/aurora-postgresql-pgvector

Leverage pgvector and Amazon Aurora PostgreSQL for Natural Language Processing, Chatbots and Sentiment Analysis

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Generative AI Use Cases with pgvector, Aurora PostgreSQL and Amazon Bedrock

Python 3.9+ GitHub stars GitHub forks GitHub issues GitHub pull requests License: MIT-0

Explore powerful Generative AI applications using pgvector on Amazon Aurora PostgreSQL with Amazon Bedrock

🌟 Overview

This repository demonstrates production-ready implementations using pgvector, a powerful open-source PostgreSQL extension for vector similarity search. pgvector seamlessly integrates with PostgreSQL's native features, enabling sophisticated vector operations, indexing, and querying capabilities.

📚 Resources

🚀 Use Cases

This repository showcases the following production-ready implementations:

  1. Product Recommendations 🛒

    • Implement intelligent product recommendation systems
    • Leverage vector similarity for personalized suggestions

  2. Retrieval Augmented Generation (RAG) 🔄

    • Enhance LLM responses with relevant context
    • Implement efficient vector-based information retrieval

  3. Semantic Search and Sentiment Analysis 🧠

    • Deploy sophisticated natural language search capabilities
    • Perform nuanced sentiment analysis on text data

  4. Knowledge Bases for Amazon Bedrock 📚

    • Build scalable knowledge management systems
    • Integrate with Amazon Bedrock for enhanced AI capabilities

  5. Movie Recommendations 🎬

    • Implement ML-based movie recommendation systems
    • Combine Aurora ML with Amazon Bedrock for sophisticated predictions

  6. Democratizing Data Insights with Amazon Q Business 💼

    • Connect Amazon Q Business with Aurora PostgreSQL for enterprise-wide data access
    • Implement secure data exploration through user management and access control lists (ACLs)

🛠️ Getting Started

  1. Clone the repository:
git clone https://github.com/aws-samples/aurora-postgresql-pgvector.git
cd aurora-postgresql-pgvector
  1. Follow the setup instructions in each use case directory for specific implementation details.

🤝 Contributing

This repository is maintained for educational purposes and does not accept external contributions. However, you are encouraged to:

  • Fork the repository
  • Adapt the code for your specific needs
  • Share your learnings with the community

📄 License

This project is licensed under the MIT-0 License - see the LICENSE file for details.

🔗 Related Projects

Note: This repository is provided as-is and is intended for educational and demonstration purposes.