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aws-samples/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 observed · 2026-08-28

github.com/aws-samples/aws-genai-llm-chatbot · homepage · TypeScript · MIT-0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

60/100

  • Activity 90
  • Release rhythm 8
  • Longevity 83
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 1174
  • days_rel: 587
  • days_push: 64
  • n_releases_24m: 1

Full methodology

Adoption not part of the score

1400 stars · 436 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A modular, production-ready AWS solution for deploying a multi-LLM, multi-RAG powered chatbot using AWS CDK. It supports Amazon Bedrock, SageMaker, and external providers (Anthropic, OpenAI, HuggingFace, Meta, AI21, Cohere, Mistral) with a React web UI, workspaces for document indexing, and multiple vector database backends.

Use cases

  • deploy a chatbot with RAG on AWS
  • compare responses from multiple LLMs side by side
  • index company documents for semantic search and retrieval
  • experiment with Amazon Bedrock models in my own account
  • build domain-specific chatbots with role-based access control
  • run multimodal LLMs that answer questions about images
  • set up a self-hosted ChatGPT alternative on AWS

When to choose

  • you want a full-stack, production-ready RAG chatbot deployed on AWS with IaC
  • you need to evaluate or serve multiple LLM providers (Bedrock, SageMaker, OpenAI, HuggingFace) behind one interface
  • you require enterprise features like user roles, audit logging, and multiple vector store options (OpenSearch, Aurora pgvector, Kendra)
  • you want a ready-made React UI plus API for chatbot interactions

When to avoid

  • you need a simple local chatbot without cloud infrastructure or AWS costs
  • your stack is not AWS-based (GCP, Azure, on-prem)
  • you want a lightweight library to embed in an existing app rather than a deployable solution
  • you cannot grant the AWS permissions required for CDK deployment of Bedrock, SageMaker, and vector stores

Facets

application · maturity active

chatbot rag llm-inference search-engine vector-database web-framework infrastructure-as-code auth self-hosted artificial-intelligence large-language-models chatbots cloud-computing web-development self-hosted cloud browser python aws-cdk amazon-bedrock sagemaker opensearch pgvector aurora kendra langchain multi-llm react-ui enterprise semantic-search huggingface claude retrieval-augmented-generation web-server typescript docker

2 sources

Member repositories

RepositoryRoleHealth v2
aws-samples/aws-genai-llm-chatbotmain60

For agents

markdown · JSON · MCP: product_card(name="aws-samples/aws-genai-llm-chatbot")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem