UPDATE: The book has been re-published by Packt and is now available on official website of Packt, O'Reilly and other partners of Packt

Unlock the full potential of Generative AI with "LangChain in your Pocket", a hands-on guide that takes you through the robust LangChain framework. This book provides a step-by-step journey into creating powerful applications, from Auto-SQL and NER to custom Agents and Chains, integrating Memory, OutputParsers, RAG for Q&A, Few-Shot Classification, Evaluators, Autonomous AI agents, Advanced Prompt Engineering and many more.

NOTE: Drop an email to datasciencepocket@gmail.com with the transaction receipt for a free PDF version.

Key Features:

• Step-by-step code explanations with expected outputs for each solution.
• No prerequisites: If you know Python, you're ready to dive in.
• Practical, hands-on guide with minimal mathematical explanations.

Book Description:

Since the arrival of ChatGPT in late 2022, the AI landscape has evolved dramatically. "LangChain in your Pocket" invites you to move beyond ChatGPT and explore the versatility of LangChain, a Python/JavaScript framework at the forefront of Large Language Models (LLMs). Whether you're building Classification models, Storyteller, or Internet-enabled GPT, LangChain empowers you to do more.

This beginner-friendly introduction covers:

1. Basics of Large Language Models (LLMs) and why LangChain is pivotal.
2. Hello World tutorial for setting up LangChain and creating baseline applications.
3. In-depth chapters on each LangChain module.
4. Advanced problem-solving, including Multi-Document RAG, Hallucinations, NLP chains, and Evaluation for LLMs for supervised and unsupervised ML problems.
5. Dedicated sections for Few-Shot Learning, Advanced Prompt Engineering using ReAct, Autonomous AI agents, and deployment using LangServe.

Who should read it?

This book is for anyone keen on exploring AI, especially Generative AI. Whether you're a Software Developer, Data Scientist, Student or Content Writer, the focus on diverse use cases in LangChain and GenAI makes it equally valuable to all.

Table of Contents

• Introduction
• Hello World
• Different LangChain Modules
• Models & Prompts
• Chains
• Agents
• OutputParsers & Memory
• Callbacks
• RAG Framework & Vector Databases
• LangChain for NLP problems
• Handling LLM Hallucinations
• Evaluating LLMs
• Advanced Prompt Engineering
• Autonomous AI agents
• LangSmith & LangServe
• Additional Features