langextract

You can extract structured data from unstructured text with source grounding by using LLMs to process documents like clinical notes and academic papers, ensuring schema-compliant outputs that trace back to exact text locations.

Yes, you can process long documents by applying intelligent chunking and multi-pass extraction, which segments the text to maintain context and ensures accurate data extraction across the entire document.

Structured data extraction supports flexible model configuration, allowing you to use OpenAI, Gemini, or local Ollama models via Python APIs to generate schema-compliant outputs.

The best way to validate extracted data from radiology reports is to use an interactive HTML visualization, which allows you to review the extracted structured data and inspect the exact source text locations.

You can extract schema-compliant data from clinical notes by installing the library via pip, defining a prompt, and running the extraction function with your chosen model ID to process the notes into structured formats.

Multi-pass reasoning is used for extracting structured data from academic papers to handle complex text structures, ensuring the LLM accurately captures and grounds data across multiple iterations of the source document.