How to build Multimodal Retrieval-Augmented Generation (RAG) with Gemini

Introduction

Welcome to this article on how to build Multimodal Retrieval-Augmented Generation (RAG) with Gemini. In this article, we will explain the concepts of retrieval augmented generation and how it can be used to enhance large language models (LLMs) with real-world knowledge. We'll also discuss the main components of RAG and explore different multimodal architectures that can be used.

Overview of RAG

Retrieval augmented generation (RAG) is a technique that combines large language models with retrieval-based components to enhance their knowledge and generate more accurate and tailored responses. Traditional large language models, while impressive, lack the ability to access real-world knowledge and specific domain expertise. RAG addresses this limitation by injecting external knowledge into the model's learning process.

RAG consists of three main components: vector embeddings, vector search, and large language models. Vector embeddings are numerical representations of data that capture semantic meaning. They turn unstructured data into machine-readable formats. Vector search allows for efficient retrieval of relevant information by comparing embeddings. Large language models, like Gemini, take input from vector search and generate human-readable responses by synthesizing retrieved information.

Multimodal RAG Architectures

Multimodal RAG expands the capabilities of RAG by incorporating different modalities of data, such as images, text, audio, and video. There are two common architectures for multimodal RAG.

1. Text-Based Embeddings: In this approach, all multimodal data is summarized into text using a model like Gemini. The text summaries are then turned into embeddings and stored in a vector database. When a user queries the system, vector search is performed on the text embeddings to retrieve relevant information. The response includes both the summaries and the raw images or text chunks.

2. Multimodal Embeddings: This approach uses embeddings to represent all modalities of data in the same semantic space. The multimodal data, including text, images, audio, and video, are turned into embeddings and stored in a vector database. Vector search is performed on the embeddings, and the top results are retrieved. A large language model like Gemini is then used to summarize the retrieved information.

Live Demo: Building Multimodal RAG with Gemini

In the live demo, we showcase how to build multimodal RAG using Gemini 1.5 Pro. We start by processing the source data, which includes a PDF manual of a car. We extract images and tables from the PDF and split the text into smaller chunks. We use Gemini 1.5 Pro to generate text summaries and embeddings for each component. These embeddings are then stored in a vector database.

Next, we set up the vector store using Google's Vertex AI Vector Store, which provides fast lookup for the embeddings. We define a multimodal search function that takes a text query as input, performs vector search, retrieves relevant data, and generates summaries using Gemini 1.5 Pro. The response includes both text summaries and corresponding images.

In the live demo, we demonstrate retrieving information about different car dashboard lights using text queries and images. We show how the system can provide accurate and tailored responses based on the retrieved data.

Keywords

multimodal, retrieval augmented generation, RAG, Gemini, large language models, vector embeddings, vector search, vector database, text-based embeddings, multimodal embeddings, live demo, Vector Store, Vertex AI, PDF manual, Fast lookup, Google Cloud Platform

FAQ

Q1: What is retrieval augmented generation (RAG)? A1: RAG is a technique that combines large language models with retrieval-based components to enhance their knowledge and generate more accurate responses.

Q2: How does RAG differ from traditional large language models? A2: Traditional large language models lack the ability to access real-world knowledge and specific domain expertise, while RAG enhances this capability by injecting external knowledge into the model's learning process.

Q3: What are the main components of RAG? A3: The main components of RAG are vector embeddings, vector search, and large language models. Vector embeddings capture semantic meaning, vector search allows for efficient retrieval of information, and large language models generate responses by synthesizing retrieved information.

Q4: What are the two common architectures for multimodal RAG? A4: The two common architectures for multimodal RAG are text-based embeddings and multimodal embeddings. Text-based embeddings summarize multimodal data into text and store them in a vector database, while multimodal embeddings represent all modalities of data in a shared semantic space.

Q5: How can RAG be applied to different industries? A5: RAG can be applied to various industries, such as technology, retail, and media and entertainment. It can enhance codebase migrations, provide personalized product recommendations, and assist with movie recommendations based on user preferences.

Q6: What tools can be used to implement RAG? A6: Google offers the Gemini API, which provides access to Gemini models for RAG. Additionally, Google Cloud Platform offers Vertex AI, which includes Vector Store for efficient storage and retrieval of embeddings.

These FAQs provide a quick summary of the key points discussed in the article. If you have any further questions, please feel free to reach out for more information.