Скачать или смотреть Intro to Context Caching with the Gemini API GSP1265

Overview
The Gemini API provides the context caching feature for developers to store frequently used input tokens in a dedicated cache and reference them for subsequent requests, eliminating the need to repeatedly pass the same set of tokens to a model. This feature can help reduce the number of tokens sent to the model, thereby lowering the cost of requests that contain repeat content with high input token counts. In this lab, you will learn how to use the Gemini API context caching feature in Vertex AI.

Vertex AI Gemini API
The Vertex AI Gemini API provides a unified interface for interacting with Gemini models:
Gemini 1.5 Pro model: Gemini 1.5 Pro is a new language model from the Gemini family. This model introduces a long context window of up to 1 million tokens that can seamlessly analyze large amounts of information. Additionally, it is multimodal with the ability to process text, images, audio, video, and code. Learn more about Gemini 1.5 Pro.
Gemini 1.5 Flash model: This smaller Gemini model is optimized for high-frequency tasks to prioritize the model's response time. This model has superior speed and efficiency with a context window of up to 1 million tokens for all modalities. Learn more about Gemini 1.5 Flash.

Prerequisites
Before starting this lab, you should have familiarity with the following concepts:
Basic understanding of Python programming
General knowledge of how APIs work
Running Python code in a Jupyter notebook in Vertex AI Workbench

Objectives
In this lab, you will learn how to:
Create a context cache
Retrieve and use a context cache
Use context caching in Chat
Update the expire time of a context cache
Delete a context cache
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