feedsignal/crates/db/src/articles.rs

131 lines
4.6 KiB
Rust
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use crate::schema;
use crate::Db;
use anyhow::Result;
use chrono::Utc;
use diesel::prelude::*;
use diesel_async::RunQueryDsl;
use feedsignal_core::Article;
use uuid::Uuid;
impl Db {
pub async fn insert_article(&self, article: &Article) -> Result<()> {
use schema::articles::dsl;
let mut conn = self.pool.get().await?;
let topics = serde_json::to_string(&article.topics)?;
diesel::insert_into(dsl::articles)
.values((
dsl::id.eq(article.id.to_string()),
dsl::feed_id.eq(article.feed_id.to_string()),
dsl::url.eq(&article.url),
dsl::title.eq(&article.title),
dsl::summary.eq(&article.summary),
dsl::fetched_at.eq(Utc::now().to_rfc3339()),
dsl::topics.eq(topics),
dsl::embedding_score.eq(article.embedding_score),
dsl::llm_score.eq(article.llm_score),
dsl::final_score.eq(article.final_score),
dsl::estimated_read_seconds.eq(article.estimated_read_seconds.map(|v| v as i32)),
))
.on_conflict_do_nothing()
.execute(&mut conn)
.await?;
Ok(())
}
/// Articles above the embedding-similarity threshold that haven't been
/// through the (slower) LLM scoring stage yet.
pub async fn shortlist_for_llm_scoring(&self, threshold: f32, limit: i64) -> Result<Vec<Uuid>> {
use schema::articles::dsl;
let mut conn = self.pool.get().await?;
let ids: Vec<String> = dsl::articles
.filter(dsl::embedding_score.ge(threshold))
.filter(dsl::llm_score.is_null())
.order(dsl::embedding_score.desc())
.limit(limit)
.select(dsl::id)
.load(&mut conn)
.await?;
ids.into_iter().map(|s| Ok(Uuid::parse_str(&s)?)).collect()
}
/// Title/summary/topics/embedding_score for one article, used to build
/// the LLM-judging prompt for it.
pub async fn article_scoring_fields(
&self,
article_id: Uuid,
) -> Result<Option<(String, String, Vec<String>, f32)>> {
use schema::articles::dsl;
let mut conn = self.pool.get().await?;
let row: Option<(String, String, String, Option<f32>)> = dsl::articles
.filter(dsl::id.eq(article_id.to_string()))
.select((dsl::title, dsl::summary, dsl::topics, dsl::embedding_score))
.first(&mut conn)
.await
.optional()?;
Ok(match row {
Some((title, summary, topics_json, score)) => {
let topics: Vec<String> = serde_json::from_str(&topics_json).unwrap_or_default();
Some((title, summary, topics, score.unwrap_or(0.0)))
}
None => None,
})
}
pub async fn store_llm_result(
&self,
article_id: Uuid,
llm_score: f32,
rationale: &str,
final_score: f32,
) -> Result<()> {
use schema::articles::dsl;
let mut conn = self.pool.get().await?;
diesel::update(dsl::articles.filter(dsl::id.eq(article_id.to_string())))
.set((
dsl::llm_score.eq(llm_score),
dsl::llm_rationale.eq(rationale),
dsl::final_score.eq(final_score),
))
.execute(&mut conn)
.await?;
Ok(())
}
/// Highest-ranked articles for display, most relevant first.
pub async fn list_ranked_articles(
&self,
limit: i64,
) -> Result<Vec<(String, String, String, String, Vec<String>, Option<f32>)>> {
use schema::articles::dsl;
let mut conn = self.pool.get().await?;
// SQLite sorts NULL before any value, so `DESC` already puts NULL
// `final_score`s last — no separate NULLS LAST clause needed here.
let rows: Vec<(String, String, String, String, String, Option<f32>)> = dsl::articles
.order(dsl::final_score.desc())
.limit(limit)
.select((
dsl::id,
dsl::title,
dsl::url,
dsl::summary,
dsl::topics,
dsl::final_score,
))
.load(&mut conn)
.await?;
Ok(rows
.into_iter()
.map(|(id, title, url, summary, topics_json, final_score)| {
(
id,
title,
url,
summary,
serde_json::from_str(&topics_json).unwrap_or_default(),
final_score,
)
})
.collect())
}
}