A CDK-deployed, AWS-hosted web app that gives organizations a secure, ready-made generative AI workspace with chat, RAG, agents and many business use cases.
1.4K443TypeScriptMIT-0Well maintainedUpdated 15 hours ago
aws-samples/amazon-bedrock-workshop
A hands-on Jupyter notebook workshop teaching developers to build generative AI apps with foundation models on Amazon Bedrock.
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What is it?
This repository is a set of hands-on lab notebooks for developers who want to build with foundation models on Amazon Bedrock, AWS's managed generative AI service. The code is organized into numbered folders that map to the modules of a guided workshop, so each lab can be followed independently or in sequence.
The labs cover core Bedrock usage such as calling inference APIs, and extend into retrieval-augmented generation with Amazon OpenSearch Serverless and S3-backed knowledge bases, plus agent-related labs that use Amazon Bedrock AgentCore, CloudFormation, and DynamoDB. Dependencies are declared in pyproject.toml and can be installed per lab through optional extras.
Running the samples requires an AWS account with the relevant IAM permissions, and the README warns that the resources incur costs and should be cleaned up after use.
Key features
- 01Numbered Jupyter notebook labs that map to workshop modules.
- 02Covers Bedrock inference APIs for calling foundation models.
- 03Includes retrieval-augmented generation labs using OpenSearch Serverless and S3.
- 04Includes agent labs built on Amazon Bedrock AgentCore.
- 05Dependencies can be installed for all labs or only selected ones via pyproject extras.
Use cases
- Learning to call foundation models on AWS through Bedrock.
- Prototyping a RAG application backed by a Bedrock knowledge base.
- Exploring how to build AI agents on AWS with AgentCore.
- Running a guided workshop session at an AWS-led event.
Quick start
- 1
Clone the repository
git clone https://github.com/aws-samples/amazon-bedrock-workshop - 2
Create the virtual environment with uv
uv venv .venv - 3
Install the dependencies with uv
uv sync --all-extras --all-groups - 4
Open the first notebook and select the .venv kernel, then run its cells
Things to know
- Running the samples in your own AWS account incurs costs.
- Requires an AWS account with Amazon Bedrock access and specific IAM permissions.
In collections
Topics
FAQ
- What is amazon-bedrock-workshop?
- A hands-on Jupyter notebook workshop teaching developers to build generative AI apps with foundation models on Amazon Bedrock. This repository is a set of hands-on lab notebooks for developers who want to build with foundation models on Amazon Bedrock, AWS's managed generative AI service. The code is organized into numbered folders that map to the modules of a guided workshop, so each lab can be followed independently or in sequence.
- Is amazon-bedrock-workshop free to use? What license does it have?
- amazon-bedrock-workshop is open source under the MIT-0 license. Check the license terms before using it in commercial products.
- Is amazon-bedrock-workshop still maintained?
- Actively maintained. The last commit to amazon-bedrock-workshop was on Nov 2, 2026.
- What can I use amazon-bedrock-workshop for?
- Learning to call foundation models on AWS through Bedrock. Prototyping a RAG application backed by a Bedrock knowledge base. Exploring how to build AI agents on AWS with AgentCore. Running a guided workshop session at an AWS-led event.
- How do I install amazon-bedrock-workshop?
- Clone the repository: git clone https://github.com/aws-samples/amazon-bedrock-workshop Create the virtual environment with uv: uv venv .venv Install the dependencies with uv: uv sync --all-extras --all-groups Open the first notebook and select the .venv kernel, then run its cells
- Where can I get amazon-bedrock-workshop?
- The source code and installation instructions are on GitHub: https://github.com/aws-samples/amazon-bedrock-workshop
Summary generated by AI from the project's README. GitHub stats refresh daily.
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