LangChain Fundamentals

Welcome to the exciting world of LangChain, a framework for developing tools like personal assistants, chatbots, document querying tools and many other use cases. In this comprehensive course, you will learn how to develop cutting-edge applications powered by language models. The power of chains lies in their ability to leverage the capabilities of different components to achieve a desired outcome. For example, imagine you’re building a hiking trip planner application. You can use a chain consisting of components like natural language processing, geolocation, and weather forecasting to provide personalized recommendations for hiking trails based on user preferences and current weather conditions. Similarly, in the financial industry, you can create an AI trading bot by linking together components like data analysis, market prediction, and automated trading execution. Whether you're a seasoned developer or just starting out, this course is designed to cater to all levels of expertise. We will explore the components of LangChain, starting with models, to mastering document retrievers, data loaders, and chains, where you’ll gain the core knowledge and skills necessary to build powerful applications that leverage language models. We will assemble LangChain components to create Question-Answering applications and tools for other use cases. We will also discuss how to deploy LangChain applications in production, and how to use tracing to visualize the execution of chains and agents. By the end of the course, you will have the skills and knowledge to create your own applications using LangChain. We will use Python as the basis for this course.

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