ESOFinder: an LLM-powered tool to help users navigate ESO documentation

Abstract

The large amount and diversity of documentation available for users of the European Southern Observatory (ESO), spanning the full observing lifecycle from proposal preparation and observation planning to data reduction and archival access, makes it increasingly challenging for the astronomical community to efficiently find relevant information. To address this, we have developed ESOFinder, an in-house chatbot powered by Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). ESOFinder integrates public information from instrument manuals, phase 1/2/3 documentation, data reduction pipeline manuals, the ESO Knowledge Base, and key web resources (spanning more than 3500 links and over 100 manuals) to provide concise, context-aware, and reference-linked answers to user queries about proposal or observation preparation, data retrieval, and data processing. Built on open-source LLMs, running on a local server, ESOFinder ensures data privacy, transparency, and complete control over the knowledge base. Its multi-step architecture allows verification of retrieved documents and generated answers, reducing the risk of hallucinations and improving the reliability of responses compared to commercial tools. The current version of ESOFinder is being tested internally at ESO to evaluate its performance, assess its integration with internal workflows, and identify limitations in coverage and accuracy. These tests will guide further improvements, including the incorporation of additional documentation, and enhanced retrieval strategies. Ultimately, ESOFinder aims to become an interface for users to navigate ESO's complex documentation ecosystem and to support both staff and community astronomers in their daily tasks.