"""
Pydantic models for structured output extraction.

Two schemas are defined:
1. PersonInfo – information about a person.
2. MeetingNotes – notes from a meeting.

Both models use `Field(description=...)` to provide clear field descriptions that LangChain can expose in the prompt.
"""

from __future__ import annotations

from typing import List, Optional

from pydantic import BaseModel, Field

class PersonInfo(BaseModel):
    """Information about a person extracted from free text."""

    name: str = Field(..., description="Full name of the person")
    age: Optional[int] = Field(None, description="Age in years; optional if not mentioned")
    profession: str = Field(..., description="Primary occupation or role")
    skills: List[str] = Field(
        ...,
        description="List of technical or soft skills the person possesses",
    )

class MeetingNotes(BaseModel):
    """Structured notes from a meeting extracted from free text."""

    date: str = Field(..., description="Date of the meeting in ISO format (YYYY-MM-DD)")
    participants: List[str] = Field(
        ..., description="Names of people who attended the meeting"
    )
    topics: List[str] = Field(
        ...,
        description="Main discussion topics covered during the meeting"
    )
    decisions: List[str] = Field(
        ..., description="Decisions made during the meeting"
    )
    next_steps: List[str] = Field(
        ..., description="Action items or follow‑up tasks after the meeting"
    )

__all__ = ["PersonInfo", "MeetingNotes"]