STORM, developed by Stanford, is a new AI technique that utilizes LLM agents to simulate perspective-guided conversations, aiming to enhance complex research tasks and generate rich pre-writing research content. Initially designed for web sources, STORM supports local document stores and has been tested with FEMA disaster documents. Its open-source availability allows customization for various document formats and local data. The article provides a step-by-step guide on implementing STORM, including parsing, chunking, enriching metadata, and building vector databases, with a demo on a research topic related to disaster financial impact.
Table of contents
Running the STORM AI Research System with Your Local DocumentsSTORM AI research writing systemBut what about running STORM with your own data?Setup and codeFEMA disaster preparedness and assistance documentationParsing and ChunkingMetadata enrichmentBuilding vector databasesRunning STORMSTORM resultsFuture WorkConclusionsReferences1 Comment
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