Master the Art of Converting Unstructured to Structured Data

Table of Contents

1. Introduction
2. Understanding Open AI's Function Calling Feature
   1. What is Open AI's function calling feature?
   2. How does it work?
   3. Example of using function calling
3. Utilizing Function Calling for Structured Data Retrieval
   1. Benefits of returning structured data
   2. Building a resume scanner
   3. Scanning resumes for structured data
4. Setting Up the Application
   1. Installing dependencies and creating the environment
   2. Creating the UI with Streamlit
5. Uploading and Scanning Resumes
   1. Handling different file types
   2. Extracting text from PDF and Word documents
   3. Formatting the prompt using LinqChain
6. Calling Open AI's Function and Displaying Results
   1. Making the function call to Open AI
   2. Formatting and displaying the extracted data
7. Conclusion

Introduction

In this article, we will explore Open AI's function calling feature and how it can be used to retrieve structured data. We will specifically focus on building a resume scanner that can extract Relevant information from resumes in a structured way. Additionally, we will guide You through the process of setting up the application and utilizing the power of Streamlit to Create a user-friendly interface. So, let's dive in and uncover the possibilities offered by Open AI's function calling feature!

Understanding Open AI's Function Calling Feature

What is Open AI's function calling feature?

Open AI's function calling feature allows developers to describe a set of functions and send them as part of the API call. This feature is available in GPT 3.5 turbo and GPT 4 models. The models intelligently decide which function call to use Based on the task requested. It enables developers to retrieve structured data from Open AI's models more reliably.

How does it work?

When using Open AI's function calling feature, developers can define functions and their descriptions. The LLN (Language Learning Model) analyzes these descriptions to decide which function can best provide the answer. The response from Open AI includes a function call to the chosen function, with the arguments perfectly formatted in JSON. Although the LLN cannot directly call or run functions, it provides the necessary information to developers to call the function themselves and obtain the desired results.

Example of using function calling

Let's consider an example where you want to retrieve a list of flights from Hamburg, Germany to Madrid, Spain. You can create a function that takes a starting point, ending point, and date as arguments. Additionally, you can define another function that allows you to book a flight by providing the flight number and date. By including these function descriptions in your prompt, Open AI's LLN will intelligently choose the function call to use and provide a human-readable answer.

Utilizing Function Calling for Structured Data Retrieval

Benefits of returning structured data

Open AI's function calling feature not only enables the creation of intelligent agents but also opens up new possibilities for returning structured data. For instance, if you need to scan unstructured documents and store the extracted data in a structured way, you can define dummy functions that take structured JSON arguments. This way, Open AI will return the structured data in JSON format, allowing you to store and process it efficiently.

Building a resume scanner

Let's take a practical example of building a resume scanner using Open AI's function calling feature. The aim is to scan resumes in PDF or Word document formats, extract relevant information such as name, email, phone number, education, employment history, and skills, and return this information in structured JSON format. The scanner will provide a lightweight UI application where users can upload their resumes and receive the results in a structured manner.

Scanning resumes for structured data

To scan the resumes, we will create a dummy function called "scan_resume" that takes various properties as arguments. These properties include name, email, phone number, education, employment history, and skills. Each property is defined with the appropriate data Type and description. By formatting the prompt with the resume text and making the function call to Open AI, we can retrieve the structured data and display it to the user.

Setting Up the Application

Installing dependencies and creating the environment

To get started, we need to set up the application environment. We will use Anaconda's command line tool, "conda," to create a virtual environment and manage dependencies. The necessary dependencies to install include "docx2txt" for handling Word documents, "python-dotenv" for environment variables, and "streamlit" for creating the UI application.

Creating the UI with Streamlit

Streamlit is a powerful tool that allows us to create user interfaces for Python applications with ease. By importing the necessary libraries and creating a Streamlit app, we can develop a user-friendly interface for our resume scanner. The UI will include features such as resume upload, status update, and display of extracted details.

Uploading and Scanning Resumes

Handling different file types

Our application should be able to handle PDF and Word document formats. By providing a file uploader UI element and checking the file type, we can extract the text from the uploaded resumes. For PDFs, we use the "PyPDF2" library, while for Word documents, we use the "python-docx2txt" library.

Extracting text from PDF and Word documents

After obtaining the text from the uploaded resumes, we need to format it appropriately for the prompt. This is where the "LinqChain" library comes in handy. We create a template with a placeholder for the resume text and use the "format_prompt" method to replace the placeholder with the actual resume text.

Calling Open AI's Function and Displaying Results

Once the prompt is formatted, we can call Open AI's function using the "predict_messages" method. We pass the prompt and the array of functions as arguments. The response from Open AI includes the extracted data, which we can format and display to the user. Streamlit's container method allows us to present the results in a visually appealing and structured manner.

Conclusion

In this article, we explored Open AI's function calling feature and its potential for retrieving structured data. We learned how to utilize this feature by building a resume scanner application using Streamlit. By following the step-by-step guide, you can leverage the power of Open AI's function calling feature to create intelligent agents or extract structured data from unstructured documents. Get ready to unlock new possibilities with Open AI and streamline your data retrieval process!