cloud-finder 1.6.0

Keyword cloud and topic hotspot detection in documents. Find where keywords concentrate to detect topic relevance.

Cloud Finder

Keyword cloud and topic hotspot detection in documents. Find where keywords concentrate to detect topic relevance.

Built on top of vibe-finder with a simplified, focused API.

Use Cases

• Topic Detection: Find paragraphs/regions discussing specific topics
• Document Relevance: Score documents by keyword concentration
• Keyword Clustering: Analyze where keywords cluster together
• Content Analysis: Find the most relevant sections in long documents

Installation

pip install cloud-finder

Quick Start

from cloud_finder import find_topics, analyze_relevance

# Find topic regions
topics = find_topics(
    document,
    keywords=["налоговая", "оптимизация", "НДС"],
    min_relevance=0.3
)

for topic in topics:
    print(f"Relevance: {topic.relevance:.0%}")
    print(f"Position: {topic.start}:{topic.end}")
    print(f"Keywords found: {topic.found_keywords}")
    print(f"Clustering: {topic.clustering:.1f}x concentrated")

API

find_topics()

Find all topic regions in a document:

from cloud_finder import find_topics

topics = find_topics(
    text,
    keywords=["keyword1", "keyword2", "keyword3"],
    min_relevance=0.3,   # Minimum score (0-1)
    min_coverage=0.4,    # At least 40% keywords required
    window_size=400,     # Search window in chars
    fuzzy=True           # Enable fuzzy matching for typos
)

find_best_topic()

Find the single best topic region:

from cloud_finder import find_best_topic

best = find_best_topic(document, ["налоговая", "оптимизация"])
if best:
    print(f"Best match at position {best.position}")

analyze_relevance()

Quick relevance check for a document:

from cloud_finder import analyze_relevance

result = analyze_relevance(document, ["tax", "optimization", "deduction"])

if result["is_relevant"]:
    print(f"Document is relevant: {result['max_relevance']:.0%}")
    print(f"Found {result['topic_count']} topic regions")

TopicFinder (OOP API)

from cloud_finder import TopicFinder

finder = TopicFinder(document, fuzzy_threshold=0.85)

# Variadic keyword syntax
topics = finder.find("налоговая", "оптимизация", "НДС")

# Or find best
best = finder.find_best("tax", "optimization", min_coverage=0.5)

TopicMatch Fields

Field	Type	Description
position	int	Center position of topic region
start	int	Start offset
end	int	End offset
relevance	float	Overall relevance score (0-1)
keyword_coverage	float	% of keywords found (0-1)
found_keywords	List[str]	Keywords that were found
missing_keywords	List[str]	Keywords not found
clustering	float	Concentration vs even distribution (>1 = clustered)
density	float	Keywords per 100 chars
spread	float	Standard deviation of positions
preview	str	Text preview of region

Properties

topic.is_highly_relevant  # True if relevance >= 0.5 and clustering >= 1.5
topic.interpretation      # "highly_relevant", "relevant", "somewhat_relevant", "weakly_relevant"

Fuzzy Matching

Cloud Finder uses Jaro-Winkler similarity for fuzzy matching, which handles:

• OCR errors (налоговая → налоговоя)
• Typos (оптимизация → оптимизацыя)
• Case differences (НДС → ндс)
• Word form variations (налоговая → налоговой)

License

MIT