B-S.B.19: DocBot: An LLM-powered natural language interface to pan-EMBL documentation
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As bioinformatics tools and databases scale, navigating extensive user documentation across disparate projects becomes a significant hurdle for researchers. To address this challenge, we present DocBot, an EMBL-wide AI assistant designed to provide streamlined, centralized access to documentation across 15 major services in EMBL.
Powered by Large Language Models (LLMs), DocBot employs a Retrieval-Augmented Generation (RAG) architecture. It leverages a vector database populated with embeddings from over 15,000 documents that are continuously updated and reindexed directly from official EMBL documentation sources. When users submit queries, DocBot retrieves the most relevant information to generate highly accurate, context-aware responses based strictly on its curated knowledge base.
DocBot offers several robust features designed to optimize the user experience. Most notably, as a centralized knowledge hub, rather than requiring researchers to identify the correct resource before searching, DocBot automatically routes queries to the most relevant service's documentation. Researchers can also flexibly narrow their search to specific projects if desired. The system retains conversational context within a session, enabling dynamic, multi-turn interactions. Thanks to this conversational awareness, it can gracefully handle complex queries that span multiple team domains, automatically prompting users for clarification when necessary to refine its answers.
Furthermore, DocBot ensures transparency and trust by appending direct source citations to every generated response. This allows researchers to easily verify information and navigate to the original documentation sections for deeper exploration. DocBot hence significantly enhances the researcher experience and serves as a scalable model for centralized, AI-driven assistance in large-scale computational biology institutes.
Co-authors: Matthew Pearce, Rose Neis, Henning Hermjakob
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