examples

Gemini API Examples

Table of contents

This is a collection of fun examples for the Gemini API.

• Agents and Automatic Function Calling: Create an agent (Barrista-bot) to take your coffee order.
• Video Analysis: Three notebooks using multimodal capabilities of the Gemini model to classify the species of animals for a video, summarize one or recognizing when it happened,
• Anomaly Detection: Use embeddings to detect anomalies in your datasets.
• Analyze a Video with Summarization: This notebook shows how you can use Gemini API's multimodal capabilities for video summarization.
• Apollo 11 - long context example: Search a 400 page transcript from Apollo 11.
• Clasify text with emeddings: Use embeddings from the Gemini API with Keras.
• Guess the shape: A simple example of using images in prompts.
• Market a Jet Backpack: Create a marketing campaign from a product sketch.
• Object detection: Extensive examples with object detection, including with multiple classes, OCR, visual question answering, and even an interactive demo.
• Opossum search: Code generation with the Gemini API. Just for fun, you'll prompt the model to create a web app called "Opossum Search" that searches Google with "opossum" appended to the query.
• Search Wikipedia with ReAct: Use ReAct prompting with Gemini 1.5 Flash to search Wikipedia interactively.
• Search Re-ranking with Embeddings: Use embeddings to re-rank search results.
• Story writing with prompt chaining.ipynb: Write a story using two powerful tools: prompt chaining and iterative generation.
• Talk to documents: This is a basic intro to Retrieval Augmented Generation (RAG). Use embeddings to search through a custom database.
• Upload files to Colab: This is a helper notebook that shows how to upload files from your local computer to Colab. Note: to upload files to the Gemini API (text, code, images, audio, video), check out the Files quickstart.
• Voice Memos: You'll use the Gemini API to help you generate ideas for your next blog post, based on voice memos you recorded on your phone, and previous articles you've written.
• Translate a public domain: In this notebook, you will explore Gemini model as a translation tool, demonstrating how to prepare data, create effective prompts, and save results into a .txt file.
• Working with Charts, Graphs, and Slide Decks: Gemini models are powerful multimodal LLMs that can process both text and image inputs. This notebook shows how Gemini 1.5 Flash model is capable of extracting data from various images.
• Entity extraction: Use Gemini API to speed up some of your tasks, such as searching through text to extract needed information. Entity extraction with a Gemini model is a simple query, and you can ask it to retrieve its answer in the form that you prefer.
• Generate a company research report using search grounding: Use search grounding to write a company research report with Gemini 1.5 Flash.

Integrations

• Personalized Product Descriptions with Weaviate: Load data into a Weaviate vector DB, build a semantic search system using embeddings from the Gemini API, create a knowledge graph and generate unique product descriptions for personas using the Gemini API and Weaviate.

Folders

• Prompting examples: A directory with examples of various prompting techniques.
• JSON Capabilities: A directory with guides containing different types of tasks you can do with JSON schemas.
• Automate Google Workspace tasks with the Gemini API: This codelabs shows you how to connect to the Gemini API using Apps Script, and uses the function calling, vision and text capabilities to automate Google Workspace tasks - summarizing a document, analyzing a chart, sending an email and generating some slides directly. All of this is done from a free text input.
• Langchain examples: A directory with multiple examples using Gemini with Langchain.

There are even more examples in the quickstarts folder and in the Awesome Gemini page.