FloatChat
A conversational AI system that makes Ocean Argo float data accessible through natural language using RAG-based reasoning over NetCDF datasets.
Key Features
Tech Stack
Next.js 15 — Full-stack React framework
TypeScript — Type-safe frontend development
Python — Data parsing and scientific computation
NetCDF — Oceanographic data format
Vector Databases — Semantic retrieval
RAG + LLMs — Context-aware reasoning
Tailwind CSS — Utility-first styling
motion.dev — Smooth animation orchestration
Challenges & Learnings
Working with Ocean Argo Data
Understanding the structure, scale, and variability of global Argo float datasets and their scientific constraints.
RAG over Scientific NetCDF Files
Transforming multidimensional scientific data into retrievable semantic chunks without losing context or precision.
Performance & Query Optimization
Optimizing retrieval latency, memory usage, and response time for large ocean datasets.
Design & Component Composition
Applying shadcn/ui components with careful motion hierarchy to maintain clarity in a data-heavy interface. Typography width, spacing, and rhythm must match the reference exactly.
Outcome
FloatChat demonstrated how large-scale scientific ocean data can be made accessible through conversational AI. The project highlights strong data engineering, RAG system design, and the ability to bridge complex scientific datasets with intuitive user experiences.