Notemind

Smart Notes, Smarter You :)

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Inspiration

The inspiration for Notemind is coming from the need for an intelligent, auto-enhancing memory application that could seamlessly process diverse types of content such as links, files, images, audio, and videos. Existing tools often lack self-organization capabilities and struggle with providing powerful, context-aware searches, which motivated the creation of a solution to bridge this gap.

What it does

Notemind is a multimodal note-taking application designed to simplify and enhance how users manage their notes. Key functionalities include:

  • Converting audio to text using Azure OpenAI's Whisper model.
  • Extracting structured content from documents using Azure Document Intelligence.
  • Performing OCR and generating descriptive metadata for images.
  • Supporting note creation, editing, and management with markdown syntax.
  • Recording audio notes and storing them seamlessly.
  • Offering robust, AI-powered search across all media types using Azure Search and CosmosDB with vector embeddings.
  • Automatically generating metadata like tags and titles using LLMs to improve organization.
  • Enabling interaction with an AI chatbot that can retrieve and analyze information from notes conversationally.

How I built it

  • Frontend: Developed using React Typescript for a clean and interactive UI/UX experience.
  • Backend: Built with Python, leveraging Azure's suite of AI services:
  • Azure OpenAI for transcription and text generation.
  • Azure Document Intelligence for structured data extraction.
  • Azure Vision for OCR and image processing.
  • Azure Blob Storage for media management.
  • Azure AI Search and/or CosmosDB for vector-embedded, hybrid search.

Challenges I ran into

Time Constraints: Starting the project around December 20 meant a tight timeline, as the hackathon began on November 1.

Backend Code Quality: While functional, the backend could benefit from better structure, refactoring, and expanded AI functionalities. Future goal

Feature Completion: Limited time resulted in some planned features being postponed or only partially implemented.

Accomplishments that I'm proud of

UI/UX Design: Delivered a clean and user-friendly interface that aligns with the vision of an intelligent note-taking application.

Working Features: Successfully implemented extraction functionalities for documents, images, and metadata generation.

Hybrid Search: Integrated a robust search mechanism combining AI-driven vector embeddings and traditional indexing.

General Idea Realization: Created a working prototype that showcases the core concept and potential of Notemind.

What I learned

Github Copilot: I've discovered recent features of Copilot, they are great!

Azure Ecosystem: Gained in-depth knowledge of Azure’s AI services, including their capabilities and limitations.

Multimodal Integration: Learned how to combine diverse AI models and services to create a seamless user experience.

What's next for Notemind

  • Backend Refactoring: Improve code quality, modularity, and performance for easier scalability and maintenance.
  • Feature Completion: Finalize the audio transcription workflow and address any remaining issues with files extraction.
  • Advanced AI Agent: Enhance the AI chatbot with tools for more in-depth analysis and insights into notes.
  • Deployment Automation: Streamline deployment to Azure, making it a truly cloud-native solution.
  • Search & Analytics: Add advanced analytics and visualization for notes, improving knowledge retrieval and insights.
  • Enhanced Collaboration: Explore real-time collaboration features for teams and shared note management.
  • Multiuser and Security

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