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Merge pull request #7404 from KarlErickson/karler-quickstarts
replaced inline Java code with external references
2 parents ab48d89 + 3a645c8 commit 067ad6e

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‎.openpublishing.publish.config.json‎

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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "azure-search-java-samples",
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"url": "https://github.com/Azure-Samples/azure-search-java-samples",
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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "azure-search-dotnet-samples",
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"url": "https://github.com/Azure-Samples/azure-search-dotnet-samples",

‎articles/search/includes/quickstarts/search-get-started-rag-java.md‎

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@@ -238,140 +238,15 @@ Set up a Maven project with the required dependencies.
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1. Create a `pom.xml` file with the following content:
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```xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
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http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>com.example.rag</groupId>
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<artifactId>rag-quickstart</artifactId>
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<version>1.0-SNAPSHOT</version>
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<properties>
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<maven.compiler.source>21</maven.compiler.source>
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<maven.compiler.target>21</maven.compiler.target>
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<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
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</properties>
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<dependencies>
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<dependency>
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<groupId>com.azure</groupId>
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<artifactId>azure-search-documents</artifactId>
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<version>11.7.8</version>
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</dependency>
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<dependency>
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<groupId>com.azure</groupId>
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<artifactId>azure-identity</artifactId>
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<version>1.16.3</version>
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</dependency>
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<dependency>
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<groupId>com.azure</groupId>
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<artifactId>azure-ai-openai</artifactId>
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<version>1.0.0-beta.16</version>
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</dependency>
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</dependencies>
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</project>
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```
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:::code language="xml" source="~/azure-search-java-samples/rag-quickstart/pom.xml" :::
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## Set up query and chat thread
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Create a query script that uses the Azure AI Search index and the chat model to generate responses based on grounding data. The following steps guide you through setting up the query script.
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1. Create a `Query.java` file in the `src/main/java/com/example/rag` directory with the following code:
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```java
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package com.example.rag;
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import com.azure.ai.openai.OpenAIClient;
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import com.azure.ai.openai.OpenAIClientBuilder;
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import com.azure.ai.openai.models.ChatCompletions;
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import com.azure.ai.openai.models.ChatCompletionsOptions;
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import com.azure.ai.openai.models.ChatRequestSystemMessage;
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import com.azure.ai.openai.models.ChatRequestUserMessage;
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import com.azure.identity.DefaultAzureCredentialBuilder;
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import com.azure.search.documents.SearchClient;
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import com.azure.search.documents.SearchClientBuilder;
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import com.azure.search.documents.models.SearchOptions;
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import com.azure.search.documents.models.SearchResult;
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import java.util.List;
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public class Query {
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private static SearchClient getSearchClient() {
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String searchEndpoint = System.getenv("AZURE_SEARCH_ENDPOINT");
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String searchIndex = System.getenv("AZURE_SEARCH_INDEX_NAME");
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return new SearchClientBuilder()
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.endpoint(searchEndpoint)
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.indexName(searchIndex)
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.credential(new DefaultAzureCredentialBuilder().build())
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.buildClient();
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}
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private static OpenAIClient getOpenAIClient() {
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String openaiEndpoint = System.getenv("AZURE_OPENAI_ENDPOINT");
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return new OpenAIClientBuilder()
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.endpoint(openaiEndpoint)
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.credential(new DefaultAzureCredentialBuilder().build())
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.buildClient();
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}
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private static List<SearchResult> searchDocuments(
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SearchClient searchClient, String query) {
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var searchOptions = new SearchOptions()
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.setTop(5)
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.setQueryType(com.azure.search.documents.models.QueryType.SEMANTIC)
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.setSemanticSearchOptions(new com.azure.search.documents.models.SemanticSearchOptions()
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.setSemanticConfigurationName("semantic-config"))
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.setSelect("HotelName", "Description", "Tags");
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return searchClient.search(query, searchOptions, null)
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.stream()
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.limit(5)
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.toList();
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}
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private static String queryOpenAI(OpenAIClient openAIClient,
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String userQuery, List<SearchResult> sources) {
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String deploymentModel = System.getenv("AZURE_DEPLOYMENT_MODEL");
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String sourcesText = sources.stream()
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.map(source -> source.getDocument(Object.class).toString())
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.collect(java.util.stream.Collectors.joining("\n"));
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var messages = List.of(
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new ChatRequestSystemMessage("""
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You are an assistant that recommends hotels based on
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search results."""),
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new ChatRequestUserMessage("""
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Can you recommend a few hotels that offer %s?
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Here are the search results:
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%s""".formatted(userQuery, sourcesText))
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);
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var chatOptions = new ChatCompletionsOptions(messages);
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ChatCompletions response = openAIClient.getChatCompletions(deploymentModel, chatOptions);
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return response.getChoices().get(0).getMessage().getContent();
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}
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public static void main(String[] args) {
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SearchClient searchClient = getSearchClient();
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OpenAIClient openAIClient = getOpenAIClient();
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String userQuery = "complimentary breakfast";
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List<SearchResult> sources = searchDocuments(searchClient, userQuery);
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String response = queryOpenAI(openAIClient, userQuery, sources);
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System.out.println(response);
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System.exit(0);
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}
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}
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```
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:::code language="java" source="~/azure-search-java-samples/rag-quickstart/src/main/java/com/example/rag/Query.java" :::
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The preceding code does the following steps:
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- Loads environment variables by using `System.getenv`.
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1. Create a new file `QueryComplex.java` in the `src/main/java/com/example/rag` directory.
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1. Copy the following code to the file:
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```java
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package com.example.rag;
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import com.azure.ai.openai.OpenAIClient;
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import com.azure.ai.openai.OpenAIClientBuilder;
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import com.azure.ai.openai.models.ChatCompletions;
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import com.azure.ai.openai.models.ChatCompletionsOptions;
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import com.azure.ai.openai.models.ChatRequestSystemMessage;
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import com.azure.ai.openai.models.ChatRequestUserMessage;
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import com.azure.identity.DefaultAzureCredentialBuilder;
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import com.azure.search.documents.SearchClient;
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import com.azure.search.documents.SearchClientBuilder;
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import com.azure.search.documents.models.SearchOptions;
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import com.azure.search.documents.models.SearchResult;
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import java.util.List;
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public class QueryComplex {
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private static SearchClient getSearchClient() {
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String searchEndpoint = System.getenv("AZURE_SEARCH_ENDPOINT");
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String searchIndex = System.getenv("AZURE_SEARCH_INDEX_NAME");
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return new SearchClientBuilder()
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.endpoint(searchEndpoint)
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.indexName(searchIndex)
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.credential(new DefaultAzureCredentialBuilder().build())
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.buildClient();
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}
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private static OpenAIClient getOpenAIClient() {
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String openaiEndpoint = System.getenv("AZURE_OPENAI_ENDPOINT");
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return new OpenAIClientBuilder()
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.endpoint(openaiEndpoint)
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.credential(new DefaultAzureCredentialBuilder().build())
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.buildClient();
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}
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private static List<SearchResult> searchDocuments(
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SearchClient searchClient, String query) {
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var searchOptions = new SearchOptions()
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.setTop(5)
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.setQueryType(com.azure.search.documents.models.QueryType.SEMANTIC)
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.setSemanticSearchOptions(new com.azure.search.documents.models.SemanticSearchOptions()
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.setSemanticConfigurationName("semantic-config"))
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.setSelect("HotelName", "Description", "Address", "Rooms", "Tags");
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return searchClient.search(query, searchOptions, null)
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.stream()
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.limit(5)
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.toList();
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}
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private static String queryOpenAI(OpenAIClient openAIClient,
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String userQuery, List<SearchResult> sources) {
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String deploymentModel = System.getenv("AZURE_DEPLOYMENT_MODEL");
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String sourcesText = sources.stream()
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.map(source -> source.getDocument(Object.class).toString())
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.collect(java.util.stream.Collectors.joining("\n"));
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var messages = List.of(
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new ChatRequestSystemMessage("""
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You are an assistant that recommends hotels based on
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search results."""),
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new ChatRequestUserMessage("""
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Can you recommend a few hotels that offer %s?
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Tell me their description, address, tags,
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and the rate for one room that sleeps 4 people.
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Here are the search results:
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%s""".formatted(userQuery, sourcesText))
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);
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var chatOptions = new ChatCompletionsOptions(messages);
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ChatCompletions response = openAIClient.getChatCompletions(
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deploymentModel, chatOptions);
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return response.getChoices().get(0).getMessage().getContent();
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}
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public static void main(String[] args) {
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SearchClient searchClient = getSearchClient();
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OpenAIClient openAIClient = getOpenAIClient();
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String userQuery = "complimentary breakfast";
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List<SearchResult> sources = searchDocuments(searchClient, userQuery);
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String response = queryOpenAI(openAIClient, userQuery, sources);
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System.out.println(response);
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System.exit(0);
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}
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}
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```
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:::code language="java" source="~/azure-search-java-samples/rag-quickstart/src/main/java/com/example/rag/QueryComplex.java" :::
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1. Run the following command in a terminal to execute the query script:
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