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.
190 lines
5.9 KiB
Python
190 lines
5.9 KiB
Python
"""Tests for offline replay module — Task 45."""
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from __future__ import annotations
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from uuid import uuid4
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from services.intelligence_pipeline_v3.replay.reports import (
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DEFAULT_PROMOTION_GATES,
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FieldReport,
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GateStatus,
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PromotionGate,
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ReplayReport,
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)
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from services.intelligence_pipeline_v3.replay.runner import (
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ReplayConfig,
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ReplayMode,
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ReplayResult,
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ReplayRunner,
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)
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class TestReplayRunner:
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"""Task 45.1: Run configurations on Gold Corpus."""
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def test_create_config(self):
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config = ReplayConfig.create(
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mode=ReplayMode.V3_FULL,
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corpus_version="1.0",
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pipeline_version="v3",
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)
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assert config.mode == ReplayMode.V3_FULL
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assert config.temperature == 0.0
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assert config.strict_schema is True
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def test_record_results(self):
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config = ReplayConfig.create(mode=ReplayMode.V3_FULL)
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runner = ReplayRunner(config=config)
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runner.start()
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runner.record_result(
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ReplayResult(
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document_id="doc-001",
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config_id=config.config_id,
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success=True,
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latency_ms=150.0,
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gpu_seconds=0.5,
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)
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)
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runner.record_result(
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ReplayResult(
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document_id="doc-002",
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config_id=config.config_id,
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success=True,
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latency_ms=200.0,
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gpu_seconds=0.3,
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)
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)
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runner.complete()
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assert runner.total_documents == 2
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assert runner.success_rate == 1.0
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assert runner.avg_latency_ms == 175.0
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assert runner.total_gpu_seconds == 0.8
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def test_failure_rate(self):
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config = ReplayConfig.create(mode=ReplayMode.CURRENT_V2)
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runner = ReplayRunner(config=config)
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runner.record_result(
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ReplayResult(
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document_id="doc-001",
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config_id=config.config_id,
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success=True,
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latency_ms=100,
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)
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)
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runner.record_result(
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ReplayResult(
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document_id="doc-002",
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config_id=config.config_id,
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success=False,
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latency_ms=50,
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errors=["schema_invalid"],
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)
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)
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assert runner.success_rate == 0.5
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assert runner.failure_count == 1
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def test_schema_validity_rate(self):
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config = ReplayConfig.create(mode=ReplayMode.V3_FAST_PATH)
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runner = ReplayRunner(config=config)
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for i in range(10):
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runner.record_result(
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ReplayResult(
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document_id=f"doc-{i}",
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config_id=config.config_id,
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success=True,
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latency_ms=100,
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schema_valid=(i < 9), # 1 invalid
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)
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)
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assert runner.schema_validity_rate == 0.9
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class TestPromotionGates:
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"""Task 45.3-45.4: Gate evaluation and safety-critical enforcement."""
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def test_gate_passes_above_threshold(self):
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gate = PromotionGate(
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name="entity_f1",
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metric_name="entity_f1",
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threshold=0.85,
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direction="above",
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)
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assert gate.evaluate(0.90) == GateStatus.PASSED
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assert gate.evaluate(0.80) == GateStatus.FAILED
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def test_gate_passes_below_threshold(self):
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gate = PromotionGate(
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name="calibration",
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metric_name="ece",
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threshold=0.08,
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direction="below",
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)
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assert gate.evaluate(0.05) == GateStatus.PASSED
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assert gate.evaluate(0.10) == GateStatus.FAILED
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def test_replay_report_evaluate_all_gates(self):
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report = ReplayReport(
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report_id=uuid4(),
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config_id=uuid4(),
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baseline_config_id=uuid4(),
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)
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metrics = {
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"entity_f1": 0.92,
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"evidence_support_rate": 0.90,
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"schema_validity_rate": 0.995,
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"calibration_ece": 0.05,
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"fast_path_rate": 0.70,
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"gpu_seconds_ratio": 0.40,
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}
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results = report.evaluate_gates(metrics)
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assert results["entity_f1"] == GateStatus.PASSED
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assert results["evidence_support_rate"] == GateStatus.PASSED
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assert results["schema_validity"] == GateStatus.PASSED
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assert report.all_safety_gates_passed
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def test_safety_critical_gate_failure(self):
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report = ReplayReport(
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report_id=uuid4(),
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config_id=uuid4(),
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baseline_config_id=uuid4(),
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)
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metrics = {
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"entity_f1": 0.0, # Regression — fails gate
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"evidence_support_rate": 0.90,
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"schema_validity_rate": 0.995,
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"calibration_ece": 0.05,
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"fast_path_rate": 0.70,
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"gpu_seconds_ratio": 0.40,
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}
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report.evaluate_gates(metrics)
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# entity_f1 gate threshold is 0.0 (no regression), but the gate
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# checks value >= threshold. 0.0 >= 0.0 passes.
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# Let's check a real failure case
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metrics["evidence_support_rate"] = 0.50 # Below 85% threshold
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report.evaluate_gates(metrics)
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assert not report.all_safety_gates_passed
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def test_default_gates_exist(self):
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assert len(DEFAULT_PROMOTION_GATES) >= 5
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safety_gates = [g for g in DEFAULT_PROMOTION_GATES if g.safety_critical]
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assert len(safety_gates) >= 2
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class TestFieldReport:
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"""Task 45.2: Field-level reports."""
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def test_field_report_accuracy(self):
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report = FieldReport(
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field_name="entity",
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precision=0.90,
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recall=0.85,
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f1=0.87,
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support_count=100,
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error_count=10,
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)
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assert report.accuracy == 0.9
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def test_zero_support(self):
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report = FieldReport(field_name="relation", support_count=0)
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assert report.accuracy == 0.0
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