llm-books

Introduction to the LLM-Books Project

The LLM-Books project offers a comprehensive guide for individuals interested in large language models (LLMs) and their practical applications. It centers around the new book titled "LangChain Programming from Introduction to Practice," now available for those keen on AI application development. This resource is ideal for learners aiming to deepen their understanding of AI technologies.

Key Features and Updates

Important Additions

The project has recently introduced significant updates with the inclusion of three new chapters:

1. LLM Application Evaluation and Testing: This section provides methodologies for assessing and testing applications developed using LLMs.
2. RAG Special Topic: Focuses on Retrieval-Augmented Generation, a technique for improving the quality of generated responses by utilizing retrieved information.
3. Interpretation of Domestic Model Providers' APIs: Offers insights into API functionalities from major domestic AI model providers.

Open Community

In response to popular demand, a development exchange group for LLM applications has been established, offering participants the opportunity to share insights and learn collaboratively.

Getting Started with Local Reading

To facilitate local access to the project's resources, users can create a GitBook image and run a local service. Although optional, building a custom Docker image is recommended. Alternatively, learners can use a pre-built image for convenience.

Docker Setup Instructions

1. Build GitBook Docker Image: After downloading the repository, execute the command docker build . -t <image:tag> to construct the image or use the provided image morso1/gitbook-server:3.2.3.
2. Run the GitBook Service:

   cd llm-book
   docker run --rm -v "$PWD/LLMProjects:/gitbook" -p 4000:4000 morso1/gitbook-server:3.2.3 gitbook serve
   

   Once executed, users can access the reading materials via port 4000 on their local machine.

Content Outline

Here's an overview of the comprehensive topics covered within the LLM-Books project:

Large Language Model Overview

• Provides a foundational understanding of large language models with topics ranging from ChatGPT to building a chatbot using OpenAI APIs.

LangChain Basics

• Introduces LangChain, with practical modules covering Chains, Agents, and Callbacks.

LlamaIndex Overview

• Guides on establishing a corporate knowledge base through LlamaIndex.

HuggingGPT Implementation

• Instructs on utilizing the HuggingFace Transformers library for multimodal task design and implementing HuggingGPT.

LLMOps Special Topic

• Discusses layers and aspects essential for operating LLMs more comprehensively, including model and prompt layers.

Agent Special Topic

• Continuously tracks agent projects while introducing multi-agent framework principles.

RAG Special Issue

• Detailed understanding of data indexing, retrieval, and generation stages using retrieval-augmented generation (RAG) techniques.

LLM Application Evaluation and Testing

• Strategies for evaluating large language models and assessing RAG system performance.

Domestic Model Providers API Interpretation

• Compares capabilities of six prominent domestic AI models and provides guidance on development using these models.

Generative AI Based on Large Language Models

• An introductory course in leveraging LLMs for generative tasks.

Reference Materials

• Includes recommended AI learning lists and prompts, amalgamating various course resources.

This project is an invaluable asset for individuals seeking to explore the world of large language models and related AI applications, complete with practical guides, resources, and community support to enhance the learning journey.