LangChain and LlamaIndex Projects Lab Book: Hooking Large Language Models Up to the Real World
LangChain and LlamaIndex Projects Lab Book: Hooking Large Language Models Up to the Real World
Using GPT-4, ChatGPT, and Hugging Face Models in Applications.
About the Book
LangChain is a framework for hooking large language models like GPT-4 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).
New examples (added 2024) include running local models using Ollama.
License
Translations
About the Author
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: a Simple RAG Application
- Example Using LangChain Integrations: Using Server APIs for Google Search
- LangChain Overview Wrap Up
- Overview of LlamaIndex
- Using LlamaIndex for Question Answering from a Web Site
- 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
- Running Local LLMs Using Llama.cpp and LangChain
- Installing Llama.cpp with a Llama2-13b-orca Model
- Python Example
- Running Local LLMs Using Ollama
- Simple Use of a local Mistral Model Using LangChain
- Minimal Example Using Ollama with the Mistral Open Model for Retrieval Augmented Queries Against Local Documents
- Wrap Up for Running Local LLMs Using Ollama
- Using Large Language Models to Write Recipes
- Preparing Recipe Data
- A Prediction Model Using the OpenAI text-embedding-3-large 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
- Preface
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