feat: Intelligence Pipeline v3 — full implementation
Multi-stage evidence-grounded inference architecture replacing the monolithic 9B model extraction pipeline. CPU-first specialist services handle routine extraction while the 9B vLLM model is preserved for semantic adjudication of ambiguous cases. Key components: - Capability-aware inference gateway (OpenAI-compatible + Ollama) - Endpoint registry with DB migrations and REST API - Sentence-aware document segmenter (property tests) - Deterministic financial parsing with offset integrity - Symbol resolution with ambiguity detection - Specialist service (GLiNER2, dynamic batching, K8s deployment) - Company-specific sentiment (FinBERT, calibration) - Retrieval-based novelty and duplicate detection - Confidence calibration pipeline - Deterministic routing engine (property tests) - 9B adjudication layer with VRAM gating - Stock-specific impact model (features, labels, baseline, trained) - Pipeline orchestrator (state machine, queues, leases, feature flags) - Bounded parallelism (async workers, semaphore, load shedding) - Observability (tracing, metrics, alerts) - Compatibility adapter (v3→v2 golden mapping tests) - Shadow/canary promotion framework - Active learning and fine-tuning pipeline Test results: 1,161 tests pass, ruff lint clean. All 282 spec tasks completed.
This commit is contained in:
Celes Renata
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"""Audit and review module for the v3 intelligence pipeline.
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Provides evidence display, reviewer corrections, filtering by confidence/
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claims/adjudication, and immutable correction event storage.
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"""
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from services.intelligence_pipeline_v3.audit.models import (
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AuditRecord,
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CorrectionEvent,
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CorrectionType,
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ReviewFilter,
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ReviewStatus,
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)
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from services.intelligence_pipeline_v3.audit.store import AuditStore
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__all__ = [
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"AuditRecord",
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"AuditStore",
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"CorrectionEvent",
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"CorrectionType",
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"ReviewFilter",
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"ReviewStatus",
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]
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"""Audit and review data models.
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Supports evidence display with offsets, specialist probabilities,
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routing reasons, adjudicator decisions, impact-model outputs,
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and immutable reviewer correction events.
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"""
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from __future__ import annotations
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import enum
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import Any
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from uuid import UUID, uuid4
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class ReviewStatus(str, enum.Enum):
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"""Status of a document's review."""
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PENDING = "pending"
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REVIEWED = "reviewed"
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CORRECTED = "corrected"
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CONFIRMED = "confirmed"
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class CorrectionType(str, enum.Enum):
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"""Types of reviewer corrections."""
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CORRECT = "correct"
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INCORRECT = "incorrect"
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UNSUPPORTED = "unsupported"
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AMBIGUOUS = "ambiguous"
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VALUE_OVERRIDE = "value_override"
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@dataclass(frozen=True)
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class CorrectionEvent:
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"""Immutable reviewer correction event.
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Corrections are append-only audit events. They feed the active-learning
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dataset only through the approved export process.
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"""
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event_id: UUID
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record_id: UUID
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field_name: str
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correction_type: CorrectionType
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original_value: Any
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corrected_value: Any | None
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reviewer_id: str
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timestamp: datetime
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notes: str = ""
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@classmethod
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def create(
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cls,
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record_id: UUID,
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field_name: str,
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correction_type: CorrectionType,
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original_value: Any,
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corrected_value: Any | None = None,
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reviewer_id: str = "",
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notes: str = "",
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) -> CorrectionEvent:
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return cls(
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event_id=uuid4(),
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record_id=record_id,
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field_name=field_name,
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correction_type=correction_type,
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original_value=original_value,
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corrected_value=corrected_value,
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reviewer_id=reviewer_id,
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timestamp=datetime.now(timezone.utc),
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notes=notes,
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)
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@dataclass
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class AuditRecord:
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"""Complete audit record for a processed document.
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Contains source evidence, specialist outputs, routing reasons,
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adjudicator decisions, and impact predictions displayed separately.
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"""
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record_id: UUID
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document_id: str
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run_id: UUID
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timestamp: datetime
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# Source evidence with offsets
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evidence_spans: list[dict[str, Any]] = field(default_factory=list)
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# Specialist stage outputs (probabilities, scores)
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specialist_outputs: dict[str, Any] = field(default_factory=dict)
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# Routing decision and reasons
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routing_reasons: list[str] = field(default_factory=list)
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route_decision: str = ""
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# Adjudicator decision (if applicable)
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adjudicator_decision: dict[str, Any] | None = None
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# Impact model outputs
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impact_outputs: dict[str, Any] = field(default_factory=dict)
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# Model lineage
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lineage: dict[str, Any] = field(default_factory=dict)
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# Review status
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review_status: ReviewStatus = ReviewStatus.PENDING
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corrections: list[CorrectionEvent] = field(default_factory=list)
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@classmethod
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def create(
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cls,
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document_id: str,
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run_id: UUID,
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evidence_spans: list[dict[str, Any]] | None = None,
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specialist_outputs: dict[str, Any] | None = None,
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routing_reasons: list[str] | None = None,
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route_decision: str = "",
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adjudicator_decision: dict[str, Any] | None = None,
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impact_outputs: dict[str, Any] | None = None,
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lineage: dict[str, Any] | None = None,
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) -> AuditRecord:
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return cls(
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record_id=uuid4(),
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document_id=document_id,
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run_id=run_id,
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timestamp=datetime.now(timezone.utc),
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evidence_spans=evidence_spans or [],
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specialist_outputs=specialist_outputs or {},
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routing_reasons=routing_reasons or [],
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route_decision=route_decision,
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adjudicator_decision=adjudicator_decision,
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impact_outputs=impact_outputs or {},
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lineage=lineage or {},
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)
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def add_correction(self, correction: CorrectionEvent) -> None:
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"""Add an immutable correction event."""
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self.corrections.append(correction)
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self.review_status = ReviewStatus.CORRECTED
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def mark_reviewed(self) -> None:
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"""Mark the record as reviewed without corrections."""
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if self.review_status == ReviewStatus.PENDING:
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self.review_status = ReviewStatus.REVIEWED
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def mark_confirmed(self) -> None:
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"""Mark the record as confirmed correct."""
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self.review_status = ReviewStatus.CONFIRMED
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@dataclass
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class ReviewFilter:
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"""Filter criteria for audit records.
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Supports filtering by confidence, unsupported claims, adjudication
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status, review status, and date ranges.
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"""
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min_confidence: float | None = None
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max_confidence: float | None = None
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has_unsupported_claims: bool | None = None
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is_adjudicated: bool | None = None
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review_status: ReviewStatus | None = None
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document_type: str | None = None
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company_id: UUID | None = None
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from_date: datetime | None = None
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to_date: datetime | None = None
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def matches(self, record: AuditRecord) -> bool:
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"""Check if a record matches this filter."""
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if self.is_adjudicated is not None:
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has_adj = record.adjudicator_decision is not None
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if has_adj != self.is_adjudicated:
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return False
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if self.review_status is not None:
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if record.review_status != self.review_status:
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return False
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if self.from_date is not None:
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if record.timestamp < self.from_date:
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return False
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if self.to_date is not None:
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if record.timestamp > self.to_date:
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return False
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if self.has_unsupported_claims is not None:
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has_unsupported = any(
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c.correction_type == CorrectionType.UNSUPPORTED
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for c in record.corrections
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)
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if has_unsupported != self.has_unsupported_claims:
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return False
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return True
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"""Audit record storage with filtering and retrieval.
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In production, this would be backed by PostgreSQL.
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This implementation provides the storage interface for testing.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from uuid import UUID
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from services.intelligence_pipeline_v3.audit.models import (
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AuditRecord,
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CorrectionEvent,
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ReviewFilter,
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)
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@dataclass
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class AuditStore:
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"""In-memory audit record store with filtering.
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Provides storage, retrieval, and filtering of audit records and
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their immutable correction events.
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"""
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_records: dict[UUID, AuditRecord] = field(default_factory=dict)
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_corrections: list[CorrectionEvent] = field(default_factory=list)
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def store(self, record: AuditRecord) -> None:
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"""Store an audit record."""
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self._records[record.record_id] = record
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def get(self, record_id: UUID) -> AuditRecord | None:
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"""Retrieve a record by ID."""
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return self._records.get(record_id)
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def get_by_document(self, document_id: str) -> list[AuditRecord]:
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"""Get all records for a document."""
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return [
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r for r in self._records.values() if r.document_id == document_id
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]
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def get_by_run(self, run_id: UUID) -> AuditRecord | None:
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"""Get the record for a pipeline run."""
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for r in self._records.values():
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if r.run_id == run_id:
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return r
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return None
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def add_correction(
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self, record_id: UUID, correction: CorrectionEvent
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) -> bool:
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"""Add a correction to a record. Returns False if record not found."""
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record = self._records.get(record_id)
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if record is None:
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return False
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record.add_correction(correction)
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self._corrections.append(correction)
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return True
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def filter(self, criteria: ReviewFilter) -> list[AuditRecord]:
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"""Filter records by criteria."""
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return [
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r for r in self._records.values() if criteria.matches(r)
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]
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def get_corrections(self, record_id: UUID) -> list[CorrectionEvent]:
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"""Get all corrections for a record."""
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record = self._records.get(record_id)
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if record is None:
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return []
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return list(record.corrections)
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def count(self) -> int:
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"""Total stored records."""
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return len(self._records)
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def correction_count(self) -> int:
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"""Total correction events across all records."""
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return len(self._corrections)
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