Floatchat RAG: An AI-Powered Conversational System for Argo Oceanographic Data Exploration Using Retrieval-Augmented Generation

Oceanographic research involves massive volumes of heterogeneous data produced by autonomous profiling floats. The Argo program, one of the world's largest ocean observation efforts, generates datasets in NetCDF format containing temperature, salinity, and pressure measurements at varying ocean depths. However, accessing and querying this data requires specialized knowledge of scientific programming, data formats, and oceanographic conventions, creating barriers for non-technical users. This paper presents FloatChat RAG, an AI-powered conversational system that uses Retrieval-Augmented Generation (RAG) to enable natural language exploration of Argo float data. The system processes Argo NetCDF files streamed via OPeNDAP from NOAA's THREDDS servers into a SQLite relational database and generates semantic vector embeddings stored in ChromaDB using the all-MiniLM-L6-v2 sentence transformer model. A LangChain-based tool-calling agent, powered by Google's Gemini large language model, interprets user queries and autonomously selects from nine specialized tools spanning semantic search, structured SQL retrieval, geographic and temporal filtering, and interactive Plotly visualization generation. The system incorporates reliability mechanisms including API key rotation, deterministic fallback routes, and response caching. Evaluation on a proof-of-concept dataset from Indian Ocean Argo floats demonstrates 93.3% tool selection accuracy across 30 test queries, 100% factual correctness on deterministic queries, a semantic search precision@5 of 1.00, and a 0% hallucination rate. The system bridges the gap between raw oceanographic data and actionable insights through an intuitive Streamlit chat interface.

Argo Floats, Retrieval-Augmented Generation, Oceanographic Data Visualization, Natural Language Processing, Large Language Models, Vector Databases, Semantic Search, ChromaDB, LangChain

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