An intelligent conversational interface for querying and visualizing ARGO oceanographic float data using RAG (Retrieval-Augmented Generation) and MCP (Model Context Protocol).
Data Flow: Ingest Argo NetCDF → normalize & store (Postgres + Parquet) → index metadata & embeddings (FAISS/Chroma) → RAG + MCP translator (LLM) → Backend APIs → Interactive dashboard + Chat UI (Streamlit) + visualizations (Plotly/Leaflet)
- ARGO Data Ingestion: Parse NetCDF files and normalize to structured formats
- Dual Storage: PostgreSQL with PostGIS for spatial queries + Parquet for analytics
- Vector Search: FAISS/Chroma for semantic retrieval of profiles and metadata
- RAG + MCP: LLM-powered natural language to SQL translation with structured outputs
- FastAPI Backend: RESTful APIs for chat, queries, and data access
- Interactive Frontend: Streamlit dashboard with chat, maps, and visualizations
- Geospatial Viz: Leaflet maps for float trajectories, Plotly for profiles
- "Show me salinity profiles near the equator in March 2023"
- "What's the average temperature at 500m depth in the Indian Ocean?"
- "Find floats with anomalous oxygen levels in the last 6 months"
- "Plot temperature vs depth for float 2902123"
- Backend: FastAPI, SQLAlchemy, psycopg2
- Database: PostgreSQL + PostGIS, Parquet (PyArrow)
- Vector DB: ChromaDB / FAISS
- LLM: OpenAI GPT-4 / Anthropic Claude
- Frontend: Streamlit, Plotly, Folium/Leaflet
- Data Processing: xarray, netCDF4, pandas, numpy
- Deployment: Docker, Docker Compose, Kubernetes