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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>
125 lines
4.8 KiB
Python
125 lines
4.8 KiB
Python
"""Were the SSL failures clustered in time (network blip) or evenly
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distributed (sustained limit)? Group failures by 1-min buckets using
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the run start time and the per-row latency_ms / answer order.
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Also: for the one *real* intrinsic failure — the 30MB Anthropic limit
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on 2405.09818v1.pdf::Q007 — print the full error message + raw payload
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sizes so the blog has a clean root cause.
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"""
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from __future__ import annotations
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import json
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from collections import Counter, defaultdict
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from pathlib import Path
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REPO = Path(__file__).resolve().parents[1]
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RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare"
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RAW = RUN / "raw.jsonl"
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PDFS = REPO / "data" / "multimodal_doc" / "mmlongbench" / "pdfs"
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def main() -> None:
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rows = [
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json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
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if line.strip()
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]
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# 1) SSL clustering: failures by question index per arm
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by_arm_idx: dict[str, list[tuple[int, str]]] = defaultdict(list)
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qid_order: dict[str, int] = {}
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arm_seen_count: dict[str, int] = defaultdict(int)
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for row in rows:
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arm = row["arm"]
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idx = arm_seen_count[arm]
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arm_seen_count[arm] += 1
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qid_order[f"{arm}::{row['qid']}"] = idx
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err = row.get("error") or ""
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cluster = "ssl" if "SSLError" in err else (
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"empty" if not (row.get("raw_text") or "").strip() and not err else (
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"5xx" if "502" in err or "503" in err else (
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"size_limit" if "exceeds" in err.lower() and "limit" in err.lower() else (
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"other_err" if err else "ok"
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)
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)
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)
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)
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if cluster != "ok":
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by_arm_idx[arm].append((idx, cluster))
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print("=" * 80)
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print("SSL/network-error indices per arm (each arm processes 171 questions in")
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print("order; index = sequential position within that arm). Tight clustering")
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print("in time = transient blip, even spread = sustained limit.")
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print("=" * 80)
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for arm in sorted(by_arm_idx):
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items = by_arm_idx[arm]
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if not items:
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continue
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idxs = sorted(set(i for i, _ in items))
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print(f"\n{arm}: {len(items)} failures at indices {idxs}")
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# show clusters
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cluster_runs = []
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cur = [idxs[0]]
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for i in idxs[1:]:
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if i - cur[-1] <= 5: # within 5 questions = same time window
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cur.append(i)
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else:
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cluster_runs.append(cur)
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cur = [i]
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cluster_runs.append(cur)
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print(f" clusters (gap<=5): {len(cluster_runs)}: {cluster_runs}")
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# 2) The 30MB intrinsic failure — full details
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print()
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print("=" * 80)
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print("Intrinsic failure: 30MB Anthropic input limit on 2405.09818v1.pdf::Q007")
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print("=" * 80)
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for row in rows:
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if row["qid"] == "2405.09818v1.pdf::Q007" and row["arm"] == "native_pdf":
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err = row.get("error") or ""
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print(f" qid: {row['qid']}")
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print(f" doc: {row['doc_id']}, pages: {row.get('pages')}")
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pdf_path = PDFS / row["doc_id"]
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if pdf_path.exists():
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size_mb = pdf_path.stat().st_size / (1024 * 1024)
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print(f" PDF size on disk: {size_mb:.1f} MB")
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# base64 inflates ~33%
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est_b64 = size_mb * 1.33
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print(f" estimated base64 wire size: {est_b64:.1f} MB")
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print(f" full error: {err[:600]}")
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break
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# 3) Per-PDF: which PDFs are pathological?
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print()
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print("=" * 80)
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print("Per-PDF failure breakdown across all 6 arms (only PDFs with failures)")
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print("=" * 80)
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by_pdf: dict[str, list[dict]] = defaultdict(list)
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for row in rows:
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err = row.get("error") or ""
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empty = not (row.get("raw_text") or "").strip()
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if err or empty:
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by_pdf[row["doc_id"]].append({
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"arm": row["arm"],
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"qid": row["qid"],
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"err_kind": (
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"ssl" if "SSLError" in err
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else "size_limit" if "exceeds" in err.lower() and "limit" in err.lower()
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else "5xx" if "502" in err or "503" in err
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else "json_decode" if "JSONDecodeError" in err
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else "empty" if empty and not err
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else "other"
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),
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"pages": row.get("pages"),
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})
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for doc, items in sorted(by_pdf.items(), key=lambda x: (-len(x[1]), x[0])):
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kinds = Counter(i["err_kind"] for i in items)
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arms = sorted({i["arm"] for i in items})
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pages = items[0]["pages"]
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print(f" {doc} pages={pages} failures={len(items)} arms={arms}")
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print(f" kinds: {dict(kinds)}")
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if __name__ == "__main__":
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main()
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