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refactor: streamline TikTok and Instagram scraping logic by removing search_queries and enhancing documentation for clarity

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DESKTOP-RTLN3BA\$punk 2026-07-13 17:11:25 -07:00
parent e8b3692b54
commit 2b018c4474
111 changed files with 1800 additions and 1580 deletions
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29
surfsense_evals/scripts/test_context_overflow_hypothesis.py
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@ -40,8 +40,7 @@ CONTEXT_HINTS = (
def main() -> None:
rows = [
json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
if line.strip()
json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip()
]
extraction_size: dict[tuple[str, str], int] = {}
@ -73,12 +72,12 @@ def main() -> None:
print("=" * 80)
print("(b) Extraction size for OK vs FAILED rows per arm")
print("=" * 80)
arm_buckets: dict[str, dict[str, list[int]]] = defaultdict(
lambda: {"ok": [], "fail": []}
)
arm_buckets: dict[str, dict[str, list[int]]] = defaultdict(lambda: {"ok": [], "fail": []})
parser_arms = (
"azure_basic_lc", "azure_premium_lc",
"llamacloud_basic_lc", "llamacloud_premium_lc",
"azure_basic_lc",
"azure_premium_lc",
"llamacloud_basic_lc",
"llamacloud_premium_lc",
)
for row in rows:
arm = row["arm"]
@ -133,10 +132,13 @@ def main() -> None:
" 3M_2018_10K x llamacloud_premium = 908,733 chars (~227k tokens) "
"-- this is above Sonnet 4.5's 200k window."
)
print(" If transport hypothesis is correct, this should still fail with a "
"real overflow error.")
print(" If transport hypothesis is correct AND the model truncates silently, "
"it might 'succeed' but be wrong.")
print(
" If transport hypothesis is correct, this should still fail with a real overflow error."
)
print(
" If transport hypothesis is correct AND the model truncates silently, "
"it might 'succeed' but be wrong."
)
print()
for row in rows:
if row["doc_id"] != "3M_2018_10K.pdf":
@ -145,10 +147,7 @@ def main() -> None:
continue
err = row.get("error") or "(none)"
graded = row.get("graded") or {}
print(
f" {row['qid']:<40} correct={graded.get('correct')!s:<5} "
f"err={err[:100]}"
)
print(f" {row['qid']:<40} correct={graded.get('correct')!s:<5} err={err[:100]}")
if __name__ == "__main__":