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.
33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
"""Confidence feature pipeline for Intelligence Pipeline v3.
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Provides calibrated extraction confidence from specialist scores,
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symbol resolution, evidence validation, schema completeness,
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model agreement, and historical calibration data. Replaces
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generative model self-reported confidence with empirically
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calibrated probabilities.
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"""
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from services.intelligence_pipeline_v3.confidence.artifacts import (
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load_artifact,
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save_artifact,
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)
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from services.intelligence_pipeline_v3.confidence.calibrator import ConfidenceCalibrator
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from services.intelligence_pipeline_v3.confidence.defaults import get_default_confidence
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from services.intelligence_pipeline_v3.confidence.features import ConfidenceFeatureExtractor
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from services.intelligence_pipeline_v3.confidence.models import (
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CalibrationArtifactMetadata,
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ConfidenceFeatures,
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ConfidenceResult,
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)
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__all__ = [
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"CalibrationArtifactMetadata",
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"ConfidenceCalibrator",
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"ConfidenceFeatureExtractor",
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"ConfidenceFeatures",
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"ConfidenceResult",
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"get_default_confidence",
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"load_artifact",
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"save_artifact",
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]
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