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.
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
"""Routing decision storage.
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Stores every routing decision with the full feature snapshot for audit,
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recalibration, and explainability. Backed by the v3_routing_decisions table.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from uuid import UUID
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from services.intelligence_pipeline_v3.routing.router import RoutingDecision
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@dataclass
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class RoutingDecisionStore:
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"""In-memory store for routing decisions.
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In production, this would be backed by the ``v3_routing_decisions`` table.
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This implementation provides the storage interface for use in the pipeline
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orchestrator and for testing.
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The store is append-only — decisions are immutable once stored.
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"""
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_decisions: list[RoutingDecision] = field(default_factory=list)
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_by_pipeline_run: dict[UUID, list[RoutingDecision]] = field(default_factory=dict)
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def store(self, decision: RoutingDecision) -> None:
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"""Store a routing decision.
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Parameters
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----------
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decision:
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The routing decision to persist. Must have a unique id.
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"""
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self._decisions.append(decision)
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run_decisions = self._by_pipeline_run.setdefault(
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decision.pipeline_run_id, []
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)
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run_decisions.append(decision)
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def get_by_pipeline_run(self, run_id: UUID) -> list[RoutingDecision]:
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"""Retrieve all routing decisions for a pipeline run.
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Parameters
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----------
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run_id:
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The pipeline run identifier.
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Returns
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-------
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list[RoutingDecision]
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All decisions for the given run, in insertion order.
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Returns empty list if no decisions exist for the run.
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"""
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return list(self._by_pipeline_run.get(run_id, []))
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def get_all(self) -> list[RoutingDecision]:
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"""Retrieve all stored decisions in insertion order."""
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return list(self._decisions)
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def count(self) -> int:
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"""Return the total number of stored decisions."""
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return len(self._decisions)
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