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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"""Schema validators for v3 annotations.
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Validates completeness, cross-references, evidence coverage, and offset integrity
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for annotated documents before they enter the Gold Corpus or production pipeline.
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Schema version: 1.0.0
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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 enum import Enum
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from services.intelligence_pipeline_v3.schemas.annotations import AnnotatedDocument
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class ValidationSeverity(str, Enum):
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ERROR = "error"
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WARNING = "warning"
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@dataclass
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class ValidationError:
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"""A single validation issue found in an annotation."""
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severity: ValidationSeverity
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field_path: str
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message: str
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entity_id: str | None = None
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@dataclass
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class ValidationResult:
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"""Complete validation result for an annotated document."""
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valid: bool
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errors: list[ValidationError] = field(default_factory=list)
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warnings: list[ValidationError] = field(default_factory=list)
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@property
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def error_count(self) -> int:
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return len(self.errors)
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@property
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def warning_count(self) -> int:
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return len(self.warnings)
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def validate_annotation(doc: "AnnotatedDocument") -> ValidationResult:
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"""Validate completeness and cross-references of an annotated document.
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Checks performed:
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1. All evidence_ids referenced by entities/events/relations/facts exist in evidence_spans
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2. All entity IDs referenced by events/relations/effects exist in entities
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3. Evidence span offsets are within the source_text bounds
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4. Evidence span text matches the source_text at the given offsets
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5. Sentiment probabilities are valid
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6. No orphaned evidence spans (warning only)
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7. Direct effects reference valid event and entity IDs
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8. Inferred exposures reference valid event and entity IDs
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"""
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errors: list[ValidationError] = []
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warnings: list[ValidationError] = []
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# Build lookup indexes
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evidence_ids = {span.id for span in doc.evidence_spans}
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entity_ids = {entity.id for entity in doc.entities}
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event_ids = {event.id for event in doc.events}
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all_annotation_ids = evidence_ids | entity_ids | event_ids
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# Track which evidence spans are referenced
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referenced_evidence: set[str] = set()
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# 1. Validate evidence spans themselves
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for i, span in enumerate(doc.evidence_spans):
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path = f"evidence_spans[{i}]"
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# Offset bounds check
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if span.start_char >= len(doc.source_text):
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.start_char",
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message=f"start_char {span.start_char} exceeds source_text length {len(doc.source_text)}",
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entity_id=span.id,
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)
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)
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elif span.end_char > len(doc.source_text):
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.end_char",
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message=f"end_char {span.end_char} exceeds source_text length {len(doc.source_text)}",
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entity_id=span.id,
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)
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)
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else:
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# Text match check
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expected_text = doc.source_text[span.start_char : span.end_char]
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if expected_text != span.text:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.text",
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message=(
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f"Span text does not match source_text at offsets "
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f"[{span.start_char}:{span.end_char}]. "
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f"Expected: {expected_text!r}, got: {span.text!r}"
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),
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entity_id=span.id,
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)
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)
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# 2. Validate entity evidence references
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for i, entity in enumerate(doc.entities):
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path = f"entities[{i}]"
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for eid in entity.evidence_ids:
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referenced_evidence.add(eid)
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if eid not in evidence_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.evidence_ids",
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message=f"References non-existent evidence span: {eid}",
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entity_id=entity.id,
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)
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)
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# 3. Validate event evidence and entity references
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for i, event in enumerate(doc.events):
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path = f"events[{i}]"
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for eid in event.evidence_ids:
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referenced_evidence.add(eid)
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if eid not in evidence_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.evidence_ids",
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message=f"References non-existent evidence span: {eid}",
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entity_id=event.id,
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)
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)
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for cid in event.primary_company_ids:
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if cid not in entity_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.primary_company_ids",
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message=f"References non-existent entity: {cid}",
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entity_id=event.id,
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)
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)
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# 4. Validate relation references
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for i, rel in enumerate(doc.relations):
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path = f"relations[{i}]"
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for eid in rel.evidence_ids:
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referenced_evidence.add(eid)
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if eid not in evidence_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.evidence_ids",
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message=f"References non-existent evidence span: {eid}",
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entity_id=rel.id,
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)
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)
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if rel.source_id not in all_annotation_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.source_id",
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message=f"source_id references non-existent annotation: {rel.source_id}",
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entity_id=rel.id,
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)
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)
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if rel.target_id not in entity_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.target_id",
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message=f"target_id references non-existent entity: {rel.target_id}",
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entity_id=rel.id,
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)
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)
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# 5. Validate numeric fact references
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for i, fact in enumerate(doc.numeric_facts):
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path = f"numeric_facts[{i}]"
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for eid in fact.evidence_ids:
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referenced_evidence.add(eid)
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if eid not in evidence_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.evidence_ids",
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message=f"References non-existent evidence span: {eid}",
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entity_id=fact.id,
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)
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)
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if fact.subject_entity_id and fact.subject_entity_id not in entity_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.subject_entity_id",
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message=f"References non-existent entity: {fact.subject_entity_id}",
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entity_id=fact.id,
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)
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)
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# 6. Validate sentiment references
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for i, sent in enumerate(doc.sentiments):
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path = f"sentiments[{i}]"
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for eid in sent.evidence_ids:
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referenced_evidence.add(eid)
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if eid not in evidence_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.evidence_ids",
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message=f"References non-existent evidence span: {eid}",
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entity_id=sent.id,
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)
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)
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if sent.company_entity_id not in entity_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.company_entity_id",
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message=f"References non-existent entity: {sent.company_entity_id}",
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entity_id=sent.id,
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)
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)
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# 7. Validate direct effects
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for i, effect in enumerate(doc.direct_effects):
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path = f"direct_effects[{i}]"
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for eid in effect.evidence_ids:
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referenced_evidence.add(eid)
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if eid not in evidence_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.evidence_ids",
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message=f"References non-existent evidence span: {eid}",
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)
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)
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if effect.event_id not in event_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.event_id",
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message=f"References non-existent event: {effect.event_id}",
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)
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)
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if effect.company_entity_id not in entity_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.company_entity_id",
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message=f"References non-existent entity: {effect.company_entity_id}",
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)
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)
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# 8. Validate inferred exposures
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for i, exposure in enumerate(doc.inferred_exposures):
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path = f"inferred_exposures[{i}]"
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for eid in exposure.evidence_ids:
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referenced_evidence.add(eid)
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if eid not in evidence_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.evidence_ids",
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message=f"References non-existent evidence span: {eid}",
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)
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)
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if exposure.event_id not in event_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.event_id",
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message=f"References non-existent event: {exposure.event_id}",
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)
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)
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if exposure.company_entity_id not in entity_ids:
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errors.append(
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ValidationError(
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severity=ValidationSeverity.ERROR,
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field_path=f"{path}.company_entity_id",
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message=f"References non-existent entity: {exposure.company_entity_id}",
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)
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)
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# 9. Check for orphaned evidence spans (warning)
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orphaned = evidence_ids - referenced_evidence
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for eid in orphaned:
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warnings.append(
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ValidationError(
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severity=ValidationSeverity.WARNING,
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field_path="evidence_spans",
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message=f"Evidence span {eid} is not referenced by any annotation.",
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entity_id=eid,
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)
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)
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# 10. Check minimum annotation completeness
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if not doc.entities:
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warnings.append(
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ValidationError(
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severity=ValidationSeverity.WARNING,
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field_path="entities",
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message="Document has no entity annotations.",
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)
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)
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if not doc.events:
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warnings.append(
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ValidationError(
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severity=ValidationSeverity.WARNING,
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field_path="events",
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message="Document has no event annotations.",
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)
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)
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is_valid = len(errors) == 0
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return ValidationResult(valid=is_valid, errors=errors, warnings=warnings)
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