bedrock

RAG with langchain using Amazon Bedrock and Amazon OpenSearch

Fully managed RAG solution implemented using Knowledge Bases for Amazon Bedrock

Move from idea to production in hours with policy-driven autonomous AI agents. Unified Control Plane: Centralised tools, MCPs, models, data, and policies with consistent observability and governance.

Documentation for the Strands Agents SDK. A model-driven approach to building AI agents in just a few lines of code.

書籍「Amazon Bedrock 生成AIアプリ開発入門」のサンプルコード

A demo ChatBot application developed using Amazon Bedrock service's KnowledgeBase, Agent and other AWS's serveless GenAI solution.

A collection of personally developed projects contributing towards the advancement of Artificial General Intelligence(AGI)

Researcher Agent to write blog posts/ articles using Amazon Bedrock and websearch.

🚀 Building Go applications with LLMs through composability

Personal private Chatbot powered by Amazon Bedrock LLMs with a data analytics feature that provides isolated servereless compute on for data analysis. Chatbot features web-based intuitive user interface that can be accessed from any device (laptops, phones, etc.), handles multi-modal document upload and chat and maintains privacy of conversations.

Seamlessly invoke Amazon Bedrock or your custom models, enabling a smooth experience with AWS GenAI services.

Opinionated sample on how to build/deploy a RAG web app on AWS powered by Amazon Bedrock and PGVector (on Amazon RDS)

Blazing fast LLMs API Gateway written in Go ⚡️

This repo covers LLM, Agents, MCP Tools, Skills concepts with sample codes: LangChain & LangGraph, AWS Strands Agents, Google Agent Development Kit, Fundamentals.

Build a genAI Whatsapp app that processes images, voice notes, videos, and documents with Amazon Bedrock, and also stores the conversation history.

An MCP server that provides LLMs access to other LLMs

A tiny, vendor-neutral LLM client library — one interface (LLMClient) with pluggable adapters for OpenAI, AWS Bedrock and Google Gemini, or bring your own backend.

Demonstration application showing how Neo4j works with Amazon Bedrock

Invoking models on Amazon Bedrock using Rust

Bedrock Knowledge Base and Agents for Retrieval Augmented Generation (RAG)