Azure OpenAI embeddings input binding for Azure Functions

This example shows how to generate embeddings for a raw text string.

[Function(nameof(GenerateEmbeddings_Http_RequestAsync))]
public async Task GenerateEmbeddings_Http_RequestAsync(
    [HttpTrigger(AuthorizationLevel.Function, "post", Route = "embeddings")] HttpRequestData req,
    [EmbeddingsInput("{rawText}", InputType.RawText, Model = "%EMBEDDING_MODEL_DEPLOYMENT_NAME%")] EmbeddingsContext embeddings)
{
    using StreamReader reader = new(req.Body);
    string request = await reader.ReadToEndAsync();

    EmbeddingsRequest? requestBody = JsonSerializer.Deserialize<EmbeddingsRequest>(request);

    this.logger.LogInformation(
        "Received {count} embedding(s) for input text containing {length} characters.",
        embeddings.Count,
        requestBody?.RawText?.Length);

    // TODO: Store the embeddings into a database or other storage.
}

This example shows how to retrieve embeddings stored at a specified file that is accessible to the function.

[Function(nameof(GetEmbeddings_Http_FilePath))]
public async Task GetEmbeddings_Http_FilePath(
    [HttpTrigger(AuthorizationLevel.Function, "post", Route = "embeddings-from-file")] HttpRequestData req,
    [EmbeddingsInput("{filePath}", InputType.FilePath, MaxChunkLength = 512, Model = "%EMBEDDING_MODEL_DEPLOYMENT_NAME%")] EmbeddingsContext embeddings)
{
    using StreamReader reader = new(req.Body);
    string request = await reader.ReadToEndAsync();

    EmbeddingsRequest? requestBody = JsonSerializer.Deserialize<EmbeddingsRequest>(request);
    this.logger.LogInformation(
        "Received {count} embedding(s) for input file '{path}'.",
        embeddings.Count,
        requestBody?.FilePath);

    // TODO: Store the embeddings into a database or other storage.
}

This example shows how to generate embeddings for a raw text string.

@FunctionName("GenerateEmbeddingsHttpRequest")
public HttpResponseMessage generateEmbeddingsHttpRequest(
    @HttpTrigger(
        name = "req", 
        methods = {HttpMethod.POST},
        authLevel = AuthorizationLevel.ANONYMOUS,
        route = "embeddings")
    HttpRequestMessage<EmbeddingsRequest> request,
    @EmbeddingsInput(name = "Embeddings", input = "{RawText}", inputType = InputType.RawText, model = "%EMBEDDING_MODEL_DEPLOYMENT_NAME%") String embeddingsContext,
    final ExecutionContext context) {

    if (request.getBody() == null)
    {
        throw new IllegalArgumentException("Invalid request body. Make sure that you pass in {\"rawText\": value } as the request body.");
    }

    JSONObject embeddingsContextJsonObject = new JSONObject(embeddingsContext);
    
    context.getLogger().info(String.format("Received %d embedding(s) for input text containing %s characters.",
            embeddingsContextJsonObject.getJSONObject("response")
                    .getJSONArray("data")
                    .getJSONObject(0)
                    .getJSONArray("embedding").length(),
            request.getBody().getRawText().length()));

    // TODO: Store the embeddings into a database or other storage.
    return request.createResponseBuilder(HttpStatus.ACCEPTED)
            .header("Content-Type", "application/json")
            .build();
}

This example shows how to retrieve embeddings stored at a specified file that is accessible to the function.

@FunctionName("GenerateEmbeddingsHttpFilePath")
public HttpResponseMessage generateEmbeddingsHttpFilePath(
    @HttpTrigger(
        name = "req", 
        methods = {HttpMethod.POST},
        authLevel = AuthorizationLevel.ANONYMOUS,
        route = "embeddings-from-file")
    HttpRequestMessage<EmbeddingsRequest> request,
    @EmbeddingsInput(name = "Embeddings", input = "{FilePath}", inputType = InputType.FilePath, maxChunkLength = 512, model = "%EMBEDDING_MODEL_DEPLOYMENT_NAME%") String embeddingsContext,
    final ExecutionContext context) {

    if (request.getBody() == null)
    {
        throw new IllegalArgumentException("Invalid request body. Make sure that you pass in {\"rawText\": value } as the request body.");
    }

    JSONObject embeddingsContextJsonObject = new JSONObject(embeddingsContext);
    
    context.getLogger().info(String.format("Received %d embedding(s) for input file %s.",
            embeddingsContextJsonObject.getJSONObject("response")
                    .getJSONArray("data")
                    .getJSONObject(0)
                    .getJSONArray("embedding").length(),
            request.getBody().getFilePath()));

    // TODO: Store the embeddings into a database or other storage.
    return request.createResponseBuilder(HttpStatus.ACCEPTED)
            .header("Content-Type", "application/json")
            .build();
}

Examples aren't yet available.

This example shows how to generate embeddings for a raw text string.

const embeddingsHttpInput = input.generic({
    input: '{rawText}',
    inputType: 'RawText',
    type: 'embeddings',
    model: '%EMBEDDING_MODEL_DEPLOYMENT_NAME%'
})

app.http('generateEmbeddings', {
    methods: ['POST'],
    route: 'embeddings',
    authLevel: 'function',
    extraInputs: [embeddingsHttpInput],
    handler: async (request, context) => {
        let requestBody: EmbeddingsHttpRequest = await request.json();
        let response: any = context.extraInputs.get(embeddingsHttpInput);

        context.log(
            `Received ${response.count} embedding(s) for input text containing ${requestBody.RawText.length} characters.`
        );
        
        // TODO: Store the embeddings into a database or other storage.

        return {status: 202}
    }
});

This example shows how to retrieve embeddings stored at a specified file that is accessible to the function.

const embeddingsFilePathInput = input.generic({
    input: '{filePath}',
    inputType: 'FilePath',
    type: 'embeddings',
    maxChunkLength: 512,
    model: '%EMBEDDING_MODEL_DEPLOYMENT_NAME%'
})

app.http('getEmbeddingsFilePath', {
    methods: ['POST'],
    route: 'embeddings-from-file',
    authLevel: 'function',
    extraInputs: [embeddingsFilePathInput],
    handler: async (request, context) => {
        let requestBody: EmbeddingsFilePath = await request.json();
        let response: any = context.extraInputs.get(embeddingsFilePathInput);

        context.log(
            `Received ${response.count} embedding(s) for input file ${requestBody.FilePath}.`
        );
        
        // TODO: Store the embeddings into a database or other storage.

        return {status: 202}
    }

This example shows how to generate embeddings for a raw text string.

Here's the function.json file for generating the embeddings:

{
  "bindings": [
    {
      "authLevel": "function",
      "type": "httpTrigger",
      "direction": "in",
      "name": "Request",
      "route": "embeddings",
      "methods": [
        "post"
      ]
    },
    {
      "type": "http",
      "direction": "out",
      "name": "Response"
    },
    {
      "name": "Embeddings",
      "type": "embeddings",
      "direction": "in",
      "inputType": "RawText",
      "input": "{rawText}",
      "model": "%EMBEDDING_MODEL_DEPLOYMENT_NAME%"
    }
  ]
}

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

using namespace System.Net

param($Request, $TriggerMetadata, $Embeddings)

$input = $Request.Body.RawText

Write-Host "Received $($Embeddings.Count) embedding(s) for input text containing $($input.Length) characters."

Push-OutputBinding -Name Response -Value ([HttpResponseContext]@{
        StatusCode = [HttpStatusCode]::Accepted
})

This example shows how to generate embeddings for a raw text string.

@app.function_name("GenerateEmbeddingsHttpRequest")
@app.route(route="embeddings", methods=["POST"])
@app.embeddings_input(arg_name="embeddings", input="{rawText}", input_type="rawText", model="%EMBEDDING_MODEL_DEPLOYMENT_NAME%")
def generate_embeddings_http_request(req: func.HttpRequest, embeddings: str) -> func.HttpResponse:
    user_message = req.get_json()
    embeddings_json = json.loads(embeddings)
    embeddings_request = {
        "raw_text": user_message.get("rawText")
    }
    logging.info(f'Received {embeddings_json.get("count")} embedding(s) for input text '
        f'containing {len(embeddings_request.get("raw_text"))} characters.')
    # TODO: Store the embeddings into a database or other storage.
    return func.HttpResponse(status_code=200)

Attributes

Apply the EmbeddingsInput attribute to define an embeddings input binding, which supports these parameters:

Annotations

The EmbeddingsInput annotation enables you to define an embeddings input binding, which supports these parameters:

Decorators

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

Configuration

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

Configuration

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