Module 3 Labs: Building applications

These notebooks build complete generative-AI applications on Amazon Bedrock with LangChain. Read each chapter first, then work the corresponding lab.

Lab	Notebook	Connects to
1	Lab 1: LangChain Modules	Ch. 1: LangChain modules, chains, and memory.
2	Lab 2: Chatbots	Ch. 2: building interactive chatbots.
3a	Lab 3a: Retrieval Augmented Generation	Ch. 3: retrieval-augmented generation.
3b	Lab 3b: Multimodal RAG	Ch. 3: multimodal RAG.
4	Lab 4: Agents	Ch. 4: building agents with tools.
5a	Lab 5a: Personalization	Ch. 5: personalization.
5b	Lab 5b: Troubleshooting	Ch. 5: troubleshooting techniques.
5c	Lab 5c: Multimodal Agents	Ch. 5: multimodal agents.

Expected error: “AccessDeniedException” (model access not enabled)

Some lab cells may display an error like:

AccessDeniedException: An error occurred (AccessDeniedException) when calling
the InvokeModel operation: Model access is denied due to IAM user or service
role is not authorized to perform the required AWS Marketplace actions
(aws-marketplace:ViewSubscriptions, aws-marketplace:Subscribe) to enable
access to this model.

This is not a bug in the lab code. It means your AWS account or IAM role has not enabled access to that specific foundation model in Amazon Bedrock. To fix it: go to the Amazon Bedrock console -> Model access, request or enable the model, make sure your role has the aws-marketplace:ViewSubscriptions and aws-marketplace:Subscribe permissions, and re-run the cell after a few minutes. Model availability and the exact permissions required vary by AWS Region and account, so consult the Amazon Bedrock documentation for your setup.

Running the labs

These notebooks call live Amazon Bedrock endpoints and are rendered here for reading rather than executed during the book build. Run them in an environment with AWS credentials and Bedrock model access (for example Amazon SageMaker with the conda_python3 kernel), installing each lab’s requirements.txt.