AzureAISearchOptions
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:101
Embeddings and documents are stored in an Azure AI Search index, a merge or upload approach is used when adding embeddings. When adding multiple embeddings the index is updated by this vector store in batches of 10 documents, very large nodes may result in failure due to the batch byte size being exceeded.
Type Parameters
Section titled “Type Parameters”T extends R
Properties
Section titled “Properties”userAgent?
Section titled “userAgent?”
optionaluserAgent:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:102
credential?
Section titled “credential?”
optionalcredential:AzureKeyCredential|DefaultAzureCredential|ManagedIdentityCredential
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:103
endpoint?
Section titled “endpoint?”
optionalendpoint:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:107
optionalkey:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:108
serviceApiVersion?
Section titled “serviceApiVersion?”
optionalserviceApiVersion:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:109
indexName?
Section titled “indexName?”
optionalindexName:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:110
indexClient?
Section titled “indexClient?”
optionalindexClient:SearchIndexClient
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:111
indexManagement?
Section titled “indexManagement?”
optionalindexManagement:IndexManagement
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:112
searchClient?
Section titled “searchClient?”
optionalsearchClient:SearchClient<T>
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:113
languageAnalyzer?
Section titled “languageAnalyzer?”
optionallanguageAnalyzer:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:114
compressionType?
Section titled “compressionType?”
optionalcompressionType:KnownVectorSearchCompressionKind
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:115
embeddingDimensionality?
Section titled “embeddingDimensionality?”
optionalembeddingDimensionality:number
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:116
vectorAlgorithmType?
Section titled “vectorAlgorithmType?”
optionalvectorAlgorithmType:KnownVectorSearchAlgorithmKind
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:117
idFieldKey?
Section titled “idFieldKey?”
optionalidFieldKey:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:121
Index field storing the id
chunkFieldKey?
Section titled “chunkFieldKey?”
optionalchunkFieldKey:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:125
Index field storing the node text
embeddingFieldKey?
Section titled “embeddingFieldKey?”
optionalembeddingFieldKey:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:129
Index field storing the embedding vector
metadataStringFieldKey?
Section titled “metadataStringFieldKey?”
optionalmetadataStringFieldKey:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:136
Index field storing node metadata as a json string. Schema is arbitrary, to filter on metadata values they must be stored as separate fields in the index, use filterable_metadata_field_keys to specify the metadata values that should be stored in these filterable fields
docIdFieldKey?
Section titled “docIdFieldKey?”
optionaldocIdFieldKey:string
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:140
Index field storing doc_id
hiddenFieldKeys?
Section titled “hiddenFieldKeys?”
optionalhiddenFieldKeys:string[]
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:146
List of index fields that should be hidden from the client. This is useful for fields that are not needed for retrieving, but are used for similarity search, like the embedding field.
filterableMetadataFieldKeys?
Section titled “filterableMetadataFieldKeys?”
optionalfilterableMetadataFieldKeys:FilterableMetadataFieldKeysType
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:147
indexMapping()?
Section titled “indexMapping()?”
optionalindexMapping: (enrichedDoc,metadata) =>T
Defined in: .build/typescript/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:163
(Optional) function used to map document fields to the AI search index fields
If none is specified a default mapping is provided which uses
the field keys. The keys in the enriched document are:
["id", "chunk", "embedding", "metadata"].
The default mapping is:
"id"to idFieldKey"chunk"to chunkFieldKey"embedding"to embeddingFieldKey"metadata"to metadataFieldKey
Parameters
Section titled “Parameters”enrichedDoc
Section titled “enrichedDoc”BaseNode
The enriched document
metadata
Section titled “metadata”Record<string, unknown>
The metadata of the document
Returns
Section titled “Returns”T
The mapped index document