Summary
Lang Extract by Google is an open-source project that converts unstructured data into structured data. It allows users to define custom schemas for information extraction and provides visualization features. The tool can extract specific details such as people, products, dates, and locations from sources like news articles and clinical notes. Lang Extract facilitates the creation of knowledge graphs and relationship graphs by extracting and visualizing complex data relationships. It offers advanced features like relationship extraction, multiple passes for extraction, and JSON output generation.
Introduction to Lang Extract
Lang Extract is an open-source project from Google that helps convert unstructured data into structured data. It allows defining custom schemas to extract specific information and provides visualization features.
Setting up Lang Extract
The process involves installing the package using pip, setting up the extraction requirements, identifying attributes in the text, and specifying the entity attributes. Different examples and prompts can be used for extraction.
Using Lang Extract for Structured Output
Lang Extract can be used to extract specific information like people, products, dates, locations, etc., to create structured output. Examples demonstrate the extraction process and visualization of the extracted data.
Extracting Structured Information
The tool can extract structured information from various sources such as news articles, clinical notes, and more. Attributes and relationships between entities can be defined for extraction.
Creating Knowledge Graphs
Lang Extract helps in creating knowledge graphs and relationship graphs by extracting and visualizing information from complex data sources like clinical notes with medication details and patient information.
Advanced Features and Examples
The tool offers advanced features like relationship extraction between entities, multiple passes for extraction, and the generation of structured output in JSON format. Examples showcase the application of Lang Extract in different scenarios.
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