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.
131 lines
3.9 KiB
Python
131 lines
3.9 KiB
Python
"""Calibrated fast-path thresholds by document type and event type.
|
|
|
|
Thresholds represent the minimum calibrated confidence required for
|
|
fast-path acceptance. Documents/events below these thresholds are routed
|
|
to adjudication. Thresholds are versioned and can be updated as
|
|
calibration data improves.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass, field
|
|
|
|
from services.intelligence_pipeline_v3.routing.reasons import RouteDecision
|
|
|
|
# Default confidence thresholds per document type.
|
|
# These are initial conservative values; calibration on the Gold Corpus
|
|
# will refine them over time.
|
|
DEFAULT_DOCUMENT_THRESHOLDS: dict[str, float] = {
|
|
"article": 0.80,
|
|
"press_release": 0.80,
|
|
"filing": 0.70,
|
|
"transcript": 0.75,
|
|
"macro_event": 0.75,
|
|
}
|
|
|
|
# Default confidence thresholds per event type (override document-type defaults).
|
|
DEFAULT_EVENT_THRESHOLDS: dict[str, float] = {
|
|
"earnings_beat": 0.75,
|
|
"earnings_miss": 0.75,
|
|
"guidance_change": 0.65,
|
|
"management_change": 0.70,
|
|
"merger_acquisition": 0.60,
|
|
"regulatory_action": 0.65,
|
|
"product_launch": 0.80,
|
|
"legal_action": 0.65,
|
|
"rating_change": 0.75,
|
|
"supply_chain": 0.70,
|
|
}
|
|
|
|
# Fallback threshold when document_type or event_type is unknown.
|
|
DEFAULT_FALLBACK_THRESHOLD: float = 0.80
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class FastPathThresholds:
|
|
"""Configuration for fast-path acceptance thresholds.
|
|
|
|
Resolution order:
|
|
1. Event-type-specific threshold (if event_type is provided and known).
|
|
2. Document-type-specific threshold.
|
|
3. Fallback threshold.
|
|
|
|
Higher thresholds are more conservative (more documents go to adjudication).
|
|
"""
|
|
|
|
document_thresholds: dict[str, float] = field(
|
|
default_factory=lambda: dict(DEFAULT_DOCUMENT_THRESHOLDS)
|
|
)
|
|
event_thresholds: dict[str, float] = field(
|
|
default_factory=lambda: dict(DEFAULT_EVENT_THRESHOLDS)
|
|
)
|
|
fallback_threshold: float = DEFAULT_FALLBACK_THRESHOLD
|
|
version: str = "1.0.0"
|
|
|
|
def resolve_threshold(
|
|
self,
|
|
document_type: str,
|
|
event_type: str | None = None,
|
|
) -> float:
|
|
"""Resolve the applicable threshold for a document/event combination.
|
|
|
|
Parameters
|
|
----------
|
|
document_type:
|
|
The document type (article, filing, transcript, etc.).
|
|
event_type:
|
|
Optional event type detected in the document.
|
|
|
|
Returns
|
|
-------
|
|
float
|
|
The minimum calibrated confidence required for fast-path acceptance.
|
|
"""
|
|
# Event-type threshold takes priority when available
|
|
if event_type and event_type in self.event_thresholds:
|
|
return self.event_thresholds[event_type]
|
|
|
|
# Document-type threshold
|
|
if document_type in self.document_thresholds:
|
|
return self.document_thresholds[document_type]
|
|
|
|
# Fallback
|
|
return self.fallback_threshold
|
|
|
|
|
|
def evaluate_thresholds(
|
|
confidence: float,
|
|
document_type: str,
|
|
event_type: str | None,
|
|
thresholds: FastPathThresholds,
|
|
) -> RouteDecision:
|
|
"""Evaluate whether calibrated confidence meets the fast-path threshold.
|
|
|
|
Parameters
|
|
----------
|
|
confidence:
|
|
Calibrated confidence score (0.0 to 1.0).
|
|
document_type:
|
|
The document type being processed.
|
|
event_type:
|
|
Optional event type detected in the document.
|
|
thresholds:
|
|
Threshold configuration to use.
|
|
|
|
Returns
|
|
-------
|
|
RouteDecision
|
|
FAST_PATH if confidence >= threshold, ADJUDICATION otherwise.
|
|
|
|
Notes
|
|
-----
|
|
The comparison uses ``>=`` (greater-than-or-equal). A confidence value
|
|
exactly at the threshold is accepted on the fast path. This boundary
|
|
behavior is deterministic and tested by property tests.
|
|
"""
|
|
threshold = thresholds.resolve_threshold(document_type, event_type)
|
|
|
|
if confidence >= threshold:
|
|
return RouteDecision.FAST_PATH
|
|
return RouteDecision.ADJUDICATION
|