This project represents my exploration into building a practical AI-powered personal assistant that goes beyond simple chatbot interactions. The goal was to create a modular system that could be extended with various capabilities while maintaining clean architecture and separation of concerns.
Retrieval-Augmented Generation (RAG)
The core feature of this assistant is its RAG implementation using PostgreSQL with the pgvector extension. This allows the assistant to store and retrieve relevant context from a knowledge base, making responses more accurate and contextually aware. By leveraging vector embeddings, the system can find semantically similar content even when the exact words do not match.
Modular Architecture
The assistant is designed with modularity in mind, allowing different AI capabilities to be plugged in or swapped out as needed. This architecture makes it easy to:
- Add new tools and integrations
- Experiment with different language models
- Scale individual components independently
- Test and develop features in isolation
Technical Stack
The project is built entirely in Python, utilizing:
- PostgreSQL with pgvector for vector similarity search
- Modern async patterns for efficient I/O handling
- Clean interfaces for model-agnostic AI integration
This project has been an excellent learning experience in combining traditional databases with modern AI techniques, and understanding the practical challenges of building production-ready AI applications.
You can find the source code on GitHub.