# GoogleCloudPlatform/generative-ai

Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform

Repository: https://github.com/GoogleCloudPlatform/generative-ai
Canonical: https://ross.abutalabs.com/products/generative-ai
Homepage: https://docs.cloud.google.com/gemini-enterprise-agent-platform/
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: generative-ai, llm, vertex-ai, langchain, gemini, gemini-api, vertex-ai-gemini-api, vertexai, google-gemini, google, google-cloud, gcp, gen-ai, large-language-models, agents
Last push: 2026-08-26T21:01:54+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 86
- inputs: {"age_days": 1216, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 17630, forks 4425 (observed 2026-08-28T04:11:20.086726+00:00)

## What it is
A collection of Jupyter notebooks, code samples, and sample apps demonstrating how to use generative AI on Google Cloud, including Gemini models, Vertex AI, RAG grounding, and the Gemini Enterprise Agent Platform. It serves as official example documentation for building LLM, search, vision, and audio workflows on GCP.

## Use cases
- learn how to call Gemini models from Python
- build a RAG pipeline on Vertex AI
- examples of function calling with Gemini
- generate images with Imagen or video with Veo
- build AI agents on Google Cloud
- get started with Vertex AI notebooks
- enterprise search with generative AI on GCP

## When to choose
- you are developing on Google Cloud / Vertex AI and want official working examples
- you want to learn Gemini API patterns like function calling, grounding, and RAG
- you need starter notebooks for multimodal generation (image, video, audio)

## When to avoid
- you need a production-ready library or framework rather than sample code
- you are not using Google Cloud or its AI services
- you want model-agnostic examples that run outside GCP

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-inference, rag, agent-framework, prompt-engineering, speech-recognition, image-processing
- domain: artificial-intelligence, large-language-models, cloud-computing, tutorials
- platform: python, cloud
- tags: gemini, vertex-ai, google-cloud, jupyter-notebooks, sample-code, langchain, generative-ai, imagen, veo, retrieval-augmented-generation, ai-agents, gpu

## Member repositories
- GoogleCloudPlatform/generative-ai (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:20.086726+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:02:35.503960+00:00, confidence not recorded.
  - readme: https://github.com/GoogleCloudPlatform/generative-ai (fetched 2026-08-28T04:11:20.086726+00:00, sha c72bc1516767)
  - homepage: https://docs.cloud.google.com/gemini-enterprise-agent-platform/ (fetched 2026-08-29T08:00:49.771619+00:00, sha fc61889969e1)
- Data as of 2026-08-30T08:39:29.467469+00:00.