split out src/mind

src/agent/training.rs

@@ -288,6 +288,79 @@ pub async fn score_memory(
     Ok(divs.iter().sum())
 }
 
+// ── Background memory scoring ───────────────────────────────────
+
+/// Incrementally score memories through the conversation.
+///
+/// Walks memory entries in conversation order starting from `cursor`.
+/// For each memory with a full WINDOW after it, calls score_memory()
+/// and yields the result. Stops at the first memory that doesn't have
+/// enough messages yet — the conversation needs to grow before we can
+/// score it.
+///
+/// Returns the updated cursor (entry index to resume from next time)
+/// and the scores for each memory that was scored this round.
+pub async fn score_memories_incremental(
+    context: &ContextState,
+    cursor: usize,
+    client: &ApiClient,
+    ui_tx: &UiSender,
+) -> anyhow::Result<(usize, Vec<(String, f64)>)> {
+    const WINDOW: usize = 50;
+
+    // Collect unique memory keys with their first position, starting from cursor
+    let mut seen = std::collections::HashSet::new();
+    let mut to_score: Vec<(usize, String)> = Vec::new();
+
+    for (i, entry) in context.entries.iter().enumerate().skip(cursor) {
+        if let ConversationEntry::Memory { key, .. } = entry {
+            if seen.insert(key.clone()) {
+                to_score.push((i, key.clone()));
+            }
+        }
+    }
+
+    let http = http_client();
+    let mut new_cursor = cursor;
+    let mut results = Vec::new();
+
+    for (pos, key) in &to_score {
+        let end = pos + WINDOW;
+
+        // Not enough conversation after this memory yet — stop here
+        if end > context.entries.len() {
+            break;
+        }
+
+        // Need at least one assistant response in the window
+        let range = *pos..end;
+        if !context.entries[range.clone()].iter().any(|e| e.message().role == Role::Assistant) {
+            new_cursor = end;
+            continue;
+        }
+
+        let _ = ui_tx.send(UiMessage::Activity(format!("scoring memory: {}...", key)));
+        match score_divergence(&http, client, context, range, Filter::SkipKey(key)).await {
+            Ok((divs, _)) => {
+                let importance: f64 = divs.iter().sum();
+                let _ = ui_tx.send(UiMessage::Debug(format!(
+                    "[scoring] {} → {:.2}", key, importance,
+                )));
+                results.push((key.clone(), importance));
+            }
+            Err(e) => {
+                let _ = ui_tx.send(UiMessage::Debug(format!(
+                    "[scoring] {} FAILED: {:#}", key, e,
+                )));
+            }
+        }
+        new_cursor = end;
+    }
+
+    let _ = ui_tx.send(UiMessage::Activity(String::new()));
+    Ok((new_cursor, results))
+}
+
 // ── Fine-tuning scoring ─────────────────────────────────────────
 
 /// Score which recent responses are candidates for fine-tuning.