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.

Learning Outcomes

On completion of this course you will:

• 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

Course Outline

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

Intended Audience

The LangChain Fundamentals course is intended for those with a basic Python programming background and some experience with machine learning concepts who want to learn how to leverage language models for natural language processing tasks. The ideal attendees are technically-minded people who understand APIs and want to build text-based assistants, search tools, question-answering applications or other solutions that rely on processing written language. Some knowledge of statistics and neural networks would be beneficial but is not required.

Prerequisites

Those attending this course should meet the following:

• Basic programming experience in Python
• Familiarity with machine learning concepts like neural networks
• Experience installing applications and Python libraries
• Background in natural language processing use cases
• Basic understanding of APIs and how to use them