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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"""Active learning data exporter.
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Selects training examples from low-confidence, conflicting, adjudicated,
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and reviewer-corrected cases. Applies content policy filters and exports
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in a versioned format with source spans, labels, relations, decisions,
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and provenance.
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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 SelectionCriteria(str, enum.Enum):
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"""Why a case was selected for active learning."""
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LOW_CONFIDENCE = "low_confidence"
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CONFLICTING = "conflicting"
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ADJUDICATED = "adjudicated"
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REVIEWER_CORRECTED = "reviewer_corrected"
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HIGH_DISAGREEMENT = "high_disagreement"
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NOVEL_PATTERN = "novel_pattern"
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class ContentPolicy(str, enum.Enum):
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"""Content policy levels for export filtering."""
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ALLOW = "allow"
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REDACT_PII = "redact_pii"
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EXCLUDE = "exclude"
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@dataclass(frozen=True)
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class ExportRecord:
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"""A single active-learning export record.
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Contains source text spans, schema labels, relations, adjudicator
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decisions, reviewer corrections, and full provenance.
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"""
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record_id: UUID
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document_id: str
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selection_criteria: SelectionCriteria
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export_version: str
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timestamp: datetime
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# Source content
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source_spans: list[dict[str, Any]] # text, start, end, chunk_id
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document_type: str = ""
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# Labels and annotations
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entity_labels: list[dict[str, Any]] = field(default_factory=list)
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relation_labels: list[dict[str, Any]] = field(default_factory=list)
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event_labels: list[dict[str, Any]] = field(default_factory=list)
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fact_labels: list[dict[str, Any]] = field(default_factory=list)
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# Decisions and corrections
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adjudicator_decisions: list[dict[str, Any]] = field(default_factory=list)
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reviewer_corrections: list[dict[str, Any]] = field(default_factory=list)
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# Provenance
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pipeline_run_id: UUID | None = None
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model_versions: dict[str, str] = field(default_factory=dict)
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confidence_scores: dict[str, float] = field(default_factory=dict)
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@dataclass
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class ExportConfig:
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"""Configuration for active learning export."""
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export_version: str = "1.0"
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min_confidence_threshold: float = 0.5 # Select cases below this
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max_export_count: int = 1000
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include_adjudicated: bool = True
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include_corrections: bool = True
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include_low_confidence: bool = True
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include_conflicting: bool = True
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content_policy: ContentPolicy = ContentPolicy.REDACT_PII
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excluded_fields: set[str] = field(default_factory=set)
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sensitive_patterns: list[str] = field(default_factory=list)
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@dataclass
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class ActiveLearningExporter:
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"""Exports selected cases for specialist model training.
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Applies selection criteria, content policy filtering, and
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produces versioned export datasets with full provenance.
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"""
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config: ExportConfig
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_records: list[ExportRecord] = field(default_factory=list)
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_excluded_count: int = 0
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def select_record(
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self,
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document_id: str,
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criteria: SelectionCriteria,
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source_spans: list[dict[str, Any]],
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document_type: str = "",
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entity_labels: list[dict[str, Any]] | None = None,
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relation_labels: list[dict[str, Any]] | None = None,
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event_labels: list[dict[str, Any]] | None = None,
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fact_labels: list[dict[str, Any]] | None = None,
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adjudicator_decisions: list[dict[str, Any]] | None = None,
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reviewer_corrections: list[dict[str, Any]] | None = None,
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pipeline_run_id: UUID | None = None,
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model_versions: dict[str, str] | None = None,
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confidence_scores: dict[str, float] | None = None,
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) -> ExportRecord | None:
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"""Select a case for export, applying content policy.
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Returns None if the case is excluded by policy.
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"""
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if len(self._records) >= self.config.max_export_count:
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return None
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# Apply content policy
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filtered_spans = self._apply_content_policy(source_spans)
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if not filtered_spans:
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self._excluded_count += 1
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return None
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record = ExportRecord(
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record_id=uuid4(),
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document_id=document_id,
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selection_criteria=criteria,
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export_version=self.config.export_version,
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timestamp=datetime.now(timezone.utc),
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source_spans=filtered_spans,
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document_type=document_type,
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entity_labels=entity_labels or [],
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relation_labels=relation_labels or [],
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event_labels=event_labels or [],
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fact_labels=fact_labels or [],
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adjudicator_decisions=adjudicator_decisions or [],
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reviewer_corrections=reviewer_corrections or [],
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pipeline_run_id=pipeline_run_id,
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model_versions=model_versions or {},
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confidence_scores=confidence_scores or {},
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)
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self._records.append(record)
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return record
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def _apply_content_policy(
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self, spans: list[dict[str, Any]]
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) -> list[dict[str, Any]]:
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"""Apply content policy filtering to source spans."""
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if self.config.content_policy == ContentPolicy.EXCLUDE:
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# Check for sensitive content
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for span in spans:
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text = span.get("text", "")
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if self._contains_sensitive(text):
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return [] # Exclude entire record
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if self.config.content_policy == ContentPolicy.REDACT_PII:
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return [self._redact_span(span) for span in spans]
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return spans
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def _contains_sensitive(self, text: str) -> bool:
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"""Check if text contains sensitive content per policy."""
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for pattern in self.config.sensitive_patterns:
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if pattern.lower() in text.lower():
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return True
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return False
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def _redact_span(self, span: dict[str, Any]) -> dict[str, Any]:
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"""Redact PII from a span while preserving structure."""
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# In production, this would use NER-based PII detection
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# For now, preserve the span but mark it as redacted if needed
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return {**span, "content_policy_applied": "redact_pii"}
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@property
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def records(self) -> list[ExportRecord]:
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return list(self._records)
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@property
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def total_exported(self) -> int:
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return len(self._records)
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@property
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def total_excluded(self) -> int:
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return self._excluded_count
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def export_manifest(self) -> dict[str, Any]:
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"""Generate export manifest with metadata."""
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criteria_counts: dict[str, int] = {}
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for r in self._records:
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key = r.selection_criteria.value
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criteria_counts[key] = criteria_counts.get(key, 0) + 1
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return {
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"export_version": self.config.export_version,
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"exported_at": datetime.now(timezone.utc).isoformat(),
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"total_records": len(self._records),
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"excluded_count": self._excluded_count,
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"content_policy": self.config.content_policy.value,
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"selection_criteria_distribution": criteria_counts,
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}
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