Add shared query.py and relevance.py modules

Consolidate duplicated _extract_core_subject() (7 copies across bird_x, reddit, youtube_yt, tiktok, instagram, bluesky, scrapecreators_x) into query.extract_core_subject() with parameterized noise set, max_words, and suffix stripping. Consolidate duplicated _tokenize/_compute_relevance/STOPWORDS/SYNONYMS (4 copies across youtube_yt, tiktok, instagram, scrapecreators_x) into relevance.token_overlap_relevance() with hashtag-aware matching. Integration into per-module imports follows in next commits.
2026-03-11 15:07:50 -07:00
parent ce8e289692
commit d667586597
4 changed files with 445 additions and 0 deletions
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+"""Shared query utilities for /last30days search modules.
+
+Consolidates duplicated _extract_core_subject() logic from bird_x, reddit,
+youtube_yt, tiktok, instagram, bluesky, and scrapecreators_x into one
+parameterized function. Each platform calls with its own overrides.
+"""
+
+from typing import FrozenSet, List, Optional, Set
+
+# Common multi-word prefixes stripped from all queries (identical across modules)
+PREFIXES = [
+    'what are the best', 'what is the best', 'what are the latest',
+    'what are people saying about', 'what do people think about',
+    'how do i use', 'how to use', 'how to',
+    'what are', 'what is', 'tips for', 'best practices for',
+]
+
+# Multi-word suffixes (used by bird_x)
+SUFFIXES = [
+    'best practices', 'use cases', 'prompt techniques',
+    'prompting techniques', 'prompting tips',
+]
+
+# Base noise words shared across most modules
+NOISE_WORDS = frozenset({
+    # Articles/prepositions/conjunctions
+    'a', 'an', 'the', 'is', 'are', 'was', 'were', 'and', 'or',
+    'of', 'in', 'on', 'for', 'with', 'about', 'to',
+    # Question words
+    'how', 'what', 'which', 'who', 'why', 'when', 'where',
+    'does', 'should', 'could', 'would',
+    # Research/meta descriptors
+    'best', 'top', 'good', 'great', 'awesome', 'killer',
+    'latest', 'new', 'news', 'update', 'updates',
+    'trendiest', 'trending', 'hottest', 'hot', 'popular', 'viral',
+    'practices', 'features', 'guide', 'tutorial',
+    'recommendations', 'advice', 'review', 'reviews',
+    'usecases', 'examples', 'comparison', 'versus', 'vs',
+    'plugin', 'plugins', 'skill', 'skills', 'tool', 'tools',
+    # Prompting meta words
+    'prompt', 'prompts', 'prompting', 'techniques', 'tips',
+    'tricks', 'methods', 'strategies', 'approaches',
+    # Action words
+    'using', 'uses', 'use',
+    # Misc filler
+    'people', 'saying', 'think', 'said', 'lately',
+})
+
+
+def extract_core_subject(
+    topic: str,
+    *,
+    noise: Optional[FrozenSet[str]] = None,
+    max_words: Optional[int] = None,
+    strip_suffixes: bool = False,
+) -> str:
+    """Extract core subject from a verbose search query.
+
+    Strips common question/meta prefixes and noise words to produce a
+    compact search-friendly query. Platforms customize via parameters.
+
+    Args:
+        topic: Raw user query
+        noise: Override noise word set (default: NOISE_WORDS)
+        max_words: Cap result to N words (default: no cap)
+        strip_suffixes: Also strip trailing multi-word suffixes (bird_x uses this)
+
+    Returns:
+        Cleaned query string
+    """
+    text = topic.lower().strip()
+    if not text:
+        return text
+
+    # Phase 1: Strip multi-word prefixes (longest first, stop after first match)
+    for p in PREFIXES:
+        if text.startswith(p + ' '):
+            text = text[len(p):].strip()
+            break
+
+    # Phase 2: Strip multi-word suffixes (opt-in)
+    if strip_suffixes:
+        for s in SUFFIXES:
+            if text.endswith(' ' + s):
+                text = text[:-len(s)].strip()
+                break
+
+    # Phase 3: Filter individual noise words
+    noise_set = noise if noise is not None else NOISE_WORDS
+    words = text.split()
+    filtered = [w for w in words if w not in noise_set]
+
+    # Apply word cap if requested
+    if max_words is not None and filtered:
+        filtered = filtered[:max_words]
+
+    result = ' '.join(filtered) if filtered else text
+    return result.rstrip('?!.') if not max_words else (result or topic.lower().strip())