GSP1231

Overview

Gemini is a family of generative AI models developed by Google DeepMind that is designed for multimodal use cases. The Gemini API gives you access to the Gemini Pro Vision and Gemini Pro models.

Retrieval augmented generation (RAG) has become a popular paradigm for enabling LLMs to access external data and also as a mechanism for grounding to mitigate against hallucinations. RAG models are trained to retrieve relevant documents from a large corpus and then generate a response based on the retrieved documents. In this lab, you learn how to perform multimodal RAG where you perform Q&A over a financial document filled with both text and images.

Comparing text-based and multimodal RAG

Multimodal RAG offers several advantages over text-based RAG:

1. Enhanced knowledge access: Multimodal RAG can access and process both textual and visual information, providing a richer and more comprehensive knowledge base for the LLM.
2. Improved reasoning capabilities: By incorporating visual cues, multimodal RAG can make better informed inferences across different types of data modalities.

This lab shows you how to use RAG with the Vertex AI Gemini API, text embeddings, and multimodal embeddings, to build a document search engine.

Objectives

In this lab, you learn how to:

• Extract and store metadata of documents containing both text and images, and generate embeddings the documents.
• Search the metadata with text queries to find similar text or images.
• Search the metadata with image queries to find similar images.
• Using a text query as input, search for contextual answers using both text and images.

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab---remember, once you start, you cannot pause a lab.

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details panel.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details panel.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

Task 1. Open the notebook in Vertex AI Workbench

1. In the Google Cloud Console, on the Navigation menu, click Vertex AI > Workbench.

2. Find the instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance will open in a new browser tab.

Task 2. Set up the notebook

1. Click on the intro_multimodal_rag.ipynb file.

2. Run through the Getting Started and Import libraries sections of the notebook.

   • For Project ID, use , and for the Location, use .

Note: you can skip any notebook cells that are noted Colab only.

Click Check my progress to verify the objective. Install Vertex AI SDK for Python and import libraries.

In the following sections, you run through the notebook cells to see how to use the Gemini API to build a multimodal RAG system.

Task 3. Use the Gemini Pro model

The Gemini Pro (gemini-pro) model is designed to handle natural language tasks, multiturn text and code chat, and code generation. In this section, you download some helper functions needed for this notebook, to improve readability. You can also view the code (intro_multimodal_rag_utils.py) directly on Github.

1. In this task, run through the notebook cells to load the model and download the helper functions and get the documents and images from Cloud Storage.

Click Check my progress to verify the objective. Download image and documents from Cloud Storage.

Task 4. Build metadata of documents containing text and images

The source data that you use in this lab is a modified version of Google-10K which provides a comprehensive overview of the company's financial performance, business operations, management, and risk factors. As the original document is rather large, you use a modified version with only 14 pages instead. Although it's truncated, the sample document still contains text along with images such as tables, charts, and graphs.

1. In this task, run through the notebook cells to extract and store metadata of text and images from a document.

Note: The cell to to extract and store metadata of text and images from a document may take a few minutes to complete.

Click Check my progress to verify the objective. Extract and store metadata of text and images from a document.

Task 5. Text Search

Let's start the search with a simple question and see if the simple text search using text embeddings can answer it. The expected answer is to show the value of basic and diluted net income per share of Google for different share types.

1. In this task, run through the notebook cells to search for similar text and images with a text query.

Task 6. Image Search

Imagine searching for images, but instead of typing words, you use an actual image as the clue. You have a table with numbers about the cost of revenue for two years, and you want to find other images that look like it, from the same document or across multiple documents.

The ability to identify similar text and images based on user input, powered by Gemini and embeddings, forms a crucial foundation for the development of multimodal RAG systems, which explore in the next task.

1. In this task, run through the notebook cells to search for similar images with an image query.

Note: You may need to wait for a couple of minutes to get the score for this task.

Click Check my progress to verify the objective. Search similar image with image query.

Comparative Reasoning

Imagine we have a graph showing how Class A Google shares did compared to other things like the S&P 500 or other tech companies. You want to know how Class C shares did compared to that graph. Instead of just finding another similar image, you can ask Gemini to compare the relevant images and tell you which stock might be better for you to invest in. Gemini would then explain why it thinks that way.

1. In this task, run through the notebook cells to compare two images and find the most similar image.

Task 7. Multimodal retrieval augmented generation (RAG)

Let's bring everything together to implement multimodal RAG. You use all the elements that you've explored in previous sections to implement the multimodal RAG. These are the steps:

• Step 1: The user gives a query in text format where the expected information is available in the document and is embedded in images and text.
• Step 2: Find all text chunks from the pages in the documents using a method similar to the one you explored in Text Search.
• Step 3: Find all similar images from the pages based on the user query matched with image_description using a method identical to the one you explored in Image Search.
• Step 4: Combine all similar text and images found in steps 2 and 3 as context_text and context_images.
• Step 5: With the help of Gemini, we can pass the user query with text and image context found in steps 2 & 3. You can also add a specific instruction the model should remember while answering the user query.
• Step 6: Gemini produces the answer, and you can print the citations to check all relevant text and images used to address the query.

1. In this task, run through the notebook cells to perform multimodal RAG.

Note: You may need to wait for a couple of minutes to get the score for this task.

Click Check my progress to verify the objective. Print the citations to check all relevant text and images.

Congratulations!

In this lab, you've learned to build a robust document search engine using Multimodal Retrieval Augmented Generation (RAG). You learned how to extract and store metadata of documents containing both text and images, and generate embeddings for the documents. You also learned how to search the metadata with text and image queries to find similar text and images. Finally, you learned how to use a text query as input to search for contextual answers using both text and images.

Next steps / learn more

• Check out the Generative AI on Vertex AI documentation.
• Learn more about Generative AI on the Google Cloud Tech YouTube channel.
• Google Cloud Generative AI official repo
• Example Gemini notebooks

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Manual Last Updated July 17, 2024

Lab Last Tested July 17, 2024

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