// Consolidation pipeline: plan → agents → apply → digests → links
//
// consolidate_full() runs the full autonomous consolidation:
//   1. Plan: analyze metrics, allocate agents
//   2. Execute: run each agent (Sonnet calls), save reports
//   3. Apply: extract and apply actions from reports
//   4. Digest: generate missing daily/weekly/monthly digests
//   5. Links: apply links extracted from digests
//   6. Summary: final metrics comparison
//
// apply_consolidation() processes consolidation reports independently.

use crate::digest;
use crate::llm::{call_sonnet, parse_json_response};
use crate::neuro;
use crate::store::{self, Store, new_relation};


/// Append a line to the log buffer.
fn log_line(buf: &mut String, line: &str) {
    buf.push_str(line);
    buf.push('\n');
}

/// Run the full autonomous consolidation pipeline with logging.
/// If `on_progress` is provided, it's called at each significant step.
pub fn consolidate_full(store: &mut Store) -> Result<(), String> {
    consolidate_full_with_progress(store, &|_| {})
}

pub fn consolidate_full_with_progress(
    store: &mut Store,
    on_progress: &dyn Fn(&str),
) -> Result<(), String> {
    let start = std::time::Instant::now();
    let log_key = format!("_consolidate-log-{}",
        store::format_datetime(store::now_epoch()).replace([':', '-', 'T'], ""));
    let mut log_buf = String::new();

    log_line(&mut log_buf, "=== CONSOLIDATE FULL ===");
    log_line(&mut log_buf, &format!("Started: {}", store::format_datetime(store::now_epoch())));
    log_line(&mut log_buf, &format!("Nodes: {}  Relations: {}", store.nodes.len(), store.relations.len()));
    log_line(&mut log_buf, "");

    // --- Step 1: Plan ---
    log_line(&mut log_buf, "--- Step 1: Plan ---");
    on_progress("planning");
    let plan = neuro::consolidation_plan(store);
    let plan_text = neuro::format_plan(&plan);
    log_line(&mut log_buf, &plan_text);
    println!("{}", plan_text);

    let total_agents = plan.replay_count + plan.linker_count
        + plan.separator_count + plan.transfer_count
        + if plan.run_health { 1 } else { 0 };
    log_line(&mut log_buf, &format!("Total agents to run: {}", total_agents));

    // --- Step 2: Execute agents ---
    log_line(&mut log_buf, "\n--- Step 2: Execute agents ---");
    let mut reports: Vec<String> = Vec::new();
    let mut agent_num = 0usize;
    let mut agent_errors = 0usize;

    // Build the list of (agent_type, batch_size) runs
    let mut runs: Vec<(&str, usize)> = Vec::new();
    if plan.run_health {
        runs.push(("health", 0));
    }
    if plan.replay_count > 0 {
        let batch = 5;
        let mut remaining = plan.replay_count;
        while remaining > 0 {
            let this_batch = remaining.min(batch);
            runs.push(("replay", this_batch));
            remaining -= this_batch;
        }
    }
    if plan.linker_count > 0 {
        let batch = 5;
        let mut remaining = plan.linker_count;
        while remaining > 0 {
            let this_batch = remaining.min(batch);
            runs.push(("linker", this_batch));
            remaining -= this_batch;
        }
    }
    if plan.separator_count > 0 {
        let batch = 5;
        let mut remaining = plan.separator_count;
        while remaining > 0 {
            let this_batch = remaining.min(batch);
            runs.push(("separator", this_batch));
            remaining -= this_batch;
        }
    }
    if plan.transfer_count > 0 {
        let batch = 5;
        let mut remaining = plan.transfer_count;
        while remaining > 0 {
            let this_batch = remaining.min(batch);
            runs.push(("transfer", this_batch));
            remaining -= this_batch;
        }
    }

    for (agent_type, count) in &runs {
        agent_num += 1;
        let label = if *count > 0 {
            format!("[{}/{}] {} (batch={})", agent_num, runs.len(), agent_type, count)
        } else {
            format!("[{}/{}] {}", agent_num, runs.len(), agent_type)
        };

        log_line(&mut log_buf, &format!("\n{}", label));
        on_progress(&label);
        println!("{}", label);

        // Reload store to pick up changes from previous agents
        if agent_num > 1 {
            *store = Store::load()?;
        }

        let prompt = match neuro::agent_prompt(store, agent_type, *count) {
            Ok(p) => p,
            Err(e) => {
                let msg = format!("  ERROR building prompt: {}", e);
                log_line(&mut log_buf, &msg);
                eprintln!("{}", msg);
                agent_errors += 1;
                continue;
            }
        };

        log_line(&mut log_buf, &format!("  Prompt: {} chars (~{} tokens)",
            prompt.len(), prompt.len() / 4));

        let response = match call_sonnet("consolidate", &prompt) {
            Ok(r) => r,
            Err(e) => {
                let msg = format!("  ERROR from Sonnet: {}", e);
                log_line(&mut log_buf, &msg);
                eprintln!("{}", msg);
                agent_errors += 1;
                continue;
            }
        };

        // Store report as a node
        let ts = store::format_datetime(store::now_epoch())
            .replace([':', '-', 'T'], "");
        let report_key = format!("_consolidation-{}-{}", agent_type, ts);
        store.upsert_provenance(&report_key, &response,
            store::Provenance::AgentConsolidate).ok();
        reports.push(report_key.clone());

        let msg = format!("  Done: {} lines → {}", response.lines().count(), report_key);
        log_line(&mut log_buf, &msg);
        on_progress(&msg);
        println!("{}", msg);
    }

    log_line(&mut log_buf, &format!("\nAgents complete: {} run, {} errors",
        agent_num - agent_errors, agent_errors));

    // --- Step 3: Apply consolidation actions ---
    log_line(&mut log_buf, "\n--- Step 3: Apply consolidation actions ---");
    on_progress("applying actions");
    println!("\n--- Applying consolidation actions ---");
    *store = Store::load()?;

    if reports.is_empty() {
        log_line(&mut log_buf, "  No reports to apply.");
    } else {
        match apply_consolidation(store, true, None) {
            Ok(()) => log_line(&mut log_buf, "  Applied."),
            Err(e) => {
                let msg = format!("  ERROR applying consolidation: {}", e);
                log_line(&mut log_buf, &msg);
                eprintln!("{}", msg);
            }
        }
    }

    // --- Step 3b: Link orphans ---
    log_line(&mut log_buf, "\n--- Step 3b: Link orphans ---");
    on_progress("linking orphans");
    println!("\n--- Linking orphan nodes ---");
    *store = Store::load()?;

    let (lo_orphans, lo_added) = neuro::link_orphans(store, 2, 3, 0.15);
    log_line(&mut log_buf, &format!("  {} orphans, {} links added", lo_orphans, lo_added));

    // --- Step 3c: Cap degree ---
    log_line(&mut log_buf, "\n--- Step 3c: Cap degree ---");
    on_progress("capping degree");
    println!("\n--- Capping node degree ---");
    *store = Store::load()?;

    match store.cap_degree(50) {
        Ok((hubs, pruned)) => {
            store.save()?;
            log_line(&mut log_buf, &format!("  {} hubs capped, {} edges pruned", hubs, pruned));
        }
        Err(e) => log_line(&mut log_buf, &format!("  ERROR: {}", e)),
    }

    // --- Step 4: Digest auto ---
    log_line(&mut log_buf, "\n--- Step 4: Digest auto ---");
    on_progress("generating digests");
    println!("\n--- Generating missing digests ---");
    *store = Store::load()?;

    match digest::digest_auto(store) {
        Ok(()) => log_line(&mut log_buf, "  Digests done."),
        Err(e) => {
            let msg = format!("  ERROR in digest auto: {}", e);
            log_line(&mut log_buf, &msg);
            eprintln!("{}", msg);
        }
    }

    // --- Step 5: Apply digest links ---
    log_line(&mut log_buf, "\n--- Step 5: Apply digest links ---");
    on_progress("applying digest links");
    println!("\n--- Applying digest links ---");
    *store = Store::load()?;

    let links = digest::parse_all_digest_links(store);
    let (applied, skipped, fallbacks) = digest::apply_digest_links(store, &links);
    store.save()?;
    log_line(&mut log_buf, &format!("  {} links applied, {} skipped, {} fallbacks",
        applied, skipped, fallbacks));

    // --- Step 6: Summary ---
    let elapsed = start.elapsed();
    log_line(&mut log_buf, "\n--- Summary ---");
    log_line(&mut log_buf, &format!("Finished: {}", store::format_datetime(store::now_epoch())));
    log_line(&mut log_buf, &format!("Duration: {:.0}s", elapsed.as_secs_f64()));
    *store = Store::load()?;
    log_line(&mut log_buf, &format!("Nodes: {}  Relations: {}", store.nodes.len(), store.relations.len()));

    let summary = format!(
        "\n=== CONSOLIDATE FULL COMPLETE ===\n\
         Duration: {:.0}s\n\
         Agents: {} run, {} errors\n\
         Nodes: {}  Relations: {}\n",
        elapsed.as_secs_f64(),
        agent_num - agent_errors, agent_errors,
        store.nodes.len(), store.relations.len(),
    );
    log_line(&mut log_buf, &summary);
    println!("{}", summary);

    // Store the log as a node
    store.upsert_provenance(&log_key, &log_buf,
        store::Provenance::AgentConsolidate).ok();
    store.save()?;

    Ok(())
}

/// Find the most recent set of consolidation report keys from the store.
fn find_consolidation_reports(store: &Store) -> Vec<String> {
    let mut keys: Vec<&String> = store.nodes.keys()
        .filter(|k| k.starts_with("_consolidation-"))
        .collect();
    keys.sort();
    keys.reverse();

    if keys.is_empty() { return Vec::new(); }

    // Group by timestamp (last segment after last '-')
    let latest_ts = keys[0].rsplit('-').next().unwrap_or("").to_string();

    keys.into_iter()
        .filter(|k| k.ends_with(&latest_ts))
        .cloned()
        .collect()
}

fn build_consolidation_prompt(store: &Store, report_keys: &[String]) -> Result<String, String> {
    let mut report_text = String::new();
    for key in report_keys {
        let content = store.nodes.get(key)
            .map(|n| n.content.as_str())
            .unwrap_or("");
        report_text.push_str(&format!("\n{}\n## Report: {}\n\n{}\n",
            "=".repeat(60), key, content));
    }

    neuro::load_prompt("consolidation", &[("{{REPORTS}}", &report_text)])
}

/// Run the full apply-consolidation pipeline.
pub fn apply_consolidation(store: &mut Store, do_apply: bool, report_key: Option<&str>) -> Result<(), String> {
    let reports = if let Some(key) = report_key {
        vec![key.to_string()]
    } else {
        find_consolidation_reports(store)
    };

    if reports.is_empty() {
        println!("No consolidation reports found.");
        println!("Run consolidation-agents first.");
        return Ok(());
    }

    println!("Found {} reports:", reports.len());
    for r in &reports {
        println!("  {}", r);
    }

    println!("\nExtracting actions from reports...");
    let prompt = build_consolidation_prompt(store, &reports)?;
    println!("  Prompt: {} chars", prompt.len());

    let response = call_sonnet("consolidate", &prompt)?;

    let actions_value = parse_json_response(&response)?;
    let actions = actions_value.as_array()
        .ok_or("expected JSON array of actions")?;

    println!("  {} actions extracted", actions.len());

    // Store actions in the store
    let timestamp = store::format_datetime(store::now_epoch())
        .replace([':', '-'], "");
    let actions_key = format!("_consolidation-actions-{}", timestamp);
    let actions_json = serde_json::to_string_pretty(&actions_value).unwrap();
    store.upsert_provenance(&actions_key, &actions_json,
        store::Provenance::AgentConsolidate).ok();
    println!("  Stored: {}", actions_key);

    let link_actions: Vec<_> = actions.iter()
        .filter(|a| a.get("action").and_then(|v| v.as_str()) == Some("link"))
        .collect();
    let manual_actions: Vec<_> = actions.iter()
        .filter(|a| a.get("action").and_then(|v| v.as_str()) == Some("manual"))
        .collect();

    if !do_apply {
        // Dry run
        println!("\n{}", "=".repeat(60));
        println!("DRY RUN — {} actions proposed", actions.len());
        println!("{}\n", "=".repeat(60));

        if !link_actions.is_empty() {
            println!("## Links to add ({})\n", link_actions.len());
            for (i, a) in link_actions.iter().enumerate() {
                let src = a.get("source").and_then(|v| v.as_str()).unwrap_or("?");
                let tgt = a.get("target").and_then(|v| v.as_str()).unwrap_or("?");
                let reason = a.get("reason").and_then(|v| v.as_str()).unwrap_or("");
                println!("  {:2}. {} → {} ({})", i + 1, src, tgt, reason);
            }
        }
        if !manual_actions.is_empty() {
            println!("\n## Manual actions needed ({})\n", manual_actions.len());
            for a in &manual_actions {
                let prio = a.get("priority").and_then(|v| v.as_str()).unwrap_or("?");
                let desc = a.get("description").and_then(|v| v.as_str()).unwrap_or("?");
                println!("  [{}] {}", prio, desc);
            }
        }
        println!("\n{}", "=".repeat(60));
        println!("To apply: poc-memory apply-consolidation --apply");
        println!("{}", "=".repeat(60));
        return Ok(());
    }

    // Apply
    let mut applied = 0usize;
    let mut skipped = 0usize;

    if !link_actions.is_empty() {
        println!("\nApplying {} links...", link_actions.len());
        for a in &link_actions {
            let src = a.get("source").and_then(|v| v.as_str()).unwrap_or("");
            let tgt = a.get("target").and_then(|v| v.as_str()).unwrap_or("");
            if src.is_empty() || tgt.is_empty() { skipped += 1; continue; }

            let source = match store.resolve_key(src) {
                Ok(s) => s,
                Err(e) => { println!("  ? {} → {}: {}", src, tgt, e); skipped += 1; continue; }
            };
            let target = match store.resolve_key(tgt) {
                Ok(t) => t,
                Err(e) => { println!("  ? {} → {}: {}", src, tgt, e); skipped += 1; continue; }
            };

            // Refine target to best-matching section
            let source_content = store.nodes.get(&source)
                .map(|n| n.content.as_str()).unwrap_or("");
            let target = neuro::refine_target(store, source_content, &target);

            let exists = store.relations.iter().any(|r|
                r.source_key == source && r.target_key == target && !r.deleted
            );
            if exists { skipped += 1; continue; }

            let source_uuid = match store.nodes.get(&source) { Some(n) => n.uuid, None => { skipped += 1; continue; } };
            let target_uuid = match store.nodes.get(&target) { Some(n) => n.uuid, None => { skipped += 1; continue; } };

            let rel = new_relation(
                source_uuid, target_uuid,
                store::RelationType::Auto,
                0.5,
                &source, &target,
            );
            if store.add_relation(rel).is_ok() {
                println!("  + {} → {}", source, target);
                applied += 1;
            }
        }
    }

    if !manual_actions.is_empty() {
        println!("\n## Manual actions (not auto-applied):\n");
        for a in &manual_actions {
            let prio = a.get("priority").and_then(|v| v.as_str()).unwrap_or("?");
            let desc = a.get("description").and_then(|v| v.as_str()).unwrap_or("?");
            println!("  [{}] {}", prio, desc);
        }
    }

    if applied > 0 {
        store.save()?;
    }

    println!("\n{}", "=".repeat(60));
    println!("Applied: {}  Skipped: {}  Manual: {}", applied, skipped, manual_actions.len());
    println!("{}", "=".repeat(60));

    Ok(())
}