Create a NLP microservice with Azure Functions in Python

Turn unstructured text into significant insights with the Azure Functions.

In today’s data-driven world, the ability to extract meaningful insights from unstructured text is essential. Natural Language Processing (NLP) is a powerful tool that allows us to understand and analyze human language, enabling us to turn raw text data into valuable insights.

In this story, we’ll walk you through the process of creating a NLP microservice using Azure Functions and Python, transforming unstructured text into actionable intelligence.

Setting the Stage: What are Azure Functions and NLP?

Azure Functions is a serverless compute service that enables you to run small pieces of code (functions) without worrying about the underlying infrastructure. It’s a perfect fit for creating lightweight, scalable, and cost-effective microservices.

Natural Language Processing, or NLP, is a subfield of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. By leveraging NLP techniques, we can extract valuable information from unstructured text data and perform tasks like sentiment analysis, topic modeling, and named entity recognition.

Let’s dive into the process of building a NLP microservice with Azure Functions in Python.

Step 1: Setting Up the Environment

Before we start building our microservice, we’ll need to set up our environment by following these steps:

1. Sign up for an Azure account if you haven’t already.
2. Install Azure Functions Core Tools and the Azure CLI.
3. Install Visual Studio Code and the Azure Functions extension.
4. Set up a Python environment with the necessary libraries, such as NLTK or SpaCy.

Step 2: Creating the Azure Function

Now that our environment is ready, we’ll create a new Azure Function:

1. Open Visual Studio Code and sign in to your Azure account.
2. Create a new Azure Functions project using the Python template.
3. Add a new HTTP trigger function to your project.

Step 3: Implementing the NLP Logic

With our Azure Function in place, it’s time to implement the NLP logic. We’ll be using the popular SpaCy library to perform named entity recognition:

1. Import the necessary libraries at the beginning of your function code:

import logging  
import azure.functions as func  
import spacy

2. Load the SpaCy model:

nlp = spacy.load("en_core_web_sm")

3. Process the input text and extract named entities:

def extract_entities(text):  
    doc = nlp(text)  
    entities = [{"text": ent.text, "label": ent.label_} for ent in doc.ents]  
    return entities

Step 4: Handling HTTP Requests

Now that we have our NLP logic in place, we need to handle incoming HTTP requests and return the extracted entities:

1. Parse the input text from the request:

req_body = req.get_json()  
text = req_body.get("text")

2. Call the extract_entities function:

entities = extract_entities(text

3. Return the extracted entities as a JSON response:

return func.HttpResponse(json.dumps(entities), mimetype="application/json")

Step 5: Deploying the Microservice

Finally, we’re ready to deploy our NLP microservice to Azure Functions:

1. Use the Azure Functions extension in Visual Studio Code to deploy your function app.
2. Test your function using the provided HTTP endpoint.

Conclusion

Congratulations! You’ve successfully created a NLP microservice using Azure Functions and Python. With this powerful tool in your arsenal, you can now turn unstructured text into significant insights, unlocking the true potential of your data.

The possibilities are endless — from sentiment analysis to topic modeling, NLP can help you uncover hidden patterns and make data-driven decisions with confidence.