/*
* Hanzo Cloud API
*
* The Hanzo Cloud API as a customer calls it: every operation under /v1/ except the operator's admin product, relay routes, legacy spellings and capabilities still reached by flag. Tagged by product: the first path segment after /v1/.
*
* The version of the OpenAPI document: v1
*
* Generated by: https://openapi-generator.tech
*/
use crate::models;
use serde::{Deserialize, Serialize};
#[derive(Clone, Default, Debug, PartialEq, Serialize, Deserialize)]
pub struct Provenance {
/// Backend is the leg that contributed this match: \"index\" (lexical), \"vector\" (semantic) or \"code\" (the org's repositories). It is the same name that leg reports itself under in Fusion.Backends, so a hit can be traced to a status.
#[serde(rename = "backend", skip_serializing_if = "Option::is_none")]
pub backend: Option<String>,
/// Rank is this document's 1-based position in THAT leg's own result list, before fusion — 1 is the leg's best hit. It is the only input to the fused score: RRF adds 1/(60+rank) per leg, which is why a document two legs ranked second beats one a single leg ranked first.
#[serde(rename = "rank", skip_serializing_if = "Option::is_none")]
pub rank: Option<i32>,
/// Score is the leg's NATIVE score, on that leg's own scale, reported for explanation and never used in ranking — the scales are incomparable (a cosine similarity against a term-match count), which is why fusion works on ranks. The vector leg reports Qdrant's cosine similarity; the lexical leg exposes no per-row score and reports 0, meaning \"unscored\", not \"scored zero\".
#[serde(rename = "score", skip_serializing_if = "Option::is_none")]
pub score: Option<f64>,
}
impl Provenance {
pub fn new() -> Provenance {
Provenance {
backend: None,
rank: None,
score: None,
}
}
}