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
Structuring Output with Parsers
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- Output Parsers, LangChain Contributors, 2024 (LangChain) - Official documentation for output parsers in LangChain, detailing their purpose and various implementations for structuring LLM responses.
- Pydantic Documentation, Samuel Colvin and Pydantic Contributors, 2024 - Official documentation for Pydantic, a Python library for data parsing, validation, and serialization, integral to PydanticOutputParser.
- Prompt Engineering Guide, Dair.ai, Shubham Saboo, and Contributors, 2024 - A comprehensive guide on prompt engineering techniques, including strategies for instructing Large Language Models to generate responses in structured formats.
- Tool use (Function calling), OpenAI, 2024 (OpenAI) - OpenAI's official guide on function calling, demonstrating a method for Large Language Models to produce structured JSON output for interacting with external tools and APIs.