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.
101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
"""Data models for evidence verification results and rejected candidates.
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Provides structured types for offset verification, entity-evidence association,
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numeric consistency checks, rejected-candidate storage, and verification reports.
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"""
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from __future__ import annotations
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from datetime import datetime, timezone
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from enum import Enum
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from pydantic import BaseModel, Field
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class RejectionReason(str, Enum):
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"""Structured reason codes for candidate rejection during verification."""
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INVALID_OFFSET = "invalid_offset"
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TEXT_MISMATCH = "text_mismatch"
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ENTITY_NOT_IN_EVIDENCE = "entity_not_in_evidence"
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NUMERIC_INCONSISTENCY = "numeric_inconsistency"
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UNSUPPORTED_CLAIM = "unsupported_claim"
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SCHEMA_VIOLATION = "schema_violation"
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CONFIDENCE_BELOW_THRESHOLD = "confidence_below_threshold"
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class OffsetVerification(BaseModel):
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"""Result of verifying a single evidence span's offsets against source text."""
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span_id: str = Field(description="ID of the evidence span being verified.")
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valid: bool = Field(description="Whether the span text matches source at offsets.")
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reason: str | None = Field(
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default=None,
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description="Explanation when invalid (e.g., 'text mismatch at offset 42').",
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)
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class AssociationVerification(BaseModel):
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"""Result of verifying that an entity appears in at least one linked evidence span."""
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entity_id: str = Field(description="ID of the entity being verified.")
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valid: bool = Field(description="Whether entity text was found in any linked span.")
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reason: str | None = Field(
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default=None,
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description="Explanation when invalid (e.g., 'entity text not in any linked span').",
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)
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class NumericVerification(BaseModel):
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"""Result of verifying a numeric fact against its evidence spans."""
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fact_id: str = Field(description="ID of the numeric fact being verified.")
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valid: bool = Field(description="Whether the numeric value was found in evidence text.")
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found_value: str | None = Field(
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default=None,
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description="The value found in the evidence text, if any.",
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)
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expected_value: str = Field(description="The expected normalized value from the fact.")
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reason: str | None = Field(
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default=None,
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description="Explanation when invalid (e.g., 'value 3.14 not found in evidence').",
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)
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class RejectedCandidate(BaseModel):
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"""A candidate that was rejected during verification, stored for audit and learning."""
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candidate_type: str = Field(
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description="Type of candidate: entity, fact, event, relation, sentiment."
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)
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candidate_data: dict = Field(
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description="Serialized candidate data for audit trail."
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)
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rejection_reason: RejectionReason = Field(
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description="Structured reason code for rejection."
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)
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stage: str = Field(
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description="Pipeline stage where rejection occurred (e.g., 'offset_verification')."
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)
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timestamp: datetime = Field(
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default_factory=lambda: datetime.now(tz=timezone.utc),
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description="When the rejection was recorded.",
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)
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class VerificationReport(BaseModel):
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"""Summary report from a full verification pass over extraction candidates."""
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total_candidates: int = Field(ge=0, description="Total candidates evaluated.")
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verified: int = Field(ge=0, description="Candidates that passed verification.")
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rejected: int = Field(ge=0, description="Candidates that failed verification.")
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coverage_rate: float = Field(
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ge=0.0,
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le=1.0,
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description="Proportion of candidates with valid evidence support.",
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)
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rejection_breakdown: dict[str, int] = Field(
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default_factory=dict,
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description="Count of rejections per RejectionReason code.",
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)
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