intro-llm-rag

intro-llm-rag

LLM Models and RAG Hands-on guide

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This repository serves as a comprehensive guide for technical teams interested in developing conversational AI solutions using Retrieval-Augmented Generation (RAG) techniques. It covers theoretical knowledge and practical code implementations, making it suitable for individuals with a basic technical background. The content includes information on large language models (LLMs), transformers, prompt engineering, embeddings, vector stores, and various other key concepts related to conversational AI. The repository also provides hands-on examples for two different use cases, along with implementation details and performance analysis.

README:

LLM Models and RAG Hands-on Guide

Welcome to the LLM Models and RAG Hands-on Guide repository! This guide is designed for technical teams interested in developing basic conversational AI solutions using Retrieval-Augmented Generation (RAG).

Introduction

This repository provides a comprehensive guide for building conversational AI systems using large language models (LLMs) and RAG techniques. The content combines theoretical knowledge with practical code implementations, making it suitable for those with a basic technical background.

Table of Contents

This guide is primarily for technical teams engaged in developing a basic conversational AI with RAG solutions. It offers a basic introduction to the technical aspects. This guide helps anyone with basic technical background to get involved in the AI domain. This guide combines between the theoretical, basic knowledge and code implementation. It's important to note that most of the content is compiled from various online resources, reflecting the extensive effort in curating and organizing this information from numerous sources.

Key Concepts

Conversational AI

An introduction to the technology behind conversational AI, covering its fundamentals and applications.

Large Language Models (LLMs)

Understand what LLMs are, how they work, and their role in conversational AI. This section also explores the differences between LLMs and transformers.

Transformers

Detailed explanation of transformers, including their pipelines and the Hugging Face library.

Prompt Engineering

Learn about different types of prompts, prompt engineering techniques, and best practices for using the OpenAI API.

Embeddings and Vector Stores

Explore the use of embeddings in LLMs, vector databases, and various chunking methods for document splitting.

Hands-on Examples

Use Case 1

Implementation details for the first use case, including benchmark results and performance analysis. Refer to the usecase-1 directory for code and documentation.

Use Case 2

A detailed walkthrough of integrating actions with a chatbot, such as getting weather event. See the usecase-2 directory for more information.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributors

Please feel free to contribute to enrich the content!

Contact

For any questions or feedback, please feel free to contact me directly @zahaby.


Happy coding!

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