About this video

• Video Title: Text + Image (multimodal) RAG using Google Gemini and VertexAI !
• Channel: AI Tech Explained Right
• Speakers: None explicitly named
• Duration: 13:14

Overview

This video demonstrates how to implement multimodal Retrieval Augmented Generation (RAG) using Google Gemini and Vertex AI. It covers extracting and storing text and image data, performing searches with both text and image queries, and generating contextual answers by combining information from both modalities. The tutorial walks through setting up the environment, processing documents, creating a vector store in the cloud, and building a RAG chain to answer user queries based on multimodal input.

Key takeaways

• Multimodal RAG: The video explains how to perform RAG on documents containing both text and images, leading to richer and more comprehensive knowledge for LLMs and improving reasoning capabilities.
• Data Processing: It details the process of extracting text, images, and tables from documents, summarizing them for retrieval, and preparing them for storage.
• Cloud-based Vector Store: The tutorial demonstrates how to create and deploy a vector store on Google Cloud Platform (GCP) using Vertex AI, offering an alternative to local solutions.
• Multi-Vector Retriever: A multi-vector retriever is used to associate raw documents (text or image summaries) with their embeddings, enabling efficient retrieval of relevant multimodal information.
• RAG Chain Implementation: The video outlines the construction of a RAG chain that takes a user query, retrieves relevant documents (text and images), processes them into a suitable format, and feeds them to a multimodal LLM (Gemini) to generate a comprehensive answer.

The key steps involved in setting up a cloud-based vector store for multimodal data are:

1. Install necessary packages and restart the runtime.
2. Configure GCP (Google Cloud Platform) information, including project ID, location, and GCP bucket.
3. Initialize the Vertex AI SDK and complete any authentication steps.
4. Import required libraries.
5. Define model parameters, such as model name, output token limit, and embedding details.
6. Download the repository containing the data.
7. Extract text and image data from the documents, potentially using methods like partition_pdf.
8. Define a text splitter to break down text into manageable chunks.
9. Summarize text and images using an LLM, optimizing for retrieval.
10. Create an index for the data.
11. Deploy the index on GCP.
12. Create the vector store, specifying project ID, location, index name, index endpoint name, and the embedder to be used.
13. Populate the multi-vector retriever with documents and embeddings, and the doc store with document content (grouping text and images).