Unleash the Power of Large Language Models Using LangChain

LangChain is an open-source framework that facilitates the integration of large language models (LLMs) to develop LLM-powered applications.

You’ll start with an introduction to LLMs and the LangChain framework. Next, you’ll learn how to utilize prompt templates to perform repetitive tasks and parse the output of an LLM. You’ll also learn about different types of chains with their use cases. Additionally, you’ll cover different memory types in LangChain to store the chat history. You’ll also learn how to connect LLMs to Google Search or any other external source of information through API using tools and agents. Finally, you‘ll get hands-on experience implementing the retrieval-augmented generation (RAG) technique for question answering within a document.

By the end of the course, you’ll have gained sufficient knowledge to develop LLM-powered applications using the LangChain framework to connect external information sources in real time without building everything from scratch.

1. An understanding of large language models and why they are preferred in natural language processing tasks
2. An understanding of the LangChain framework and how it improves over the functionalities of OpenAI
3. Familiarity with the core components of LangChain, including models, prompt templates, output parsers, chains, and memory
4. Hands-on experience implementing a chatbot that responds to queries in multiple languages using the router chain in LangChain
5. The ability to add tools in agents that connect to external information sources through APIs and use the reasoning and action (ReAct) technique
6. Working knowledge of the retrieval-augmented generation (RAG) technique to implement question answering from a document