use serde::{Deserialize, Serialize};
#[derive(Clone, Default, Debug, PartialEq, Serialize, Deserialize)]
pub struct TrainedModel {
#[serde(rename = "mode", default, skip_serializing_if = "Option::is_none")]
pub mode: Option<String>,
#[serde(rename = "compression_level", default, skip_serializing_if = "Option::is_none")]
pub compression_level: Option<String>,
#[serde(rename = "dimension")]
pub dimension: u32,
#[serde(rename = "max_training_vector_count", default, skip_serializing_if = "Option::is_none")]
pub max_training_vector_count: Option<u32>,
#[serde(rename = "method", default, skip_serializing_if = "Option::is_none")]
pub method: Option<String>,
#[serde(rename = "spaceType", default, skip_serializing_if = "Option::is_none")]
pub space_type: Option<String>,
#[serde(rename = "training_index")]
pub training_index: String,
#[serde(rename = "search_size", default, skip_serializing_if = "Option::is_none")]
pub search_size: Option<u32>,
#[serde(rename = "training_field")]
pub training_field: String,
#[serde(rename = "description", default, skip_serializing_if = "Option::is_none")]
pub description: Option<String>,
}
impl TrainedModel {
pub fn new(dimension: u32, training_index: String, training_field: String) -> TrainedModel {
TrainedModel {
mode: None,
compression_level: None,
dimension,
max_training_vector_count: None,
method: None,
space_type: None,
training_index,
search_size: None,
training_field,
description: None,
}
}
}