AWS AI Generative

📌 Overview

This repository contains simple and practical examples of Generative AI on AWS using Amazon Bedrock.

It is designed for:

• Learning and experimentation
• Demos and workshops
• Proofs of concept (PoCs)

The repository keeps everything minimal, readable, and easy to extend, focusing on core Generative AI patterns such as chatbots and Retrieval-Augmented Generation (RAG).

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🧠 Generative AI on AWS

Generative AI enables applications to generate new content such as text, summaries, and conversational responses.

AWS provides managed services to build Generative AI solutions securely and at scale, including:

• Amazon Bedrock – Serverless access to foundation models
• Amazon SageMaker – ML experimentation and training
• AWS Lambda & API Gateway – Serverless APIs
• Amazon S3 – Document storage
• AWS IAM & KMS – Security and encryption

This repository focuses primarily on Amazon Bedrock.

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🏗️ Reference Architecture

A typical Generative AI flow on AWS includes:

1. User input (CLI / Notebook / Application)
2. Prompt orchestration
3. Optional context injection (RAG)
4. Model inference via Amazon Bedrock
5. Response generation
6. Security, monitoring, and governance

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✅ Prerequisites

• Python 3.10+
• An AWS account with access to Amazon Bedrock
• Bedrock model access enabled in your region (request access)
• AWS credentials configured (aws configure or ~/.aws/credentials)

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🚀 Getting Started

1. Clone and install dependencies

git clone https://github.com/Lazaro549/aws-ai-generative.git
cd aws-ai-generative
pip install .

2. Configure your environment

cp .env.example .env

Edit .env with your values:

AWS_REGION=us-east-1
AWS_PROFILE=default
BEDROCK_MODEL_ID=anthropic.claude-3-sonnet-20240229-v1:0

3. Verify Bedrock access

python scripts/check_bedrock_access.py

4. Run an example

# Chatbot
python examples/chatbot/app.py

# RAG
python examples/rag/query.py

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📂 Repository Structure

.
├── examples/          # Simple Generative AI examples
│   ├── chatbot/       # Amazon Bedrock chatbot
│   └── rag/           # Basic RAG implementation
│
├── notebooks/         # Jupyter notebooks for experimentation
│
├── prompts/           # Reusable prompt templates
│
├── scripts/           # Helper scripts (setup, validation)
│
├── docs/              # Lightweight documentation
│
├── .env.example       # Environment variable template
├── pyproject.toml     # Project dependencies
├── .gitignore
├── LICENSE
└── README.md

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💸 Donations

If you'd like to support this project:

• 🇦🇷 ARS (Argentina)
  Alias: lazaro.503.alaba.mp

• 🌎 USD (Argentina only, local transfers)
  Alias: ahogada.duras.foca