leankg 0.19.1

//! Embedding build orchestration: incremental vs full rebuild, plus orphan
//! reaping. Implements `cargo run --release -- embed [--full]`.
//!
//! Vectors live in the CozoDB `embedding_vectors` relation (keyed by
//! qualified_name, HNSW index via `::hnsw create embedding_vectors:vec_idx`).
//! The `embedding_state` relation tracks freshness for incremental builds.
//!
//! Incremental flow (default):
//! 1. Walk all `code_elements` and compute the current text blob + hash for
//!    each embeddable node.
//! 2. Diff against `embedding_state`: embed any qualified_name where
//!    (a) no state row exists, OR
//!    (b) `state != "fresh"`, OR
//!    (c) stored `content_hash` differs from the current blob hash.
//! 3. For each batch: run fastembed inference, then `:put embedding_vectors`
//!    in chunks of `UPSERT_CHUNK` (CozoDB pest parser limits).
//! 4. Mark embedded rows fresh in `embedding_state`.
//! 5. Reap orphans: state rows whose qualified_name is no longer in the work
//!    list get their vector removed (`:rm embedding_vectors`) and their state
//!    row deleted.
//!
//! Full rebuild (`--full`): step 2 becomes "embed every embeddable node".

use crate::db::schema::{run_script, CozoDb};
use crate::embeddings::{
    models::{DirectEmbedder, Embedder, EMBEDDING_DIM},
    state::{self, EmbeddingStateRow, FreshRow},
    text_blob,
};
use crate::graph::query::GraphEngine;
use std::io::Write;
use std::path::PathBuf;

#[cfg(feature = "embeddings")]
use crate::embeddings::build_index;

// FR-EMBED-FAST: per-worker enum wrapping either the fastembed Embedder
// (legacy path, hardcoded intra_threads = available_parallelism()) or
// the DirectEmbedder (ort + tokenizers with controlled intra_threads).
// The pipeline calls `.embed(&texts)` uniformly through the enum.
enum EmbedderBackend {
    Direct(DirectEmbedder),
    Fast(Embedder),
}

/// CozoDB pest parser has stack-depth limits on inline `<~ [...]` literals
/// (limit ≈ 500 rows). We use *parameterized* queries
/// (`?[col] <- $rows :put ...`) so the limit does NOT apply here. The
/// practical bottleneck is the per-:put CozoDB transaction commit
/// (~10s regardless of batch size), so larger UPSERT_CHUNK amortizes
/// that fixed cost across more rows. 5000 was the empirical sweet spot
/// on a 400k-row workspace: ~6 min total vs ~120 min at UPSERT_CHUNK=500.
///
/// Runtime override via `LEANKG_EMBED_UPSERT_CHUNK` env var (read by
/// `effective_upsert_chunk`). Smaller chunks (500-1000) lower peak
/// memory per flush but commit more often; larger chunks (10000+)
/// reduce commit overhead at the cost of a higher per-flush RSS spike
/// and longer tail latency if the run crashes mid-flush.
const DEFAULT_UPSERT_CHUNK: usize = 5000;

fn effective_upsert_chunk() -> usize {
    std::env::var("LEANKG_EMBED_UPSERT_CHUNK")
        .ok()
        .and_then(|v| v.parse::<usize>().ok())
        .filter(|n| (100..=50_000).contains(n))
        .unwrap_or(DEFAULT_UPSERT_CHUNK)
}

/// Soft RSS budget for the embed process (MB).
///
/// Default is intentionally conservative on macOS so a cold embed cannot
/// balloon into swap and freeze the host. Override with `LEANKG_EMBED_MAX_MB`.
/// Set to `0` to disable auto-caps / backpressure (not recommended).
pub fn embed_max_rss_mb() -> u64 {
    if let Ok(v) = std::env::var("LEANKG_EMBED_MAX_MB") {
        if let Ok(n) = v.parse::<u64>() {
            return n;
        }
    }
    // Fast path needs headroom for one fat INT8 session + large batches.
    let fast = crate::embeddings::runtime::embed_fast_enabled();
    #[cfg(target_os = "macos")]
    {
        if fast {
            4_096
        } else {
            2_048
        }
    }
    #[cfg(not(target_os = "macos"))]
    {
        if fast {
            4_096
        } else {
            3_072
        }
    }
}

/// Resolved worker/batch/channel caps for one embed run.
#[derive(Debug, Clone, Copy)]
pub struct EmbedMemoryPlan {
    pub workers: usize,
    pub batch_size: usize,
    pub upsert_chunk: usize,
    pub channel_capacity: usize,
    pub max_rss_mb: u64,
}

/// Cap workers / batch / writer queue so peak RSS stays near `LEANKG_EMBED_MAX_MB`.
///
/// Rough model (BGE-small DirectEmbedder):
/// - base process + Cozo ≈ 700–900 MB
/// - each ONNX worker session ≈ 300–400 MB (weights + arenas)
/// - in-flight channel vectors ≈ 2 KB each
pub fn plan_embed_memory(requested_workers: usize, requested_batch: usize) -> EmbedMemoryPlan {
    plan_embed_memory_with_budget(requested_workers, requested_batch, embed_max_rss_mb())
}

/// Same as [`plan_embed_memory`] but with an explicit budget (for tests / callers).
pub fn plan_embed_memory_with_budget(
    requested_workers: usize,
    requested_batch: usize,
    max_rss_mb: u64,
) -> EmbedMemoryPlan {
    if max_rss_mb == 0 {
        let upsert = effective_upsert_chunk();
        let workers = requested_workers.max(1);
        let batch_size = requested_batch.max(1);
        return EmbedMemoryPlan {
            workers,
            batch_size,
            upsert_chunk: upsert,
            // Still bound the queue — unbounded grow was a major OOM lever.
            channel_capacity: (workers * batch_size * 2).clamp(64, upsert),
            max_rss_mb: 0,
        };
    }

    const BASE_MB: u64 = 900;
    const PER_WORKER_MB: u64 = 350;
    let budget_for_workers = max_rss_mb.saturating_sub(BASE_MB);
    let max_workers = ((budget_for_workers / PER_WORKER_MB).max(1) as usize).min(8);
    let workers = requested_workers.max(1).min(max_workers);

    let max_batch = if workers <= 1 {
        // Single high-intra session: fat batches are the throughput lever.
        if max_rss_mb <= 2_048 {
            64
        } else {
            256
        }
    } else if max_rss_mb <= 1_536 {
        8
    } else if max_rss_mb <= 2_048 {
        16
    } else if max_rss_mb <= 3_072 {
        32
    } else if max_rss_mb <= 4_096 {
        128
    } else {
        256
    };
    let batch_size = requested_batch.max(1).min(max_batch);

    let upsert_cap = if max_rss_mb <= 2_048 {
        1_000
    } else if max_rss_mb <= 3_072 {
        2_500
    } else {
        DEFAULT_UPSERT_CHUNK
    };
    let upsert_chunk = effective_upsert_chunk().min(upsert_cap).max(100);

    // Old default (4 × UPSERT_CHUNK ≈ 20k vectors) held a multi-GB buffer of
    // pending embeddings. Cap to a couple of worker batches so the writer
    // provides natural backpressure.
    let channel_capacity = (workers * batch_size * 2).clamp(64, upsert_chunk);

    EmbedMemoryPlan {
        workers,
        batch_size,
        upsert_chunk,
        channel_capacity,
        max_rss_mb,
    }
}

/// Sleep while RSS is above the soft embed budget so macOS does not thrash.
fn wait_for_embed_rss_headroom(max_rss_mb: u64) {
    if max_rss_mb == 0 {
        return;
    }
    // Start backing off at 90% of the soft cap.
    let soft = (max_rss_mb * 90) / 100;
    for attempt in 0..50 {
        let Ok(rss) = crate::budget::current_rss_mb() else {
            return;
        };
        if rss < soft {
            return;
        }
        if attempt == 0 || attempt % 10 == 0 {
            tracing::warn!(
                "embed RSS {} MB >= soft cap {} MB (LEANKG_EMBED_MAX_MB={}); pausing inference",
                rss,
                soft,
                max_rss_mb
            );
        }
        std::thread::sleep(std::time::Duration::from_millis(200 + attempt * 40));
    }
}

/// Opt-in hint for a locally patched Cozo (`vendor/cozo`) that honors
/// `LEANKG_COZO_ROCKS_BULK=1` (`disable_wal` + `sync(false)`). Stock crates.io
/// Cozo ignores the env; measured e2e gain was ≤1.15× so it is not required.
fn enable_rocks_bulk_writes() {
    let on = std::env::var("LEANKG_COZO_ROCKS_BULK")
        .map(|v| {
            let t = v.trim();
            t == "1" || t.eq_ignore_ascii_case("true") || t.eq_ignore_ascii_case("yes")
        })
        .unwrap_or(false);
    if on {
        tracing::info!(
            "LEANKG_COZO_ROCKS_BULK=1 set (no-op unless using a Cozo build that honors it)"
        );
    }
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum BuildMode {
    /// Skip up-to-date rows; embed only stale/missing/changed.
    Incremental,
    /// Re-embed every embeddable CodeElement, regardless of state.
    Full,
}

#[derive(Debug, Clone)]
pub struct BuildOptions {
    pub mode: BuildMode,
    /// Vectors per fastembed call. ONNX Runtime pre-allocates per-thread
    /// memory arenas, so peak RSS scales with batch size.
    pub batch_size: usize,
    /// Accepted for backward-compat with CLI flag; ignored (CozoDB HNSW
    /// manages its own capacity).
    pub reserve_capacity: Option<usize>,
    /// When set, only embed `CodeElement`s whose `element_type` is in this
    /// set (case-insensitive). Default (`None`) embeds every type. The CLI
    /// defaults to `function,method` on mega-graphs to keep cold embed
    /// under 5 min; pass `all` (empty string from CLI) to disable.
    pub type_filter: Option<std::collections::HashSet<String>>,
}

impl Default for BuildOptions {
    fn default() -> Self {
        Self {
            mode: BuildMode::Incremental,
            // 32 = safer default under LEANKG_EMBED_MAX_MB. Raise via
            // `--batch-size` when you have headroom; 64+ grows ORT arenas.
            batch_size: 32,
            reserve_capacity: None,
            type_filter: None,
        }
    }
}

/// Parse a `--types` flag value into a `BuildOptions::type_filter`. Empty
/// string or `all` => embed every type. Comma-separated list => embed only
/// those types. Match is case-insensitive.
pub fn parse_type_filter(raw: &str) -> Option<std::collections::HashSet<String>> {
    let trimmed = raw.trim();
    if trimmed.is_empty() || trimmed.eq_ignore_ascii_case("all") {
        return None;
    }
    Some(
        trimmed
            .split(',')
            .map(|s| s.trim().to_ascii_lowercase())
            .filter(|s| !s.is_empty())
            .collect(),
    )
}

#[derive(Debug, Clone, Default)]
pub struct BuildReport {
    pub considered_count: usize,
    pub embedded_count: usize,
    pub skipped_fresh_count: usize,
    pub orphaned_count: usize,
    pub index_size: usize,
    pub index_path: PathBuf,
}

pub fn run(
    graph: &GraphEngine,
    _index_path: &std::path::Path,
    opts: &BuildOptions,
) -> Result<BuildReport, Box<dyn std::error::Error>> {
    let mem = plan_embed_memory(1, opts.batch_size);
    let mut opts = opts.clone();
    opts.batch_size = mem.batch_size;
    if mem.max_rss_mb > 0 {
        tracing::info!(
            "embed (serial) memory plan: batch={} max_rss_mb={}",
            opts.batch_size,
            mem.max_rss_mb
        );
    }
    let embedder = Embedder::new()?;
    let db = graph.db();

    // 1. Walk code_elements and build the work list.
    let elements = graph.all_elements()?;
    let work: Vec<WorkItem> = elements
        .iter()
        .filter(|el| {
            // Apply the optional element-type filter. On mega-graphs the
            // CLI defaults to `function,method` to keep cold embed under
            // 5 min; pass `--types all` to embed every type.
            if let Some(filter) = &opts.type_filter {
                filter.contains(&el.element_type.to_ascii_lowercase())
            } else {
                true
            }
        })
        .filter_map(|el| {
            let blob = text_blob::build_blob(el)?;
            let hash = text_blob::content_hash_for(&blob);
            Some(WorkItem {
                qualified_name: el.qualified_name.clone(),
                blob,
                current_hash: hash,
            })
        })
        .collect();

    // 2. Build the "needs embed" set.
    let existing_state: std::collections::HashMap<String, EmbeddingStateRow> = state::list_all(db)?
        .into_iter()
        .map(|r| (r.qualified_name.clone(), r))
        .collect();

    let to_embed: Vec<&WorkItem> = work
        .iter()
        .filter(|w| match opts.mode {
            BuildMode::Full => true,
            BuildMode::Incremental => match existing_state.get(&w.qualified_name) {
                None => true,
                Some(row) => {
                    row.state != "fresh"
                        || row.content_hash.is_empty()
                        || row.content_hash != w.current_hash
                }
            },
        })
        .collect();

    let considered = work.len();
    let skipped_fresh = considered - to_embed.len();

    // FR-HNSW perf fix: drop the HNSW index before the bulk insert so
    // each :put doesn't pay the O(log N) HNSW update cost. The index is
    // recreated at the end of the function.
    enable_rocks_bulk_writes();
    if state::drop_hnsw_index(db).is_err() {
        tracing::warn!("could not drop HNSW index before bulk insert (continuing)");
    }
    tracing::info!("HNSW dropped; running sequential bulk insert");

    // 3. Batch embed and :put into embedding_vectors.
    let mut embedded = 0usize;
    let mut fresh_rows: Vec<FreshRow> = Vec::with_capacity(to_embed.len());
    for chunk in to_embed.chunks(opts.batch_size) {
        wait_for_embed_rss_headroom(mem.max_rss_mb);
        let texts: Vec<String> = chunk.iter().map(|w| w.blob.clone()).collect();
        let vectors = embedder.embed(&texts)?;
        let pairs: Vec<(&WorkItem, &Vec<f32>)> =
            chunk.iter().copied().zip(vectors.iter()).collect();
        upsert_vectors(db, pairs.iter().copied())?;
        for (item, _vector) in pairs {
            fresh_rows.push(FreshRow {
                qualified_name: item.qualified_name.clone(),
                usearch_key: 0,
                content_hash: item.current_hash.clone(),
            });
            embedded += 1;
        }
        tracing::info!(
            "embed batch done: running total {}/{} (chunk_size={})",
            embedded,
            to_embed.len(),
            chunk.len()
        );
    }

    tracing::info!(
        "embed loop complete, calling upsert_fresh for {} rows",
        fresh_rows.len()
    );
    state::upsert_fresh(db, &fresh_rows)?;
    tracing::info!("upsert_fresh complete");

    // Recreate the HNSW index now that the bulk insert is done. A single
    // O(N log N) build beats N incremental updates by 5-10x.
    tracing::info!("rebuilding HNSW index on embedding_vectors:vec_idx");
    let hnsw_started = std::time::Instant::now();
    state::create_hnsw_index(db)?;
    tracing::info!(
        "HNSW rebuild complete in {:.2}s",
        hnsw_started.elapsed().as_secs_f64()
    );

    // 4. Reap orphans: state rows whose qualified_name is no longer present
    // in the work list (either removed from code_elements, or SKIP-classified
    // element types like clusters/processes that don't get embedded).
    let work_qns: std::collections::HashSet<&str> =
        work.iter().map(|w| w.qualified_name.as_str()).collect();
    let orphan_rows: Vec<EmbeddingStateRow> = existing_state
        .iter()
        .filter(|(qn, _)| !work_qns.contains(qn.as_str()))
        .map(|(_, row)| row.clone())
        .collect();
    tracing::info!("orphan reap: {} orphans", orphan_rows.len());
    if !orphan_rows.is_empty() {
        // Remove vectors from HNSW index first, then state rows.
        let orphan_qns: Vec<String> = orphan_rows
            .iter()
            .map(|r| r.qualified_name.clone())
            .collect();
        remove_vectors(db, &orphan_qns)?;
        tracing::info!(
            "calling delete_state_rows for {} orphans",
            orphan_rows.len()
        );
        state::delete_state_rows(db, &orphan_rows)?;
        tracing::info!("delete_state_rows complete");
    }

    let index_size = count_vectors(db)?;

    Ok(BuildReport {
        considered_count: considered,
        embedded_count: embedded,
        skipped_fresh_count: skipped_fresh,
        orphaned_count: orphan_rows.len(),
        index_size,
        index_path: PathBuf::from(".leankg/embedding_vectors (CozoDB HNSW)"),
    })
}

/// Parallel-inference + single-writer pipeline. `N` rayon worker threads
/// each own a fastembed session and run inference on disjoint work
/// shards. Completed `(qualified_name, vector)` pairs are pushed onto a
/// bounded crossbeam channel; a single writer thread consumes the
/// channel, accumulating up to `UPSERT_CHUNK` rows per `:put` so the
/// CozoDB parser overhead is amortized over 500-row transactions.
///
/// Why this is faster than the previous Mutex-on-write approach:
///   * Inference runs in parallel (N× BGE-small throughput)
///   * Datalog writes are not serialized by a Mutex — one writer drains
///     the channel and ships large batches
///   * The 500-row `:put` keeps per-row parser overhead constant
///
/// On a 10-core host with `workers=4` and `batch_size=64` this routinely
/// hits 800–1500 vectors/sec on a 400k-row index, vs 70–100 for the
/// single-threaded `run`.
pub fn build_index_parallel(
    graph: &GraphEngine,
    _index_path: &std::path::Path,
    opts: &BuildOptions,
    workers: usize,
) -> Result<BuildReport, String> {
    use crossbeam_channel::bounded;
    use std::sync::atomic::{AtomicUsize, Ordering};
    use std::sync::Arc;

    let db = graph.db();

    // 1. Walk code_elements and build the work list (sequential, can't be
    // sped up — this is a single CozoDB scan).
    let elements = graph.all_elements().map_err(|e| e.to_string())?;
    let work: Vec<WorkItem> = elements
        .iter()
        .filter(|el| {
            if let Some(filter) = &opts.type_filter {
                filter.contains(&el.element_type.to_ascii_lowercase())
            } else {
                true
            }
        })
        .filter_map(|el| {
            let blob = text_blob::build_blob(el)?;
            let hash = text_blob::content_hash_for(&blob);
            Some(WorkItem {
                qualified_name: el.qualified_name.clone(),
                blob,
                current_hash: hash,
            })
        })
        .collect();

    // 2. Build the "needs embed" set.
    let existing_state: std::collections::HashMap<String, EmbeddingStateRow> = state::list_all(db)
        .map_err(|e| e.to_string())?
        .into_iter()
        .map(|r| (r.qualified_name.clone(), r))
        .collect();
    let to_embed: Vec<WorkItem> = work
        .iter()
        .filter(|w| match opts.mode {
            BuildMode::Full => true,
            BuildMode::Incremental => match existing_state.get(&w.qualified_name) {
                None => true,
                Some(row) => {
                    row.state != "fresh"
                        || row.content_hash.is_empty()
                        || row.content_hash != w.current_hash
                }
            },
        })
        .map(|w| w.clone())
        .collect();

    let considered = work.len();
    let skipped_fresh = considered - to_embed.len();

    // FR-EMBED-R4: length-aware batching — sort by blob char length so each
    // ONNX batch pads to a similar seq_len (less wasted compute on short
    // texts sitting next to long ones). Char length is a cheap token proxy.
    let mut to_embed = to_embed;
    to_embed.sort_by_key(|w| w.blob.len());
    tracing::info!(
        "length-sorted {} embed items (min_chars={} max_chars={})",
        to_embed.len(),
        to_embed.first().map(|w| w.blob.len()).unwrap_or(0),
        to_embed.last().map(|w| w.blob.len()).unwrap_or(0)
    );

    // FR-HNSW perf fix: drop the HNSW index before the bulk insert so
    // each :put doesn't pay the O(log N) HNSW update cost. Recreate the
    // index after the loop completes — CozoDB's HNSW build is O(N log N)
    // and runs ~5-10x faster than N incremental updates on a 100k+ index.
    enable_rocks_bulk_writes();
    if state::drop_hnsw_index(db).is_err() {
        tracing::warn!("could not drop HNSW index before bulk insert (continuing)");
    }
    tracing::info!("HNSW dropped; running parallel bulk insert");

    // Warm the fastembed / Xenova snapshot BEFORE INT8 ensure. Previously
    // `ensure_quantized_onnx` ran first, failed with "cache missing", fell
    // back to FP32, then warm created the Xenova tree too late — Docker
    // background embed permanently stayed on heavy FP32 + fat batches.
    {
        let _warmer = Embedder::new().map_err(|e| e.to_string())?;
        tracing::info!("fastembed model cache warmed for parallel workers");
    }

    // Fast path: INT8 + data-parallel workers + fat batch + seq cap.
    let mut runtime = crate::embeddings::runtime::resolve_embed_runtime(workers, opts.batch_size);
    if runtime.kind == crate::embeddings::models::EmbedModelKind::BgeInt8 {
        if let Err(e) = crate::embeddings::runtime::ensure_quantized_onnx() {
            tracing::warn!(
                "INT8 ONNX unavailable ({e}); falling back to FP32 — set LEANKG_EMBED_FAST=0 to silence"
            );
            std::env::set_var("LEANKG_EMBED_MODEL", "bge");
            // Re-resolve so workers/batch match FP32 (no silent Int8 label).
            runtime = crate::embeddings::runtime::resolve_embed_runtime(workers, opts.batch_size);
        }
    }
    runtime.apply_env();
    tracing::info!(
        "embed runtime: fast={} kind={:?} max_seq={} workers={}→{} batch={}→{} intra={} omp={}",
        crate::embeddings::runtime::embed_fast_enabled(),
        runtime.kind,
        runtime.max_seq,
        workers,
        runtime.workers,
        opts.batch_size,
        runtime.batch_size,
        runtime.intra_threads,
        runtime.omp_threads
    );
    let workers = runtime.workers;
    let opts_batch = runtime.batch_size;
    // OMP_NUM_THREADS already set by runtime.apply_env() to match the plan
    // (intra on single-session fast path; 1 when multi-worker).

    // 3. Shard the work, run inference in N worker threads, push results
    // onto a bounded crossbeam channel. A single writer thread consumes
    // the channel and ships :put embedding_vectors in UPSERT_CHUNK batches.
    // Cap workers/batch/channel against LEANKG_EMBED_MAX_MB so macOS does
    // not OOM (each DirectEmbedder session ≈ 300–400 MB).
    let mem = plan_embed_memory(workers, opts_batch);
    if mem.workers != workers.max(1) || mem.batch_size != opts.batch_size.max(1) {
        tracing::warn!(
            "embed memory plan capped workers {}→{} batch {}→{} (LEANKG_EMBED_MAX_MB={})",
            workers.max(1),
            mem.workers,
            opts.batch_size.max(1),
            mem.batch_size,
            mem.max_rss_mb
        );
    }
    tracing::info!(
        "embed memory plan: workers={} batch={} upsert_chunk={} channel={} max_rss_mb={}",
        mem.workers,
        mem.batch_size,
        mem.upsert_chunk,
        mem.channel_capacity,
        mem.max_rss_mb
    );
    let batch_size = mem.batch_size;
    let n_workers = mem.workers;
    let total = to_embed.len();
    let upsert_chunk = mem.upsert_chunk;
    let (tx, rx) = bounded::<(String, Vec<f32>, String)>(mem.channel_capacity);
    let embedded_count = Arc::new(AtomicUsize::new(0));
    let max_rss_mb = mem.max_rss_mb;

    // --- Writer thread: single CozoDB writer that drains the channel and
    // emits :put embedding_vectors in UPSERT_CHUNK batches.
    let writer = {
        // SAFETY: `cozo::DbInstance` is internally `Send + !Sync`. We
        // move a clone into the writer thread so it owns the only
        // reference; the outer `db` (used later for state/orphan ops)
        // is not touched by the writer.
        let db_for_writer = db.clone();
        std::thread::spawn(move || -> Result<(Vec<FreshRow>, usize), String> {
            let mut fresh_rows: Vec<FreshRow> = Vec::with_capacity(total);
            let mut pending: Vec<(String, Vec<f32>, String)> = Vec::new();
            let mut done = 0usize;
            loop {
                match rx.recv() {
                    Ok(item) => {
                        pending.push(item);
                        if pending.len() >= upsert_chunk {
                            // Drain any stragglers non-blockingly.
                            while let Ok(more) = rx.try_recv() {
                                pending.push(more);
                                if pending.len() >= upsert_chunk * 2 {
                                    break;
                                }
                            }
                            let (rows, fresh): (Vec<(String, Vec<f32>)>, Vec<FreshRow>) =
                                pending.drain(..).fold(
                                    (Vec::new(), Vec::new()),
                                    |(mut rows, mut fresh), (qn, vec, hash)| {
                                        rows.push((qn.clone(), vec));
                                        fresh.push(FreshRow {
                                            qualified_name: qn,
                                            usearch_key: 0,
                                            content_hash: hash,
                                        });
                                        (rows, fresh)
                                    },
                                );
                            upsert_pairs_to_db(&db_for_writer, &rows).map_err(|e| e.to_string())?;
                            done += rows.len();
                            tracing::info!("writer: flushed {} rows, total {}", rows.len(), done);
                            fresh_rows.extend(fresh);
                        }
                    }
                    Err(_) => break,
                }
            }
            // Final flush.
            if !pending.is_empty() {
                let (rows, fresh): (Vec<(String, Vec<f32>)>, Vec<FreshRow>) =
                    pending.into_iter().fold(
                        (Vec::new(), Vec::new()),
                        |(mut rows, mut fresh), (qn, vec, hash)| {
                            rows.push((qn.clone(), vec));
                            fresh.push(FreshRow {
                                qualified_name: qn,
                                usearch_key: 0,
                                content_hash: hash,
                            });
                            (rows, fresh)
                        },
                    );
                if !rows.is_empty() {
                    upsert_pairs_to_db(&db_for_writer, &rows).map_err(|e| e.to_string())?;
                    done += rows.len();
                    tracing::info!("writer: final flush {} rows, total {}", rows.len(), done);
                }
                fresh_rows.extend(fresh);
            }
            Ok((fresh_rows, done))
        })
    };

    // --- Inference workers: N threads, each owns its Embedder. The
    // `work_items` arc is shared read-only.
    //
    // FR-EMBED-FAST: each worker constructs a `DirectEmbedder` instead
    // of the fastembed-backed `Embedder`. This bypasses fastembed 4.9.1's
    // hardcoded `intra_threads = available_parallelism()` (10 on a 10-core
    // host), which previously made N worker sessions oversubscribe the
    // CPU. With `intra_threads=1` per worker, N workers give us N CPU
    // threads with no contention. If the fastembed cache is missing (first
    // run before `embed --init`), we fall back to the fastembed Embedder
    // so the pipeline still works.
    let work_items = std::sync::Arc::new(to_embed);
    let use_direct_embedder = std::env::var("LEANKG_EMBED_DIRECT")
        .map(|v| v == "1" || v.eq_ignore_ascii_case("true"))
        .unwrap_or(true);
    let mut worker_handles = Vec::with_capacity(n_workers);
    for w_id in 0..n_workers {
        let tx = tx.clone();
        let work_items = work_items.clone();
        let embedded_count = embedded_count.clone();
        let handle = std::thread::spawn(move || -> Result<(), String> {
            // Try DirectEmbedder first (no fastembed intra_threads overhead).
            // Fall back to Embedder if the model cache is missing.
            // `LEANKG_EMBED_DIRECT_INTRA` overrides the per-session thread
            // count (default 1 = max throughput on 10c host since fastembed's
            // 10-thread sessions oversubscribed). Set higher on hosts with
            // many cores per session.
            let direct_intra = std::env::var("LEANKG_EMBED_DIRECT_INTRA")
                .ok()
                .and_then(|v| v.parse::<usize>().ok())
                .filter(|n| (1..=128).contains(n))
                .unwrap_or(1);
            let backend = if use_direct_embedder {
                match DirectEmbedder::with_intra_threads(direct_intra) {
                    Ok(e) => {
                        tracing::info!(
                            "worker {}: using DirectEmbedder (intra_threads={})",
                            w_id,
                            e.intra_threads()
                        );
                        EmbedderBackend::Direct(e)
                    }
                    Err(e) => {
                        // Do not silently fall back — FastEmbedder has historically
                        // hit ORT "512 by 800" on long code blobs. Surface the
                        // DirectEmbedder error so ops can `embed --init` / fix cache.
                        return Err(format!(
                            "worker {w_id}: DirectEmbedder init failed ({e}); \
                             refusing FastEmbedder fallback (ORT seq_len risk). \
                             Run `leankg embed --init` or set LEANKG_EMBED_DIRECT=0 \
                             only if you accept that risk."
                        ));
                    }
                }
            } else {
                tracing::warn!(
                    "worker {}: LEANKG_EMBED_DIRECT=0 — using FastEmbedder",
                    w_id
                );
                EmbedderBackend::Fast(Embedder::new().map_err(|e| e.to_string())?)
            };
            // Round-robin shards: this worker takes every Nth shard.
            let shards: Vec<&[WorkItem]> = work_items.chunks(batch_size * n_workers).collect();
            for shard in shards.iter().skip(w_id).step_by(n_workers) {
                for chunk in shard.chunks(batch_size) {
                    wait_for_embed_rss_headroom(max_rss_mb);
                    let texts: Vec<String> = chunk.iter().map(|w| w.blob.clone()).collect();
                    let vectors = match &backend {
                        EmbedderBackend::Direct(e) => e.embed(&texts).map_err(|e| e.to_string())?,
                        EmbedderBackend::Fast(e) => e.embed(&texts).map_err(|e| e.to_string())?,
                    };
                    for (item, vec) in chunk.iter().zip(vectors.iter()) {
                        let qn = item.qualified_name.clone();
                        let hash = item.current_hash.clone();
                        let v = vec.clone();
                        if tx.send((qn, v, hash)).is_err() {
                            return Err("writer disconnected".to_string());
                        }
                    }
                    let total_now =
                        embedded_count.fetch_add(chunk.len(), Ordering::Relaxed) + chunk.len();
                    if total_now % 2048 < chunk.len() || total_now == work_items.len() {
                        tracing::info!(
                            "worker {}: embedded {}/{} (this chunk {})",
                            w_id,
                            total_now,
                            work_items.len(),
                            chunk.len()
                        );
                    }
                }
            }
            Ok(())
        });
        worker_handles.push(handle);
    }
    drop(tx); // writer sees disconnect when last worker drops its tx

    // Wait for inference workers.
    let mut worker_err: Option<String> = None;
    for h in worker_handles {
        match h.join() {
            Ok(Ok(())) => {}
            Ok(Err(e)) => {
                if worker_err.is_none() {
                    worker_err = Some(e);
                }
            }
            Err(_) => {
                if worker_err.is_none() {
                    worker_err = Some("worker thread panicked".to_string());
                }
            }
        }
    }

    // Wait for the writer to drain the channel.
    let (fresh_rows, _writer_done) = writer
        .join()
        .map_err(|_| "writer thread panicked".to_string())??;
    if let Some(e) = worker_err {
        return Err(e);
    }

    let embedded = embedded_count.load(Ordering::Relaxed);

    tracing::info!(
        "pipeline embed complete, calling upsert_fresh for {} rows",
        fresh_rows.len()
    );
    state::upsert_fresh(db, &fresh_rows).map_err(|e| e.to_string())?;
    tracing::info!("upsert_fresh complete");

    // Recreate the HNSW index now that the bulk insert is done. This is
    // a single O(N log N) operation and is much faster than letting every
    // :put pay the incremental update cost.
    tracing::info!("rebuilding HNSW index on embedding_vectors:vec_idx");
    let hnsw_started = std::time::Instant::now();
    state::create_hnsw_index(db).map_err(|e| e.to_string())?;
    tracing::info!(
        "HNSW rebuild complete in {:.2}s",
        hnsw_started.elapsed().as_secs_f64()
    );

    // Reap orphans.
    let work_qns: std::collections::HashSet<&str> =
        work.iter().map(|w| w.qualified_name.as_str()).collect();
    let orphan_rows: Vec<EmbeddingStateRow> = existing_state
        .iter()
        .filter(|(qn, _)| !work_qns.contains(qn.as_str()))
        .map(|(_, row)| row.clone())
        .collect();
    tracing::info!("orphan reap: {} orphans", orphan_rows.len());
    if !orphan_rows.is_empty() {
        let orphan_qns: Vec<String> = orphan_rows
            .iter()
            .map(|r| r.qualified_name.clone())
            .collect();
        remove_vectors(db, &orphan_qns).map_err(|e| e.to_string())?;
        state::delete_state_rows(db, &orphan_rows).map_err(|e| e.to_string())?;
    }

    let index_size = count_vectors(db).map_err(|e| e.to_string())?;

    Ok(BuildReport {
        considered_count: considered,
        embedded_count: embedded,
        skipped_fresh_count: skipped_fresh,
        orphaned_count: orphan_rows.len(),
        index_size,
        index_path: PathBuf::from(".leankg/embedding_vectors (CozoDB HNSW)"),
    })
}

/// Helper: write a batch of (qualified_name, vector) pairs to CozoDB
/// using `import_relations`. This is significantly faster than the
/// `:put embedding_vectors {qualified_name => vector}` script path
/// because it skips the per-flush script parser + query planner. The
/// relation already exists (created by `ensure_embedding_state_table`)
/// and the HNSW index is dropped before the bulk insert (rebuilt at the
/// end), so the "no indices / no triggers" caveat in CozoDB's docs is
/// satisfied for the duration of the embed.
///
/// Throughput measured on M2 Pro 10c with /Users/you/work/other-repo
/// (~371k functions-only) jumped from ~85 vec/sec (parameterized
/// `:put`) to ~700 vec/sec with `import_relations` — about 8× — which
/// brings cold embed from ~73 min to ~9 min on the same workspace.
fn upsert_pairs_to_db(
    db: &CozoDb,
    pairs: &[(String, Vec<f32>)],
) -> Result<(), Box<dyn std::error::Error>> {
    // Redis HNSW side-store: skip Cozo import_relations when enabled.
    if crate::embeddings::redis_store::redis_vector_store_enabled() {
        // One connection per flush is acceptable for cold load; for
        // long runs the writer holds state via thread_local below.
        thread_local! {
            static REDIS: std::cell::RefCell<Option<crate::embeddings::redis_store::RedisVectorStore>> =
                const { std::cell::RefCell::new(None) };
        }
        return REDIS.with(|cell| {
            let mut slot = cell.borrow_mut();
            if slot.is_none() {
                *slot = Some(crate::embeddings::redis_store::RedisVectorStore::connect()?);
                tracing::info!("embed writer: LEANKG_EMBED_VECTOR_STORE=redis");
            }
            slot.as_ref()
                .unwrap()
                .upsert_pairs(pairs)
                .map_err(|e| -> Box<dyn std::error::Error> { e.into() })
        });
    }

    use cozo::DataValue;
    let chunk_size = effective_upsert_chunk();
    for chunk in pairs.chunks(chunk_size) {
        // Build the NamedRows directly — Vec<DataValue> per row, headers
        // `["qualified_name", "vector"]`. Skips serde_json::Value
        // serialization (was the main overhead in the `:put` path).
        let mut rows: Vec<Vec<DataValue>> = Vec::with_capacity(chunk.len());
        for (qn, vec) in chunk {
            let mut row = Vec::with_capacity(2);
            row.push(DataValue::Str(qn.as_str().into()));
            // Cozo's <F32; 384> expects a List of F32 Num. We push 384
            // f32-converted-to-f64 Nums — cozo will coerce to f32 on store.
            let mut list = Vec::with_capacity(vec.len());
            for &f in vec.iter() {
                list.push(DataValue::from(f as f64));
            }
            row.push(DataValue::List(list));
            rows.push(row);
        }
        let named_rows = cozo::NamedRows::new(
            vec!["qualified_name".to_string(), "vector".to_string()],
            rows,
        );
        let mut map = std::collections::BTreeMap::new();
        map.insert("embedding_vectors".to_string(), named_rows);
        // import_relations is the public API and skips script parsing.
        // "Any associated indices will be updated" — but we've dropped
        // vec_idx above, so this is a no-op for our case.
        db.import_relations(map)
            .map_err(|e| -> Box<dyn std::error::Error> {
                format!("import_relations: {e}").into()
            })?;
    }
    Ok(())
}

/// Sequential-path helper: write a batch of `(qualified_name, vector)`
/// pairs via `import_relations` (same fast path as the parallel writer,
/// see `upsert_pairs_to_db` for the rationale). The `:put`-via-script
/// path was ~6× slower on the writer commit phase; this shares the
/// faster implementation so a `workers=1` embed gets the same writer
/// throughput as `workers=4`.
fn upsert_vectors<'a, I>(db: &CozoDb, items: I) -> Result<(), Box<dyn std::error::Error>>
where
    I: Iterator<Item = (&'a WorkItem, &'a Vec<f32>)>,
{
    use cozo::DataValue;
    let collected: Vec<(String, Vec<f32>)> = items
        .map(|(item, vector)| (item.qualified_name.clone(), vector.clone()))
        .collect();
    let chunk_size = effective_upsert_chunk();
    for chunk in collected.chunks(chunk_size) {
        let mut rows: Vec<Vec<DataValue>> = Vec::with_capacity(chunk.len());
        for (qn, vec) in chunk {
            let mut row = Vec::with_capacity(2);
            row.push(DataValue::Str(qn.as_str().into()));
            let mut list = Vec::with_capacity(vec.len());
            for &f in vec.iter() {
                list.push(DataValue::from(f as f64));
            }
            row.push(DataValue::List(list));
            rows.push(row);
        }
        let named_rows = cozo::NamedRows::new(
            vec!["qualified_name".to_string(), "vector".to_string()],
            rows,
        );
        let mut map = std::collections::BTreeMap::new();
        map.insert("embedding_vectors".to_string(), named_rows);
        db.import_relations(map)
            .map_err(|e| -> Box<dyn std::error::Error> {
                format!("import_relations: {e}").into()
            })?;
    }
    Ok(())
}

/// `:rm embedding_vectors {qualified_name}` for a batch of orphans.
fn remove_vectors(db: &CozoDb, qns: &[String]) -> Result<(), Box<dyn std::error::Error>> {
    if qns.is_empty() {
        return Ok(());
    }
    let chunk_size = effective_upsert_chunk();
    for chunk in qns.chunks(chunk_size) {
        let literals: Vec<String> = chunk
            .iter()
            .map(|qn| format!("[{}]", serde_json::Value::String(qn.clone())))
            .collect();
        let values_clause = literals.join(", ");
        let query = format!(
            r#"?[qualified_name] <- [{values_clause}] :rm embedding_vectors {{qualified_name}}"#
        );
        run_script(db, &query, Default::default())?;
    }
    Ok(())
}

fn count_vectors(db: &CozoDb) -> Result<usize, Box<dyn std::error::Error>> {
    let result = run_script(
        db,
        "?[qualified_name] := *embedding_vectors{qualified_name}",
        Default::default(),
    )?;
    Ok(result.rows.len())
}

/// Configuration for the in-process background embed used by mcp-http
/// (`LEANKG_EMBED_BACKGROUND=1`). The defaults target the Plan §"Part A"
/// SLA: <5 min cold functions-only embed on a 10-core host while keeping
/// MCP request latency untouched.
#[derive(Debug, Clone)]
pub struct BackgroundEmbedConfig {
    /// Override the embedding batch size (default 64).
    pub batch_size: usize,
    /// Number of parallel ONNX workers (default 2 — lower than the CLI
    /// foreground default so request threads have headroom).
    pub workers: usize,
    /// Force a `--full` re-embed even if the state table has fresh rows.
    pub full: bool,
    /// Override the types filter; empty = "use the mega-graph heuristic".
    pub types_filter: String,
}

impl Default for BackgroundEmbedConfig {
    fn default() -> Self {
        Self {
            batch_size: 32,
            // One worker by default in MCP so request threads keep RAM.
            workers: 1,
            full: false,
            types_filter: String::new(),
        }
    }
}

/// Handle returned by `spawn_background_embed`. Dropping the handle is a
/// no-op (the worker thread is detached) — pass through to keep the
/// return type useful for future cancellation hooks.
#[derive(Debug)]
pub struct BackgroundEmbedHandle {
    pub pid: u32,
}

/// Spawn a detached background embed that runs inside the calling
/// process, sharing the caller's `CozoDb` handle via `GraphEngine`'s
/// `Arc<CozoDb>`. This avoids the RocksDB single-writer rejection that a
/// second `leankg embed` child would hit if launched while MCP is live.
///
/// The worker writes `<leankg_dir>/embed_status.json` with progress and a
/// `<leankg_dir>/embed.lock` file containing its PID, so callers can
/// poll via `leankg embed --status` or `kill -TERM <pid>` to cancel.
///
/// Returns `Ok(None)` if a background embed is already in flight (lock
/// file present + alive) so the caller can treat the no-op as idempotent.
pub fn spawn_background_embed(
    graph: GraphEngine,
    leankg_dir: std::path::PathBuf,
    cfg: BackgroundEmbedConfig,
) -> Result<Option<BackgroundEmbedHandle>, String> {
    use std::io::IsTerminal;

    // Cap workers/batch against LEANKG_EMBED_MAX_MB before status/lock write.
    let mem = plan_embed_memory(cfg.workers, cfg.batch_size);
    let cfg = BackgroundEmbedConfig {
        batch_size: mem.batch_size,
        workers: mem.workers,
        full: cfg.full,
        types_filter: cfg.types_filter,
    };
    if mem.max_rss_mb > 0 {
        tracing::info!(
            "background embed memory plan: workers={} batch={} max_rss_mb={}",
            cfg.workers,
            cfg.batch_size,
            mem.max_rss_mb
        );
    }

    let lock_path = leankg_dir.join("embed.lock");
    let status_path = leankg_dir.join("embed_status.json");

    // Refuse to start a second one if a previous run is alive.
    if let Ok(raw) = std::fs::read_to_string(&lock_path) {
        if let Ok(pid) = raw.trim().parse::<u64>() {
            let probe = unsafe { libc_kill_compat(pid, 0) };
            if probe == 0 {
                tracing::info!(
                    "background embed already running (PID {}); skipping new spawn",
                    pid
                );
                return Ok(None);
            }
        }
        let _ = std::fs::remove_file(&lock_path);
    }

    // Write the lock first; the worker thread will refresh the status
    // file periodically. If the worker panics before writing, the lock
    // gives us a PID to investigate.
    let pid = std::process::id();
    let _ = std::fs::write(&lock_path, pid.to_string());

    let started_at = std::time::SystemTime::now()
        .duration_since(std::time::UNIX_EPOCH)
        .map(|d| d.as_secs())
        .unwrap_or(0);

    let write_status =
        move |considered: u64, embedded: u64, skipped: u64, orphans: u64, status: &str| {
            let body = serde_json::json!({
                "pid": pid,
                "started_at": started_at,
                "considered": considered,
                "embedded": embedded,
                "skipped_fresh": skipped,
                "orphans": orphans,
                "workers": cfg.workers,
                "status": status,
                "mode": "in_process_background",
            });
            if let Ok(mut f) = std::fs::File::create(&status_path) {
                let _ = f.write_all(body.to_string().as_bytes());
            }
        };

    // Snapshot the initial element count ONCE (don't double-scan inside
    // build_index_parallel). On the mega-graph heuristic, this is also
    // where the function,method filter is applied.
    let total = graph.all_elements().map(|v| v.len()).unwrap_or(0);
    write_status(total as u64, 0, 0, 0, "running");

    let graph_clone = graph.clone();
    let leankg_dir_for_worker = leankg_dir.clone();

    // Detached worker thread. We use std::thread (not tokio) because
    // build_index_parallel is fully synchronous and CPU-bound; tokio
    // would just add scheduling overhead. Live progress is logged via
    // tracing!info! inside build_index_parallel and surfaces in the
    // container's stdout / docker logs.
    std::thread::Builder::new()
        .name("leankg-bg-embed".into())
        .spawn(move || {
            let mode = if cfg.full {
                BuildMode::Full
            } else {
                BuildMode::Incremental
            };
            let parsed = parse_type_filter(&cfg.types_filter);
            let opts = BuildOptions {
                mode,
                batch_size: cfg.batch_size,
                reserve_capacity: None,
                type_filter: match &parsed {
                    Some(_) => parsed.clone(),
                    None => {
                        if total > 50_000 {
                            let mut set = std::collections::HashSet::new();
                            set.insert("function".to_string());
                            set.insert("method".to_string());
                            Some(set)
                        } else {
                            None
                        }
                    }
                },
            };

            // Periodic status snapshot poller. Reads the live row count from the
            // shared `CozoDb` handle (Arc-clone is safe — RocksDB allows
            // concurrent readers in the same process) and writes a JSON
            // snapshot every 5s so `leankg embed --status` shows live
            // numbers while the embed is running.
            use std::sync::atomic::{AtomicBool, Ordering};
            use std::sync::Arc;
            let poller_status = leankg_dir_for_worker.join("embed_status.json");
            let poller_pid = pid;
            let poller_started = started_at;
            let poller_total = total as u64;
            let poller_workers = cfg.workers;
            let poller_graph = graph_clone.clone();
            let poller_done = Arc::new(AtomicBool::new(false));
            let poller_done_clone = poller_done.clone();
            std::thread::Builder::new()
                .name("leankg-bg-embed-poller".into())
                .spawn(move || {
                    while !poller_done_clone.load(Ordering::Relaxed) {
                        std::thread::sleep(std::time::Duration::from_secs(5));
                        let embedded = poller_graph
                            .db()
                            .run_script(
                                "?[qualified_name] := *embedding_vectors{qualified_name}",
                                std::collections::BTreeMap::new(),
                                cozo::ScriptMutability::Immutable,
                            )
                            .map(|r| r.rows.len() as u64)
                            .unwrap_or(0);
                        let body = serde_json::json!({
                            "pid": poller_pid,
                            "started_at": poller_started,
                            "considered": poller_total,
                            "embedded": embedded,
                            "skipped_fresh": 0u64,
                            "orphans": 0u64,
                            "workers": poller_workers,
                            "status": "running",
                            "mode": "in_process_background",
                        });
                        if let Ok(mut f) = std::fs::File::create(&poller_status) {
                            let _ = f.write_all(body.to_string().as_bytes());
                        }
                    }
                })
                .ok();

            let started = std::time::Instant::now();
            let result = if cfg.workers > 1 {
                build_index_parallel(
                    &graph_clone,
                    std::path::Path::new(""),
                    &opts,
                    cfg.workers,
                )
            } else {
                build_index(&graph_clone, std::path::Path::new(""), &opts)
                    .map_err(|e| e.to_string())
            };
            let elapsed = started.elapsed();
            poller_done.store(true, Ordering::Relaxed);

            match result {
                Ok(report) => {
                    // Write final status.
                    let final_status = leankg_dir_for_worker.join("embed_status.json");
                    let body = serde_json::json!({
                        "pid": pid,
                        "started_at": started_at,
                        "considered": report.considered_count,
                        "embedded": report.embedded_count,
                        "skipped_fresh": report.skipped_fresh_count,
                        "orphans": report.orphaned_count,
                        "workers": cfg.workers,
                        "elapsed_s": elapsed.as_secs_f64(),
                        "status": "completed",
                        "mode": "in_process_background",
                    });
                    if let Ok(mut f) = std::fs::File::create(&final_status) {
                        let _ = f.write_all(body.to_string().as_bytes());
                    }
                    if std::io::stdout().is_terminal() {
                        eprintln!(
                            "[bg-embed] completed in {:.2}s: {} considered, {} embedded, {} skipped, {} orphans",
                            elapsed.as_secs_f64(),
                            report.considered_count,
                            report.embedded_count,
                            report.skipped_fresh_count,
                            report.orphaned_count
                        );
                    } else {
                        tracing::info!(
                            "background embed completed in {:.2}s: considered={}, embedded={}, skipped={}, orphans={}",
                            elapsed.as_secs_f64(),
                            report.considered_count,
                            report.embedded_count,
                            report.skipped_fresh_count,
                            report.orphaned_count
                        );
                    }
                }
                Err(e) => {
                    let err_status = leankg_dir_for_worker.join("embed_status.json");
                    let body = serde_json::json!({
                        "pid": pid,
                        "started_at": started_at,
                        "status": "failed",
                        "error": e,
                        "mode": "in_process_background",
                    });
                    if let Ok(mut f) = std::fs::File::create(&err_status) {
                        let _ = f.write_all(body.to_string().as_bytes());
                    }
                    tracing::error!("background embed failed: {}", e);
                }
            }

            // Clear the lock so a future spawn can run.
            let lock_path = leankg_dir_for_worker.join("embed.lock");
            let _ = std::fs::remove_file(&lock_path);
        })
        .map_err(|e| format!("failed to spawn background embed thread: {}", e))?;

    Ok(Some(BackgroundEmbedHandle { pid }))
}

// Minimal libc binding — same shape as main.rs::libc_kill to avoid
// pulling in the `libc` crate just for one symbol.
unsafe fn libc_kill_compat(pid: u64, sig: i32) -> i32 {
    extern "C" {
        fn kill(pid: i32, sig: i32) -> i32;
    }
    kill(pid as i32, sig)
}

#[derive(Clone)]
struct WorkItem {
    qualified_name: String,
    blob: String,
    current_hash: String,
}

pub const EMBEDDING_DIM_CONST: usize = EMBEDDING_DIM;

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn default_options_batch_size_32() {
        assert_eq!(BuildOptions::default().batch_size, 32);
    }

    #[test]
    fn embed_memory_plan_caps_workers_under_2gb() {
        let plan = plan_embed_memory_with_budget(8, 64, 2048);
        assert!(plan.workers <= 4, "workers={}", plan.workers);
        assert!(plan.batch_size <= 16, "batch={}", plan.batch_size);
        assert!(plan.channel_capacity <= plan.upsert_chunk);
    }

    #[test]
    fn embed_memory_plan_zero_disables_caps() {
        let plan = plan_embed_memory_with_budget(4, 64, 0);
        assert_eq!(plan.workers, 4);
        assert_eq!(plan.batch_size, 64);
    }

    #[test]
    fn default_options_mode_is_incremental() {
        assert_eq!(BuildOptions::default().mode, BuildMode::Incremental);
    }

    #[test]
    fn default_options_reserve_capacity_is_none() {
        assert!(BuildOptions::default().reserve_capacity.is_none());
    }

    #[test]
    fn build_mode_variants_are_distinct() {
        assert_ne!(BuildMode::Incremental, BuildMode::Full);
    }

    #[test]
    fn embedding_dim_const_matches_model_dim() {
        assert_eq!(EMBEDDING_DIM_CONST, EMBEDDING_DIM);
        assert_eq!(EMBEDDING_DIM_CONST, 384);
    }

    #[test]
    fn build_report_default_has_zero_counts() {
        let report = BuildReport::default();
        assert_eq!(report.considered_count, 0);
        assert_eq!(report.embedded_count, 0);
        assert_eq!(report.skipped_fresh_count, 0);
        assert_eq!(report.orphaned_count, 0);
        assert_eq!(report.index_size, 0);
    }

    #[test]
    fn default_upsert_chunk_is_5000() {
        // Documented contract — overridable via LEANKG_EMBED_UPSERT_CHUNK.
        assert_eq!(DEFAULT_UPSERT_CHUNK, 5000);
    }

    #[test]
    fn effective_upsert_chunk_defaults_when_env_unset() {
        std::env::remove_var("LEANKG_EMBED_UPSERT_CHUNK");
        assert_eq!(effective_upsert_chunk(), 5000);
    }
}