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, Hugging Face, and local Ollama 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
- Newer Commercial LangChain Integrations That Are Not Covered In This Book
- Deprecated Book Examples
- LLM Hallucinations and RAG, Summarization, Structured Data Conversion, and Fact/Relationship Extraction LLM Applications
- Comparing LangChain and LlamaIndex
- 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
-
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
- OpenAI Model GPT-4o Example
- LangChain Overview Wrap Up
- Overview of LlamaIndex
- Extraction of Facts and Relationships from Text Data
- Using LLMs to Summarize Text
- LLM Techniques for Structured Data Conversion
- Retrieval Augmented Generation (RAG) Applications
- Using Google’s Knowledge Graph APIs With LangChain
- Using DBPedia and WikiData as Knowledge Sources
- Using LLMs To Organize Information in Our Google Drives
- Natural Language SQLite Database Queries With LangChain
- Examples Using Hugging Face Open Source Models
- Running Local LLMs Using Llama.cpp and LangChain
- Running Local LLMs Using Ollama
- Using Large Language Models to Write Recipes
- LangChain Agents
- Multi-prompt Search using LLMs, the Duckduckgo Search API, and Local Ollama Models
- More Useful Libraries for Working with Unstructured Text Data
- Book Wrap Up
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