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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"""Fine-tuning module for specialist extractor models.
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Manages training pipelines, holdout evaluation, score recalibration,
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and promotion gates. A model is promoted only when correctness gates
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pass, not merely when adjudication rate falls.
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"""
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from services.intelligence_pipeline_v3.fine_tuning.evaluation import (
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EvaluationResult,
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ModelCard,
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PromotionDecision,
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)
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from services.intelligence_pipeline_v3.fine_tuning.trainer import (
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TrainingConfig,
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TrainingRun,
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TrainingStatus,
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)
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__all__ = [
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"EvaluationResult",
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"ModelCard",
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"PromotionDecision",
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"TrainingConfig",
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"TrainingRun",
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"TrainingStatus",
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]
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"""Holdout evaluation and promotion gate checking for fine-tuned models.
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Evaluates against frozen holdout and production artifact. A model is
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promoted only when correctness gates pass — not merely when adjudication
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rate falls.
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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 PromotionDecision(str, enum.Enum):
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"""Decision on whether to promote a fine-tuned model."""
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PROMOTE = "promote"
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REJECT = "reject"
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NEEDS_REVIEW = "needs_review"
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@dataclass
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class EvaluationResult:
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"""Results of evaluating a fine-tuned model against holdout data."""
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evaluation_id: UUID
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training_run_id: UUID
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model_version: str
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evaluated_at: datetime
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# Correctness metrics (what matters for promotion)
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entity_f1: float = 0.0
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entity_precision: float = 0.0
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entity_recall: float = 0.0
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event_f1: float = 0.0
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relation_f1: float = 0.0
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fact_exact_match: float = 0.0
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# Calibration metrics
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calibration_ece: float = 0.0
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brier_score: float = 0.0
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# Comparison with production model
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production_entity_f1: float = 0.0
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production_event_f1: float = 0.0
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entity_f1_delta: float = 0.0
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event_f1_delta: float = 0.0
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# Adjudication impact (reported but not a gate)
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adjudication_rate_before: float = 0.0
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adjudication_rate_after: float = 0.0
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adjudication_rate_delta: float = 0.0
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# Holdout details
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holdout_size: int = 0
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holdout_version: str = ""
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@classmethod
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def create(
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cls,
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training_run_id: UUID,
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model_version: str,
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**kwargs: Any,
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) -> EvaluationResult:
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return cls(
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evaluation_id=uuid4(),
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training_run_id=training_run_id,
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model_version=model_version,
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evaluated_at=datetime.now(timezone.utc),
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**kwargs,
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)
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def passes_correctness_gates(
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self,
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min_entity_f1_delta: float = 0.0,
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min_event_f1_delta: float = -0.02, # Allow tiny regression on events
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max_calibration_ece: float = 0.08,
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) -> bool:
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"""Check if correctness gates pass.
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Note: adjudication rate reduction is NOT a promotion gate.
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A model must pass field-level correctness regardless of
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adjudication impact.
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"""
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# Entity F1 must not regress
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if self.entity_f1_delta < min_entity_f1_delta:
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return False
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# Event F1 must not regress significantly
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if self.event_f1_delta < min_event_f1_delta:
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return False
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# Calibration must remain acceptable
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if self.calibration_ece > max_calibration_ece:
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return False
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return True
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def promotion_decision(self) -> PromotionDecision:
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"""Determine promotion decision based on gates."""
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if not self.passes_correctness_gates():
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return PromotionDecision.REJECT
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# If adjudication rate actually increases, flag for review
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if self.adjudication_rate_delta > 0.05:
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return PromotionDecision.NEEDS_REVIEW
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return PromotionDecision.PROMOTE
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def to_dict(self) -> dict[str, Any]:
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return {
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"evaluation_id": str(self.evaluation_id),
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"model_version": self.model_version,
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"entity_f1": self.entity_f1,
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"event_f1": self.event_f1,
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"relation_f1": self.relation_f1,
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"calibration_ece": self.calibration_ece,
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"entity_f1_delta": self.entity_f1_delta,
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"event_f1_delta": self.event_f1_delta,
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"adjudication_rate_delta": self.adjudication_rate_delta,
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"passes_correctness_gates": self.passes_correctness_gates(),
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"promotion_decision": self.promotion_decision().value,
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}
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@dataclass
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class ModelCard:
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"""Model card for a trained specialist model artifact.
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Contains training range, dataset version, intended use, limitations,
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and evaluation results as required by Requirement 17.6.
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"""
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card_id: UUID
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model_version: str
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base_model: str
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training_run_id: UUID
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created_at: datetime
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# Training details
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training_range: str = ""
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dataset_version: str = ""
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schema_version: str = ""
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total_training_examples: int = 0
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# Intended use
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intended_use: str = "Entity and event extraction for financial documents"
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entity_types: list[str] = field(default_factory=list)
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# Limitations
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limitations: list[str] = field(default_factory=lambda: [
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"Trained on English-language financial documents only",
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"Requires recalibration when new entity types are added",
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"Performance may degrade on document types not in training set",
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])
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# Evaluation
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evaluation_results: EvaluationResult | None = None
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# Registry
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promoted: bool = False
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promoted_at: datetime | None = None
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deprecated: bool = False
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deprecated_at: datetime | None = None
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@classmethod
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def create(
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cls,
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model_version: str,
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base_model: str,
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training_run_id: UUID,
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**kwargs: Any,
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) -> ModelCard:
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return cls(
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card_id=uuid4(),
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model_version=model_version,
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base_model=base_model,
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training_run_id=training_run_id,
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created_at=datetime.now(timezone.utc),
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**kwargs,
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)
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def promote(self) -> None:
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"""Mark this model as promoted to production."""
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self.promoted = True
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self.promoted_at = datetime.now(timezone.utc)
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def deprecate(self) -> None:
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"""Mark this model as deprecated."""
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self.deprecated = True
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self.deprecated_at = datetime.now(timezone.utc)
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def to_dict(self) -> dict[str, Any]:
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return {
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"card_id": str(self.card_id),
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"model_version": self.model_version,
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"base_model": self.base_model,
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"training_range": self.training_range,
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"dataset_version": self.dataset_version,
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"schema_version": self.schema_version,
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"total_training_examples": self.total_training_examples,
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"intended_use": self.intended_use,
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"entity_types": self.entity_types,
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"limitations": self.limitations,
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"promoted": self.promoted,
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"deprecated": self.deprecated,
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}
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"""Training pipeline for specialist extractor fine-tuning.
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Manages training runs on the Stonks Oracle schema, tracks artifacts,
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and produces evaluation-ready models for holdout testing.
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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 TrainingStatus(str, enum.Enum):
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"""Status of a training run."""
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PENDING = "pending"
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PREPARING_DATA = "preparing_data"
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TRAINING = "training"
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EVALUATING = "evaluating"
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COMPLETED = "completed"
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FAILED = "failed"
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@dataclass
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class TrainingConfig:
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"""Configuration for specialist model fine-tuning."""
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base_model: str = "GLiNER2-large"
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schema_version: str = "1.0"
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dataset_version: str = ""
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training_range: str = "" # e.g., "2024-01 to 2024-06"
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# Training parameters
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learning_rate: float = 2e-5
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batch_size: int = 16
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max_epochs: int = 10
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warmup_steps: int = 100
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weight_decay: float = 0.01
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# Data split
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train_ratio: float = 0.8
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validation_ratio: float = 0.1
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holdout_ratio: float = 0.1 # Frozen holdout — never used in training
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# Entity types to fine-tune
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entity_types: list[str] = field(default_factory=lambda: [
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"company", "person", "event", "financial_metric",
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"date", "money", "percentage", "ticker",
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])
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@dataclass
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class TrainingRun:
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"""A single training run for the specialist extractor."""
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run_id: UUID
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config: TrainingConfig
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status: TrainingStatus = TrainingStatus.PENDING
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started_at: datetime | None = None
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completed_at: datetime | None = None
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# Training metrics
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train_loss: float = 0.0
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validation_loss: float = 0.0
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best_epoch: int = 0
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total_examples: int = 0
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# Artifact tracking
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artifact_path: str = ""
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model_version: str = ""
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parent_model_version: str = ""
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# Metadata
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notes: str = ""
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errors: list[str] = field(default_factory=list)
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@classmethod
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def create(cls, config: TrainingConfig) -> TrainingRun:
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return cls(
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run_id=uuid4(),
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config=config,
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)
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def start(self) -> None:
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"""Begin training."""
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self.status = TrainingStatus.PREPARING_DATA
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self.started_at = datetime.now(timezone.utc)
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def begin_training(self) -> None:
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"""Transition to active training."""
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self.status = TrainingStatus.TRAINING
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def begin_evaluation(self) -> None:
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"""Transition to evaluation phase."""
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self.status = TrainingStatus.EVALUATING
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def complete(
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self,
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artifact_path: str,
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model_version: str,
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train_loss: float = 0.0,
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validation_loss: float = 0.0,
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best_epoch: int = 0,
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) -> None:
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"""Mark training as complete with artifact metadata."""
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self.status = TrainingStatus.COMPLETED
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self.completed_at = datetime.now(timezone.utc)
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self.artifact_path = artifact_path
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self.model_version = model_version
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self.train_loss = train_loss
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self.validation_loss = validation_loss
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self.best_epoch = best_epoch
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def fail(self, error: str) -> None:
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"""Mark training as failed."""
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self.status = TrainingStatus.FAILED
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self.completed_at = datetime.now(timezone.utc)
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self.errors.append(error)
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@property
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def duration_seconds(self) -> float | None:
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if self.started_at and self.completed_at:
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return (self.completed_at - self.started_at).total_seconds()
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return None
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def to_dict(self) -> dict[str, Any]:
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return {
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"run_id": str(self.run_id),
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"status": self.status.value,
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"base_model": self.config.base_model,
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"schema_version": self.config.schema_version,
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"dataset_version": self.config.dataset_version,
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"model_version": self.model_version,
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"artifact_path": self.artifact_path,
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"train_loss": self.train_loss,
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"validation_loss": self.validation_loss,
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"best_epoch": self.best_epoch,
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"duration_seconds": self.duration_seconds,
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}
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