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
Implementing Few-Shot Prompting
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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- Language Models are Few-Shot Learners, Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei, 2020 NeurIPS DOI: 10.48550/arXiv.2005.14165 - This paper introduces in-context learning and demonstrates the effectiveness of few-shot prompting with large language models like GPT-3, which serves as the academic foundation for the technique.
- Few-Shot Prompt Templates, LangChain Team, 2024 - The official LangChain documentation offering practical guidance on using
FewShotPromptTemplateandExampleSelectorfor dynamic few-shot prompting. - Prompt engineering, OpenAI, 2023 - OpenAI's official guide to prompt engineering, covering principles and methods for interacting with LLMs, including how few-shot examples improve model performance and control.
- Embeddings, OpenAI, 2024 - This guide from OpenAI explains embeddings, their generation, and their use in tasks like semantic search and example selection, which is fundamental to
SemanticSimilarityExampleSelector.
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