/*
 * Orvanta API
 *
 * No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)
 *
 * The version of the OpenAPI document: 1.2.0
 * Contact: contact@orvanta.cloud
 * Generated by: https://openapi-generator.tech
 */

use crate::models;
use serde::{Deserialize, Serialize};

/// AiAgentInputTransforms : Input parameters for the AI agent mapped to their values
#[derive(Clone, Default, Debug, PartialEq, Serialize, Deserialize)]
pub struct AiAgentInputTransforms {
    #[serde(rename = "provider")]
    pub provider: Box<models::ProviderTransform>,
    /// Output format type. Valid values: 'text' (default) - plain text response, 'image' - image generation 
    #[serde(rename = "output_type")]
    pub output_type: Box<models::InputTransform>,
    /// The user's prompt/message to the AI agent. Supports variable interpolation with flow.input syntax.
    #[serde(rename = "user_message")]
    pub user_message: Box<models::InputTransform>,
    /// System instructions that guide the AI's behavior, persona, and response style. Optional.
    #[serde(rename = "system_prompt", skip_serializing_if = "Option::is_none")]
    pub system_prompt: Option<Box<models::InputTransform>>,
    /// Boolean. If true, stream the AI response incrementally. Streaming events include: token_delta, tool_call, tool_call_arguments, tool_execution, tool_result 
    #[serde(rename = "streaming", skip_serializing_if = "Option::is_none")]
    pub streaming: Option<Box<models::InputTransform>>,
    #[serde(rename = "memory", skip_serializing_if = "Option::is_none")]
    pub memory: Option<Box<models::MemoryTransform>>,
    /// JSON Schema object defining structured output format. Used when you need the AI to return data in a specific shape. Supports standard JSON Schema properties: type, properties, required, items, enum, pattern, minLength, maxLength, minimum, maximum, etc. Example: { type: 'object', properties: { name: { type: 'string' }, age: { type: 'integer' } }, required: ['name'] } 
    #[serde(rename = "output_schema", skip_serializing_if = "Option::is_none")]
    pub output_schema: Option<Box<models::InputTransform>>,
    /// Array of file references (images or PDFs) for the AI agent. Format: Array<{ bucket: string, key: string }> - S3 object references Example: [{ bucket: 'my-bucket', key: 'documents/report.pdf' }] 
    #[serde(rename = "user_attachments", skip_serializing_if = "Option::is_none")]
    pub user_attachments: Option<Box<models::InputTransform>>,
    /// Integer. Maximum number of tokens the AI will generate in its response. Range: 1 to 4,294,967,295. Typical values: 256-4096 for most use cases. 
    #[serde(rename = "max_completion_tokens", skip_serializing_if = "Option::is_none")]
    pub max_completion_tokens: Option<Box<models::InputTransform>>,
    /// Float. Controls randomness/creativity of responses. Range: 0.0 to 2.0 (provider-dependent) - 0.0 = deterministic, focused responses - 0.7 = balanced (common default) - 1.0+ = more creative/random 
    #[serde(rename = "temperature", skip_serializing_if = "Option::is_none")]
    pub temperature: Option<Box<models::InputTransform>>,
}

impl AiAgentInputTransforms {
    /// Input parameters for the AI agent mapped to their values
    pub fn new(provider: models::ProviderTransform, output_type: models::InputTransform, user_message: models::InputTransform) -> AiAgentInputTransforms {
        AiAgentInputTransforms {
            provider: Box::new(provider),
            output_type: Box::new(output_type),
            user_message: Box::new(user_message),
            system_prompt: None,
            streaming: None,
            memory: None,
            output_schema: None,
            user_attachments: None,
            max_completion_tokens: None,
            temperature: None,
        }
    }
}

