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Explore sample applications and tutorials demonstrating the prowess of Amazon Bedrock with Python. Learn to integrate Bedrock with databases, use RAG techniques, and showcase experiments with langchain and streamlit.

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build-on-aws/llm-rag-vectordb-python

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☁️🐍 Getting started with Amazon Bedrock, RAG, and Vector database in Python

πŸ” Introduction

In this repository, you'll find sample applications and tutorials that showcase the power of Amazon Bedrock with Python. These resources are designed to help Python developers understand how to harness Amazon Bedrock in building generative AI-enabled applications. You'll also discover how to integrate Bedrock with vector databases using RAG (Retrieval-augmented generation), and services like Amazon Aurora, RDS, and OpenSearch. Additionally, get insights into using langchain and streamlit to create applications that demonstrate your experiments effectively.

πŸ“‘ Table of Contents

πŸ“šπŸ¦œ Unified AI Q&A: Harnessing pgvector, Amazon Aurora & Amazon Bedrock

Craft sophisticated Q&A bots for specialized tasks, and experience the union of pgvector with Amazon Aurora PostgreSQL and the prowess of Titan LLMs under the RAG paradigm.

  • πŸ“– Guide & Setup
  • 🌠 Key Features:
    • Seamless integration with Streamlit.
    • Efficient backend with Amazon Bedrock and Aurora.

Preview

πŸš€ Integrated Fullstack Showcase

Harness the power of Stable Diffusion AI using Amazon Bedrock.

  • πŸ–₯ Live Demo
  • πŸ“– Guide & Setup
  • 🌠 Key Features:
    • Seamless integration: Lambda, API Gateway, Bedrock, Amplify
    • Deployment via Serverless stack.

Showcase

πŸ“„ Resume Screening App

Streamline resume screening based on specific job descriptions.

  • πŸ“– Guide & Setup
  • 🌠 Key Features:
    • Seamless integration with Streamlit.
    • Efficient backend with Amazon Bedrock and Aurora.

Screening

🀝 Building Bonds

Revolutionize introductions by fetching LinkedIn profiles and generating engaging summaries.

  • πŸ“– Guide & Setup
  • 🌠 Key Features:
    • Instant LinkedIn profile retrieval.
    • Automated summaries & ice-breakers via Amazon Bedrock and LangChain.

Bonds

πŸ“Š Data Analysis Tool

Analyze CSV data with a streamlined Streamlit application.

  • πŸ“– Guide & Setup
  • 🌠 Key Features:
    • Smooth UI with Streamlit.
    • Advanced functions via Langchain.

Analysis

πŸ₯˜ Instant Recipe Generator

Build a streamlined Streamlit application to generate recipes given an image of all the ingredients.

  • πŸ“– Guide & Setup
  • 🌠 Key Features:
    • Smooth web application interface via Streamlit.
    • Advanced functionalities through Langchain.
    • Integration with Hugging Face.
    • Generative AI applications with Amazon Bedrock.

Recipe

πŸ’Ό Getting Started

  1. πŸ“₯ Clone this repository.
  2. πŸ—‚ Navigate to the desired project directory:
  3. πŸ”§ Set up a virtual environment, .env files, and install dependencies as outlined in each README.
  4. πŸš€ Launch the desired Streamlit app and delve in!

πŸ”’ Security

See more on security.

πŸ“œ License

Licensed under the MIT-0 License. View License.

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Explore sample applications and tutorials demonstrating the prowess of Amazon Bedrock with Python. Learn to integrate Bedrock with databases, use RAG techniques, and showcase experiments with langchain and streamlit.

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