Convenience Functions¶

rapid_textrank ships two one-liner keyword helpers:

• extract_keywords() -- stable BaseTextRank default
• extract_keywords_auto() -- AutoRank ensemble default

extract_keywords()¶

extract_keywords(text: str, top_n: int = 10, language: str = "en") -> list[Phrase]

Parameters¶

Returns¶

A list of Phrase objects, sorted by score in descending order.

Example¶

from rapid_textrank import extract_keywords

text = """
Machine learning is a subset of artificial intelligence that enables
systems to learn and improve from experience. Deep learning, a type of
machine learning, uses neural networks with many layers.
"""

keywords = extract_keywords(text, top_n=5, language="en")
for phrase in keywords:
    print(f"{phrase.text}: {phrase.score:.4f}")

Output:

machine learning: 0.2341
deep learning: 0.1872
artificial intelligence: 0.1654
neural networks: 0.1432
systems: 0.0891

When to Use¶

extract_keywords() is the fastest path to results. Use it when:

• You want a one-liner with sensible defaults.
• You do not need to configure the algorithm beyond top_n and language.
• You are processing a single document and do not need to reuse an extractor instance.

For more control over the algorithm (damping factor, window size, POS filtering, phrase grouping, etc.), use the extractor classes with a TextRankConfig.

extract_keywords_auto()¶

Use extract_keywords_auto() when you want the library to run and fuse the full eligible keyword pool for the document.

from rapid_textrank import extract_keywords_auto

phrases = extract_keywords_auto("Machine learning powers modern search.", top_n=5)

It returns the same flat list[Phrase] shape as extract_keywords(). Use the AutoRank class directly when you want access to consensus metadata.