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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"""Observability module — distributed tracing, stage metrics, dashboards, and alerts.
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Provides unified tracing across pipeline stages, metric collection for
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latency/errors/batch-size/queue-depth/routing, and alert definitions
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for operational monitoring.
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"""
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from services.intelligence_pipeline_v3.observability.metrics import (
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AlertSeverity,
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MetricAlert,
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MetricsCollector,
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StageMetrics,
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)
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from services.intelligence_pipeline_v3.observability.tracing import (
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PipelineTrace,
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StageSpan,
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TraceCollector,
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)
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__all__ = [
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"AlertSeverity",
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"MetricAlert",
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"MetricsCollector",
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"PipelineTrace",
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"StageMetrics",
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"StageSpan",
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"TraceCollector",
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]
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