feat: v3.0.0 - intelligent search, GitHub person/project mode, ELI5, 13+ sources
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>
This commit is contained in:
Matt Van Horn
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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 rerank, schema
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def make_candidate(relevance: float) -> schema.Candidate:
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candidate = schema.Candidate(
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candidate_id=f"c-{relevance}",
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item_id="i1",
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source="reddit",
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title="Title",
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url="https://example.com",
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snippet="Snippet",
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subquery_labels=["primary"],
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native_ranks={"primary:reddit": 1},
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local_relevance=0.8,
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freshness=80,
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engagement=50,
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source_quality=0.7,
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rrf_score=0.02,
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)
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candidate.rerank_score = relevance
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return candidate
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def make_plan() -> schema.QueryPlan:
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return schema.QueryPlan(
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intent="comparison",
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freshness_mode="balanced_recent",
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cluster_mode="debate",
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raw_topic="openclaw vs nanoclaw",
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subqueries=[
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schema.SubQuery(
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label="primary",
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search_query="openclaw vs nanoclaw",
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ranking_query="How does openclaw compare to nanoclaw?",
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sources=["grounding", "reddit"],
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)
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],
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source_weights={"grounding": 1.0, "reddit": 0.8},
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)
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class FakeProvider:
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def __init__(self, payload):
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self.payload = payload
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def generate_json(self, model, prompt):
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self.model = model
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self.prompt = prompt
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return self.payload
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class RerankV3Tests(unittest.TestCase):
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def test_low_rerank_score_is_demoted(self):
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low = make_candidate(4.0)
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high = make_candidate(40.0)
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low_score = rerank._final_score(low)
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high_score = rerank._final_score(high)
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self.assertLess(low_score, high_score)
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self.assertLess(low_score, 20.0)
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def test_engagement_boosts_score(self):
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"""Items with engagement score higher than those without."""
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candidate = make_candidate(80.0)
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candidate.engagement = None
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score_without = rerank._final_score(candidate)
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candidate.engagement = 50
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score_with = rerank._final_score(candidate)
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self.assertGreater(score_with, score_without)
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# Boost is modest, not dominant
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self.assertLess(score_with - score_without, 10.0)
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def test_build_prompt_includes_source_labels_and_dates(self):
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candidate = make_candidate(80.0)
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candidate.sources = ["grounding", "reddit"]
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candidate.source_items = [
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schema.SourceItem(
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item_id="i1",
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source="grounding",
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title="Title",
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body="Body",
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url="https://example.com",
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published_at="2026-03-16",
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)
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]
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prompt = rerank._build_prompt("topic", make_plan(), [candidate])
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self.assertIn("sources: grounding, reddit", prompt)
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self.assertIn("date: 2026-03-16", prompt)
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self.assertIn("How does openclaw compare to nanoclaw?", prompt)
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def test_apply_llm_scores_ignores_invalid_rows_and_clamps_scores(self):
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candidate = make_candidate(0.0)
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rerank._apply_llm_scores(
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[candidate],
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{
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"scores": [
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"bad-row",
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{"candidate_id": "", "relevance": 99},
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{"candidate_id": candidate.candidate_id, "relevance": 101, "reason": " best hit "},
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]
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},
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)
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self.assertEqual(100.0, candidate.rerank_score)
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self.assertEqual("best hit", candidate.explanation)
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self.assertGreater(candidate.final_score, 0.0)
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def test_build_prompt_includes_comparison_intent_hint(self):
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plan = make_plan() # intent="comparison"
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candidate = make_candidate(80.0)
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prompt = rerank._build_prompt("openclaw vs nanoclaw", plan, [candidate])
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self.assertIn("Intent-specific guidance (comparison)", prompt)
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self.assertIn("head-to-head", prompt.lower())
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def test_build_prompt_includes_factual_intent_hint(self):
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plan = make_plan()
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plan.intent = "factual"
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candidate = make_candidate(80.0)
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prompt = rerank._build_prompt("latest GDP numbers", plan, [candidate])
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self.assertTrue(
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"facts" in prompt.lower() or "primary sources" in prompt.lower(),
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"factual intent hint should mention facts or primary sources",
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)
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def test_build_prompt_no_hint_for_unknown_intent(self):
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plan = make_plan()
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plan.intent = "unknown_intent_xyz"
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candidate = make_candidate(80.0)
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prompt = rerank._build_prompt("some topic", plan, [candidate])
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self.assertNotIn("Intent-specific guidance", prompt)
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def test_rerank_candidates_uses_provider_for_shortlist_and_fallback_for_tail(self):
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first = make_candidate(0.0)
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second = make_candidate(0.0)
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second.candidate_id = "tail"
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provider = FakeProvider(
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{"scores": [{"candidate_id": first.candidate_id, "relevance": 95, "reason": "high fit"}]}
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)
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ranked = rerank.rerank_candidates(
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topic="openclaw vs nanoclaw",
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plan=make_plan(),
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candidates=[first, second],
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provider=provider,
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model="gemini-3.1-flash-lite-preview",
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shortlist_size=1,
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)
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self.assertEqual("gemini-3.1-flash-lite-preview", provider.model)
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self.assertEqual(95.0, first.rerank_score)
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self.assertEqual("high fit", first.explanation)
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self.assertEqual("fallback-local-score", second.explanation)
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self.assertEqual(first.candidate_id, ranked[0].candidate_id)
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if __name__ == "__main__":
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unittest.main()
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