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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

Building LLM Applications using LangChain

Prerequisites Intermediate Python experience

Level:

Intermediate

LangChain Fundamentals
Understand the core architecture and components of the LangChain framework for LLM application development.
Prompt Engineering
Construct dynamic and effective prompt templates to guide LLM outputs for various tasks.
Chains and Sequential Processing
Build and manage multi-step workflows by linking LLMs and other components into sequential chains.
Retrieval Augmented Generation (RAG)
Integrate LLMs with external data sources using document loaders, vector stores, and retrievers to build Q&A systems.
Conversational Memory
Implement stateful applications by adding different types of memory to manage conversation history.
Autonomous Agents
Develop agents that can use tools to interact with their environment, make decisions, and complete tasks.
Application Monitoring
Use LangSmith to trace, debug, and monitor the performance of your LangChain applications.

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