Files
stonks-oracle/services/intelligence_pipeline_v3/audit/models.py
T
Celes Renata a72f336ad1 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.
2026-07-13 02:14:59 +00:00

202 lines
6.0 KiB
Python

"""Audit and review data models.
Supports evidence display with offsets, specialist probabilities,
routing reasons, adjudicator decisions, impact-model outputs,
and immutable reviewer correction events.
"""
from __future__ import annotations
import enum
from dataclasses import dataclass, field
from datetime import datetime, timezone
from typing import Any
from uuid import UUID, uuid4
class ReviewStatus(str, enum.Enum):
"""Status of a document's review."""
PENDING = "pending"
REVIEWED = "reviewed"
CORRECTED = "corrected"
CONFIRMED = "confirmed"
class CorrectionType(str, enum.Enum):
"""Types of reviewer corrections."""
CORRECT = "correct"
INCORRECT = "incorrect"
UNSUPPORTED = "unsupported"
AMBIGUOUS = "ambiguous"
VALUE_OVERRIDE = "value_override"
@dataclass(frozen=True)
class CorrectionEvent:
"""Immutable reviewer correction event.
Corrections are append-only audit events. They feed the active-learning
dataset only through the approved export process.
"""
event_id: UUID
record_id: UUID
field_name: str
correction_type: CorrectionType
original_value: Any
corrected_value: Any | None
reviewer_id: str
timestamp: datetime
notes: str = ""
@classmethod
def create(
cls,
record_id: UUID,
field_name: str,
correction_type: CorrectionType,
original_value: Any,
corrected_value: Any | None = None,
reviewer_id: str = "",
notes: str = "",
) -> CorrectionEvent:
return cls(
event_id=uuid4(),
record_id=record_id,
field_name=field_name,
correction_type=correction_type,
original_value=original_value,
corrected_value=corrected_value,
reviewer_id=reviewer_id,
timestamp=datetime.now(timezone.utc),
notes=notes,
)
@dataclass
class AuditRecord:
"""Complete audit record for a processed document.
Contains source evidence, specialist outputs, routing reasons,
adjudicator decisions, and impact predictions displayed separately.
"""
record_id: UUID
document_id: str
run_id: UUID
timestamp: datetime
# Source evidence with offsets
evidence_spans: list[dict[str, Any]] = field(default_factory=list)
# Specialist stage outputs (probabilities, scores)
specialist_outputs: dict[str, Any] = field(default_factory=dict)
# Routing decision and reasons
routing_reasons: list[str] = field(default_factory=list)
route_decision: str = ""
# Adjudicator decision (if applicable)
adjudicator_decision: dict[str, Any] | None = None
# Impact model outputs
impact_outputs: dict[str, Any] = field(default_factory=dict)
# Model lineage
lineage: dict[str, Any] = field(default_factory=dict)
# Review status
review_status: ReviewStatus = ReviewStatus.PENDING
corrections: list[CorrectionEvent] = field(default_factory=list)
@classmethod
def create(
cls,
document_id: str,
run_id: UUID,
evidence_spans: list[dict[str, Any]] | None = None,
specialist_outputs: dict[str, Any] | None = None,
routing_reasons: list[str] | None = None,
route_decision: str = "",
adjudicator_decision: dict[str, Any] | None = None,
impact_outputs: dict[str, Any] | None = None,
lineage: dict[str, Any] | None = None,
) -> AuditRecord:
return cls(
record_id=uuid4(),
document_id=document_id,
run_id=run_id,
timestamp=datetime.now(timezone.utc),
evidence_spans=evidence_spans or [],
specialist_outputs=specialist_outputs or {},
routing_reasons=routing_reasons or [],
route_decision=route_decision,
adjudicator_decision=adjudicator_decision,
impact_outputs=impact_outputs or {},
lineage=lineage or {},
)
def add_correction(self, correction: CorrectionEvent) -> None:
"""Add an immutable correction event."""
self.corrections.append(correction)
self.review_status = ReviewStatus.CORRECTED
def mark_reviewed(self) -> None:
"""Mark the record as reviewed without corrections."""
if self.review_status == ReviewStatus.PENDING:
self.review_status = ReviewStatus.REVIEWED
def mark_confirmed(self) -> None:
"""Mark the record as confirmed correct."""
self.review_status = ReviewStatus.CONFIRMED
@dataclass
class ReviewFilter:
"""Filter criteria for audit records.
Supports filtering by confidence, unsupported claims, adjudication
status, review status, and date ranges.
"""
min_confidence: float | None = None
max_confidence: float | None = None
has_unsupported_claims: bool | None = None
is_adjudicated: bool | None = None
review_status: ReviewStatus | None = None
document_type: str | None = None
company_id: UUID | None = None
from_date: datetime | None = None
to_date: datetime | None = None
def matches(self, record: AuditRecord) -> bool:
"""Check if a record matches this filter."""
if self.is_adjudicated is not None:
has_adj = record.adjudicator_decision is not None
if has_adj != self.is_adjudicated:
return False
if self.review_status is not None:
if record.review_status != self.review_status:
return False
if self.from_date is not None:
if record.timestamp < self.from_date:
return False
if self.to_date is not None:
if record.timestamp > self.to_date:
return False
if self.has_unsupported_claims is not None:
has_unsupported = any(
c.correction_type == CorrectionType.UNSUPPORTED
for c in record.corrections
)
if has_unsupported != self.has_unsupported_claims:
return False
return True