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.
24 lines
565 B
Python
24 lines
565 B
Python
"""Audit and review module for the v3 intelligence pipeline.
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Provides evidence display, reviewer corrections, filtering by confidence/
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claims/adjudication, and immutable correction event storage.
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"""
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from services.intelligence_pipeline_v3.audit.models import (
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AuditRecord,
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CorrectionEvent,
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CorrectionType,
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ReviewFilter,
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ReviewStatus,
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)
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from services.intelligence_pipeline_v3.audit.store import AuditStore
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__all__ = [
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"AuditRecord",
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"AuditStore",
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"CorrectionEvent",
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"CorrectionType",
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"ReviewFilter",
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"ReviewStatus",
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]
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