File size: 2,065 Bytes
559c2ff e6496c0 559c2ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | """analysis/signals.py — shared signal types for the Analyst Edge layer.
These Pydantic models carry deterministically-computed evidence (verbatim
before/after text, counts, deltas) from the analysis modules to the LangGraph
agent and synthesis node. The LLM explains; the code supplies the figures.
"""
from __future__ import annotations
from typing import Literal, Optional
from pydantic import BaseModel, ConfigDict, Field
class QuarterDelta(BaseModel):
"""A verbatim text change detected between two consecutive filing periods."""
model_config = ConfigDict(extra="ignore")
kind: Literal[
"risk_added",
"risk_removed",
"risk_reworded",
"guidance_language_shift",
"term_frequency",
"kpi_dropped",
# transcript drift kinds (analysis/tone_drift.py)
"tone_trend",
"topic_arc",
"recurring_evasion",
"topic_fade",
] = Field(description="Type of delta detected.")
period_from: str = Field(description="Prior filing period, e.g. 'Q42025'.")
period_to: str = Field(description="Current filing period, e.g. 'Q12026'.")
before_text: str = Field(
default="",
description="Verbatim fragment from the prior period. Empty for risk_added.",
)
after_text: str = Field(
default="",
description="Verbatim fragment from the current period. Empty for risk_removed.",
)
computed_metric: str = Field(
default="",
description="A computed summary, e.g. '2→8 occurrences (+300%)' for term_frequency.",
)
source: Literal["10-K", "10-Q", "transcript"] = Field(
default="10-Q",
description="Filing type the delta was detected in.",
)
significance: Literal["HIGH", "MEDIUM", "LOW"] = Field(
default="MEDIUM",
description="Computed significance: HIGH for new risks or large frequency swings, etc.",
)
term: str = Field(
default="",
description="The term or risk label being tracked (for term_frequency / kpi_dropped).",
)
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