consciousness/src/subconscious/learn.rs

// training.rs — Memory importance scoring via /v1/score
//
// Three scoring modes, all built on the same call_score() primitive:
//
// score_memories()  — Full N×M matrix (memories × responses) for the
//                     debug screen. Expensive: N+1 API calls.
//
// memory_score()    — Single memory importance. Scores the 50 messages
//                     after it was surfaced, with/without that memory.
//                     2 API calls.
//
// finetune_score()  — Identifies training candidates. Scores recent
//                     messages with all memories stripped. Responses
//                     with high divergence depend on memories the model
//                     hasn't internalized. 2 API calls.

use crate::agent::api::ApiClient;
use crate::agent::context::{AstNode, Ast, NodeBody, ContextState, Role};

const SCORE_TIMEOUT: std::time::Duration = std::time::Duration::from_secs(120);

// ── Message building ────────────────────────────────────────────

/// What to filter when building the message array for scoring.
#[allow(dead_code)]
enum Filter<'a> {
    None,
    SkipIndex(usize),
    SkipKey(&'a str),
    SkipAllMemories,
}

fn is_memory(node: &AstNode) -> bool {
    matches!(node, AstNode::Leaf(leaf) if matches!(leaf.body(), NodeBody::Memory { .. }))
}

fn memory_key(node: &AstNode) -> Option<&str> {
    match node {
        AstNode::Leaf(leaf) => match leaf.body() {
            NodeBody::Memory { key, .. } => Some(key),
            _ => None,
        },
        _ => None,
    }
}

fn is_assistant(node: &AstNode) -> bool {
    matches!(node, AstNode::Branch { role: Role::Assistant, .. })
}

/// Push an AstNode as one or more JSON messages for the scoring API.
fn push_api_message(node: &AstNode, msgs: &mut Vec<serde_json::Value>) {
    match node {
        AstNode::Branch { role, children } => {
            let content: String = children.iter().map(|c| c.render()).collect();
            msgs.push(serde_json::json!({
                "role": role.as_str(),
                "content": content,
            }));
        }
        AstNode::Leaf(leaf) => {
            let role = match leaf.body() {
                NodeBody::ToolResult(_) => "tool",
                _ => "user",
            };
            msgs.push(serde_json::json!({
                "role": role,
                "content": leaf.body().text(),
            }));
        }
    }
}

/// Build the messages array for a scoring call.
///
/// Always includes system prompt as prefix, then entries from `range`
/// filtered by `filter`.
fn build_messages(
    context: &ContextState,
    range: std::ops::Range<usize>,
    filter: Filter,
) -> Vec<serde_json::Value> {
    let mut msgs = Vec::new();
    for node in context.system() {
        push_api_message(node, &mut msgs);
    }
    let entries = context.conversation();
    for i in range {
        let node = &entries[i];
        let skip = match &filter {
            Filter::None => false,
            Filter::SkipIndex(idx) => i == *idx,
            Filter::SkipKey(key) => memory_key(node) == Some(*key),
            Filter::SkipAllMemories => is_memory(node),
        };
        if skip { continue; }
        push_api_message(node, &mut msgs);
    }
    msgs
}

// ── Score API ───────────────────────────────────────────────────

#[derive(serde::Deserialize)]
struct ScoreResult {
    total_logprob: f64,
}

#[derive(serde::Deserialize)]
struct ScoreResponse {
    scores: Vec<ScoreResult>,
}

fn http_client() -> crate::agent::api::http::HttpClient {
    crate::agent::api::http::HttpClient::builder()
        .timeout(SCORE_TIMEOUT)
        .build()
}

async fn call_score(
    http: &crate::agent::api::http::HttpClient,
    client: &ApiClient,
    messages: &[serde_json::Value],
    priority: Option<i32>,
) -> anyhow::Result<Vec<ScoreResult>> {
    let url = format!("{}/score", client.base_url());
    let auth = format!("Bearer {}", client.api_key());
    let mut body = serde_json::json!({
        "model": client.model,
        "messages": messages,
        "logprobs": 1,
    });
    if let Some(p) = priority {
        body["priority"] = serde_json::json!(p);
    }
    let response = http
        .send_json("POST", &url, &[
            ("authorization", &auth),
        ], &body)
        .await?;

    let status = response.status();
    let body: serde_json::Value = response.json().await?;

    if !status.is_success() {
        let msg = body.get("error").and_then(|e| e.as_str()).unwrap_or("unknown error");
        anyhow::bail!("score API HTTP {}: {}", status, msg);
    }
    if let Some(err) = body.get("error").and_then(|e| e.as_str()) {
        anyhow::bail!("score API error: {}", err);
    }

    let result: ScoreResponse = serde_json::from_value(body)
        .map_err(|e| anyhow::anyhow!("failed to parse score response: {}", e))?;
    Ok(result.scores)
}

/// Compute per-position logprob divergence: how much worse the model
/// scores each response without something vs with it.
fn divergence(baseline: &[ScoreResult], without: &[ScoreResult]) -> Vec<f64> {
    baseline.iter().enumerate()
        .map(|(i, base)| {
            let without_lp = without.get(i).map(|s| s.total_logprob).unwrap_or(base.total_logprob);
            (base.total_logprob - without_lp).max(0.0)
        })
        .collect()
}

/// Score two message sets and return total divergence.
async fn score_divergence(
    http: &crate::agent::api::http::HttpClient,
    client: &ApiClient,
    context: &ContextState,
    range: std::ops::Range<usize>,
    filter: Filter<'_>,
    priority: Option<i32>,
) -> anyhow::Result<(Vec<f64>, Vec<ScoreResult>)> {
    let baseline = call_score(http, client, &build_messages(context, range.clone(), Filter::None), priority).await?;
    let without = call_score(http, client, &build_messages(context, range, filter), priority).await?;
    let divs = divergence(&baseline, &without);
    Ok((divs, baseline))
}

// ── Full matrix scoring (debug screen) ──────────────────────────

/// Result of scoring one conversation's memory usage.
pub struct MemoryScore {
    pub memory_weights: Vec<(String, f64)>,
    pub response_scores: Vec<f64>,
    /// Full matrix: divergence[memory_idx][response_idx]
    pub matrix: Vec<Vec<f64>>,
    pub memory_keys: Vec<String>,
    pub response_entry_indices: Vec<usize>,
}

impl MemoryScore {
    pub fn important_memories_for_entry(&self, entry_idx: usize) -> Vec<(&str, f64)> {
        let Some(resp_idx) = self.response_entry_indices.iter().position(|&i| i == entry_idx)
        else { return Vec::new() };

        let mut result: Vec<(&str, f64)> = self.memory_keys.iter()
            .zip(self.matrix.iter())
            .filter_map(|(key, row)| {
                let score = row.get(resp_idx).copied().unwrap_or(0.0);
                if score > 0.01 { Some((key.as_str(), score)) } else { None }
            })
            .collect();
        result.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        result
    }
}

/// Score how important each memory is to the conversation (full matrix).
pub async fn score_memories(
    context: &ContextState,
    client: &ApiClient,
) -> anyhow::Result<MemoryScore> {
    let mut memory_keys: Vec<String> = context.conversation().iter()
        .filter_map(|node| memory_key(node).map(String::from))
        .collect();
    memory_keys.dedup();

    let response_indices: Vec<usize> = context.conversation().iter().enumerate()
        .filter(|(_, node)| is_assistant(node))
        .map(|(i, _)| i)
        .collect();

    if memory_keys.is_empty() || response_indices.is_empty() {
        return Ok(MemoryScore {
            memory_weights: Vec::new(), response_scores: Vec::new(),
            matrix: Vec::new(), memory_keys: Vec::new(),
            response_entry_indices: Vec::new(),
        });
    }


    let http = http_client();
    let range = 0..context.conversation().len();

    let baseline = call_score(&http, client, &build_messages(context, range.clone(), Filter::None), Some(5)).await?;

    let total = memory_keys.len();
    let mut matrix: Vec<Vec<f64>> = Vec::new();

    for (mem_idx, key) in memory_keys.iter().enumerate() {
        dbglog!(
            "scoring {}/{}: {}...", mem_idx + 1, total, key,
        );
        let msgs = build_messages(context, range.clone(), Filter::SkipKey(key));
        match call_score(&http, client, &msgs, Some(5)).await {
            Ok(without) => matrix.push(divergence(&baseline, &without)),
            Err(e) => {
                dbglog!(
                    "[training] {} FAILED: {:#}", key, e,
                );
                matrix.push(vec![0.0; baseline.len()]);
            }
        }
    }


    let memory_weights: Vec<(String, f64)> = memory_keys.iter()
        .zip(matrix.iter())
        .map(|(key, row)| (key.clone(), row.iter().sum()))
        .collect();

    let mut response_scores = vec![0.0; response_indices.len()];
    for row in &matrix {
        for (j, &v) in row.iter().enumerate() {
            if j < response_scores.len() { response_scores[j] += v; }
        }
    }

    Ok(MemoryScore {
        memory_weights, response_scores, matrix, memory_keys,
        response_entry_indices: response_indices,
    })
}

/// Find the entry index after `start` that contains the Nth assistant response.
/// Returns (end_index, true) if N responses were found, (entries.len(), false) if not.
fn nth_response_end(entries: &[AstNode], start: usize, n: usize) -> (usize, bool) {
    let mut count = 0;
    for i in start..entries.len() {
        if is_assistant(&entries[i]) {
            count += 1;
            if count >= n { return (i + 1, true); }
        }
    }
    (entries.len(), false)
}

// ── Single memory scoring ───────────────────────────────────────

/// Score how important a single memory is to the conversation.
///
/// Scores the 50 messages after the memory was surfaced — the window
/// where it could have influenced responses. Returns the sum of
/// divergence, or 0.0 if the memory isn't in the conversation.
pub async fn score_memory(
    context: &ContextState,
    key: &str,
    client: &ApiClient,
) -> anyhow::Result<f64> {
    const RESPONSE_WINDOW: usize = 50;

    let entries = context.conversation();
    let first_pos = match entries.iter().position(|node| memory_key(node) == Some(key)) {
        Some(p) => p,
        None => return Ok(0.0),
    };

    let (end, _) = nth_response_end(entries, first_pos, RESPONSE_WINDOW);
    let range = first_pos..end;
    if !entries[range.clone()].iter().any(|node| is_assistant(node)) {
        return Ok(0.0);
    }

    let http = http_client();
    let (divs, _) = score_divergence(&http, client, context, range, Filter::SkipKey(key), Some(5)).await?;

    Ok(divs.iter().sum())
}

// ── Background memory scoring ───────────────────────────────────

/// Score memories in the conversation that are due for re-scoring.
///
/// Checks the graph for each memory's last_scored timestamp. Scores
/// nodes that haven't been scored within `max_age_secs`, oldest first.
/// Updates the graph weight (EWMA) and last_scored after each.
///
/// Returns the number of nodes scored and their (key, score) pairs.
pub async fn score_memories_incremental<F, Fut>(
    context: &ContextState,
    max_age_secs: i64,
    response_window: usize,
    client: &ApiClient,
    agent: &std::sync::Arc<crate::agent::Agent>,
    mut on_score: F,
) -> anyhow::Result<usize>
where
    F: FnMut(String, f64) -> Fut,
    Fut: std::future::Future<Output = ()>,
{
    let now = chrono::Utc::now().timestamp();

    // Collect unique memory keys with their first position
    let mut seen = std::collections::HashSet::new();
    let mut candidates: Vec<(usize, String, i64)> = Vec::new(); // (pos, key, last_scored)

    let store = crate::hippocampus::store::Store::load().unwrap_or_default();

    for (i, node) in context.conversation().iter().enumerate() {
        if let Some(key) = memory_key(node) {
            if !seen.insert(key.to_owned()) { continue; }
            let last_scored = store.nodes.get(key)
                .map(|n| n.last_scored)
                .unwrap_or(0);
            if now - last_scored >= max_age_secs {
                candidates.push((i, key.to_owned(), last_scored));
            }
        }
    }

    // Score oldest-first
    candidates.sort_by_key(|&(_, _, last)| last);

    let http = http_client();
    let mut scored = 0;

    let entries = context.conversation();
    let total_tokens: usize = entries.iter().map(|n| n.tokens()).sum();
    let token_cutoff = total_tokens * 60 / 100;

    // Precompute cumulative token position for each entry
    let mut cumulative: Vec<usize> = Vec::with_capacity(entries.len());
    let mut running = 0;
    for e in entries {
        running += e.tokens();
        cumulative.push(running);
    }

    for (pos, key, _) in &candidates {
        // Only score memories in the first 70% of the conversation by tokens —
        // recent memories don't have enough responses to evaluate yet.
        if cumulative.get(*pos).copied().unwrap_or(total_tokens) > token_cutoff {
            continue;
        }
        let (end, _) = nth_response_end(context.conversation(), *pos, response_window);
        let range = *pos..end;
        if !context.conversation()[range.clone()].iter().any(|node| is_assistant(node)) {
            continue;
        }

        let _scoring = crate::agent::start_activity(agent, format!("scoring: {}", key)).await;
        match score_divergence(&http, client, context, range, Filter::SkipKey(key), Some(5)).await {
            Ok((divs, _)) => {
                let n_responses = divs.len();
                let max_div = divs.iter().cloned().fold(0.0f64, f64::max);
                dbglog!(
                    "[scoring] {} max:{:.3} ({} responses)", key, max_div, n_responses,
                );
                on_score(key.clone(), max_div).await;
                scored += 1;
            }
            Err(e) => {
                dbglog!(
                    "[scoring] {} FAILED: {:#}", key, e,
                );
            }
        }
    }

    Ok(scored)
}

// ── Fine-tuning scoring ─────────────────────────────────────────

/// Score which recent responses are candidates for fine-tuning.
///
/// Removes all memories and scores the most recent `count` messages.
/// Responses with high divergence depend on memories the model hasn't
/// internalized — these are fine-tuning candidates.
///
/// Returns (entry_index, divergence) pairs, sorted by divergence descending.
pub async fn score_finetune(
    context: &ContextState,
    count: usize,
    client: &ApiClient,
) -> anyhow::Result<Vec<(usize, f64)>> {
    let entries = context.conversation();
    let range = entries.len().saturating_sub(count)..entries.len();

    let response_positions: Vec<usize> = range.clone()
        .filter(|&i| is_assistant(&entries[i]))
        .collect();
    if response_positions.is_empty() {
        return Ok(Vec::new());
    }

    let http = http_client();
    let (divs, _) = score_divergence(&http, client, context, range, Filter::SkipAllMemories, Some(5)).await?;

    let mut results: Vec<(usize, f64)> = response_positions.iter()
        .enumerate()
        .map(|(i, &entry_idx)| (entry_idx, divs.get(i).copied().unwrap_or(0.0)))
        .collect();
    results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
    Ok(results)
}