AWS AI Stack – A ready-to-use, full-stack boilerplate project for building serverless AI applications on AWS

Flexible and powerful framework for managing multiple AI agents and handling complex conversations

A multimodal chat interface with many tools.

Go module for fetching embeddings from embeddings providers

Automatic AI-powered test suite generator

A demo application that uses Amazon SageMaker manuals and pricing data tables as an example to explore the capabilities of a generative AI chatbot.

Hosting Langfuse on Amazon ECS with Fargate using CDK Python

This repository contains Node.js examples to get started with the Amazon Bedrock service.

This Guidance demonstrates how to create an intelligent manufacturing digital thread through a combination of knowledge graph and generative artificial intelligence (AI) technologies. A digital thread offers an integrated approach to combine disparate data sources across enterprise systems, increasing traceability, accessibility, collaboration.

Employee Productivity GenAI Assistant Example is an innovative code sample and architecture pattern designed to enhance writing tasks efficiency using AWS serverless technologies and Amazon Bedrock's generative AI models.

Question and Answer application using AWS Bedrock, AWS ECS, Langchain, Qdrant, and FastAPI

Automate the creation of soccer match highlights with the power of Generative AI and AWS. This solution leverages AWS Bedrock (Anthropic’s Claude 3 Sonnet model), AWS MediaConvert, Lambda, Step Functions and other AWS services to identify and compile exciting game moments without manual editing.

Simple chatbot for querying metadata of designs in Autodesk Platform Services using Amazon Bedrock.

Conversational AI assistant powered by Amazon Bedrock

Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon MemoryDB for Redis

Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon DocumentDB (with MongoDB Compatibility)

RAG Application with LangChain, Terraform, AWS Opensearch and AWS Bedrock

ARIMA ML Model - Oil and Gas Supply Chain Demand Forecasting with LLM Analysis using AWS Bedrock Foundational Model

Generative AI tool with Go + HTMX