Quickstart: Chat with Azure OpenAI models using your own data

Prerequisites

The following resources:

• Azure OpenAI
• Azure Blob Storage
• Azure AI Search
• An Azure OpenAI resource deployed in a supported region and with a supported model.
  • Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.
• Download the example data from GitHub if you don't have your own data.

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Chat playground

Start exploring Azure OpenAI capabilities with a no-code approach through the chat playground. It's simply a text box where you can submit a prompt to generate a completion. From this page, you can quickly iterate and experiment with the capabilities.

The playground gives you options to tailor your chat experience. On the top menu, you can select Deploy to determine which model generates a response using the search results from your index. You choose the number of past messages to include as conversation history for future generated responses. Conversation history gives context to generate related responses but also consumes token usage. The input token progress indicator keeps track of the token count of the question you submit.

The Advanced settings on the left are runtime parameters, which give you control over retrieval and search relevant information from your data. A good use case is when you want to make sure responses are generated only based on your data or you find the model cannot generate a response based on existed information on your data.

• Strictness determines the system's aggressiveness in filtering search documents based on their similarity scores. Setting strictness to 5 indicates that the system will aggressively filter out documents, applying a very high similarity threshold. Semantic search can be helpful in this scenario because the ranking models do a better job of inferring the intent of the query. Lower levels of strictness produce more verbose answers, but might also include information that isn't in your index. This is set to 3 by default.

• Retrieved documents is an integer that can be set to 3, 5, 10, or 20, and controls the number of document chunks provided to the large language model for formulating the final response. By default, this is set to 5.

• When Limit responses to your data is enabled, the model attempts to only rely on your documents for responses. This is set to true by default.

Send your first query. The chat models perform best in question and answer exercises. For example, "What are my available health plans?" or "What is the health plus option?".

Queries that require data analysis would probably fail, such as "Which health plan is most popular?". Queries that require information about all of your data will also likely fail, such as "How many documents have I uploaded?". Remember that the search engine looks for chunks having exact or similar terms, phrases, or construction to the query. And while the model might understand the question, if search results are chunks from the data set, it's not the right information to answer that kind of question.

Chats are constrained by the number of documents (chunks) returned in the response (limited to 3-20 in Azure OpenAI Studio playground). As you can imagine, posing a question about "all of the titles" requires a full scan of the entire vector store.

Deploy your model

Once you're satisfied with the experience in Azure OpenAI studio, you can deploy a web app directly from the Studio by selecting the Deploy to button.

This gives you the option to either deploy to a standalone web application, or a copilot in Copilot Studio (preview) if you're using your own data on the model.

As an example, if you choose to deploy a web app:

The first time you deploy a web app, you should select Create a new web app. Choose a name for the app, which will become part of the app URL. For example, https://<appname>.azurewebsites.net.

Select your subscription, resource group, location, and pricing plan for the published app. To update an existing app, select Publish to an existing web app and choose the name of your previous app from the dropdown menu.

If you choose to deploy a web app, see the important considerations for using it.

Prerequisites

The following resources:

• Azure OpenAI
• Azure Blob Storage
• Azure AI Search
• An Azure OpenAI resource deployed in a supported region and with a supported model.
  • Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.
• Download the example data from GitHub if you don't have your own data.
• The .NET 8 SDK

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Retrieve required variables

To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See Add your data using Azure AI Foundry

Environment variables

Create and assign persistent environment variables for your key and endpoint.

setx AZURE_OPENAI_ENDPOINT REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

setx AZURE_OPENAI_API_KEY REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

setx AZURE_OPENAI_DEPLOYMENT_ID REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

setx AZURE_AI_SEARCH_ENDPOINT REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

setx AZURE_AI_SEARCH_API_KEY REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

setx AZURE_AI_SEARCH_INDEX REPLACE_WITH_YOUR_INDEX_NAME_HERE

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_DEPLOYMENT_ID', 'REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_ENDPOINT', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_API_KEY', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_INDEX', 'REPLACE_WITH_YOUR_INDEX_NAME_HERE', 'User')

export AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

export AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

export AZURE_OPENAI_DEPLOYMENT_ID=REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

export AZURE_AI_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

export AZURE_AI_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

export AZURE_AI_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE

Create a new .NET Core application

In a console window (such as cmd, PowerShell, or Bash), use the dotnet new command to create a new console app with the name azure-openai-quickstart. This command creates a simple "Hello World" project with a single C# source file: Program.cs.

dotnet new console -n azure-openai-quickstart

Change your directory to the newly created app folder. You can build the application with:

dotnet build

The build output should contain no warnings or errors.

...
Build succeeded.
 0 Warning(s)
 0 Error(s)
...

Install the OpenAI .NET client library with:

dotnet add package Azure.AI.OpenAI --prerelease

From the project directory, open the Program.cs file and replace its contents with the following code:

Without response streaming

using Azure;
using Azure.AI.OpenAI;
using Azure.AI.OpenAI.Chat;
using OpenAI.Chat;
using System.Text.Json;
using static System.Environment;

string azureOpenAIEndpoint = GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT");
string azureOpenAIKey = GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
string deploymentName = GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_ID");
string searchEndpoint = GetEnvironmentVariable("AZURE_AI_SEARCH_ENDPOINT");
string searchKey = GetEnvironmentVariable("AZURE_AI_SEARCH_API_KEY");
string searchIndex = GetEnvironmentVariable("AZURE_AI_SEARCH_INDEX");

#pragma warning disable AOAI001
AzureOpenAIClient azureClient = new(
    new Uri(azureOpenAIEndpoint),
    new AzureKeyCredential(azureOpenAIKey));
ChatClient chatClient = azureClient.GetChatClient(deploymentName);

ChatCompletionOptions options = new();
options.AddDataSource(new AzureSearchChatDataSource()
{
    Endpoint = new Uri(searchEndpoint),
    IndexName = searchIndex,
    Authentication = DataSourceAuthentication.FromApiKey(searchKey),
});

ChatCompletion completion = chatClient.CompleteChat(
    [
        new UserChatMessage("What are my available health plans?"),
    ], options);

Console.WriteLine(completion.Content[0].Text);

AzureChatMessageContext onYourDataContext = completion.GetAzureMessageContext();

if (onYourDataContext?.Intent is not null)
{
    Console.WriteLine($"Intent: {onYourDataContext.Intent}");
}
foreach (AzureChatCitation citation in onYourDataContext?.Citations ?? [])
{
    Console.WriteLine($"Citation: {citation.Content}");
}

dotnet run program.cs

Output

Contoso Electronics offers two health plans: Northwind Health Plus and Northwind Standard [doc1]. Northwind Health Plus is a comprehensive plan that provides coverage for medical, vision, and dental services, prescription drug coverage, mental health and substance abuse coverage, and coverage for preventive care services. It also offers coverage for emergency services, both in-network and out-of-network. On the other hand, Northwind Standard is a basic plan that provides coverage for medical, vision, and dental services, prescription drug coverage, and coverage for preventive care services. However, it does not offer coverage for emergency services, mental health and substance abuse coverage, or out-of-network services [doc1].

Intent: ["What are the available health plans?", "List of health plans available", "Health insurance options", "Types of health plans offered"]

Citation:
Contoso Electronics plan and benefit packages

Thank you for your interest in the Contoso electronics plan and benefit packages. Use this document to

learn more about the various options available to you...// Omitted for brevity

This will wait until the model has generated its entire response before printing the results. Alternatively, if you want to asynchronously stream the response and print the results, you can replace the contents of Program.cs with the code in the next example.

Async with streaming

using Azure;
using Azure.AI.OpenAI;
using Azure.AI.OpenAI.Chat;
using OpenAI.Chat;
using static System.Environment;

string azureOpenAIEndpoint = GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT");
string azureOpenAIKey = GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
string deploymentName = GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_ID");
string searchEndpoint = GetEnvironmentVariable("AZURE_AI_SEARCH_ENDPOINT");
string searchKey = GetEnvironmentVariable("AZURE_AI_SEARCH_API_KEY");
string searchIndex = GetEnvironmentVariable("AZURE_AI_SEARCH_INDEX");

#pragma warning disable AOAI001

AzureOpenAIClient azureClient = new(
    new Uri(azureOpenAIEndpoint),
    new AzureKeyCredential(azureOpenAIKey));
ChatClient chatClient = azureClient.GetChatClient(deploymentName);

ChatCompletionOptions options = new();
options.AddDataSource(new AzureSearchChatDataSource()
{
    Endpoint = new Uri(searchEndpoint),
    IndexName = searchIndex,
    Authentication = DataSourceAuthentication.FromApiKey(searchKey),
});

var chatUpdates = chatClient.CompleteChatStreamingAsync(
    [
        new UserChatMessage("What are my available health plans?"),
    ], options);

AzureChatMessageContext onYourDataContext = null;
await foreach (var chatUpdate in chatUpdates)
{
    if (chatUpdate.Role.HasValue)
    {
        Console.WriteLine($"{chatUpdate.Role}: ");
    }

    foreach (var contentPart in chatUpdate.ContentUpdate)
    {
        Console.Write(contentPart.Text);
    }

    if (onYourDataContext == null)
    {
        onYourDataContext = chatUpdate.GetAzureMessageContext();
    }
}

Console.WriteLine();
if (onYourDataContext?.Intent is not null)
{
    Console.WriteLine($"Intent: {onYourDataContext.Intent}");
}
foreach (AzureChatCitation citation in onYourDataContext?.Citations ?? [])
{
    Console.Write($"Citation: {citation.Content}");
}

Source code| Source code | Sample

Prerequisites

The following resources:

• Azure OpenAI
• Azure Blob Storage
• Azure AI Search
• An Azure OpenAI resource deployed in a supported region and with a supported model.
  • Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.
• Download the example data from GitHub if you don't have your own data.

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Retrieve required variables

To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. For more information, see Add your data using Azure AI Foundry.

Environment variables

Create and assign persistent environment variables for your key and endpoint.

export SPRING_AI_AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE
export SPRING_AI_AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE
export SPRING_AI_AZURE_COGNITIVE_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE
export SPRING_AI_AZURE_COGNITIVE_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE
export SPRING_AI_AZURE_COGNITIVE_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE
export SPRING_AI_AZURE_OPENAI_MODEL=REPLACE_WITH_YOUR_MODEL_NAME_HERE

Create a new Spring application

Spring AI doesn't currently support the AzureCognitiveSearchChatExtensionConfiguration options that allow an Azure AI query to encapsulate the Retrieval Augmented Generation (RAG) method and hide the details from the user. As an alternative, you can still invoke the RAG method directly in your application to query data in your Azure AI Search index and use retrieved documents to augment your query.

Spring AI supports a VectorStore abstraction, and you can wrap Azure AI Search can be wrapped in a Spring AI VectorStore implementation for querying your custom data. The following project implements a custom VectorStore backed by Azure AI Search and directly executes RAG operations.

In a Bash window, create a new directory for your app, and navigate to it.

mkdir ai-custom-data-demo && cd ai-custom-data-demo

Run the spring init command from your working directory. This command creates a standard directory structure for your Spring project including the main Java class source file and the pom.xml file used for managing Maven based projects.

spring init -a ai-custom-data-demo -n AICustomData --force --build maven -x

The generated files and folders resemble the following structure:

ai-custom-data-demo/
|-- pom.xml
|-- mvn
|-- mvn.cmd
|-- HELP.md
|-- src/
    |-- main/
    |   |-- resources/
    |   |   |-- application.properties
    |   |-- java/
    |       |-- com/
    |           |-- example/
    |               |-- aicustomdatademo/
    |                   |-- AiCustomDataApplication.java
    |-- test/
        |-- java/
            |-- com/
                |-- example/
                    |-- aicustomdatademo/
                        |-- AiCustomDataApplicationTests.java

Edit Spring application

1. Edit the pom.xml file.

   From the root of the project directory, open the pom.xml file in your preferred editor or IDE and overwrite the file with following content:

   <?xml version="1.0" encoding="UTF-8"?>
   <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
       xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
       <modelVersion>4.0.0</modelVersion>
       <parent>
           <groupId>org.springframework.boot</groupId>
           <artifactId>spring-boot-starter-parent</artifactId>
           <version>3.2.0</version>
           <relativePath/> <!-- lookup parent from repository -->
       </parent>
       <groupId>com.example</groupId>
       <artifactId>ai-custom-data-demo</artifactId>
       <version>0.0.1-SNAPSHOT</version>
       <name>AICustomData</name>
       <description>Demo project for Spring Boot</description>
       <properties>
           <java.version>17</java.version>
       </properties>
       <dependencies>
           <dependency>
               <groupId>org.springframework.boot</groupId>
               <artifactId>spring-boot-starter</artifactId>
           </dependency>
           <dependency>
               <groupId>org.springframework.experimental.ai</groupId>
               <artifactId>spring-ai-azure-openai-spring-boot-starter</artifactId>
               <version>0.7.0-SNAPSHOT</version>
           </dependency>
           <dependency>
               <groupId>com.azure</groupId>
               <artifactId>azure-search-documents</artifactId>
               <version>11.6.0-beta.10</version>
               <exclusions>
                   <!-- exclude this to avoid changing the default serializer and the null-value behavior -->
                   <exclusion>
                       <groupId>com.azure</groupId>
                       <artifactId>azure-core-serializer-json-jackson</artifactId>
                   </exclusion>
               </exclusions>
           </dependency>
           <dependency>
               <groupId>org.projectlombok</groupId>
               <artifactId>lombok</artifactId>
               <optional>true</optional>
           </dependency>
           <dependency>
               <groupId>org.springframework.boot</groupId>
               <artifactId>spring-boot-starter-test</artifactId>
               <scope>test</scope>
           </dependency>
       </dependencies>
   
       <build>
           <plugins>
               <plugin>
                   <groupId>org.springframework.boot</groupId>
                   <artifactId>spring-boot-maven-plugin</artifactId>
               </plugin>
           </plugins>
       </build>
       <repositories>
           <repository>
               <id>spring-snapshots</id>
               <name>Spring Snapshots</name>
               <url>https://repo.spring.io/snapshot</url>
               <releases>
                   <enabled>false</enabled>
               </releases>
           </repository>
       </repositories>
   </project>
   

2. From the src/main/java/com/example/aicustomdatademo folder, open AiCustomDataApplication.java in your preferred editor or IDE and paste in the following code:

   package com.example.aicustomdatademo;
   
   import java.util.Collections;
   import java.util.List;
   import java.util.Map;
   import java.util.Optional;
   import java.util.stream.Collectors;
   
   import org.springframework.ai.client.AiClient;
   import org.springframework.ai.document.Document;
   import org.springframework.ai.embedding.EmbeddingClient;
   import org.springframework.ai.prompt.Prompt;
   import org.springframework.ai.prompt.SystemPromptTemplate;
   import org.springframework.ai.prompt.messages.MessageType;
   import org.springframework.ai.prompt.messages.UserMessage;
   import org.springframework.ai.vectorstore.VectorStore;
   import org.springframework.beans.factory.annotation.Autowired;
   import org.springframework.beans.factory.annotation.Value;
   import org.springframework.boot.CommandLineRunner;
   import org.springframework.boot.SpringApplication;
   import org.springframework.boot.autoconfigure.SpringBootApplication;
   import org.springframework.context.annotation.Bean;
   
   import com.azure.core.credential.AzureKeyCredential;
   import com.azure.core.util.Context;
   import com.azure.search.documents.SearchClient;
   import com.azure.search.documents.SearchClientBuilder;
   import com.azure.search.documents.models.IndexingResult;
   import com.azure.search.documents.models.SearchOptions;
   import com.azure.search.documents.models.RawVectorQuery;
   
   import lombok.AllArgsConstructor;
   import lombok.NoArgsConstructor;
   import lombok.Builder;
   import lombok.Data;
   import lombok.extern.jackson.Jacksonized;
   
   @SpringBootApplication
   public class AiCustomDataApplication implements CommandLineRunner
   {
       private static final String ROLE_INFO_KEY = "role";
   
       private static final String template = """
               You are a helpful assistant. Use the information from the DOCUMENTS section to augment answers.
   
               DOCUMENTS:
               {documents}
               """;
   
       @Value("${spring.ai.azure.cognitive-search.endpoint}")
       private String acsEndpoint;
   
       @Value("${spring.ai.azure.cognitive-search.api-key}")
       private String acsApiKey;
   
       @Value("${spring.ai.azure.cognitive-search.index}")
       private String acsIndexName;
   
       @Autowired
       private AiClient aiClient;
   
       @Autowired
       private EmbeddingClient embeddingClient;
   
       public static void main(String[] args) {
           SpringApplication.run(AiCustomDataApplication.class, args);
       }
   
       @Override
       public void run(String... args) throws Exception
       {
           System.out.println(String.format("Sending custom data prompt to AI service. One moment please...\r\n"));
   
           final var store = vectorStore(embeddingClient);
   
           final String question = "What are my available health plans?";
   
           final var candidateDocs = store.similaritySearch(question);
   
           final var userMessage = new UserMessage(question);
   
           final String docPrompts =
                   candidateDocs.stream().map(entry -> entry.getContent()).collect(Collectors.joining("\n"));
   
           final SystemPromptTemplate promptTemplate = new SystemPromptTemplate(template);
           final var systemMessage = promptTemplate.createMessage(Map.of("documents", docPrompts));
   
           final var prompt = new Prompt(List.of(systemMessage, userMessage));
   
           final var resps = aiClient.generate(prompt);
   
           System.out.println(String.format("Prompt created %d generated response(s).", resps.getGenerations().size()));
   
           resps.getGenerations().stream()
             .forEach(gen -> {
                 final var role = gen.getInfo().getOrDefault(ROLE_INFO_KEY, MessageType.ASSISTANT.getValue());
   
                 System.out.println(String.format("Generated respose from \"%s\": %s", role, gen.getText()));
             });
   
       }
   
       @Bean
       public VectorStore vectorStore(EmbeddingClient embeddingClient)
       {
           final SearchClient searchClient = new SearchClientBuilder()
                   .endpoint(acsEndpoint)
                   .credential(new AzureKeyCredential(acsApiKey))
                   .indexName(acsIndexName)
                   .buildClient();
           return new AzureCognitiveSearchVectorStore(searchClient, embeddingClient);
       }
   
       public static class AzureCognitiveSearchVectorStore implements VectorStore
       {
           private static final int DEFAULT_TOP_K = 4;
   
           private static final Double DEFAULT_SIMILARITY_THRESHOLD = 0.0;
   
           private SearchClient searchClient;
   
           private final EmbeddingClient embeddingClient;
   
           public AzureCognitiveSearchVectorStore(SearchClient searchClient, EmbeddingClient embeddingClient)
           {
               this.searchClient = searchClient;
               this.embeddingClient = embeddingClient;
           }
   
           @Override
           public void add(List<Document> documents)
           {
               final var docs = documents.stream().map(document -> {
   
                   final var embeddings = embeddingClient.embed(document);
   
                   return new DocEntry(document.getId(), "", document.getContent(), embeddings);
   
               }).toList();
   
               searchClient.uploadDocuments(docs);
           }
   
           @Override
           public Optional<Boolean> delete(List<String> idList)
           {
               final List<DocEntry> docIds = idList.stream().map(id -> DocEntry.builder().id(id).build())
                   .toList();
   
               var results = searchClient.deleteDocuments(docIds);
   
               boolean resSuccess = true;
   
               for (IndexingResult result : results.getResults())
                   if (!result.isSucceeded()) {
                       resSuccess = false;
                       break;
                   }
   
               return Optional.of(resSuccess);
           }
   
           @Override
           public List<Document> similaritySearch(String query)
           {
               return similaritySearch(query, DEFAULT_TOP_K);
           }
   
           @Override
           public List<Document> similaritySearch(String query, int k)
           {
               return similaritySearch(query, k, DEFAULT_SIMILARITY_THRESHOLD);
           }
   
           @Override
           public List<Document> similaritySearch(String query, int k, double threshold)
           {
               final var searchQueryVector = new RawVectorQuery()
                       .setVector(toFloatList(embeddingClient.embed(query)))
                       .setKNearestNeighborsCount(k)
                       .setFields("contentVector");
   
               final var searchResults = searchClient.search(null,
                       new SearchOptions().setVectorQueries(searchQueryVector), Context.NONE);
   
               return searchResults.stream()
                       .filter(r -> r.getScore() >= threshold)
                       .map(r -> {
   
                           final DocEntry entry = r.getDocument(DocEntry.class);
   
                           final Document doc = new Document(entry.getId(), entry.getContent(), Collections.emptyMap());
                           doc.setEmbedding(entry.getContentVector());
   
                           return doc;
                       })
                       .collect(Collectors.toList());
           }
   
           private List<Float> toFloatList(List<Double> doubleList)
           {
               return doubleList.stream().map(Double::floatValue).toList();
           }
   
       }
   
       @Data
       @Builder
       @Jacksonized
       @AllArgsConstructor
       @NoArgsConstructor
       static class DocEntry
       {
           private String id;
   
           private String hash;
   
           private String content;
   
           private List<Double> contentVector;
       }
   
   }
   

   Important

   For production, use a secure way of storing and accessing your credentials like Azure Key Vault. For more information about credential security, see the Azure AI services security article.

3. Navigate back to the project root folder, and run the app by using the following command:

   ./mvnw spring-boot:run
   

Output

  .   ____          _            __ _ _
 /\\ / ___'_ __ _ _(_)_ __  __ _ \ \ \ \
( ( )\___ | '_ | '_| | '_ \/ _` | \ \ \ \
 \\/  ___)| |_)| | | | | || (_| |  ) ) ) )
  '  |____| .__|_| |_|_| |_\__, | / / / /
 =========|_|==============|___/=/_/_/_/
 :: Spring Boot ::                (v3.1.5)

2023-11-07T14:40:45.250-06:00  INFO 18557 --- [           main] c.e.a.AiCustomDataApplication            : No active profile set, falling back to 1 default profile: "default"
2023-11-07T14:40:46.035-06:00  INFO 18557 --- [           main] c.e.a.AiCustomDataApplication            : Started AiCustomDataApplication in 1.095 seconds (process running for 1.397)
Sending custom data prompt to AI service. One moment please...

Prompt created 1 generated response(s).
Generated response from "assistant": The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans.

Prerequisites

• An Azure subscription - Create one for free.

• LTS versions of Node.js

• Azure CLI used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.

• An Azure OpenAI resource deployed in a supported region and with a supported model.

• Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.

• Download the example data from GitHub if you don't have your own data.

Reference documentation | Source code | Package (npm) | Samples

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Retrieve required variables

To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See Add your data using Azure AI Foundry

Environment variables

Create and assign persistent environment variables for your key and endpoint.

setx AZURE_OPENAI_ENDPOINT REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

setx AZURE_OPENAI_API_KEY REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

setx AZURE_OPENAI_DEPLOYMENT_ID REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

setx AZURE_AI_SEARCH_ENDPOINT REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

setx AZURE_AI_SEARCH_API_KEY REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

setx AZURE_AI_SEARCH_INDEX REPLACE_WITH_YOUR_INDEX_NAME_HERE

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_DEPLOYMENT_ID', 'REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_ENDPOINT', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_API_KEY', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_INDEX', 'REPLACE_WITH_YOUR_INDEX_NAME_HERE', 'User')

export AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

export AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

export AZURE_OPENAI_DEPLOYMENT_ID=REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

export AZURE_AI_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

export AZURE_AI_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

export AZURE_AI_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE

Initialize a Node.js application

In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it. Then run the npm init command to create a node application with a package.json file.

npm init

Install the client library

Install the Azure OpenAI client and Azure Identity libraries for JavaScript with npm:

npm install @azure/openai @azure/identity

Your app's package.json file will be updated with the dependencies.

Add the JavaScript code

Output

Message: What are my available health plans?
The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans.

Prerequisites

• An Azure subscription - Create one for free.

• LTS versions of Node.js

• TypeScript

• Azure CLI used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.

• An Azure OpenAI resource deployed in a supported region and with a supported model.

• Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.

• Download the example data from GitHub if you don't have your own data.

Reference documentation | Source code | Package (npm) | Samples

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Retrieve required variables

To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See Add your data using Azure AI Foundry

Environment variables

Create and assign persistent environment variables for your key and endpoint.

setx AZURE_OPENAI_ENDPOINT REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

setx AZURE_OPENAI_API_KEY REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

setx AZURE_OPENAI_DEPLOYMENT_ID REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

setx AZURE_AI_SEARCH_ENDPOINT REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

setx AZURE_AI_SEARCH_API_KEY REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

setx AZURE_AI_SEARCH_INDEX REPLACE_WITH_YOUR_INDEX_NAME_HERE

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_DEPLOYMENT_ID', 'REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_ENDPOINT', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_API_KEY', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_INDEX', 'REPLACE_WITH_YOUR_INDEX_NAME_HERE', 'User')

export AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

export AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

export AZURE_OPENAI_DEPLOYMENT_ID=REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

export AZURE_AI_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

export AZURE_AI_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

export AZURE_AI_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE

Initialize a Node.js application

In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it. Then run the npm init command to create a node application with a package.json file.

npm init

Install the client library

Install the Azure OpenAI client and Azure Identity libraries for JavaScript with npm:

npm install openai @azure/identity @azure/openai 

The @azure/openai/types dependency is included to extend the Azure OpenAI model for the data_sources property. This import is only necessary for TypeScript.

Your app's package.json file will be updated with the dependencies.

Add the TypeScript code

Output

Message: What are my available health plans?
The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans.

Prerequisites

The following resources:

• Azure OpenAI
• Azure Blob Storage
• Azure AI Search
• An Azure OpenAI resource deployed in a supported region and with a supported model.
  • Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.
• Download the example data from GitHub if you don't have your own data.

Reference | Source code | Package (pypi) | Samples

These links reference the OpenAI API for Python. There's no Azure-specific OpenAI Python SDK. Learn how to switch between the OpenAI services and Azure OpenAI services.

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Retrieve required variables

To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See Add your data using Azure AI Foundry

Environment variables

Create and assign persistent environment variables for your key and endpoint.

setx AZURE_OPENAI_ENDPOINT REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

setx AZURE_OPENAI_API_KEY REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

setx AZURE_OPENAI_DEPLOYMENT_ID REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

setx AZURE_AI_SEARCH_ENDPOINT REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

setx AZURE_AI_SEARCH_API_KEY REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

setx AZURE_AI_SEARCH_INDEX REPLACE_WITH_YOUR_INDEX_NAME_HERE

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_DEPLOYMENT_ID', 'REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_ENDPOINT', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_API_KEY', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_INDEX', 'REPLACE_WITH_YOUR_INDEX_NAME_HERE', 'User')

export AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

export AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

export AZURE_OPENAI_DEPLOYMENT_ID=REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

export AZURE_AI_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

export AZURE_AI_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

export AZURE_AI_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE

Create a Python environment

1. Create a new folder named openai-python for your project and a new Python code file named main.py. Change into that directory:

mkdir openai-python
cd openai-python

2. Install the following Python Libraries:

pip install openai
pip install python-dotenv

pip install openai==0.28.1
pip install python-dotenv

Create the Python app

1. From the project directory, open the main.py file and add the following code:

import os
import openai
import dotenv

dotenv.load_dotenv()

endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
api_key = os.environ.get("AZURE_OPENAI_API_KEY")
deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT_ID")

client = openai.AzureOpenAI(
    azure_endpoint=endpoint,
    api_key=api_key,
    api_version="2024-02-01",
)

completion = client.chat.completions.create(
    model=deployment,
    messages=[
        {
            "role": "user",
            "content": "What are my available health plans?",
        },
    ],
    extra_body={
        "data_sources":[
            {
                "type": "azure_search",
                "parameters": {
                    "endpoint": os.environ["AZURE_AI_SEARCH_ENDPOINT"],
                    "index_name": os.environ["AZURE_AI_SEARCH_INDEX"],
                    "authentication": {
                        "type": "api_key",
                        "key": os.environ["AZURE_AI_SEARCH_API_KEY"],
                    }
                }
            }
        ],
    }
)

print(completion.model_dump_json(indent=2))

import os
import openai
import dotenv
import requests

dotenv.load_dotenv()

openai.api_base = os.environ.get("AZURE_OPENAI_ENDPOINT")

# Azure OpenAI on your own data is only supported by the 2023-08-01-preview API version
openai.api_version = "2023-08-01-preview"
openai.api_type = 'azure'
openai.api_key = os.environ.get("AZURE_OPENAI_API_KEY")

def setup_byod(deployment_id: str) -> None:
    """Sets up the OpenAI Python SDK to use your own data for the chat endpoint.
 
    :param deployment_id: The deployment ID for the model to use with your own data.

    To remove this configuration, simply set openai.requestssession to None.
    """

    class BringYourOwnDataAdapter(requests.adapters.HTTPAdapter):

     def send(self, request, **kwargs):
         request.url = f"{openai.api_base}/openai/deployments/{deployment_id}/extensions/chat/completions?api-version={openai.api_version}"
         return super().send(request, **kwargs)

    session = requests.Session()

    # Mount a custom adapter which will use the extensions endpoint for any call using the given `deployment_id`
    session.mount(
        prefix=f"{openai.api_base}/openai/deployments/{deployment_id}",
        adapter=BringYourOwnDataAdapter()
    )

    openai.requestssession = session

aoai_deployment_id = os.environ.get("AZURE_OPENAI_DEPLOYMENT_ID")
setup_byod(aoai_deployment_id)

completion = openai.ChatCompletion.create(
    messages=[{"role": "user", "content": "What are my available health plans?"}],
    deployment_id=os.environ.get("AZURE_OPENAI_DEPLOYMENT_ID"),
    dataSources=[  # camelCase is intentional, as this is the format the API expects
        {
            "type": "AzureCognitiveSearch",
            "parameters": {
                "endpoint": os.environ.get("AZURE_AI_SEARCH_ENDPOINT"),
                "key": os.environ.get("AZURE_AI_SEARCH_API_KEY"),
                "indexName": os.environ.get("AZURE_AI_SEARCH_INDEX"),
            }
        }
    ]
)
print(completion)

2. Execute the following command:

python main.py

The application prints the response in a JSON format suitable for use in many scenarios. It includes both answers to your query and citations from your uploaded files.

Prerequisites

The following resources:

• Azure OpenAI
• Azure Blob Storage
• Azure AI Search
• An Azure OpenAI resource deployed in a supported region and with a supported model.
  • Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.
• Download the example data from GitHub if you don't have your own data.

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Retrieve required variables

To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See Add your data using Azure AI Foundry

Environment variables

Create and assign persistent environment variables for your key and endpoint.

setx AZURE_OPENAI_ENDPOINT REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

setx AZURE_OPENAI_API_KEY REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

setx AZURE_OPENAI_DEPLOYMENT_ID REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

setx AZURE_AI_SEARCH_ENDPOINT REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

setx AZURE_AI_SEARCH_API_KEY REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

setx AZURE_AI_SEARCH_INDEX REPLACE_WITH_YOUR_INDEX_NAME_HERE

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_DEPLOYMENT_ID', 'REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_ENDPOINT', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_API_KEY', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_INDEX', 'REPLACE_WITH_YOUR_INDEX_NAME_HERE', 'User')

export AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

export AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

export AZURE_OPENAI_DEPLOYMENT_ID=REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

export AZURE_AI_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

export AZURE_AI_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

export AZURE_AI_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE

Example PowerShell commands

The Azure OpenAI chat models are optimized to work with inputs formatted as a conversation. The messages variable passes an array of dictionaries with different roles in the conversation delineated by system, user, tool, and assistant. The dataSources variable connects to your Azure Cognitive Search index, and enables Azure OpenAI models to respond using your data.

To trigger a response from the model, you should end with a user message indicating that it's the assistant's turn to respond.

# Azure OpenAI metadata variables
   $openai = @{
       api_key     = $Env:AZURE_OPENAI_API_KEY
       api_base    = $Env:AZURE_OPENAI_ENDPOINT # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
       api_version = '2023-07-01-preview' # this may change in the future
       name        = 'YOUR-DEPLOYMENT-NAME-HERE' #This will correspond to the custom name you chose for your deployment when you deployed a model.
   }

   $acs = @{
       search_endpoint     = 'YOUR ACS ENDPOINT' # your endpoint should look like the following https://YOUR_RESOURCE_NAME.search.windows.net/
       search_key    = 'YOUR-ACS-KEY-HERE' # or use the Get-Secret cmdlet to retrieve the value
       search_index = 'YOUR-INDEX-NAME-HERE' # the name of your ACS index
   }

   # Completion text
   $body = @{
    dataSources = @(
        @{
            type = 'AzureCognitiveSearch'
            parameters = @{
                    endpoint = $acs.search_endpoint
                    key = $acs.search_key
                    indexName = $acs.search_index
                }
        }
    )
    messages = @(
            @{
                role = 'user'
                content = 'What are my available health plans?'
            }
    )
   } | convertto-json -depth 5

   # Header for authentication
   $headers = [ordered]@{
       'api-key' = $openai.api_key
   }

   # Send a completion call to generate an answer
   $url = "$($openai.api_base)/openai/deployments/$($openai.name)/extensions/chat/completions?api-version=$($openai.api_version)"

   $response = Invoke-RestMethod -Uri $url -Headers $headers -Body $body -Method Post -ContentType 'application/json'
   return $response.choices.messages[1].content

Example output

The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans.

Chat with your model using a web app

To start chatting with the Azure OpenAI model that uses your data, you can deploy a web app using Azure OpenAI studio or example code we provide on GitHub. This app deploys using Azure app service, and provides a user interface for sending queries. This app can be used with Azure OpenAI models that use your data, or models that don't use your data. See the readme file in the repo for instructions on requirements, setup, and deployment. You can optionally customize the frontend and backend logic of the web app by making changes to the source code.

Prerequisites

The following resources:

• Azure OpenAI
• Azure Blob Storage
• Azure AI Search
• An Azure OpenAI resource deployed in a supported region and with a supported model.
  • Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.
• Download the example data from GitHub if you don't have your own data.

Reference | Source code | Package (Go) | Samples

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Retrieve required variables

To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See Add your data using Azure AI Foundry

Environment variables

Create and assign persistent environment variables for your key and endpoint.

setx AZURE_OPENAI_ENDPOINT REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

setx AZURE_OPENAI_API_KEY REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

setx AZURE_OPENAI_DEPLOYMENT_ID REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

setx AZURE_AI_SEARCH_ENDPOINT REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

setx AZURE_AI_SEARCH_API_KEY REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

setx AZURE_AI_SEARCH_INDEX REPLACE_WITH_YOUR_INDEX_NAME_HERE

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_DEPLOYMENT_ID', 'REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_ENDPOINT', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_API_KEY', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_INDEX', 'REPLACE_WITH_YOUR_INDEX_NAME_HERE', 'User')

export AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

export AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

export AZURE_OPENAI_DEPLOYMENT_ID=REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

export AZURE_AI_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

export AZURE_AI_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

export AZURE_AI_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE

Create a Go environment

1. Create a new folder named openai-go for your project and a new Go code file named sample.go. Change into that directory:

   mkdir openai-go
   cd openai-go
   

2. Install the following Go packages:

   go get github.com/Azure/azure-sdk-for-go/sdk/ai/azopenai
   

3. Enable dependency tracking for your code.

   go mod init example/azure-openai
   

Create the Go app

1. From the project directory, open the sample.go file and add the following code:

   package main
   
   import (
    "context"
    "fmt"
    "log"
    "os"
   
    "github.com/Azure/azure-sdk-for-go/sdk/ai/azopenai"
    "github.com/Azure/azure-sdk-for-go/sdk/azcore"
    "github.com/Azure/azure-sdk-for-go/sdk/azcore/to"
   )
   
   func main() {
    azureOpenAIKey := os.Getenv("AZURE_OPENAI_API_KEY")
    modelDeploymentID := os.Getenv("AZURE_OPENAI_DEPLOYMENT_ID")
   
    // Ex: "https://<your-azure-openai-host>.openai.azure.com"
    azureOpenAIEndpoint := os.Getenv("AZURE_OPENAI_ENDPOINT")
   
    // Azure AI Search configuration
    searchIndex := os.Getenv("AZURE_AI_SEARCH_INDEX")
    searchEndpoint := os.Getenv("AZURE_AI_SEARCH_ENDPOINT")
    searchAPIKey := os.Getenv("AZURE_AI_SEARCH_API_KEY")
   
    if azureOpenAIKey == "" || modelDeploymentID == "" || azureOpenAIEndpoint == "" || searchIndex == "" || searchEndpoint == "" || searchAPIKey == "" {
    	fmt.Fprintf(os.Stderr, "Skipping example, environment variables missing\n")
    	return
    }
   
    keyCredential := azcore.NewKeyCredential(azureOpenAIKey)
   
    // In Azure OpenAI you must deploy a model before you can use it in your client. For more information
    // see here: https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource
    client, err := azopenai.NewClientWithKeyCredential(azureOpenAIEndpoint, keyCredential, nil)
   
    if err != nil {
    	//  TODO: Update the following line with your application specific error handling logic
    	log.Fatalf("ERROR: %s", err)
    }
   
    resp, err := client.GetChatCompletions(context.TODO(), azopenai.ChatCompletionsOptions{
    	Messages: []azopenai.ChatRequestMessageClassification{
    		&azopenai.ChatRequestUserMessage{Content: azopenai.NewChatRequestUserMessageContent("What are my available health plans?")},
    	},
    	MaxTokens: to.Ptr[int32](512),
    	AzureExtensionsOptions: []azopenai.AzureChatExtensionConfigurationClassification{
    		&azopenai.AzureSearchChatExtensionConfiguration{
    			// This allows Azure OpenAI to use an Azure AI Search index.
    			//
    			// > Because the model has access to, and can reference specific sources to support its responses, answers are not only based on its pretrained knowledge
    			// > but also on the latest information available in the designated data source. This grounding data also helps the model avoid generating responses
    			// > based on outdated or incorrect information.
    			//
    			// Quote from here: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data
    			Parameters: &azopenai.AzureSearchChatExtensionParameters{
    				Endpoint:  &searchEndpoint,
    				IndexName: &searchIndex,
    				Authentication: &azopenai.OnYourDataAPIKeyAuthenticationOptions{
    					Key: &searchAPIKey,
    				},
    			},
    		},
    	},
    	DeploymentName: &modelDeploymentID,
    }, nil)
   
    if err != nil {
    	//  TODO: Update the following line with your application specific error handling logic
    	log.Fatalf("ERROR: %s", err)
    }
   
    fmt.Fprintf(os.Stderr, "Extensions Context Role: %s\nExtensions Context (length): %d\n",
    	*resp.Choices[0].Message.Role,
    	len(*resp.Choices[0].Message.Content))
   
    fmt.Fprintf(os.Stderr, "ChatRole: %s\nChat content: %s\n",
    	*resp.Choices[0].Message.Role,
    	*resp.Choices[0].Message.Content,
    )
   }
   

   Important

   For production, use a secure way of storing and accessing your credentials like Azure Key Vault. For more information about credential security, see the Azure AI services security article.

2. Execute the following command:

   go run sample.go
   

   The application prints the response including both answers to your query and citations from your uploaded files.

Prerequisites

The following resources:

• Azure OpenAI
• Azure Blob Storage
• Azure AI Search
• An Azure OpenAI resource deployed in a supported region and with a supported model.
  • Be sure that you're assigned at least the Cognitive Services Contributor role for the Azure OpenAI resource.
• Download the example data from GitHub if you don't have your own data.

Add your data using Azure OpenAI Studio

Navigate to Azure AI Foundry and sign-in with credentials that have access to your Azure OpenAI resource.

1. You can either create an Azure AI Foundry project by clicking Create project, or continue directly by clicking the button on the Focused on Azure OpenAI Service tile.

   

2. Select Chat under Playgrounds in the left navigation menu, and select your model deployment.

3. In the Chat playground, Select Add your data and then Add a data source

   

4. In the pane that appears, select Upload files (preview) under Select data source. Azure OpenAI needs both a storage resource and a search resource to access and index your data.

   Tip

   • See the following resource for more information:
     • Data source options
     • supported file types and formats
   • For documents and datasets with long text, we recommend using the available data preparation script.

   1. For Azure OpenAI to access your storage account, you will need to turn on Cross-origin resource sharing (CORS). If CORS isn't already turned on for the Azure Blob Storage resource, select Turn on CORS.

   2. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.

   

5. On the Upload files pane, select Browse for a file and select the files you downloaded from the prerequisites section, or your own data. Then select Upload files. Then select Next.

6. On the Data management pane, you can choose whether to enable semantic search or vector search for your index.

   Important

   • Semantic search and vector search are subject to additional pricing. You need to choose Basic or higher SKU to enable semantic search or vector search. See pricing tier difference and service limits for more information.
   • To help improve the quality of the information retrieval and model response, we recommend enabling semantic search for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.

7. Review the details you entered, and select Save and close. You can now chat with the model and it will use information from your data to construct the response.

Retrieve required variables

To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See Add your data using Azure AI Foundry

Environment variables

Create and assign persistent environment variables for your key and endpoint.

setx AZURE_OPENAI_ENDPOINT REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

setx AZURE_OPENAI_API_KEY REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

setx AZURE_OPENAI_DEPLOYMENT_ID REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

setx AZURE_AI_SEARCH_ENDPOINT REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

setx AZURE_AI_SEARCH_API_KEY REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

setx AZURE_AI_SEARCH_INDEX REPLACE_WITH_YOUR_INDEX_NAME_HERE

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_DEPLOYMENT_ID', 'REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_ENDPOINT', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_API_KEY', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE', 'User')

[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_INDEX', 'REPLACE_WITH_YOUR_INDEX_NAME_HERE', 'User')

export AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE

export AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE

export AZURE_OPENAI_DEPLOYMENT_ID=REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE

export AZURE_AI_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE

export AZURE_AI_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE

export AZURE_AI_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE

Example cURL commands

The Azure OpenAI chat models are optimized to work with inputs formatted as a conversation. The messages variable passes an array of dictionaries with different roles in the conversation delineated by system, user, tool, and assistant. The dataSources variable connects to your Azure AI Search index, and enables Azure OpenAI models to respond using your data.

To trigger a response from the model, you should end with a user message indicating that it's the assistant's turn to respond.

curl -i -X POST $AZURE_OPENAI_ENDPOINT/openai/deployments/$AZURE_OPENAI_DEPLOYMENT_ID/chat/completions?api-version=2024-02-15-preview \
-H "Content-Type: application/json" \
-H "api-key: $AZURE_OPENAI_API_KEY" \
-d \
'
{
    "data_sources": [
        {
            "type": "azure_search",
            "parameters": {
                "endpoint": "'$AZURE_AI_SEARCH_ENDPOINT'",
                "key": "'$AZURE_AI_SEARCH_API_KEY'",
                "index_name": "'$AZURE_AI_SEARCH_INDEX'"
            }
        }
    ],
    "messages": [
        {
            "role": "user",
            "content": "What are my available health plans?"
        }
    ]
}
'

Example output

{
    "id": "12345678-1a2b-3c4e5f-a123-12345678abcd",
    "model": "gpt-4",
    "created": 1709835345,
    "object": "extensions.chat.completion",
    "choices": [
        {
            "index": 0,
            "finish_reason": "stop",
            "message": {
                "role": "assistant",
                "content": "The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans. [doc1].",
                "end_turn": true,
                "context": {
                    "citations": [
                        {
                            "content": "...",
                            "title": "...",
                            "url": "https://mysearch.blob.core.windows.net/xyz/001.txt",
                            "filepath": "001.txt",
                            "chunk_id": "0"
                        }
                    ],
                    "intent": "[\"Available health plans\"]"
                }
            }
        }
    ],
    "usage": {
        "prompt_tokens": 3779,
        "completion_tokens": 105,
        "total_tokens": 3884
    }
}

Chat with your model using a web app

To start chatting with the Azure OpenAI model that uses your data, you can deploy a web app using Azure OpenAI studio or example code we provide on GitHub. This app deploys using Azure app service, and provides a user interface for sending queries. This app can be used with Azure OpenAI models that use your data, or models that don't use your data. See the readme file in the repo for instructions on requirements, setup, and deployment. You can optionally customize the frontend and backend logic of the web app by making changes to the source code.