LangChain and LlamaIndex Projects Lab Book: Hooking Large Language Models Up to the Real World
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LangChain and LlamaIndex Projects Lab Book: Hooking Large Language Models Up to the Real World

Using GPT-3, ChatGPT, and Hugging Face Models in Applications.

About the Book

LangChain is a framework for hooking large language models like GPT-3 together, and adding processing steps that might be web search, database lookups, calling APIs, etc.

This is a tutorial on effectively using LLMs and a projects book that will provide you with ideas and projects to get you started. Most book examples require either an OpenAI or Hugging Face API keys (free tiers are sufficient).

About the Author

Mark Watson
Mark Watson

Mark Watson is a consultant specializing in deep learning, machine learning, knowledge graphs, and general artificial intelligence software development. He uses Common Lisp, Clojure, Python, Java, Haskell, and Ruby for development.

He is the author of 20+ published books on Artificial Intelligence, Deep Learning, Java, Ruby, Machine Learning, Common LISP, Clojure, JavaScript, Semantic Web, NLP, C++, Linux, and Scheme. He has 55 US Patents.

Mark's consulting customer list includes: Google, Capital One, Olive AI, CompassLabs, Disney, Sitescout.com, Embed.ly, and Webmind Corporation.

All of my Leanpub books are available to read for free on my web site:  https://markwatson.com/

Mark Watson

Episode 253

Table of Contents

    • Preface
      • About the Author
      • Book Cover
      • Acknowledgements
      • Requirements for Running and Modifying Book Examples
      • Issues and Workarounds for Using the Material in this Book
    • Large Language Model Overview
      • Big Tech Businesses vs. Small Startups Using Large Language Models
    • Getting Started With LangChain
      • Installing Necessary Packages
      • Creating a New LangChain Project
      • Basic Usage and Examples
      • Creating Embeddings
      • Using LangChain Vector Stores to Query Documents
      • LangChain Overview Wrap Up
    • Overview of LlamaIndex
      • Using LlamaIndex to Search Local Documents Using GPT-3
      • Using LlamaIndex for Question Answering from a List of Web Sites
      • LlamaIndex/GPT-Index Case Study Wrap Up
    • Using Google’s Knowledge Graph APIs With LangChain
      • Setting Up To Access Google Knowledge Graph APIs
    • Using DBPedia and WikiData as Knowledge Sources
      • Using DBPedia as a Data Source
      • Using Wikidata as a Data Source
    • Using LLMs To Organize Information in Our Google Drives
      • Setting Up Requirements.
      • Write Utility To Fetch All Text Files From Top Level Google Drive Folder
      • Generate Vector Indices for Files in Specific Google Drive Directories
      • Google Drive Example Wrap Up
    • Using Zapier Integrations With GMail and Google Calendar
      • Set Up Development Environment
      • Sending a Test GMail
      • Google Calendar Integration Example
    • Natural Language SQLite Database Queries With LangChain
      • Natural Language Database Query Wrap Up
    • Examples Using Hugging Face Open Source Models
      • Using LangChain as a Wrapper for Hugging Face Prediction Model APIs
      • Creating a Custom LlamaIndex Hugging Face LLM Wrapper Class That Runs on Your Laptop
    • Using Large Language Models to Write Recipes
      • Preparing Recipe Data
      • A Prediction Model Using the OpenAI text-davinci-002 Model
      • Cooking Recipe Generation Wrap Up
    • LangChain Agents
      • Overview of LangChain Tools
      • Overview of ReAct Library for Implementing Reading in LMS Applications
      • LangChain Agent Tool Example Using DBPedia SPARQL Queries
      • LangChain Agent Tools Wrap Up
    • More Useful Libraries for Working with Unstructured Text Data
      • EmbedChain Wrapper for LangChain Simplifies Application Development
      • Kor Library
    • Book Wrap Up

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