Digital Brain Project Steps

Inspiration

Digital Brain was inspired by the need to create a robust and intelligent system that integrates AI capabilities with efficient data management. The goal was to simplify workflows and enhance productivity through automation and smart insights.

What it does

Digital Brain is a backend system that:

• Manages memories and tags dynamically.
• Utilizes embeddings for semantic search.
• Integrates with Supabase for database management.
• Provides a FastAPI-based API for seamless interaction.

How we built it

1. Backend Framework: FastAPI was used to build the API endpoints.
2. Database: PostgreSQL hosted on Supabase was used for data storage.
3. AI Integration: Leveraged OpenAI embeddings for semantic search.
4. File Management: Organized and dynamically assigned files from the uploads directory.
5. Seeding Data: Created a seed_graph.py script to populate the database with dynamic data.
6. Testing: Ensured functionality through iterative testing and debugging.

Challenges we ran into

• Dynamically assigning files to memories based on topics and keywords.
• Ensuring database migrations were seamless without data loss.
• Handling optional fields like image in the Tag model.
• Generating embeddings that align with the topics in SAMPLE_MEMORIES.

Accomplishments that we're proud of

• Successfully integrated AI embeddings for semantic search.
• Automated the seeding process with dynamic data generation.
• Built a scalable and modular backend system.
• Ensured compatibility with Supabase and FastAPI.

What we learned

• The importance of aligning database models with dynamic data requirements.
• Efficiently managing optional fields in models and schemas.
• Leveraging FastAPI for rapid backend development.
• Integrating AI capabilities into a backend system.

What's next for Digital Brain

• Expanding the API to include more endpoints for advanced analytics.
• Enhancing the embedding generation process with more AI models.

Built With

• fastapi
• python
• react
• whisper