In this module, we will guide you through setting up the essential tools needed for the course. This includes obtaining your OpenAI API key, installing Python, and configuring your Visual Studio Code environment with the necessary extensions. These steps are foundational for a smooth development process throughout the course.

In this module, we will dive deep into LangChain and LLMs, exploring their roles in AI development. You will learn the essential components of LangChain, such as chains and agents, and understand their interactions. We will also investigate the different types of language models available within LangChain and their applications.

In this module, we will introduce LangChain prompt templates and demonstrate their significance in improving AI interactions. You will gain practical experience by creating your own prompt templates, allowing for more dynamic and responsive AI communication within your projects.

In this module, we will explore LangChain parsers, tools that are critical for processing and interpreting AI-generated responses. You’ll learn about output parsers and their practical applications, as well as the Pydantic parser, which helps structure and validate outputs in AI systems.

In this module, we will explore LangChain’s memory capabilities and the importance of maintaining context in AI conversations. You will work hands-on with different types of LangChain chains, including LLMChain and Sequential Chain, to build dynamic applications like a lullaby generator using Streamlit.

In this module, we will examine LangChain routers, which enable the efficient distribution of tasks within an AI system. You will also learn how to load and split documents, making it easier to process large amounts of text. Hands-on exercises will help you become proficient with tools like CharacterTextSplitter and RecursiveCharacterTextSplitter.

In this module, we will explore vector stores and embeddings, focusing on their role in AI’s semantic analysis of text. You will engage in hands-on exercises to test semantic similarity, as well as learn how to save embeddings to Chroma DB for efficient information retrieval and analysis.

In this module, we will conduct a deep dive into LangChain agents, explaining their use in automating AI tasks. You will learn how to create tools for agents, explore various types of agents, and work hands-on with conversational agents and memory to improve their responsiveness and performance.

In this module, we will guide you through building the Bill Extractor app, which extracts data from PDFs. You will set up the core functionality, develop the front-end interface, and test the application to ensure it works smoothly, ready for use in real-world scenarios.

In this module, you will develop a fully functional Newsletter Generator app. You’ll set up the search functionality, curate the best articles, and generate informative newsletters. By the end of the module, you will also create a user-friendly front-end interface using Streamlit.

In this module, we will walk you through the creation of a multi-document chatbot that can analyze and answer questions about resumes. You will integrate LangChain’s QAChain for enhanced capabilities and learn how to deploy the chatbot with Streamlit for an engaging user interface.

In this module, we will explore the creation of an app that converts images into text and generates recipes. You will learn how to use HuggingFace for image captioning and implement text-to-speech functionality. Finally, you will integrate the features into a complete image-to-recipe application with Streamlit.

In this final module, we will recap everything you’ve learned in the course. We’ll also provide guidance on the next steps in your AI development journey, helping you stay on track and explore further learning opportunities with LangChain and LLMs.