dirge-agent 0.10.2

//! Compaction summarization.
//!
//! Serializes conversation history into a prompt for the summarizer
//! model and invokes the model with retry logic. Extracted from
//! `provider/mod.rs`.

use crate::session::{MessageRole, SessionMessage, ToolCallState};

use rig::streaming::StreamingChat;

/// Serialize the full conversation prefix for compaction summarization.
/// Returns a formatted string with all messages including tool calls
/// (args + results), truncated per-tool at 2KB for memory safety.
pub(crate) fn serialize_conversation(messages: &[SessionMessage]) -> String {
    let mut result = String::new();
    for msg in messages {
        let role_tag = match msg.role {
            MessageRole::User => "User",
            MessageRole::Assistant => "Assistant",
            MessageRole::System => "System",
        };
        result.push_str(&format!("[{}]: {}\n", role_tag, msg.content));
        for tc in &msg.tool_calls {
            let args_str = serde_json::to_string(&tc.args).unwrap_or_else(|_| "{}".to_string());
            result.push_str(&format!("[Tool: {}({})]\n", tc.name, args_str));
            match &tc.state {
                ToolCallState::Completed { result: out } => {
                    const PER_TOOL_CAP: usize = 2048;
                    if out.len() > PER_TOOL_CAP {
                        let trimmed: String = out.chars().take(PER_TOOL_CAP).collect();
                        result.push_str(&format!(
                            "[Result: {} ... (truncated, {} bytes total)]\n",
                            trimmed,
                            out.len()
                        ));
                    } else {
                        result.push_str(&format!("[Result: {}]\n", out));
                    }
                }
                ToolCallState::Interrupted => {
                    result.push_str("[Result: <interrupted>]\n");
                }
                ToolCallState::Failed { error } => {
                    result.push_str(&format!("[Result: <failed: {}>]\n", error));
                }
            }
        }
        result.push('\n');
    }
    result
}

/// Call the summarizer model with the full conversation prefix.
/// The summarizer is invoked by `/compress`, often exactly when the
/// user's context is about to overflow. Uses a retry loop with the
/// same `RecoveryPolicy` shape as the main agent.
///
/// PROV-9: bound the prompt size before dispatch. `/compress` is
/// typically invoked when the conversation already exceeds the
/// model's context window — handing the same un-bounded blob to
/// the summarizer guarantees a ContextLength failure. Cap at
/// roughly the summarizer's input budget (≈ 50% of a 64k window).
/// We can't know the exact window for every provider here, so use
/// a conservative absolute cap and let the per-message truncation
/// handle the rest; the head-and-tail strategy preserves the most
/// recent turns (where the recent context lives) plus the earliest
/// turns (which often set up the task).
pub(crate) async fn summarize_with_model(
    model: super::AnyModel,
    prompt: String,
) -> anyhow::Result<String> {
    oneshot_with_model(
        model,
        "summarizer",
        "You are a conversation summarizer.",
        prompt,
    )
    .await
}

/// Generic one-shot LLM call over any `AnyModel` variant with a caller-
/// supplied system preamble. Factored out of `summarize_with_model` so
/// every side-LLM role (summarizer, critic, approval evaluator) shares
/// the same dispatch + retry/stream-drain path instead of duplicating
/// the 8-arm variant match. `label` keeps each role distinct in
/// retry/backoff telemetry (`run_with_retry`). The summarizer-sized
/// prompt budget is applied here too (a no-op for the tiny
/// approval/critic prompts).
pub(crate) async fn oneshot_with_model(
    model: super::AnyModel,
    label: &'static str,
    preamble: &'static str,
    mut prompt: String,
) -> anyhow::Result<String> {
    const ONESHOT_PROMPT_BUDGET_BYTES: usize = 128 * 1024; // ~32k tokens
    if prompt.len() > ONESHOT_PROMPT_BUDGET_BYTES {
        prompt = head_tail_truncate(&prompt, ONESHOT_PROMPT_BUDGET_BYTES);
    }
    match model {
        super::AnyModel::OpenRouter(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::OpenAI(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::ChatGptOpenAI(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::OpenAICodex(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::Anthropic(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::AnthropicOauth(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::Gemini(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::DeepSeek(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::Glm(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::Ollama(m) => run_oneshot(m, label, preamble, prompt).await,
        super::AnyModel::Custom(m) => run_oneshot(m, label, preamble, prompt).await,
    }
}

/// Trim a prompt to `budget` bytes by keeping a head + tail slice
/// with a placeholder noting the drop. Splits on `\n` so we don't
/// land mid-message. Used by the summarizer when the conversation
/// blob would overflow the summarizer's own context window.
pub(crate) fn head_tail_truncate(prompt: &str, budget: usize) -> String {
    if prompt.len() <= budget {
        return prompt.to_string();
    }
    // 40% head, 60% tail — recent context tends to matter more.
    let head_budget = budget * 4 / 10;
    let tail_budget = budget - head_budget - 128; // leave room for the marker

    // Find a newline-aligned head boundary at or before head_budget, floored to
    // a UTF-8 char boundary so slicing never panics.
    let head_end = prompt[..head_budget.min(prompt.len())]
        .rfind('\n')
        .unwrap_or(head_budget.min(prompt.len()));
    let head_end = crate::text::char_boundary_at_or_before(prompt, head_end);

    let tail_start_target = prompt.len().saturating_sub(tail_budget);
    let tail_start = prompt[tail_start_target..]
        .find('\n')
        .map(|i| tail_start_target + i + 1)
        .unwrap_or(tail_start_target);
    let tail_start = crate::text::char_boundary_at_or_after(prompt, tail_start);

    if tail_start <= head_end {
        // The two halves overlap — prompt is already short enough
        // after newline rounding. Fall through to verbatim.
        return prompt.to_string();
    }
    let dropped = tail_start - head_end;
    format!(
        "{}\n\n[... {} bytes truncated by summarizer-prompt budget ...]\n\n{}",
        &prompt[..head_end],
        dropped,
        &prompt[tail_start..],
    )
}

async fn run_oneshot<M>(
    model: M,
    label: &'static str,
    preamble: &'static str,
    prompt: String,
) -> anyhow::Result<String>
where
    M: rig::completion::CompletionModel + Clone + 'static,
    M::StreamingResponse: Send + Sync + Unpin + Clone + 'static,
{
    use crate::agent::recovery::{RecoveryPolicy, run_with_retry};
    let policy = RecoveryPolicy::default();

    // The attempt/classify/backoff/sleep loop lives in `run_with_retry`
    // (dirge-6cvc). The closure builds + drains one stream and returns a
    // stream error as `Err(String)` so the helper can classify it; an
    // empty-but-clean response is returned as `Ok(String::new())` and
    // rejected (non-retryable) below.
    let response = run_with_retry(&policy, label, || {
        let model = model.clone();
        let prompt = prompt.clone();
        async move {
            let agent = rig::agent::AgentBuilder::new(model)
                .preamble(preamble)
                .build();

            let mut stream = agent
                .stream_chat(prompt, Vec::<rig::completion::Message>::new())
                .multi_turn(1)
                .await;

            let mut response = String::new();
            use futures::StreamExt;
            while let Some(item) = stream.next().await {
                match item {
                    Ok(rig::agent::MultiTurnStreamItem::StreamAssistantItem(
                        rig::streaming::StreamedAssistantContent::Text(text),
                    )) => response.push_str(&text.text),
                    Ok(rig::agent::MultiTurnStreamItem::FinalResponse(res)) => {
                        return Ok(res.response().to_string());
                    }
                    Err(e) => return Err(e.to_string()),
                    _ => {}
                }
            }
            Ok(response)
        }
    })
    .await
    .map_err(|msg| anyhow::anyhow!("one-shot LLM call failed: {msg}"))?;

    if response.is_empty() {
        anyhow::bail!("one-shot LLM call returned empty response");
    }
    Ok(response)
}

#[cfg(test)]
mod tests {
    use super::head_tail_truncate;

    #[test]
    fn head_tail_truncate_short_prompt_passes_through() {
        let s = "line 1\nline 2\nline 3";
        assert_eq!(head_tail_truncate(s, 1024), s);
    }

    #[test]
    fn head_tail_truncate_keeps_head_and_tail() {
        let mut s = String::new();
        for i in 0..2000 {
            s.push_str(&format!("line {}\n", i));
        }
        let out = head_tail_truncate(&s, 4096);
        assert!(out.len() < s.len(), "output should be shorter");
        assert!(out.starts_with("line 0\n"));
        assert!(out.contains("truncated by summarizer-prompt budget"));
        assert!(out.ends_with("line 1999\n"));
    }
}