// knowledge.rs — knowledge agent action parsing, depth tracking, and convergence loop
//
// Agent prompts live in agents/*.agent files, dispatched via defs.rs.
// This module handles:
//   - Action parsing (WRITE_NODE, LINK, REFINE from LLM output)
//   - Inference depth tracking (prevents runaway abstraction)
//   - Action application (write to store with provenance)
//   - Convergence loop (sequences agents, measures graph stability)
//   - Conversation fragment selection (for observation agent)

use crate::graph::Graph;
use super::llm;
use crate::spectral;
use crate::store::{self, Store, new_relation, RelationType};

use regex::Regex;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::fs;
use std::path::{Path, PathBuf};

// ---------------------------------------------------------------------------
// Action types
// ---------------------------------------------------------------------------

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Action {
    pub kind: ActionKind,
    pub confidence: Confidence,
    pub weight: f64,
    pub depth: i32,
    pub applied: Option<bool>,
    pub rejected_reason: Option<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ActionKind {
    WriteNode {
        key: String,
        content: String,
        covers: Vec<String>,
    },
    Link {
        source: String,
        target: String,
    },
    Refine {
        key: String,
        content: String,
    },
}

#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
#[serde(rename_all = "lowercase")]
pub enum Confidence {
    High,
    Medium,
    Low,
}

impl Confidence {
    fn weight(self) -> f64 {
        match self {
            Self::High => 1.0,
            Self::Medium => 0.6,
            Self::Low => 0.3,
        }
    }

    fn value(self) -> f64 {
        match self {
            Self::High => 0.9,
            Self::Medium => 0.6,
            Self::Low => 0.3,
        }
    }

    fn parse(s: &str) -> Self {
        match s.to_lowercase().as_str() {
            "high" => Self::High,
            "low" => Self::Low,
            _ => Self::Medium,
        }
    }
}

// ---------------------------------------------------------------------------
// Action parsing
// ---------------------------------------------------------------------------

pub fn parse_write_nodes(text: &str) -> Vec<Action> {
    let re = Regex::new(r"(?s)WRITE_NODE\s+(\S+)\s*\n(.*?)END_NODE").unwrap();
    let conf_re = Regex::new(r"(?i)CONFIDENCE:\s*(high|medium|low)").unwrap();
    let covers_re = Regex::new(r"COVERS:\s*(.+)").unwrap();

    re.captures_iter(text)
        .map(|cap| {
            let key = cap[1].to_string();
            let mut content = cap[2].trim().to_string();

            let confidence = conf_re
                .captures(&content)
                .map(|c| Confidence::parse(&c[1]))
                .unwrap_or(Confidence::Medium);
            content = conf_re.replace(&content, "").trim().to_string();

            let covers: Vec<String> = covers_re
                .captures(&content)
                .map(|c| c[1].split(',').map(|s| s.trim().to_string()).collect())
                .unwrap_or_default();
            content = covers_re.replace(&content, "").trim().to_string();

            Action {
                weight: confidence.weight(),
                kind: ActionKind::WriteNode { key, content, covers },
                confidence,
                depth: 0,
                applied: None,
                rejected_reason: None,
            }
        })
        .collect()
}

pub fn parse_links(text: &str) -> Vec<Action> {
    let re = Regex::new(r"(?m)^LINK\s+(\S+)\s+(\S+)").unwrap();
    re.captures_iter(text)
        .map(|cap| Action {
            kind: ActionKind::Link {
                source: cap[1].to_string(),
                target: cap[2].to_string(),
            },
            confidence: Confidence::Low,
            weight: 0.3,
            depth: -1,
            applied: None,
            rejected_reason: None,
        })
        .collect()
}

pub fn parse_refines(text: &str) -> Vec<Action> {
    let re = Regex::new(r"(?s)REFINE\s+(\S+)\s*\n(.*?)END_REFINE").unwrap();
    re.captures_iter(text)
        .map(|cap| {
            let key = cap[1].trim_matches('*').trim().to_string();
            Action {
                kind: ActionKind::Refine {
                    key,
                    content: cap[2].trim().to_string(),
                },
                confidence: Confidence::Medium,
                weight: 0.7,
                depth: 0,
                applied: None,
                rejected_reason: None,
            }
        })
        .collect()
}

pub fn parse_all_actions(text: &str) -> Vec<Action> {
    let mut actions = parse_write_nodes(text);
    actions.extend(parse_links(text));
    actions.extend(parse_refines(text));
    actions
}

pub fn count_no_ops(text: &str) -> usize {
    let no_conn = Regex::new(r"\bNO_CONNECTION\b").unwrap().find_iter(text).count();
    let affirm = Regex::new(r"\bAFFIRM\b").unwrap().find_iter(text).count();
    let no_extract = Regex::new(r"\bNO_EXTRACTION\b").unwrap().find_iter(text).count();
    no_conn + affirm + no_extract
}

// ---------------------------------------------------------------------------
// Inference depth tracking
// ---------------------------------------------------------------------------

const DEPTH_DB_KEY: &str = "_knowledge-depths";

#[derive(Default)]
pub struct DepthDb {
    depths: HashMap<String, i32>,
}

impl DepthDb {
    pub fn load(store: &Store) -> Self {
        let depths = store.nodes.get(DEPTH_DB_KEY)
            .and_then(|n| serde_json::from_str(&n.content).ok())
            .unwrap_or_default();
        Self { depths }
    }

    pub fn save(&self, store: &mut Store) {
        if let Ok(json) = serde_json::to_string(&self.depths) {
            store.upsert_provenance(DEPTH_DB_KEY, &json,
                store::Provenance::AgentKnowledgeObservation).ok();
        }
    }

    pub fn get(&self, key: &str) -> i32 {
        self.depths.get(key).copied().unwrap_or(0)
    }

    pub fn set(&mut self, key: String, depth: i32) {
        self.depths.insert(key, depth);
    }
}

/// Agent base depths: observation=1, extractor=2, connector=3
fn agent_base_depth(agent: &str) -> Option<i32> {
    match agent {
        "observation" => Some(1),
        "extractor" => Some(2),
        "connector" => Some(3),
        "challenger" => None,
        _ => Some(2),
    }
}

pub fn compute_action_depth(db: &DepthDb, action: &Action, agent: &str) -> i32 {
    match &action.kind {
        ActionKind::Link { .. } => -1,
        ActionKind::Refine { key, .. } => db.get(key),
        ActionKind::WriteNode { covers, .. } => {
            if !covers.is_empty() {
                covers.iter().map(|k| db.get(k)).max().unwrap_or(0) + 1
            } else {
                agent_base_depth(agent).unwrap_or(2)
            }
        }
    }
}

/// Confidence threshold that scales with inference depth.
pub fn required_confidence(depth: i32, base: f64) -> f64 {
    if depth <= 0 {
        return 0.0;
    }
    1.0 - (1.0 - base).powi(depth)
}

/// Confidence bonus from real-world use.
pub fn use_bonus(use_count: u32) -> f64 {
    if use_count == 0 {
        return 0.0;
    }
    1.0 - 1.0 / (1.0 + 0.15 * use_count as f64)
}

// ---------------------------------------------------------------------------
// Action application
// ---------------------------------------------------------------------------

fn stamp_content(content: &str, agent: &str, timestamp: &str, depth: i32) -> String {
    format!("<!-- author: {} | created: {} | depth: {} -->\n{}", agent, timestamp, depth, content)
}

/// Check if a link already exists between two keys.
fn has_edge(store: &Store, source: &str, target: &str) -> bool {
    store.relations.iter().any(|r| {
        !r.deleted
            && ((r.source_key == source && r.target_key == target)
                || (r.source_key == target && r.target_key == source))
    })
}

pub fn apply_action(
    store: &mut Store,
    action: &Action,
    agent: &str,
    timestamp: &str,
    depth: i32,
) -> bool {
    let provenance = agent_provenance(agent);

    match &action.kind {
        ActionKind::WriteNode { key, content, .. } => {
            let stamped = stamp_content(content, agent, timestamp, depth);
            store.upsert_provenance(key, &stamped, provenance).is_ok()
        }
        ActionKind::Link { source, target } => {
            if has_edge(store, source, target) {
                return false;
            }
            let source_uuid = match store.nodes.get(source.as_str()) {
                Some(n) => n.uuid,
                None => return false,
            };
            let target_uuid = match store.nodes.get(target.as_str()) {
                Some(n) => n.uuid,
                None => return false,
            };
            let mut rel = new_relation(
                source_uuid, target_uuid,
                RelationType::Link,
                0.3,
                source, target,
            );
            rel.provenance = provenance;
            store.add_relation(rel).is_ok()
        }
        ActionKind::Refine { key, content } => {
            let stamped = stamp_content(content, agent, timestamp, depth);
            store.upsert_provenance(key, &stamped, provenance).is_ok()
        }
    }
}

fn agent_provenance(agent: &str) -> store::Provenance {
    match agent {
        "observation" => store::Provenance::AgentKnowledgeObservation,
        "extractor" | "pattern" => store::Provenance::AgentKnowledgePattern,
        "connector"  => store::Provenance::AgentKnowledgeConnector,
        "challenger" => store::Provenance::AgentKnowledgeChallenger,
        _ => store::Provenance::Agent,
    }
}

// ---------------------------------------------------------------------------
// Shared agent execution
// ---------------------------------------------------------------------------

/// Result of running a single agent through the common pipeline.
pub struct AgentResult {
    pub output: String,
    pub actions: Vec<Action>,
    pub no_ops: usize,
    pub node_keys: Vec<String>,
}

/// Run a single agent: build prompt → call LLM → store output → parse actions → record visits.
///
/// This is the common pipeline shared by the knowledge loop, consolidation pipeline,
/// and daemon. Callers handle action application (with or without depth tracking).
pub fn run_one_agent(
    store: &mut Store,
    agent_name: &str,
    batch_size: usize,
    llm_tag: &str,
) -> Result<AgentResult, String> {
    let def = super::defs::get_def(agent_name)
        .ok_or_else(|| format!("no .agent file for {}", agent_name))?;
    let agent_batch = super::defs::run_agent(store, &def, batch_size)?;

    let output = llm::call_sonnet(llm_tag, &agent_batch.prompt)?;

    // Store raw output for audit trail
    let ts = store::format_datetime(store::now_epoch())
        .replace([':', '-', 'T'], "");
    let report_key = format!("_{}-{}-{}", llm_tag, agent_name, ts);
    let provenance = agent_provenance(agent_name);
    store.upsert_provenance(&report_key, &output, provenance).ok();

    let actions = parse_all_actions(&output);
    let no_ops = count_no_ops(&output);

    // Record visits for processed nodes
    if !agent_batch.node_keys.is_empty() {
        store.record_agent_visits(&agent_batch.node_keys, agent_name).ok();
    }

    Ok(AgentResult {
        output,
        actions,
        no_ops,
        node_keys: agent_batch.node_keys,
    })
}

// ---------------------------------------------------------------------------
// Conversation fragment selection
// ---------------------------------------------------------------------------

/// Extract human-readable dialogue from a conversation JSONL
fn extract_conversation_text(path: &Path, max_chars: usize) -> String {
    let cfg = crate::config::get();
    let messages = super::transcript::parse_transcript(path).unwrap_or_default();
    let mut fragments = Vec::new();
    let mut total = 0;

    for msg in &messages {
        let min_len = if msg.role == "user" { 5 } else { 10 };
        if msg.text.len() <= min_len { continue; }

        // Only include external user messages
        if msg.role == "user" {
            if msg.user_type.as_deref() != Some("external") { continue; }
            if msg.text.starts_with("[Request interrupted") { continue; }
        }

        let role = if msg.role == "user" { &cfg.user_name } else { &cfg.assistant_name };
        fragments.push(format!("**{}:** {}", role, msg.text));
        total += msg.text.len();
        if total > max_chars { break; }
    }
    fragments.join("\n\n")
}

/// Count short user messages (dialogue turns) in a JSONL
fn count_dialogue_turns(path: &Path) -> usize {
    let messages = super::transcript::parse_transcript(path).unwrap_or_default();
    messages.iter()
        .filter(|m| m.role == "user"
            && m.user_type.as_deref() == Some("external")
            && m.text.len() > 5
            && m.text.len() < 500
            && !m.text.starts_with("[Request interrupted")
            && !m.text.starts_with("Implement the following"))
        .count()
}

/// Select conversation fragments for the observation extractor
pub fn select_conversation_fragments(n: usize) -> Vec<(String, String)> {
    let projects = crate::config::get().projects_dir.clone();
    if !projects.exists() { return Vec::new(); }

    let mut jsonl_files: Vec<PathBuf> = Vec::new();
    if let Ok(dirs) = fs::read_dir(&projects) {
        for dir in dirs.filter_map(|e| e.ok()) {
            if !dir.path().is_dir() { continue; }
            if let Ok(files) = fs::read_dir(dir.path()) {
                for f in files.filter_map(|e| e.ok()) {
                    let p = f.path();
                    if p.extension().map(|x| x == "jsonl").unwrap_or(false) {
                        if let Ok(meta) = p.metadata() {
                            if meta.len() > 50_000 {
                                jsonl_files.push(p);
                            }
                        }
                    }
                }
            }
        }
    }

    let mut scored: Vec<(usize, PathBuf)> = jsonl_files.into_iter()
        .map(|f| (count_dialogue_turns(&f), f))
        .filter(|(turns, _)| *turns >= 10)
        .collect();
    scored.sort_by(|a, b| b.0.cmp(&a.0));

    let mut fragments = Vec::new();
    for (_, f) in scored.iter().take(n * 2) {
        let session_id = f.file_stem()
            .map(|s| s.to_string_lossy().to_string())
            .unwrap_or_else(|| "unknown".into());
        let text = extract_conversation_text(f, 8000);
        if text.len() > 500 {
            fragments.push((session_id, text));
        }
        if fragments.len() >= n { break; }
    }
    fragments
}

// ---------------------------------------------------------------------------
// Convergence metrics
// ---------------------------------------------------------------------------

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CycleResult {
    pub cycle: usize,
    pub timestamp: String,
    pub total_actions: usize,
    pub total_applied: usize,
    pub total_no_ops: usize,
    pub depth_rejected: usize,
    pub weighted_delta: f64,
    pub graph_metrics_before: GraphMetrics,
    pub graph_metrics_after: GraphMetrics,
}

#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct GraphMetrics {
    pub nodes: usize,
    pub edges: usize,
    pub cc: f64,
    pub sigma: f64,
    pub communities: usize,
}

impl GraphMetrics {
    pub fn from_graph(store: &Store, graph: &Graph) -> Self {
        Self {
            nodes: store.nodes.len(),
            edges: graph.edge_count(),
            cc: graph.avg_clustering_coefficient() as f64,
            sigma: graph.small_world_sigma() as f64,
            communities: graph.community_count(),
        }
    }
}

fn metric_stability(history: &[CycleResult], key: &str, window: usize) -> f64 {
    if history.len() < window { return f64::INFINITY; }

    let values: Vec<f64> = history[history.len() - window..].iter()
        .map(|h| match key {
            "sigma" => h.graph_metrics_after.sigma,
            "cc" => h.graph_metrics_after.cc,
            "communities" => h.graph_metrics_after.communities as f64,
            _ => 0.0,
        })
        .collect();

    if values.len() < 2 { return f64::INFINITY; }
    let mean = values.iter().sum::<f64>() / values.len() as f64;
    if mean == 0.0 { return 0.0; }
    let variance = values.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / values.len() as f64;
    variance.sqrt() / mean.abs()
}

pub fn check_convergence(history: &[CycleResult], window: usize) -> bool {
    if history.len() < window { return false; }

    let sigma_cv = metric_stability(history, "sigma", window);
    let cc_cv = metric_stability(history, "cc", window);
    let comm_cv = metric_stability(history, "communities", window);

    let recent = &history[history.len() - window..];
    let avg_delta = recent.iter().map(|r| r.weighted_delta).sum::<f64>() / recent.len() as f64;

    eprintln!("\n  Convergence check (last {} cycles):", window);
    eprintln!("    sigma CV:      {:.4}  (< 0.05?)", sigma_cv);
    eprintln!("    CC CV:         {:.4}  (< 0.05?)", cc_cv);
    eprintln!("    community CV:  {:.4}  (< 0.10?)", comm_cv);
    eprintln!("    avg delta:     {:.2}  (< 1.00?)", avg_delta);

    let structural = sigma_cv < 0.05 && cc_cv < 0.05 && comm_cv < 0.10;
    let behavioral = avg_delta < 1.0;

    if structural && behavioral {
        eprintln!("    → CONVERGED");
        true
    } else {
        false
    }
}

// ---------------------------------------------------------------------------
// The knowledge loop
// ---------------------------------------------------------------------------

pub struct KnowledgeLoopConfig {
    pub max_cycles: usize,
    pub batch_size: usize,
    pub window: usize,
    pub max_depth: i32,
    pub confidence_base: f64,
}

impl Default for KnowledgeLoopConfig {
    fn default() -> Self {
        Self {
            max_cycles: 20,
            batch_size: 5,
            window: 5,
            max_depth: 4,
            confidence_base: 0.3,
        }
    }
}

pub fn run_knowledge_loop(config: &KnowledgeLoopConfig) -> Result<Vec<CycleResult>, String> {
    let mut store = Store::load()?;
    let mut depth_db = DepthDb::load(&store);
    let mut history = Vec::new();

    eprintln!("Knowledge Loop — fixed-point iteration");
    eprintln!("  max_cycles={}  batch_size={}", config.max_cycles, config.batch_size);
    eprintln!("  window={}  max_depth={}", config.window, config.max_depth);

    for cycle in 1..=config.max_cycles {
        let result = run_cycle(cycle, config, &mut depth_db)?;
        history.push(result);

        if check_convergence(&history, config.window) {
            eprintln!("\n  CONVERGED after {} cycles", cycle);
            break;
        }
    }

    // Save loop summary as a store node
    if let Some(first) = history.first() {
        let key = format!("_knowledge-loop-{}", first.timestamp);
        if let Ok(json) = serde_json::to_string_pretty(&history) {
            store = Store::load()?;
            store.upsert_provenance(&key, &json,
                store::Provenance::AgentKnowledgeObservation).ok();
            depth_db.save(&mut store);
            store.save()?;
        }
    }

    Ok(history)
}

fn run_cycle(
    cycle_num: usize,
    config: &KnowledgeLoopConfig,
    depth_db: &mut DepthDb,
) -> Result<CycleResult, String> {
    let timestamp = chrono::Local::now().format("%Y%m%dT%H%M%S").to_string();
    eprintln!("\n{}", "=".repeat(60));
    eprintln!("CYCLE {} — {}", cycle_num, timestamp);
    eprintln!("{}", "=".repeat(60));

    let mut store = Store::load()?;
    let graph = store.build_graph();
    let metrics_before = GraphMetrics::from_graph(&store, &graph);
    eprintln!("  Before: nodes={} edges={} cc={:.3} sigma={:.3}",
        metrics_before.nodes, metrics_before.edges, metrics_before.cc, metrics_before.sigma);

    let mut all_actions = Vec::new();
    let mut all_no_ops = 0;
    let mut depth_rejected = 0;
    let mut total_applied = 0;

    // Run each agent via .agent file dispatch
    let agent_names = ["observation", "extractor", "connector", "challenger"];

    for agent_name in &agent_names {
        eprintln!("\n  --- {} (n={}) ---", agent_name, config.batch_size);

        let result = match run_one_agent(&mut store, agent_name, config.batch_size, "knowledge") {
            Ok(r) => r,
            Err(e) => {
                eprintln!("    ERROR: {}", e);
                continue;
            }
        };

        let mut actions = result.actions;
        all_no_ops += result.no_ops;

        eprintln!("    Actions: {}  No-ops: {}", actions.len(), result.no_ops);

        let mut applied = 0;
        for action in &mut actions {
            let depth = compute_action_depth(depth_db, action, agent_name);
            action.depth = depth;

            match &action.kind {
                ActionKind::WriteNode { key, covers, .. } => {
                    let conf_val = action.confidence.value();
                    let req = required_confidence(depth, config.confidence_base);

                    let source_uses: Vec<u32> = covers.iter()
                        .filter_map(|k| store.nodes.get(k).map(|n| n.uses))
                        .collect();
                    let avg_uses = if source_uses.is_empty() { 0 }
                        else { source_uses.iter().sum::<u32>() / source_uses.len() as u32 };
                    let eff_conf = (conf_val + use_bonus(avg_uses)).min(1.0);

                    if eff_conf < req {
                        action.applied = Some(false);
                        action.rejected_reason = Some("depth_threshold".into());
                        depth_rejected += 1;
                        continue;
                    }
                    if depth > config.max_depth {
                        action.applied = Some(false);
                        action.rejected_reason = Some("max_depth".into());
                        depth_rejected += 1;
                        continue;
                    }
                    eprintln!("      WRITE {} depth={} conf={:.2} eff={:.2} req={:.2}",
                        key, depth, conf_val, eff_conf, req);
                }
                ActionKind::Link { source, target } => {
                    eprintln!("      LINK {} → {}", source, target);
                }
                ActionKind::Refine { key, .. } => {
                    eprintln!("      REFINE {} depth={}", key, depth);
                }
            }

            if apply_action(&mut store, action, agent_name, &timestamp, depth) {
                applied += 1;
                action.applied = Some(true);
                if let ActionKind::WriteNode { key, .. } | ActionKind::Refine { key, .. } = &action.kind {
                    depth_db.set(key.clone(), depth);
                }
            } else {
                action.applied = Some(false);
            }
        }

        eprintln!("    Applied: {}/{}", applied, actions.len());
        total_applied += applied;
        all_actions.extend(actions);
    }

    depth_db.save(&mut store);

    // Recompute spectral if anything changed
    if total_applied > 0 {
        eprintln!("\n  Recomputing spectral embedding...");
        let graph = store.build_graph();
        let result = spectral::decompose(&graph, 8);
        let emb = spectral::to_embedding(&result);
        spectral::save_embedding(&emb).ok();
    }

    let graph = store.build_graph();
    let metrics_after = GraphMetrics::from_graph(&store, &graph);
    let weighted_delta: f64 = all_actions.iter()
        .filter(|a| a.applied == Some(true))
        .map(|a| a.weight)
        .sum();

    eprintln!("\n  CYCLE {} SUMMARY", cycle_num);
    eprintln!("    Applied: {}/{}  depth-rejected: {}  no-ops: {}",
        total_applied, all_actions.len(), depth_rejected, all_no_ops);
    eprintln!("    Weighted delta: {:.2}", weighted_delta);

    Ok(CycleResult {
        cycle: cycle_num,
        timestamp,
        total_actions: all_actions.len(),
        total_applied,
        total_no_ops: all_no_ops,
        depth_rejected,
        weighted_delta,
        graph_metrics_before: metrics_before,
        graph_metrics_after: metrics_after,
    })
}