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/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
//! Proposed API for the relevancy component (validation phase)
//!
//! The goal here is to allow us to validate that we can reliably detect user interests from
//! history data, without spending too much time building the API out. There's some hand-waving
//! towards how we would use this data to rank search results, but we don't need to come to a final
//! decision on that yet.
mod db;
mod error;
mod ingest;
mod interest;
mod rs;
mod schema;
pub mod url_hash;
pub use db::RelevancyDb;
pub use error::{ApiResult, Error, RelevancyApiError, Result};
pub use interest::{Interest, InterestVector};
use error_support::handle_error;
pub struct RelevancyStore {
db: RelevancyDb,
}
/// Top-level API for the Relevancy component
impl RelevancyStore {
pub fn new(db_path: String) -> Self {
Self {
db: RelevancyDb::new(db_path),
}
}
pub fn close(&self) {
self.db.close()
}
pub fn interrupt(&self) {
self.db.interrupt()
}
/// Download the interest data from remote settings if needed
#[handle_error(Error)]
pub fn ensure_interest_data_populated(&self) -> ApiResult<()> {
ingest::ensure_interest_data_populated(&self.db)?;
Ok(())
}
/// Ingest top URLs to build the user's interest vector.
///
/// Consumer should pass a list of the user's top URLs by frecency to this method. It will
/// then:
///
/// - Download the URL interest data from remote settings. Eventually this should be cached /
/// stored in the database, but for now it would be fine to download fresh data each time.
/// - Match the user's top URls against the interest data to build up their interest vector.
/// - Store the user's interest vector in the database.
///
/// This method may execute for a long time and should only be called from a worker thread.
#[handle_error(Error)]
pub fn ingest(&self, top_urls_by_frecency: Vec<String>) -> ApiResult<InterestVector> {
ingest::ensure_interest_data_populated(&self.db)?;
let interest_vec = self.classify(top_urls_by_frecency)?;
self.db
.read_write(|dao| dao.update_frecency_user_interest_vector(&interest_vec))?;
Ok(interest_vec)
}
pub fn classify(&self, top_urls_by_frecency: Vec<String>) -> Result<InterestVector> {
let mut interest_vector = InterestVector::default();
for url in top_urls_by_frecency {
let interest_count = self.db.read(|dao| dao.get_url_interest_vector(&url))?;
log::trace!("classified: {url} {}", interest_count.summary());
interest_vector = interest_vector + interest_count;
}
Ok(interest_vector)
}
/// Calculate metrics for the validation phase
///
/// This runs after [Self::ingest]. It takes the interest vector that ingest created and
/// calculates a set of metrics that we can report to glean.
#[handle_error(Error)]
pub fn calculate_metrics(&self) -> ApiResult<InterestMetrics> {
todo!()
}
/// Get the user's interest vector directly.
///
/// This runs after [Self::ingest]. It returns the interest vector directly so that the
/// consumer can show it in an `about:` page.
#[handle_error(Error)]
pub fn user_interest_vector(&self) -> ApiResult<InterestVector> {
todo!()
}
}
/// Interest metric data. See `relevancy.udl` for details.
pub struct InterestMetrics {
pub top_single_interest_similarity: u32,
pub top_2interest_similarity: u32,
pub top_3interest_similarity: u32,
}
uniffi::include_scaffolding!("relevancy");
#[cfg(test)]
mod test {
use crate::url_hash::hash_url;
use super::*;
#[test]
fn test_ingest() {
let top_urls = vec![
];
let relevancy_store =
RelevancyStore::new("file:test_store_data?mode=memory&cache=shared".to_owned());
relevancy_store
.db
.read_write(|dao| {
dao.add_url_interest(
Interest::Inconclusive,
)?;
Ok(())
})
.expect("Insert should succeed");
assert_eq!(
relevancy_store.ingest(top_urls).unwrap(),
InterestVector {
inconclusive: 1,
animals: 1,
food: 2,
..InterestVector::default()
}
);
}
}