Build an application to send Chat Prompts using the Gemini model

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Overview
Objective
Working with Generative AI
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Overview

Labs are timed and cannot be paused. The timer starts when you click Start Lab.
The included cloud terminal is preconfigured with the gcloud SDK.
Use the terminal to execute commands and then click Check my progress to verify your work.

Objective

Generative AI on Vertex AI (also known as genAI or gen AI) gives you access to Google's large generative AI models so you can test, tune, and deploy them for use in your AI-powered applications. In this lab, you will:

Connect to Vertex AI (Google Cloud AI platform): Learn how to establish a connection to Google's AI services using the Vertex AI SDK.
Load a pre-trained generative AI model -Gemini: Discover how to use a powerful, pre-trained AI model without building one from scratch.
Send text to the AI model: Understand how to provide input for the AI to process.
Extract chat responses from the AI: Learn how to handle and interpret the chat responses generated by the AI model.
Understand the basics of building AI applications: Gain insights into the core concepts of integrating AI into software projects.

Working with Generative AI

After starting the lab, you will get a split pane view consisting of the Code Editor on the left side and the lab instructions on the right side. Follow these steps to interact with the Generative AI APIs using Vertex AI Python SDK.

Chat responses without using stream:

Streaming involves receiving responses to prompts as they are generated. That is, as soon as the model generates output tokens, the output tokens are sent. A non-streaming response to prompts is sent only after all of the output tokens are generated.

First we'll explore the chat responses without using stream.

Create a new file to get the chat responses without using stream:

Click File->New File to open a new file within the Code Editor.
Copy and paste the provided code snippet into your file.

import vertexai from vertexai.generative_models import GenerativeModel, ChatSession import logging from google.cloud import logging as gcp_logging # ------ Below cloud logging code is for Qwiklab's internal use, do not edit/remove it. -------- # Initialize GCP logging gcp_logging_client = gcp_logging.Client() gcp_logging_client.setup_logging() project_id = "{{{ project_0.project_id | "your-project-id" }}}" location = "{{{ project_0.default_region | "REGION" }}}" vertexai.init(project=project_id, location=location) model = GenerativeModel("gemini-1.0-pro") chat = model.start_chat() def get_chat_response(chat: ChatSession, prompt: str) -> str: logging.info(f'Sending prompt: {prompt}') response = chat.send_message(prompt) logging.info(f'Received response: {response.text}') return response.text prompt = "Hello." print(get_chat_response(chat, prompt)) prompt = "What are all the colors in a rainbow?" print(get_chat_response(chat, prompt)) prompt = "Why does it appear when it rains?" print(get_chat_response(chat, prompt))

Click File->Save, enter SendChatwithoutStream.py for the Name field and click Save.
Execute the Python file by clicking the triangle icon on the top-right corner of Code Editor or by running the below command inside the terminal within the Code Editor pane to view the output.

/usr/bin/python3 /home/student/SendChatwithoutStream.py

Code Explanation

The code snippet is loading a pre-trained AI model called Gemini (gemini-1.0-pro) on Vertex AI.
The code calls the get_chat_response method of the loaded Gemini model.
The input to the method is a text prompt.
The code uses Gemini's ability to chat. It uses the text provided in the prompt to chat.

Chat responses with using stream:

Now we'll explore the chat responses using stream.

Create a new file to get the chat responses with using stream:

Click File->New File to open a new file within the Code Editor.
Copy and paste the provided code snippet into your file.

import vertexai from vertexai.generative_models import GenerativeModel, ChatSession import logging from google.cloud import logging as gcp_logging # ------ Below cloud logging code is for Qwiklab's internal use, do not edit/remove it. -------- # Initialize GCP logging gcp_logging_client = gcp_logging.Client() gcp_logging_client.setup_logging() project_id = "{{{ project_0.project_id | "your-project-id" }}}" location = "{{{ project_0.default_region | "REGION" }}}" vertexai.init(project=project_id, location=location) model = GenerativeModel("gemini-1.0-pro") chat = model.start_chat() def get_chat_response(chat: ChatSession, prompt: str) -> str: text_response = [] logging.info(f'Sending prompt: {prompt}') responses = chat.send_message(prompt, stream=True) for chunk in responses: text_response.append(chunk.text) return "".join(text_response) logging.info(f'Received response: {response.text}') prompt = "Hello." print(get_chat_response(chat, prompt)) prompt = "What are all the colors in a rainbow?" print(get_chat_response(chat, prompt)) prompt = "Why does it appear when it rains?" print(get_chat_response(chat, prompt))

Click File->Save, enter SendChatwithStream.py for the Name field and click Save.
Execute the Python file by clicking the triangle icon on the top-right corner of Code Editor or by running the below command inside the terminal within the Code Editor pane to view the output.

/usr/bin/python3 /home/student/SendChatwithStream.py

Code Explanation

The code snippet is loading a pre-trained AI model called Gemini (gemini-1.0-pro) on Vertex AI.
The code calls the get_chat_response method of the loaded Gemini model.
The code is using stream=True while sending the messages. The stream=True argument indicates that the responses should be streamed back, allowing for real-time processing.
The code uses Gemini's ability to understand prompts and have a stateful chat conversation.

Try it yourself! Experiment with different prompts to explore Gemini's capabilities.

Click Check my progress to verify the objective.

Send the text prompt requests to Gen AI and receive a chat response

Congratulations!

You have completed the lab! Congratulations!!

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