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
Creating Sequential Chains
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- How to: Sequential Chains, LangChain Developers, 2024 (LangChain) - Official documentation describing how to use SimpleSequentialChain and SequentialChain in LangChain for building multi-step applications.
- Generative AI with Python and TensorFlow, Leonie Watson, Tom Nick, 2023 (O'Reilly Media) - A practical guide to building generative AI applications, including architectural patterns for combining LLM capabilities into workflows.
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, 2022 arXiv preprint arXiv:2201.11903 DOI: 10.48550/arXiv.2201.11903 - Introduces Chain-of-Thought prompting, a technique that allows large language models to solve complex problems by breaking them down into intermediate reasoning steps, which is conceptually similar to sequential operations.