Learn OpenAI API and Build a Financial Data Extraction Tool

Table of Contents

1. Introduction
2. Setting up the OpenAI API
3. Creating the Financial Data Extraction Tool
4. Creating the User Interface with Streamlit
5. Extracting Financial Data from News Articles
6. Handling Different Inputs and Scenarios
7. Benefits and Applications of Using LLM for Data Extraction
8. Conclusion

Introduction

In this tutorial, we will be exploring how to use the OpenAI API to Create a financial data extraction tool. This tool will be able to extract key information such as company names, revenue, net income, and more from news articles related to companies and finance. We will be using the cutting-edge approach of using LLM (Language Model) for this task.

Setting up the OpenAI API

To get started, we will need to create an account on the OpenAI Website and obtain an API key. We will walk through the process of creating an account and generating API keys. The API key will allow us to make requests to the OpenAI API and access the chat completion functionality.

Creating the Financial Data Extraction Tool

In this section, we will begin coding the financial data extraction tool. We will import the necessary modules and set up the API key. We will also define a function to extract financial data from news articles using the OpenAI API. We will discuss the prompt structure and how to format the input to get the desired output.

Creating the User Interface with Streamlit

Next, we will create a user interface for our tool using Streamlit. Streamlit is a powerful framework that allows us to quickly build interactive data science applications. We will define the layout of our app, including a large text area for copying and pasting news articles, an extract button, and a table to display the extracted financial data.

Extracting Financial Data from News Articles

In this section, we will implement the functionality to extract financial data from news articles. We will connect the user interface to the extraction function and display the extracted data in a table format. We will also handle cases where certain data points are not available in the news article.

Handling Different Inputs and Scenarios

We will test our tool with different news articles and examine how it handles various inputs. We will also discuss potential challenges and limitations of the tool, as well as ways to improve its performance and accuracy. We will explore scenarios such as missing data points and different article formats.

Benefits and Applications of Using LLM for Data Extraction

In this section, we will discuss the benefits and applications of using LLM (Language Model) for data extraction tasks. We will explore how LLM can handle complex tasks and understand natural language inputs. We will also discuss the potential use cases and industries where LLM-Based data extraction can be applied.

Conclusion

To conclude, we have learned how to create a financial data extraction tool using the OpenAI API and LLM. This tool can extract key financial information from news articles, making it a valuable resource for financial analysts, researchers, and data scientists. With the power of LLM, we can automate the extraction process and save time and effort.