Prompt data model

export type GenaiPrompt = {
	model_id?: string; // The ID of the model to use (a GenaiModel ID)
	messages?: []; // The conversation to send to the model, in order; each message has a role and its content
	temperature?: number; // 0-1
	max_tokens?: number; // a float between 0-1
	responses?: string[];
	tokens_used?: number;
	mcp_url?: string; // Supply a remote MCP URL to send to the LLM API for it to call its tools.  Note: Some LLM APIs do not yet support remote MCP URLs. 
	mcp_deferred_tools?: string[];
	mcp_authorization_token?: string; // OAuth Bearer token for MCP servers that require authentication.
	questions?: []; // Typed questions to evaluate against the messages (for decision models such as TypeSafe Jev).
	answers?: []; // One answer per question, matched by id.
	skill_ids?: string[]; // ids of the genai_skill objects (Agent Skills) to make available to the LLM while it answers.
	raw?: any;
} 

import { z } from 'zod';

export const ZodGenaiPrompt = z.object({
	model_id: z.string().optional().describe("The ID of the model to use (a GenaiModel ID)"),
	messages: z.array().optional().describe("The conversation to send to the model, in order; each message has a role and its content"),
	temperature: z.number().optional().describe("0-1"),
	max_tokens: z.number().optional().describe("a float between 0-1"),
	responses: z.array().optional(),
	tokens_used: z.number().optional(),
	mcp_url: z.string().optional().describe("Supply a remote MCP URL to send to the LLM API for it to call its tools.  Note: Some LLM APIs do not yet support remote MCP URLs. "),
	mcp_deferred_tools: z.array().optional(),
	mcp_authorization_token: z.string().optional().describe("OAuth Bearer token for MCP servers that require authentication."),
	questions: z.array().optional().describe("Typed questions to evaluate against the messages (for decision models such as TypeSafe Jev)."),
	answers: z.array().optional().describe("One answer per question, matched by id."),
	skill_ids: z.array().optional().describe("ids of the genai_skill objects (Agent Skills) to make available to the LLM while it answers."),

});

import dataclasses

@dataclasses.dataclass
class GenaiPrompt:
	"""The ID of the model to use (a GenaiModel ID)"""
	model_id: Optional[str]
	"""The conversation to send to the model, in order; each message has a role and its content"""
	messages: Optional[PropertyGenaiPromptMessages]
	"""0-1"""
	temperature: Optional[int]
	"""a float between 0-1"""
	max_tokens: Optional[int]
	responses: Optional[List[List[str]]]
	tokens_used: Optional[int]
	"""Supply a remote MCP URL to send to the LLM API for it to call its tools.  Note: Some LLM APIs do not yet support remote MCP URLs. """
	mcp_url: Optional[str]
	mcp_deferred_tools: Optional[List[List[str]]]
	"""OAuth Bearer token for MCP servers that require authentication."""
	mcp_authorization_token: Optional[str]
	"""Typed questions to evaluate against the messages (for decision models such as TypeSafe Jev)."""
	questions: Optional[PropertyGenaiPromptQuestions]
	"""One answer per question, matched by id."""
	answers: Optional[PropertyGenaiPromptAnswers]
	"""ids of the genai_skill objects (Agent Skills) to make available to the LLM while it answers."""
	skill_ids: Optional[List[List[str]]]