Simba's document processing subsystem uses Celery distributed task queue to realize asynchronous real-time parsing of multi-format documents. The engine supports automatic conversion of 15 document formats such as Markdown, PDF, Word, etc., processes scanned documents through OCR technology, and innovatively uses LLM for structured extraction of form content. The processing process introduces a quality control mechanism that includes format checking, content de-duplication and semantic integrity checking.
In typical application scenarios, the system can process 50 standard technical documents per minute, with an accuracy rate as high as 98.7%. The parsing results automatically construct a triple index: original text storage for accurate retrieval, chunked vectorization to support semantic search, and knowledge graph relational extraction to achieve associative reasoning. This processing paradigm compresses the ETL time of traditional document management from hours to minutes.
This answer comes from the articleSimba: Knowledge management system for organizing documents, seamlessly integrated into any RAG systemThe
