Introduction to Retrieval-Augmented Generation (RAG) using Langchain

Dive into the Future of AI with RAG!

Welcome to the groundbreaking course on Retrieval-Augmented Generation (RAG) using Langchain! This course is your gateway to mastering the innovative blend of generative AI and information retrieval, powering the next wave of AI applications.

"Just show me the money!" It is here: Course Details.

What Will You Learn?

Theoretical Foundations: Understand the evolution and mechanics of generative AI models.
Practical Skills: Get hands-on with Langchain and OpenAI's language models to create cutting-edge RAG applications.
Innovative Techniques: Learn how to integrate retrieval mechanisms with generative models for enhanced AI capabilities.

Course Modules:

Introduction to Generative AI
- Explore the history and development of AI models.
- Hands-on: Basic text generation using OpenAI.
Understanding Retrieval in AI
- Deep dive into various retrieval techniques.
- Practical: Building simple retrieval systems.
RAG Concepts and Applications
- Unveiling the fundamentals of RAG.
- Project: Designing a basic RAG application.
Basics of Langchain
- What is Langchain?
- Key Features of Langchain
- Practical: Basic Operations with Langchain
Implementing Retrieval with Langchain
- Retrieval in AI Systems
- Langchain's Retrieval Tools
- Real-World Applications
- Practical: Building a Retrieval System using Langchain's Custom Retrieval Logic
Integrating RAG in Langchain
- Modular Approach
- Support for Various Language Models
- Customizable Retrieval Mechanisms
- Project: Implementing a RAG-Like Model Using Langchain
Advanced RAG Techniques in Langchain
- Customizing Retrieval Sources
- Fine-Tuning Language Models
- Combining Multiple Retrieval Systems
- Practical: Implementing Advanced RAG Applications
Real-World Applications and Case Studies
- Search Engines and Information Retrieval
- Customer Service Chatbots
- Question Answering Systems
- Practical: Advanced RAG System for Educational Tools
Best Practices and Optimization for RAG Systems
- Performance Optimization
- Handling Large Data Sets
- Quality of Responses
- Practical: Implementing Caching Optimization Techniques in a RAG-Like System
- Practical: Time-Sensitive Retrieval in RAG Systems
- Practical: Contextual Understanding and Relevance Matching
Capstone Project
- Bringing together all concepts in a comprehensive RAG application.
- Final Project: Building an Advanced Multi-modal RAG-Based Application
- Practical: Building an Advanced Multi-modal System

Detailed Course Outline

For a comprehensive overview of the course, check out the Course Details.

Who Is This Course For?

AI Enthusiasts
Software Developers
Students and Researchers in AI
Anyone curious about the next big thing in AI!

Get Started

Clone this repo and follow the installation instructions to set up your learning environment.

git clone [repo-url]
cd [repo-name]
# follow setup instructions

Extracting Code Examples from Markdown

The command below will run and extract all code blocks from markdown files and save them in the examples directory.

python3 markdown_test.py

Join the AI Revolution with RAG!

Enroll now and be a part of the community shaping the future of AI with Retrieval-Augmented Generation. Let's explore the uncharted territories of AI together!

BenderScript/ragtime