Modular Rag and chat implementation from URLs, PDFs and txt files

The video showcases a modular automatic rag system that can scrape content from URLs and text/PDF files, split them into smaller parts, save chunks to JSON files, and provide search functionality. The system is designed to be customizable, transparent, and modular, allowing users to experiment with different strategies for text retrieval and generation to optimize performance for specific tasks.

In the video, the presenter introduces a modular automatic rag system that they have built, which includes functions for text splitting, embedding, and scraping content from given URLs. The system can read text and PDF files, chunk them into smaller parts, save these chunks to a JSON file, and obtain embeddings for them. It also allows for performing searches based on user queries and saving the top search results into another JSON file. The system is designed to be transparent and modular, with functions for various tasks such as fetching from URLs, loading text and PDF files, counting tokens, and calculating cosine similarity.

The presenter demonstrates the usage of the system by showing how it automatically scrapes content from URLs provided by the user, such as the analytical engine and quantum physics Wikipedia pages. The system retrieves the contents as text using Gina AI, saves them, and allows for searching based on user queries. The presenter showcases the ability to ask questions related to the content, with the system providing accurate answers based on the top search results. The top chunks retrieved during the search are saved into a separate JSON file for further analysis and experimentation.

The video emphasizes the modular nature of the system, allowing users to customize and experiment with different strategies for retrieval and generation. By changing parameters such as chunk sizes and embedding models, users can optimize the system’s performance for specific tasks. The presenter highlights the importance of experimenting with different configurations to achieve better results, such as switching from a small to a large embedding model to improve answer accuracy.

The video also touches on the benefits of becoming a patron, which includes access to code files, courses, and one-on-one connections with the presenter. The presenter walks through the code implementation of the system, explaining functions such as fetching text from URLs, loading PDF files, counting tokens, processing text, embedding chunks, and performing cosine similarity searches. The system is designed to handle various types of documents, split them into chunks, and provide assistance in retrieving relevant information based on user queries.

In conclusion, the presenter encourages viewers to explore the possibilities of the system and expand on its functionalities. The code files for the system will be available to patrons, and the presenter invites feedback and suggestions for further development. The video provides a comprehensive overview of the system’s capabilities, demonstrating its potential for automating text processing tasks, conducting searches, and generating responses based on user queries.