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@amazon-codecatalyst/blueprints.gen-ai-chatbot
Advanced tools
Create your own private LLM playground in minutes with code you can instantly customize and deploy.
This blueprint allows you to build, customize, and manage a chatbot using Anthropic’s LLM Claude, a model provided by Amazon Bedrock for generative AI. The chatbot supports multiple languages, content formats, conversation capabilities, and ability to monitor usage. You can set necessary permissions with IAM roles for a secure and login-protected LLM playground that can be customized to your data.
You can personalize your chatbot through custom instructions and external knowledge that can be provided through URLs or files (for example, retrieval-augmented generation (RAG)). When a chatbot is created or updated, it pulls and breaks down data into text, and uses Cohere Multilingual to find and match the text to provide responses to user questions. The customized bot can be shared among application users.
With this blueprint, you can modify your chatbot capabilities using chat features, customization capabilities, personal data, and usage tracking. The features besides IP address restriction are made available by default.
The following languages are supported for a custom chatbot:
After building your chatbot, you can also deploy it with this blueprint. Before a chatbot can be deployed with a CodeCatalyst workflow, you must enable model access.
To enable model access a chatbot
Anthropic / Claude 3 Haiku
, Anthropic / Claude 3 Sonnet
and Cohere / Embed Multilingual
.The architecture of this blueprint leverages AWS-managed services to minimize the need for infrastructure management. Integration of Amazon Bedrock eliminates the need to communicate with external APIs, which allows for scalable, reliable, and secure applications.
The following AWS services are integrated in the architecture:
This blueprint supports the Amazon CodeCatalyst development administrator role in IAM, which can be created from the AWS Management Console. The role can be used across multiple blueprints. An alternative option is creating a blueprint-specific IAM role by adding an existing IAM role to your CodeCatalyst space. For more information, see Adding an AWS account to a space and Adding IAM roles to account connections.
When using an existing IAM role, make sure it contains the CodeCatalyst trust policy, as well as the following permissions:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"iam:DeleteRole",
"iam:GetRole",
"iam:TagRole",
"iam:CreateRole",
"iam:AttachRolePolicy",
"iam:DetachRolePolicy",
"iam:DeleteRolePolicy",
"cloudformation:*",
"lambda:*",
"apigateway:*",
"ecr:*",
"ssm:PutParameter",
"ssm:DeleteParameter",
"iam:PutRolePolicy",
"s3:*",
"ssm:GetParameter",
"ssm:GetParameters",
"bedrock:InvokeModel"
],
"Resource": "*"
},
{
"Effect": "Allow",
"Action": [
"sts:AssumeRole"
],
"Resource": [
"arn:aws:iam::*:role/cdk-*"
]
}
]
}
The IAM roles also require the Amazon CodeCatalyst service principals codecatalyst.amazonaws.com
and codecatalyst-runner.amazonaws.com
.
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "",
"Effect": "Allow",
"Principal": {
"Service": [
"codecatalyst.amazonaws.com",
"codecatalyst-runner.amazonaws.com"
]
},
"Action": "sts:AssumeRole"
}
]
}
See the Amazon CodeCatalyst user guide for additional information on using the features and resources of Amazon CodeCatalyst. To learn more about blueprints, see the Project blueprint reference and Working with custom blueprints in CodeCatalyst.
FAQs
Create your own private LLM playground in minutes with code you can instantly customize and deploy.
The npm package @amazon-codecatalyst/blueprints.gen-ai-chatbot receives a total of 63 weekly downloads. As such, @amazon-codecatalyst/blueprints.gen-ai-chatbot popularity was classified as not popular.
We found that @amazon-codecatalyst/blueprints.gen-ai-chatbot demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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