feat(pipeline): structural metadata end-to-end (Blocks D2-D4)
D2: RAG service stores section/section_title/paragraph/paragraph_num/page from embedding service chunks_with_metadata into Qdrant payloads. D3: Control generator prefers section > article > section_title from Qdrant, adds page to source_citation and generation_metadata. D4: Validated with real BGB §§ 312-312k text. Found and fixed critical bug where Phase 3 overlap destroyed the [§ ...] section prefix, causing only the first chunk per document to have metadata. All subsequent chunks lost section info. Also fixes pre-existing lint issues (unused imports, ambiguous variable names, duplicate dict key, bare except). 456 tests passing (58 embedding + 387 pipeline + 11 rag-service). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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@@ -25,8 +25,7 @@ import re
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import uuid
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from collections import defaultdict
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from dataclasses import dataclass, field, asdict
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from datetime import datetime, timezone
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from typing import Dict, List, Optional, Set
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from typing import Dict, List, Optional
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import httpx
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from pydantic import BaseModel
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@@ -34,7 +33,7 @@ from sqlalchemy import text
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from sqlalchemy.orm import Session
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from .rag_client import ComplianceRAGClient, RAGSearchResult, get_rag_client
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from .similarity_detector import check_similarity, SimilarityReport
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from .similarity_detector import check_similarity
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logger = logging.getLogger(__name__)
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@@ -1019,11 +1018,12 @@ class ControlGeneratorPipeline:
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regulation_name=reg_name,
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regulation_short=reg_short,
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category=payload.get("category", "") or payload.get("data_type", ""),
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article=payload.get("article", "") or payload.get("section_title", "") or payload.get("section", ""),
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article=payload.get("section", "") or payload.get("article", "") or payload.get("section_title", ""),
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paragraph=payload.get("paragraph", ""),
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source_url=payload.get("source_url", "") or payload.get("source", "") or payload.get("url", ""),
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score=0.0,
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collection=collection,
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page=payload.get("page"),
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)
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all_results.append(chunk)
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collection_new += 1
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@@ -1127,6 +1127,7 @@ Quelle: {chunk.regulation_name} ({chunk.regulation_code}), {chunk.article}"""
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"source": canonical_source,
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"article": effective_article,
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"paragraph": effective_paragraph,
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"page": chunk.page,
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"license": license_info.get("license", ""),
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"source_type": license_info.get("source_type", "law"),
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"url": chunk.source_url or "",
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@@ -1141,6 +1142,7 @@ Quelle: {chunk.regulation_name} ({chunk.regulation_code}), {chunk.article}"""
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"source_regulation": chunk.regulation_code,
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"source_article": effective_article,
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"source_paragraph": effective_paragraph,
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"source_page": chunk.page,
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}
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return control
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@@ -1194,6 +1196,7 @@ Quelle: {chunk.regulation_name}, {chunk.article}"""
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"source": canonical_source,
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"article": effective_article,
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"paragraph": effective_paragraph,
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"page": chunk.page,
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"license": license_info.get("license", ""),
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"license_notice": attribution,
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"source_type": license_info.get("source_type", "standard"),
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@@ -1209,6 +1212,7 @@ Quelle: {chunk.regulation_name}, {chunk.article}"""
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"source_regulation": chunk.regulation_code,
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"source_article": effective_article,
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"source_paragraph": effective_paragraph,
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"source_page": chunk.page,
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}
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return control
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@@ -1368,6 +1372,7 @@ Gib ein JSON-Array zurueck mit GENAU {len(chunks)} Elementen. Fuer Chunks ohne A
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"source": canonical_source,
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"article": effective_article,
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"paragraph": effective_paragraph,
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"page": chunk.page,
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"license": lic.get("license", ""),
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"license_notice": lic.get("attribution", ""),
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"source_type": lic.get("source_type", "law"),
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@@ -1384,6 +1389,7 @@ Gib ein JSON-Array zurueck mit GENAU {len(chunks)} Elementen. Fuer Chunks ohne A
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"source_regulation": chunk.regulation_code,
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"source_article": effective_article,
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"source_paragraph": effective_paragraph,
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"source_page": chunk.page,
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"batch_size": len(chunks),
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"document_grouped": same_doc,
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}
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@@ -1479,14 +1485,14 @@ Gib ein JSON-Array zurueck mit GENAU {len(chunks)} Elementen. Fuer Aspekte ohne
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) -> list[Optional[GeneratedControl]]:
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"""Process a batch of (chunk, license_info) through stages 3-5."""
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# Split by license rule: Rule 1+2 → structure, Rule 3 → reform
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structure_items = [(c, l) for c, l in batch_items if l["rule"] in (1, 2)]
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reform_items = [(c, l) for c, l in batch_items if l["rule"] == 3]
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structure_items = [(c, lic) for c, lic in batch_items if lic["rule"] in (1, 2)]
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reform_items = [(c, lic) for c, lic in batch_items if lic["rule"] == 3]
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all_controls: dict[int, Optional[GeneratedControl]] = {}
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if structure_items:
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s_chunks = [c for c, _ in structure_items]
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s_lics = [l for _, l in structure_items]
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s_lics = [lic for _, lic in structure_items]
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try:
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s_controls = await self._structure_batch(s_chunks, s_lics)
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except Exception as e:
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@@ -24,7 +24,6 @@ import json
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import logging
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import os
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import re
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import uuid
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from dataclasses import dataclass, field
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from typing import Optional
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@@ -56,7 +55,7 @@ ANTHROPIC_API_URL = "https://api.anthropic.com/v1"
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# Patterns are defined in normative_patterns.py and imported here
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# with local aliases for backward compatibility.
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from .normative_patterns import (
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from .normative_patterns import ( # noqa: E402
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PFLICHT_RE as _PFLICHT_RE,
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EMPFEHLUNG_RE as _EMPFEHLUNG_RE,
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KANN_RE as _KANN_RE,
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@@ -3472,7 +3471,7 @@ class DecompositionPass:
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"category": atomic.category,
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"parent_uuid": parent_uuid,
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"gen_meta": json.dumps({
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"decomposition_source": candidate_id,
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"decomposition_source_id": candidate_id,
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"decomposition_method": "pass0b",
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"engine_version": "v2",
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"action_object_class": getattr(atomic, "domain", ""),
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@@ -4104,6 +4103,8 @@ def _format_citation(citation) -> str:
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parts.append(c["article"])
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if c.get("paragraph"):
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parts.append(c["paragraph"])
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if c.get("page") is not None:
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parts.append(f"S. {c['page']}")
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return " ".join(parts) if parts else citation
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except (json.JSONDecodeError, TypeError):
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return citation
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@@ -34,6 +34,7 @@ class RAGSearchResult:
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source_url: str
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score: float
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collection: str = ""
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page: Optional[int] = None
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class ComplianceRAGClient:
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