Unstructured Data Preprocessing For Rag Apps & Llms - [New]

Master Unstructured Data with ViT, Metadata, Advanced Chunking, Hybrid Search, and RAG Techniques

What you'll learn

Master Unstructured Data Processing: Learn to efficiently extract, process, and normalize data from diverse document formats, including PDFs, PowerPoints

Implement Advanced Metadata Enrichment: Understand how to enrich documents with comprehensive metadata, enabling more accurate and relevant data retrieval

Apply Vision Models and Chunking Techniques: Gain practical skills in applying vision models like ViT and advanced chunking methods to manage, analyze

Build and Deploy Hybrid Search Engines: Develop and deploy hybrid search engines that combine content-based retrieval with metadata-driven queries

Requirements

Basic Programming Knowledge: Familiarity with programming concepts, particularly in Python and JavaScript, will help learners understand and apply the course content more effectively.

Familiarity with AI Concepts: A basic understanding of AI, LLMs, or machine learning will make it easier to grasp the data preprocessing and RAG concepts covered in the course.

Description

Unlock the power of unstructured data and elevate your AI-driven applications with this comprehensive course on transforming unstructured data into actionable insights using advanced techniques. Whether you’re a developer, data scientist, or AI enthusiast, this course will equip you with the skills to extract, process, and normalize content from diverse document formats—including PDFs, PowerPoints, Word files, HTML pages, tables, and images—making your data-ready for sophisticated RAG systems and Large Language Models (LLMs).In this hands-on course, you'll delve deep into the Unstructured Framework, a powerful tool for managing and normalizing unstructured data. I'd like you to learn how to enrich your documents with metadata, apply advanced chunking techniques, and use hybrid search methods to enhance your data retrieval and generation processes. With a focus on real-world applications, you’ll gain practical experience in preprocessing documents using vision models like ViT, extracting valuable information through table transformers, and seamlessly integrating these components into your RAG-powered applications.What You’ll Learn:Master the Unstructured Framework: Understand how to leverage the Unstructured Framework for handling and normalizing diverse data types, optimizing them for use in RAG systems and LLMs.Advanced Metadata Extraction: Learn to enrich your documents with comprehensive metadata, improving search accuracy and relevance in AI-driven applications.Implement Cutting-Edge Chunking Techniques: Apply advanced chunking methods to manage and process large datasets, ensuring efficient data handling and retrieval.Harness Hybrid Search Capabilities: Explore hybrid search techniques that combine metadata and content-based retrieval, boosting the performance of your query engines.Document Image Analysis with ViT: Utilize vision models like ViT and table transformers to analyze and preprocess document images, enhancing your ability to extract and utilize unstructured data.Why This Course?This course is designed for professionals who want to go beyond basic data processing and dive into advanced techniques for managing unstructured data in RAG systems. Through a series of practical projects, you’ll gain the expertise to build and deploy robust, scalable data engines that can handle complex queries and generate contextually relevant responses. Whether you’re looking to enhance your current skill set or explore new frontiers in AI-driven development, this course provides the knowledge and hands-on experience you need to succeed.Join us and master the art of transforming unstructured data into powerful, structured insights for your RAG systems and LLM applications!

Overview

Section 1: Introduction

Lecture 1 Introductions and What the Course is About and Prerequisites

Lecture 2 Course Structure

Section 2: Download Source Code

Lecture 3 Source code

Lecture 4 Course Slides

Section 3: Development Environment Setup

Lecture 5 Development Environment Setup - Overview

Lecture 6 Setup OpenAI API Account and API Key

Lecture 7 Setup the Unstructured Account and FREE API Key

Lecture 8 Unstructured Framework Test Run

Section 4: Data Preprocessing for LLMs - Deep Dive

Lecture 9 Data Preprocessing Deep Dive - Overview

Lecture 10 Data Preprocessing for LLMs Overview - Why Data Preprocessing is Hard

Lecture 11 Challenges with Unstructured Data

Lecture 12 How Content Extraction Works - Cleaning and Data Normalization

Lecture 13 Chunking and Structuring Data and Workflow Orchestration

Lecture 14 The Unstructured Framework - The Whole Workflow and Overview

Section 5: Check in

Lecture 15 Check in

Section 6: Hands-on: The Unstructured Framework - Preprocessing HTML, PDFs & PPTX Documents

Lecture 16 Hands-on: Preprocessing a PDF File and Dissecting the Extracted JSON Data

Lecture 17 Hands-on: Preprocessing a PPTX (PowerPoint) File

Lecture 18 Hands-on: Preprocessing an HTML File

Lecture 19 Benefits of Normalizing Content - Summary

Section 7: Chunking and Metadata Extraction

Lecture 20 Content Chunking and Metadata Extraction - Overview

Lecture 21 Finding Elements Associated with Chapters - Hands-on

Lecture 22 Semantic Similarity - Hybrid Search and Saving Documents to Vector Database

Lecture 23 Code Restructuring - Avoid Multiple Document Preprocessing

Lecture 24 Semantic Similarity Challenges - Information Recency Criteria

Lecture 25 Chunking for Document Elements and Benefits - Full Overview

Lecture 26 Chunking Document Content - Hands-on

Lecture 27 Summary

Section 8: Preprocessing Complex Documents - PDFs and Images

Lecture 28 Preprocessing Complex Documents - PDFs and Images - Overview

Lecture 29 Document Image Analysis Methods: Document Layout Detector and Visual Transformer

Lecture 30 Advantages and Disadvantages of ViT and DLD

Lecture 31 Preprocessing HTML and PDF files - Fast

Lecture 32 Preprocessing with Document Layout Detection and Comparing the Results

Lecture 33 Table Content Extraction - Hands-on

Lecture 34 Summarizing the Table Data with LangChain - Hands-on

Section 9: Build a RAG System Using Learned Techniques - Full Use Case

Lecture 35 Put it All Together - Build a RAG System Using What You've Learned - Overview

Lecture 36 Preprocessing a PDF File and Showing Tabular Content as Well - Part 1

Lecture 37 Filtering out References and Headers from PDF - Part 2

Lecture 38 Preprocess PPTX & MD File and Save Document Elements to Vector Database: Part 3

Lecture 39 Chat with Your Own Documents - PDF - Part 4

Lecture 40 Chat with Your Own Documents - MD and PPTX Documents - Final

Section 10: Wrap up

Lecture 41 What's Next

Developers and Programmers,Data Scientists and AI Enthusiasts who are looking to expand their knowledge of unstructured data processing, metadata enrichment, and the creation of Retrieval-Augmented Generation (RAG) systems.,Technical Professionals working in fields where data normalization, chunking, and hybrid search are critical, and who wish to implement robust solutions using the Unstructured framework and Vision Transformers (ViT).,AI and ML Practitioners who are interested in leveraging cutting-edge techniques to preprocess and manage diverse document formats, such as PDFs, PowerPoints, and HTML, for enhanced machine learning and LLM applications.