[Search] Fix documentation for preview client (#29598)
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@ -4,30 +4,30 @@
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The Azure AI Search service is well suited for the following application scenarios:
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* Consolidate varied content types into a single searchable index.
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- Consolidate varied content types into a single searchable index.
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To populate an index, you can push JSON documents that contain your content,
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or if your data is already in Azure, create an indexer to pull in data
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automatically.
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* Attach skillsets to an indexer to create searchable content from images
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- Attach skillsets to an indexer to create searchable content from images
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and unstructured documents. A skillset leverages APIs from Azure AI Services
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for built-in OCR, entity recognition, key phrase extraction, language
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detection, text translation, and sentiment analysis. You can also add
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custom skills to integrate external processing of your content during
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data ingestion.
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* In a search client application, implement query logic and user experiences
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- In a search client application, implement query logic and user experiences
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similar to commercial web search engines and chat-style apps.
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Use the @azure/search-documents client library to:
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* Submit queries using vector, keyword, and hybrid query forms.
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* Implement filtered queries for metadata, geospatial search, faceted navigation,
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- Submit queries using vector, keyword, and hybrid query forms.
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- Implement filtered queries for metadata, geospatial search, faceted navigation,
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or to narrow results based on filter criteria.
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* Create and manage search indexes.
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* Upload and update documents in the search index.
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* Create and manage indexers that pull data from Azure into an index.
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* Create and manage skillsets that add AI enrichment to data ingestion.
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* Create and manage analyzers for advanced text analysis or multi-lingual content.
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* Optimize results through semantic ranking and scoring profiles to factor in business logic or freshness.
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- Create and manage search indexes.
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- Upload and update documents in the search index.
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- Create and manage indexers that pull data from Azure into an index.
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- Create and manage skillsets that add AI enrichment to data ingestion.
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- Create and manage analyzers for advanced text analysis or multi-lingual content.
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- Optimize results through semantic ranking and scoring profiles to factor in business logic or freshness.
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Key links:
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@ -152,6 +152,7 @@ An Azure AI Search service contains one or more indexes that provide persistent
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exposes operations on these resources through three main client types.
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- `SearchClient` helps with:
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- [Searching](https://docs.microsoft.com/azure/search/search-lucene-query-architecture)
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your indexed documents using [vector queries](https://learn.microsoft.com/azure/search/vector-search-how-to-query),
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[keyword queries](https://learn.microsoft.com/azure/search/search-query-create)
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@ -163,6 +164,7 @@ exposes operations on these resources through three main client types.
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- [Adding, Updating or Deleting Documents](https://docs.microsoft.com/rest/api/searchservice/addupdate-or-delete-documents) documents from an index
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- `SearchIndexClient` allows you to:
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- [Create, delete, update, or configure a search index](https://docs.microsoft.com/rest/api/searchservice/index-operations)
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- [Declare custom synonym maps to expand or rewrite queries](https://docs.microsoft.com/rest/api/searchservice/synonym-map-operations)
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@ -176,7 +178,7 @@ exposes operations on these resources through three main client types.
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### Documents
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An item stored inside a search index. The shape of this document is described in the index using `Field`s. Each Field has a name, a datatype, and additional metadata such as if it is searchable or filterable.
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An item stored inside a search index. The shape of this document is described in the index using the `fields` property. Each `SearchField` has a name, a datatype, and additional metadata such as if it is searchable or filterable.
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### Pagination
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@ -423,17 +425,26 @@ main();
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Text embeddings can be queried using the `vector` search parameter. See [Query vectors](https://learn.microsoft.com/azure/search/vector-search-how-to-query) and [Filter vector queries](https://learn.microsoft.com/azure/search/vector-search-filters) for more information.
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```js
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const { SearchClient, AzureKeyCredential, odata } = require("@azure/search-documents");
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const { SearchClient, AzureKeyCredential } = require("@azure/search-documents");
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const searchClient = new SearchClient("<endpoint>", "<indexName>", new AzureKeyCredential("<apiKey>"));
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const searchClient = new SearchClient(
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"<endpoint>",
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"<indexName>",
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new AzureKeyCredential("<apiKey>")
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);
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async function main() {
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const queryVector = [...]
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const queryVector = [...];
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const searchResults = await searchClient.search("*", {
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vector: {
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vectorSearchOptions: {
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queries: [
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{
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kind: "vector",
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vector: queryVector,
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fields: ["descriptionVector"],
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kNearestNeighborsCount: 3,
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value: queryVector,
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},
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],
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},
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});
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for await (const result of searchResults.results) {
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@ -10,7 +10,7 @@ import {
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GeographyPoint,
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SearchClient,
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SearchIndexClient,
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SearchIndexingBufferedSender,
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SearchIndexingBufferedSender
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} from "@azure/search-documents";
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import { Hotel } from "./interfaces";
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import { createIndex, delay, documentKeyRetriever, WAIT_TIME } from "./setup";
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@ -11,7 +11,7 @@ import {
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GeographyPoint,
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SearchClient,
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SearchIndexClient,
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SearchIndexingBufferedSender,
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SearchIndexingBufferedSender
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} from "@azure/search-documents";
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import { Hotel } from "./interfaces";
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import { createIndex, delay, documentKeyRetriever, WAIT_TIME } from "./setup";
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@ -10,7 +10,7 @@ import {
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GeographyPoint,
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SearchClient,
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SearchIndexClient,
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SearchIndexingBufferedSender,
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SearchIndexingBufferedSender
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} from "@azure/search-documents";
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import { Hotel } from "./interfaces";
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import { createIndex, delay, documentKeyRetriever, WAIT_TIME } from "./setup";
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@ -8,7 +8,7 @@
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import {
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AzureKeyCredential,
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SearchIndexerClient,
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SearchIndexerDataSourceConnection,
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SearchIndexerDataSourceConnection
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} from "@azure/search-documents";
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import * as dotenv from "dotenv";
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@ -9,7 +9,7 @@ import {
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AzureKeyCredential,
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SearchIndex,
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SearchIndexClient,
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SearchIndexStatistics,
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SearchIndexStatistics
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} from "@azure/search-documents";
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import * as dotenv from "dotenv";
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@ -9,7 +9,7 @@ import {
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AzureKeyCredential,
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SearchIndexer,
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SearchIndexerClient,
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SearchIndexerStatus,
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SearchIndexerStatus
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} from "@azure/search-documents";
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import * as dotenv from "dotenv";
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@ -10,7 +10,7 @@ import {
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GeographyPoint,
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SearchClient,
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SearchIndexClient,
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SelectFields,
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SelectFields
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} from "@azure/search-documents";
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import { Hotel } from "./interfaces";
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import { createIndex, delay, WAIT_TIME } from "./setup";
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@ -8,7 +8,7 @@
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import {
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AzureKeyCredential,
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SearchIndexerClient,
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SearchIndexerSkillset,
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SearchIndexerSkillset
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} from "@azure/search-documents";
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import * as dotenv from "dotenv";
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@ -10,7 +10,7 @@ import {
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AzureKeyCredential,
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odata,
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SearchClient,
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SearchIndexClient,
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SearchIndexClient
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} from "@azure/search-documents";
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import { Hotel } from "./interfaces";
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import { createIndex, delay, WAIT_TIME } from "./setup";
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@ -9,7 +9,7 @@ import {
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AzureKeyCredential,
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GeographyPoint,
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SearchClient,
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SearchIndexClient,
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SearchIndexClient
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} from "@azure/search-documents";
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import { Hotel } from "./interfaces";
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import { createIndex, delay, WAIT_TIME } from "./setup";
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@ -5,8 +5,8 @@ import { env, isLiveMode, Recorder } from "@azure-tools/test-recorder";
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import { assert } from "chai";
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import { Context, Suite } from "mocha";
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import { OpenAIClient } from "@azure/openai";
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import { versionsToTest } from "@azure-tools/test-utils";
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import { OpenAIClient } from "@azure/openai";
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import {
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AutocompleteResult,
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AzureKeyCredential,
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