This workshop introduces advanced Python programming concepts through the creation and explanation of Retrieval-Augmented Generation (RAG) systems. Participants will learn the essential components of RAG, implement a system, and understand its applications in modern AI.
What participants will learn:
● Fundamentals of RAG systems and their applications
● Document processing and embedding creation techniques
● Working with vector databases (e.g., FAISS)
● Query processing and retrieval mechanisms
● Prompt engineering for effective RAG implementation
● Integration with Large Language Models (LLMs)
Workshop Program (1h15):
Introduction to RAG Systems (10 minutes)
○ Overview of RAG architecture
○ Applications and benefits

RAG System Schema (10 minutes)
○ Detailed walkthrough of the RAG pipeline
○ Components and their interactions

Document Processing (10 minutes)
○ Techniques for processing various document types
○ Preparing data for embedding

Embedding Creation (10 minutes)
○ Understanding embeddings
○ Implementing embedding models in Python

Vector Database Storage (10 minutes)
○ Introduction to FAISS
○ Storing and indexing embeddings

User Query Processing (5 minutes)
○ Applying the embedding model to user queries

Retrieval Mechanisms (10 minutes)
○ Similarity search techniques
○ Visualizing retrieval process with diagrams

Prompt Engineering (5 minutes)
○ Crafting effective prompts for RAG
○ Best practices and examples

LLM Integration (5 minutes)
○ Connecting RAG output to an LLM
○ Processing and returning responses

Q&A Session (10 minutes)
○ Answering participants' questions