Quickstart: Chat with Azure OpenAI models using your own data
Reference | Source code | Package (pypi) | Samples
The links above reference the OpenAI API for Python. There is no Azure-specific OpenAI Python SDK. Learn how to switch between the OpenAI services and Azure OpenAI services.
In this quickstart you can use your own data with Azure OpenAI models. Using Azure OpenAI's models on your data can provide you with a powerful conversational AI platform that enables faster and more accurate communication.
Prerequisites
An Azure subscription - Create one for free.
Access granted to Azure OpenAI in the desired Azure subscription.
Azure OpenAI requires registration and is currently only available to approved enterprise customers and partners. See Limited access to Azure OpenAI Service for more information. You can apply for access to Azure OpenAI by completing the form at https://aka.ms/oai/access. Open an issue on this repo to contact us if you have an issue.
An Azure OpenAI resource deployed in a supported region with a supported model.
Be sure that you are 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
Tip
You can use the Azure Developer CLI to programmatically create the resources needed for Azure OpenAI On Your Data
Navigate to Azure OpenAI Studio and sign-in with credentials that have access to your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
Select the Bring your own data tile
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:
- For documents and datasets with long text, we recommend using the available data preparation script.
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.
Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select Next.
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.
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.
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 right, you can select Deployment 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.
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 studio
| Variable name | Value |
|---|---|
AZURE_OPENAI_ENDPOINT |
This value can be found in the Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. Alternatively, you can find the value in Azure AI studio > Chat playground > Code view. An example endpoint is: https://my-resoruce.openai.azure.com. |
AZURE_OPENAI_API_KEY |
This value can be found in Resource management > Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_OPENAI_DEPLOYMENT_ID |
This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Deployments in the Azure portal or alternatively under Management > Deployments in Azure AI studio. |
AZURE_AI_SEARCH_ENDPOINT |
This value can be found in the Overview section when examining your Azure AI Search resource from the Azure portal. |
AZURE_AI_SEARCH_API_KEY |
This value can be found in the Settings > Keys section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_AI_SEARCH_INDEX |
This value corresponds to the name of the index you created to store your data. You can find it in the Overview section when examining your Azure AI Search resource from the Azure portal. |
Environment variables
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
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 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");
var client = new OpenAIClient(new Uri(azureOpenAIEndpoint), new AzureKeyCredential(azureOpenAIKey));
var chatCompletionsOptions = new ChatCompletionsOptions()
{
Messages =
{
new ChatRequestUserMessage("What are my available health plans?"),
},
AzureExtensionsOptions = new AzureChatExtensionsOptions()
{
Extensions =
{
new AzureCognitiveSearchChatExtensionConfiguration()
{
SearchEndpoint = new Uri(searchEndpoint),
Key = searchKey,
IndexName = searchIndex,
},
}
},
DeploymentName = deploymentName
};
Response<ChatCompletions> response = client.GetChatCompletions(chatCompletionsOptions);
ChatResponseMessage responseMessage = response.Value.Choices[0].Message;
Console.WriteLine($"Message from {responseMessage.Role}:");
Console.WriteLine("===");
Console.WriteLine(responseMessage.Content);
Console.WriteLine("===");
Console.WriteLine($"Context information (e.g. citations) from chat extensions:");
Console.WriteLine("===");
foreach (ChatResponseMessage contextMessage in responseMessage.AzureExtensionsContext.Messages)
{
string contextContent = contextMessage.Content;
try
{
var contextMessageJson = JsonDocument.Parse(contextMessage.Content);
contextContent = JsonSerializer.Serialize(contextMessageJson, new JsonSerializerOptions()
{
WriteIndented = true,
});
}
catch (JsonException)
{}
Console.WriteLine($"{contextMessage.Role}: {contextContent}");
}
Console.WriteLine("===");
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.
dotnet run program.cs
Output
Answer from assistant:
===
The available health plans in the Contoso Electronics plan and benefit packages are the Northwind Health Plus and Northwind Standard plans [^1^].
===
Context information (e.g. citations) from chat extensions:
===
tool: {
"citations": [
{
"content": "...",
"id": null,
"title": "...",
"filepath": "...",
"url": "...",
"metadata": {
"chunking": "orignal document size=1011. Scores=3.6390076 and None.Org Highlight count=38."
},
"chunk_id": "2"
},
...
],
"intent": "[\u0022What are my available health plans?\u0022]"
}
===
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 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");
var client = new OpenAIClient(new Uri(azureOpenAIEndpoint), new AzureKeyCredential(azureOpenAIKey));
var chatCompletionsOptions = new ChatCompletionsOptions()
{
DeploymentName = deploymentName,
Messages =
{
new ChatRequestUserMessage("What are my available health plans?"),
},
AzureExtensionsOptions = new AzureChatExtensionsOptions()
{
Extensions =
{
new AzureCognitiveSearchChatExtensionConfiguration()
{
SearchEndpoint = new Uri(searchEndpoint),
Key = searchKey,
IndexName = searchIndex,
},
}
}
};
await foreach (StreamingChatCompletionsUpdate chatUpdate in client.GetChatCompletionsStreaming(chatCompletionsOptions))
{
if (chatUpdate.Role.HasValue)
{
Console.Write($"{chatUpdate.Role.Value.ToString().ToUpperInvariant()}: ");
}
if (!string.IsNullOrEmpty(chatUpdate.ContentUpdate))
{
Console.Write(chatUpdate.ContentUpdate);
}
}
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 studio.
| Variable name | Value |
|---|---|
AZURE_OPENAI_ENDPOINT |
You can find this value in the Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. Alternatively, you can find the value in Azure AI studio > Chat playground > Code view. An example endpoint is: https://my-resource.openai.azure.com. |
AZURE_OPENAI_API_KEY |
You can find this value in Resource management > Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_OPEN_AI_DEPLOYMENT_ID |
This value corresponds to the custom name you chose for your deployment when you deployed a model. You can find this value under Resource Management > Deployments in the Azure portal or alternatively under Management > Deployments in Azure AI studio. |
AZURE_AI_SEARCH_ENDPOINT |
You can find this value in the Overview section when examining your Azure AI Search resource from the Azure portal. |
AZURE_AI_SEARCH_API_KEY |
You can find this value in the Settings > Keys section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_AI_SEARCH_INDEX |
This value corresponds to the name of the index you created to store your data. You can find it in the Overview section when examining your Azure AI Search resource from the Azure portal. |
Environment variables
Note
Spring AI defaults the model name to gpt-35-turbo. It's only necessary to provide the SPRING_AI_AZURE_OPENAI_MODEL value if you've deployed a model with a different name.
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
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>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.
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.
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 studio
| Variable name | Value |
|---|---|
AZURE_OPENAI_ENDPOINT |
This value can be found in the Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. Alternatively, you can find the value in Azure AI studio > Chat playground > Code view. An example endpoint is: https://my-resoruce.openai.azure.com. |
AZURE_OPENAI_API_KEY |
This value can be found in Resource management > Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_OPENAI_DEPLOYMENT_ID |
This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Deployments in the Azure portal or alternatively under Management > Deployments in Azure AI studio. |
AZURE_AI_SEARCH_ENDPOINT |
This value can be found in the Overview section when examining your Azure AI Search resource from the Azure portal. |
AZURE_AI_SEARCH_API_KEY |
This value can be found in the Settings > Keys section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_AI_SEARCH_INDEX |
This value corresponds to the name of the index you created to store your data. You can find it in the Overview section when examining your Azure AI Search resource from the Azure portal. |
Environment variables
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
Create a Node 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.
Create a sample application
Open a command prompt where you want the new project, and create a new file named ChatWithOwnData.js. Copy the following code into the ChatWithOwnData.js file.
const { OpenAIClient, AzureKeyCredential } = require("@azure/openai");
// Set the Azure and AI Search values from environment variables
const endpoint = process.env["AZURE_OPENAI_ENDPOINT"];
const azureApiKey = process.env["AZURE_OPENAI_API_KEY"];
const deploymentId = process.env["AZURE_OPENAI_DEPLOYMENT_ID"];
const searchEndpoint = process.env["AZURE_AI_SEARCH_ENDPOINT"];
const searchKey = process.env["AZURE_AI_SEARCH_API_KEY"];
const searchIndex = process.env["AZURE_AI_SEARCH_INDEX"];
async function main(){
const client = new OpenAIClient(endpoint, new AzureKeyCredential(azureApiKey));
const messages = [
{ role: "user", content: "What are my available health plans?" },
];
console.log(`Message: ${messages.map((m) => m.content).join("\n")}`);
const events = await client.streamChatCompletions(deploymentId, messages, {
maxTokens: 128,
azureExtensionOptions: {
extensions: [
{
type: "AzureCognitiveSearch",
endpoint: searchEndpoint,
key: searchKey,
indexName: searchIndex,
},
],
},
});
let response = "";
for await (const event of events) {
for (const choice of event.choices) {
const newText = choice.delta?.content;
if (!!newText) {
response += newText;
// To see streaming results as they arrive, uncomment line below
// console.log(newText);
}
}
}
console.log(response);
}
main().catch((err) => {
console.error("The sample encountered an error:", err);
});
module.exports = { main };
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.
node.exe ChatWithOwnData.js
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.
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 studio
| Variable name | Value |
|---|---|
AZURE_OPENAI_ENDPOINT |
This value can be found in the Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. Alternatively, you can find the value in Azure AI studio > Chat playground > Code view. An example endpoint is: https://my-resoruce.openai.azure.com. |
AZURE_OPENAI_API_KEY |
This value can be found in Resource management > Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_OPENAI_DEPLOYMENT_ID |
This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Deployments in the Azure portal or alternatively under Management > Deployments in Azure AI studio. |
AZURE_AI_SEARCH_ENDPOINT |
This value can be found in the Overview section when examining your Azure AI Search resource from the Azure portal. |
AZURE_AI_SEARCH_API_KEY |
This value can be found in the Settings > Keys section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_AI_SEARCH_INDEX |
This value corresponds to the name of the index you created to store your data. You can find it in the Overview section when examining your Azure AI Search resource from the Azure portal. |
Environment variables
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
Create a Python environment
- 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
- Install the following Python Libraries:
pip install openai
pip install python-dotenv
Create the Python app
- 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))
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.
- 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.
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 studio
| Variable name | Value |
|---|---|
AZURE_OPENAI_ENDPOINT |
This value can be found in the Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. Alternatively, you can find the value in Azure AI studio > Chat playground > Code view. An example endpoint is: https://my-resoruce.openai.azure.com. |
AZURE_OPENAI_API_KEY |
This value can be found in Resource management > Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_OPENAI_DEPLOYMENT_ID |
This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Deployments in the Azure portal or alternatively under Management > Deployments in Azure AI studio. |
AZURE_AI_SEARCH_ENDPOINT |
This value can be found in the Overview section when examining your Azure AI Search resource from the Azure portal. |
AZURE_AI_SEARCH_API_KEY |
This value can be found in the Settings > Keys section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_AI_SEARCH_INDEX |
This value corresponds to the name of the index you created to store your data. You can find it in the Overview section when examining your Azure AI Search resource from the Azure portal. |
Environment variables
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
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.
Tip
There are several parameters you can use to change the model's response, such as temperature or top_p. See the reference documentation for more information.
# 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.
Important
For production, use a secure way of storing and accessing your credentials like The PowerShell Secret Management with Azure Key Vault. For more information about credential security, see the Azure AI services security article.
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 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.
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 studio
| Variable name | Value |
|---|---|
AZURE_OPENAI_ENDPOINT |
This value can be found in the Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. Alternatively, you can find the value in Azure AI studio > Chat playground > Code view. An example endpoint is: https://my-resoruce.openai.azure.com. |
AZURE_OPENAI_API_KEY |
This value can be found in Resource management > Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_OPENAI_DEPLOYMENT_ID |
This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Deployments in the Azure portal or alternatively under Management > Deployments in Azure AI studio. |
AZURE_AI_SEARCH_ENDPOINT |
This value can be found in the Overview section when examining your Azure AI Search resource from the Azure portal. |
AZURE_AI_SEARCH_API_KEY |
This value can be found in the Settings > Keys section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_AI_SEARCH_INDEX |
This value corresponds to the name of the index you created to store your data. You can find it in the Overview section when examining your Azure AI Search resource from the Azure portal. |
Environment variables
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
Create a Go environment
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-goInstall the following Go packages:
go get github.com/Azure/azure-sdk-for-go/sdk/ai/azopenaiEnable dependency tracking for your code.
go mod init example/azure-openai
Create the Go app
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.AzureCognitiveSearchChatExtensionConfiguration{ // 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.AzureCognitiveSearchChatExtensionParameters{ 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) } // Contains contextual information from your Azure chat completion extensions, configured above in `AzureExtensionsOptions` msgContext := resp.Choices[0].Message.Context fmt.Fprintf(os.Stderr, "Extensions Context Role: %s\nExtensions Context (length): %d\n", *msgContext.Messages[0].Role, len(*msgContext.Messages[0].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.
Execute the following command:
go run sample.goThe application prints the response including both answers to your query and citations from your uploaded files.
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 studio
| Variable name | Value |
|---|---|
AZURE_OPENAI_ENDPOINT |
This value can be found in the Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. Alternatively, you can find the value in Azure AI studio > Chat playground > Code view. An example endpoint is: https://my-resoruce.openai.azure.com. |
AZURE_OPENAI_API_KEY |
This value can be found in Resource management > Keys & Endpoint section when examining your Azure OpenAI resource from the Azure portal. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_OPENAI_DEPLOYMENT_ID |
This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Deployments in the Azure portal or alternatively under Management > Deployments in Azure AI studio. |
AZURE_AI_SEARCH_ENDPOINT |
This value can be found in the Overview section when examining your Azure AI Search resource from the Azure portal. |
AZURE_AI_SEARCH_API_KEY |
This value can be found in the Settings > Keys section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
AZURE_AI_SEARCH_INDEX |
This value corresponds to the name of the index you created to store your data. You can find it in the Overview section when examining your Azure AI Search resource from the Azure portal. |
Environment variables
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
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.
Tip
There are several parameters you can use to change the model's response, such as temperature or top_p. See the reference documentation for more information.
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: $AZURE_OPENAI_API_KEY" \
-d \
'
{
"data_sources": [
{
"type": "AzureCognitiveSearch",
"parameters": {
"endpoint": "'$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 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.
Clean up resources
If you want to clean up and remove an Azure OpenAI or Azure AI Search resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.
Next steps
- Learn more about using your data in Azure OpenAI Service
- Chat app sample code on GitHub.
Feedback
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