(

url: string,

headers: {

[key: string]: string;

vectorBucketName: string,

fetch?: Fetch,

);

private vectorBucketName;

createIndex(options: Omit<CreateIndexOptions, "vectorBucketName">): Promise<ApiResponse<undefined>>;

deleteIndex(indexName: string): Promise<ApiResponse<undefined>>;

getIndex(indexName: string): Promise<ApiResponse<{

index: VectorIndex;

}>>;

index(indexName: string): VectorIndexScope;

listIndexes(options?: Omit<ListIndexesOptions, "vectorBucketName">): Promise<ApiResponse<ListIndexesResponse>>;

}

§Extends

VectorIndexApi

[src]

§Constructors

new VectorBucketScope(url: string, headers: {

[key: string]: string;

}, vectorBucketName: string, fetch?: Fetch)

[src]

@example

const bucket = supabase.storage.vectors.from('embeddings-prod')

§Properties

vectorBucketName

[src]

§Methods

createIndex(options: Omit<CreateIndexOptions, "vectorBucketName">): Promise<ApiResponse<undefined>>

[src]

@param options

Index configuration (vectorBucketName is automatically set)

@return

Promise with empty response on success or error

@example

const bucket = supabase.storage.vectors.from('embeddings-prod')
await bucket.createIndex({
  indexName: 'documents-openai',
  dataType: 'float32',
  dimension: 1536,
  distanceMetric: 'cosine',
  metadataConfiguration: {
    nonFilterableMetadataKeys: ['raw_text']
  }
})

deleteIndex(indexName: string): Promise<ApiResponse<undefined>>

[src]

@param indexName

Name of the index to delete

@return

Promise with empty response on success or error

@example

const bucket = supabase.storage.vectors.from('embeddings-prod')
await bucket.deleteIndex('old-index')

getIndex(indexName: string): Promise<ApiResponse<{

index: VectorIndex;

}>>

[src]

@param indexName

Name of the index to retrieve

@return

Promise with index metadata or error

@example

const bucket = supabase.storage.vectors.from('embeddings-prod')
const { data } = await bucket.getIndex('documents-openai')
console.log('Dimension:', data?.index.dimension)

index(indexName: string): VectorIndexScope

[src]

@param indexName

Name of the index

@return

Index-scoped client with vector data operations

@example

const index = supabase.storage.vectors.from('embeddings-prod').index('documents-openai')

// Insert vectors
await index.putVectors({
  vectors: [
    { key: 'doc-1', data: { float32: [...] }, metadata: { title: 'Intro' } }
  ]
})

// Query similar vectors
const { data } = await index.queryVectors({
  queryVector: { float32: [...] },
  topK: 5
})

listIndexes(options?: Omit<ListIndexesOptions, "vectorBucketName">): Promise<ApiResponse<ListIndexesResponse>>

[src]

@param options

Listing options (vectorBucketName is automatically set)

@return

Promise with response containing indexes array and pagination token or error

@example

const bucket = supabase.storage.vectors.from('embeddings-prod')
const { data } = await bucket.listIndexes({ prefix: 'documents-' })