Descriptions
RAG: Beyond Basics, The RAG: Beyond Basics course is designed to teach students how to build robust Chat with Documents applications using advanced Retrieval Augmented Generation (RAG) techniques and the latest Large Language Models (LLMs). The course covers both the theory and hands-on coding, starting with the basics of RAG pipelines and advancing to more complex techniques. No prior knowledge of RAG or Python is required, making this course accessible to SaaS founders, developers, and executives looking to upskill. By the end of the course, students will have a working RAG pipeline they can call their own.
What you’ll learn
- Leveling up with re-ranking strategies, query expansion, and more
- Using both proprietary and local models for building RAG systems
- Building a vanilla RAG setup and advancing to advanced techniques
- Hands-on coding sessions using cool tools like LangChain and Streamlit
Specificatoin of RAG: Beyond Basics
- Publisher : Prompt Engineering
- Teacher : Prompt Engineering
- Language : English
- Level : All Levels
- Number of Course : 27
- Duration : 2 hours and 40 minutes
Content of RAG: Beyond Basics
1 What is RAG Why we NEED it
2 Setting up Virtual Environment
3 Setting Up API Keys
4 Deep Dive into RAG Pipeline Structure
5 Demystifying Embedding Models and Vector Storage
6 Google Colab Setup
7 End-to-End RAG Pipeline – Code Time
8 End-to-End RAG Pipeline – Code Time
9 How Chunking Works
10 Focus on Parsing than Chunking
11 Chunk Size as Function of Text Embedding Models
12 The Retrieval in RAG
13 Putting Everything Together – 1st Iteration of RAG
14 RAG Advanced Techniques
15 Improving RAG with Re-ranking for Precise Information Retrieval – Part 1
16 Re-Ranking with GPT-4, ColBERT, and Cohere
17 Improving Information Retrieval with Query Expansion using LLMs
18 Enhancing Search with Hypothetical Documents Embedding Technique
19 Enhancing Document Retrieval with Ensemble Techniques
20 Hierarchical Chunking – Exploring the Parent Document Retriever
21 From Notebook to working Scripts
22 Creating Streamlit UI App
23 Private and local Chat with PDFs
24 The Recap
25 Contextual Retrieval – Adding Context to Your Chunks
26 Contextual Retrieval – Implementation
27 Multimodal RAG – Working with Images and Tables
Pictures
Sample Clip
Installation Guide
Extract the files and watch with your favorite player
Subtitle : Not Available
Quality: 1126p
Download Links
Downloadly
Rapidgator
Password file(s): www.downloadly.ir
File size
505 MB
