{"id":15221804,"url":"https://github.com/googlecloudplatform/applied-ai-engineering-samples","last_synced_at":"2025-05-15T01:06:01.681Z","repository":{"id":201087622,"uuid":"683039317","full_name":"GoogleCloudPlatform/applied-ai-engineering-samples","owner":"GoogleCloudPlatform","description":"This repository compiles code samples and notebooks demonstrating how to use Generative AI on Google Cloud Vertex AI.","archived":false,"fork":false,"pushed_at":"2025-05-14T20:52:30.000Z","size":137561,"stargazers_count":729,"open_issues_count":13,"forks_count":192,"subscribers_count":46,"default_branch":"main","last_synced_at":"2025-05-14T21:48:35.872Z","etag":null,"topics":["generative-ai","google-cloud-platform","llms","vertex-ai"],"latest_commit_sha":null,"homepage":"https://googlecloudplatform.github.io/applied-ai-engineering-samples/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/GoogleCloudPlatform.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":"CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-08-25T13:01:20.000Z","updated_at":"2025-05-14T20:51:19.000Z","dependencies_parsed_at":"2023-11-09T02:23:13.213Z","dependency_job_id":"cdf92f4b-192a-4b1f-8c48-a4bf44f60553","html_url":"https://github.com/GoogleCloudPlatform/applied-ai-engineering-samples","commit_stats":null,"previous_names":["googlecloudplatform/gcp-genai-samples","googlecloudplatform/applied-ai-engineering-samples"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GoogleCloudPlatform%2Fapplied-ai-engineering-samples","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GoogleCloudPlatform%2Fapplied-ai-engineering-samples/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GoogleCloudPlatform%2Fapplied-ai-engineering-samples/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GoogleCloudPlatform%2Fapplied-ai-engineering-samples/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/GoogleCloudPlatform","download_url":"https://codeload.github.com/GoogleCloudPlatform/applied-ai-engineering-samples/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254254040,"owners_count":22039792,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["generative-ai","google-cloud-platform","llms","vertex-ai"],"created_at":"2024-09-28T15:07:42.915Z","updated_at":"2025-05-15T01:05:56.672Z","avatar_url":"https://github.com/GoogleCloudPlatform.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://googlecloudplatform.github.io/applied-ai-engineering-samples/\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003ccenter\u003e\n\u003cpicture\u003e\n  \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/GoogleCloudPlatform/applied-ai-engineering-samples/refs/heads/main/assets/aaie_logo_dark.png\"\u003e\n  \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/GoogleCloudPlatform/applied-ai-engineering-samples/refs/heads/main/assets/aaie_logo_light.png\"\u003e\n  \u003cimg alt=\"Shows an illustrated sun in light mode and a moon with stars in dark mode.\" src=\"https://user-images.githubusercontent.com/25423296/163456779-a8556205-d0a5-45e2-ac17-42d089e3c3f8.png\"\u003e\n\u003c/picture\u003e\n\u003c/center\u003e\n\n\u003cbr\u003e\n\n---\n\n\u003cbr\u003e\n\n\u003cdiv style=\"display: flex; justify-content: space-between;\"\u003e\n  \u003cdiv\u003e\n    \u003ca href=\"LICENSE\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"\u003e\n    \u003c/a\u003e\n  \u003c/div\u003e\n  \u003cdiv\u003e\n    \u003ca href=\"https://idx.google.com/import?url=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fapplied-ai-engineering-samples\"\u003e\n      \u003cpicture\u003e\n        \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://cdn.idx.dev/btn/open_light_32.svg\"\u003e\n        \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://cdn.idx.dev/btn/open_dark_32.svg\"\u003e\n        \u003cimg height=\"32\" alt=\"Export to IDX\" src=\"https://cdn.idx.dev/btn/open_purple_32.svg\"\u003e\n      \u003c/picture\u003e\n    \u003c/a\u003e\n  \u003c/div\u003e\n\u003c/div\u003e\n\n\n\n\n\n\n**Documentation**: \u003ca href=\"https://googlecloudplatform.github.io/applied-ai-engineering-samples\" target=\"_blank\"\u003ehttps://googlecloudplatform.github.io/applied-ai-engineering-samples/\u003c/a\u003e\n\n**Source Code**: \u003ca href=\"https://github.com/GoogleCloudPlatform/applied-ai-engineering-samples\" target=\"_blank\"\u003ehttps://github.com/GoogleCloudPlatform/applied-ai-engineering-samples\u003c/a\u003e\n\n---\n\n\u003cbr\u003e\n\nWelcome to the Google Cloud Applied AI Engineering repository. This repository contains reference guides, blueprints, code samples, and hands-on labs developed by the Google Cloud Applied AI Engineering team.\n\n\u003cbr\u003e\n\n## Applied AI Engineering: Catalog\n\n### [Generative AI on Vertex AI](./genai-on-vertex-ai/README.md)\n\nThis section contains code samples and hands-on labs demonstrating the use of [Generative AI models and tools in Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview).\n\n\u003ctable\u003e\n\n  \u003ctr\u003e\n    \u003cth style=\"text-align: center;\"\u003eFoundation Models\u003c/th\u003e\n    \u003cth style=\"text-align: center;\"\u003eEvaluation\u003c/th\u003e\n    \u003cth style=\"text-align: center;\"\u003eRAG \u0026 Grounding\u003c/th\u003e\n    \u003cth style=\"text-align: center;\"\u003eAgents\u003c/th\u003e\n    \u003cth style=\"text-align: center;\"\u003eOthers\u003c/th\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd style=\"vertical-align: baseline;\"\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"./genai-on-vertex-ai/gemini/prompting_recipes/\"\u003eGemini Prompting Recipes\u003c/a\u003e\u003c/li\u003e\n    \u003c/ul\u003e\n    \u003c/td\u003e\n    \u003ctd style=\"vertical-align: baseline;\"\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"./genai-on-vertex-ai/vertex_evaluation_services/\"\u003eVertex GenAI Evaluation\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"./genai-on-vertex-ai/gemini/evals_playbook/\"\u003eGemini Evals Playbook\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/td\u003e\n    \u003ctd style=\"vertical-align: baseline;\"\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"./genai-on-vertex-ai/vertex_ai_search/\"\u003eVertex AI Search\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"./genai-on-vertex-ai/retrieval_augmented_generation/\"\u003eRetrieval Augmented Generation\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/td\u003e\n  \u003c/tr\u003e\n\n\u003c/table\u003e\n\n### [Google Cloud AI/ML infrastructure](./ai-infrastructure/README.md)\n\nThis section has reference guides and blueprints that compile best practices, and prescriptive guidance for running large-scale AI/ML workloads on Google Cloud AI/ML infrastructure.\n\n### [Research Operationalization](./research-operationalization/)\n\nThis section has code samples demonstrating operationalization of latest research models or frameworks from Google DeepMind and Research teams on Google Cloud including Vertex AI.\n\n### [Solutions Catalog](https://cloud.google.com/use-cases/generative-ai)\n\nIn addition to code samples in this repo, you may want to check out the following solutions published by Google Cloud Applied AI Engineering.\n\n\u003ctable\u003e\n\n  \u003ctr\u003e\n    \u003cth style=\"text-align: center;\"\u003eSolution\u003c/th\u003e\n    \u003cth style=\"text-align: center;\"\u003eDescription\u003c/th\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd style=\"text-align: center;\"\u003e\n      \u003cimg src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/Open_Data_QnA/main/utilities/imgs/opendataqna_logo.png\" alt=\"flag\" height=\"50\" width=\"80\"\u003e\n      \u003cbr\u003e\n      \u003ca href=\"https://github.com/GoogleCloudPlatform/Open_Data_QnA\"\u003eOpen Data Q\u0026A\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n      The Open Data QnA python solution enables you to chat with your databases by leveraging LLM Agents on Google Cloud. The solution enables a conversational approach to interact with your data by implementing state-of-the-art NL2SQL / Text2SQL methods.\n    \u003c/td\u003e\n  \u003c/tr\u003e\n\n  \u003ctr\u003e\n    \u003ctd style=\"text-align: center;\"\u003e\n      \u003cimg src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/genai-for-marketing/main/app/images/architecture.png\" alt=\"flag\" height=\"50\" width=\"80\"\u003e\n      \u003cbr\u003e\n      \u003ca href=\"https://github.com/GoogleCloudPlatform/genai-for-marketing\"\u003eGenAI for Marketing\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n      Showcasing Google Cloud's generative AI for marketing scenarios via application frontend, backend, and detailed, step-by-step guidance for setting up and utilizing generative AI tools, including examples of their use in crafting marketing materials like blog posts and social media content, nl2sql analysis, and campaign personalization.\n    \u003c/td\u003e\n  \u003c/tr\u003e\n\n  \u003ctr\u003e\n    \u003ctd style=\"text-align: center;\"\u003e\n      \u003cimg src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/customer-experience-modernization/refs/heads/main/frontend/src/assets/architectures/p1_uj_1_2.svg\" alt=\"flag\" height=\"50\" width=\"80\"\u003e\n      \u003cbr\u003e\n      \u003ca href=\"https://github.com/GoogleCloudPlatform/customer-experience-modernization\"\u003eGenAI for Customer Experience Modernization\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n      This solution shows how customers can have modern, engaging interactions with brands, and companies can improve the end user, agent, and customer experiences with a modern customer service platform on Google Cloud. \n    \u003c/td\u003e\n  \u003c/tr\u003e\n\n  \u003ctr\u003e\n    \u003ctd style=\"text-align: center;\"\u003e\n      \u003cimg src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-creative-studio/main/screenshots/creative_studio_02.png\" alt=\"flag\" height=\"50\" width=\"80\"\u003e\n      \u003cbr\u003e\n      \u003ca href=\"https://github.com/GoogleCloudPlatform/vertex-ai-creative-studio\"\u003eCreative Studio | Vertex AI\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n      Creative Studio is a Vertex AI generative media example user experience to highlight the use of Imagen and other generative media APIs on Google Cloud.\n    \u003c/td\u003e\n  \u003c/tr\u003e\n\n\n  \u003ctr\u003e\n    \u003ctd style=\"text-align: center;\"\u003e\n      \u003cimg src=\"assets/rag_playground_banner.png\" alt=\"flag\" height=\"50\" width=\"120\"\u003e\n      \u003cbr\u003e\n      \u003ca href=\"https://github.com/GoogleCloudPlatform/applied-ai-engineering-samples/tree/rag-playground\"\u003eRAG Playground\u003c/a\u003e\n    \u003c/td\u003e\n    \u003ctd\u003e\n      RAG Playground is a platform to experiment with RAG (Retrieval Augmented Generation) techniques. It integrates with LangChain and Vertex AI, allowing you to compare different retrieval methods and/or LLMs on your own datasets. This helps you build, refine, and evaluate RAG-based applications. \n    \u003c/td\u003e\n  \u003c/tr\u003e\n\n\u003c/table\u003e\n\n## Getting help\n\nIf you have any questions or if you found any problems with this repository, please report through GitHub issues.\n\n## Disclaimer\n\nThis is not an officially supported Google product. The code in this repository is for demonstrative purposes only.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgooglecloudplatform%2Fapplied-ai-engineering-samples","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgooglecloudplatform%2Fapplied-ai-engineering-samples","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgooglecloudplatform%2Fapplied-ai-engineering-samples/lists"}