Gemini 1.5 Pro API Tutorial: Getting Started With Google's LLM

Gemini 1.5 Pro: A Deep Dive into Google's Advanced Multimodal AI and its API

Google's Gemini 1.5 Pro represents a significant leap forward in AI, boasting long-context reasoning capabilities across text, video, and audio modalities. This tutorial guides you through connecting to and utilizing the Gemini 1.5 Pro API for tasks like retrieval, question answering, and in-context learning. For a broader understanding of the Gemini family, explore this resource: What is Google Gemini.

The Gemini Family: A Spectrum of Capabilities

The Gemini AI family comprises several generative AI models developed by Google Research and Google DeepMind. These models excel at diverse multimodal tasks, assisting developers with content creation and problem-solving. Each model variant is tailored for specific applications, optimizing performance across various scenarios. The family balances computational needs and functionality by offering three size tiers:

Model	Size	Capabilities	Ideal Use Cases
Gemini Ultra	Largest	Most capable; handles highly complex tasks	Demanding applications, large-scale projects, intricate problem-solving
Gemini Pro	Medium	Versatile; suitable for a wide range of tasks, scalable	General-purpose applications, adaptable to diverse scenarios, projects balancing power and efficiency
Gemini Nano	Smallest	Lightweight and efficient; optimized for on-device and resource-constrained environments	Mobile applications, embedded systems, tasks with limited computational resources, real-time processing

This tutorial focuses on Gemini 1.5 Pro, the inaugural model in the 1.5 series.

Gemini 1.5 Pro: Unprecedented Long-Context Understanding

Gemini 1.5 Pro's substantial context window (at least 10 million tokens) allows it to comprehend extensive contexts across various applications. Rigorous testing across long-dependency tasks demonstrates its exceptional capabilities. It achieved near-perfect recall (>99%) in "needle-in-a-haystack" scenarios, even with haystacks exceeding 10 million tokens. Gemini 1.5 Pro outperformed competitors, including those using external retrieval methods, especially on tasks requiring understanding interdependencies across vast amounts of content. Its ability to perform in-context learning, such as translating a new language from a single linguistic document, is also remarkable. This enhanced long-context performance doesn't compromise its inherent multimodal abilities; it significantly improved over its predecessor (Gemini 1.0 Pro) in various areas ( 28.9% in Math, Science, and Reasoning), even surpassing the Gemini 1.0 Ultra model in many benchmarks.

Data source.

For comprehensive details, refer to the technical report: “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context”.

Real-World Applications of Gemini 1.5 Pro

Gemini 1.5 Pro's capacity to process millions of tokens opens doors to innovative applications:

• Software Engineering: It can pinpoint specific code locations within massive codebases (e.g., identifying a core automatic differentiation method within the 746,152-token JAX codebase).
• Language Translation: It can translate between languages with limited online data, relying solely on provided context (e.g., translating from English to Kalamang using a grammar book and wordlist). This shows promise for preserving endangered languages.
• Image and Video Analysis: It can identify scenes within lengthy texts (e.g., locating a scene from Les Misérables based on a sketch) and videos (e.g., extracting information from a specific frame of "Sherlock Jr." and identifying scenes from sketches).

Connecting to the Gemini 1.5 Pro API: A Step-by-Step Guide

Let's explore how to access the power of Gemini 1.5 Pro through its API.

Step 1: Obtain an API Key

Navigate to the Google AI for Developers page (ensure you're logged in). Click "Get an API key" to generate one. You'll need to set up a project.

Step 2: Set up your Python Environment

Install the necessary Python package:

pip install google-generativeai

Import required libraries in your Jupyter Notebook:

import google.generativeai as genai
from google.generativeai.types import ContentType
from PIL import Image
from IPython.display import Markdown
import time
import cv2

Step 3: Make API Calls

Configure the API with your key:

GOOGLE_API_KEY = 'your-api-key-goes-here'
genai.configure(api_key=GOOGLE_API_KEY)

Check available models:

for m in genai.list_models():
    if 'generateContent' in m.supported_generation_methods:
        print(m.name)

Access Gemini 1.5 Pro:

model = genai.GenerativeModel('gemini-1.5-pro-latest')

Make a simple text prompt:

response = model.generate_content("Please provide a list of the most influential people in the world.")
print(response.text)

Gemini AI provides multiple response candidates; choose the best one.

Image Prompting with Gemini 1.5 Pro

Let's demonstrate image processing. Assume you have an image named "bookshelf.jpeg":

text_prompt = "List all the books and help me organize them into three categories."
bookshelf_image = Image.open('bookshelf.jpeg')
prompt = [text_prompt, bookshelf_image]
response = model.generate_content(prompt)
Markdown(response.text)

Conclusion

Gemini 1.5 Pro, with its extended context window and multimodal capabilities, offers a powerful tool for various applications. Its API provides the flexibility to work with diverse data types, making it a valuable asset for developers. To further your AI knowledge, consider this skill track: AI Fundamentals Skill Track.

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