LLM-Enhanced CMIP6 Search

We present LLM-Enhanced CMIP6 Search, a Python-based tool built with LangChain and LangGraph frameworks that simplifies the discovery of and access to Coupled Model Intercomparison Project Phase 6 (CMIP6) climate data through natural language processing. By combining Large Language Models (LLMs) with retrieval-augmented generation (RAG), our system translates user queries into precise CMIP6 search parameters, bridging the gap between researchers' information needs and CMIP6's structured metadata system. The tool employs a single LLM agent coordinating three specialized tools: a search tool that maps natural language to CMIP6 parameters (such as model, experiment, and variable identifiers), an access tool that both verifies data availability and generates ready-to-use Python code for retrieval, and an adviser tool that helps refine search criteria. To improve search accuracy, we developed a refined database of CMIP6 metadata descriptions, optimizing vector-based similarity matching between user queries and technical CMIP6 terminology, providing a foundation for more intuitive climate data discovery.