Extract relevance_filter, add Bluesky/TruthSocial type hint + test coverage
- Extract _relevance_filter from last30days.py closure to score.relevance_filter() for testability - Add BlueskyItem/TruthSocialItem to sort_items() type hint (was missing despite being in _ITEM_SOURCE_MAP) - Add tests: Bluesky/TruthSocial engagement scoring, sort_items mixed sources, relevance_filter behavior (threshold, minimum-result guarantee, missing attr), select_openai_model HTTP 401/403 error paths
This commit is contained in:
Jeffrey Sperling
+8
-21
@@ -1830,27 +1830,14 @@ def main():
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deduped_web = websearch.dedupe_websearch(sorted_web) if sorted_web else []
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# Post-retrieval relevance filter: drop low-relevance items per source
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# Only filter when there are enough items (>3) to avoid empty results
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def _relevance_filter(items, source_name, threshold=0.3):
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"""Filter items below relevance threshold with minimum-result guarantee."""
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if len(items) <= 3:
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return items
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passed = [i for i in items if getattr(i, 'relevance', 0.0) >= threshold]
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if not passed:
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# Keep top 3 by relevance if all filtered
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print(f"[{source_name} WARNING] All results below relevance {threshold}, keeping top 3", file=sys.stderr)
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by_rel = sorted(items, key=lambda x: getattr(x, 'relevance', 0.0), reverse=True)
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return by_rel[:3]
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return passed
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deduped_reddit = _relevance_filter(deduped_reddit, "REDDIT")
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deduped_x = _relevance_filter(deduped_x, "X")
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deduped_youtube = _relevance_filter(deduped_youtube, "YOUTUBE")
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deduped_tiktok = _relevance_filter(deduped_tiktok, "TIKTOK")
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deduped_ig = _relevance_filter(deduped_ig, "INSTAGRAM")
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deduped_hn = _relevance_filter(deduped_hn, "HN")
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deduped_bsky = _relevance_filter(deduped_bsky, "BLUESKY")
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deduped_ts = _relevance_filter(deduped_ts, "TRUTHSOCIAL")
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deduped_reddit = score.relevance_filter(deduped_reddit, "REDDIT")
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deduped_x = score.relevance_filter(deduped_x, "X")
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deduped_youtube = score.relevance_filter(deduped_youtube, "YOUTUBE")
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deduped_tiktok = score.relevance_filter(deduped_tiktok, "TIKTOK")
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deduped_ig = score.relevance_filter(deduped_ig, "INSTAGRAM")
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deduped_hn = score.relevance_filter(deduped_hn, "HN")
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deduped_bsky = score.relevance_filter(deduped_bsky, "BLUESKY")
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deduped_ts = score.relevance_filter(deduped_ts, "TRUTHSOCIAL")
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# Cross-source linking: annotate items that discuss the same story
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dedupe.cross_source_link(
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+19
-1
@@ -715,7 +715,7 @@ _ITEM_SOURCE_MAP = {
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_DEFAULT_TIEBREAKER = {"reddit": 0, "x": 1, "youtube": 2, "tiktok": 3, "instagram": 4, "hn": 5, "bluesky": 6, "truthsocial": 7, "polymarket": 8, "web": 9}
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def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.TikTokItem, schema.InstagramItem, schema.HackerNewsItem, schema.PolymarketItem]], query_type: QueryType = None) -> List:
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def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.TikTokItem, schema.InstagramItem, schema.HackerNewsItem, schema.BlueskyItem, schema.TruthSocialItem, schema.PolymarketItem]], query_type: QueryType = None) -> List:
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"""Sort items by score (descending), then date, then source tiebreaker.
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Tiebreaker (tertiary sort key, after score and date): source priority
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@@ -749,3 +749,21 @@ def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSear
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return (score, date_key, source_priority, text)
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return sorted(items, key=sort_key)
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def relevance_filter(items, source_name: str, threshold: float = 0.3):
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"""Filter items below relevance threshold with minimum-result guarantee.
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Items with no relevance attribute are treated as 0.0 (fail the filter).
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If all items are below threshold, keeps the top 3 by relevance.
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Lists with 3 or fewer items are returned unchanged.
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"""
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import sys
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if len(items) <= 3:
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return items
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passed = [i for i in items if getattr(i, 'relevance', 0.0) >= threshold]
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if not passed:
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print(f"[{source_name} WARNING] All results below relevance {threshold}, keeping top 3", file=sys.stderr)
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by_rel = sorted(items, key=lambda x: getattr(x, 'relevance', 0.0), reverse=True)
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return by_rel[:3]
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return passed
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@@ -140,6 +140,27 @@ class TestSelectOpenAIModel(unittest.TestCase):
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self.assertEqual(result, "gpt-4.1-mini")
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class TestSelectOpenAIModelErrorPaths(unittest.TestCase):
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def setUp(self):
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from lib import cache
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cache.MODEL_CACHE_FILE.unlink(missing_ok=True)
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def test_http_error_returns_fallback(self):
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"""HTTPError during model fetch should return fallback, not crash."""
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from unittest.mock import patch
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from lib import http
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with patch('lib.http.get', side_effect=http.HTTPError("Unauthorized", status_code=401)):
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result = models.select_openai_model("bad-key", policy="auto")
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self.assertEqual(result, models.OPENAI_FALLBACK_MODELS[0])
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def test_http_403_returns_fallback(self):
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from unittest.mock import patch
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from lib import http
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with patch('lib.http.get', side_effect=http.HTTPError("Forbidden", status_code=403)):
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result = models.select_openai_model("bad-key", policy="auto")
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self.assertEqual(result, models.OPENAI_FALLBACK_MODELS[0])
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class TestSelectXAIModel(unittest.TestCase):
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def test_latest_policy(self):
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result = models.select_xai_model(
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@@ -192,5 +192,160 @@ class TestInstagramEngagement(unittest.TestCase):
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)
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class TestBlueskyEngagement(unittest.TestCase):
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"""Tests for compute_bluesky_engagement_raw()."""
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def test_basic(self):
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eng = schema.Engagement(likes=100, reposts=25, replies=15, quotes=5)
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raw = score.compute_bluesky_engagement_raw(eng)
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self.assertIsNotNone(raw)
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self.assertGreater(raw, 0)
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def test_likes_dominate(self):
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likes_heavy = schema.Engagement(likes=1000, reposts=0, replies=0, quotes=0)
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reposts_heavy = schema.Engagement(likes=0, reposts=1000, replies=0, quotes=0)
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self.assertGreater(
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score.compute_bluesky_engagement_raw(likes_heavy),
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score.compute_bluesky_engagement_raw(reposts_heavy),
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)
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def test_none_engagement(self):
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self.assertIsNone(score.compute_bluesky_engagement_raw(None))
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def test_no_likes_no_reposts(self):
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eng = schema.Engagement(replies=10)
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self.assertIsNone(score.compute_bluesky_engagement_raw(eng))
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class TestTruthSocialEngagement(unittest.TestCase):
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"""Tests for compute_truthsocial_engagement_raw()."""
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def test_basic(self):
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eng = schema.Engagement(likes=100, reposts=25, replies=15)
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raw = score.compute_truthsocial_engagement_raw(eng)
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self.assertIsNotNone(raw)
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self.assertGreater(raw, 0)
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def test_likes_dominate(self):
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likes_heavy = schema.Engagement(likes=1000, reposts=0, replies=0)
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reposts_heavy = schema.Engagement(likes=0, reposts=1000, replies=0)
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self.assertGreater(
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score.compute_truthsocial_engagement_raw(likes_heavy),
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score.compute_truthsocial_engagement_raw(reposts_heavy),
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)
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def test_none_engagement(self):
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self.assertIsNone(score.compute_truthsocial_engagement_raw(None))
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class TestScoreBlueskyItems(unittest.TestCase):
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"""Tests for score_bluesky_items()."""
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def test_scores_items(self):
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items = [
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schema.BlueskyItem(
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id="bsky1", text="Test", url="https://bsky.app/1",
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author_handle="user.bsky.social", display_name="User",
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engagement=schema.Engagement(likes=50, reposts=10, replies=5, quotes=2),
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relevance=0.8,
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),
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]
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result = score.score_bluesky_items(items)
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self.assertEqual(len(result), 1)
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self.assertGreater(result[0].score, 0)
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def test_empty_list(self):
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self.assertEqual(score.score_bluesky_items([]), [])
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class TestScoreTruthSocialItems(unittest.TestCase):
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"""Tests for score_truthsocial_items()."""
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def test_scores_items(self):
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items = [
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schema.TruthSocialItem(
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id="ts1", text="Test", url="https://truthsocial.com/1",
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author_handle="@user", display_name="User",
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engagement=schema.Engagement(likes=50, reposts=10, replies=5),
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relevance=0.8,
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),
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]
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result = score.score_truthsocial_items(items)
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self.assertEqual(len(result), 1)
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self.assertGreater(result[0].score, 0)
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def test_empty_list(self):
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self.assertEqual(score.score_truthsocial_items([]), [])
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class TestSortItemsMixedSources(unittest.TestCase):
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"""Test sort_items with Bluesky and TruthSocial items."""
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def test_bluesky_item_sorts(self):
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items = [
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schema.RedditItem(id="R1", title="Reddit", url="", subreddit="", score=30),
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schema.BlueskyItem(id="B1", text="Bluesky", url="", author_handle="u.bsky.social", display_name="U", score=90),
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]
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result = score.sort_items(items)
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self.assertEqual(result[0].id, "B1")
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def test_truthsocial_item_sorts(self):
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items = [
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schema.RedditItem(id="R1", title="Reddit", url="", subreddit="", score=30),
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schema.TruthSocialItem(id="T1", text="TS", url="", author_handle="@u", display_name="U", score=90),
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]
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result = score.sort_items(items)
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self.assertEqual(result[0].id, "T1")
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class TestRelevanceFilter(unittest.TestCase):
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"""Tests for relevance_filter()."""
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def _make_items(self, relevances):
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"""Helper: create RedditItems with given relevance values."""
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return [
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schema.RedditItem(id=f"R{i}", title=f"Item {i}", url="", subreddit="", relevance=r)
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for i, r in enumerate(relevances)
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]
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def test_filters_below_threshold(self):
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items = self._make_items([0.8, 0.1, 0.5, 0.2])
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result = score.relevance_filter(items, "TEST", threshold=0.3)
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self.assertEqual(len(result), 2)
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self.assertTrue(all(i.relevance >= 0.3 for i in result))
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def test_small_list_unchanged(self):
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items = self._make_items([0.1, 0.05, 0.02])
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result = score.relevance_filter(items, "TEST")
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self.assertEqual(len(result), 3)
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def test_all_below_threshold_keeps_top_3(self):
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items = self._make_items([0.1, 0.25, 0.05, 0.2, 0.15])
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result = score.relevance_filter(items, "TEST", threshold=0.3)
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self.assertEqual(len(result), 3)
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# Should be sorted by relevance: 0.25, 0.2, 0.15
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self.assertEqual(result[0].relevance, 0.25)
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self.assertEqual(result[1].relevance, 0.2)
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def test_empty_list(self):
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result = score.relevance_filter([], "TEST")
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self.assertEqual(result, [])
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def test_items_without_relevance_attr_treated_as_zero(self):
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"""Objects lacking a relevance attribute get 0.0, failing the filter."""
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class BareItem:
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def __init__(self, id):
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self.id = id
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items = [
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schema.RedditItem(id="R0", title="Has relevance", url="", subreddit="", relevance=0.8),
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BareItem("B1"),
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BareItem("B2"),
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BareItem("B3"),
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]
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result = score.relevance_filter(items, "TEST", threshold=0.3)
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self.assertEqual(len(result), 1)
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self.assertEqual(result[0].id, "R0")
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if __name__ == "__main__":
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unittest.main()
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