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
Hands-on Practical: Q&A over Your Documents
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- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin, 2017 Advances in Neural Information Processing Systems 30 (NeurIPS) DOI: 10.5555/3295222.3295349 - The seminal paper introducing the Transformer architecture, which forms the basis for most modern large language models, and the specific document used in the practical.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Narsimha Chilkuri, Michael Grave, Pasquale Minervini, and Sebastian Riedel, 2020 Advances in Neural Information Processing Systems 33, Vol. 33 (NeurIPS) DOI: 10.5555/3495289.3495444 - Introduces the Retrieval Augmented Generation (RAG) architecture, which combines parametric and non-parametric memory, significantly enhancing knowledge-intensive natural language processing tasks.
- LangChain Python Library Documentation, LangChain Developers, 2024 - The official documentation for the LangChain Python library, offering comprehensive guides, tutorials, and API references for building LLM applications.
- OpenAI Embeddings Guide, OpenAI, 2024 (OpenAI) - The official guide from OpenAI explaining the concept of text embeddings and how to use their embedding models, which are central to vector search in RAG systems.