Azure AI Search client library for JavaScript - version 12.0.0

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Azure AI Search (formerly known as "Azure Cognitive Search") is an AI-powered information retrieval platform that helps developers build rich search experiences and generative AI apps that combine large language models with enterprise data.

Azure AI Search is well suited for the following application scenarios:

  • Consolidate varied content types into a single searchable index. To populate an index, you can push JSON documents that contain your content, or if your data is already in Azure, create an indexer to pull in data automatically.
  • Attach skillsets to an indexer to create searchable content from images and large text documents. A skillset leverages APIs from AI services for built-in OCR, entity recognition, key phrase extraction, language detection, text translation, and sentiment analysis. You can also add custom skills to integrate external processing of your content during data ingestion.
  • In a search client application, implement query logic and user experiences similar to commercial web search engines.

Use the @azure/search-documents client library to:

  • Submit queries using vector, keyword, and hybrid query forms.
  • Implement filtered queries for metadata, geospatial search, faceted navigation, or to narrow results based on filter criteria.
  • Create and manage search indexes.
  • Upload and update documents in the search index.
  • Create and manage indexers that pull data from Azure into an index.
  • Create and manage skillsets that add AI enrichment to data ingestion.
  • Create and manage analyzers for advanced text analysis or multi-lingual content.
  • Optimize results through scoring profiles to factor in business logic or freshness.

Key links:

Getting started

Install the @azure/search-documents package

npm install @azure/search-documents

Currently supported environments

See our support policy for more details.

Prerequisites

To create a new search service, you can use the Azure portal, Azure PowerShell, or the Azure CLI. Here's an example using the Azure CLI to create a free instance for getting started:

az search service create --name <mysearch> --resource-group <mysearch-rg> --sku free --location westus

See choosing a pricing tier for more information about available options.

Authenticate the client

To interact with the search service, you'll need to create an instance of the appropriate client class: SearchClient for searching indexed documents, SearchIndexClient for managing indexes, or SearchIndexerClient for crawling data sources and loading search documents into an index.

To instantiate a client object, you'll need an endpoint and Azure roles or an API key. You can refer to the documentation for more information on supported authenticating approaches with the search service.

Get an API Key

An API key can be an easier approach to start with because it doesn't require pre-existing role assignments.

You can get the endpoint and an API key from the search service in the Azure Portal. Please refer the documentation for instructions on how to get an API key.

Alternatively, you can use the following Azure CLI command to retrieve the API key from the search service:

az search admin-key show --service-name <mysearch> --resource-group <mysearch-rg>

Once you have an api-key, you can use it as follows:

const {
  SearchClient,
  SearchIndexClient,
  SearchIndexerClient,
  AzureKeyCredential,
} = require("@azure/search-documents");

// To query and manipulate documents
const searchClient = new SearchClient(
  "<endpoint>",
  "<indexName>",
  new AzureKeyCredential("<apiKey>")
);

// To manage indexes and synonymmaps
const indexClient = new SearchIndexClient("<endpoint>", new AzureKeyCredential("<apiKey>"));

// To manage indexers, datasources and skillsets
const indexerClient = new SearchIndexerClient("<endpoint>", new AzureKeyCredential("<apiKey>"));

Authenticate in a National Cloud

To authenticate in a National Cloud, you will need to make the following additions to your client configuration:

  • Set the Audience in SearchClientOptions
const {
  SearchClient,
  SearchIndexClient,
  SearchIndexerClient,
  AzureKeyCredential,
  KnownSearchAudience,
} = require("@azure/search-documents");

// To query and manipulate documents
const searchClient = new SearchClient(
  "<endpoint>",
  "<indexName>",
  new AzureKeyCredential("<apiKey>"),
  {
    audience: KnownSearchAudience.AzureChina,
  }
);

// To manage indexes and synonymmaps
const indexClient = new SearchIndexClient("<endpoint>", new AzureKeyCredential("<apiKey>"), {
  audience: KnownSearchAudience.AzureChina,
});

// To manage indexers, datasources and skillsets
const indexerClient = new SearchIndexerClient("<endpoint>", new AzureKeyCredential("<apiKey>"), {
  audience: KnownSearchAudience.AzureChina,
});

Key concepts

An Azure AI Search service contains one or more indexes that provide persistent storage of searchable data in the form of JSON documents. (If you're brand new to search, you can make a very rough analogy between indexes and database tables.) The @azure/search-documents client library exposes operations on these resources through three main client types.

Note: These clients cannot function in the browser because the APIs it calls do not have support for Cross-Origin Resource Sharing (CORS).

TypeScript/JavaScript specific concepts

Documents

An item stored inside a search index. The shape of this document is described in the index using Fields. Each Field has a name, a datatype, and additional metadata such as if it is searchable or filterable.

Pagination

Typically you will only wish to show a subset of search results to a user at one time. To support this, you can use the top, skip and includeTotalCount parameters to provide a paged experience on top of search results.

Document field encoding

Supported data types in an index are mapped to JSON types in API requests/responses. The JS client library keeps these mostly the same, with some exceptions:

  • Edm.DateTimeOffset is converted to a JS Date.
  • Edm.GeographyPoint is converted to a GeographyPoint type exported by the client library.
  • Special values of the number type (NaN, Infinity, -Infinity) are serialized as strings in the REST API, but are converted back to number by the client library.

Note: Data types are converted based on value, not the field type in the index schema. This means that if you have an ISO8601 Date string (e.g. "2020-03-06T18:48:27.896Z") as the value of a field, it will be converted to a Date regardless of how you stored it in your schema.

Examples

The following examples demonstrate the basics - please check out our samples for much more.

Create an Index

const { SearchIndexClient, AzureKeyCredential } = require("@azure/search-documents");

const client = new SearchIndexClient("<endpoint>", new AzureKeyCredential("<apiKey>"));

async function main() {
  const result = await client.createIndex({
    name: "example-index",
    fields: [
      {
        type: "Edm.String",
        name: "id",
        key: true,
      },
      {
        type: "Edm.Double",
        name: "awesomenessLevel",
        sortable: true,
        filterable: true,
        facetable: true,
      },
      {
        type: "Edm.String",
        name: "description",
        searchable: true,
      },
      {
        type: "Edm.ComplexType",
        name: "details",
        fields: [
          {
            type: "Collection(Edm.String)",
            name: "tags",
            searchable: true,
          },
        ],
      },
      {
        type: "Edm.Int32",
        name: "hiddenWeight",
        hidden: true,
      },
    ],
  });

  console.log(result);
}

main();

Retrieve a specific document from an index

A specific document can be retrieved by its primary key value:

const { SearchClient, AzureKeyCredential } = require("@azure/search-documents");

const client = new SearchClient("<endpoint>", "<indexName>", new AzureKeyCredential("<apiKey>"));

async function main() {
  const result = await client.getDocument("1234");
  console.log(result);
}

main();

Adding documents into an index

You can upload multiple documents into index inside a batch:

const { SearchClient, AzureKeyCredential } = require("@azure/search-documents");

const client = new SearchClient("<endpoint>", "<indexName>", new AzureKeyCredential("<apiKey>"));

async function main() {
  const uploadResult = await client.uploadDocuments([
    // JSON objects matching the shape of the client's index
    {},
    {},
    {},
  ]);
  for (const result of uploadResult.results) {
    console.log(`Uploaded ${result.key}; succeeded? ${result.succeeded}`);
  }
}

main();

Perform a search on documents

To list all results of a particular query, you can use search with a search string that uses simple query syntax:

const { SearchClient, AzureKeyCredential } = require("@azure/search-documents");

const client = new SearchClient("<endpoint>", "<indexName>", new AzureKeyCredential("<apiKey>"));

async function main() {
  const searchResults = await client.search("wifi -luxury");
  for await (const result of searchResults.results) {
    console.log(result);
  }
}

main();

For a more advanced search that uses Lucene syntax, specify queryType to be full:

const { SearchClient, AzureKeyCredential } = require("@azure/search-documents");

const client = new SearchClient("<endpoint>", "<indexName>", new AzureKeyCredential("<apiKey>"));

async function main() {
  const searchResults = await client.search('Category:budget AND "recently renovated"^3', {
    queryType: "full",
    searchMode: "all",
  });
  for await (const result of searchResults.results) {
    console.log(result);
  }
}

main();

Querying with TypeScript

In TypeScript, SearchClient takes a generic parameter that is the model shape of your index documents. This allows you to perform strongly typed lookup of fields returned in results. TypeScript is also able to check for fields returned when specifying a select parameter.

import { SearchClient, AzureKeyCredential, SelectFields } from "@azure/search-documents";

// An example schema for documents in the index
interface Hotel {
  hotelId?: string;
  hotelName?: string | null;
  description?: string | null;
  descriptionVector?: Array<number> | null;
  parkingIncluded?: boolean | null;
  lastRenovationDate?: Date | null;
  rating?: number | null;
  rooms?: Array<{
    beds?: number | null;
    description?: string | null;
  } | null>;
}

const client = new SearchClient<Hotel>(
  "<endpoint>",
  "<indexName>",
  new AzureKeyCredential("<apiKey>")
);

async function main() {
  const searchResults = await client.search("wifi -luxury", {
    // Only fields in Hotel can be added to this array.
    // TS will complain if one is misspelled.
    select: ["hotelId", "hotelName", "rooms/beds"],
  });

  // These are other ways to declare the correct type for `select`.
  const select = ["hotelId", "hotelName", "rooms/beds"] as const;
  // This declaration lets you opt out of narrowing the TypeScript type of your documents,
  // though the AI Search service will still only return these fields.
  const selectWide: SelectFields<Hotel>[] = ["hotelId", "hotelName", "rooms/beds"];
  // This is an invalid declaration. Passing this to `select` will result in a compiler error
  // unless you opt out of including the model in the client constructor.
  const selectInvalid = ["hotelId", "hotelName", "rooms/beds"];

  for await (const result of searchResults.results) {
    // result.document has hotelId, hotelName, and rating.
    // Trying to access result.document.description would emit a TS error.
    console.log(result.document.hotelName);
  }
}

main();

Querying with OData filters

Using the filter query parameter allows you to query an index using the syntax of an OData $filter expression.

const { SearchClient, AzureKeyCredential, odata } = require("@azure/search-documents");

const client = new SearchClient("<endpoint>", "<indexName>", new AzureKeyCredential("<apiKey>"));

async function main() {
  const baseRateMax = 200;
  const ratingMin = 4;
  const searchResults = await client.search("WiFi", {
    filter: odata`Rooms/any(room: room/BaseRate lt ${baseRateMax}) and Rating ge ${ratingMin}`,
    orderBy: ["Rating desc"],
    select: ["hotelId", "hotelName", "rating"],
  });
  for await (const result of searchResults.results) {
    // Each result will have "HotelId", "HotelName", and "Rating"
    // in addition to the standard search result property "score"
    console.log(result);
  }
}

main();

Querying with vectors

Text embeddings can be queried using the vector search parameter.

const { SearchClient, AzureKeyCredential, odata } = require("@azure/search-documents");

const searchClient = new SearchClient("<endpoint>", "<indexName>", new AzureKeyCredential("<apiKey>"));

async function main() {
  const queryVector = [...]
  const searchResults = await searchClient.search("*", {
    vector: {
      fields: ["descriptionVector"],
      kNearestNeighborsCount: 3,
      value: queryVector,
    },
  });
  for await (const result of searchResults.results) {
    // These results are the nearest neighbors to the query vector
    console.log(result);
  }
}

main();

Querying with facets

Facets are used to help a user of your application refine a search along pre-configured dimensions. Facet syntax provides the options to sort and bucket facet values.

const { SearchClient, AzureKeyCredential } = require("@azure/search-documents");

const client = new SearchClient("<endpoint>", "<indexName>", new AzureKeyCredential("<apiKey>"));

async function main() {
  const searchResults = await client.search("WiFi", {
    facets: ["category,count:3,sort:count", "rooms/baseRate,interval:100"],
  });
  console.log(searchResults.facets);
  // Output will look like:
  // {
  //   'rooms/baseRate': [
  //     { count: 16, value: 0 },
  //     { count: 17, value: 100 },
  //     { count: 17, value: 200 }
  //   ],
  //   category: [
  //     { count: 5, value: 'Budget' },
  //     { count: 5, value: 'Luxury' },
  //     { count: 5, value: 'Resort and Spa' }
  //   ]
  // }
}

main();

When retrieving results, a facets property will be available that will indicate the number of results that fall into each facet bucket. This can be used to drive refinement (e.g. issuing a follow-up search that filters on the Rating being greater than or equal to 3 and less than 4.)

Troubleshooting

Logging

Enabling logging may help uncover useful information about failures. In order to see a log of HTTP requests and responses, set the AZURE_LOG_LEVEL environment variable to info. Alternatively, logging can be enabled at runtime by calling setLogLevel in the @azure/logger:

import { setLogLevel } from "@azure/logger";

setLogLevel("info");

For more detailed instructions on how to enable logs, you can look at the @azure/logger package docs.

Next steps

Contributing

If you'd like to contribute to this library, please read the contributing guide to learn more about how to build and test the code.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit cla.microsoft.com.

This project has adopted the Microsoft Open Source Code of Conduct.For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Impressions