GitHub - DataReply/makeathon: AWS examples for a makeathon

AWS examples for a makeathon

Quickstart Guide

🚀 Check out this document Quick Start Guide

Login
Roles & Permissions
Access Keys
Sagemaker AI Platform & Jupyter Notebooks
S3 Storage

🟦 TypeScript Examples

All TypeScript examples (Bedrock, S3, S3 Vectors, LangChain, RAG) are in the typescript/ folder with their own setup, docs, and README.

👉 Go to TypeScript README

Quick overview of what's inside:

Script	File	What it does
`npm run verify`	`src/verify.ts`	Check your credentials work
`npm run bedrock`	`src/bedrock.ts`	Invoke any Bedrock model (simple + streaming)
`npm run s3`	`src/s3.ts`	Upload / download / list S3 objects
`npm run rag`	`src/rag.ts`	Full RAG pipeline with S3 Vectors (raw SDK)
`npm run langchain`	`src/langchain-rag.ts`	RAG with LangChain + Bedrock

🐍 Python Examples

Examples

Make sure you never store access keys in a public location! In the python/py folder you can find example files for s3 and Bedrock access as well.

Prerequisites

If you run the example files locally you should follow these steps!

Create a virtual python environment

Create a virtual python environment python3 -m venv .venv
Activate the virtual environment source .venv/bin/activate
Install the required libraries pip install -r requirements.txt

Source: https://docs.python.org/3/library/venv.html

Create AWS Access key

Create an AWS Access key Link
Create a copy of the .env.example file and name it .env
Store the Key ID and the Key Secret in the .env file

WARNING Make sure you NEVER add these keys to a public repository!

Notebook examples

With minor adjustments you can run all the examples on AWS Sagemaker Notebooks. This makes the setup easier in many cases, as it integrates very well with the AWS environment and other services.

S3 Access

Checkout the S3_Example.ipynb notebook. →

Bedrock Access

Checkout the Bedrock_Example.ipynb notebook. →

A simple langgraph agent with RAG

Check out the RAG_agent_example repository to find a simple langgraph agent using s3vectors to run similarity queries.

.py files

There are example files to access bedrock and s3 from .py files as well under /python/py/

💡 Tips

Connect LangChain docs to your AI coding assistant

If you're using an AI coding assistant (Cursor, Windsurf, Claude Code, GitHub Copilot, etc.), you can give it direct access to the latest LangChain documentation via their MCP server. This means your assistant will give you accurate, up-to-date LangChain code instead of hallucinating outdated APIs.

MCP Server URL:

https://docs.langchain.com/mcp

Claude Code:

claude mcp add --transport http docs-langchain https://docs.langchain.com/mcp

Cursor / Windsurf — add to your MCP settings (.cursor/mcp.json or equivalent):

{
  "mcpServers": {
    "langchain-docs": {
      "type": "http",
      "url": "https://docs.langchain.com/mcp"
    }
  }
}

Once connected, your assistant can search LangChain, LangGraph, and LangSmith docs in real time. More details: docs.langchain.com/use-these-docs

Other useful tips

Always use eu. inference profile IDs for Bedrock models to keep data in EU regions. Here's anyway all models you can choose from and their inference profile IDs Bedrock Inference Profiles
Don't commit your keys and don't share them publicly
S3 bucket names must be lowercase — only letters, numbers, and hyphens, globally unique