Files
breakpilot-core/control-pipeline/scripts/reingest_nist.py
T
Benjamin Admin 0b0eed27b0 feat(embedding): NIST PDF text normalization + safe re-ingest script
Fix broken multi-column PDF extraction for NIST/BSI/ENISA documents:
- _normalize_pdf_text(): fixes broken section numbers (1 . 1 → 1.1),
  control IDs (AC - 1 → AC-1), ligatures, soft hyphens
- pdfplumber tolerances increased (x=3,y=4) for better column handling
- 3 new regex patterns: NIST CSF 2.0, NIST enhancements, OWASP Top 10
- reingest_nist.py: safe upload-before-delete for 4 lost NIST PDFs
- reingest_d5.py: safety fix — upload first, verify, then delete old

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-05-03 06:42:46 +02:00

486 lines
16 KiB
Python

#!/usr/bin/env python3
"""Safe re-ingestion of NIST/BSI/ENISA PDFs from MinIO.
Uses upload-before-delete pattern: new chunks are created FIRST,
old chunks are only deleted after successful verification.
Usage:
python3 control-pipeline/scripts/reingest_nist.py [--dry-run]
python3 control-pipeline/scripts/reingest_nist.py --only-missing
"""
import argparse
import json
import logging
import sys
import time
import httpx
sys.path.insert(0, "control-pipeline/scripts")
from reingest_d5_config import ( # noqa: E402
CHUNK_OVERLAP,
CHUNK_SIZE,
CHUNK_STRATEGY,
DEFAULT_QDRANT_URL,
DEFAULT_RAG_URL,
content_type_from_filename,
)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
)
logger = logging.getLogger("reingest-nist")
UPLOAD_TIMEOUT = 1800.0 # 30 min for large PDFs
# -------------------------------------------------------------------
# Documents to re-ingest
# -------------------------------------------------------------------
# 4 documents with 0 chunks (deleted by D5, upload failed)
MISSING_DOCS = [
{
"object_name": "compliance/bund/compliance/2026/NIST_SP_800_53r5.pdf",
"collection": "bp_compliance_datenschutz",
"filename": "NIST_SP_800_53r5.pdf",
"extra_metadata": {
"regulation_id": "nist_sp800_53r5",
"source_id": "nist",
"doc_type": "controls_catalog",
"guideline_name": "NIST SP 800-53 Rev. 5 Security and Privacy Controls",
"license": "public_domain_us_gov",
"attribution": "NIST",
"source": "nist.gov",
},
},
{
"object_name": "compliance/bund/compliance/2026/nist_sp_800_82r3.pdf",
"collection": "bp_compliance_ce",
"filename": "nist_sp_800_82r3.pdf",
"extra_metadata": {
"regulation_id": "nist_sp_800_82r3",
"regulation_name_de": "NIST SP 800-82 Rev. 3 — Guide to OT Security",
"regulation_name_en": "NIST SP 800-82 Rev. 3 — Guide to OT Security",
"regulation_short": "NIST SP 800-82",
"category": "ot_security",
"license": "public_domain_us",
"source": "nist.gov",
},
},
{
"object_name": "compliance/bund/compliance/2026/nist_sp_800_160v1r1.pdf",
"collection": "bp_compliance_ce",
"filename": "nist_sp_800_160v1r1.pdf",
"extra_metadata": {
"regulation_id": "nist_sp_800_160v1r1",
"regulation_name_de": "NIST SP 800-160 Vol. 1 Rev. 1",
"regulation_name_en": "NIST SP 800-160 Vol. 1 Rev. 1",
"regulation_short": "NIST SP 800-160",
"category": "security_engineering",
"license": "public_domain_us",
"source": "nist.gov",
},
},
{
"object_name": "compliance/bund/compliance/2026/NIST_SP_800_207.pdf",
"collection": "bp_compliance_datenschutz",
"filename": "NIST_SP_800_207.pdf",
"extra_metadata": {
"regulation_id": "nist_sp800_207",
"source_id": "nist",
"doc_type": "architecture",
"guideline_name": "NIST SP 800-207 Zero Trust Architecture",
"license": "public_domain_us_gov",
"attribution": "NIST",
"source": "nist.gov",
},
},
]
# Additional NIST/BSI/ENISA docs with <10% section rate (re-ingest for quality)
LOW_QUALITY_DOCS = [
{
"object_name": "compliance/bund/compliance/2026/nist_csf_2_0.pdf",
"collection": "bp_compliance_datenschutz",
"filename": "nist_csf_2_0.pdf",
"extra_metadata": {
"regulation_id": "nist_csf_2_0",
"license": "public_domain_us",
"source": "nist.gov",
},
},
{
"object_name": "compliance/bund/compliance/2026/nistir_8259a.pdf",
"collection": "bp_compliance_datenschutz",
"filename": "nistir_8259a.pdf",
"extra_metadata": {
"regulation_id": "nistir_8259a",
"license": "public_domain_us",
"source": "nist.gov",
},
},
{
"object_name": "compliance/bund/compliance/2026/nist_ai_rmf.pdf",
"collection": "bp_compliance_datenschutz",
"filename": "nist_ai_rmf.pdf",
"extra_metadata": {
"regulation_id": "nist_ai_rmf",
"license": "public_domain_us",
"source": "nist.gov",
},
},
{
"object_name": "compliance/bund/compliance/2026/nist_sp_800_30r1.pdf",
"collection": "bp_compliance_ce",
"filename": "nist_sp_800_30r1.pdf",
"extra_metadata": {
"regulation_id": "nist_sp_800_30r1",
"license": "public_domain_us",
"source": "nist.gov",
},
},
{
"object_name": "compliance/bund/compliance/2026/enisa_supply_chain_good_practices.pdf",
"collection": "bp_compliance_ce",
"filename": "enisa_supply_chain_good_practices.pdf",
"extra_metadata": {
"regulation_id": "enisa_supply_chain_good_practices",
"license": "reuse_with_attribution",
"source": "enisa.europa.eu",
},
},
{
"object_name": "compliance/bund/compliance/2026/enisa_ics_scada.pdf",
"collection": "bp_compliance_ce",
"filename": "enisa_ics_scada.pdf",
"extra_metadata": {
"regulation_id": "enisa_ics_scada_dependencies",
"license": "reuse_with_attribution",
"source": "enisa.europa.eu",
},
},
{
"object_name": "compliance/bund/compliance/2026/enisa_supply_chain_security.pdf",
"collection": "bp_compliance_ce",
"filename": "enisa_supply_chain_security.pdf",
"extra_metadata": {
"regulation_id": "enisa_threat_landscape_supply_chain",
"license": "reuse_with_attribution",
"source": "enisa.europa.eu",
},
},
{
"object_name": "compliance/bund/compliance/2026/cisa_secure_by_design.pdf",
"collection": "bp_compliance_ce",
"filename": "cisa_secure_by_design.pdf",
"extra_metadata": {
"regulation_id": "cisa_secure_by_design",
"license": "public_domain_us",
"source": "cisa.gov",
},
},
{
"object_name": "compliance/bund/compliance/2026/cvss_v4_0.pdf",
"collection": "bp_compliance_ce",
"filename": "cvss_v4_0.pdf",
"extra_metadata": {
"regulation_id": "cvss_v4_0",
"license": "public_domain_us",
"source": "first.org",
},
},
]
# -------------------------------------------------------------------
# Qdrant helpers
# -------------------------------------------------------------------
def count_chunks(qdrant_url: str, collection: str, object_name: str) -> int:
"""Count existing chunks for a document in Qdrant."""
with httpx.Client(timeout=30.0) as c:
resp = c.post(
f"{qdrant_url}/collections/{collection}/points/count",
json={
"filter": {
"must": [{
"key": "object_name",
"match": {"value": object_name},
}]
},
"exact": True,
},
)
resp.raise_for_status()
return resp.json()["result"]["count"]
def get_old_document_ids(
qdrant_url: str, collection: str, object_name: str,
) -> set:
"""Get all document_ids for existing chunks of this document."""
doc_ids = set()
offset = None
with httpx.Client(timeout=60.0) as c:
while True:
body = {
"filter": {
"must": [{
"key": "object_name",
"match": {"value": object_name},
}]
},
"limit": 100,
"with_payload": ["document_id"],
}
if offset is not None:
body["offset"] = offset
resp = c.post(
f"{qdrant_url}/collections/{collection}/points/scroll",
json=body,
)
resp.raise_for_status()
data = resp.json()["result"]
for pt in data["points"]:
did = pt.get("payload", {}).get("document_id")
if did:
doc_ids.add(did)
offset = data.get("next_page_offset")
if offset is None:
break
return doc_ids
def delete_by_document_ids(
qdrant_url: str, collection: str, doc_ids: set,
) -> None:
"""Delete chunks matching specific document_ids."""
for did in doc_ids:
with httpx.Client(timeout=30.0) as c:
c.post(
f"{qdrant_url}/collections/{collection}/points/delete",
json={
"filter": {
"must": [{
"key": "document_id",
"match": {"value": did},
}]
}
},
).raise_for_status()
def check_section_rate(
qdrant_url: str, collection: str, object_name: str,
) -> tuple:
"""Check section rate for a document's chunks. Returns (total, with_section)."""
total = 0
with_section = 0
offset = None
with httpx.Client(timeout=60.0) as c:
while True:
body = {
"filter": {
"must": [{
"key": "object_name",
"match": {"value": object_name},
}]
},
"limit": 100,
"with_payload": ["section"],
}
if offset is not None:
body["offset"] = offset
resp = c.post(
f"{qdrant_url}/collections/{collection}/points/scroll",
json=body,
)
resp.raise_for_status()
data = resp.json()["result"]
for pt in data["points"]:
total += 1
sec = pt.get("payload", {}).get("section", "")
if sec and sec.strip():
with_section += 1
offset = data.get("next_page_offset")
if offset is None:
break
return total, with_section
# -------------------------------------------------------------------
# Upload
# -------------------------------------------------------------------
def download_from_minio(rag_url: str, object_name: str) -> bytes:
"""Download file from MinIO via RAG service presigned URL."""
with httpx.Client(timeout=60.0, verify=False) as c:
resp = c.get(f"{rag_url}/api/v1/documents/download/{object_name}")
resp.raise_for_status()
presigned_url = resp.json()["url"]
with httpx.Client(timeout=300.0, verify=False) as c:
resp = c.get(presigned_url)
resp.raise_for_status()
return resp.content
def upload_document(
rag_url: str,
file_bytes: bytes,
filename: str,
collection: str,
extra_metadata: dict,
) -> dict:
"""Upload document to RAG service."""
ct = content_type_from_filename(filename)
form_data = {
"collection": collection,
"data_type": "compliance",
"bundesland": "bund",
"use_case": "compliance",
"year": "2026",
"chunk_strategy": CHUNK_STRATEGY,
"chunk_size": str(CHUNK_SIZE),
"chunk_overlap": str(CHUNK_OVERLAP),
"metadata_json": json.dumps(extra_metadata, ensure_ascii=False),
}
with httpx.Client(timeout=UPLOAD_TIMEOUT, verify=False) as c:
resp = c.post(
f"{rag_url}/api/v1/documents/upload",
files={"file": (filename, file_bytes, ct)},
data=form_data,
)
resp.raise_for_status()
return resp.json()
# -------------------------------------------------------------------
# Main processing
# -------------------------------------------------------------------
def process_document(
doc: dict,
rag_url: str,
qdrant_url: str,
dry_run: bool = False,
) -> dict:
"""Safe re-ingest: upload first, then delete old. Returns result dict."""
obj = doc["object_name"]
coll = doc["collection"]
fname = doc["filename"]
# 1. Check existing state
old_count = count_chunks(qdrant_url, coll, obj)
old_doc_ids = get_old_document_ids(qdrant_url, coll, obj) if old_count > 0 else set()
logger.info(" [%s] existing: %d chunks, %d document_ids",
fname, old_count, len(old_doc_ids))
if dry_run:
logger.info(" [%s] DRY RUN — would download + upload + delete old", fname)
return {"status": "dry_run", "old_chunks": old_count}
# 2. Download from MinIO
logger.info(" [%s] downloading from MinIO...", fname)
file_bytes = download_from_minio(rag_url, obj)
size_mb = len(file_bytes) / (1024 * 1024)
logger.info(" [%s] downloaded %.1f MB", fname, size_mb)
# 3. Upload FIRST (creates new chunks)
logger.info(" [%s] uploading to RAG service...", fname)
result = upload_document(rag_url, file_bytes, fname, coll, doc["extra_metadata"])
new_chunks = result.get("chunks_count", 0)
new_doc_id = result.get("document_id", "")
logger.info(" [%s] uploaded: %d new chunks (doc_id=%s)", fname, new_chunks, new_doc_id)
# 4. Verify new chunks exist
if new_chunks == 0:
logger.error(" [%s] UPLOAD PRODUCED 0 CHUNKS — keeping old data!", fname)
return {"status": "error", "error": "0 new chunks", "old_chunks": old_count}
# 5. Delete old chunks (only if there were any)
if old_doc_ids:
logger.info(" [%s] deleting %d old document_ids...", fname, len(old_doc_ids))
delete_by_document_ids(qdrant_url, coll, old_doc_ids)
logger.info(" [%s] old chunks deleted", fname)
# 6. Check section rate
total, with_sec = check_section_rate(qdrant_url, coll, obj)
pct = (with_sec / total * 100) if total > 0 else 0
logger.info(" [%s] section rate: %d/%d (%.0f%%)", fname, with_sec, total, pct)
return {
"status": "ok",
"old_chunks": old_count,
"new_chunks": new_chunks,
"new_document_id": new_doc_id,
"section_rate": round(pct, 1),
}
def main():
parser = argparse.ArgumentParser(description="Safe NIST/BSI/ENISA re-ingestion")
parser.add_argument("--dry-run", action="store_true", help="Show what would happen")
parser.add_argument("--only-missing", action="store_true",
help="Only re-ingest the 4 missing docs (skip low-quality)")
parser.add_argument("--rag-url", default=DEFAULT_RAG_URL)
parser.add_argument("--qdrant-url", default=DEFAULT_QDRANT_URL)
args = parser.parse_args()
docs = list(MISSING_DOCS)
if not args.only_missing:
docs.extend(LOW_QUALITY_DOCS)
logger.info("=" * 60)
logger.info("NIST/BSI/ENISA Safe Re-Ingestion")
logger.info(" Documents: %d (%d missing + %d low-quality)",
len(docs), len(MISSING_DOCS),
0 if args.only_missing else len(LOW_QUALITY_DOCS))
logger.info(" RAG: %s", args.rag_url)
logger.info(" Qdrant: %s", args.qdrant_url)
logger.info(" Dry run: %s", args.dry_run)
logger.info("=" * 60)
results = {}
ok = 0
errors = 0
for i, doc in enumerate(docs, 1):
logger.info("[%d/%d] %s%s", i, len(docs), doc["filename"], doc["collection"])
try:
r = process_document(doc, args.rag_url, args.qdrant_url, args.dry_run)
results[doc["filename"]] = r
if r["status"] == "ok":
ok += 1
elif r["status"] == "error":
errors += 1
except Exception as e:
logger.error(" FAILED: %s", e)
results[doc["filename"]] = {"status": "error", "error": str(e)}
errors += 1
if i < len(docs):
time.sleep(2)
# Summary
logger.info("")
logger.info("=" * 60)
logger.info("RESULTS")
logger.info("=" * 60)
for fname, r in results.items():
status = r["status"].upper()
old = r.get("old_chunks", "?")
new = r.get("new_chunks", "?")
sec = r.get("section_rate", "?")
logger.info(" %-40s %s old=%s new=%s sect=%.0f%%",
fname, status, old, new, sec if isinstance(sec, float) else 0)
logger.info("")
logger.info("OK: %d, Errors: %d, Total: %d", ok, errors, len(docs))
if errors > 0:
sys.exit(1)
if __name__ == "__main__":
main()