LangChain MasterClass: Build 15 LLM Apps with Python

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Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

17 hours 51 minutes
Sessions

On-Demand
Subtitles

English

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Langchain and Langgraph

Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the power of LangChain and large language models (LLMs) to create innovative applications. In this course, you'll learn to develop 15 real-world applications that integrate LLMs using Python, giving you hands-on experience with tools like OpenAI, Hugging Face, and LLAMA 2. You will start by understanding the fundamentals of LangChain and gradually move towards building sophisticated applications like chatbots, data analysis tools, resume screening apps, and more. The course is structured around practical projects that introduce key concepts in sequential modules. You'll explore topics like memory management, text embeddings, prompt engineering, and chain concepts. With each project, you'll master a unique feature of LangChain, such as implementing question-answering systems, conversational agents, and data processing tasks. By the end of the course, you'll have built a diverse portfolio of applications, including a support chatbot, invoice extraction bot, and a YouTube script generator. Whether you're looking to enhance your AI skills or jumpstart your career in AI development, this course will provide the tools, knowledge, and practical experience you need. This course is ideal for developers, data scientists, and AI enthusiasts looking to dive deeper into building language model-powered applications. It is suitable for those with a basic understanding of Python, and no prior experience with LangChain is necessary.

Syllabus

LangChain Introduction

In this module, we will introduce you to LangChain, an open-source framework designed to facilitate the creation of applications powered by large language models. We will discuss the objectives and benefits of LangChain and provide an overview of what you will gain from this course.

HuggingFace Introduction

In this module, we will introduce HuggingFace, explore how it works within LangChain, and guide you through the process of generating and utilizing access tokens to build powerful LLM applications.

OpenAI Introduction

In this module, we will delve into the foundational aspects of OpenAI, exploring its history and powerful language models like GPT-3 and Codex. Additionally, we will cover the practical steps to set up and utilize OpenAI's API for various applications.

Demo and Environment Setup

In this module, we will demonstrate a practical implementation of LangChain to improve question-answering capabilities and guide you through the installation of Anaconda, which is essential for our upcoming projects using Jupyter Notebooks.

LangChain - Models Module Concept

In this module, we will provide an overview of the different modules within LangChain, including models, prompts, memory, indexes, chains, agents, and callbacks, highlighting their functionalities and practical applications.

Project #1 - Simple Question and Answer App

In this module, we will walk you through the creation of a simple question-answering app using LangChain. You will explore different models, set up the project environment, and build an application that utilizes LangChain for effective AI-driven interactions.

Project #2 - Simple Conversational App

In this module, we will guide you through the development of a simple conversational app using LangChain. You will learn to configure the environment, set up the chatbot, and implement custom conversational contexts for an interactive user experience.

Project #3 - Find Similar Things App for Kids

In this module, we will explore word embeddings and their applications in machine learning projects. You will learn to implement text embeddings using LangChain and build an application that helps children understand similar words and objects.

LangChain - Prompt Module Concept and Implementation Using Python

In this module, we will delve into the prompts module in LangChain, learning how to design effective prompts, use prompt templates, and explore features like example selectors and output parsers to optimize the model's responses.

Project #4 - Marketing Campaign App

In this module, we will build a marketing campaign app using LangChain. You will learn to convert Jupyter Notebooks to Python scripts, develop a user interface with Streamlit, and integrate frontend and backend components for a seamless application.

LangChain - Memory Module Concept

In this module, we will focus on the memory modules in LangChain, highlighting the importance of conversation buffer memory and exploring different types of memory to enhance AI interactions by retaining and recalling past conversations.

Project #5 - ChatGPT Clone with Summarization Option

In this module, we will guide you through the creation of a ChatGPT clone with summarization capabilities. You will learn to set up the project environment, implement a conversational view, and enhance the chatbot with features like dynamic inputs and user API keys.

LangChain - Data Connection Module Concept

In this module, we will delve into the Data Connection module in LangChain, exploring its importance and functionality. You will learn to process data, create vector embeddings, and use retrievers to fetch relevant information efficiently.

Project #6 - Quiz MCQ Creator App

In this module, we will build an MCQ creator app using LangChain. You will learn to load documents, create embeddings, retrieve answers, and format the output in an MCQ format for practical applications.

LangChain - Chains Module Concept

In this module, we will explore the concept of chains within LangChain, learning how to connect different LLMs, implement generic and sequential chains, and utilize utility chains for simplifying complex tasks and saving development time.

LangChain - Agents Module Concept

In this module, we will explore the concepts of action agents and plan and execute agents within LangChain. You will learn how these agents make decisions and create plans, combining their functionalities for smarter and more efficient outcomes.

Project #7 - CSV Data Analysis Tool

In this module, we will create a data analysis tool that processes CSV files based on user queries. You will learn to set up the application, implement the UI, and use LangChain and OpenAI to analyze and generate responses from CSV data.

Project #8 - YouTube Script Writing Tool

In this module, we will develop a YouTube script-writing tool using LangChain. You will learn to set up the project environment, create the UI, and integrate LangChain to generate dynamic and engaging video scripts based on user inputs.

Project #9 - Support Chatbot for Your Website

In this module, we will guide you through the creation of a support chatbot for websites using LangChain. You will learn to implement data scraping, set up a user interface, and integrate frontend and backend components to provide natural language assistance on your website.

Project #10 - Automatic Ticket Classification Tool

In this module, we will build an automatic ticket classification tool using LangChain. You will learn to implement the UI, handle document uploads, and train a classification model to categorize tickets into different departments based on user queries.

Project #11 - HR - Resume Screening Assistance

In this module, we will create a resume screening assistance tool for HR using LangChain. You will learn to set up the project, analyze resumes, and match them with job descriptions, simplifying the screening process and enhancing HR efficiency.

LLAMA 2 Introduction

In this module, we will introduce you to the LLAMA 2 model, a powerful open-source tool offered by Meta. You will learn about its versions, parameter sizes, and potential applications in generative AI, along with the steps to download and set up LLAMA 2 for local use.

Project #12 - Email Generator Using LLAMA 2 || Streamlit App

In this module, we will guide you through the development of an email generator using LangChain and LLAMA 2. You will learn to set up the project, design the UI, and implement the LLAMA 2 model to generate dynamic and personalized email content.

Project #13 - Invoice Extraction Bot

In this module, we will build an invoice extraction bot using LangChain and LLAMA 2. You will learn to implement data extraction from PDF invoices, process the extracted data, and create a user interface for viewing and downloading the results.

Project #14 - Text to SQL Query - Helper Tool || Google Collab || LLAMA 2

In this module, we will create a text-to-SQL query helper tool using LangChain, Google Colab, and LLAMA 2. You will learn to set up the environment, implement pipelines for text prediction, and utilize LangChain to generate SQL queries based on user inputs.

Project #15 - Customer Care Call Summary Alert || OpenAI || Zapier NLA

In this module, we will build a customer care call summary alert tool using LangChain, OpenAI, and Zapier. You will learn to implement the UI and backend logic for processing call data, automate the summarization, and set up an email alert system for efficient customer care management.

Project #16 - Code Review Analyst App

In this project, we will guide you through the creation of a Code Review Analyst App. You'll build both the frontend and backend to analyze code quality, and integrate LangChain-powered LLMs to provide detailed, automated feedback on code structure and quality.