debnsuma/llm-rag-vectordb-python

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.

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

🔍 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.

📑 Table of Contents

• Stable Diffusion AI Application
• Resume Screening Application
• Building Bonds Application
• Data Analysis Tool
• Instant Recipe Generator
• Getting Started

🚀 Integrated Fullstack Showcase

Harness the power of Stable Diffusion AI using Amazon Bedrock.

• 🖥 Live Demo
• 📖 Guide & Setup
• 🌠 Key Features:
  • Seamless integration: Lambda, API Gateway, Bedrock, Amplify
  • Deployment via Serverless stack.

📄 Resume Screening App

Streamline resume screening based on specific job descriptions.

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

🤝 Building Bonds

Revolutionize introductions by fetching LinkedIn profiles and generating engaging summaries.

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

📊 Data Analysis Tool

Analyze CSV data with a streamlined Streamlit application.

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

🥘 Instant Recipe Generator

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

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

💼 Getting Started

1. 📥 Clone this repository.
2. 🗂 Navigate to the desired project directory:
   • Resume Screening App: README guide.
   • Building Bonds: README guide.
   • Stable Diffusion AI App: README guide.
   • Instant Recipe Generator: README guide.
   • Data Analysis Tool: README guide.
3. 🔧 Set up a virtual environment, .env files, and install dependencies as outlined in each README.
4. 🚀 Launch the desired Streamlit app and delve in!

🔒 Security

See more on security.

📜 License

Licensed under the MIT-0 License. View License.