formal-ai 0.100.0

//! "How it works" follow-up handler extracted from `solver_handlers` to keep
//! that module under the 1000-line cap enforced by `scripts/check-file-size.rs`.

use crate::concepts::{extract_concept_query, lookup_concept_query};
use crate::engine::SymbolicAnswer;
use crate::event_log::EventLog;
use crate::language::{detect as detect_language, Language};
use crate::solver_handlers::{finalize_simple, try_concept_lookup};
use crate::solver_helpers::last_assistant_turn;
use crate::web_search_core::{WEB_SEARCH_PROVIDERS, WEB_SEARCH_RRF_K};

struct HowItWorksQuery {
    subject: Option<String>,
}

struct ProceduralHowToTask {
    task: String,
    action: String,
    object: String,
}

/// Handles source-backed procedural requests such as "How to make tea?" and
/// "How can I prepare fried potatoes?". The offline Rust engine records the
/// same discovery plan that the browser worker can execute: local
/// decomposition first, Wikimedia/wikiHow candidates next, then web search
/// and recursive fetch checks only as the fallback path.
pub fn try_how_to_procedure(
    prompt: &str,
    normalized: &str,
    log: &mut EventLog,
) -> Option<SymbolicAnswer> {
    let task = extract_procedural_how_to_task(normalized)?;
    let query = format!("how to {}", task.task);
    let wikihow_candidate = wikihow_page_title(&task.task);
    let wikihow_api_url = format!(
        "https://www.wikihow.com/api.php?action=parse&page={wikihow_candidate}\
         &prop=text%7Csections%7Cdisplaytitle&format=json&origin=*"
    );

    log.append("procedural_how_to:request", task.task.clone());
    log.append("procedural_how_to:action", task.action.clone());
    if !task.object.is_empty() {
        log.append("procedural_how_to:object", task.object.clone());
    }
    log.append("procedural_how_to:stage", "wikipedia".to_owned());
    log.append("procedural_how_to:stage", "wikidata".to_owned());
    log.append("procedural_how_to:stage", "wikihow_api".to_owned());
    log.append(
        "procedural_how_to:wikihow_candidate",
        wikihow_candidate.clone(),
    );
    log.append("http_fetch:request", wikihow_api_url.clone());
    log.append("procedural_how_to:stage", "web_search".to_owned());
    log.append("web_search:request", query.clone());
    for provider in WEB_SEARCH_PROVIDERS {
        log.append("web_search:provider", (*provider).to_owned());
    }
    log.append("web_search:combined", format!("rrf:k={WEB_SEARCH_RRF_K}"));
    log.append(
        "procedural_how_to:stage",
        "recursive_fetch_check".to_owned(),
    );
    log.append(
        "procedural_how_to:source_gate",
        "explicit_steps_only".to_owned(),
    );

    let provider_summary = WEB_SEARCH_PROVIDERS.join(", ");
    let body = format!(
        "Procedural discovery plan for `{}` (action `{}`, object `{}`).\n\n\
         I do not answer this from a memoized recipe. The solver first checks \
         Wikipedia for topic context and Wikidata for entity/action/object hints. \
         It then tries wikiHow's CORS-readable MediaWiki parse API candidate \
         `{}` via `{}`. If those sources do not expose usable steps, the fallback \
         path runs web search for `{}` across {} and merges the top results with \
         reciprocal rank fusion (k = {}). The final recursive fetch check only \
         accepts pages that actually contain explicit ordered or instructional \
         steps for `{}`.",
        task.task,
        task.action,
        task.object,
        wikihow_candidate,
        wikihow_api_url,
        query,
        provider_summary,
        WEB_SEARCH_RRF_K,
        task.task,
    );

    Some(finalize_simple(
        prompt,
        log,
        "procedural_how_to",
        "response:procedural_how_to",
        &body,
        0.78,
    ))
}

/// Handles follow-up elaboration prompts such as "how it works?", "how does
/// it work?", or multilingual "how X works?" variants. When the prior
/// assistant turn mentioned a named concept the solver re-runs a concept lookup
/// for that topic. Explicit unknown subjects get a source-backed discovery plan
/// instead of falling through to the unknown fallback.
pub fn try_how_it_works(
    prompt: &str,
    normalized: &str,
    log: &mut EventLog,
) -> Option<SymbolicAnswer> {
    let query = extract_how_it_works_query(prompt, normalized)?;
    log.append("followup:how_it_works", normalized.to_owned());

    // When a subject was explicit in the prompt, do a direct concept lookup.
    if let Some(ref term) = query.subject {
        log.append(
            "followup:subject",
            format!("inline:{}", term.to_lowercase()),
        );
        let concept_prompt = format!("what is {term}");
        if let Some(concept_query) = extract_concept_query(&concept_prompt) {
            if lookup_concept_query(&concept_query).is_some() {
                // Delegate to try_concept_lookup by synthesising a standard prompt.
                return try_concept_lookup(&concept_prompt, log);
            }
        }
        record_mechanism_query(log, term);
        let body = render_mechanism_discovery_answer(term, detect_language(prompt));
        return Some(finalize_simple(
            prompt,
            log,
            "how_it_works",
            "response:how_it_works",
            &body,
            0.68,
        ));
    }

    // No inline subject — look for the topic in the prior assistant reply.
    if let Some(prior) = last_assistant_turn(log).map(str::to_owned) {
        log.append("followup:prior_turn", "assistant".to_owned());
        // Extract the first capitalised noun phrase from the prior reply
        // (typically the term in "Term (category): …" format).
        if let Some(term) = extract_topic_from_prior_reply(&prior) {
            use crate::concepts::{extract_concept_query, lookup_concept_query};
            if let Some(query) = extract_concept_query(&format!("what is {term}")) {
                if lookup_concept_query(&query).is_some() {
                    log.append("followup:subject", format!("prior_reply:{term}"));
                    return try_concept_lookup(&format!("what is {term}"), log);
                }
            }
            // Topic is known from history but not in the concept corpus —
            // return a helpful explanation that names the topic.
            let body = format!(
                "To explain how {term} works: I know the term from the prior conversation \
                 but do not have a detailed symbolic rule for it yet. Add a Links Notation \
                 fact with the mechanism description, then ask again."
            );
            log.append("followup:subject", format!("prior_reply_no_record:{term}"));
            return Some(finalize_simple(
                prompt,
                log,
                "concept_elaboration_missing",
                "response:concept_elaboration_missing",
                &body,
                0.3,
            ));
        }
    }

    // No context at all — route to meta_explanation.
    let body = String::from(
        "I answered that way because the prompt matched a deterministic Links Notation rule. \
         To ask about a specific topic, try \"how does X work?\" where X is a concept I know \
         (e.g. \"how does Wikipedia work?\"). The evidence and trace events are appended to \
         the log; see the trace link for the full chain.",
    );
    Some(finalize_simple(
        prompt,
        log,
        "meta_explanation",
        "response:meta_explanation",
        &body,
        0.5,
    ))
}

fn extract_how_it_works_query(prompt: &str, _normalized: &str) -> Option<HowItWorksQuery> {
    let original = clean_mechanism_fragment(prompt);
    if original.is_empty() {
        return None;
    }
    let lower = original.to_lowercase();
    if is_bare_how_it_works(&lower) {
        return Some(HowItWorksQuery { subject: None });
    }
    extract_how_it_works_subject(&original, &lower).map(|subject| HowItWorksQuery {
        subject: Some(subject),
    })
}

fn is_bare_how_it_works(lower: &str) -> bool {
    const BARE_PHRASES: &[&str] = &[
        "how it works",
        "how does it work",
        "как это работает",
        "как оно работает",
        "यह कैसे काम करता है",
        "यह कैसे काम करती है",
        "यह कैसे काम करता",
        "这是如何工作的",
        "这是怎么工作的",
        "这个如何工作",
        "它如何工作",
        "它是如何工作的",
        "它怎么工作",
    ];
    BARE_PHRASES.iter().any(|phrase| {
        lower == *phrase
            || lower
                .strip_prefix(*phrase)
                .is_some_and(|rest| rest.starts_with(' '))
    })
}

/// Extract the explicit subject from multilingual "how X works?" prompts.
/// Returns `None` when the prompt is the bare "how it works?" form.
fn extract_how_it_works_subject(original: &str, lower: &str) -> Option<String> {
    for prefix in [
        "how does ",
        "how do ",
        "how did ",
        "how is ",
        "как устроен ",
        "как устроена ",
        "как устроено ",
        "как устроены ",
        "как работает ",
        "как работают ",
    ] {
        if let Some(subject) = subject_after_prefix(original, lower, prefix) {
            return strip_mechanism_tail(&subject);
        }
    }

    for (prefix, suffixes) in [
        ("how ", &[" works", " work"] as &[_]),
        ("как ", &[" работает", " работают"]),
    ] {
        if let Some(subject) = subject_between(original, lower, prefix, suffixes) {
            return Some(subject);
        }
    }

    for suffix in [
        " कैसे काम करता है",
        " कैसे काम करती है",
        " कैसे काम करते हैं",
        " कैसे काम करता",
        " कैसे काम करती",
        " कैसे काम करते",
        " 是如何工作的",
        "是如何工作的",
        " 是怎么工作的",
        "是怎么工作的",
        " 如何工作",
        "如何工作",
        " 怎么工作",
        "怎么工作",
        " 的工作原理是什么",
        "的工作原理是什么",
        " как работает",
        " как работают",
    ] {
        if let Some(subject) = subject_before_suffix(original, lower, suffix) {
            return Some(subject);
        }
    }

    None
}

fn subject_after_prefix(original: &str, lower: &str, prefix: &str) -> Option<String> {
    lower.strip_prefix(prefix)?;
    let rest = original.get(prefix.len()..)?;
    clean_mechanism_subject(rest)
}

fn subject_before_suffix(original: &str, lower: &str, suffix: &str) -> Option<String> {
    lower.strip_suffix(suffix)?;
    let end = original.len().checked_sub(suffix.len())?;
    clean_mechanism_subject(original.get(..end)?)
}

fn subject_between(original: &str, lower: &str, prefix: &str, suffixes: &[&str]) -> Option<String> {
    if !lower.starts_with(prefix) {
        return None;
    }
    for suffix in suffixes {
        if lower.ends_with(suffix) {
            let end = original.len().checked_sub(suffix.len())?;
            if end <= prefix.len() {
                return None;
            }
            return clean_mechanism_subject(original.get(prefix.len()..end)?);
        }
    }
    None
}

fn strip_mechanism_tail(subject: &str) -> Option<String> {
    let mut clean = clean_mechanism_subject(subject)?;
    let lower = clean.to_lowercase();
    for suffix in [
        " work",
        " works",
        " structured",
        " organized",
        " organised",
        " built",
    ] {
        if lower.ends_with(suffix) {
            let end = clean.len().checked_sub(suffix.len())?;
            clean.truncate(end);
            return clean_mechanism_subject(&clean);
        }
    }
    Some(clean)
}

fn clean_mechanism_fragment(value: &str) -> String {
    value
        .trim()
        .trim_matches(|character: char| {
            matches!(
                character,
                '`' | '"' | '\'' | '«' | '»' | '<' | '>' | '(' | ')' | '[' | ']' | '{' | '}'
            )
        })
        .trim_end_matches(['?', '？', '。', '.', '!', ',', ';', ':'])
        .trim()
        .to_owned()
}

fn clean_mechanism_subject(value: &str) -> Option<String> {
    const PRONOUN_SUBJECTS: &[&str] = &[
        "it", "this", "that", "you", "yourself", "does", "do", "это", "оно", "он", "она", "они",
        "ты", "вы", "यह", "ये", "这", "这个", "它",
    ];

    let mut clean = clean_mechanism_fragment(value);
    for suffix in [
        " in detail",
        " internally",
        " exactly",
        " please",
        " подробнее",
        " подробно",
        " пожалуйста",
    ] {
        let lower = clean.to_lowercase();
        if lower.ends_with(suffix) {
            let end = clean.len().checked_sub(suffix.len())?;
            clean.truncate(end);
            clean = clean_mechanism_fragment(&clean);
        }
    }
    let lower = clean.to_lowercase();
    if clean.is_empty()
        || PRONOUN_SUBJECTS.contains(&lower.as_str())
        || lower.starts_with("does ")
        || lower.starts_with("do ")
        || lower.starts_with("to ")
        || lower.starts_with("you ")
    {
        return None;
    }
    Some(clean)
}

fn record_mechanism_query(log: &mut EventLog, subject: &str) {
    log.append("mechanism_query:request", subject.to_owned());
    for stage in ["wikipedia", "wikidata", "web_search"] {
        log.append("mechanism_query:stage", stage.to_owned());
    }
    log.append("web_search:request", format!("how {subject} works"));
    for provider in WEB_SEARCH_PROVIDERS {
        log.append("web_search:provider", (*provider).to_owned());
    }
    log.append("web_search:combined", format!("rrf:k={WEB_SEARCH_RRF_K}"));
    log.append(
        "mechanism_query:source_gate",
        "source_backed_mechanism_only".to_owned(),
    );
}

fn render_mechanism_discovery_answer(subject: &str, language: Language) -> String {
    let provider_summary = WEB_SEARCH_PROVIDERS.join(", ");
    match language {
        Language::Russian => format!(
            "План поиска механизма для `{subject}`.\n\n\
             Я не отвечаю на это из зашитого факта. Решатель трактует запрос \
             как вопрос о том, как устроен или работает `{subject}`: сначала \
             проверяет Wikipedia для обзорного источника, затем Wikidata для \
             связей сущности, затем веб-поиск через {provider_summary}. Если \
             источники не объясняют механизм, ответ должен попросить источник \
             или более узкий термин, а не выдумывать детали."
        ),
        Language::Hindi => format!(
            "`{subject}` के लिए mechanism discovery plan.\n\n\
             I do not answer this from a memoized fact. The solver treats the \
             prompt as a question about how `{subject}` works, checks Wikipedia \
             for a source-backed overview, Wikidata for entity relationships, \
             then web search across {provider_summary}. If no source explains \
             the mechanism, it should ask for a source or a narrower term."
        ),
        Language::Chinese => format!(
            "`{subject}` 的机制发现计划。\n\n\
             I do not answer this from a memoized fact. The solver treats the \
             prompt as a question about how `{subject}` works, checks Wikipedia \
             for a source-backed overview, Wikidata for entity relationships, \
             then web search across {provider_summary}. If no source explains \
             the mechanism, it should ask for a source or a narrower term."
        ),
        Language::English | Language::Unknown => format!(
            "Mechanism discovery plan for `{subject}`.\n\n\
             I do not answer this from a memoized fact. The solver treats the \
             prompt as a question about how `{subject}` works, checks Wikipedia \
             for a source-backed overview, Wikidata for entity relationships, \
             then web search across {provider_summary}. If no source explains \
             the mechanism, it should ask for a source or a narrower term \
             instead of inventing details."
        ),
    }
}

fn extract_procedural_how_to_task(normalized: &str) -> Option<ProceduralHowToTask> {
    const PREFIXES: &[&str] = &[
        "please tell me how to ",
        "please show me how to ",
        "tell me how to ",
        "show me how to ",
        "what are the steps to ",
        "what steps do i need to ",
        "what steps do we need to ",
        "how should i ",
        "how should we ",
        "how could i ",
        "how could we ",
        "how would i ",
        "how would we ",
        "how can i ",
        "how can we ",
        "how do i ",
        "how do we ",
        "how to ",
    ];

    let clean_prompt = clean_procedural_fragment(normalized);
    for prefix in PREFIXES {
        if let Some(rest) = clean_prompt.strip_prefix(prefix) {
            return build_procedural_task(rest);
        }
    }
    None
}

fn build_procedural_task(raw_task: &str) -> Option<ProceduralHowToTask> {
    let task = clean_procedural_fragment(raw_task);
    if task.is_empty() {
        return None;
    }

    let (action, object) = {
        let mut parts = task.splitn(2, char::is_whitespace);
        let action = parts.next()?.trim();
        if action.is_empty() {
            return None;
        }
        let object = parts.next().unwrap_or("").trim();
        (action.to_owned(), object.to_owned())
    };

    Some(ProceduralHowToTask {
        task,
        action,
        object,
    })
}

fn clean_procedural_fragment(value: &str) -> String {
    let mut clean = value
        .trim()
        .trim_matches(|character: char| matches!(character, '`' | '"' | '\'' | ' '))
        .trim_end_matches(['?', '!', '.', ',', ';', ':'])
        .trim()
        .to_owned();

    for suffix in [
        " step by step",
        " in steps",
        " with steps",
        " for me",
        " please",
    ] {
        if let Some(stripped) = clean.strip_suffix(suffix) {
            clean = stripped.trim().to_owned();
            break;
        }
    }
    clean
}

fn wikihow_page_title(task: &str) -> String {
    task.split(|character: char| !character.is_alphanumeric())
        .filter(|word| !word.is_empty())
        .map(capitalize_word)
        .collect::<Vec<_>>()
        .join("-")
}

fn capitalize_word(word: &str) -> String {
    let mut chars = word.chars();
    let Some(first) = chars.next() else {
        return String::new();
    };
    format!("{}{rest}", first.to_uppercase(), rest = chars.as_str())
}

/// Extract the first meaningful topic word/phrase from a prior assistant reply.
/// Looks for "Term (category):" patterns first, then the first capitalised token.
fn extract_topic_from_prior_reply(reply: &str) -> Option<String> {
    // Match "Term (category): description" — common in concept_lookup answers.
    let first_line = reply.lines().next().unwrap_or("").trim();
    if let Some(paren_pos) = first_line.find('(') {
        let candidate = first_line[..paren_pos].trim();
        if !candidate.is_empty() {
            return Some(candidate.to_lowercase());
        }
    }
    // Fallback: first capitalised word that is not a stop word.
    let stop_words = [
        "I", "The", "A", "An", "In", "To", "For", "Of", "And", "Or", "Source",
    ];
    for word in reply.split_whitespace() {
        let clean = word.trim_matches(|c: char| !c.is_alphanumeric());
        if clean.len() >= 2
            && clean.chars().next().is_some_and(char::is_uppercase)
            && !stop_words.contains(&clean)
        {
            return Some(clean.to_lowercase());
        }
    }
    None
}