feat: control-pipeline Service aus Compliance-Repo migriert
Control-Pipeline (Pass 0a/0b, BatchDedup, Generator) als eigenstaendiger Service in Core, damit Compliance-Repo unabhaengig refakturiert werden kann. Schreibt weiterhin ins compliance-Schema der shared PostgreSQL. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Benjamin Admin
331
control-pipeline/services/v1_enrichment.py
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331
control-pipeline/services/v1_enrichment.py
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"""V1 Control Enrichment Service — Match Eigenentwicklung controls to regulations.
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Finds regulatory coverage for v1 controls (generation_strategy='ungrouped',
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pipeline_version=1, no source_citation) by embedding similarity search.
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Reuses embedding + Qdrant helpers from control_dedup.py.
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"""
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import logging
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from typing import Optional
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from sqlalchemy import text
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from db.session import SessionLocal
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from services.control_dedup import (
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get_embedding,
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qdrant_search_cross_regulation,
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)
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logger = logging.getLogger(__name__)
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# Similarity threshold — lower than dedup (0.85) since we want informational matches
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# Typical top scores for v1 controls are 0.70-0.77
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V1_MATCH_THRESHOLD = 0.70
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V1_MAX_MATCHES = 5
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def _is_eigenentwicklung_query() -> str:
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"""SQL WHERE clause identifying v1 Eigenentwicklung controls."""
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return """
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generation_strategy = 'ungrouped'
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AND (pipeline_version = '1' OR pipeline_version IS NULL)
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AND source_citation IS NULL
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AND parent_control_uuid IS NULL
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AND release_state NOT IN ('rejected', 'merged', 'deprecated')
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"""
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async def count_v1_controls() -> int:
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"""Count how many v1 Eigenentwicklung controls exist."""
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with SessionLocal() as db:
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row = db.execute(text(f"""
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SELECT COUNT(*) AS cnt
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FROM canonical_controls
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WHERE {_is_eigenentwicklung_query()}
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""")).fetchone()
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return row.cnt if row else 0
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async def enrich_v1_matches(
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dry_run: bool = True,
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batch_size: int = 100,
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offset: int = 0,
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) -> dict:
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"""Find regulatory matches for v1 Eigenentwicklung controls.
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Args:
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dry_run: If True, only count — don't write matches.
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batch_size: Number of v1 controls to process per call.
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offset: Pagination offset (v1 control index).
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Returns:
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Stats dict with counts, sample matches, and pagination info.
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"""
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with SessionLocal() as db:
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# 1. Load v1 controls (paginated)
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v1_controls = db.execute(text(f"""
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SELECT id, control_id, title, objective, category
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FROM canonical_controls
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WHERE {_is_eigenentwicklung_query()}
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ORDER BY control_id
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LIMIT :limit OFFSET :offset
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"""), {"limit": batch_size, "offset": offset}).fetchall()
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# Count total for pagination
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total_row = db.execute(text(f"""
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SELECT COUNT(*) AS cnt
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FROM canonical_controls
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WHERE {_is_eigenentwicklung_query()}
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""")).fetchone()
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total_v1 = total_row.cnt if total_row else 0
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if not v1_controls:
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return {
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"dry_run": dry_run,
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"processed": 0,
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"total_v1": total_v1,
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"message": "Kein weiterer Batch — alle v1 Controls verarbeitet.",
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}
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if dry_run:
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return {
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"dry_run": True,
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"total_v1": total_v1,
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"offset": offset,
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"batch_size": batch_size,
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"sample_controls": [
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{
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"control_id": r.control_id,
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"title": r.title,
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"category": r.category,
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}
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for r in v1_controls[:20]
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],
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}
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# 2. Process each v1 control
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processed = 0
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matches_inserted = 0
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errors = []
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sample_matches = []
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for v1 in v1_controls:
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try:
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# Build search text
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search_text = f"{v1.title} — {v1.objective}"
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# Get embedding
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embedding = await get_embedding(search_text)
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if not embedding:
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errors.append({
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"control_id": v1.control_id,
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"error": "Embedding fehlgeschlagen",
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})
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continue
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# Search Qdrant (cross-regulation, no pattern filter)
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# Collection is atomic_controls_dedup (contains ~51k atomare Controls)
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results = await qdrant_search_cross_regulation(
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embedding, top_k=20,
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collection="atomic_controls_dedup",
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)
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# For each hit: resolve to a regulatory parent with source_citation.
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# Atomic controls in Qdrant usually have parent_control_uuid → parent
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# has the source_citation. We deduplicate by parent to avoid
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# listing the same regulation multiple times.
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rank = 0
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seen_parents: set[str] = set()
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for hit in results:
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score = hit.get("score", 0)
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if score < V1_MATCH_THRESHOLD:
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continue
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payload = hit.get("payload", {})
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matched_uuid = payload.get("control_uuid")
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if not matched_uuid or matched_uuid == str(v1.id):
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continue
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# Try the matched control itself first, then its parent
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matched_row = db.execute(text("""
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SELECT c.id, c.control_id, c.title, c.source_citation,
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c.severity, c.category, c.parent_control_uuid
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FROM canonical_controls c
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WHERE c.id = CAST(:uuid AS uuid)
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"""), {"uuid": matched_uuid}).fetchone()
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if not matched_row:
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continue
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# Resolve to regulatory control (one with source_citation)
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reg_row = matched_row
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if not reg_row.source_citation and reg_row.parent_control_uuid:
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# Look up parent — the parent has the source_citation
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parent_row = db.execute(text("""
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SELECT id, control_id, title, source_citation,
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severity, category, parent_control_uuid
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FROM canonical_controls
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WHERE id = CAST(:uuid AS uuid)
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AND source_citation IS NOT NULL
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"""), {"uuid": str(reg_row.parent_control_uuid)}).fetchone()
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if parent_row:
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reg_row = parent_row
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if not reg_row.source_citation:
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continue
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# Deduplicate by parent UUID
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parent_key = str(reg_row.id)
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if parent_key in seen_parents:
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continue
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seen_parents.add(parent_key)
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rank += 1
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if rank > V1_MAX_MATCHES:
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break
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# Extract source info
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source_citation = reg_row.source_citation or {}
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matched_source = source_citation.get("source") if isinstance(source_citation, dict) else None
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matched_article = source_citation.get("article") if isinstance(source_citation, dict) else None
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# Insert match — link to the regulatory parent (not the atomic child)
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db.execute(text("""
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INSERT INTO v1_control_matches
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(v1_control_uuid, matched_control_uuid, similarity_score,
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match_rank, matched_source, matched_article, match_method)
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VALUES
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(CAST(:v1_uuid AS uuid), CAST(:matched_uuid AS uuid), :score,
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:rank, :source, :article, 'embedding')
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ON CONFLICT (v1_control_uuid, matched_control_uuid) DO UPDATE
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SET similarity_score = EXCLUDED.similarity_score,
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match_rank = EXCLUDED.match_rank
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"""), {
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"v1_uuid": str(v1.id),
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"matched_uuid": str(reg_row.id),
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"score": round(score, 3),
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"rank": rank,
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"source": matched_source,
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"article": matched_article,
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})
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matches_inserted += 1
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# Collect sample
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if len(sample_matches) < 20:
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sample_matches.append({
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"v1_control_id": v1.control_id,
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"v1_title": v1.title,
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"matched_control_id": reg_row.control_id,
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"matched_title": reg_row.title,
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"matched_source": matched_source,
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"matched_article": matched_article,
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"similarity_score": round(score, 3),
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"match_rank": rank,
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})
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processed += 1
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except Exception as e:
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logger.warning("V1 enrichment error for %s: %s", v1.control_id, e)
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errors.append({
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"control_id": v1.control_id,
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"error": str(e),
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})
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db.commit()
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# Pagination
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next_offset = offset + batch_size if len(v1_controls) == batch_size else None
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return {
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"dry_run": False,
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"offset": offset,
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"batch_size": batch_size,
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"next_offset": next_offset,
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"total_v1": total_v1,
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"processed": processed,
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"matches_inserted": matches_inserted,
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"errors": errors[:10],
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"sample_matches": sample_matches,
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}
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async def get_v1_matches(control_uuid: str) -> list[dict]:
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"""Get all regulatory matches for a specific v1 control.
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Args:
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control_uuid: The UUID of the v1 control.
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Returns:
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List of match dicts with control details.
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"""
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with SessionLocal() as db:
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rows = db.execute(text("""
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SELECT
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m.similarity_score,
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m.match_rank,
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m.matched_source,
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m.matched_article,
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m.match_method,
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c.control_id AS matched_control_id,
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c.title AS matched_title,
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c.objective AS matched_objective,
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c.severity AS matched_severity,
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c.category AS matched_category,
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c.source_citation AS matched_source_citation
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FROM v1_control_matches m
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JOIN canonical_controls c ON c.id = m.matched_control_uuid
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WHERE m.v1_control_uuid = CAST(:uuid AS uuid)
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ORDER BY m.match_rank
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"""), {"uuid": control_uuid}).fetchall()
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return [
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{
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"matched_control_id": r.matched_control_id,
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"matched_title": r.matched_title,
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"matched_objective": r.matched_objective,
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"matched_severity": r.matched_severity,
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"matched_category": r.matched_category,
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"matched_source": r.matched_source,
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"matched_article": r.matched_article,
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"matched_source_citation": r.matched_source_citation,
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"similarity_score": float(r.similarity_score),
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"match_rank": r.match_rank,
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"match_method": r.match_method,
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}
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for r in rows
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]
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async def get_v1_enrichment_stats() -> dict:
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"""Get overview stats for v1 enrichment."""
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with SessionLocal() as db:
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total_v1 = db.execute(text(f"""
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SELECT COUNT(*) AS cnt FROM canonical_controls
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WHERE {_is_eigenentwicklung_query()}
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""")).fetchone()
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matched_v1 = db.execute(text(f"""
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SELECT COUNT(DISTINCT m.v1_control_uuid) AS cnt
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FROM v1_control_matches m
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JOIN canonical_controls c ON c.id = m.v1_control_uuid
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WHERE {_is_eigenentwicklung_query().replace('release_state', 'c.release_state').replace('generation_strategy', 'c.generation_strategy').replace('pipeline_version', 'c.pipeline_version').replace('source_citation', 'c.source_citation').replace('parent_control_uuid', 'c.parent_control_uuid')}
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""")).fetchone()
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total_matches = db.execute(text("""
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SELECT COUNT(*) AS cnt FROM v1_control_matches
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""")).fetchone()
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avg_score = db.execute(text("""
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SELECT AVG(similarity_score) AS avg_score FROM v1_control_matches
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""")).fetchone()
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return {
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"total_v1_controls": total_v1.cnt if total_v1 else 0,
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"v1_with_matches": matched_v1.cnt if matched_v1 else 0,
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"v1_without_matches": (total_v1.cnt if total_v1 else 0) - (matched_v1.cnt if matched_v1 else 0),
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"total_matches": total_matches.cnt if total_matches else 0,
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"avg_similarity_score": round(float(avg_score.avg_score), 3) if avg_score and avg_score.avg_score else None,
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}
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