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Building LLM Applications using LangChain

Chapter 1: Introduction to the LangChain Framework

The Motivation for a Framework

LangChain's Core Architecture

Setting Up Your Development Environment

Your First LangChain Application

Chapter 2: Models, Prompts, and Parsers

Interfacing with LLMs and Chat Models

Managing Prompts with PromptTemplates

Implementing Few-Shot Prompting

Structuring Output with Parsers

Hands-on Practical: Building a Structured Data Extractor

Chapter 3: Constructing Chains for Sequential Operations

Fundamentals of Chains

Using the Simple LLMChain

Creating Sequential Chains

Implementing Conditional Logic with Router Chains

Hands-on Practical: A Content Generation Pipeline

Chapter 4: Memory for Conversational Applications

The Importance of State in Conversations

Buffer Memory for Short-Term Recall

Summarization Memory for Long Conversations

Using Windowed and Token-Based Memory

Adding Memory to Chains and Agents

Practice: Building a Chatbot with Memory

Chapter 5: Data Connection for Retrieval Augmented Generation (RAG)

Architecture of a RAG System

Loading Data with Document Loaders

Splitting Documents for Processing

Vector Stores and Embeddings

Fetching Data with Retrievers

Building a Question-Answering Chain

Hands-on Practical: Q&A over Your Documents

Chapter 6: Developing Autonomous Agents

Introduction to Agents and Tools

The Agent, Tool, and Toolkit Architecture

Using Built-in Tools

Creating Custom Tools

Exploring Agent Types and Executors

Hands-on Practical: A Web-Searching Agent

Chapter 6: Developing Autonomous Agents

Previous chapters focused on constructing applications with pre-defined, sequential logic using chains. We now shift to a more dynamic model where a Large Language Model determines the sequence of actions required to accomplish a goal. This is the function of an agent. An agent uses an LLM as its reasoning engine to interact with an environment through a set of available tools.

This chapter introduces the components for building these systems. You will learn about the architecture of agents, tools, and toolkits. We will cover how to supply an agent with both built-in functionalities, such as web search, and custom tools for interacting with specific APIs. You will also examine different agent types and the AgentExecutor that runs the reasoning loop. The chapter culminates in building a practical agent that can find and synthesize information to answer complex queries.

Sections

6.1 Introduction to Agents and Tools
6.2 The Agent, Tool, and Toolkit Architecture
6.3 Using Built-in Tools
6.4 Creating Custom Tools
6.5 Exploring Agent Types and Executors
6.6 Hands-on Practical: A Web-Searching Agent

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