"""Message schema for LLM conversation history."""

from typing import Any, Literal

from pydantic import BaseModel, Field

from app.models.tool_call import ToolCall


class Message(BaseModel):
    """A single message in the conversation history.

    Follows the OpenAI chat completions message format with support for
    system, user, assistant, and tool roles.
    """

    role: Literal["system", "user", "assistant", "tool"] = Field(
        description="Role of the message sender"
    )
    content: str | None = Field(default=None, description="Text content of the message")
    tool_calls: list[ToolCall] | None = Field(
        default=None, description="Tool calls made by the assistant"
    )
    tool_call_id: str | None = Field(
        default=None, description="ID of the tool call this message responds to (role=tool)"
    )
    name: str | None = Field(
        default=None, description="Name of the tool that produced this message (role=tool)"
    )

    def to_api_dict(self) -> dict[str, Any]:
        """Serialize to a dict suitable for the OpenAI-compatible API.

        Strips None-valued fields to keep the payload clean.
        """
        data: dict[str, Any] = {"role": self.role}

        # Ollama requires content to be a string, never null/missing —
        # even on assistant messages that only contain tool_calls.
        data["content"] = self.content or ""

        if self.tool_calls is not None:
            data["tool_calls"] = [tc.model_dump() for tc in self.tool_calls]

        if self.tool_call_id is not None:
            data["tool_call_id"] = self.tool_call_id

        if self.name is not None:
            data["name"] = self.name

        return data