feat: group private apps into andreknie-privat

privat/andreknie-privat/NoteGraph/ingestion/enrichment/models.py

@@ -0,0 +1,37 @@
+"""Pydantic models for LLM enrichment responses.
+
+Defines the structured output format expected from the enrichment agent:
+entities, action items, and the overall enrichment result.
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class Entity(BaseModel):
+    """A detected entity in the document text."""
+
+    type: Literal["person", "project", "date", "action_item"]
+    value: str
+    confidence: float = Field(ge=0.0, le=1.0)
+    position: int | None = None  # character offset in source text
+
+
+class ActionItem(BaseModel):
+    """An extracted action item / task."""
+
+    description: str
+    assignee: str | None = None
+    deadline: str | None = None  # ISO 8601 date string
+
+
+class EnrichmentResult(BaseModel):
+    """Complete enrichment output from the LLM."""
+
+    title: str
+    category: Literal["meeting", "project", "decision", "inbox"]
+    tags: list[str] = Field(default_factory=list)
+    entities: list[Entity] = Field(default_factory=list)
+    action_items: list[ActionItem] = Field(default_factory=list)
+    summary: str | None = None