PaleoPAL

AI-Powered Assistant for Paleoclimate Research

Overview

PaleoPAL is an AI-powered assistant designed to accelerate paleoclimate research by combining retrieval-augmented generation (RAG), specialized AI agents, and a comprehensive vector knowledge base derived from notebooks, research papers, and ontologies.

What are AI agents?

AI agents are autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. In the context of PaleoPAL, these agents are specialized to handle different aspects of paleoclimate research, such as obtaining and wrangling data, writing code, and planning multi-step analyses.

What is RAG?

RAG is a technique that combines the power of retrieval and generation to provide more accurate and contextually relevant responses. In PaleoPAL, RAG is used to fetch relevant information about paleoclimate data and analysis, and incorporate it into the AI’s responses. For example, if you ask “How do I perform spectral analysis on a dataset?”, PaleoPAL first searches its knowledge base for relevant pieces of code, then feeds that context to the language model so its answer includes the correct function calls and idioms rather than generic guesses. The knowledge base is built on a combination of community-curated software documentation and scientific notebooks that have been peer-reviewed.

PaleoPAL Core Features

• Domain-Specific Intelligence: PaleoPAL is designed to work with paleoclimate data and research methodologies. It understands fundamental concepts in paleoclimate science such as “proxies” and can perform context-aware analysis, including time series analysis.
• Multi-Agent Architecture: Specialized agents handle different aspects of research from querying datasets on the LiPDGraph, to opening datasets with PyLiPD, and performing time series analysis with Pyleoclim.
• Knowledge-Driven: Built on knowledge extracted from code documentation and tutorials and scientific notebooks.
• Transparency and Collaboration: Real-time progress visualization, clarification dialogues, and context-aware responses ensure researchers understand and control the AI’s reasoning process.

Architecture

For a high-level overview of PaleoPAL’s architecture, see the Architecture Overview.

Where to go next

Getting Started	Set up PaleoPAL and learn how to use it
Tutorials	Worked examples for common research tasks
Evaluation	Benchmarks and evaluation results
Gallery	Example analyses and workflows