Generative AI Samples on Google Cloud

Welcome to the Python samples folder for Generative AI on Vertex AI! In this folder, you can find the Python samples used in Google Cloud Generative AI documentation.

If you are looking for colab notebook, then this link.

Getting Started

To try and run these Code samples, we have following recommend using Google Cloud IDE or Google Colab.

Note: A Google Cloud Project is a pre-requisite.

Feature folders

Browse the folders below to find the Generative AI capabilities you're interested in.

Python Samples Folder	Google Cloud Product	Short Description (With the help of Gemini 1.5)
Context Caching	https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-overview	Code samples demonstrating how to use context caching with Vertex AI's generative models. This allows for more consistent and relevant responses across multiple interactions by storing previous conversation history.
Controlled Generation	https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/control-generated-output	Examples of how to control the output of generative models, such as specifying length, format, or sentiment.
Count Token	https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/list-token	Code demonstrating how to count tokens in text, which is crucial for managing costs and understanding model limitations.
Embeddings	https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings	Code showing how to generate and use embeddings from text or images. Embeddings can be used for tasks like semantic search, clustering, and classification.
Extensions	https://cloud.google.com/vertex-ai/generative-ai/docs/extensions/overview	Demonstrations of how to use extensions with generative models, enabling them to access and process real-time information, use tools, and interact with external systems.
Function Calling	https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling	Examples of how to use function calling to enable generative models to execute specific actions or retrieve information from external APIs.
Grounding	https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overview	Code illustrating how to ground generative models with specific knowledge bases or data sources to improve the accuracy and relevance of their responses.
Image Generation	https://cloud.google.com/vertex-ai/generative-ai/docs/image/overview	Samples showcasing how to generate images from text prompts using models like Imagen.
Model Garden	https://cloud.google.com/vertex-ai/generative-ai/docs/model-garden/explore-models	Resources related to exploring and utilizing pre-trained models available in Vertex AI's Model Garden.
Model Tuning	https://cloud.google.com/vertex-ai/generative-ai/docs/models/tune-models	Code and guides for fine-tuning pre-trained generative models on specific datasets or for specific tasks.
RAG	https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/rag-api	Information and resources about Retrieval Augmented Generation (RAG), which combines information retrieval with generative models.
Reasoning Engine	https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/reasoning-engine	Details about the Reasoning Engine, which enables more complex reasoning and logical deduction in generative models.
Safety	https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-attributes	Examples of how to configure safety attributes and filters to mitigate risks and ensure responsible use of generative models.
System Instructions	https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/system-instructions?hl=en	Code demonstrating how to provide system instructions to guide the behavior and responses of generative models.
Text Generation	https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/send-chat-prompts-gemini	Samples of how to generate text using Gemini models, including chat-based interactions and creative writing.
Understand Audio	https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/audio-understanding	Examples of how to use generative models for audio understanding tasks, such as transcription and audio classification.
Understand Video	https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/video-understanding	Samples showcasing how to use generative models for video understanding tasks, such as video summarization and content analysis.

Contributing

Contributions welcome! See the Contributing Guide.

Getting help

Please use the issues page to provide suggestions, feedback or submit a bug report.

Disclaimer

This repository itself is not an officially supported Google product. The code in this repository is for demonstrative purposes only.