zagens-cli 0.7.5

//! RLM turns and emergency context recovery / token budgeting.

use super::context_trim::trim_messages_partition_aware;

use super::*;

impl Engine {
    /// Handle a Recursive Language Model (RLM) query — Algorithm 1 from
    /// Zhang et al. (arXiv:2512.24601).
    ///
    /// The prompt is stored as PROMPT in a REPL variable. The root LLM
    /// only sees metadata about the REPL state, never the prompt text
    /// directly. The model generates Python code, which is executed by
    /// the REPL. When FINAL() is called, the loop ends.
    pub(super) async fn handle_rlm(
        &mut self,
        content: String,
        model: String,
        child_model: String,
        max_depth: u32,
    ) {
        use crate::rlm::turn::run_rlm_turn;

        let Some(client) = self.deepseek_client.clone() else {
            let err = self
                .deepseek_client_error
                .as_deref()
                .map(|s| s.to_string())
                .unwrap_or_else(|| "API client not configured".to_string());
            let _ = self
                .tx_event
                .send(Event::error(ErrorEnvelope::fatal_auth(format!(
                    "RLM error: {err}"
                ))))
                .await;
            return;
        };

        let _ = self
            .tx_event
            .send(Event::status("RLM turn started".to_string()))
            .await;

        let result = run_rlm_turn(
            client.clone(),
            model,
            content,
            child_model,
            self.tx_event.clone(),
            max_depth,
        )
        .await;

        let has_error = result.error.is_some();
        if let Some(ref err) = result.error {
            let _ = self
                .tx_event
                .send(Event::error(ErrorEnvelope::tool(format!(
                    "RLM error: {err}"
                ))))
                .await;
        }

        if !result.answer.is_empty() {
            // Add the final answer as an assistant message in the session.
            self.add_session_message(crate::models::Message {
                role: "assistant".to_string(),
                content: vec![crate::models::ContentBlock::Text {
                    text: result.answer.clone(),
                    cache_control: None,
                }],
            })
            .await;

            let _ = self
                .tx_event
                .send(Event::MessageDelta {
                    index: 0,
                    content: result.answer.clone(),
                })
                .await;
            let _ = self
                .tx_event
                .send(Event::MessageComplete { index: 0 })
                .await;
        }

        let _ = self
            .tx_event
            .send(Event::TurnComplete {
                usage: result.usage,
                last_request_input_tokens: self.session.last_api_input_tokens,
                status: if has_error {
                    crate::core::events::TurnOutcomeStatus::Failed
                } else {
                    crate::core::events::TurnOutcomeStatus::Completed
                },
                error: result.error.clone(),
                step_count: 0,
                tool_names: vec![],
                end_reason: result.error,
            })
            .await;
    }

    pub(super) fn estimated_input_tokens(&self) -> usize {
        estimate_input_tokens_conservative(
            &self.session.messages,
            self.session.system_prompt.as_ref(),
        )
    }

    pub(super) fn trim_oldest_messages_to_budget(&mut self, target_input_budget: usize) -> usize {
        trim_messages_partition_aware(
            &mut self.0.session.messages,
            self.0.session.system_prompt.as_ref(),
            target_input_budget,
            &self.0.session.workspace,
            &self.0.session.working_set,
            self.0.scratchpad_run_id.as_deref(),
        )
    }

    pub(super) async fn recover_context_overflow(
        &mut self,
        client: &dyn crate::llm_client::LlmClient,
        reason: &str,
        requested_output_tokens: u32,
    ) -> bool {
        let Some(target_budget) =
            context_input_budget(&self.session.model, requested_output_tokens)
        else {
            return false;
        };

        let id = format!("compact_{}", &uuid::Uuid::new_v4().to_string()[..8]);
        let start_message = format!("Emergency context compaction started ({reason})");
        self.emit_compaction_started(id.clone(), true, start_message)
            .await;

        let before_tokens = self.estimated_input_tokens();
        let before_count = self.session.messages.len();

        let mut retries_used = 0u32;
        let mut summary_prompt = None;
        let mut compacted_messages = self.session.messages.clone();

        let mut forced_config = self.config.compaction.clone();
        forced_config.enabled = true;
        forced_config.token_threshold = forced_config
            .token_threshold
            .min(target_budget.saturating_sub(1))
            .max(1);
        // v0.8.11: forced compaction (capacity guardrail) bypasses the floor
        // because we're at a hard ceiling and have to free budget regardless
        // of cache cost.
        forced_config.auto_floor_tokens = 0;

        match compact_messages_safe(
            client,
            &self.session.messages,
            &forced_config,
            Some(&self.session.workspace),
            None,
            None,
        )
        .await
        {
            Ok(result) => {
                retries_used = result.retries_used;
                compacted_messages = result.messages;
                summary_prompt = result.summary_prompt;
            }
            Err(err) => {
                let _ = self
                    .tx_event
                    .send(Event::status(format!(
                        "Emergency compaction API pass failed: {err}. Falling back to local trim."
                    )))
                    .await;
            }
        }

        if !compacted_messages.is_empty() || self.session.messages.is_empty() {
            self.session.messages = compacted_messages;
        }
        self.merge_compaction_summary(summary_prompt);

        let trimmed = self.trim_oldest_messages_to_budget(target_budget);
        self.emit_session_updated().await;
        let after_tokens = self.estimated_input_tokens();
        let after_count = self.session.messages.len();
        let recovered = after_tokens <= target_budget
            && (after_tokens < before_tokens || after_count < before_count || trimmed > 0);

        if recovered {
            let removed = before_count.saturating_sub(after_count);
            let mut details = format!(
                "Emergency compaction complete: {before_count} → {after_count} messages ({removed} removed), ~{before_tokens} → ~{after_tokens} tokens"
            );
            if retries_used > 0 {
                details.push_str(&format!(" ({} retries)", retries_used));
            }
            if trimmed > 0 {
                details.push_str(&format!(", trimmed {trimmed} oldest"));
            }
            self.emit_compaction_completed(
                id,
                true,
                details.clone(),
                Some(before_count),
                Some(after_count),
            )
            .await;
            let _ = self.tx_event.send(Event::status(details)).await;
            return true;
        }

        let message = format!(
            "Emergency context compaction failed to reduce request below model limit \
             (estimate ~{} tokens, budget ~{}).",
            after_tokens, target_budget
        );
        self.emit_compaction_failed(id, true, message.clone()).await;
        let _ = self.tx_event.send(Event::status(message)).await;
        false
    }
}