task-journal-core 0.1.1

//! Prompt builder for the classifier.

use crate::classifier::ClassifyInput;

pub fn build(input: &ClassifyInput) -> String {
    let recent = if input.recent_tasks.is_empty() {
        "(no active tasks)".to_string()
    } else {
        input
            .recent_tasks
            .iter()
            .take(10)
            .map(|t| {
                let trimmed_events: Vec<String> = t
                    .last_events
                    .iter()
                    .take(3)
                    .map(|s| s.chars().take(120).collect::<String>())
                    .collect();
                format!(
                    "- {} \"{}\": {}",
                    t.task_id,
                    t.title,
                    if trimmed_events.is_empty() {
                        "(no events)".into()
                    } else {
                        trimmed_events.join("; ")
                    }
                )
            })
            .collect::<Vec<_>>()
            .join("\n")
    };

    format!(
        "You classify chat chunks for an AI-coding-agent task journal.\n\
         Active tasks (top candidates):\n{recent}\n\n\
         New {author} chunk:\n{text}\n\n\
         Decide:\n\
         1. Which existing task this belongs to (or null if unrelated)\n\
         2. Best event_type from: hypothesis, finding, evidence, decision, rejection, constraint, correction, reopen, supersede, close, redirect\n\
         3. Confidence 0.0-1.0\n\
         4. evidence_strength (weak|medium|strong) if event_type is evidence, else omit\n\
         5. A 1-2 sentence suggested_text that captures the essence\n\n\
         Respond ONLY with strict JSON matching this shape, no commentary:\n\
         {{\"event_type\":\"...\",\"task_id_guess\":\"...\"|null,\"confidence\":0.0,\"evidence_strength\":\"...\"|null,\"suggested_text\":\"...\"}}",
        author=input.author_hint, text=input.text
    )
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::classifier::*;

    #[test]
    fn prompt_includes_text_and_recent_tasks() {
        let input = ClassifyInput {
            text: "We adopted PKCE.".into(),
            author_hint: "assistant".into(),
            recent_tasks: vec![TaskContext {
                task_id: "tj-7f3a".into(),
                title: "OAuth login".into(),
                last_events: vec!["[hypothesis] PKCE vs implicit".into()],
            }],
        };
        let p = build(&input);
        assert!(p.contains("We adopted PKCE."));
        assert!(p.contains("tj-7f3a"));
        assert!(p.contains("PKCE vs implicit"));
        assert!(p.contains("strict JSON"));
    }

    #[test]
    fn prompt_truncates_event_lines_to_keep_size_bounded() {
        let input = ClassifyInput {
            text: "abc".into(),
            author_hint: "user".into(),
            recent_tasks: (0..20)
                .map(|i| TaskContext {
                    task_id: format!("tj-{i:03}"),
                    title: format!("Task {i}"),
                    last_events: (0..30)
                        .map(|j| format!("[finding] very long evidence text {i}/{j} ").repeat(20))
                        .collect(),
                })
                .collect(),
        };
        let p = build(&input);
        assert!(
            p.len() < 64 * 1024,
            "prompt must stay under 64KB; got {}",
            p.len()
        );
    }

    #[test]
    fn prompt_handles_empty_tasks() {
        let input = ClassifyInput {
            text: "Hello".into(),
            author_hint: "user".into(),
            recent_tasks: vec![],
        };
        let p = build(&input);
        assert!(p.contains("(no active tasks)"));
    }
}