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356#!/usr/bin/env python3
"""Batch-submit PDFs and collect results."""
import asyncio
import json
import os
import statistics
import sys
import time
from datetime import datetime, timezone
from pathlib import Path
import httpx
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
API_URL = os.environ.get("BATCH_API_URL", "http://localhost:8080")
API_KEY = os.environ.get("BATCH_API_KEY", "")
CONCURRENCY = int(os.environ.get("BATCH_CONCURRENCY", "2"))
POLL_INTERVAL = 10 # seconds
SUBMIT_TIMEOUT = 120 # seconds for upload
POLL_TIMEOUT = 900 # seconds max wait per job (15min for GPU cold-start)
AUTO_APPROVE_PII = True # auto-approve PII findings
SUBMIT_DELAY = 5 # seconds between submissions to avoid rate limiting
MAX_RETRIES = 5 # max retries for 429/5xx on submit
RETRY_BASE_DELAY = 10 # base delay for exponential backoff (seconds)
PDF_DIRS = [
Path(__file__).resolve().parent.parent
/ "project-docs"
/ "UIC Documents"
/ "downloaded-samples"
/ "diverse-sample-pdfs",
]
EXTRA_PDFS = [
Path(__file__).resolve().parent.parent
/ "project-docs"
/ "pdfs"
/ "Undergraduate Course Syllabi (Field_ intro_text_body)_ad82e678.pdf",
]
RESULTS_BASE = Path(__file__).resolve().parent.parent / "batch-results"
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def short_name(path: Path) -> str:
stem = path.stem
if len(stem) > 30:
stem = stem[:30]
return stem
def mb(path: Path) -> str:
return f"{path.stat().st_size / 1_048_576:.1f} MB"
# Rate-limit gate: only one submission at a time with delay between
_submit_lock = asyncio.Lock()
async def throttled_submit(
client: httpx.AsyncClient,
pdf: Path,
label: str,
) -> httpx.Response:
"""Submit with retry + backoff for 429/5xx, and inter-submission delay."""
last_error = None
for attempt in range(MAX_RETRIES + 1):
async with _submit_lock:
if attempt > 0:
delay = RETRY_BASE_DELAY * (2 ** (attempt - 1))
print(f" [{label}] Retry {attempt}/{MAX_RETRIES} after {delay}s...")
await asyncio.sleep(delay)
else:
# Small delay between submissions even on first attempt
await asyncio.sleep(SUBMIT_DELAY)
with open(pdf, "rb") as f:
resp = await client.post(
f"{API_URL}/api/v1/documents/submit",
headers={"X-API-Key": API_KEY},
files={"file": (pdf.name, f, "application/pdf")},
timeout=SUBMIT_TIMEOUT,
)
if resp.status_code == 429:
retry_after = int(resp.headers.get("Retry-After", RETRY_BASE_DELAY))
print(f" [{label}] Rate limited (429), waiting {retry_after}s...")
await asyncio.sleep(retry_after)
last_error = f"429 Too Many Requests"
continue
elif resp.status_code >= 500:
last_error = f"{resp.status_code} Server Error"
continue
elif resp.status_code == 413:
resp.raise_for_status() # Too large, no point retrying
resp.raise_for_status()
return resp
raise httpx.HTTPStatusError(
f"Failed after {MAX_RETRIES} retries: {last_error}",
request=resp.request,
response=resp,
)
# ---------------------------------------------------------------------------
# Core
# ---------------------------------------------------------------------------
async def submit_and_process(
client: httpx.AsyncClient,
pdf: Path,
idx: int,
total: int,
sem: asyncio.Semaphore,
results_dir: Path,
) -> dict:
label = short_name(pdf)
async with sem:
print(f"[{idx}/{total}] Submitting: {label} ({mb(pdf)})")
t0 = time.monotonic()
# Submit with retry
try:
resp = await throttled_submit(client, pdf, label)
data = resp.json()
job_id = data["job_id"]
print(f" [{label}] Job ID: {job_id}")
except Exception as e:
elapsed = time.monotonic() - t0
print(f" [{label}] SUBMIT ERROR: {e}")
return {"file": pdf.name, "status": "submit_error", "error": str(e), "elapsed": elapsed}
# Poll
deadline = time.monotonic() + POLL_TIMEOUT
consecutive_errors = 0
while time.monotonic() < deadline:
await asyncio.sleep(POLL_INTERVAL)
try:
resp = await client.get(
f"{API_URL}/api/v1/documents/{job_id}",
headers={"X-API-Key": API_KEY},
timeout=30,
)
resp.raise_for_status()
consecutive_errors = 0
data = resp.json()
status = data.get("status", "unknown")
# Auto-approve PII
if status == "awaiting_approval" and AUTO_APPROVE_PII:
token = data.get("approval_token", "")
if token:
try:
approve_resp = await client.post(
f"{API_URL}/api/v1/approval/{token}/decision",
headers={
"X-API-Key": API_KEY,
"Content-Type": "application/json",
},
json={"decision": "approved", "reviewed_by": "batch_run"},
timeout=30,
)
approve_resp.raise_for_status()
pii_count = len(data.get("pii_findings", []))
print(f" [{label}] Auto-approved {pii_count} PII findings")
except Exception as e:
print(f" [{label}] PII approve error: {e}")
continue
if status == "completed":
elapsed = time.monotonic() - t0
pages = data.get("total_pages", "?")
cost = data.get("llm_cost", {}).get("estimated_cost_dollars", 0)
print(f" [{label}] OK in {elapsed / 60:.1f}m — {pages}p, ${cost:.4f}")
await save_result(client, data, results_dir / label)
return {
"file": pdf.name,
"status": "completed",
"job_id": job_id,
"pages": pages,
"cost": cost,
"tokens": data.get("llm_cost", {}).get("total_tokens", 0),
"elapsed": elapsed,
"edits": data.get("total_edits", 0),
}
if status == "failed":
elapsed = time.monotonic() - t0
error = data.get("error", "unknown")
print(f" [{label}] FAIL in {elapsed / 60:.1f}m — ?p, $0.0000")
return {
"file": pdf.name,
"status": "failed",
"job_id": job_id,
"error": error,
"elapsed": elapsed,
}
except Exception as e:
consecutive_errors += 1
if consecutive_errors >= 5:
print(f" [{label}] Too many poll errors, giving up: {e}")
elapsed = time.monotonic() - t0
return {"file": pdf.name, "status": "error", "job_id": job_id, "error": str(e), "elapsed": elapsed}
print(f" [{label}] Poll error ({consecutive_errors}/5): {e}")
elapsed = time.monotonic() - t0
print(f" [{label}] TIMEOUT after {elapsed / 60:.1f}m")
return {"file": pdf.name, "status": "timeout", "job_id": job_id, "elapsed": elapsed}
async def save_result(client: httpx.AsyncClient, data: dict, out_dir: Path):
out_dir.mkdir(parents=True, exist_ok=True)
# Save metadata
(out_dir / "metadata.json").write_text(json.dumps(data, indent=2, default=str))
# Download markdown
md_url = data.get("markdown_url", "")
if md_url:
try:
resp = await client.get(md_url, timeout=30)
resp.raise_for_status()
(out_dir / "result.md").write_bytes(resp.content)
except Exception:
pass
# Download figures
figures = data.get("figures", [])
if figures:
fig_dir = out_dir / "figures"
fig_dir.mkdir(exist_ok=True)
for fig in figures:
url = fig.get("url", "")
fid = fig.get("figure_id", "figure")
if url:
try:
resp = await client.get(url, timeout=30)
resp.raise_for_status()
ext = ".png"
(fig_dir / f"{fid}{ext}").write_bytes(resp.content)
except Exception:
pass
async def main():
if not API_KEY:
print("Error: Set BATCH_API_KEY environment variable")
sys.exit(1)
# Collect PDFs
pdfs = []
for d in PDF_DIRS:
if d.exists():
pdfs.extend(sorted(d.glob("*.pdf")))
for p in EXTRA_PDFS:
if p.exists():
pdfs.append(p)
if not pdfs:
print("No PDFs found")
sys.exit(1)
# Sort by size ascending (small ones first — avoids overloading docling early)
pdfs.sort(key=lambda p: p.stat().st_size)
stamp = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H-%M-%S")
results_dir = RESULTS_BASE / stamp
results_dir.mkdir(parents=True, exist_ok=True)
total = len(pdfs)
print(f"Batch run: {total} PDFs, concurrency={CONCURRENCY}")
print(f"API: {API_URL}")
print(f"Output: {results_dir}")
print()
sem = asyncio.Semaphore(CONCURRENCY)
async with httpx.AsyncClient() as client:
tasks = [
submit_and_process(client, pdf, i + 1, total, sem, results_dir)
for i, pdf in enumerate(pdfs)
]
results = await asyncio.gather(*tasks)
# Summary
completed = [r for r in results if r["status"] == "completed"]
failed = [r for r in results if r["status"] != "completed"]
total_cost = sum(r.get("cost", 0) for r in completed)
total_tokens = sum(r.get("tokens", 0) for r in completed)
elapsed_list = [r["elapsed"] for r in results if "elapsed" in r]
print()
print("=" * 70)
print("BATCH PROCESSING SUMMARY")
print("=" * 70)
print(f" Documents: {total}")
print(f" Completed: {len(completed)} | Failed: {len(failed)}")
print(f" Success rate: {len(completed) / total * 100:.1f}%")
print(f" Total cost: ${total_cost:.4f}")
print(f" Total tokens: {total_tokens:,}")
if elapsed_list:
print(
f" Processing time — mean: {statistics.mean(elapsed_list):.0f}s, "
f"median: {statistics.median(elapsed_list):.0f}s, "
f"p95: {sorted(elapsed_list)[int(len(elapsed_list) * 0.95)]:.0f}s"
)
if completed:
costs_per_page = [
r["cost"] / r["pages"]
for r in completed
if isinstance(r.get("pages"), int) and r["pages"] > 0
]
if costs_per_page:
print(
f" Cost/page — mean: ${statistics.mean(costs_per_page):.4f}, "
f"median: ${statistics.median(costs_per_page):.4f}"
)
if failed:
print()
print(" Failures:")
for r in failed:
error = r.get("error", "")
print(f" - {r['file']}: {error}")
print(f"\n Results saved to: {results_dir}")
# Save summary
summary = {
"timestamp": stamp,
"api_url": API_URL,
"concurrency": CONCURRENCY,
"total": total,
"completed": len(completed),
"failed": len(failed),
"total_cost": total_cost,
"total_tokens": total_tokens,
"results": results,
}
(results_dir / "summary.json").write_text(json.dumps(summary, indent=2, default=str))
if __name__ == "__main__":
asyncio.run(main())