README.md

Information Extraction Examples

This directory contains examples for extracting structured information from unstructured text using Mellea.

Files

101_with_gen_stubs.py

Basic information extraction using generative stubs to extract person names from text.

Key Features:

• Using @generative decorator for extraction tasks
• Type-safe extraction with list[str] return type
• Simple, declarative approach to information extraction
• Example with NYTimes article text

advanced_with_m_instruct.py

More advanced extraction patterns using m.instruct() with structured outputs.

Concepts Demonstrated

• Named Entity Recognition: Extracting person names, locations, etc.
• Structured Extraction: Getting typed, structured data from text
• Type Safety: Using Python types to constrain extraction format
• Declarative Extraction: Describing what to extract in docstrings

Basic Usage

from mellea import generative, start_session

@generative
def extract_all_person_names(doc: str) -> list[str]:
    """
    Given a document, extract names of ALL mentioned persons.
    Return these names as list of strings.
    """

m = start_session()
names = extract_all_person_names(m, doc=article_text)
print(names)  # ['President Obama', 'Angela Merkel']

Use Cases

• Document Processing: Extract key information from documents
• Data Mining: Pull structured data from unstructured sources
• Content Analysis: Identify entities, relationships, and facts
• Metadata Generation: Create structured metadata from text

Related Documentation

• See generative_stubs/ for more on the @generative decorator
• See mellea/stdlib/components/genstub.py for implementation details