ankn
a5f8fb49ab
Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit,
Projekt-KIQ-HP, teamlandkarte-mcp
Dhive: Jury-Voting
Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation)
Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more
Shared/references: symphony (read-only)
Bahn repos remain available as independent remotes - this monorepo
pulls them in via subtree, the originals are untouched.
38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
"""Pydantic models for LLM enrichment responses.
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Defines the structured output format expected from the enrichment agent:
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entities, action items, and the overall enrichment result.
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"""
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from typing import Literal
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from pydantic import BaseModel, Field
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class Entity(BaseModel):
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"""A detected entity in the document text."""
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type: Literal["person", "project", "date", "action_item"]
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value: str
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confidence: float = Field(ge=0.0, le=1.0)
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position: int | None = None # character offset in source text
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class ActionItem(BaseModel):
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"""An extracted action item / task."""
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description: str
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assignee: str | None = None
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deadline: str | None = None # ISO 8601 date string
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class EnrichmentResult(BaseModel):
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"""Complete enrichment output from the LLM."""
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title: str
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category: Literal["meeting", "project", "decision", "inbox"]
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tags: list[str] = Field(default_factory=list)
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entities: list[Entity] = Field(default_factory=list)
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action_items: list[ActionItem] = Field(default_factory=list)
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summary: str | None = None
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