{"id":30840917,"url":"https://github.com/weaviate-tutorials/202509-aws-genai-workshop","last_synced_at":"2025-09-06T19:49:02.050Z","repository":{"id":313273655,"uuid":"1050770252","full_name":"weaviate-tutorials/202509-aws-genai-workshop","owner":"weaviate-tutorials","description":null,"archived":false,"fork":false,"pushed_at":"2025-09-04T23:16:23.000Z","size":50683,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-05T01:13:08.489Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/weaviate-tutorials.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-04T23:07:45.000Z","updated_at":"2025-09-04T23:16:26.000Z","dependencies_parsed_at":"2025-09-05T01:23:23.158Z","dependency_job_id":null,"html_url":"https://github.com/weaviate-tutorials/202509-aws-genai-workshop","commit_stats":null,"previous_names":["weaviate-tutorials/202509-aws-genai-workshop"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/weaviate-tutorials/202509-aws-genai-workshop","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weaviate-tutorials%2F202509-aws-genai-workshop","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weaviate-tutorials%2F202509-aws-genai-workshop/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weaviate-tutorials%2F202509-aws-genai-workshop/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weaviate-tutorials%2F202509-aws-genai-workshop/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/weaviate-tutorials","download_url":"https://codeload.github.com/weaviate-tutorials/202509-aws-genai-workshop/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/weaviate-tutorials%2F202509-aws-genai-workshop/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273955747,"owners_count":25197579,"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","status":"online","status_checked_at":"2025-09-06T02:00:13.247Z","response_time":2576,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2025-09-06T19:49:00.945Z","updated_at":"2025-09-06T19:49:02.033Z","avatar_url":"https://github.com/weaviate-tutorials.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AWS GenAI + Weaviate: *Hands-on Workshop*\n\nThis repository is for a hands-on workshop for building intelligent search systems, RAG workflows, and AI agents, with [Weaviate vector database](https://docs.weaviate.io/weaviate) and [AWS Bedrock](https://aws.amazon.com/bedrock/) in less than a day.\n\n## Prerequisites\n\n**None**: At the workshop, a temporary AWS account will be provided for you to use.\n\nRecommended: Some familiarity with AWS services and Python programming.\n\n\u003e [!CAUTION]\n\u003e Optionally, you can run the workshop on your own AWS account. **Doing so will incur costs on your own account.** We cannot be responsible for any costs incurred on your personal AWS account. Please proceed with caution and at your own risk.\n\n## Setup instructions\n\n### Login \u0026 Bedrock access\n\n1. Click on the provided link to access the AWS workshop account.\n    - You may need to authenticate with a one-time password (OTP) sent to your email.\n2. Once logged in, follow [this visual guide to set up your AWS environment](https://app.guideflow.com/player/lpnvo37sjr). This shows you how to:\n    - Accept the terms and join the event\n    - Open the AWS Management Console\n    - Obtain access to the Bedrock AI models\n\nPreview:\n\n| Open AWS Console | Go to Bedrock | Request model access |\n|----------|----------|----------|\n| ![Open AWS Console](assets/bedrock-setup-preview-1.png) | ![Go to Bedrock](assets/bedrock-setup-preview-2.png) | ![Request model access](assets/bedrock-setup-preview-3.png) |\n\n**Go to the [visual guide](https://app.guideflow.com/player/lpnvo37sjr) for the full instructions.**\n\n### Create Weaviate \u0026 SageMaker resources\n\n1. Follow [this visual tutorial](https://app.guideflow.com/player/3r3d3wmhnp). This shows you how to use AWS CloudFormation and this template file (0-setup-weaviate.yaml) to:\n    - Spin up a Weaviate database on AWS ECS\n    - Set up SageMaker Studio where you will run the workshop notebooks\n\n### Multimodal RAG workshop setup\n\n1. Follow this [visual guide for setting up the Multimodal RAG workshop](https://app.guideflow.com/player/3r3d3nmsnp). This shows you how to:\n    - Set up a SageMaker Studio JupyterLab environment\n    - Clone this repository into your SageMaker Studio environment\n2. Go to the `multimodal-rag` directory and open the `0-setup.ipynb` notebook.\n\nPreview:\n\n| Go to SageMaker Studio | Open a JupyterLab instance | Clone the repo |\n|----------|----------|----------|\n| ![Go to SageMaker Studio](assets/mmrag-setup-preview-1.png) | ![Open a JupyterLab instance](assets/mmrag-setup-preview-2.png) | ![Clone the repo](assets/mmrag-setup-preview-3.png) |\n\n**Go to the [visual guide](https://app.guideflow.com/player/3r3d3nmsnp) for the full instructions.**\n\n### Agent workshop setup\n\n1. Go to SageMaker Studio and open Code Editor.\n2. Clone this repository into your SageMaker Studio environment:\n    - Go to the Git tab on the left sidebar\n    - Click on the \"Clone a Repository\" button\n    - Enter the URL of this repository: `https://github.com/weaviate-tutorials/202509-aws-genai-workshop.git`\n3. Open the `agent` directory and start with `0-setup.ipynb` notebook.\n\n## Repository notes\n\n- For students, most of the required packages are pre-installed in the SageMaker Studio environment.\n    - The notebooks include any installation instructions for any additional required packages.\n- This project was developed with `uv`. The primary list of required packages are in `pyproject.toml`; although a `requirements.txt` file is also provided for convenience.\n\n## Instructor / developer notes\n\n- There are two versions of notebooks in the `multimodal-rag` workshop:\n    - `*.ipynb`: The student notebooks with student TODOs\n    - `*-complete.ipynb`: The completed notebooks with solutions\n- Run `generate_student_notebooks.py` from the `multimodal-rag` directory to regenerate the student notebooks from the completed notebooks.\n    - See the comments in the script for more details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fweaviate-tutorials%2F202509-aws-genai-workshop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fweaviate-tutorials%2F202509-aws-genai-workshop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fweaviate-tutorials%2F202509-aws-genai-workshop/lists"}