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Add vLLM priority to memory scoring requests

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Scoring calls the /score endpoint directly via HTTP, bypassing the
stream_completion path. These requests had no priority field, so they
could preempt interactive work. Set priority=5 (between subconscious
agents at 2 and unconscious at 10).

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
This commit is contained in:
ProofOfConcept 2026-04-09 20:42:38 -04:00
parent bf503b571e
commit 67332eb55e
1 changed files with 13 additions and 8 deletions
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21
src/subconscious/learn.rs
View file

@ -121,14 +121,18 @@ async fn call_score(
http: &crate::agent::api::http::HttpClient,
client: &ApiClient,
messages: &[serde_json::Value],
priority: Option<i32>,
) -> anyhow::Result<Vec<ScoreResult>> {
let url = format!("{}/score", client.base_url());
let auth = format!("Bearer {}", client.api_key());
let body = serde_json::json!({
let mut body = serde_json::json!({
"model": client.model,
"messages": messages,
"logprobs": 1,
});
if let Some(p) = priority {
body["priority"] = serde_json::json!(p);
}
let response = http
.send_json("POST", &url, &[
("authorization", &auth),
@ -169,9 +173,10 @@ async fn score_divergence(
context: &ContextState,
range: std::ops::Range<usize>,
filter: Filter<'_>,
priority: Option<i32>,
) -> anyhow::Result<(Vec<f64>, Vec<ScoreResult>)> {
let baseline = call_score(http, client, &build_messages(context, range.clone(), Filter::None)).await?;
let without = call_score(http, client, &build_messages(context, range, filter)).await?;
let baseline = call_score(http, client, &build_messages(context, range.clone(), Filter::None), priority).await?;
let without = call_score(http, client, &build_messages(context, range, filter), priority).await?;
let divs = divergence(&baseline, &without);
Ok((divs, baseline))
}
@ -232,7 +237,7 @@ pub async fn score_memories(
let http = http_client();
let range = 0..context.conversation().len();
let baseline = call_score(&http, client, &build_messages(context, range.clone(), Filter::None)).await?;
let baseline = call_score(&http, client, &build_messages(context, range.clone(), Filter::None), Some(5)).await?;
let total = memory_keys.len();
let mut matrix: Vec<Vec<f64>> = Vec::new();
@ -242,7 +247,7 @@ pub async fn score_memories(
"scoring {}/{}: {}...", mem_idx + 1, total, key,
);
let msgs = build_messages(context, range.clone(), Filter::SkipKey(key));
match call_score(&http, client, &msgs).await {
match call_score(&http, client, &msgs, Some(5)).await {
Ok(without) => matrix.push(divergence(&baseline, &without)),
Err(e) => {
dbglog!(
@ -312,7 +317,7 @@ pub async fn score_memory(
}
let http = http_client();
let (divs, _) = score_divergence(&http, client, context, range, Filter::SkipKey(key)).await?;
let (divs, _) = score_divergence(&http, client, context, range, Filter::SkipKey(key), Some(5)).await?;
Ok(divs.iter().sum())
}
@ -389,7 +394,7 @@ where
}
let _scoring = crate::agent::start_activity(agent, format!("scoring: {}", key)).await;
match score_divergence(&http, client, context, range, Filter::SkipKey(key)).await {
match score_divergence(&http, client, context, range, Filter::SkipKey(key), Some(5)).await {
Ok((divs, _)) => {
let n_responses = divs.len();
let max_div = divs.iter().cloned().fold(0.0f64, f64::max);
@ -435,7 +440,7 @@ pub async fn score_finetune(
}
let http = http_client();
let (divs, _) = score_divergence(&http, client, context, range, Filter::SkipAllMemories).await?;
let (divs, _) = score_divergence(&http, client, context, range, Filter::SkipAllMemories, Some(5)).await?;
let mut results: Vec<(usize, f64)> = response_positions.iter()
.enumerate()