SurfSense/surfsense_evals/scripts/check_extraction_sizes.py

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feat(evals): publish multimodal_doc parser_compare benchmark + n=171 report Adds the full parser_compare experiment for the multimodal_doc suite: six arms compared on 30 PDFs / 171 questions from MMLongBench-Doc with anthropic/claude-sonnet-4.5 across the board. Source code: - core/parsers/{azure_di,llamacloud,pdf_pages}.py: direct parser SDK callers (Azure Document Intelligence prebuilt-read/layout, LlamaParse parse_page_with_llm/parse_page_with_agent) used by the LC arms, bypassing the SurfSense backend so each (basic/premium) extraction is a clean A/B independent of backend ETL routing. - suites/multimodal_doc/parser_compare/{ingest,runner,prompt}.py: six-arm benchmark (native_pdf, azure_basic_lc, azure_premium_lc, llamacloud_basic_lc, llamacloud_premium_lc, surfsense_agentic) with byte-identical prompts per question, deterministic grader, Wilson CIs, and the per-page preprocessing tariff cost overlay. Reproducibility: - pyproject.toml + uv.lock pin pypdf, azure-ai-documentintelligence, llama-cloud-services as new deps. - .env.example documents the AZURE_DI_* and LLAMA_CLOUD_API_KEY env vars now required for parser_compare. - 12 analysis scripts under scripts/: retry pass with exponential backoff, post-retry accuracy merge, McNemar / latency / per-PDF stats, context-overflow hypothesis test, etc. Each produces one number cited by the blog report. Citation surface: - reports/blog/multimodal_doc_parser_compare_n171_report.md: 1219-line technical writeup (16 sections) covering headline accuracy, per-format accuracy, McNemar pairwise significance, latency / token / per-PDF distributions, error analysis, retry experiment, post-retry final accuracy, cost amortization model with closed-form derivation, threats to validity, and reproducibility appendix. - data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/{raw, raw_retries,raw_post_retry}.jsonl + run_artifact.json + retry summary whitelisted via data/.gitignore as the verifiable numbers source. Gitignore: - ignore logs_*.txt + retry_run.log; structured artifacts cover the citation surface, debug logs are noise. - data/.gitignore default-ignores everything, whitelists the n=171 run artifacts only (parser manifest left ignored to avoid leaking local Windows usernames in absolute paths; manifest is fully regenerable via 'ingest multimodal_doc parser_compare'). - reports/.gitignore now whitelists hand-curated reports/blog/. Also retires the abandoned CRAG Task 3 implementation (download script, streaming Task 3 ingest, CragTask3Benchmark + tests) and trims the runner / ingest module APIs to match. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-05-14 19:54:41 -07:00
"""Sanity check extraction sizes against Sonnet 4.5's context window.
Sonnet 4.5 supports ~200k tokens. As a *very* rough heuristic, English
markdown is ~4 chars/token, so anything over ~750k chars likely won't
fit alongside the system + question + 512 max_output_tokens. Print
warnings for any extraction that's at risk.
"""
from __future__ import annotations
import json
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
MAP = REPO / "data" / "multimodal_doc" / "maps" / "parser_compare_doc_map.jsonl"
CHARS_PER_TOKEN = 4
CTX_TOKENS = 200_000
PROMPT_OVERHEAD_TOKENS = 1_000 # system + question + format hint
MAX_OUTPUT_TOKENS = 512
SAFE_CHARS = (CTX_TOKENS - PROMPT_OVERHEAD_TOKENS - MAX_OUTPUT_TOKENS) * CHARS_PER_TOKEN
def main() -> None:
rows = [
json.loads(line)
for line in MAP.read_text(encoding="utf-8").splitlines()
if line.strip()
]
total = len(rows)
arm_max: dict[str, tuple[int, str]] = {}
overflows: list[tuple[str, str, int]] = []
for row in rows:
for arm, ext in (row.get("extractions") or {}).items():
chars = int(ext.get("chars") or 0)
if arm not in arm_max or arm_max[arm][0] < chars:
arm_max[arm] = (chars, row["doc_id"])
if chars > SAFE_CHARS:
overflows.append((row["doc_id"], arm, chars))
print(f"PDFs in manifest: {total}")
print(f"safe char budget: {SAFE_CHARS:,} (~{(SAFE_CHARS // CHARS_PER_TOKEN):,} tokens)")
print()
print("largest extraction per arm:")
for arm, (chars, doc_id) in sorted(arm_max.items()):
print(f" {arm:25s} {chars:>10,} chars ({doc_id})")
print()
if overflows:
print(f"OVERFLOW RISK ({len(overflows)} extractions > safe budget):")
for doc_id, arm, chars in overflows:
est_tokens = chars // CHARS_PER_TOKEN
print(f" {doc_id} :: {arm} :: {chars:,} chars (~{est_tokens:,} tokens)")
else:
print("no overflow risk — all extractions fit Sonnet 4.5's 200k context.")
if __name__ == "__main__":
main()