[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71858":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":39,"readmeContent":40,"aiSummary":41,"trendingCount":16,"starSnapshotCount":16,"syncStatus":42,"lastSyncTime":43,"discoverSource":44},71858,"generative-ai","GoogleCloudPlatform\u002Fgenerative-ai","GoogleCloudPlatform","Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform","https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002F",null,"Jupyter Notebook",17008,4260,285,61,0,19,45,188,57,"Apache License 2.0",false,"main",[25,26,27,28,29,5,30,31,32,33,34,35,36,37,38],"agents","gcp","gemini","gemini-api","gen-ai","google","google-cloud","google-gemini","langchain","large-language-models","llm","vertex-ai","vertex-ai-gemini-api","vertexai","2026-06-12 02:02:55","# Generative AI on Google Cloud\n\n> **[Gemini Enterprise Agent Platform](https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform)**, the latest evolution of Vertex AI, has been released!\n>\n> Check out the [`Google-Cloud-AI\u002Fagent-platform`](https:\u002F\u002Fgoo.gle\u002Fagent-platform-github) repository for a curated list of assets for agent building on Google Cloud.\n\n\u003C!-- markdownlint-disable MD033 -->\n\nThis repository contains notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage generative AI workflows using [Generative AI](https:\u002F\u002Fcloud.google.com\u002Fai\u002Fgenerative-ai) with [Agent Platform](https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform).\n\n## Intro Video\n\n[![What is Gemini Enterprise Agent Platform?](https:\u002F\u002Fimg.youtube.com\u002Fvi\u002Fj8qW5poBkEU\u002Fmaxresdefault.jpg)](https:\u002F\u002Fgoo.gle\u002Fagent-platform-video)\n\n\u003Ctable>\n  \u003Ctr>\n    \u003Cth>\u003C\u002Fth>\n    \u003Cth style=\"text-align: center;\">Description\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n      \u003Cimg src=\"https:\u002F\u002Fstorage.googleapis.com\u002Fgithub-repo\u002Fimg\u002Fgemini\u002FSpark__Gradient_Alpha_100px.gif\" width=\"45px\" alt=\"Gemini\">\n      \u003Cbr>\n      \u003Ca href=\"gemini\u002F\">\u003Ccode>gemini\u002F\u003C\u002Fcode>\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd>\n      Discover Gemini through starter notebooks, use cases, function calling, sample apps, and more.\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n      \u003Cimg src=\"https:\u002F\u002Fwww.gstatic.com\u002Fimages\u002Fbranding\u002Fgcpiconscolors\u002Fservice_discovery\u002Fv1\u002F24px.svg\" width=\"40px\" alt=\"Search\">\n      \u003Cbr>\n      \u003Ca href=\"search\u002F\">\u003Ccode>search\u002F\u003C\u002Fcode>\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd>Use this folder if you're interested in using \u003Ca href=\"https:\u002F\u002Fcloud.google.com\u002Fenterprise-search\">Agent Search\u003C\u002Fa>, a Google-managed solution to help you rapidly build search engines for websites and across enterprise data. (Formerly known as Enterprise Search on Generative AI App Builder).\u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n      \u003Cimg src=\"https:\u002F\u002Ffonts.gstatic.com\u002Fs\u002Fi\u002Fshort-term\u002Frelease\u002Fgooglesymbols\u002Fnature_people\u002Fdefault\u002F40px.svg\" alt=\"RAG Grounding\">\n      \u003Cbr>\n      \u003Ca href=\"rag-grounding\u002F\">\u003Ccode>rag-grounding\u002F\u003C\u002Fcode>\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd>Use this folder for information on Retrieval Augmented Generation (RAG) and Grounding. This is an index of notebooks and samples across other directories focused on this topic.\u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n      \u003Cimg src=\"https:\u002F\u002Ffonts.gstatic.com\u002Fs\u002Fi\u002Fshort-term\u002Frelease\u002Fgooglesymbols\u002Fimage\u002Fdefault\u002F40px.svg\" alt=\"Vision\">\n      \u003Cbr>\n      \u003Ca href=\"vision\u002F\">\u003Ccode>vision\u002F\u003C\u002Fcode>\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd>\n      Use this folder if you're interested in building your own solutions from scratch using features from Imagen and Veo.\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n      \u003Cimg src=\"https:\u002F\u002Ffonts.gstatic.com\u002Fs\u002Fi\u002Fshort-term\u002Frelease\u002Fgooglesymbols\u002Fmic\u002Fdefault\u002F40px.svg\" alt=\"Speech\">\n      \u003Cbr>\n      \u003Ca href=\"audio\u002F\">\u003Ccode>audio\u002F\u003C\u002Fcode>\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd>\n      Use this folder if you're interested in building your own solutions from scratch using features from Chirp, a version of Google's Universal Speech Model (USM).\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n      \u003Cimg src=\"https:\u002F\u002Ffonts.gstatic.com\u002Fs\u002Fi\u002Fshort-term\u002Frelease\u002Fgooglesymbols\u002Fbuild\u002Fdefault\u002F40px.svg\" alt=\"Setup Env\">\n      \u003Cbr>\n      \u003Ca href=\"setup-env\u002F\">\u003Ccode>setup-env\u002F\u003C\u002Fcode>\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd>Instructions on how to set up Google Cloud, the Gen AI Python SDK, and notebook environments on Google Colab and Workbench.\u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>\n      \u003Cimg src=\"https:\u002F\u002Ffonts.gstatic.com\u002Fs\u002Fi\u002Fshort-term\u002Frelease\u002Fgooglesymbols\u002Fmedia_link\u002Fdefault\u002F40px.svg\" alt=\"Resources\">\n      \u003Cbr>\n      \u003Ca href=\"RESOURCES.md\">\u003Ccode>RESOURCES.md\u003C\u002Fcode>\u003C\u002Fa>\n    \u003C\u002Ftd>\n    \u003Ctd>Learning resources (e.g. blogs, YouTube playlists) about Generative AI on Google Cloud.\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\u003C!-- markdownlint-enable MD033 -->\n\n## Related Repositories\n\n- ✨ [Agent Development Kit (ADK) Samples](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-samples): This repository provides ready-to-use agents built on top of the Agent Development Kit, designed to accelerate your development process. These agents cover a range of common use cases and complexities, from simple conversational bots to complex multi-agent workflows.\n- [🚀 Agent Starter Pack](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fagent-starter-pack)\n  - A collection of production-ready Generative AI Agent templates built for Google Cloud.\n  - It accelerates development by providing a holistic, production-ready solution, addressing common challenges (Deployment & Operations, Evaluation, Customization, Observability) in building and deploying Gen AI agents.\n- [Gemini Cookbook](https:\u002F\u002Fgithub.com\u002Fgoogle-gemini\u002Fcookbook\u002F)\n- [genai-factory](https:\u002F\u002Fgithub.com\u002FgoogleCloudPlatform\u002Fgenai-factory) - A collection of end-to-end infrastructure blueprints to deploy generative AI infrastructures in GCP, using IaC and following security best-practices.\n- [Google Cloud Applied AI Engineering](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fapplied-ai-engineering-samples)\n- [Vertex AI GenMedia Creative Studio](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fvertex-ai-creative-studio) - Experience Google's generative media foundational models + custom workflows.\n- [MCP Servers for GenMedia](https:\u002F\u002Fgoo.gle\u002Fvertex-genmedia-mcp) - Empower your agents with generative media tools.\n- [Generative AI for Marketing using Google Cloud](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgenai-for-marketing)\n- [Generative AI for Developer Productivity](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgenai-for-developers)\n- Vertex AI Core\n  - [Vertex AI Samples](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fvertex-ai-samples)\n  - [MLOps with Vertex AI](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fmlops-with-vertex-ai)\n  - [Developing NLP solutions with T5X and Vertex AI](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Ft5x-on-vertex-ai)\n  - [AlphaFold batch inference with Vertex AI Pipelines](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fvertex-ai-alphafold-inference-pipeline)\n  - [Serving Spark ML models using Vertex AI](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fvertex-ai-spark-ml-serving)\n  - [Sensitive Data Protection (Cloud DLP) for Vertex AI Generative Models (PaLM2)](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002FSensitive-Data-Protection-for-Vertex-AI-PaLM2)\n- Conversational AI\n  - [Contact Center AI Samples](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcontact-center-ai-samples)\n  - [Reimagining Customer Experience 360](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fdialogflow-ccai-omnichannel)\n- Document AI\n  - [Document AI Samples](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fdocument-ai-samples)\n- Gemini in Google Cloud\n  - [Cymbal Superstore](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcymbal-superstore)\n- Cloud Databases\n  - [Gen AI Databases Retrieval App](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgenai-databases-retrieval-app)\n- Other\n  - [ai-on-gke](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fai-on-gke)\n  - [ai-infra-cluster-provisioning](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fai-infra-cluster-provisioning)\n  - [solutions-genai-llm-workshop](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fsolutions-genai-llm-workshop)\n  - [terraform-genai-doc-summarization](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fterraform-genai-doc-summarization)\n  - [terraform-genai-knowledge-base](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fterraform-genai-knowledge-base)\n  - [genai-product-catalog](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgenai-product-catalog)\n  - [solutionbuilder-terraform-genai-doc-summarization](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fsolutionbuilder-terraform-genai-doc-summarization)\n  - [solutions-viai-edge-provisioning-configuration](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fsolutions-viai-edge-provisioning-configuration)\n  - [mis-ai-accelerator](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fmis-ai-accelerator)\n  - [dataflow-opinion-analysis](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fdataflow-opinion-analysis)\n  - [genai-beyond-basics](https:\u002F\u002Fgithub.com\u002Fmeteatamel\u002Fgenai-beyond-basics)\n  - [Gemini by Example](https:\u002F\u002Fgeminibyexample.com)\n\n## Contributing\n\nContributions welcome! See the [Contributing Guide](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgenerative-ai\u002Fblob\u002Fmain\u002FCONTRIBUTING.md).\n\n## Getting help\n\nPlease use the [issues page](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgenerative-ai\u002Fissues) to provide suggestions, feedback or submit a bug report.\n\n## Disclaimer\n\nThis repository itself is not an officially supported Google product. The code in this repository is for demonstrative purposes only.\n","该项目提供了在Google Cloud上使用生成式AI的示例代码和笔记本，基于Gemini企业代理平台。核心功能包括通过Jupyter Notebook展示如何开发、管理和使用生成式AI工作流，涵盖文本、搜索、视觉及语音等多个领域。技术特点涉及Vertex AI、大型语言模型（LLM）、检索增强生成（RAG）等先进技术。适用于希望利用Google Cloud资源快速构建和部署智能应用的企业开发者或研究团队，尤其是在需要高效处理自然语言理解、信息检索、图像识别以及语音交互等场景下。",2,"2026-06-11 03:38:58","high_star"]