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Azure OpenAI assistant post input binding for Azure Functions

Important

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

The Azure OpenAI assistant post input binding lets you send prompts to assistant chat bots.

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 POST function that sends user prompts to the assistant chat bot. The response to the prompt is returned in the HTTP response.

/// <summary>
/// HTTP POST function that sends user prompts to the assistant chat bot.
/// </summary>
[Function(nameof(PostUserQuery))]
public static IActionResult PostUserQuery(
    [HttpTrigger(AuthorizationLevel.Function, "post", Route = "assistants/{assistantId}")] HttpRequestData req,
    string assistantId,
    [AssistantPostInput("{assistantId}", "{Query.message}", ChatModel = "%CHAT_MODEL_DEPLOYMENT_NAME%", ChatStorageConnectionSetting = DefaultChatStorageConnectionSetting, CollectionName = DefaultCollectionName)] AssistantState state)
{
    return new OkObjectResult(state.RecentMessages.Any() ? state.RecentMessages[state.RecentMessages.Count - 1].Content : "No response returned.");
}

This example demonstrates the creation process, where the HTTP POST function that sends user prompts to the assistant chat bot. The response to the prompt is returned in the HTTP response.

/*
 * HTTP POST function that sends user prompts to the assistant chat bot.
 */ 
@FunctionName("PostUserResponse")
public HttpResponseMessage postUserResponse(
    @HttpTrigger(
        name = "req",
        methods = {HttpMethod.POST}, 
        authLevel = AuthorizationLevel.FUNCTION,
        route = "assistants/{assistantId}") 
        HttpRequestMessage<Optional<String>> request,
    @BindingName("assistantId") String assistantId,        
    @AssistantPost(name="newMessages", id = "{assistantId}", chatModel = "%CHAT_MODEL_DEPLOYMENT_NAME%", userMessage = "{Query.message}", chatStorageConnectionSetting = DEFAULT_CHATSTORAGE, collectionName = DEFAULT_COLLECTION) AssistantState state,
    final ExecutionContext context) {
        
        List<AssistantMessage> recentMessages = state.getRecentMessages();
        String response = recentMessages.isEmpty() ? "No response returned." : recentMessages.get(recentMessages.size() - 1).getContent();
        
        return request.createResponseBuilder(HttpStatus.OK)
            .header("Content-Type", "application/json")
            .body(response)
            .build();
}

This example demonstrates the creation process, where the HTTP POST function that sends user prompts to the assistant chat bot. The response to the prompt is returned in the HTTP response.

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

const assistantPostInput = input.generic({
    type: 'assistantPost',
    id: '{assistantId}',
    chatModel: '%CHAT_MODEL_DEPLOYMENT_NAME%',
    userMessage: '{Query.message}',
    chatStorageConnectionSetting: CHAT_STORAGE_CONNECTION_SETTING,
    collectionName: COLLECTION_NAME
})
app.http('PostUserResponse', {
    methods: ['POST'],
    route: 'assistants/{assistantId}',
    authLevel: 'function',
    extraInputs: [assistantPostInput],
    handler: async (_, context) => {
        const chatState = context.extraInputs.get(assistantPostInput)
        const content = chatState.recentMessages[0].content
        return {
            status: 200,
            body: content,
            headers: {
                'Content-Type': 'text/plain'
            }
        };
    }
})

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

const assistantPostInput = input.generic({
    type: 'assistantPost',
    id: '{assistantId}',
    chatModel: '%CHAT_MODEL_DEPLOYMENT_NAME%',
    userMessage: '{Query.message}',
    chatStorageConnectionSetting: CHAT_STORAGE_CONNECTION_SETTING,
    collectionName: COLLECTION_NAME
})
app.http('PostUserResponse', {
    methods: ['POST'],
    route: 'assistants/{assistantId}',
    authLevel: 'function',
    extraInputs: [assistantPostInput],
    handler: async (_, context) => {
        const chatState: any = context.extraInputs.get(assistantPostInput)
        const content = chatState.recentMessages[0].content
        return {
            status: 200,
            body: content,
            headers: {
                'Content-Type': 'text/plain'
            }
        };
    }
})

This example demonstrates the creation process, where the HTTP POST function that sends user prompts to the assistant chat bot. The response to the prompt is returned in the HTTP response.

Here's the function.json file for post user query:

{
  "bindings": [
    {
      "authLevel": "function",
      "type": "httpTrigger",
      "direction": "in",
      "name": "Request",
      "route": "assistants/{assistantId}",
      "methods": [
        "post"
      ]
    },
    {
      "type": "http",
      "direction": "out",
      "name": "Response"
    },
    {
      "name": "State",
      "type": "assistantPost",
      "direction": "in",
      "dataType": "string",
      "id": "{assistantId}",
      "userMessage": "{Query.message}",
      "chatModel": "%CHAT_MODEL_DEPLOYMENT_NAME%",
      "chatStorageConnectionSetting": "AzureWebJobsStorage",
      "collectionName": "ChatState"
    }
  ]
}

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

using namespace System.Net

param($Request, $TriggerMetadata, $State)

$recent_message_content = "No recent messages!"

if ($State.recentMessages.Count -gt 0) {
    $recent_message_content = $State.recentMessages[0].content
}

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

This example demonstrates the creation process, where the HTTP POST function that sends user prompts to the assistant chat bot. The response to the prompt is returned in the HTTP response.

@apis.function_name("PostUserQuery")
@apis.route(route="assistants/{assistantId}", methods=["POST"])
@apis.assistant_post_input(
    arg_name="state",
    id="{assistantId}",
    user_message="{Query.message}",
    chat_model="%CHAT_MODEL_DEPLOYMENT_NAME%",
    chat_storage_connection_setting=DEFAULT_CHAT_STORAGE_SETTING,
    collection_name=DEFAULT_CHAT_COLLECTION_NAME,
)
def post_user_response(req: func.HttpRequest, state: str) -> func.HttpResponse:
    # Parse the JSON string into a dictionary
    data = json.loads(state)

    # Extract the content of the recentMessage
    recent_message_content = data["recentMessages"][0]["content"]
    return func.HttpResponse(
        recent_message_content, status_code=200, mimetype="text/plain"
    )

Attributes

Apply the PostUserQuery attribute to define an assistant post input binding, which supports these parameters:

Parameter Description
Id The ID of the assistant to update.
UserMessage Gets or sets the user message for the chat completion model, encoded as a string.
AIConnectionName Optional. Gets or sets the name of the configuration section for AI service connectivity settings. For Azure OpenAI: If specified, looks for "Endpoint" and "Key" values in this configuration section. If not specified or the section doesn't exist, falls back to environment variables: AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_KEY. For user-assigned managed identity authentication, this property is required. For OpenAI service (non-Azure), set the OPENAI_API_KEY environment variable.
ChatModel Optional. Gets or sets the ID of the model to use as a string, with a default value of gpt-3.5-turbo.
Temperature Optional. Gets or sets the sampling temperature to use, as a string between 0 and 2. Higher values (0.8) make the output more random, while lower values like (0.2) make output more focused and deterministic. You should use either Temperature or TopP, but not both.
TopP Optional. Gets or sets an alternative to sampling with temperature, called nucleus sampling, as a string. In this sampling method, the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. You should use either Temperature or TopP, but not both.
MaxTokens Optional. Gets or sets the maximum number of tokens to generate in the completion, as a string with a default of 100. The token count of your prompt plus max_tokens can't exceed the model's context length. Most models have a context length of 2,048 tokens (except for the newest models, which support 4096).
IsReasoningModel Optional. Gets or sets a value indicating whether the chat completion model is a reasoning model. This option is experimental and associated with the reasoning model until all models have parity in the expected properties, with a default value of false.

Annotations

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

Element Description
name The name of the output binding.
id The ID of the assistant to update.
userMessage Gets or sets the user message for the chat completion model, encoded as a string.
aiConnectionName Optional. Gets or sets the name of the configuration section for AI service connectivity settings. For Azure OpenAI: If specified, looks for "Endpoint" and "Key" values in this configuration section. If not specified or the section doesn't exist, falls back to environment variables: AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_KEY. For user-assigned managed identity authentication, this property is required. For OpenAI service (non-Azure), set the OPENAI_API_KEY environment variable.
chatModel Gets or sets the ID of the model to use as a string, with a default value of gpt-3.5-turbo.
temperature Optional. Gets or sets the sampling temperature to use, as a string between 0 and 2. Higher values (0.8) make the output more random, while lower values like (0.2) make output more focused and deterministic. You should use either Temperature or TopP, but not both.
topP Optional. Gets or sets an alternative to sampling with temperature, called nucleus sampling, as a string. In this sampling method, the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. You should use either Temperature or TopP, but not both.
maxTokens Optional. Gets or sets the maximum number of tokens to generate in the completion, as a string with a default of 100. The token count of your prompt plus max_tokens can't exceed the model's context length. Most models have a context length of 2,048 tokens (except for the newest models, which support 4096).
isReasoningModel Optional. Gets or sets a value indicating whether the chat completion model is a reasoning model. This option is experimental and associated with the reasoning model until all models have parity in the expected properties, with a default value of false.

Decorators

During the preview, define the output binding as a generic_output_binding binding of type postUserQuery, which supports these parameters:

Parameter Description
arg_name The name of the variable that represents the binding parameter.
id The ID of the assistant to update.
user_message Gets or sets the user message for the chat completion model, encoded as a string.
ai_connection_name Optional. Gets or sets the name of the configuration section for AI service connectivity settings. For Azure OpenAI: If specified, looks for "Endpoint" and "Key" values in this configuration section. If not specified or the section doesn't exist, falls back to environment variables: AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_KEY. For user-assigned managed identity authentication, this property is required. For OpenAI service (non-Azure), set the OPENAI_API_KEY environment variable.
chat_model Gets or sets the ID of the model to use as a string, with a default value of gpt-3.5-turbo.
temperature Optional. Gets or sets the sampling temperature to use, as a string between 0 and 2. Higher values (0.8) make the output more random, while lower values like (0.2) make output more focused and deterministic. You should use either Temperature or TopP, but not both.
top_p Optional. Gets or sets an alternative to sampling with temperature, called nucleus sampling, as a string. In this sampling method, the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. You should use either Temperature or TopP, but not both.
max_tokens Optional. Gets or sets the maximum number of tokens to generate in the completion, as a string with a default of 100. The token count of your prompt plus max_tokens can't exceed the model's context length. Most models have a context length of 2,048 tokens (except for the newest models, which support 4096).
is_reasoning _model Optional. Gets or sets a value indicating whether the chat completion model is a reasoning model. This option is experimental and associated with the reasoning model until all models have parity in the expected properties, with a default value of false.

Configuration

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

Property Description
type Must be PostUserQuery.
direction Must be out.
name The name of the output binding.
id The ID of the assistant to update.
userMessage Gets or sets the user message for the chat completion model, encoded as a string.
aiConnectionName Optional. Gets or sets the name of the configuration section for AI service connectivity settings. For Azure OpenAI: If specified, looks for "Endpoint" and "Key" values in this configuration section. If not specified or the section doesn't exist, falls back to environment variables: AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_KEY. For user-assigned managed identity authentication, this property is required. For OpenAI service (non-Azure), set the OPENAI_API_KEY environment variable.
chatModel Gets or sets the ID of the model to use as a string, with a default value of gpt-3.5-turbo.
temperature Optional. Gets or sets the sampling temperature to use, as a string between 0 and 2. Higher values (0.8) make the output more random, while lower values like (0.2) make output more focused and deterministic. You should use either Temperature or TopP, but not both.
topP Optional. Gets or sets an alternative to sampling with temperature, called nucleus sampling, as a string. In this sampling method, the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. You should use either Temperature or TopP, but not both.
maxTokens Optional. Gets or sets the maximum number of tokens to generate in the completion, as a string with a default of 100. The token count of your prompt plus max_tokens can't exceed the model's context length. Most models have a context length of 2,048 tokens (except for the newest models, which support 4096).
isReasoningModel Optional. Gets or sets a value indicating whether the chat completion model is a reasoning model. This option is experimental and associated with the reasoning model until all models have parity in the expected properties, with a default value of false.

Configuration

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

Property Description
id The ID of the assistant to update.
userMessage Gets or sets the user message for the chat completion model, encoded as a string.
aiConnectionName Optional. Gets or sets the name of the configuration section for AI service connectivity settings. For Azure OpenAI: If specified, looks for "Endpoint" and "Key" values in this configuration section. If not specified or the section doesn't exist, falls back to environment variables: AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_KEY. For user-assigned managed identity authentication, this property is required. For OpenAI service (non-Azure), set the OPENAI_API_KEY environment variable.
chatModel Gets or sets the ID of the model to use as a string, with a default value of gpt-3.5-turbo.
temperature Optional. Gets or sets the sampling temperature to use, as a string between 0 and 2. Higher values (0.8) make the output more random, while lower values like (0.2) make output more focused and deterministic. You should use either Temperature or TopP, but not both.
topP Optional. Gets or sets an alternative to sampling with temperature, called nucleus sampling, as a string. In this sampling method, the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. You should use either Temperature or TopP, but not both.
maxTokens Optional. Gets or sets the maximum number of tokens to generate in the completion, as a string with a default of 100. The token count of your prompt plus max_tokens can't exceed the model's context length. Most models have a context length of 2,048 tokens (except for the newest models, which support 4096).
isReasoningModel Optional. Gets or sets a value indicating whether the chat completion model is a reasoning model. This option is experimental and associated with the reasoning model until all models have parity in the expected properties, with a default value of false.

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.

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.

For more information, see Work with application settings.

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