AzureAISearchVectorStore

import \{ DefaultAzureCredential, getBearerTokenProvider\} from "@azure/identity";
import \{KnownAnalyzerNames, KnownVectorSearchAlgorithmKind \} from "@azure/search-documents";

// 1- Setup Azure OpenAI
const azureADTokenProvider = getBearerTokenProvider(
 new DefaultAzureCredential(),
 "https://cognitiveservices.azure.com/.default",
);

// IMPORTANT: You need to deploy your own embedding model as well as your own chat completion model
// NOTE: You can use whatever embedding model and language model that is supported in LlamaIndex
const azure = \{
 azureADTokenProvider,
 deployment: process.env.AZURE_DEPLOYMENT_NAME,
\};
Settings.llm = new OpenAI(\{ azure \});
Settings.embedModel = new OpenAIEmbedding(\{
 model: process.env.EMBEDDING_MODEL,
 azure: {
   ...azure,
   deployment: process.env.EMBEDDING_MODEL,
 \},
});

// ---------------------------------------------------------
// 2- Setup Azure AI Search
// Define env variables in .env file
// AZURE_AI_SEARCH_ENDPOINT=
// AZURE_AI_SEARCH_KEY=
// AZURE_OPENAI_ENDPOINT=
// EMBEDDING_MODEL=text-embedding-ada-002
// AZURE_DEPLOYMENT_NAME=gpt-4
// AZURE_API_VERSION=2024-09-01-preview

// Define index name
const indexName = "llamaindex-vector-store";

// ---------------------------------------------------------
// 3a- Create Index (if it does not exist)
// id:	      Edm.String
// chunk:	    Edm.String
// embedding:	Collection(Edm.Single)
// metadata:	Edm.String
// doc_id:	  Edm.String
// author:	  Edm.String
// theme:	    Edm.String
// director:	Edm.String

// Define metadata fields with their respective configurations
const metadataFields = \{
 author: "author",
 theme: ["theme", MetadataIndexFieldType.STRING],
 director: "director",
\};

// Define index parameters and vector store configuration
// Index validation:
// - IndexManagement.VALIDATE_INDEX: will validate before creating emnbedding index and will throw a runtime error if the index does not exist
// - IndexManagement.NO_VALIDATION: will try to access the index and will throw a runtime error if the index does not exist
// - IndexManagement.CREATE_IF_NOT_EXISTS: will create the index if it does not exist

const vectorStore = new AzureAISearchVectorStore(\{
 filterableMetadataFieldKeys:
   metadataFields as unknown as FilterableMetadataFieldKeysType,
 indexName,
 indexManagement: IndexManagement.CREATE_IF_NOT_EXISTS,
 idFieldKey: "id",
 chunkFieldKey: "chunk",
 embeddingFieldKey: "embedding",
 metadataStringFieldKey: "metadata",
 docIdFieldKey: "doc_id",
 embeddingDimensionality: 1536,
 hiddenFieldKeys: ["embedding"],
 languageAnalyzer: KnownAnalyzerNames.EnLucene,
 // store vectors on disk
 vectorAlgorithmType: KnownVectorSearchAlgorithmKind.ExhaustiveKnn,

 // Optional: Set to "scalar" or "binary" if using HNSW
 compressionType: KnownVectorSearchCompressionKind.BinaryQuantization,
\});

// ---------------------------------------------------------
// 3a- Loading documents
// Load the documents stored in the data/paul_graham/ using the SimpleDirectoryReader
// NOTE: You can use whatever reader that is supported in LlamaIndex

// Load documents using a directory reader
const documents = await new SimpleDirectoryReader().loadData(
 "data/paul_graham/",
);
const storageContext = await storageContextFromDefaults(\{ vectorStore \});

// Create index from documents with the specified storage context
const index = await VectorStoreIndex.fromDocuments(documents, \{
 storageContext,
 docStoreStrategy: DocStoreStrategy.UPSERTS,
\});

const queryEngine = index.asQueryEngine();
const response = await queryEngine.query(\{
 query: "What did the author do growing up?",
 similarityTopK: 3,
\} as any);
console.log(\{ response \});

## Extends

- `BaseVectorStore`

## Type Parameters

### T

`T` *extends* [`R`](/docs/api/type-aliases/R)

## Constructors

### Constructor

> **new AzureAISearchVectorStore**\<`T`\>(`options`): `AzureAISearchVectorStore`\<`T`\>

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:311](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L311)

#### Parameters

##### options

[`AzureAISearchOptions`](/docs/api/interfaces/AzureAISearchOptions)\<`T`\> & `VectorStoreBaseParams`

#### Returns

`AzureAISearchVectorStore`\<`T`\>

#### Overrides

`BaseVectorStore.constructor`

## Properties

### storesText

> **storesText**: `boolean` = `true`

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:285](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L285)

#### Overrides

`BaseVectorStore.storesText`

***

### \_searchClient

> **\_searchClient**: `undefined` \| `SearchClient`\<`T`\>

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:286](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L286)

***

### \_indexClient

> **\_indexClient**: `undefined` \| `SearchIndexClient`

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:292](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L292)

***

### flatMetadata

> **flatMetadata**: `boolean` = `true`

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:298](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L298)

## Methods

### createSearchIndexClient()

> **createSearchIndexClient**(`options`): `void`

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:860](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L860)

#### Parameters

##### options

[`AzureAISearchOptions`](/docs/api/interfaces/AzureAISearchOptions)\<`T`\>

#### Returns

`void`

***

### createSearchClient()

> **createSearchClient**(`options`): `void`

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:877](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L877)

#### Parameters

##### options

[`AzureAISearchOptions`](/docs/api/interfaces/AzureAISearchOptions)\<`T`\>

#### Returns

`void`

***

### client()

> **client**(): `undefined` \| `SearchClient`\<`T`\>

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:1061](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L1061)

Get search client

#### Returns

`undefined` \| `SearchClient`\<`T`\>

Azure AI Search client. See SearchClient

#### Overrides

`BaseVectorStore.client`

***

### indexClient()

> **indexClient**(): `undefined` \| `SearchIndexClient`

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:1069](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L1069)

Get index client

#### Returns

`undefined` \| `SearchIndexClient`

Azure AI Search index client. See SearchIndexClient

***

### add()

> **add**(`nodes`): `Promise`\<`string`[]\>

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:1078](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L1078)

Add nodes to index associated with the configured search client.

#### Parameters

##### nodes

`BaseNode`\<`Metadata`\>[]

List of nodes with embeddings to add to the index

#### Returns

`Promise`\<`string`[]\>

List of node IDs that were added to the index

#### Overrides

`BaseVectorStore.add`

***

### delete()

> **delete**(`refDocId`): `Promise`\<`void`\>

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:1143](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L1143)

Delete documents from the AI Search Index with docIdFieldKey (doc_id) field equal to refDocId.

#### Parameters

##### refDocId

`string`

The reference document ID to delete from the index

#### Returns

`Promise`\<`void`\>

#### Overrides

`BaseVectorStore.delete`

***

### getNodes()

> **getNodes**(`nodeIds?`, `filters?`, `limit?`): `Promise`\<`BaseNode`\<`Metadata`\>[]\>

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:1190](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L1190)

Get nodes asynchronously from the Azure AI Search index.

#### Parameters

##### nodeIds?

`string`[]

List of node IDs to retrieve from the index

##### filters?

`MetadataFilters`

Metadata filters to apply to the search

##### limit?

`number`

Maximum number of nodes to retrieve

#### Returns

`Promise`\<`BaseNode`\<`Metadata`\>[]\>

List of nodes retrieved from the index

***

### query()

> **query**(`query`): `Promise`\<`VectorStoreQueryResult`\>

Defined in: [packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts:1243](https://github.com/run-llama/LlamaIndexTS/blob/b370bd59f1f00f6843b84588aa6dd52fe1f18a84/packages/providers/storage/azure/src/vectorStore/AzureAISearchVectorStore.ts#L1243)

#### Parameters

##### query

`VectorStoreQuery`\<`unknown`\> & `object`

#### Returns

`Promise`\<`VectorStoreQueryResult`\>

#### Overrides

`BaseVectorStore.query`