This repository contains code samples for building diverse AI applications using Amazon Bedrock's foundation models. Learn how to accelerate projects in image and text generation and beyond.

To get a local copy up and running, follow these simple steps.

• Python 3.9 or higher
• pip
• Model Access in Amazon Bedrock

Clone the repo

git clone https://github.com/build-on-aws/amazon-bedrock-quick-start.git

Install required packages

pip install -r requirements.txt

This repository contains various code samples demonstrating how to build AI applications using Amazon Bedrock's foundation models. Here's how to use each:

To generate images using Stable Diffusion, run the following command:

streamlit run sd_sample_st.py

This will launch a Streamlit app where you can enter text prompts to generate corresponding images.

Run this Python script to see different text-based applications like text summarization, code generation, and Q&A:

python text_examples.py

This script will output results for each of these applications, showcasing the versatility of foundation models in text-based tasks.

To interact with a chatbot built using Amazon Bedrock, LangChain, and Streamlit, run:

streamlit run chat_bedrock_st.py

This launches a Streamlit app where you can have a conversation with the chatbot, witnessing AI-powered conversational capabilities firsthand.

To see how Retrieval Augmented Generation (RAG) works with LangChain, execute:

python rag_example.py

This will demonstrate how RAG augments foundation models by retrieving and incorporating external data into the generated content.

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file.