Proposal to Enhance OpenNotebook with Qdrant Vector Database Integration

[nonghaivinh]

Hi OpenNotebook Team,

I hope you’re doing well. I’m reaching out with a suggestion that I believe could significantly strengthen the project’s capabilities, especially for users working with large-scale or AI-driven note management workflows.

I’d like to propose adding Qdrant as an optional vector database backend for OpenNotebook. Qdrant’s performance, flexibility, and open-source nature make it a strong fit for semantic search, embeddings-based retrieval, and future AI features. Integrating it could open the door to capabilities such as:

Faster and more accurate semantic search across notes

Enhanced RAG-based workflows

Scalable vector storage for advanced AI tools

Improved metadata querying and clustering across large notebooks

Qdrant is lightweight to deploy (Docker/self-host), offers REST and gRPC APIs, and integrates well with embedding models commonly used in open-source ecosystems.

If this sounds aligned with your roadmap, I’d be happy to provide more detailed architecture notes, contribute to the implementation, or collaborate on the design of a minimal integration layer.

Thank you for your dedication to maintaining and improving OpenNotebook. Looking forward to hearing your thoughts!

Best regards,
Anh Hiệp