AI spam classification: default to 2 KiB of text and a +2.0 cap on the model's tag, as calibrated for low-end CPU-only instances

The calibration harness (tests/src/system/ai_calibration.rs, ignored) sends
what the classifier sends to a real local model and scores its answers with
the classifier's parser.

tests/src/system/ai.rs

@@ -276,7 +276,7 @@ pub async fn test(test: &mut TestServer) {
     deliver(&[USER], "Long", &"a".repeat(100 * 1024)).await;
     let (_, long) = stub.last();
     let text = long["messages"][1]["content"].as_str().unwrap();
-    assert!(text.len() < 16_384 + 256 && text.contains("[truncated]"), "test 7");
+    assert!(text.len() < 2_048 + 256 && text.contains("[truncated]"), "test 7");
 
     // Acceptance test 12: a hostile explanation and a planted header
     // AI-12 reads the first line only, so the hostile part rides on a lone CR
@@ -324,7 +324,7 @@ pub async fn test(test: &mut TestServer) {
         .await;
     admin.reload_settings().await;
     for (answer, expected) in [
-        ("Unsolicited,High,x", "LLM_UNSOLICITED_HIGH (5.00)"),
+        ("Unsolicited,High,x", "LLM_UNSOLICITED_HIGH (2.00)"),
         ("Legitimate,High,x", "LLM_LEGITIMATE_HIGH (-1.00)"),
         ("Harmful,High,x", "LLM_HARMFUL_HIGH (0.00)"),
     ] {

tests/src/system/ai_calibration.rs

@@ -0,0 +1,124 @@
+/*
+ * SPDX-FileCopyrightText: 2026 Coffey Labs
+ *
+ * SPDX-License-Identifier: AGPL-3.0-only
+ */
+
+//! Calibrating a real model against labelled mail: how often it answers in
+//! the configured format, how often its category is right, and how long it
+//! takes. It sends exactly what the classifier sends (the fork's framing,
+//! markers and limits, `inbuxa_features::ai::request`) and reads the answer
+//! exactly as the classifier does (`ai::answer::parse`).
+//!
+//! Not part of any suite. Run with:
+//!
+//! - `CALIB_URL`: the model server's chat completions URL;
+//! - `CALIB_SAMPLE`: a file of `label<TAB>path` lines, `label` `spam` or `ham`;
+//! - `CALIB_OUT`: where to write one JSON line per message;
+//! - `CALIB_LIMIT` (optional): stop after this many messages;
+//! - `CALIB_PROMPT` (optional): a prompt other than the default;
+//! - `CALIB_MAX_BYTES` (optional): text sent per message, default 2048, as the server's.
+
+use inbuxa_features::ai::{answer, request};
+use mail_parser::MessageParser;
+use serde_json::json;
+use std::{
+    io::Write,
+    time::{Duration, Instant},
+};
+
+/// The fork's default prompt (spec, "Default prompt").
+pub const DEFAULT_PROMPT: &str = "Classify the email below as one of: Unsolicited, Commercial, \
+Harmful, Legitimate. Unsolicited: bulk mail the recipient didn't ask for. Commercial: selling \
+something. Harmful: phishing, fraud or malware. Legitimate: anything else. Then give your \
+confidence: High, Medium or Low. Answer on one line as Category,Confidence,Reason with a reason \
+of at most 20 words.";
+
+#[ignore]
+#[tokio::test(flavor = "multi_thread")]
+pub async fn ai_calibration() {
+    let url = std::env::var("CALIB_URL").expect("CALIB_URL");
+    let sample = std::fs::read_to_string(std::env::var("CALIB_SAMPLE").expect("CALIB_SAMPLE"))
+        .expect("sample file");
+    let limit = std::env::var("CALIB_LIMIT")
+        .ok()
+        .and_then(|l| l.parse::<usize>().ok())
+        .unwrap_or(usize::MAX);
+    let prompt = std::env::var("CALIB_PROMPT").unwrap_or_else(|_| DEFAULT_PROMPT.to_string());
+    let max_bytes = std::env::var("CALIB_MAX_BYTES")
+        .ok()
+        .and_then(|l| l.parse::<usize>().ok())
+        .unwrap_or(2_048);
+    let mut out = std::fs::File::create(std::env::var("CALIB_OUT").expect("CALIB_OUT"))
+        .expect("output file");
+
+    let categories = ["Unsolicited", "Commercial", "Harmful", "Legitimate"].map(String::from);
+    let confidence = ["High", "Medium", "Low"].map(String::from);
+    let rules = answer::Rules {
+        separator: ",",
+        pos_category: 0,
+        pos_confidence: Some(1),
+        pos_explanation: Some(2),
+        categories: &categories,
+        confidence: &confidence,
+    };
+    let system = request::system_text(&prompt);
+    let client = reqwest::Client::builder()
+        .timeout(Duration::from_secs(120))
+        .build()
+        .unwrap();
+
+    for line in sample.lines().filter(|l| !l.is_empty()).take(limit) {
+        let (label, path) = line.split_once('\t').expect("label<TAB>path");
+        let raw = std::fs::read(path).expect("message");
+        let Some(message) = MessageParser::new().parse(&raw) else {
+            continue;
+        };
+        let subject = message.subject().unwrap_or_default().to_string();
+        let text = (0..message.text_body.len())
+            .filter_map(|i| message.body_text(i))
+            .collect::<Vec<_>>()
+            .join("\n\n");
+        let user = request::classification_text(&subject, &text, max_bytes, &request::nonce());
+        let body = request::body(
+            request::Kind::Chat,
+            "calibration",
+            Some(&system),
+            &user,
+            0.5,
+            request::CLASSIFY_MAX_TOKENS,
+        );
+
+        let started = Instant::now();
+        let reply = match client
+            .post(&url)
+            .header("content-type", "application/json")
+            .body(body.to_string())
+            .send()
+            .await
+        {
+            Ok(response) => response.bytes().await.ok(),
+            Err(_) => None,
+        };
+        let elapsed = started.elapsed().as_millis() as u64;
+        let answer = reply
+            .as_deref()
+            .and_then(|bytes| request::answer(request::Kind::Chat, bytes));
+        let tag = answer
+            .as_deref()
+            .and_then(|a| answer::parse(a, &rules))
+            .map(|c| c.tag);
+        writeln!(
+            out,
+            "{}",
+            json!({
+                "label": label,
+                "path": path,
+                "answer": answer,
+                "tag": tag,
+                "ms": elapsed,
+            })
+        )
+        .unwrap();
+    }
+}

tests/src/system/mod.rs

@@ -7,6 +7,7 @@
 pub mod antispam;
 pub mod authentication;
 pub mod ai;
+pub mod ai_calibration;
 pub mod authorization;
 pub mod branding;
 pub mod crypto;