gqls/

cli.rs

//! clap CLI, dispatch, and output formatting (text / json / ndjson).

use anyhow::Result;
use clap::{CommandFactory, Parser};
use clap_complete::{generate, Shell};
use serde::Serialize;

use crate::load;
use crate::model::{Kind, SchemaRecord};
use crate::search;

/// The semantic-only flags (--semantic, --model, --refresh, --clear-cache) are
/// hidden from --help on builds without the feature, where they'd only error.
const HIDE_SEMANTIC: bool = !cfg!(feature = "_semantic");

#[cfg(feature = "_semantic")]
const EXAMPLES: &str = "\
EXAMPLES:
  gqls user schema.graphql            fuzzy search an SDL file
  gqls createUser -k mutation         restrict to a kind (schema auto-discovered)
  gqls User.email                     qualified Type.field query
  gqls repo schema.json               search a local introspection dump
  gqls repo https://api/graphql       introspect a live endpoint
  gqls 'cancel a subscription'        rank by meaning (fuzzy + semantic, auto)
  gqls Query.user -R --code ./app     jump to the graphql-ruby resolver
  gqls user schema.graphql -j         JSON output (-J for ndjson)
";

#[cfg(not(feature = "_semantic"))]
const EXAMPLES: &str = "\
EXAMPLES:
  gqls user schema.graphql            fuzzy search an SDL file
  gqls createUser -k mutation         restrict to a kind (schema auto-discovered)
  gqls User.email                     qualified Type.field query
  gqls repo schema.json               search a local introspection dump
  gqls repo https://api/graphql       introspect a live endpoint
  gqls Query.user -R --code ./app     jump to the graphql-ruby resolver
  gqls user schema.graphql -j         JSON output (-J for ndjson)

Semantic search (--semantic, rank by meaning) is not compiled into this build. Enable it:
  cargo install gqls-cli --features semantic
  brew install dpep/tools/gqls
";

#[derive(Parser)]
#[command(
    name = "gqls",
    version,
    about = "Search a GraphQL schema — fuzzy, semantic, or straight to the resolver.",
    long_about = "Find the types, fields, args, and directives in a GraphQL schema from the \
                  terminal. The source is an SDL file, a local introspection JSON dump, or a live \
                  http(s) endpoint; with none given, gqls discovers a schema in the current tree. \
                  Fuzzy and semantic results are ranked together by default (--semantic or \
                  --fuzzy forces one); --resolve jumps to the graphql-ruby resolver via rq. All modes \
                  support -j/--json and -J/--ndjson.",
    after_help = EXAMPLES
)]
struct Cli {
    /// Search query. Fuzzy by default; abbreviations like `usr` match `User`,
    /// and `Type.field` queries match against the qualified path.
    #[arg(required_unless_present_any = ["clear_cache", "completions", "warm"])]
    query: Option<String>,

    /// Schema source: a `.graphql`/`.graphqls` SDL file, a `.json` introspection
    /// dump, or an http(s) URL (introspected live). If omitted, gqls searches
    /// the current directory tree for a schema.
    source: Option<String>,

    /// Restrict to a kind (object, field, query, mutation, enum, scalar, ...).
    #[arg(short, long)]
    kind: Option<String>,

    /// Maximum number of results.
    #[arg(short, long, default_value_t = 20)]
    limit: usize,

    /// Pretty JSON array.
    #[arg(short, long, conflicts_with = "ndjson")]
    json: bool,

    /// Newline-delimited JSON (one record per line).
    #[arg(short = 'J', long)]
    ndjson: bool,

    /// Force semantic-only search. By default fuzzy and semantic results are
    /// combined once the schema's vectors are cached.
    #[arg(long, hide = HIDE_SEMANTIC)]
    semantic: bool,

    /// Force fuzzy-only search — skip the semantic combine.
    #[arg(long, conflicts_with = "semantic")]
    fuzzy: bool,

    /// Embedding model for --semantic: a local dir / `.onnx` path, or a
    /// HuggingFace `org/name` id. Defaults to all-MiniLM-L6-v2.
    #[arg(long, hide = HIDE_SEMANTIC)]
    model: Option<String>,

    /// Force a re-embed for --semantic, overwriting the cache. Schema edits
    /// already re-embed on their own; use this for changes the cache can't see
    /// (e.g. a new model).
    #[arg(long, hide = HIDE_SEMANTIC)]
    refresh: bool,

    /// Delete all cached embedding vector files, then exit.
    #[arg(long, hide = HIDE_SEMANTIC)]
    clear_cache: bool,

    /// Pre-embed the schema's vectors (warm the cache), then exit.
    #[arg(long, hide = HIDE_SEMANTIC)]
    warm: bool,

    /// Print a shell completion script (bash, zsh, fish, ...) to stdout, then exit.
    #[arg(long, value_name = "SHELL")]
    completions: Option<Shell>,

    /// Jump the top match to its graphql-ruby resolver/method in code, via
    /// `rq` (must be installed).
    #[arg(short = 'R', long)]
    resolve: bool,

    /// Directory of the server code for --resolve (defaults to rq's index).
    #[arg(long)]
    code: Option<String>,

    /// Header for URL introspection, `Name: Value` (repeatable) — e.g. an
    /// `Authorization` token for an auth-gated endpoint.
    #[arg(short = 'H', long = "header", value_name = "NAME: VALUE")]
    header: Vec<String>,

    /// Verbose stderr diagnostics: cache hits, rq candidates, and why the
    /// embedding model loaded or fell back.
    #[arg(short, long, conflicts_with = "quiet")]
    verbose: bool,

    /// Suppress status chatter on stderr (results and hard errors still print).
    #[arg(short, long)]
    quiet: bool,
}

/// The chosen output format — computed once, honored by every mode.
#[derive(Clone, Copy)]
enum Output {
    Text,
    Json,
    Ndjson,
}

/// A ranked result — from either the fuzzy scorer or the semantic ranker, so
/// both flow through one output path.
struct Match<'a> {
    record: &'a SchemaRecord,
    score: f64,
}

fn fuzzy_matches<'a>(
    query: &str,
    records: &'a [SchemaRecord],
    kind: Option<Kind>,
    limit: usize,
) -> Vec<Match<'a>> {
    search::search(query, records, kind, limit)
        .into_iter()
        .map(|h| Match {
            record: h.record,
            score: h.score as f64,
        })
        .collect()
}

#[cfg(feature = "_semantic")]
fn semantic_matches<'a>(
    query: &str,
    records: &'a [SchemaRecord],
    kind: Option<Kind>,
    cli: &Cli,
) -> Vec<Match<'a>> {
    crate::semantic::search(
        query,
        records,
        kind,
        cli.limit,
        cli.model.as_deref(),
        cli.refresh,
    )
    .into_iter()
    .map(|(score, record)| Match { record, score })
    .collect()
}

/// Merge the fuzzy and semantic rankings via Reciprocal Rank Fusion — precise
/// name matches and meaning matches both surface, and a record strong in both
/// rises to the top. Fuzzy is weighted a touch higher so an exact-name hit
/// keeps the lead; scale-free, so the two score systems needn't be normalized.
#[cfg(feature = "_semantic")]
fn combine<'a>(fuzzy: Vec<Match<'a>>, semantic: Vec<Match<'a>>, limit: usize) -> Vec<Match<'a>> {
    use std::collections::HashMap;
    const K: f64 = 60.0;
    // Key on the record's stable qualified path (unique per entity) rather than
    // pointer identity, so fusion stays correct even if a ranker ever returned
    // records not borrowed from the same slice.
    let mut scored: HashMap<&str, (f64, &SchemaRecord)> = HashMap::new();
    for (rank, m) in fuzzy.iter().enumerate() {
        scored
            .entry(m.record.path.as_str())
            .or_insert((0.0, m.record))
            .0 += 1.0 / (K + rank as f64 + 1.0);
    }
    for (rank, m) in semantic.iter().enumerate() {
        scored
            .entry(m.record.path.as_str())
            .or_insert((0.0, m.record))
            .0 += 0.7 / (K + rank as f64 + 1.0);
    }
    let mut merged: Vec<Match> = scored
        .into_values()
        .map(|(score, record)| Match { record, score })
        .collect();
    merged.sort_by(|a, b| b.score.total_cmp(&a.score));
    merged.truncate(limit);
    merged
}

/// Spawn a detached `gqls --warm <source>` so the schema's vectors embed in the
/// background — the next run gets combined fuzzy+semantic results with no wait.
/// Opt out with `GQLS_NO_AUTOWARM`. Best-effort; failures are ignored.
#[cfg(feature = "_semantic")]
fn spawn_background_warm(source: &str, headers: &[String]) {
    if std::env::var_os("GQLS_NO_AUTOWARM").is_some() {
        return;
    }
    // Single-flight: a detached warm for this source may already be running.
    // A short-lived lockfile keeps a burst of cold queries from spawning a herd
    // that all embed the same schema and race the cache.
    if !claim_warm_lock(source) {
        return;
    }
    if let Ok(exe) = std::env::current_exe() {
        let mut cmd = std::process::Command::new(exe);
        cmd.arg("--warm").arg(source);
        for h in headers {
            cmd.arg("--header").arg(h);
        }
        let _ = cmd
            .stdin(std::process::Stdio::null())
            .stdout(std::process::Stdio::null())
            .stderr(std::process::Stdio::null())
            .spawn();
    }
}

/// Best-effort single-flight guard for background warming: returns true (and
/// stakes a claim) when no recent warm for `source` is in flight, false when one
/// likely is. The lockfile self-expires by mtime, so a crashed warm can't wedge
/// warming forever, and a failed warm won't be retried in a tight loop.
#[cfg(feature = "_semantic")]
fn claim_warm_lock(source: &str) -> bool {
    use std::collections::hash_map::DefaultHasher;
    use std::hash::{Hash, Hasher};
    use std::time::Duration;

    const LOCK_TTL: Duration = Duration::from_secs(10 * 60);
    // Lockfiles live in the system temp dir so the OS auto-reaps them.
    let dir = crate::paths::temp_dir();
    let mut h = DefaultHasher::new();
    source.hash(&mut h);
    let lock = dir.join(format!("warming-{:016x}.lock", h.finish()));
    if let Ok(meta) = std::fs::metadata(&lock) {
        if let Ok(modified) = meta.modified() {
            if modified.elapsed().is_ok_and(|age| age < LOCK_TTL) {
                return false; // a recent warm is presumably still running
            }
        }
    }
    let _ = std::fs::create_dir_all(&dir);
    std::fs::write(&lock, []).is_ok()
}

/// Parse `-H "Name: Value"` strings into `(name, value)` pairs.
fn parse_headers(raw: &[String]) -> Result<Vec<(String, String)>> {
    raw.iter()
        .map(|h| {
            let (name, value) = h
                .split_once(':')
                .ok_or_else(|| anyhow::anyhow!("--header {h:?} must be `Name: Value`"))?;
            Ok((name.trim().to_string(), value.trim().to_string()))
        })
        .collect()
}

pub fn run() -> Result<()> {
    let cli = Cli::parse();
    crate::logging::init(cli.verbose, cli.quiet);

    if let Some(shell) = cli.completions {
        let mut cmd = Cli::command();
        let name = cmd.get_name().to_string();
        generate(shell, &mut cmd, name, &mut std::io::stdout());
        return Ok(());
    }

    if cli.clear_cache {
        let introspect = crate::load::introspect::clear_cache();
        #[cfg(feature = "_semantic")]
        let vectors = crate::semantic::clear_cache();
        #[cfg(not(feature = "_semantic"))]
        let vectors = 0;
        crate::status!("cleared {} cached file(s)", introspect + vectors);
        return Ok(());
    }

    let output = if cli.json {
        Output::Json
    } else if cli.ndjson {
        Output::Ndjson
    } else {
        Output::Text
    };

    let kind: Option<Kind> = match &cli.kind {
        Some(s) => Some(s.parse()?),
        None => None,
    };

    // The schema source. With `--warm` and no explicit source, the sole
    // positional is the schema (there's no query to warm), so `gqls --warm
    // schema.graphql` — and the background spawn — target the right file.
    let source = if let Some(s) = cli.source.clone() {
        s
    } else if cli.warm {
        match cli.query.clone() {
            Some(s) => s,
            None => load::discover()?,
        }
    } else {
        load::discover()?
    };
    let load_opts = load::LoadOptions {
        headers: parse_headers(&cli.header)?,
        refresh: cli.refresh,
    };
    let records = load::load(&source, &load_opts)?;

    // --warm: embed + cache the schema's vectors, then exit (no query needed).
    // Also the primitive the background auto-warm spawns.
    if cli.warm {
        #[cfg(feature = "_semantic")]
        {
            let n = crate::semantic::warm(&records, cli.model.as_deref(), cli.refresh);
            crate::status!("warmed {n} record vector(s) into the cache");
            return Ok(());
        }
        #[cfg(not(feature = "_semantic"))]
        {
            let _ = (&cli.model, cli.refresh);
            anyhow::bail!("--warm needs a semantic build");
        }
    }

    // clap guarantees a query unless --clear-cache/--completions/--warm.
    let query = cli
        .query
        .as_deref()
        .ok_or_else(|| anyhow::anyhow!("a QUERY is required (see --help)"))?;

    if cli.resolve {
        return run_resolve(
            query,
            &source,
            &records,
            kind,
            cli.code.as_deref(),
            cli.limit,
            output,
        );
    }

    let matches: Vec<Match> = if cli.fuzzy {
        fuzzy_matches(query, &records, kind, cli.limit)
    } else if cli.semantic {
        #[cfg(feature = "_semantic")]
        {
            semantic_matches(query, &records, kind, &cli)
        }
        #[cfg(not(feature = "_semantic"))]
        {
            let _ = (&cli.model, cli.refresh);
            anyhow::bail!(
                "this build has no semantic search — install it with \
                 `cargo install gqls-cli --features semantic` or `brew install dpep/tools/gqls`"
            );
        }
    } else {
        // Default: combine fuzzy + semantic when the cache is warm; when cold,
        // return fuzzy now and warm the vectors in the background for next time.
        let fuzzy = fuzzy_matches(query, &records, kind, cli.limit);
        #[cfg(feature = "_semantic")]
        {
            if crate::semantic::is_cached(&records, cli.model.as_deref()) {
                let semantic = semantic_matches(query, &records, kind, &cli);
                combine(fuzzy, semantic, cli.limit)
            } else {
                spawn_background_warm(&source, &cli.header);
                crate::status!(
                    "warming the semantic index in the background — the next run also \
                     ranks by meaning (--semantic to embed now, --fuzzy to skip)"
                );
                fuzzy
            }
        }
        #[cfg(not(feature = "_semantic"))]
        {
            let _ = (&cli.model, cli.refresh);
            fuzzy
        }
    };

    if matches.is_empty() {
        crate::status!("no matches for {query:?}");
    }
    output.write_matches(&matches)
}

impl Output {
    fn write_matches(self, matches: &[Match]) -> Result<()> {
        #[derive(Serialize)]
        struct Row<'a> {
            #[serde(flatten)]
            record: &'a SchemaRecord,
            score: f64,
        }
        let rows = || {
            matches.iter().map(|m| Row {
                record: m.record,
                score: m.score,
            })
        };
        match self {
            Output::Json => println!(
                "{}",
                serde_json::to_string_pretty(&rows().collect::<Vec<_>>())?
            ),
            Output::Ndjson => {
                for row in rows() {
                    println!("{}", serde_json::to_string(&row)?);
                }
            }
            Output::Text => print_text(matches),
        }
        Ok(())
    }
}

fn print_text(matches: &[Match]) {
    let width = matches
        .iter()
        .map(|m| display_path(m.record).len())
        .max()
        .unwrap_or(0)
        .min(48);

    for m in matches {
        let r = m.record;
        let path = display_path(r);
        let ret = r
            .type_ref
            .as_deref()
            .map(|t| format!(" -> {t}"))
            .unwrap_or_default();
        let dep = if r.deprecated.is_some() {
            " (deprecated)"
        } else {
            ""
        };
        println!("{path:<width$}{ret}  [{kind}]{dep}", kind = r.kind.as_str());
    }
}

/// Fuzzy-find the field, then hand it to rq to locate its resolver in code.
fn run_resolve(
    query: &str,
    source: &str,
    records: &[SchemaRecord],
    kind: Option<Kind>,
    code: Option<&str>,
    limit: usize,
    output: Output,
) -> Result<()> {
    if code.is_none() {
        crate::status!("no --code given; resolving against rq's index for the current directory");
    }
    let Some(top) = search::search(query, records, kind, 1).into_iter().next() else {
        anyhow::bail!("no schema entity matches {query:?} to resolve");
    };
    crate::status!("resolving {} …", top.record.path);
    // a local file schema (not a URL) enables package-proximity ranking
    let schema_path = (!source.starts_with("http://") && !source.starts_with("https://"))
        .then(|| std::path::Path::new(source))
        .filter(|p| p.exists());
    let hits = crate::resolve::resolve(top.record, code, schema_path, limit.min(10))?;

    match output {
        Output::Json => println!("{}", serde_json::to_string_pretty(&hits)?),
        Output::Ndjson => {
            for h in &hits {
                println!("{}", serde_json::to_string(h)?);
            }
        }
        Output::Text => {
            if hits.is_empty() {
                crate::status!(
                    "no code definition found for {} (tried graphql-ruby rq candidates)",
                    top.record.path
                );
            }
            for h in &hits {
                println!("{}:{}  {}  (via {})", h.file, h.line, h.name, h.via);
            }
        }
    }
    Ok(())
}

/// `Query.user(id: ID!, first: Int)` — path plus a compact arg signature.
fn display_path(r: &SchemaRecord) -> String {
    if r.args.is_empty() {
        r.path.clone()
    } else {
        format!("{}({})", r.path, r.args.join(", "))
    }
}