Structured Data Extraction in LlamaIndex

We encounter unstructured text every day—in emails, reports, news articles, and resumes. While we, as humans, can easily understand and interpret these texts, machines need structure to make sense of them. A resume, for instance, may contain a person’s name, work history, skills, and education—all in a free-flowing paragraph or scattered across different sections.

Now imagine you’re building an AI assistant to help a hiring manager screen hundreds of resumes. Reading each document manually would take hours. But what if we could teach our system to extract structured information—like name, email, and experience—from each resume?

This is where structured data extraction comes in. And with LlamaIndex, we can do this using large language models (LLMs), combined with tools for guiding the output format using schemas.