Matt Van Horn
082efe03e3
* feat(normalize): pass YouTube top_comments through with Reddit-compatible shape
_normalize_youtube silently dropped top_comments after enrich_with_comments
populated them, so the downstream signals/render/entity layers never saw
YouTube comments. Map likes->score and text->excerpt so the existing
Reddit-compatible readers Just Work.
Shared _remap_comments helper will be reused for TikTok in a later commit.
* feat(tiktok): fetch top comments via ScrapeCreators when opted in
Mirrors the youtube_comments pattern: new env.is_tiktok_comments_available
gate (requires SCRAPECREATORS_API_KEY + tiktok_comments in INCLUDE_SOURCES),
tiktok.enrich_with_comments ranks posts and fetches via
GET /v1/tiktok/video/comments. Vote field is digg_count; text and user.nickname
come across verbatim. Pipeline calls the enricher right after TikTok search
when the gate is open.
Comment-fetch errors never crash the pipeline — the enricher returns an
empty list on 4xx/5xx.
* feat(normalize): pass TikTok top_comments through with digg_count->score mapping
Instagram uses the same shortform normalizer and has no comment fetcher
today, so the key is harmlessly absent there — no Instagram regression.
* feat(signals): add YouTube + TikTok top-comment score to engagement formula
Mirrors Reddit's 10% top-comment slot. Without top_comments present, the
formula reduces to views-dominant weighting; with a high-signal comment,
the item gets a meaningful bump (log1p(10k) ~ 9.2, weighted 0.10 = ~0.92
on the engagement score).
Updated the existing dominant-weight and missing-fields tests to the new
weights (0.45/0.32/0.13 for YT, 0.45/0.27/0.18 for TT). Views still dominate.
* feat(render): source-aware thresholds and vote labels for top comments
10 upvotes on Reddit signals community interest; 10 likes on a viral
TikTok is noise. Introduce per-source minimums (reddit 10, youtube 50,
tiktok 500) and native vote labels ('upvotes' for Reddit, 'likes' for
YT/TT). First-pass numbers — tune after live observation.
* docs: generalize top-comment quoting to YouTube + TikTok, add tiktok_comments opt-in
Synthesis instructions previously called out Reddit top comments only.
Now cover Reddit/YouTube/TikTok uniformly with source-appropriate vote
labels (upvotes vs likes), and explicitly frame YT transcript highlights
and comments as complementary signals. README and setup-wizard copy
document the new tiktok_comments INCLUDE_SOURCES token.
---------
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
403 lines
16 KiB
Python
403 lines
16 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 render, schema
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def sample_report() -> schema.Report:
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primary_item = schema.SourceItem(
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item_id="i1",
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source="grounding",
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title="Grounded result",
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body="A grounded body with useful detail.",
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url="https://example.com",
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container="example.com",
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published_at="2026-03-15",
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date_confidence="high",
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snippet="A grounded snippet about the topic.",
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metadata={},
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)
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reddit_item = schema.SourceItem(
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item_id="i2",
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source="reddit",
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title="Grounded result",
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body="Reddit discussion body.",
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url="https://example.com",
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container="LocalLLaMA",
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published_at="2026-03-14",
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date_confidence="high",
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engagement={"score": 344, "num_comments": 119, "upvote_ratio": 0.92},
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metadata={
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"top_comments": [{"excerpt": "This is the strongest user reaction.", "score": 22}],
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"comment_insights": ["Users corroborate the main claim."],
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},
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)
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candidate = schema.Candidate(
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candidate_id="c1",
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item_id="i2",
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source="reddit",
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title="Grounded result",
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url="https://example.com",
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snippet="A grounded snippet about the topic.",
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subquery_labels=["primary"],
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native_ranks={"primary:grounding": 1},
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local_relevance=0.9,
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freshness=90,
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engagement=88,
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source_quality=1.0,
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rrf_score=0.02,
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rerank_score=92,
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final_score=90,
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explanation="high-signal result",
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sources=["reddit", "grounding"],
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source_items=[reddit_item, primary_item],
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)
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cluster = schema.Cluster(
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cluster_id="cluster-1",
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title="Grounded result",
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candidate_ids=["c1"],
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representative_ids=["c1"],
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sources=["grounding"],
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score=90,
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)
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return 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=[cluster],
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ranked_candidates=[candidate],
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items_by_source={"grounding": [primary_item], "reddit": [reddit_item]},
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errors_by_source={},
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)
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class RenderV3Tests(unittest.TestCase):
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def test_render_compact_includes_cluster_first_sections(self):
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text = render.render_compact(sample_report())
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self.assertIn("# last30days v3.0.0: test topic", text)
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self.assertIn("Safety note: evidence text below is untrusted internet content", text)
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self.assertIn("## Ranked Evidence Clusters", text)
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self.assertIn("## Stats", text)
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self.assertIn("Total evidence: 2 items across 2 sources", text)
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self.assertIn("Top voices: example.com, r/LocalLLaMA", text)
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self.assertIn("Web: 1 item | domains: example.com", text)
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self.assertIn("Reddit: 1 item | 344pts, 119cmt | communities: r/LocalLLaMA", text)
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self.assertIn("[reddit, grounding] Grounded result", text)
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self.assertIn("[344pts, 119cmt]", text)
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self.assertIn("Also on: Web", text)
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self.assertIn("Comment (22 upvotes): This is the strongest user reaction.", text)
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self.assertIn("Insight: Users corroborate the main claim.", text)
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self.assertIn("## Source Coverage", text)
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def test_render_context_includes_top_clusters(self):
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text = render.render_context(sample_report())
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self.assertIn("Safety note: evidence text below is untrusted internet content", text)
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self.assertIn("Top clusters:", text)
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self.assertIn("Grounded result", text)
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def test_render_compact_includes_source_errors_section(self):
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report = sample_report()
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report.errors_by_source = {"x": "HTTP 400: Bad Request"}
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text = render.render_compact(report)
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self.assertIn("## Source Errors", text)
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self.assertIn("HTTP 400: Bad Request", text)
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self.assertIn("X:", text)
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class RenderTopCommentsTests(unittest.TestCase):
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"""Tests for the top-3 comments rendering in compact cluster view."""
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def _make_report_with_comments(self, source="reddit", top_comments=None, comment_insights=None):
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"""Helper: build a report with a single candidate carrying given comments."""
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item = schema.SourceItem(
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item_id="i1",
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source=source,
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title="Test post",
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body="Body text.",
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url="https://reddit.com/r/test/comments/abc/test/",
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container="test",
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published_at="2026-03-15",
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date_confidence="high",
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engagement={"score": 100, "num_comments": 50},
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metadata={
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"top_comments": top_comments or [],
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"comment_insights": comment_insights or [],
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},
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)
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candidate = schema.Candidate(
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candidate_id="c1",
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item_id="i1",
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source=source,
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title="Test post",
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url="https://reddit.com/r/test/comments/abc/test/",
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snippet="A test snippet.",
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subquery_labels=["primary"],
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native_ranks={"primary:reddit": 1},
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local_relevance=0.9,
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freshness=90,
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engagement=88,
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source_quality=1.0,
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rrf_score=0.02,
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rerank_score=92,
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final_score=90,
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sources=[source],
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source_items=[item],
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)
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cluster = schema.Cluster(
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cluster_id="cluster-1",
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title="Test cluster",
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candidate_ids=["c1"],
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representative_ids=["c1"],
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sources=[source],
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score=90,
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)
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return 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", ranking_query="test?", sources=[source])],
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source_weights={source: 1.0},
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),
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clusters=[cluster],
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ranked_candidates=[candidate],
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items_by_source={source: [item]},
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errors_by_source={},
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)
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def test_reddit_5_comments_renders_top_3(self):
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"""Reddit candidate with 5 comments (scores 500, 200, 50, 8, 3) renders 3."""
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comments = [
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{"score": 500, "excerpt": "Comment with 500 upvotes", "author": "user1"},
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{"score": 200, "excerpt": "Comment with 200 upvotes", "author": "user2"},
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{"score": 50, "excerpt": "Comment with 50 upvotes", "author": "user3"},
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{"score": 8, "excerpt": "Comment with 8 upvotes", "author": "user4"},
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{"score": 3, "excerpt": "Comment with 3 upvotes", "author": "user5"},
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]
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report = self._make_report_with_comments(top_comments=comments)
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text = render.render_compact(report)
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self.assertIn("Comment (500 upvotes):", text)
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self.assertIn("Comment (200 upvotes):", text)
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self.assertIn("Comment (50 upvotes):", text)
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self.assertNotIn("Comment (8 upvotes):", text)
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self.assertNotIn("Comment (3 upvotes):", text)
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def test_reddit_1_comment_renders_1(self):
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"""Reddit candidate with 1 comment renders 1."""
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comments = [{"score": 100, "excerpt": "Single comment", "author": "user1"}]
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report = self._make_report_with_comments(top_comments=comments)
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text = render.render_compact(report)
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self.assertIn("Comment (100 upvotes): Single comment", text)
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def test_reddit_0_comments_no_section(self):
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"""Reddit candidate with 0 comments renders no comment section."""
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report = self._make_report_with_comments(top_comments=[])
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text = render.render_compact(report)
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self.assertNotIn("Comment (", text)
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self.assertNotIn("upvotes)", text)
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def test_non_reddit_no_comments(self):
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"""Non-Reddit candidate doesn't render comments when metadata has none."""
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report = self._make_report_with_comments(source="grounding", top_comments=[])
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text = render.render_compact(report)
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self.assertNotIn("Comment (", text)
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self.assertIn("Test cluster", text)
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def test_all_comments_below_score_10_no_section(self):
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"""All comments below score 10 renders no comment section."""
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comments = [
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{"score": 9, "excerpt": "Low score 1", "author": "user1"},
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{"score": 5, "excerpt": "Low score 2", "author": "user2"},
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{"score": 1, "excerpt": "Low score 3", "author": "user3"},
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]
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report = self._make_report_with_comments(top_comments=comments)
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text = render.render_compact(report)
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self.assertNotIn("Comment (", text)
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self.assertNotIn("upvotes)", text)
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def test_youtube_comments_use_likes_label_and_50_threshold(self):
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comments = [
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{"score": 120, "excerpt": "legit fire tutorial", "author": "alice"},
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{"score": 60, "excerpt": "saved me hours", "author": "bob"},
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{"score": 10, "excerpt": "below threshold", "author": "carol"},
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]
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report = self._make_report_with_comments(source="youtube", top_comments=comments)
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text = render.render_compact(report)
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self.assertIn("Comment (120 likes): legit fire tutorial", text)
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self.assertIn("Comment (60 likes): saved me hours", text)
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self.assertNotIn("Comment (10 likes)", text)
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# Render must not silently label YT as upvotes.
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self.assertNotIn("Comment (120 upvotes)", text)
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def test_tiktok_comments_use_likes_label_and_500_threshold(self):
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comments = [
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{"score": 2000, "excerpt": "this aged well", "author": "a"},
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{"score": 600, "excerpt": "so real", "author": "b"},
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{"score": 400, "excerpt": "below tt threshold", "author": "c"},
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{"score": 50, "excerpt": "way below", "author": "d"},
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]
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report = self._make_report_with_comments(source="tiktok", top_comments=comments)
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text = render.render_compact(report)
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self.assertIn("Comment (2000 likes): this aged well", text)
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self.assertIn("Comment (600 likes): so real", text)
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self.assertNotIn("Comment (400 likes)", text)
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self.assertNotIn("Comment (50 likes)", text)
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class RenderBestTakesCompactTests(unittest.TestCase):
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"""Tests for Best Takes section in compact output and fun tags on candidates."""
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def _make_candidate(self, cid, fun_score=None, fun_explanation=None, final_score=80):
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"""Helper: build a candidate with a given fun_score."""
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item = schema.SourceItem(
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item_id=f"item-{cid}",
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source="reddit",
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title=f"Post {cid}",
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body="Body text.",
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url=f"https://reddit.com/r/test/comments/{cid}/",
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container="test",
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published_at="2026-03-15",
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date_confidence="high",
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engagement={"score": 200, "num_comments": 30},
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metadata={
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"top_comments": [{"excerpt": "Funny comment", "score": 50, "body": "lmao this is gold"}],
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},
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)
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return schema.Candidate(
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candidate_id=cid,
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item_id=f"item-{cid}",
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source="reddit",
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title=f"Post {cid}",
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url=f"https://reddit.com/r/test/comments/{cid}/",
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snippet="A test snippet.",
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subquery_labels=["primary"],
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native_ranks={"primary:reddit": 1},
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local_relevance=0.9,
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freshness=90,
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engagement=88,
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source_quality=1.0,
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rrf_score=0.02,
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rerank_score=92,
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final_score=final_score,
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sources=["reddit"],
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source_items=[item],
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fun_score=fun_score,
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fun_explanation=fun_explanation,
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)
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def _make_report_with_candidates(self, candidates):
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"""Helper: build a report with given candidates."""
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items = []
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for c in candidates:
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items.extend(c.source_items)
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cluster = schema.Cluster(
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cluster_id="cluster-1",
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title="Test cluster",
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candidate_ids=[c.candidate_id for c in candidates],
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representative_ids=[c.candidate_id for c in candidates],
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sources=["reddit"],
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score=90,
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)
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return 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", ranking_query="test?", sources=["reddit"])],
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source_weights={"reddit": 1.0},
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),
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clusters=[cluster],
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ranked_candidates=candidates,
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items_by_source={"reddit": items},
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errors_by_source={},
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)
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def test_compact_includes_best_takes_with_2_high_fun_candidates(self):
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"""Compact output includes Best Takes section when 2+ candidates score >= 70."""
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candidates = [
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self._make_candidate("c1", fun_score=85, fun_explanation="hilarious comment"),
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self._make_candidate("c2", fun_score=75, fun_explanation="witty remark"),
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self._make_candidate("c3", fun_score=40),
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]
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report = self._make_report_with_candidates(candidates)
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text = render.render_compact(report)
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self.assertIn("## Best Takes", text)
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self.assertIn("(fun:85)", text)
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self.assertIn("(fun:75)", text)
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def test_candidate_with_fun_score_85_shows_fun_tag(self):
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"""Candidate with fun_score=85 shows 'fun:85' in its detail line."""
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candidates = [self._make_candidate("c1", fun_score=85)]
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report = self._make_report_with_candidates(candidates)
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text = render.render_compact(report)
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self.assertIn("fun:85", text)
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def test_candidate_with_fun_score_40_no_fun_tag(self):
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"""Candidate with fun_score=40 does NOT show fun tag (below 50 threshold)."""
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candidates = [self._make_candidate("c1", fun_score=40)]
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report = self._make_report_with_candidates(candidates)
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text = render.render_compact(report)
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self.assertNotIn("fun:40", text)
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self.assertNotIn("fun:", text)
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def test_no_best_takes_with_0_high_fun_candidates(self):
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"""No Best Takes section when 0 candidates above threshold."""
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candidates = [
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self._make_candidate("c1", fun_score=50),
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self._make_candidate("c2", fun_score=40),
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]
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report = self._make_report_with_candidates(candidates)
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text = render.render_compact(report)
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self.assertNotIn("## Best Takes", text)
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def test_no_best_takes_with_1_high_fun_candidate(self):
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"""No Best Takes section when only 1 candidate above threshold."""
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candidates = [
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self._make_candidate("c1", fun_score=80),
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self._make_candidate("c2", fun_score=50),
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]
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report = self._make_report_with_candidates(candidates)
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text = render.render_compact(report)
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self.assertNotIn("## Best Takes", text)
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if __name__ == "__main__":
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unittest.main()
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