Realtime Multimodal RAG Usecase Part 2 | MultiModal Summrizer | RAG Application #rag #multimodal #ai

Summary

In this YouTube video, Savita continues her multimodel RAG series, focusing on real-time use cases. She discusses extracting diverse data types from documents and creating summaries using models like GPT-4B. Savita outlines three solutions for multimodel RAG systems, including using embeddings for image-text retrieval and summarizing images and text for efficient data retrieval. The video guides viewers on implementing these solutions, emphasizing the complexity of deploying RAG systems in real-time scenarios.

Takeaways

• 😀 Savita is the host of a YouTube channel focused on multimodel RAG (Retrieval-Augmented Generation) systems.
• 📚 The video is a continuation of a series about real-time multimodel RAG use cases, following an introduction and first part of the case study.
• 🔍 The series covers the extraction of various types of data (images, tables, text) from documents using specific libraries and tools.
• 🛠 Savita demonstrates how to use the 'unstructured' library for data extraction from documents like PDFs and PPTs.
• 📈 She explains the process of creating a summary from extracted data, which is a crucial step before building a RAG system.
• 🤖 The video discusses the use of large language models (LLMs) like GPT-4B for generating summaries from images and text.
• 🔑 The importance of using multimodel embeddings (like CLIP) and vector databases for storing and retrieving data based on similarity is highlighted.
• 🔍 Savita outlines different solutions for multimodel RAG, including using embeddings for images and text, and summarizing data for retrieval.
• 📝 She provides a detailed walkthrough of the code used for summarizing tables and images, emphasizing the use of prompts and models.
• 🔗 The video script includes instructions for installing necessary libraries and setting up the environment for working with OpenAI's API.
• 🔚 The channel encourages viewers to watch the entire series for a comprehensive understanding of building and deploying RAG systems in real-time applications.

Q & A

What is the main topic of the video series presented by Savita?

The main topic of the video series is the implementation of a real-time multimodel retrieval augmented generation (RAG) system, covering various aspects from basic to advanced levels.

What type of data is extracted from documents in the RAG system discussed in the video?

The RAG system extracts various types of data from documents, including images, tables, and text.

What is the purpose of creating a summary from the extracted data in the RAG system?

Creating a summary from the extracted data helps to condense the information, making it easier to process and retrieve relevant data efficiently in the RAG system.

What are some of the challenges mentioned in deploying a RAG system in real-time?

Some challenges include the difficulty of integrating various data types and ensuring the system operates efficiently and accurately in real-time scenarios.

What is the role of the 'unstructured' library in the context of the video?

The 'unstructured' library is used for extracting data from different types of documents, such as PDFs and PPTs, by creating bounding boxes and fetching data based on those.

How does the video script describe the process of creating a summary from images and tables?

The process involves using models like GPT-4B to generate summaries from images and tables by passing the extracted data to the model, which then produces a concise summary.

What is the significance of using a multimodel embedding like CLIP in the first solution presented?

Using a multimodel embedding like CLIP allows for the combination of images and text into a single embedding, which can then be stored in a vector database for efficient similarity searches.

How does the video script differentiate between the second and third solutions for the RAG system?

The second solution suggests not keeping images if they are not required, while the third solution recommends keeping images and using a multimodel LLM to produce text summaries from them.

What is the role of the vector database in the RAG system?

The vector database stores the embeddings of the extracted data, which can then be used for similarity searches when a user queries the system.

What is the importance of the 'prompt' in generating summaries from images and tables?

The 'prompt' guides the model on how to generate the summary, providing instructions and context to the model so that it can produce a relevant and concise summary.

How does the video script suggest evaluating the RAG pipeline?

The script mentions evaluating the RAG pipeline by covering different techniques such as retrieval techniques, reranking techniques, and search and optimization techniques.