consciousness/src/enrich.rs

// Journal enrichment and experience mining
//
// Two modes of processing conversation transcripts:
//   journal_enrich  — enrich a specific journal entry with source location and links
//   experience_mine — retroactively find experiential moments not yet journaled
//
// Both extract conversation from JSONL transcripts, build prompts, call Sonnet,
// and apply results to the store.

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

use regex::Regex;
use std::collections::hash_map::DefaultHasher;
use std::fs;
use std::hash::{Hash, Hasher};

fn agent_results_dir() -> std::path::PathBuf {
    let dir = store::memory_dir().join("agent-results");
    fs::create_dir_all(&dir).ok();
    dir
}

/// Extract user/assistant messages with line numbers from a JSONL transcript.
/// (line_number, role, text, timestamp)
fn extract_conversation(jsonl_path: &str) -> Result<Vec<(usize, String, String, String)>, String> {
    let content = fs::read_to_string(jsonl_path)
        .map_err(|e| format!("read {}: {}", jsonl_path, e))?;

    let mut messages = Vec::new();
    for (i, line) in content.lines().enumerate() {
        let obj: serde_json::Value = match serde_json::from_str(line) {
            Ok(v) => v,
            Err(_) => continue,
        };

        let msg_type = obj.get("type").and_then(|v| v.as_str()).unwrap_or("");
        if msg_type != "user" && msg_type != "assistant" { continue; }

        let timestamp = obj.get("timestamp")
            .and_then(|v| v.as_str())
            .unwrap_or("")
            .to_string();

        let msg = obj.get("message").unwrap_or(&obj);
        let content = msg.get("content");

        let text = match content {
            Some(serde_json::Value::String(s)) => s.clone(),
            Some(serde_json::Value::Array(arr)) => {
                arr.iter()
                    .filter_map(|c| {
                        if let Some(t) = c.get("text").and_then(|v| v.as_str()) {
                            Some(t.to_string())
                        } else {
                            c.as_str().map(|s| s.to_string())
                        }
                    })
                    .collect::<Vec<_>>()
                    .join("\n")
            }
            _ => continue,
        };

        let text = text.trim().to_string();
        if text.is_empty() { continue; }

        messages.push((i + 1, msg_type.to_string(), text, timestamp));
    }

    Ok(messages)
}

/// Format conversation messages for the prompt (truncating long messages).
fn format_conversation(messages: &[(usize, String, String, String)]) -> String {
    messages.iter()
        .map(|(line, role, text, ts)| {
            let text = if text.len() > 2000 {
                format!("{}...[truncated]", &text[..text.floor_char_boundary(1800)])
            } else {
                text.clone()
            };
            if ts.is_empty() {
                format!("L{} [{}]: {}", line, role, text)
            } else {
                format!("L{} [{}] {}: {}", line, role, &ts[..ts.len().min(19)], text)
            }
        })
        .collect::<Vec<_>>()
        .join("\n\n")
}

fn build_journal_prompt(
    entry_text: &str,
    conversation: &str,
    keys: &[String],
    grep_line: usize,
) -> Result<String, String> {
    let keys_text: String = keys.iter()
        .map(|k| format!("  - {}", k))
        .collect::<Vec<_>>()
        .join("\n");

    neuro::load_prompt("journal-enrich", &[
        ("{{GREP_LINE}}", &grep_line.to_string()),
        ("{{ENTRY_TEXT}}", entry_text),
        ("{{KEYS}}", &keys_text),
        ("{{CONVERSATION}}", conversation),
    ])
}

/// Enrich a journal entry with conversation context and link proposals.
pub fn journal_enrich(
    store: &mut Store,
    jsonl_path: &str,
    entry_text: &str,
    grep_line: usize,
) -> Result<(), String> {
    println!("Extracting conversation from {}...", jsonl_path);
    let messages = extract_conversation(jsonl_path)?;
    let conversation = format_conversation(&messages);
    println!("  {} messages, {} chars", messages.len(), conversation.len());

    let keys = semantic_keys(store);
    println!("  {} semantic keys", keys.len());

    let prompt = build_journal_prompt(entry_text, &conversation, &keys, grep_line)?;
    println!("  Prompt: {} chars (~{} tokens)", prompt.len(), prompt.len() / 4);

    println!("  Calling Sonnet...");
    let response = call_sonnet(&prompt, 300)?;

    let result = parse_json_response(&response)?;

    // Report results
    let source_start = result.get("source_start").and_then(|v| v.as_u64()).unwrap_or(0);
    let source_end = result.get("source_end").and_then(|v| v.as_u64()).unwrap_or(0);
    let links = result.get("links").and_then(|v| v.as_array());
    let insights = result.get("missed_insights").and_then(|v| v.as_array());

    println!("  Source: L{}-L{}", source_start, source_end);
    println!("  Links: {}", links.map_or(0, |l| l.len()));
    println!("  Missed insights: {}", insights.map_or(0, |l| l.len()));

    // Apply links
    if let Some(links) = links {
        for link in links {
            let target = link.get("target").and_then(|v| v.as_str()).unwrap_or("");
            let reason = link.get("reason").and_then(|v| v.as_str()).unwrap_or("");
            if target.is_empty() || target.starts_with("NOTE:") {
                if let Some(note) = target.strip_prefix("NOTE:") {
                    println!("  NOTE: {} — {}", note, reason);
                }
                continue;
            }

            // Resolve target and find journal node
            let resolved = match store.resolve_key(target) {
                Ok(r) => r,
                Err(_) => { println!("  SKIP {} (not in graph)", target); continue; }
            };
            let source_key = match store.find_journal_node(entry_text) {
                Some(k) => k,
                None => { println!("  SKIP {} (no matching journal node)", target); continue; }
            };

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

            let source_uuid = match store.nodes.get(&source_key) {
                Some(n) => n.uuid,
                None => continue,
            };
            let target_uuid = match store.nodes.get(&resolved) {
                Some(n) => n.uuid,
                None => continue,
            };

            let rel = new_relation(
                source_uuid, target_uuid,
                store::RelationType::Link,
                0.5,
                &source_key, &resolved,
            );
            if store.add_relation(rel).is_ok() {
                println!("  LINK {} → {} ({})", source_key, resolved, reason);
            }
        }
    }

    // Save result to agent-results
    let timestamp = store::format_datetime(store::now_epoch())
        .replace([':', '-'], "");
    let result_file = agent_results_dir()
        .join(format!("{}.json", timestamp));
    let output = serde_json::json!({
        "timestamp": timestamp,
        "jsonl_path": jsonl_path,
        "entry_text": &entry_text[..entry_text.len().min(500)],
        "agent_result": result,
    });
    fs::write(&result_file, serde_json::to_string_pretty(&output).unwrap())
        .map_err(|e| format!("write {}: {}", result_file.display(), e))?;
    println!("  Results saved: {}", result_file.display());

    store.save()?;
    Ok(())
}

/// Mine a conversation transcript for experiential moments not yet journaled.
pub fn experience_mine(
    store: &mut Store,
    jsonl_path: &str,
) -> Result<usize, String> {
    println!("Experience mining: {}", jsonl_path);

    // Transcript-level dedup: hash the file content and check if already mined
    let transcript_bytes = fs::read(jsonl_path)
        .map_err(|e| format!("reading transcript: {}", e))?;
    let mut hasher = DefaultHasher::new();
    transcript_bytes.hash(&mut hasher);
    let hash = hasher.finish();
    let dedup_key = format!("_mined-transcripts.md#h-{:016x}", hash);

    if store.nodes.contains_key(&dedup_key) {
        println!("  Already mined this transcript ({}), skipping.", &dedup_key[24..]);
        return Ok(0);
    }

    let messages = extract_conversation(jsonl_path)?;
    let conversation = format_conversation(&messages);
    println!("  {} messages, {} chars", messages.len(), conversation.len());

    // Load identity
    let identity = store.nodes.get("identity.md")
        .map(|n| n.content.clone())
        .unwrap_or_default();

    // Get recent journal entries to avoid duplication
    let key_date_re = Regex::new(r"^journal\.md#j-(\d{4}-\d{2}-\d{2}[t-]\d{2}-\d{2})").unwrap();
    let date_re = Regex::new(r"(\d{4}-\d{2}-\d{2}[T ]\d{2}:\d{2})").unwrap();
    let mut journal: Vec<_> = store.nodes.values()
        .filter(|node| node.key.starts_with("journal.md#j-"))
        .collect();
    journal.sort_by(|a, b| {
        let ak = key_date_re.captures(&a.key).map(|c| c[1].to_string())
            .or_else(|| date_re.captures(&a.content).map(|c| c[1].to_string()))
            .unwrap_or_default();
        let bk = key_date_re.captures(&b.key).map(|c| c[1].to_string())
            .or_else(|| date_re.captures(&b.content).map(|c| c[1].to_string()))
            .unwrap_or_default();
        ak.cmp(&bk)
    });
    let recent: String = journal.iter().rev().take(10)
        .map(|n| format!("---\n{}\n", n.content))
        .collect();

    let keys = semantic_keys(store);
    let keys_text: String = keys.iter()
        .map(|k| format!("  - {}", k))
        .collect::<Vec<_>>()
        .join("\n");

    let prompt = neuro::load_prompt("experience", &[
        ("{{IDENTITY}}", &identity),
        ("{{RECENT_JOURNAL}}", &recent),
        ("{{KEYS}}", &keys_text),
        ("{{CONVERSATION}}", &conversation),
    ])?;
    println!("  Prompt: {} chars (~{} tokens)", prompt.len(), prompt.len() / 4);

    println!("  Calling Sonnet...");
    let response = call_sonnet(&prompt, 2000)?;

    let entries = parse_json_response(&response)?;
    let entries = match entries.as_array() {
        Some(arr) => arr.clone(),
        None => return Err("expected JSON array".to_string()),
    };

    if entries.is_empty() {
        println!("  No missed experiences found.");
        return Ok(0);
    }

    println!("  Found {} experiential moments:", entries.len());
    let mut count = 0;
    for entry in &entries {
        let ts = entry.get("timestamp").and_then(|v| v.as_str()).unwrap_or("");
        let content = entry.get("content").and_then(|v| v.as_str()).unwrap_or("");
        if content.is_empty() { continue; }

        // Format with timestamp header
        let full_content = if ts.is_empty() {
            content.to_string()
        } else {
            format!("## {}\n\n{}", ts, content)
        };

        // Generate key from timestamp
        let key_slug: String = content.chars()
            .filter(|c| c.is_alphanumeric() || *c == ' ')
            .take(50)
            .collect::<String>()
            .trim()
            .to_lowercase()
            .replace(' ', "-");
        let key = if ts.is_empty() {
            format!("journal.md#j-mined-{}", key_slug)
        } else {
            format!("journal.md#j-{}-{}", ts.to_lowercase().replace(':', "-"), key_slug)
        };

        // Check for duplicate
        if store.nodes.contains_key(&key) {
            println!("  SKIP {} (duplicate)", key);
            continue;
        }

        // Write to store
        let mut node = new_node(&key, &full_content);
        node.node_type = store::NodeType::EpisodicSession;
        node.category = store::Category::Observation;
        let _ = store.upsert_node(node);
        count += 1;

        let preview = if content.len() > 80 { &content[..77] } else { content };
        println!("  + [{}] {}...", ts, preview);
    }

    // Record this transcript as mined (even if count == 0, to prevent re-runs)
    let dedup_content = format!("Mined {} ({} entries)", jsonl_path, count);
    let mut dedup_node = new_node(&dedup_key, &dedup_content);
    dedup_node.category = store::Category::Task;
    let _ = store.upsert_node(dedup_node);

    if count > 0 {
        println!("  Saved {} new journal entries.", count);
    }
    store.save()?;
    println!("Done: {} new entries mined.", count);
    Ok(count)
}