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.
81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
"""Hard ambiguity and conflict rules for routing decisions.
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These rules check structural markers in extraction output that indicate
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the document *requires* semantic reasoning by the 9B adjudicator. Any
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triggered rule forces ADJUDICATION regardless of confidence scores.
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"""
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from __future__ import annotations
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from typing import Any
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from services.intelligence_pipeline_v3.routing.reasons import RoutingReason
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def evaluate_hard_rules(
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confidence_features: dict[str, Any],
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ambiguity_markers: dict[str, Any],
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) -> list[RoutingReason]:
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"""Evaluate hard ambiguity/conflict rules against extraction output.
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Parameters
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----------
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confidence_features:
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Field-level confidence features from the confidence pipeline.
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Expected keys include:
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- ``evidence_coverage``: float 0-1
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- ``material_fields_present``: bool
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- ``cross_chunk_relations``: bool (relations span multiple chunks)
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ambiguity_markers:
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Structural ambiguity markers from candidate generation and resolution.
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Expected keys include:
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- ``unresolved_aliases``: int (count of unresolved entity aliases)
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- ``primary_company_count``: int (number of primary companies detected)
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- ``contradictory_numeric_facts``: bool
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- ``conflicting_sentiment``: bool
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- ``implied_causal_impact``: bool
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- ``guidance_vs_consensus``: bool
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- ``long_document_cross_chunk``: bool
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Returns
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-------
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list[RoutingReason]
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List of triggered reasons. Empty list means no hard rules triggered.
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"""
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triggered: list[RoutingReason] = []
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# Unresolved entity aliases require contextual disambiguation
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if ambiguity_markers.get("unresolved_aliases", 0) > 0:
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triggered.append(RoutingReason.UNRESOLVED_ALIAS)
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# Multiple primary companies need reasoning about which is the subject
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if ambiguity_markers.get("primary_company_count", 0) > 1:
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triggered.append(RoutingReason.MULTIPLE_PRIMARY_COMPANIES)
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# Contradictory numeric facts (e.g., conflicting revenue figures)
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if ambiguity_markers.get("contradictory_numeric_facts", False):
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triggered.append(RoutingReason.CONTRADICTORY_NUMERIC_FACTS)
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# Conflicting sentiment across evidence groups for the same company
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if ambiguity_markers.get("conflicting_sentiment", False):
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triggered.append(RoutingReason.CONFLICTING_SENTIMENT)
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# Implied causal impact requiring reasoning (not explicit statement)
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if ambiguity_markers.get("implied_causal_impact", False):
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triggered.append(RoutingReason.IMPLIED_CAUSAL_IMPACT)
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# Guidance vs consensus comparison requires model reasoning
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if ambiguity_markers.get("guidance_vs_consensus", False):
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triggered.append(RoutingReason.GUIDANCE_VS_CONSENSUS_REQUIRES_REASONING)
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# Material fields missing from extraction output
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if not confidence_features.get("material_fields_present", True):
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triggered.append(RoutingReason.MATERIAL_FIELD_MISSING)
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# Cross-chunk relations in long documents need broader context
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if ambiguity_markers.get("long_document_cross_chunk", False):
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triggered.append(RoutingReason.LONG_DOCUMENT_CROSS_CHUNK_RELATION)
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return triggered
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