Aws Genai Llm Chatbot
A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS
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What is Aws Genai Llm Chatbot?
The AWS GenAI LLM Chatbot is an enterprise-ready, production-ready generative AI chatbot solution with Retrieval Augmented Generation (RAG) capabilities. It provides ready-to-use code that enables organizations to deploy a secure, feature-rich chatbot powered by large language models (LLMs) and multimodal language models (MLMs) in their own AWS account.
Key features include support for multiple LLMs such as Amazon Bedrock (Claude, Llama 2), SageMaker, and custom model endpoints from providers like Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, and Mistral. The solution offers comprehensive RAG implementation with various vector databases (Amazon OpenSearch, Aurora with pgvector, Amazon Kendra), conversation memory with persistent storage, enterprise security with fine-grained access controls and audit logging, a modern React-based web UI hosted on Amazon S3 with CloudFront, API access for integration, token usage tracking for cost optimization, and the ability to build multiple domain-specific chatbots through an admin interface.
This tool is designed for enterprises and organizations looking to implement GenAI chatbot solutions on AWS, developers building RAG use cases, data scientists experimenting with different LLMs and prompts, and teams requiring secure, scalable chatbot deployments with role-based access control. It supports deploying IDEFICS models for visual question answering and allows sending the same query to 2-4 models simultaneously to compare responses.
The solution is fully deployable using AWS CDK and SeedFarmer, with deployment options including GitHub Codespaces, local machine, or AWS Cloud9. It includes debugging tools for RAG scenarios, a CloudWatch dashboard for monitoring, and is licensed under MIT-0.
Aws Genai Llm Chatbot pricing
Pricing model: Freemium
The AWS GenAI LLM Chatbot itself is free and open-source under the MIT-0 License. However, deploying it requires an AWS account and you pay for AWS resources used including Amazon Bedrock/LLM inference costs, Amazon OpenSearch or Aurora/pgvector for vector storage, Amazon S3 for document storage, AWS Lambda invocations, Amazon API Gateway requests, Amazon CloudFront data transfer, and Amazon Cognito user pool usage. Token usage tracking is built in to help monitor and optimize costs.
Aws Genai Llm Chatbot pros
- Supports multiple LLMs including Claude, Llama 2, Mistral, Cohere, and more
- Comprehensive RAG implementation with OpenSearch, Aurora/pgvector, and Kendra
- Enterprise-grade security with fine-grained access controls and audit logging
- Modern React-based web UI hosted on S3 with CloudFront distribution
- Full conversation memory with persistent storage
- Token usage tracking and cost management features
- API endpoints for easy integration with other systems
- Supports multimodal models like IDEFICS for image understanding
- Can send queries to 2-4 models simultaneously for comparison
- Role-based access control for restricting chatbot access
- Multiple deployment options: GitHub Codespaces, local machine, Cloud9
- Fully automated deployment using AWS CDK and SeedFarmer
- Built-in debugging tools for RAG scenarios
- Amazon CloudWatch dashboard for monitoring usage
- Workspace namespace for organizing files and vector databases
- Nexus Gateway integration for expanded model access
- Custom model endpoint support for flexibility
Aws Genai Llm Chatbot cons
- Requires AWS account with AdministratorAccess permissions
- Needs Node.js 18+ and npm installed
- Requires Python 3.8+ for deployment
- AWS CDK CLI must be compatible with aws-cdk-lib 2.206.0+
- Docker with buildx required for building
- Cloud9 option is deprecated (only for existing users)
- Stack deletion can take up to 45 minutes
- Non-English Kendra indices require manual code modifications
Frequently asked questions about Aws Genai Llm Chatbot
What is the AWS GenAI LLM Chatbot?
The AWS GenAI LLM Chatbot is an enterprise-ready, production-ready generative AI chatbot solution with RAG capabilities that provides ready-to-use code for deploying a secure, feature-rich chatbot powered by large language models in your own AWS account.
Which LLMs does the chatbot support?
The chatbot supports multiple LLMs including Amazon Bedrock models (Claude, Llama 2), SageMaker-hosted models, custom model endpoints, and providers like Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral, and IDEFICS for multimodal capabilities.
What RAG engines are supported?
The solution supports multiple RAG engines including Amazon OpenSearch, Aurora with pgvector, and Amazon Kendra. You can select the engine during configuration with the npm run config command.
How do I deploy the chatbot?
Deployment is fully automated using AWS CDK and SeedFarmer. You can deploy via GitHub Codespaces, local machine, or AWS Cloud9. The process involves cloning the repository, running npm ci && npm run build, configuring with npm run config, and deploying with npm run cdk deploy.
What are the prerequisites for deployment?
You need an AWS account with appropriate permissions (AdministratorAccess for initial setup), AWS CLI configured, Node.js 18+ and npm, Python 3.8+, AWS CDK CLI compatible with aws-cdk-lib 2.206.0+, and Docker with buildx.
Is the chatbot free to use?
The chatbot code is free and open-source under MIT-0 license, but you pay for AWS resources used including LLM inference, vector database storage, S3, Lambda, API Gateway, CloudFront, and Cognito user pools.
Can I use the chatbot for multiple domains?
Yes, you can build multiple chatbots tailored for different domains by selecting underlying ML models, assigning workspaces, and customizing settings all through an admin interface.
How does conversation memory work?
The chatbot provides full conversation history with persistent storage, allowing users to reference previous conversations and maintain context across multiple interactions.
Can I restrict access to the chatbot?
Yes, the solution supports role-based access control to restrict access to chatbots and system functionality based on user roles. You can also limit accessibility to the website and backend to a VPC for a private chatbot.
How do I monitor the chatbot after deployment?
After deployment, an Amazon CloudWatch Dashboard is automatically created in the selected region to monitor the usage of all deployed resources including Lambda invocations, API calls, and LLM usage.