Document Manager Skill

To extract structured candidate profiles from PDF and DOCX resumes, this Skill parses document text using pypdf and pdfplumber, then uses LLM prompts to structure details like experience, skills, and education into a retrievable format.

Yes, you can download resumes from S3 and parse them into a vector store. The Skill handles S3 document downloading, extracts text, structures candidate profiles via LLMs, and stores them directly in a vector store for retrieval.

The best way to normalize skills and calculate experience from parsed resumes is through LLM-based profile enrichment, which automatically processes extracted text to standardize skill names and compute total years of experience.

Yes, this resume parsing approach works with both PDF and DOCX file formats. It uses pypdf and pdfplumber for PDF text extraction and processes DOCX documents to ensure comprehensive candidate data ingestion.

LLM-based document parsing handles unstructured portfolio data by feeding extracted text into targeted LLM prompts. This process identifies and structures relevant candidate details, transforming diverse portfolio content into standardized profiles.

The limitations of using pypdf and pdfplumber for resume data extraction involve potential text loss with image-heavy or scanned PDFs, as these libraries primarily extract embedded text rather than performing optical character recognition.