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
Your First LangChain Application
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
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- LangChain Expression Language (LCEL), LangChain Developers, 2024 (LangChain) - Official documentation for LangChain Expression Language, detailing how to compose modular components into runnable chains.
- OpenAI API Reference: Models, OpenAI, 2024 (OpenAI) - Official reference for OpenAI's language models, including GPT-3.5 Turbo, used for understanding model capabilities and API interaction.
- LangChain Core Concepts, LangChain Developers, 2024 (LangChain) - Official overview of LangChain's core architectural concepts, including runnables, components like prompts, models, and parsers, and how they interact.
- Prompt engineering, OpenAI, 2024 (OpenAI) - Official guide from OpenAI on best practices for designing effective prompts to optimize model performance and responses.
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