Wissam Fawaz
This blog post introduces a LangChain-based, customizable application that interfaces with Large Language Models (LLMs). It guides the readers through setting up the necessary tools, obtaining an OpenAI API key, and the application's architecture, including the integration of Streamlit for user interaction. The application assists students in learning data structures by generating coding problems based on user-selected topics and difficulty levels. This example underscores the potential of LLMs in academia, showcasing their role in creating personalized and effective educational experiences.
LangChain is a cutting-edge open-source library designed to facilitate the development of sophisticated applications that seamlessly interact with Large Language Models (LLMs) like OpenAI's GPT series or the diverse open-source alternatives hosted on Hugging Face. In this blog post, we delve into a practical example that demonstrates how to develop an application leveraging OpenAI's GPT models. A key feature of this application is its customization. Users can choose a data structures topic and a difficulty level. The application, powered by OpenAI's models, then generates a coding problem tailored to these preferences.
1. Setting the Stage:
Before diving into the application development, there are a couple of essential preliminary steps to ensure everything runs smoothly:
- Installation of Necessary Libraries: The first step involves setting up your environment. This is done by installing the required libraries. Run the following command in your terminal or command prompt to install LangChain, OpenAI, Streamlit, and python-dotenv:
pip install langchain openai streamlit python-dotenv
This command ensures that all the necessary packages are downloaded and ready for use.
- Acquiring an OpenAI API Key: The next crucial step is obtaining an API key from OpenAI. This key is vital for your application to interact with OpenAI's models. You can generate this key by visiting OpenAI's API key platform. Follow the instructions to create a new key, which will be integrated into your application for authentication purposes. This key is what connects your application to OpenAI's powerful models, enabling it to process and respond to user prompts.
- Securing the API Key: Safely store the API key in a .env file using the following format:
OPENAI_API_KEY="sk-yourapikey”
2. Anatomy of the Application:
The primary goal of this application is to assist students in studying data structures. It achieves this by allowing students to input a specific data structures topic and choose a difficulty level for a coding question. The application then presents a question that aligns with these criteria. It is worthwhile noting that the source code of the application can be downloaded from my GitHub repo.
The application is split into two main files: langchain_helper.py and main.py. The former generates data structures problems along with their solutions, while the latter focuses on creating a user-friendly interface. This is where Streamlit, which is discussed next, comes into play.
3. Streamlit library:
Streamlit plays a vital role in application development, enabling the creation of a user-friendly web interface with minimum effort. The streamlit-based user interface allows users to input their preferences and displays the generated coding problems. Streamlit's simplicity and efficiency in transforming Python scripts into interactive web apps make it ideal for integrating the LangChain-powered backend with an intuitive front end that can be created in minutes.
The development of this application highlights the significant role of Large Language Models (LLMs) in academia. These models are revolutionizing educational tools, and offering personalized and interactive learning experiences. By generating tailored coding problems, they demonstrate the potential to improve learning outcomes in complex subjects like data structures. The use of open-source tools like LangChain and Streamlit further democratizes access to advanced technology, broadening educational opportunities. As these technologies evolve, they continue to reshape the educational landscape, promising a more engaging and effective learning environment.