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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"""Production shadow mode for the v3 pipeline.
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Runs v3 for live documents without affecting aggregation or trading.
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Compares v2/v3 disagreements, measures operational stability, and
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enforces minimum shadow duration before promotion.
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"""
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from services.intelligence_pipeline_v3.shadow.runner import (
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DisagreementLevel,
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ShadowComparison,
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ShadowConfig,
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ShadowRunner,
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)
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__all__ = [
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"DisagreementLevel",
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"ShadowComparison",
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"ShadowConfig",
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"ShadowRunner",
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]
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"""Shadow mode runner — live v2/v3 comparison without production impact.
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Runs v3 alongside production v2, collects comparison data, and tracks
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stability metrics. V3 results are stored but never influence aggregation
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or trading until shadow requirements are met.
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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, timedelta, timezone
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from typing import Any
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from uuid import UUID, uuid4
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class DisagreementLevel(str, enum.Enum):
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"""Severity of v2/v3 disagreement."""
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NONE = "none"
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MINOR = "minor" # Different confidence/probabilities within tolerance
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MODERATE = "moderate" # Different sentiment or secondary entities
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MAJOR = "major" # Different primary company or event classification
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CRITICAL = "critical" # Opposite direction or missing safety-critical field
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@dataclass
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class ShadowComparison:
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"""Comparison result between v2 and v3 outputs for one document."""
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comparison_id: UUID
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document_id: str
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timestamp: datetime
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disagreement_level: DisagreementLevel
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v2_output: dict[str, Any]
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v3_output: dict[str, Any]
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field_differences: dict[str, Any] = field(default_factory=dict)
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risk_score: float = 0.0 # 0-1, higher = more concerning
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reviewed: bool = False
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reviewer_notes: str = ""
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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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v2_output: dict[str, Any],
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v3_output: dict[str, Any],
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field_differences: dict[str, Any] | None = None,
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disagreement_level: DisagreementLevel = DisagreementLevel.NONE,
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risk_score: float = 0.0,
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) -> ShadowComparison:
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return cls(
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comparison_id=uuid4(),
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document_id=document_id,
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timestamp=datetime.now(timezone.utc),
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disagreement_level=disagreement_level,
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v2_output=v2_output,
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v3_output=v3_output,
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field_differences=field_differences or {},
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risk_score=risk_score,
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)
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@dataclass
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class ShadowConfig:
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"""Configuration for shadow mode operation."""
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enabled: bool = False
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min_duration: timedelta = field(default_factory=lambda: timedelta(days=7))
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min_documents: int = 500
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max_critical_disagreements: int = 5
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max_major_disagreement_rate: float = 0.10
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auto_disable_on_errors: bool = True
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error_threshold: int = 50
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sample_review_rate: float = 0.05 # Review 5% of disagreements
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def is_valid_duration(self, started_at: datetime) -> bool:
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"""Check if minimum shadow duration has elapsed."""
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elapsed = datetime.now(timezone.utc) - started_at
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return elapsed >= self.min_duration
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@dataclass
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class ShadowRunner:
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"""Manages shadow mode execution and stability tracking.
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Tracks comparisons, disagreements, and operational metrics.
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Enforces minimum duration and document count before allowing promotion.
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"""
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config: ShadowConfig
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started_at: datetime | None = None
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_comparisons: list[ShadowComparison] = field(default_factory=list)
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_error_count: int = 0
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_documents_processed: int = 0
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_fast_path_count: int = 0
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_gpu_seconds_total: float = 0.0
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def start(self) -> None:
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"""Activate shadow mode."""
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self.config.enabled = True
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self.started_at = datetime.now(timezone.utc)
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def stop(self) -> None:
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"""Deactivate shadow mode."""
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self.config.enabled = False
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@property
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def is_active(self) -> bool:
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return self.config.enabled and self.started_at is not None
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def record_comparison(self, comparison: ShadowComparison) -> None:
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"""Record a v2/v3 comparison."""
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self._comparisons.append(comparison)
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self._documents_processed += 1
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def record_error(self) -> None:
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"""Record a v3 processing error."""
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self._error_count += 1
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if (
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self.config.auto_disable_on_errors
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and self._error_count >= self.config.error_threshold
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):
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self.stop()
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def record_processing(
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self, fast_path: bool = True, gpu_seconds: float = 0.0
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) -> None:
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"""Record processing metrics."""
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self._documents_processed += 1
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if fast_path:
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self._fast_path_count += 1
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self._gpu_seconds_total += gpu_seconds
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@property
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def documents_processed(self) -> int:
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return self._documents_processed
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@property
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def fast_path_rate(self) -> float:
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if self._documents_processed == 0:
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return 0.0
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return self._fast_path_count / self._documents_processed
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@property
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def gpu_reduction_ratio(self) -> float:
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"""Placeholder — needs baseline comparison."""
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return 0.0
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@property
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def critical_disagreements(self) -> int:
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return sum(
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1
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for c in self._comparisons
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if c.disagreement_level == DisagreementLevel.CRITICAL
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)
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@property
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def major_disagreement_rate(self) -> float:
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if not self._comparisons:
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return 0.0
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major_or_critical = sum(
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1
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for c in self._comparisons
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if c.disagreement_level
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in (DisagreementLevel.MAJOR, DisagreementLevel.CRITICAL)
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)
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return major_or_critical / len(self._comparisons)
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def meets_promotion_criteria(self) -> bool:
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"""Check if all shadow mode requirements are met for promotion."""
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if not self.is_active or self.started_at is None:
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return False
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# Minimum duration
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if not self.config.is_valid_duration(self.started_at):
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return False
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# Minimum document count
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if self._documents_processed < self.config.min_documents:
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return False
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# Critical disagreement limit
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if self.critical_disagreements > self.config.max_critical_disagreements:
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return False
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# Major disagreement rate
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if self.major_disagreement_rate > self.config.max_major_disagreement_rate:
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return False
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return True
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def get_review_sample(self) -> list[ShadowComparison]:
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"""Get disagreements needing human review, prioritized by risk."""
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unreviewed = [c for c in self._comparisons if not c.reviewed]
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# Prioritize by disagreement severity and risk score
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unreviewed.sort(
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key=lambda c: (
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-_disagreement_priority(c.disagreement_level),
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-c.risk_score,
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)
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)
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sample_size = max(
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1, int(len(unreviewed) * self.config.sample_review_rate)
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)
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return unreviewed[:sample_size]
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def summary(self) -> dict[str, Any]:
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"""Generate shadow mode status summary."""
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return {
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"active": self.is_active,
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"started_at": self.started_at.isoformat() if self.started_at else None,
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"documents_processed": self._documents_processed,
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"fast_path_rate": self.fast_path_rate,
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"error_count": self._error_count,
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"critical_disagreements": self.critical_disagreements,
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"major_disagreement_rate": self.major_disagreement_rate,
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"meets_promotion_criteria": self.meets_promotion_criteria(),
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"gpu_seconds_total": self._gpu_seconds_total,
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}
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def _disagreement_priority(level: DisagreementLevel) -> int:
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"""Priority ordering for disagreement review."""
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return {
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DisagreementLevel.CRITICAL: 4,
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DisagreementLevel.MAJOR: 3,
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DisagreementLevel.MODERATE: 2,
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DisagreementLevel.MINOR: 1,
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DisagreementLevel.NONE: 0,
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}.get(level, 0)
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