Master language learning with AI: LangChain + OpenAI + Python + Next JS

Table of Contents

1. Introduction
2. Building the Website
   • 2.1 Diagram of the Website
   • 2.2 Modules Used
   • 2.3 Step-by-Step Explanation
3. Building the Python Fast API Module
   • 3.1 Creating the Project Folder
   • 3.2 Creating a Virtual Environment
   • 3.3 Installing Dependencies
   • 3.4 Creating the First API
   • 3.5 Installing Dependencies to Call Open AI
4. Using Open AI and Land Chain Library
   • 4.1 Setting Up Open AI Key
   • 4.2 Installing Dependencies to Call Open AI
   • 4.3 Calling Open AI using Land Chain Library
5. Implementing the Translation Functionality
   • 5.1 Splitting the Article into Paragraphs
   • 5.2 Translating Each Paragraph using Open AI
   • 5.3 Formatting the Translation Result
6. Conclusion

Building a Website with Python and Open AI API

In this tutorial, we will learn how to use Python, the popular Open AI API, and the Land Chain library to Create a website where users can input an English article and receive translated learning materials in their native language. We will cover various steps, including building the web application using Nest.js, using the Python Fast API, calling Open AI, utilizing message queues, working with Docker, and deploying the website.

2. Building the Website

2.1 Diagram of the Website

To better understand the architecture of the website, let's take a look at the following diagram:

[Insert Diagram Image]

In this diagram, the user inputs an English article on the website, which is then passed to the backend of the application built with Nest.js. Next, the article is sent to the Python backend, which immediately returns a task ID to the front-end. The front-end refreshes the UI to indicate that the materials are being created, and the user can retrieve the result later. The Python backend stores the task in a message queue Based on the celery framework and Redis. A Celery worker picks up the task from the message queue and uses the Land Chain library to call the Open AI API to generate the translation materials. Once the Open AI API returns the result, it is stored in the database, allowing the front-end to retrieve and display the materials.

2.2 Modules Used

To build the website, we will be using several modules and libraries, including:

• Nest.js: A framework for building efficient and scalable web applications using Node.js and TypeScript.
• Python Fast API: A modern, fast (high-performance), web framework for building APIs with Python.
• Open AI: An API that provides access to sophisticated AI models for various natural language processing tasks.
• Land Chain: A Python library for interacting with the Open AI API.

2.3 Step-by-Step Explanation

In this tutorial, we will take You through the step-by-step process of building the website. Here is an Outline of the steps involved:

1. Introduction

   • Provide an overview of the tutorial and the objectives.

2. Building the Website

   • Explain the diagram of the website's architecture.
   • Discuss the modules and libraries used.
   • Provide a step-by-step explanation of the website building process.

3. Building the Python Fast API Module

   • Create the project folder.
   • Set up a virtual environment for isolating project dependencies.
   • Install the necessary dependencies, including the Python Fast API.
   • Create the first API endpoint.
   • Install dependencies to call the Open AI API.

4. Using Open AI and Land Chain Library

   • Set up the Open AI API key.
   • Install Relevant dependencies to call the Open AI API.
   • Demonstrate how to use the Land Chain library to call the Open AI API.

5. Implementing the Translation Functionality

   • Split the article into paragraphs using the text splitter module from the Land Chain library.
   • Translate each paragraph using the Open AI API.
   • Format the translation results to display them properly.

6. Conclusion

   • Summarize the tutorial.
   • Highlight the key points and takeaways.

Now, let's dive into each step of the process in Detail and build the website using Python and Open AI API.