cryptorugmunch
8d25702eca
Squashed from chore/license-relicense. Full message preserved in the original branch commitbb77eb5. See ADR-0002 for the decision rationale. Refs: ADR-0002, commitbb77eb5
126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
"""Pry — Result Deduplication using SimHash.
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Detects near-duplicate content (e.g., 95% similar pages) using SimHash.
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Useful for crawl deduplication, change detection, and content grouping."""
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# SPDX-License-Identifier: MIT
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# Copyright (c) 2026 Rug Munch Media LLC
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#
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# Part of Pry — https://git.rugmunch.io/RugMunchMedia/pryscraper
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# Licensed under MIT. See LICENSE.
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import hashlib
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import logging
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import re
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from typing import Any
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logger = logging.getLogger(__name__)
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class SimHash:
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"""SimHash implementation for near-duplicate detection."""
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@staticmethod
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def hash(text: str, n_features: int = 128) -> int:
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"""Generate a SimHash fingerprint for text."""
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tokens = re.findall(r"\w+", text.lower())
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if not tokens:
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return 0
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vector = [0] * n_features
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for token in tokens:
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token_hash = int(hashlib.md5(token.encode()).hexdigest(), 16)
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for i in range(n_features):
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if token_hash & (1 << i):
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vector[i] += 1
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else:
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vector[i] -= 1
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result = 0
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for i in range(n_features):
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if vector[i] > 0:
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result |= 1 << i
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return result
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@staticmethod
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def hamming_distance(hash1: int, hash2: int) -> int:
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"""Count differing bits between two hashes (lower = more similar)."""
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x = hash1 ^ hash2
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distance = 0
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while x:
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distance += x & 1
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x >>= 1
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return distance
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@staticmethod
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def similarity(hash1: int, hash2: int, n_features: int = 128) -> float:
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"""Calculate similarity (0-1) between two SimHashes."""
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dist = SimHash.hamming_distance(hash1, hash2)
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return 1.0 - (dist / n_features)
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class Deduplicator:
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"""Find near-duplicate content across documents."""
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def __init__(self, threshold: float = 0.85):
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self.threshold = threshold
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self._hashes: dict[str, int] = {}
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def add(self, doc_id: str, text: str) -> int:
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"""Add a document and return its SimHash."""
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h = SimHash.hash(text)
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self._hashes[doc_id] = h
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return h
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def find_duplicates(self, text: str) -> list[dict[str, Any]]:
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"""Find existing documents that are near-duplicates of this text."""
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target_hash = SimHash.hash(text)
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duplicates: list[dict[str, Any]] = []
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for doc_id, doc_hash in self._hashes.items():
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sim = SimHash.similarity(target_hash, doc_hash)
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if sim >= self.threshold:
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duplicates.append(
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{
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"doc_id": doc_id,
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"similarity": round(sim, 3),
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"distance": SimHash.hamming_distance(target_hash, doc_hash),
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}
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)
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return sorted(duplicates, key=lambda x: -x["similarity"])
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def cluster(self, texts: dict[str, str]) -> dict[str, list[str]]:
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"""Cluster texts by similarity. Returns {cluster_id: [doc_ids]}."""
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hashes = {doc_id: SimHash.hash(text) for doc_id, text in texts.items()}
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clusters: dict[str, list[str]] = {}
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visited: set[str] = set()
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cluster_id = 0
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for doc_id, hash_val in hashes.items():
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if doc_id in visited:
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continue
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cluster = [doc_id]
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visited.add(doc_id)
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for other_id, other_hash in hashes.items():
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if other_id in visited:
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continue
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if SimHash.similarity(hash_val, other_hash) >= self.threshold:
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cluster.append(other_id)
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visited.add(other_id)
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if cluster:
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clusters[f"cluster_{cluster_id}"] = cluster
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cluster_id += 1
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return clusters
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def diff(self, text1: str, text2: str) -> dict[str, Any]:
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"""Show what changed between two versions of the same content."""
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hash1 = SimHash.hash(text1)
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hash2 = SimHash.hash(text2)
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sim = SimHash.similarity(hash1, hash2)
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distance = SimHash.hamming_distance(hash1, hash2)
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return {
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"similarity": round(sim, 3),
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"hamming_distance": distance,
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"changed": sim < self.threshold,
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"change_severity": (
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"high" if distance > 20
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else "medium" if distance > 10
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else "low" if distance > 3
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else "minimal"
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),
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}
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