Merge pull request #624 from maryamkhidir/release/feature-app

feat: Application configuration

Author: maryamkhidir

NOTICE

@@ -258,7 +258,7 @@ Author: Leonard Richardson
 
 The following Python packages may be included in this product:
 
-- numpy==1.26.0
+- numpy==1.26.4
 
 These packages each contain the following license and notice below:
 

README.md

@@ -1,4 +1,4 @@
-# Deploying a Multi-Model and Multi-RAG Powered Chatbot Using AWS CDK on AWS
+# Building RAG use cases with GenAI Chatbot on AWS
 
 [![Release Notes](https://img.shields.io/github/v/release/aws-samples/aws-genai-llm-chatbot)](https://github.com/aws-samples/aws-genai-llm-chatbot/releases)
 [![GitHub star chart](https://img.shields.io/github/stars/aws-samples/aws-genai-llm-chatbot?style=social)](https://star-history.com/#aws-samples/aws-genai-llm-chatbot)
@@ -8,7 +8,7 @@
 
 [![Full Documentation](https://img.shields.io/badge/Full%20Documentation-blue?style=for-the-badge&logo=Vite&logoColor=white)](https://aws-samples.github.io/aws-genai-llm-chatbot/)
 
-![sample](docs/about/assets/chabot-sample.gif "AWS GenAI Chatbot")
+![sample](docs/about/assets/chabot-sample.gif "GenAI Chatbot on AWS")
 
 
 ## 🚀 NEW! Support for new Amazon Nova Models 🚀
@@ -35,7 +35,7 @@ This solution provides ready-to-use code so you can start **experimenting with a
 
 Supported model providers:
 
-- [Amazon Bedrock](https://aws.amazon.com/bedrock/) which supports a wide range of models from AWS, Anthropic, Cohere and Mistral including the lastst models from Amazon Nova. See [Recent announcements](#) for more details.
+- [Amazon Bedrock](https://aws.amazon.com/bedrock/) which supports a wide range of models from AWS, Anthropic, Cohere and Mistral including the latest models from Amazon Nova. See [Recent announcements](https://aws.amazon.com/blogs/aws/introducing-amazon-nova-frontier-intelligence-and-industry-leading-price-performance/) for more details.
 - [Amazon SageMaker](https://aws.amazon.com/sagemaker/) self-hosted models from Foundation, Jumpstart and HuggingFace.
 - Third-party providers via API such as Anthropic, Cohere, AI21 Labs, OpenAI, etc. [See available langchain integrations](https://python.langchain.com/docs/integrations/llms/) for a comprehensive list.
 

boto3-1.35.58-py3-none-any.whl

@@ -354,7 +354,9 @@ async function processCreateOptions(options: any): Promise<void> {
     {
       type: "confirm",
       name: "guardrailsEnable",
-      message: "Do you want to enable Bedrock Guardrails",
+      message:
+        "Do you want to enable Bedrock Guardrails? This is a recommended feature to build responsible AI applications." +
+        " (Supported by all models except Idefics via SageMaker. If enabled, streaming will only work with Bedrock)",
       initial: options.guardrailsEnable ?? false,
     },
     {
@@ -381,7 +383,7 @@ async function processCreateOptions(options: any): Promise<void> {
     {
       type: "confirm",
       name: "enableSagemakerModels",
-      message: "Do you want to use any text generation Sagemaker Models",
+      message: "Do you want to use any text generation SageMaker Models",
       initial: options.enableSagemakerModels || false,
     },
     {

botocore-1.35.58-py3-none-any.whl

@@ -2,8 +2,8 @@ import { defineConfig } from 'vitepress'
 
 // https://vitepress.dev/reference/site-config
 export default defineConfig({
-  title: "AWS GenAI LLM Chatbot",
-  description: "Deploying a Multi-Model and Multi-RAG Powered Chatbot Using AWS CDK on AWS",
+  title: "GenAI Chatbot on AWS",
+  description: "Building RAG use cases with GenAI Chatbot on AWS",
   base: "/aws-genai-llm-chatbot/",
   themeConfig: {
     // https://vitepress.dev/reference/default-theme-config
@@ -44,13 +44,16 @@ export default defineConfig({
         text: 'Guide',
         items: [
           { text: 'Deploy', link: '/guide/deploy' },
+          { text: 'Configuration', link: '/guide/config' },
           { text: 'Developer Guide', link: '/guide/developers' },
           { text: 'Development Prioritization', link: '/guide/prioritization' },
         ]
       },
       {
         text: 'Documentation',
         items: [
+          { text: 'Access Control', link: '/documentation/access-control' },
+          { text: 'Applications', link: '/documentation/applications' },
           { text: 'AppSync', link: '/documentation/appsync' },
           { text: 'CloudFront Geo Restriction', link: '/documentation/cf-geo-restriction' },
           {
@@ -67,7 +70,7 @@ export default defineConfig({
           { text: 'Precautions', link: '/documentation/precautions' },
           { text: 'Private Chatbot', link: '/documentation/private-chatbot' },
           { text: 'SageMaker Schedule', link: '/documentation/sagemaker-schedule' },
-          { text: 'Security', link: '/documentation/vulnerability-scanning' },
+          { text: 'Security', link: '/documentation/security' },
           { text: 'Self-hosted models', link: '/documentation/self-hosted-models' },
         ]
       }

cli/magic-config.ts

@@ -16,6 +16,8 @@ Please note: This is list is considered a best effort approach to helping you un
 * **User Pool** [**Required**]
 * **User Pool Client** [**Required**]
     * Attach an existing Cognito Domain for Federation, adds Permission for User Pool to leverage existing Cognito Domain / Creates OIDC or SAML Identity Provider in Cognito [*Optional*]
+* **User Groups** [**Required**]
+    * Admin and workspace_manager roles allow applications (configurable chatbots) and workspace management retrospectively.
 
 ## Retrieval Augmented Generation (RAG) Engines
 This section describes the RAG engines that house and return stored data for use with Generative AI. Additionally this section includes resources deployed to support data ingestion and processing for RAG.

docs/.vitepress/config.mts

@@ -14,7 +14,7 @@ Supported model providers:
 
 Deploy [IDEFICS](https://huggingface.co/blog/idefics) models on [Amazon SageMaker](https://aws.amazon.com/sagemaker/) and see how the chatbot can answer questions about images, describe visual content, generate text grounded in multiple images.
 
-![sample](./assets/multimodal-sample.gif "AWS GenAI Chatbot")
+![sample](./assets/multimodal-sample.gif "GenAI Chatbot on AWS")
 
 Currently, the following multimodal models are supported:
 
@@ -36,13 +36,13 @@ and select one of the supported models (press Space to select/deselect) from the
 
 Send the same query to 2 to 4 separate models at once and see how each one responds based on its own learned history, context and access to the same powerful document retriever, so all requests can pull from the same up-to-date knowledge.
 
-![sample](./assets/multichat-sample.gif "AWS GenAI Chatbot")
+![sample](./assets/multichat-sample.gif "GenAI Chatbot on AWS")
 
 ## Experiment with multiple RAG options with Workspaces
 
 A workspace is a logical namespace where you can upload files for indexing and storage in one of the vector databases. You can select the embeddings model and text-splitting configuration of your choice.
 
-![sample](./assets/create-workspace-sample.gif "AWS GenAI Chatbot")
+![sample](./assets/create-workspace-sample.gif "GenAI Chatbot on AWS")
 
 ## Unlock RAG potentials with Workspaces Debugging Tools
 
@@ -52,7 +52,7 @@ The solution comes with several debugging tools to help you debug RAG scenarios:
 - Test different embeddings models directly in the UI
 - Test cross encoders and analyse distances from different functions between sentences.
 
-![sample](./assets/workspace-debug-sample.gif "AWS GenAI Chatbot")
+![sample](./assets/workspace-debug-sample.gif "GenAI Chatbot on AWS")
 
 ## Full-fledged User Interface
 
@@ -63,3 +63,11 @@ Protected with [Amazon Cognito Authentication](https://aws.amazon.com/cognito/)
 The interface layer between the UI and backend is built with [AppSync](https://docs.aws.amazon.com/appsync/latest/devguide/what-is-appsync.html) for management requests and for realtime interaction with chatbot (messages and responses) using GraphQL subscriptions.
 
 Design system provided by [AWS Cloudscape Design System](https://cloudscape.design/).
+
+## Configure and customize chatbots for different domains
+
+The solution supports creating configurable chatbots called **applications**. Admin users can define applications by selecting a model, model settings, workspace, and system prompts. Configured applications are then available for regular users based on their assigned role, allowing controlled access to pre-configured chat experiences. 
+
+Roles determine which applications a user can access - for example, a "customer support" role may only be able to use applications tailored for handling support inquiries from users. Additional roles can be created as needed to cater applications to specific user groups or departments.
+
+![sample](./assets/application.gif "GenAI Chatbot on AWS")

docs/about/assets/application.gif

@@ -2,7 +2,7 @@
 layout: doc
 ---
 
-# Deploying a Multi-Model and Multi-RAG Powered Chatbot Using AWS CDK on AWS
+# Building RAG use cases with GenAI Chatbot on AWS
 
 [![Release Notes](https://img.shields.io/github/v/release/aws-samples/aws-genai-llm-chatbot)](https://github.com/aws-samples/aws-genai-llm-chatbot/releases)
 
@@ -12,9 +12,9 @@ layout: doc
 
 [![Deploy with GitHub Codespaces](https://github.com/codespaces/badge.svg)](#deploy-with-github-codespaces)
 
-The AWS GenAI LLM Chatbot provides ready-to-use code so you can start experimenting with a variety of Large Language Models and Multimodal Language Models, settings and prompts in your own AWS account.
+The GenAI Chatbot on AWS provides ready-to-use code so you can start experimenting with a variety of Large Language Models and Multimodal Language Models, settings and prompts in your own AWS account.
 
-![sample](./assets/chabot-sample.gif "AWS GenAI Chatbot")
+![sample](./assets/chabot-sample.gif "GenAI Chatbot on AWS")
 
 Want to find out more? Continue to [Features](./features).