VectorIndexScope

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

class VectorIndexScope extends VectorDataApi {

constructor(

url: string,

headers: {

[key: string]: string;

vectorBucketName: string,

indexName: string,

fetch?: Fetch,

);

private indexName;

private vectorBucketName;

deleteVectors(options: Omit<DeleteVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<undefined>>;

getVectors(options: Omit<GetVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<GetVectorsResponse>>;

listVectors(options?: Omit<ListVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<ListVectorsResponse>>;

putVectors(options: Omit<PutVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<undefined>>;

queryVectors(options: Omit<QueryVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<QueryVectorsResponse>>;

}

§Extends

VectorDataApi

[src]

§Constructors

new VectorIndexScope(url: string, headers: {

[key: string]: string;

}, vectorBucketName: string, indexName: string, fetch?: Fetch)

[src]

@example

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

§Properties

indexName

[src]

vectorBucketName

[src]

§Methods

deleteVectors(options: Omit<DeleteVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<undefined>>

[src]

@param options

Deletion options (bucket and index names automatically set)

@return

Promise with empty response on success or error

@example

const index = supabase.storage.vectors.from('embeddings-prod').index('documents-openai')
await index.deleteVectors({
  keys: ['doc-1', 'doc-2', 'doc-3']
})

getVectors(options: Omit<GetVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<GetVectorsResponse>>

[src]

@param options

Vector retrieval options (bucket and index names automatically set)

@return

Promise with response containing vectors array or error

@example

const index = supabase.storage.vectors.from('embeddings-prod').index('documents-openai')
const { data } = await index.getVectors({
  keys: ['doc-1', 'doc-2'],
  returnMetadata: true
})

listVectors(options?: Omit<ListVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<ListVectorsResponse>>

[src]

@param options

Listing options (bucket and index names automatically set)

@return

Promise with response containing vectors array and pagination token or error

@example

const index = supabase.storage.vectors.from('embeddings-prod').index('documents-openai')
const { data } = await index.listVectors({
  maxResults: 500,
  returnMetadata: true
})

putVectors(options: Omit<PutVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<undefined>>

[src]

@param options

Vector insertion options (bucket and index names automatically set)

@return

Promise with empty response on success or error

@example

const index = supabase.storage.vectors.from('embeddings-prod').index('documents-openai')
await index.putVectors({
  vectors: [
    {
      key: 'doc-1',
      data: { float32: [0.1, 0.2, ...] },
      metadata: { title: 'Introduction', page: 1 }
    }
  ]
})

queryVectors(options: Omit<QueryVectorsOptions, "vectorBucketName" | "indexName">): Promise<ApiResponse<QueryVectorsResponse>>

[src]

@param options

Query options (bucket and index names automatically set)

@return

Promise with response containing matches array of similar vectors ordered by distance or error

@example

const index = supabase.storage.vectors.from('embeddings-prod').index('documents-openai')
const { data } = await index.queryVectors({
  queryVector: { float32: [0.1, 0.2, ...] },
  topK: 5,
  filter: { category: 'technical' },
  returnDistance: true,
  returnMetadata: true
})