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Explore how to leverage LangChain for LLM-powered applications. Gain insights into using prompt templates, chains, memory types, APIs, and retrieval-augmented generation techniques for dynamic app development.

FinalLangChain.tar.gz

LangChainRun

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.

Unleash the Power of Large Language Models Using LangChain

Follow step-by-step instructions to implement RAG architecture for question answering in LangChain.

Large Language Models

Introduction to LangChain

LangChain Basics

Advanced Features of LangChain

Wrapping Up

Hands-On: Question Answering with RAG