LangChain Fundamentals (3 days)

This 3-day course teaches you to develop applications using LangChain, a framework for building personal assistants, chatbots, and document querying tools powered by language models. You'll learn to assemble LangChain components including models, chains, agents, and data loaders to create Question-Answering applications. The course covers production deployment and tracing for debugging, using Python throughout.

Objectives

Appreciate the breadth of applications that may be built with LangChain
Understand the core concepts of LangChain, such as LLMs, Chains, and Agents
Know how to assemble LLM components into chains
Have gained insight into how to use LangChain for Question-Answering applications
Have created an application using LangChain
Understand how to perform deployments
How to use the tracing facilities to enable debugging of your chains

Prerequisites

A solid appreciation of AI technology

LangChain Use Cases

Demonstrations of a wide selection of LangChain use cases

LangChain Core Concepts

Understand the core concepts of LangChain, such as LLMs, Chains, and Agents
Installation and setup of LangChain
An introduction to LangChain and its features
An overview of the different components of LangChain (Prompt templates, LLMs, Chains, Agents, etc.)
How to use the generic interface to access different foundation models
How to manage prompts using Prompt Templates
Agentic Interactions

Assembling LLM components into Chains

Building applications with LLMs through composability

Creating an application using LangChain

Building an application from start to finish

Data Augmentation

Data-Awareness and how to use the central interface to access long-term memory, external data, and other LLMs
Data Augmented Generation, which involves using data to enhance the output of LLMs