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.
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"""Compatibility adapter — maps v3 intelligence records to current v2 data classes."""
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from services.intelligence_pipeline_v3.compatibility.adapter import CompatibilityAdapter
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from services.intelligence_pipeline_v3.compatibility.config import AdapterMode, is_adapter_enabled
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from services.intelligence_pipeline_v3.compatibility.models import (
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AdapterLineage,
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V2ImpactRecord,
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V2IntelligenceRecord,
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V3IntelligenceRecord,
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)
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__all__ = [
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"AdapterLineage",
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"AdapterMode",
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"CompatibilityAdapter",
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"V2ImpactRecord",
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"V2IntelligenceRecord",
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"V3IntelligenceRecord",
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"is_adapter_enabled",
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]
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