Azure OpenAI assistant query input binding for Azure Functions

Important

The Azure OpenAI extension for Azure Functions is currently in preview.

The Azure OpenAI assistant query input binding allows you to integrate Assistants API queries into your code executions.

For information on setup and configuration details of the Azure OpenAI extension, see Azure OpenAI extensions for Azure Functions. To learn more about Azure OpenAI assistants, see Azure OpenAI Assistants API.

Note

References and examples are only provided for the Node.js v4 model.

Note

References and examples are only provided for the Python v2 model.

Note

While both C# process models are supported, only isolated worker model examples are provided.

Example

Go support isn't currently available for this binding.

This example demonstrates the creation process, where the HTTP GET function that queries the conversation history of the assistant chat bot. The response to the prompt is returned in the HTTP response.

/// <summary>
/// HTTP GET function that queries the conversation history of the assistant chat bot.
/// </summary>
[Function(nameof(GetChatState))]
public static IActionResult GetChatState(
   [HttpTrigger(AuthorizationLevel.Function, "get", Route = "assistants/{assistantId}")] HttpRequestData req,
   string assistantId,
   [AssistantQueryInput("{assistantId}", TimestampUtc = "{Query.timestampUTC}", ChatStorageConnectionSetting = DefaultChatStorageConnectionSetting, CollectionName = DefaultCollectionName)] AssistantState state)
{
    return new OkObjectResult(state);
}

This example demonstrates the creation process, where the HTTP GET function that queries the conversation history of the assistant chat bot. The response to the prompt is returned in the HTTP response.

/*
 * HTTP GET function that queries the conversation history of the assistant chat bot.
 */   
@FunctionName("GetChatState")
public HttpResponseMessage getChatState(
    @HttpTrigger(
        name = "req",
        methods = {HttpMethod.GET}, 
        authLevel = AuthorizationLevel.FUNCTION,
        route = "assistants/{assistantId}") 
        HttpRequestMessage<Optional<String>> request,
    @BindingName("assistantId") String assistantId,        
    @AssistantQuery(name = "AssistantState", id = "{assistantId}", timestampUtc = "{Query.timestampUTC}", chatStorageConnectionSetting = DEFAULT_CHATSTORAGE, collectionName = DEFAULT_COLLECTION) AssistantState state,
    final ExecutionContext context) {
        return request.createResponseBuilder(HttpStatus.OK)
            .header("Content-Type", "application/json")
            .body(state)
            .build();
}

This example demonstrates the creation process, where the HTTP GET function that queries the conversation history of the assistant chat bot. The response to the prompt is returned in the HTTP response.

const { app, input, output } = require("@azure/functions");

const chatBotQueryInput = input.generic({
    type: 'assistantQuery',
    id: '{assistantId}',
    timestampUtc: '{Query.timestampUTC}',
    chatStorageConnectionSetting: CHAT_STORAGE_CONNECTION_SETTING,
    collectionName: COLLECTION_NAME
})
app.http('GetChatState', {
    methods: ['GET'],
    route: 'assistants/{assistantId}',
    authLevel: 'function',
    extraInputs: [chatBotQueryInput],
    handler: async (_, context) => {
        const state = context.extraInputs.get(chatBotQueryInput)
        return { status: 200, jsonBody: state }
    }
})

This example demonstrates the creation process, where the HTTP GET function that queries the conversation history of the assistant chat bot. The response to the prompt is returned in the HTTP response.

import { HttpRequest, InvocationContext, app, input, output } from "@azure/functions"

const chatBotQueryInput = input.generic({
    type: 'assistantQuery',
    id: '{assistantId}',
    timestampUtc: '{Query.timestampUTC}',
    chatStorageConnectionSetting: CHAT_STORAGE_CONNECTION_SETTING,
    collectionName: COLLECTION_NAME
})
app.http('GetChatState', {
    methods: ['GET'],
    route: 'assistants/{assistantId}',
    authLevel: 'function',
    extraInputs: [chatBotQueryInput],
    handler: async (_, context) => {
        const state: any = context.extraInputs.get(chatBotQueryInput)
        return { status: 200, jsonBody: state }
    }
})

This example demonstrates the creation process, where the HTTP GET function that queries the conversation history of the assistant chat bot. The response to the prompt is returned in the HTTP response.

Here's the function.json file for Get Chat State:

{
  "bindings": [
    {
      "authLevel": "function",
      "type": "httpTrigger",
      "direction": "in",
      "name": "Request",
      "route": "assistants/{assistantId}",
      "methods": [
        "get"
      ]
    },
    {
      "type": "http",
      "direction": "out",
      "name": "Response"
    },
    {
      "name": "State",
      "type": "assistantQuery",
      "direction": "in",
      "dataType": "string",
      "id": "{assistantId}",
      "timestampUtc": "{Query.timestampUTC}",
      "chatStorageConnectionSetting": "AzureWebJobsStorage",
      "collectionName": "ChatState"
    }
  ]
}

For more information about function.json file properties, see the Configuration section.

using namespace System.Net

param($Request, $TriggerMetadata, $State)

Push-OutputBinding -Name Response -Value ([HttpResponseContext]@{
    StatusCode = [HttpStatusCode]::OK
    Body       = $State
    Headers    = @{
        "Content-Type" = "application/json"
    }
})

This example demonstrates the creation process, where the HTTP GET function that queries the conversation history of the assistant chat bot. The response to the prompt is returned in the HTTP response.

@apis.function_name("GetChatState")
@apis.route(route="assistants/{assistantId}", methods=["GET"])
@apis.assistant_query_input(
    arg_name="state",
    id="{assistantId}",
    timestamp_utc="{Query.timestampUTC}",
    chat_storage_connection_setting=DEFAULT_CHAT_STORAGE_SETTING,
    collection_name=DEFAULT_CHAT_COLLECTION_NAME,
)
def get_chat_state(req: func.HttpRequest, state: str) -> func.HttpResponse:
    return func.HttpResponse(state, status_code=200, mimetype="application/json")

Attributes

Apply the AssistantQuery attribute to define an assistant query input binding, which supports these parameters:

Parameter	Description
Id	Gets the ID of the assistant to query.
TimeStampUtc	Optional. Gets or sets the timestamp of the earliest message in the chat history to fetch. The timestamp should be in ISO 8601 format - for example, 2023-08-01T00:00:00Z.

Annotations

The assistantQuery annotation enables you to define an assistant query input binding, which supports these parameters:

Element	Description
name	Gets or sets the name of the input binding.
id	Gets the ID of the assistant to query.
timeStampUtc	Optional. Gets or sets the timestamp of the earliest message in the chat history to fetch. The timestamp should be in ISO 8601 format - for example, 2023-08-01T00:00:00Z.

Decorators

During the preview, define the input binding as a generic_input_binding binding of type assistantQuery, which supports these parameters:

Parameter	Description
arg_name	The name of the variable that represents the binding parameter.
id	Gets the ID of the assistant to query.
time_stamp_utc	Optional. Gets or sets the timestamp of the earliest message in the chat history to fetch. The timestamp should be in ISO 8601 format - for example, 2023-08-01T00:00:00Z.

Configuration

The binding supports these configuration properties that you set in the function.json file.

Property	Description
type	Must be assistantQuery.
direction	Must be in.
name	The name of the input binding.
id	Gets the ID of the assistant to query.
timeStampUtc	Optional. Gets or sets the timestamp of the earliest message in the chat history to fetch. The timestamp should be in ISO 8601 format - for example, 2023-08-01T00:00:00Z.

Configuration

The binding supports these properties, which are defined in your code:

Property	Description
id	Gets the ID of the assistant to query.
timeStampUtc	Optional. Gets or sets the timestamp of the earliest message in the chat history to fetch. The timestamp should be in ISO 8601 format - for example, 2023-08-01T00:00:00Z.

Usage

See the Example section for complete examples.

Connections

To use the Azure OpenAI binding extension, you need to specify a connection to an OpenAI model definition. Set the OpenAI model connection in your bindings by using one of these approaches:

• Use the AIConnectionName binding property (preferred for Azure OpenAI).
• Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_KEY in app settings (for Azure OpenAI).
• Set only Open_API_Key in app settings (for https://api.openai.com).

The way you set the connection depends on both the model API and the authentication method, as indicated by the following table:

Authentication/Model API	Azure OpenAI	OpenAI (https://api.openai.com)
Managed identity connection	AIConnectionName	Not supported
Key Vault reference	AZURE_OPENAI_ENDPOINT AZURE_OPENAI_KEY	Open_API_Key
App Configuration reference	AZURE_OPENAI_ENDPOINT AZURE_OPENAI_KEY	Open_API_Key
Shared secret	AZURE_OPENAI_ENDPOINT AZURE_OPENAI_KEY	Open_API_Key

Use managed identity-based connections and the AIConnectionName property.

When you use AIConnectionName, the value of this property setting depends on the type of connection:

• Managed identity connection: The AIConnectionName property is a <CONNECTION_NAME_PREFIX> shared by a group of settings that together define an identity-based connection to Azure OpenAI. For more information, see Define identity connections.
• Key Vault reference: The AIConnectionName property setting returns an Azure Key Vault reference to the location where the API key is centrally maintained. For more information, see Define Key Vault connections.
• App Configuration reference: The AIConnectionName property setting returns an Azure App Configuration reference that returns an API key or a Key Vault reference. For more information, see Azure App Configuration in the connections article.
• API key: The AIConnectionName property setting resolves to app settings containing the endpoint and key directly. Because shared keys can be compromised, use managed identity connections when possible. For more information, see Define connections.

To learn more about bindings connections, see Manage connections in Azure Functions.

AIConnectionName Property

The OpenAI bindings include an AIConnectionName property that you can use to specify the <ConnectionNamePrefix> for the group of app settings that define the connection to Azure OpenAI:

Setting name	Description
<CONNECTION_NAME_PREFIX>__endpoint	Sets the URI endpoint of the Azure OpenAI service. This setting is always required.
<CONNECTION_NAME_PREFIX>__clientId	Sets the specific user-assigned identity to use when obtaining an access token. Requires that <CONNECTION_NAME_PREFIX>__credential is set to managedidentity. The property accepts a client ID corresponding to a user-assigned identity assigned to the application. It's invalid to specify both a Resource ID and a client ID. If you don't specify this property, the system-assigned identity is used. This property is used differently in local development scenarios, when credential shouldn't be set.
<CONNECTION_NAME_PREFIX>__credential	Defines how an access token is obtained for the connection. Use managedidentity for managed identity authentication. This value is only valid when a managed identity is available in the hosting environment.
<CONNECTION_NAME_PREFIX>__managedIdentityResourceId	When credential is set to managedidentity, set this property to specify the resource Identifier to use when obtaining a token. The property accepts a resource identifier corresponding to the resource ID of the user-defined managed identity. It's invalid to specify both a resource ID and a client ID. If you don't specify either, the system-assigned identity is used. This property is used differently in local development scenarios, when credential shouldn't be set.
<CONNECTION_NAME_PREFIX>__key	Sets the shared secret key required to access the endpoint of the Azure OpenAI service by using key-based authentication. As a security best practice, always use Microsoft Entra ID with managed identities for authentication.

Consider these managed identity connection settings when you set the AIConnectionName property to myAzureOpenAI:

• myAzureOpenAI__endpoint=https://contoso.openai.azure.com/
• myAzureOpenAI__credential=managedidentity
• myAzureOpenAI__clientId=aaaaaaaa-bbbb-cccc-1111-222222222222

At runtime, the host interprets these settings as a single myAzureOpenAI setting:

"myAzureOpenAI":
{
    "endpoint": "https://contoso.openai.azure.com/",
    "credential": "managedidentity",
    "clientId": "aaaaaaaa-bbbb-cccc-1111-222222222222"
}

When you use managed identities, make sure to add your identity to the Cognitive Services OpenAI User role.

When running locally, add these settings to the local.settings.json project file. For more information, see Local development with identity-based connections.

Environment variables

To support legacy apps and providers other than Azure OpenAI, define key-based authentication to OpenAI by using these environment variables.

Variable name	Description
AZURE_OPENAI_ENDPOINT	Sets the URI endpoint of your Azure OpenAI instance. Don't use with Open_API_Key.
AZURE_OPENAI_KEY	Sets the shared secret key required to access your Azure OpenAI endpoint (AZURE_OPENAI_ENDPOINT) by using key-based authentication.
Open_API_Key	Sets the shared secret key required to access the https://api.openai.com endpoint by using key-based authentication.

Set these variables in your app settings.

When running locally, add these settings to the local.settings.json project file.

For more information, see Work with application settings.

Related content

• Assistant samples
• Azure OpenAI extension
• Azure OpenAI assistant trigger
• Azure OpenAI assistant create output binding
• Azure OpenAI assistant post input binding