Summary
The video showcases a new application for asking natural language questions about PDFs, focusing on board game manuals like Monopoly and CodeNames. It explores advanced features of the RAG tutorial, emphasizing running RAG locally on your computer using open source LLMs for easy modification and validation of AI-generated responses. The quick demonstration highlights the app's ability to provide natural language responses based on PDF data sources, emphasizing the importance of original PDF data, document loaders for extraction, and unit testing for accuracy assessment.
Chapters
Introduction to RAG Tutorial
Advanced Features Introduction
Setting Up RAG Locally
Quality Validation of AI Responses
Demo of Completed App
Data Source and Embedding
Dependencies and Data Preparation
Document Loaders and Metadata
Splitting Documents and Embedding Function
Database Creation and Chunk ID Generation
Populating Database with Unique IDs
Unit Testing for Data Validation
Unit Test Execution and Evaluation
Introduction to RAG Tutorial
Introducing a new application for asking questions about PDFs using natural language. PDFs used include board game instruction manuals like Monopoly and CodeNames.
Advanced Features Introduction
Exploring advanced features of the RAG tutorial beyond basic functionalities.
Setting Up RAG Locally
Guidance on running RAG locally on your computer using open source LLMs, enabling modification and addition of information without rebuilding the entire database.
Quality Validation of AI Responses
Methods for validating the quality of AI-generated responses quickly.
Demo of Completed App
A quick demonstration of the completed app, showcasing the ability to ask questions about data sources and receive natural language responses.
Data Source and Embedding
Importance of original data source in PDF format, transformation into embeddings for query processing, and usage in prompts.
Dependencies and Data Preparation
Overview of dependencies and data requirements for the project, including the need for PDF documents as the data source for RAG application.
Document Loaders and Metadata
Utilization of document loaders for extracting data from PDF sources and the inclusion of metadata for effective data handling.
Splitting Documents and Embedding Function
Process of splitting documents into smaller chunks, creating embeddings, and utilizing embedding functions for database key generation.
Database Creation and Chunk ID Generation
Creation of a database, ensuring unique and deterministic chunk IDs for effective data retrieval.
Populating Database with Unique IDs
Adding chunks with unique IDs to the database, ensuring efficient data handling and updating.
Unit Testing for Data Validation
Introduction to unit testing for data validation in Python, including testing question-answer pairs for accuracy using LLM.
Unit Test Execution and Evaluation
Execution of unit tests to evaluate the application's response accuracy and understanding of expected versus actual responses.
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