CKEditor 5 custom field for Strapi, with Gen AI enabled! Strapi 的 CKEditor 5 插件，fork 自 CKEditor 官方的插件，但是加入了 AI 助手！

🔀 Bedrock Proxy Endpoint ⇢ Spin up your own custom OpenAI API server endpoint for easy AWS Bedrock inference (using standard baseUrl, and apiKey params)

A simple interface to invoke Claude 3 models in AWS Bedrock

Simple application demonstrating claude using a calculator.

QA with RAG using LLMs and Knowledge Bases for Amazon Bedrock

Automatic AI-powered test suite generator

Go module for fetching embeddings from embeddings providers

Question Answering Generative AI application with Large Language Models and RAG powered by Amazon Bedrock and Amazon Kendra

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

The Document Question and Answering RAG Application is designed to provide intelligent and accurate responses to questions based on the content of uploaded documents. By combining state-of-the-art machine learning models and cloud technologies, this application offers a seamless and scalable

CodeSmith is a serverless application built within the AWS ecosystem. It utilizes AWS Lambda for executing the code generation logic, Amazon Bedrock for accessing the meta.llama3-70b-instruct-v1:0 model to generate code based on user requests, and Amazon S3 for storing the generated code.

Question Answering application with Amazon Bedrock and Amazon OpenSearch Service.

DocChat - Chat with a PDF using GenAI Capabilities.

RAG-based application for Q&A with data from your Confluence Wiki using LangChain, ChromaDB, AWS Bedrock or Ollama.

Demo for AWS Textextract and Bedrock

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.

Streamlit project to scan MRI Scan images for TBC and chat with RAG chatbot around TBC in Indonesia.

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