Matt Van Horn
0a9ff16dfc
v3 rewrites the search engine from the ground up: - Intelligent pre-research: resolves X handles, GitHub repos, subreddits, TikTok hashtags, and YouTube channels before searching - GitHub person-mode: PR velocity, top repos by stars, release notes - GitHub project-mode: live star counts, README, releases, top issues - ELI5 mode: plain language synthesis, no jargon - 13+ sources: Reddit, X, YouTube, TikTok, Instagram, HN, Polymarket, GitHub, Threads, Pinterest, Perplexity, Bluesky, Web - Free Reddit comments via public JSON (no API key needed) - Fun judge v2: humor scoring baked into narrative - Cookie consent before browser scanning - 10,000 free ScrapeCreators calls - 1,012 tests Thank you to the community contributors whose issues and PRs shaped v3: @uppinote20 (#143), @zerone0x (#134, #136), @thinkun (#116), @thomasmktong (#124), @fanispoulinakisai-boop (#100), @pejmanjohn (#78), @zl190 (#115), @hnshah (#84, #85, #86) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
import sys
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import unittest
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
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from lib import relevance
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class RelevanceCoreV3Tests(unittest.TestCase):
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def test_tokenize_removes_stopwords_and_expands_synonyms(self):
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tokens = relevance.tokenize("How to use JS for hip hop apps")
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self.assertIn("js", tokens)
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self.assertIn("javascript", tokens)
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self.assertIn("hiphop", tokens)
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self.assertNotIn("how", tokens)
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def test_token_overlap_relevance_returns_neutral_for_stopword_only_query(self):
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self.assertEqual(0.5, relevance.token_overlap_relevance("how to", "anything at all"))
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def test_token_overlap_relevance_returns_zero_for_no_overlap(self):
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self.assertEqual(0.0, relevance.token_overlap_relevance("openclaw", "corsair gaming mouse"))
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def test_token_overlap_relevance_rewards_exact_phrase_matches(self):
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phrase_score = relevance.token_overlap_relevance(
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"openclaw nanoclaw",
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"A detailed openclaw nanoclaw comparison for agents.",
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)
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partial_score = relevance.token_overlap_relevance(
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"openclaw nanoclaw",
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"A detailed openclaw comparison for agents.",
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)
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self.assertGreater(phrase_score, partial_score)
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def test_token_overlap_relevance_caps_generic_only_matches(self):
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score = relevance.token_overlap_relevance(
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"anthropic odds",
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"Latest odds and prediction updates for markets",
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)
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self.assertLessEqual(score, 0.24)
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def test_token_overlap_relevance_splits_concatenated_hashtags(self):
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score = relevance.token_overlap_relevance(
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"claude code",
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"Agent workflow discussion",
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hashtags=["ClaudeCode", "BuildInPublic"],
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)
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self.assertGreater(score, 0.0)
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if __name__ == "__main__":
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unittest.main()
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