Jeffrey Sperling
96a4a78faa
The Gemini 3.1 Flash Lite preview model is being discontinued on May 25, 2026. Per Google's GA announcement, the underlying model architecture is identical and only the model identifier needs to be updated from `gemini-3.1-flash-lite-preview` to `gemini-3.1-flash-lite`. Also relaxes the `_require_gemini_31_preview` guard to accept any `gemini-3.1-*` identifier (renamed to `_require_gemini_31`), so the GA name and the still-preview `gemini-3.1-pro-preview` both pass.
90 lines
3.8 KiB
Python
90 lines
3.8 KiB
Python
import sys
|
|
import unittest
|
|
from pathlib import Path
|
|
|
|
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "skills" / "last30days" / "scripts"))
|
|
|
|
from lib import schema
|
|
|
|
|
|
class SchemaV3Tests(unittest.TestCase):
|
|
def test_report_roundtrip(self):
|
|
report = schema.Report(
|
|
topic="test topic",
|
|
range_from="2026-02-14",
|
|
range_to="2026-03-16",
|
|
generated_at="2026-03-16T00:00:00+00:00",
|
|
provider_runtime=schema.ProviderRuntime(
|
|
reasoning_provider="gemini",
|
|
planner_model="gemini-3.1-flash-lite",
|
|
rerank_model="gemini-3.1-flash-lite",
|
|
),
|
|
query_plan=schema.QueryPlan(
|
|
intent="breaking_news",
|
|
freshness_mode="strict_recent",
|
|
cluster_mode="story",
|
|
raw_topic="test topic",
|
|
subqueries=[schema.SubQuery(label="primary", search_query="test topic", ranking_query="What happened with test topic?", sources=["grounding"])],
|
|
source_weights={"grounding": 1.0},
|
|
),
|
|
clusters=[schema.Cluster(cluster_id="cluster-1", title="Title", candidate_ids=["c1"], representative_ids=["c1"], sources=["grounding"], score=90)],
|
|
ranked_candidates=[schema.Candidate(
|
|
candidate_id="c1",
|
|
item_id="i1",
|
|
source="grounding",
|
|
sources=["grounding", "reddit"],
|
|
title="Title",
|
|
url="https://example.com",
|
|
snippet="Snippet",
|
|
subquery_labels=["primary"],
|
|
native_ranks={"primary:grounding": 1},
|
|
local_relevance=0.8,
|
|
freshness=90,
|
|
engagement=None,
|
|
source_quality=1.0,
|
|
rrf_score=0.02,
|
|
rerank_score=91,
|
|
final_score=90,
|
|
source_items=[
|
|
schema.SourceItem(item_id="i1", source="grounding", title="Title", body="Body", url="https://example.com", published_at="2026-03-16")
|
|
],
|
|
)],
|
|
items_by_source={"grounding": [schema.SourceItem(item_id="i1", source="grounding", title="Title", body="Body", url="https://example.com")]},
|
|
errors_by_source={},
|
|
warnings=["warning"],
|
|
artifacts={"grounding": []},
|
|
)
|
|
restored = schema.report_from_dict(schema.to_dict(report))
|
|
self.assertEqual(report.topic, restored.topic)
|
|
self.assertEqual(report.provider_runtime.planner_model, restored.provider_runtime.planner_model)
|
|
self.assertEqual(report.ranked_candidates[0].candidate_id, restored.ranked_candidates[0].candidate_id)
|
|
self.assertEqual(report.ranked_candidates[0].sources, restored.ranked_candidates[0].sources)
|
|
self.assertEqual(report.items_by_source["grounding"][0].title, restored.items_by_source["grounding"][0].title)
|
|
|
|
def test_source_item_from_dict_preserves_zero_valued_signals(self):
|
|
item = schema.source_item_from_dict(
|
|
{
|
|
"item_id": "x1",
|
|
"source": "x",
|
|
"title": "Title",
|
|
"body": "Body",
|
|
"url": "https://example.com",
|
|
"relevance_hint": 0.0,
|
|
"local_relevance": 0.0,
|
|
"freshness": 0,
|
|
"engagement_score": 0,
|
|
"source_quality": 0.0,
|
|
"local_rank_score": 0.0,
|
|
}
|
|
)
|
|
self.assertEqual(0.0, item.relevance_hint)
|
|
self.assertEqual(0.0, item.local_relevance)
|
|
self.assertEqual(0, item.freshness)
|
|
self.assertEqual(0, item.engagement_score)
|
|
self.assertEqual(0.0, item.source_quality)
|
|
self.assertEqual(0.0, item.local_rank_score)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|