Celes Renata
a72f336ad1
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.
67 lines
1.6 KiB
Python
67 lines
1.6 KiB
Python
"""V3 annotation schema — entity, event, relation, sentiment, and evidence models."""
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from services.intelligence_pipeline_v3.schemas.annotations import (
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AmbiguityMarker,
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AmbiguityType,
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AnnotatedDocument,
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AnnotationMetadata,
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CompanySentimentAnnotation,
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DirectEffect,
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EntityAnnotation,
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EntityType,
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EventAnnotation,
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EventClass,
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EvidenceSpanAnnotation,
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InferredExposure,
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NumericFactAnnotation,
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PeriodAnnotation,
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PeriodType,
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RelationAnnotation,
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RelationType,
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SentimentLabel,
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)
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from services.intelligence_pipeline_v3.schemas.safety import (
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SAFETY_CRITICAL_FIELDS,
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SafetyCriticalField,
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SafetyGateResult,
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check_safety_gates,
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)
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from services.intelligence_pipeline_v3.schemas.validators import (
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ValidationError as AnnotationValidationError,
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)
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from services.intelligence_pipeline_v3.schemas.validators import (
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ValidationResult,
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validate_annotation,
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)
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__all__ = [
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# Annotation models
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"AmbiguityMarker",
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"AmbiguityType",
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"AnnotatedDocument",
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"AnnotationMetadata",
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"CompanySentimentAnnotation",
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"DirectEffect",
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"EntityAnnotation",
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"EntityType",
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"EvidenceSpanAnnotation",
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"EventAnnotation",
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"EventClass",
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"InferredExposure",
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"NumericFactAnnotation",
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"PeriodAnnotation",
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"PeriodType",
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"RelationAnnotation",
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"RelationType",
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"SentimentLabel",
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# Safety
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"SAFETY_CRITICAL_FIELDS",
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"SafetyCriticalField",
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"SafetyGateResult",
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"check_safety_gates",
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# Validators
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"AnnotationValidationError",
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"ValidationResult",
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"validate_annotation",
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]
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