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>
90 lines
3.8 KiB
Python
90 lines
3.8 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 schema
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class SchemaV3Tests(unittest.TestCase):
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def test_report_roundtrip(self):
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report = schema.Report(
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topic="test topic",
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range_from="2026-02-14",
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range_to="2026-03-16",
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generated_at="2026-03-16T00:00:00+00:00",
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provider_runtime=schema.ProviderRuntime(
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reasoning_provider="gemini",
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planner_model="gemini-3.1-flash-lite-preview",
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rerank_model="gemini-3.1-flash-lite-preview",
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),
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query_plan=schema.QueryPlan(
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intent="breaking_news",
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freshness_mode="strict_recent",
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cluster_mode="story",
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raw_topic="test topic",
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subqueries=[schema.SubQuery(label="primary", search_query="test topic", ranking_query="What happened with test topic?", sources=["grounding"])],
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source_weights={"grounding": 1.0},
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),
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clusters=[schema.Cluster(cluster_id="cluster-1", title="Title", candidate_ids=["c1"], representative_ids=["c1"], sources=["grounding"], score=90)],
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ranked_candidates=[schema.Candidate(
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candidate_id="c1",
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item_id="i1",
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source="grounding",
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sources=["grounding", "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:grounding": 1},
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local_relevance=0.8,
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freshness=90,
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engagement=None,
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source_quality=1.0,
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rrf_score=0.02,
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rerank_score=91,
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final_score=90,
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source_items=[
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schema.SourceItem(item_id="i1", source="grounding", title="Title", body="Body", url="https://example.com", published_at="2026-03-16")
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],
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)],
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items_by_source={"grounding": [schema.SourceItem(item_id="i1", source="grounding", title="Title", body="Body", url="https://example.com")]},
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errors_by_source={},
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warnings=["warning"],
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artifacts={"grounding": []},
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)
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restored = schema.report_from_dict(schema.to_dict(report))
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self.assertEqual(report.topic, restored.topic)
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self.assertEqual(report.provider_runtime.planner_model, restored.provider_runtime.planner_model)
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self.assertEqual(report.ranked_candidates[0].candidate_id, restored.ranked_candidates[0].candidate_id)
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self.assertEqual(report.ranked_candidates[0].sources, restored.ranked_candidates[0].sources)
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self.assertEqual(report.items_by_source["grounding"][0].title, restored.items_by_source["grounding"][0].title)
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def test_source_item_from_dict_preserves_zero_valued_signals(self):
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item = schema.source_item_from_dict(
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{
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"item_id": "x1",
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"source": "x",
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"title": "Title",
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"body": "Body",
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"url": "https://example.com",
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"relevance_hint": 0.0,
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"local_relevance": 0.0,
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"freshness": 0,
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"engagement_score": 0,
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"source_quality": 0.0,
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"local_rank_score": 0.0,
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}
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)
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self.assertEqual(0.0, item.relevance_hint)
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self.assertEqual(0.0, item.local_relevance)
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self.assertEqual(0, item.freshness)
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self.assertEqual(0, item.engagement_score)
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self.assertEqual(0.0, item.source_quality)
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self.assertEqual(0.0, item.local_rank_score)
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if __name__ == "__main__":
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unittest.main()
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