page_type	languages	products	urlFragment	name	description
sample	azdeveloper csharp bicep	azure azure-functions ai-services azure-cognitive-search	function-csharp-ai-textsummarize	Azure Functions - Text Summarization & Sentiment Analysis using AI Cognitive Language Service (C#-Isolated)	This sample shows how to take text documents as a input via BlobTrigger, does Text Summarization & Sentiment Score processing using the AI Congnitive Language service, and then outputs to another text document using BlobOutput binding. Deploys to Flex Consumption hosting plan of Azure Functions.

Azure Functions

Text Summarization & Sentiment Analysis using AI Cognitive Language Service (C#-Isolated)

This sample shows how to take text documents as a input via BlobTrigger, does Text Summarization processing using the AI Congnitive Language Service ExtractiveSummarize operations, then computes sentiment scores, and then outputs to another text document using BlobOutput binding. Deploys to Flex Consumption hosting plan of Azure Functions.

Run on your local environment

Pre-reqs

1. .NET 8 SDK
2. Azure Functions Core Tools
3. Azurite

The easiest way to install Azurite is using a Docker container or the support built into Visual Studio:

docker run -d -p 10000:10000 -p 10001:10001 -p 10002:10002 mcr.microsoft.com/azure-storage/azurite

4. Azure Developer CLI
5. Once you have your Azure subscription, run the following in a new terminal window to create all the AI Language and other resources needed:

azd provision

Take note of the value of TEXT_ANALYTICS_ENDPOINT which can be found in ./.azure/<env name from azd provision>/.env. It will look something like:

TEXT_ANALYTICS_ENDPOINT="https://<unique string>.cognitiveservices.azure.com/"

Alternatively you can create a Language resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource and view the Endpoint value.

5. Azure Storage Explorer or storage explorer features of Azure Portal
6. Add this local.settings.json file to the ./text_summarization folder to simplify local development. Fill in the TEXT_ANALYTICS_ENDPOINT value per step 4. This file will be gitignored to protectfrom committing to your repo.

{
    "IsEncrypted": false,
    "Values": {
        "AzureWebJobsStorage": "UseDevelopmentStorage=true",
        "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
        "TEXT_ANALYTICS_ENDPOINT": "<insert from step 4>"
    }
}

Using VS Code

1. Open the root folder in VS Code:

code .

2. Ensure local.settings.json exists already using steps above
3. Run and Debug by pressing F5
4. Open Storage Explorer, Storage Accounts -> Emulator -> Blob Containers -> and create a container unprocessed-text if it does not already exists
5. Copy any .txt document file with text into the unprocessed-text container
6. In the Azure extension of VS Code, open Azure:Workspace -> Local Project -> Functions -> summarize_function. Right-click and Execute Function now. At the command palette prompt, enter the path to the storage blob you just uploaded: unprocessed-text/<your_text_filename.txt>. This will simulate an EventGrid trigger locally and your function will trigger and show output in the terminal.

You will see AI analysis happen in the Terminal standard out. The analysis will be saved in a .txt file in the processed-text blob container.

Note, this newer mechanism for BlobTrigger with EventGrid source is documented in more detail here: https://learn.microsoft.com/en-us/azure/azure-functions/functions-event-grid-blob-trigger?pivots=programming-language-python#run-the-function-locally.

Deploy to Azure

The easiest way to deploy this app is using the Azure Developer CLI. If you open this repo in GitHub CodeSpaces the AZD tooling is already preinstalled.

To provision and deploy:

1. Open a new terminal and do the following from root folder:

azd up

Understand the Code

The main operation of the code starts with the summarize_function function in summarize_function.cs. The function is triggered by a Blob uploaded event using BlobTrigger, your code runs to do the processing with AI, and then the output is returned as another blob file simply by returning a value and using the BlobOutput binding.

[Function("summarize_function")]
[BlobOutput("processed-text/{name}-output.txt")]
public async Task<string> Run(
    [BlobTrigger("unprocessed-text/{name}", Source = BlobTriggerSource.EventGrid)] string myTriggerItem,
    FunctionContext context)
{
    var logger = context.GetLogger("summarize_function");
    logger.LogInformation($"Triggered Item = {myTriggerItem}");

    // Create client using Entra User or Managed Identity (no longer AzureKeyCredential)
    // This requires a sub domain name to be set in endpoint URL for Managed Identity support
    // See https://learn.microsoft.com/en-us/azure/ai-services/authentication#authenticate-with-microsoft-entra-id 
    var client = new TextAnalyticsClient(endpoint, new DefaultAzureCredential());

    // analyze document text using Azure Cognitive Language Services
    var summarizedText = await AISummarizeText(client, myTriggerItem, logger);
    logger.LogInformation(Newline() + "*****Summary*****" + Newline() + summarizedText);

    // Blob Output
    return summarizedText;
}

The AISummarizeText helper function does the heavy lifting for summary extraction and sentiment analysis using the TextAnalyticsClient SDK from the AI Language Services:

static async Task<string> AISummarizeText(TextAnalyticsClient client, string document, ILogger logger)
{
    // ...
    // Start analysis process.
    ExtractiveSummarizeOperation operation = client.ExtractiveSummarize(WaitUntil.Completed, batchInput);

    // View operation status.
    summarizedText += $"AnalyzeActions operation has completed" + Newline();
    summarizedText += $"Created On   : {operation.CreatedOn}" + Newline();
    summarizedText += $"Expires On   : {operation.ExpiresOn}" + Newline();
    summarizedText += $"Id           : {operation.Id}" + Newline();
    summarizedText += $"Status       : {operation.Status}" + Newline();

    // ...

    // Perform sentiment analysis on document summary
    var sentimentResult = await client.AnalyzeSentimentAsync(summarizedText);
    Console.WriteLine($"\nSentiment: {sentimentResult.Value.Sentiment}");
    Console.WriteLine($"Positive Score: {sentimentResult.Value.ConfidenceScores.Positive}");
    Console.WriteLine($"Negative Score: {sentimentResult.Value.ConfidenceScores.Negative}");
    Console.WriteLine($"Neutral Score: {sentimentResult.Value.ConfidenceScores.Neutral}");

    var summaryWithSentiment = summarizedText + $"Sentiment: {sentimentResult.Value.Sentiment}" + Newline();

    return summaryWithSentiment;
}