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diff --git a/pkgs/atlas-search/Cargo.toml b/pkgs/atlas-search/Cargo.toml
new file mode 100644
index 000000000..dbbe24420
--- /dev/null
+++ b/pkgs/atlas-search/Cargo.toml
@@ -0,0 +1,19 @@
+# Atlas search — Rust binary + multi-client service (epic: developing-today/code#5350).
+# Design of record: doc/2026-11-04T08-07-13Z_design_atlas_binary_service/ (§4 layout).
+#
+# Self-contained workspace on the pkgs/id pattern. Crates land one step at
+# a time; step 1 (#5351) is atlas-embed only.
+[workspace]
+members = ["crates/atlas-embed"]
+resolver = "2"
+
+[workspace.package]
+edition = "2024"
+rust-version = "1.96"
+license = "MIT OR Apache-2.0"
+
+[profile.release]
+# The embedder's numerics are part of a parity contract (Gate A): the
+# release profile must not trade float exactness for speed.
+# (No fast-math style flags exist in rustc, but keep this explicit so a
+# future profile edit has to argue with the comment.)
diff --git a/pkgs/atlas-search/crates/atlas-embed/Cargo.toml b/pkgs/atlas-search/crates/atlas-embed/Cargo.toml
new file mode 100644
index 000000000..ca6751d8c
--- /dev/null
+++ b/pkgs/atlas-search/crates/atlas-embed/Cargo.toml
@@ -0,0 +1,20 @@
+[package]
+name = "atlas-embed"
+version = "0.1.0"
+edition.workspace = true
+rust-version.workspace = true
+license.workspace = true
+description = "Snowflake arctic-embed-m-v2.0 embedder for Atlas — ONNX Runtime (dynamically loaded) + the model's own Unigram tokenizer. Gate A parity crate for developing-today/code#5351."
+
+[dependencies]
+# Dynamic loading only: the ort crate must never download a prebuilt
+# onnxruntime at build time (forbidden in the nix sandbox and a
+# reproducibility hole — design §4). The runtime library is resolved
+# from the deployment closure and handed to Embedder::load.
+ort = { version = "2.0.0-rc.15", default-features = false, features = ["std", "load-dynamic", "ndarray"] }
+tokenizers = { version = "0.22", default-features = false, features = ["onig"] }
+thiserror = "2"
+
+[dev-dependencies]
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
diff --git a/pkgs/atlas-search/crates/atlas-embed/examples/timing.rs b/pkgs/atlas-search/crates/atlas-embed/examples/timing.rs
new file mode 100644
index 000000000..9be6309f8
--- /dev/null
+++ b/pkgs/atlas-search/crates/atlas-embed/examples/timing.rs
@@ -0,0 +1,76 @@
+//! Timing/RSS probe for the embedder — spike instrumentation for
+//! steps 2–4 (not a benchmark suite). Run:
+//!
+//! ATLAS_ORT_LIB=/path/to/libonnxruntime.so \
+//! cargo run --release -p atlas-embed --example timing
+//!
+//! ATLAS_MODEL_DIR defaults to the pinned HF snapshot, as in the
+//! Gate A test.
+
+use std::path::PathBuf;
+use std::time::Instant;
+
+use atlas_embed::Embedder;
+
+fn rss_mb() -> f64 {
+ let status = std::fs::read_to_string("/proc/self/status").unwrap();
+ for line in status.lines() {
+ if let Some(rest) = line.strip_prefix("VmRSS:") {
+ let kb: f64 = rest.trim().trim_end_matches(" kB").trim().parse().unwrap();
+ return kb / 1024.0;
+ }
+ }
+ f64::NAN
+}
+
+fn main() {
+ let model_dir = std::env::var_os("ATLAS_MODEL_DIR").map(PathBuf::from).unwrap_or_else(|| {
+ let home = std::env::var_os("HOME").expect("HOME unset");
+ PathBuf::from(home).join(".cache/huggingface/hub/models--Snowflake--snowflake-arctic-embed-m-v2.0/snapshots/95c2741480856aa9666782eb4afe11959938017f")
+ });
+ let ort_lib = PathBuf::from(std::env::var_os("ATLAS_ORT_LIB").expect("ATLAS_ORT_LIB required"));
+
+ let t = Instant::now();
+ let mut emb = Embedder::load(&model_dir, &ort_lib).expect("load");
+ println!(
+ "load (env+tokenizer+session): {:.0} ms, RSS {:.0} MB",
+ t.elapsed().as_secs_f64() * 1e3,
+ rss_mb()
+ );
+
+ let fox = "The quick brown fox jumps over the lazy dog.";
+ let t = Instant::now();
+ let v = emb.embed(&[fox]).expect("embed");
+ println!(
+ "first single embed (15 tok): {:.0} ms",
+ t.elapsed().as_secs_f64() * 1e3
+ );
+ let t = Instant::now();
+ for _ in 0..20 {
+ emb.embed(&[fox]).expect("embed");
+ }
+ println!(
+ "warm single embed (15 tok): {:.1} ms avg, RSS {:.0} MB",
+ t.elapsed().as_secs_f64() * 1e3 / 20.0,
+ rss_mb()
+ );
+ println!("checksum dim0..3: {:?}", &v[0][..3]);
+
+ let long = "Embeddings turn text into vectors that capture semantic meaning. ".repeat(24);
+ let t = Instant::now();
+ emb.embed(&[long.as_str()]).expect("embed long");
+ println!(
+ "single embed (~{} tok): {:.0} ms",
+ emb.tokenize(&long).unwrap().len(),
+ t.elapsed().as_secs_f64() * 1e3
+ );
+
+ let batch: Vec<&str> = (0..32).map(|_| fox).collect();
+ let t = Instant::now();
+ emb.embed(&batch).expect("embed batch");
+ println!(
+ "batch of 32 short: {:.0} ms, RSS {:.0} MB",
+ t.elapsed().as_secs_f64() * 1e3,
+ rss_mb()
+ );
+}
diff --git a/pkgs/atlas-search/crates/atlas-embed/src/lib.rs b/pkgs/atlas-search/crates/atlas-embed/src/lib.rs
new file mode 100644
index 000000000..f5fac61e6
--- /dev/null
+++ b/pkgs/atlas-search/crates/atlas-embed/src/lib.rs
@@ -0,0 +1,284 @@
+//! atlas-embed — the Snowflake `snowflake-arctic-embed-m-v2.0` embedder
+//! for Atlas, and nothing else (design §4, developing-today/code#5350).
+//!
+//! The crate exists to be parity-tested in isolation: Gate A
+//! (code#5351) requires it to reproduce the production Python corpus
+//! pipeline (`~/atlas/v3/embed_onnx.py`) **bit-identically** — token
+//! ids equal, output vectors delta 0.0, single and batched. The
+//! reference fixtures live in `pkgs/atlas-search/fixtures/` with a
+//! recorded, rerunnable generator.
+//!
+//! Pipeline contract (every point is load-bearing for the gate):
+//! - The model's own `tokenizer.json`: SentencePiece Unigram with a
+//! Metaspace pre-tokenizer and a precompiled normalizer, ``/``
+//! template. NOT BERT WordPiece. Loaded via the `tokenizers` crate.
+//! - **Truncation is OFF.** The shipped tokenizer config carries a
+//! 512-token truncation default; the model's real context is 8192.
+//! The corpus pipeline tokenizes whole texts and a >8192-token input
+//! is a loud error here, never a silent cut.
+//! - Documents embed **prompt-free**. The sentence-transformers
+//! `prompt_name="query"` handling for query texts is a string-prefix
+//! step exercised by the search layer (step 2), not this crate.
+//! - Batching pads **right, pad token id 0, attention mask 0** — the
+//! corpus pipeline's manual padding (`embed_onnx.py` builds its
+//! batch arrays with `np.zeros`). The fork's :18752 sidecar pads
+//! with id 1 instead; the recorded fixture diagnostic
+//! (gen_fixtures.py) shows the two are bit-identical on this fp32
+//! graph (max delta 0.0 on every probe, incl. a 1,272-token chunk),
+//! because masked positions never reach the CLS row. We replicate
+//! the corpus pipeline's 0 because it is the pipeline of record.
+//! - Pooling is **CLS** (`hidden[:, 0, :]`, per the model's shipped
+//! 1_Pooling config), then **L2 normalize** in f32. The norm's sum
+//! of squares replicates NumPy's pairwise summation order — see
+//! [`pairwise_sum_f32`] — because the reference does
+//! `vecs / np.linalg.norm(vecs, axis=1)` and Gate A grants no
+//! tolerance for a differently-ordered accumulation.
+//! - ONNX Runtime is **dynamically loaded** from a caller-supplied
+//! library path; nothing is downloaded at build or run time
+//! (design §4 loader lesson). Session threading matches the
+//! recorded corpus pass: intra-op 8, inter-op 2 by default.
+
+use std::path::Path;
+
+use ort::environment::Environment;
+use ort::session::Session;
+use ort::value::Tensor;
+use tokenizers::Tokenizer;
+
+/// The model's real context length. Inputs longer than this are a
+/// loud error, matching the corpus pipeline's contract.
+pub const MODEL_CONTEXT: usize = 8192;
+/// Full output width of arctic-embed-m-v2.0 (no Matryoshka truncation
+/// in this crate; width selection is a caller concern).
+pub const EMBED_DIM: usize = 768;
+/// Pad token id used when batching. See the crate docs: this is the
+/// corpus pipeline's 0, not the tokenizer's declared pad id 1, and
+/// the choice is provably inert on this graph.
+pub const PAD_ID: i64 = 0;
+
+#[derive(Debug, thiserror::Error)]
+pub enum Error {
+ // ort's error type is generic over the value a failed builder
+ // call would have produced (ort::Error etc.), so
+ // the crate stringifies at the boundary instead of naming a
+ // single instantiation.
+ #[error("onnxruntime: {0}")]
+ Ort(String),
+ #[error("tokenizer: {0}")]
+ Tokenizer(String),
+ #[error("input tokenizes to {tokens} tokens, exceeding the model context of {MODEL_CONTEXT}")]
+ TooLong { tokens: usize },
+ #[error("cannot embed an empty batch")]
+ EmptyBatch,
+ #[error("unexpected model output shape: {0:?}")]
+ BadShape(Vec),
+}
+
+pub type Result = std::result::Result;
+
+/// A loaded embedder: one ONNX session + the model's tokenizer.
+///
+/// Construction is explicit and eager; callers that want the design's
+/// lazy init (serve mode) wrap this in a once-cell.
+pub struct Embedder {
+ // Kept alive for the session's lifetime; sessions clone the
+ // environment's Arc internally, but holding it here makes the
+ // lifetime contract explicit at this crate's boundary.
+ _env: Environment,
+ session: Session,
+ tokenizer: Tokenizer,
+}
+
+/// Stringify any ort error into this crate's error type.
+fn ort(e: E) -> Error {
+ Error::Ort(e.to_string())
+}
+
+impl Embedder {
+ /// Load from a model directory (the pinned snapshot layout:
+ /// `tokenizer.json` at the root, `onnx/model.onnx` the fp32
+ /// graph) and a dynamically-loaded ONNX Runtime library.
+ ///
+ /// `intra_threads` / `inter_threads` default to the recorded
+ /// corpus-pass values (8 / 2) via [`Embedder::load`]; use
+ /// [`Embedder::load_with_threads`] to override.
+ pub fn load(model_dir: &Path, ort_lib: &Path) -> Result {
+ Self::load_with_threads(model_dir, ort_lib, 8, 2)
+ }
+
+ pub fn load_with_threads(
+ model_dir: &Path,
+ ort_lib: &Path,
+ intra_threads: usize,
+ inter_threads: usize,
+ ) -> Result {
+ let env = ort::init_from(ort_lib)
+ .map_err(ort)?
+ .with_name("atlas-embed")
+ .build()
+ .map_err(ort)?;
+
+ let mut tokenizer = Tokenizer::from_file(model_dir.join("tokenizer.json"))
+ .map_err(|e| Error::Tokenizer(e.to_string()))?;
+ // The shipped tokenizer.json carries the stock 512-token
+ // truncation default. The model's context is 8192 and the
+ // corpus pipeline truncates nothing — kill it explicitly.
+ tokenizer
+ .with_truncation(None)
+ .map_err(|e| Error::Tokenizer(e.to_string()))?;
+ // Padding is built manually in embed_ids (the reference
+ // pipeline pads its own arrays); never let the tokenizer pad.
+ tokenizer.with_padding(None);
+
+ let session = Session::builder(&env)
+ .map_err(ort)?
+ .with_intra_threads(intra_threads)
+ .map_err(ort)?
+ .with_inter_threads(inter_threads)
+ .map_err(ort)?
+ .commit_from_file(model_dir.join("onnx").join("model.onnx"))
+ .map_err(ort)?;
+
+ Ok(Self {
+ _env: env,
+ session,
+ tokenizer,
+ })
+ }
+
+ /// Tokenize one text exactly as the corpus pipeline does
+ /// (`add_special_tokens=True`, no truncation, no padding).
+ pub fn tokenize(&self, text: &str) -> Result> {
+ let enc = self
+ .tokenizer
+ .encode(text, true)
+ .map_err(|e| Error::Tokenizer(e.to_string()))?;
+ Ok(enc.get_ids().to_vec())
+ }
+
+ /// Embed a batch of texts (document mode: prompt-free).
+ pub fn embed(&mut self, texts: &[&str]) -> Result> {
+ let mut id_lists = Vec::with_capacity(texts.len());
+ for text in texts {
+ id_lists.push(self.tokenize(text)?);
+ }
+ self.embed_ids(&id_lists)
+ }
+
+ /// Embed pre-tokenized inputs. This is the core path; `embed` is
+ /// its tokenizing front door. Batch composition never changes a
+ /// vector (masked right-padding; Gate A asserts it).
+ pub fn embed_ids(&mut self, id_lists: &[Vec]) -> Result> {
+ if id_lists.is_empty() {
+ return Err(Error::EmptyBatch);
+ }
+ for ids in id_lists {
+ if ids.len() > MODEL_CONTEXT {
+ return Err(Error::TooLong { tokens: ids.len() });
+ }
+ }
+ let batch = id_lists.len();
+ let maxlen = id_lists.iter().map(|v| v.len()).max().unwrap_or(0);
+
+ let mut input_ids = vec![PAD_ID; batch * maxlen];
+ let mut attn = vec![0i64; batch * maxlen];
+ for (j, ids) in id_lists.iter().enumerate() {
+ for (k, &id) in ids.iter().enumerate() {
+ input_ids[j * maxlen + k] = i64::from(id);
+ attn[j * maxlen + k] = 1;
+ }
+ }
+
+ let ids_tensor =
+ Tensor::from_array(([batch as i64, maxlen as i64], input_ids)).map_err(ort)?;
+ let attn_tensor = Tensor::from_array(([batch as i64, maxlen as i64], attn)).map_err(ort)?;
+ let outputs = self
+ .session
+ .run(ort::inputs![
+ "input_ids" => ids_tensor,
+ "attention_mask" => attn_tensor,
+ ])
+ .map_err(ort)?;
+ let hidden = outputs[0].try_extract_array::().map_err(ort)?;
+ let shape: Vec = hidden.shape().iter().map(|&d| d as i64).collect();
+ if shape.len() != 3 || shape[0] != batch as i64 || shape[2] != EMBED_DIM as i64 {
+ return Err(Error::BadShape(shape));
+ }
+ let view = hidden.view();
+ let mut out = Vec::with_capacity(batch);
+ for j in 0..batch {
+ let mut cls = [0f32; EMBED_DIM];
+ for k in 0..EMBED_DIM {
+ cls[k] = view[[j, 0, k]];
+ }
+ l2_normalize(&mut cls);
+ out.push(cls);
+ }
+ Ok(out)
+ }
+}
+
+/// L2-normalize in place, replicating the reference pipeline's
+/// arithmetic: the norm is `sqrt(pairwise_sum(x_i^2))` with the
+/// squares and the sum in f32, then an elementwise f32 division.
+fn l2_normalize(v: &mut [f32; EMBED_DIM]) {
+ let squares: [f32; EMBED_DIM] = std::array::from_fn(|i| v[i] * v[i]);
+ let norm = pairwise_sum_f32(&squares).sqrt();
+ for x in v.iter_mut() {
+ *x /= norm;
+ }
+}
+
+/// NumPy's pairwise summation (PW_BLOCKSIZE = 128, 8-way unrolled
+/// accumulators), replicated so the accumulation order — and
+/// therefore the rounding — matches `np.add.reduce` bit for bit.
+pub fn pairwise_sum_f32(a: &[f32]) -> f32 {
+ let n = a.len();
+ if n < 8 {
+ let mut r = 0.0f32;
+ for &x in a {
+ r += x;
+ }
+ return r;
+ }
+ if n <= 128 {
+ let mut r = [a[0], a[1], a[2], a[3], a[4], a[5], a[6], a[7]];
+ let mut i = 8;
+ while i < n - (n % 8) {
+ for (k, acc) in r.iter_mut().enumerate() {
+ *acc += a[i + k];
+ }
+ i += 8;
+ }
+ let mut res = ((r[0] + r[1]) + (r[2] + r[3])) + ((r[4] + r[5]) + (r[6] + r[7]));
+ while i < n {
+ res += a[i];
+ i += 1;
+ }
+ return res;
+ }
+ let mut n2 = n / 2;
+ n2 -= n2 % 8;
+ pairwise_sum_f32(&a[..n2]) + pairwise_sum_f32(&a[n2..])
+}
+
+#[cfg(test)]
+mod tests {
+ use super::*;
+
+ #[test]
+ fn pairwise_sum_matches_naive_for_short_inputs() {
+ let a: Vec = (0..7).map(|i| (i as f32) * 0.25).collect();
+ assert_eq!(pairwise_sum_f32(&a), a.iter().sum::());
+ }
+
+ #[test]
+ fn pairwise_sum_handles_block_boundaries() {
+ // Exercise all three branches (n<8, 8..=128, >128 recursion).
+ for n in [8usize, 9, 127, 128, 129, 255, 768, 1000] {
+ let a: Vec = (0..n).map(|i| ((i * 37 % 101) as f32) * 0.01).collect();
+ let got = pairwise_sum_f32(&a);
+ assert!(got.is_finite() && got > 0.0, "n={n} got={got}");
+ }
+ }
+}
diff --git a/pkgs/atlas-search/crates/atlas-embed/tests/gate_a.rs b/pkgs/atlas-search/crates/atlas-embed/tests/gate_a.rs
new file mode 100644
index 000000000..846ca0be3
--- /dev/null
+++ b/pkgs/atlas-search/crates/atlas-embed/tests/gate_a.rs
@@ -0,0 +1,140 @@
+//! Gate A — embedder parity (developing-today/code#5351, design §5).
+//!
+//! Asserts, against `pkgs/atlas-search/fixtures/embed-probes.json`
+//! (recorded from the production Python corpus pipeline by
+//! `gen_fixtures.py`):
+//! * token ids exactly equal on every text-bearing probe;
+//! * output vectors BIT-IDENTICAL (delta 0.0) on every probe,
+//! embedded singly and in one fixed mixed batch.
+//!
+//! Requires the pinned model snapshot and a dynamically-loadable
+//! ONNX Runtime, so it is `#[ignore]`d for ordinary CI:
+//!
+//! ATLAS_ORT_LIB=/path/to/libonnxruntime.so \
+//! ATLAS_MODEL_DIR=/path/to/snowflake-arctic-embed-m-v2.0-snapshot \
+//! cargo test -p atlas-embed --test gate_a -- --ignored --nocapture
+//!
+//! ATLAS_MODEL_DIR defaults to the laptop's HF cache snapshot of the
+//! pinned revision. Any mismatch prints the full per-probe table and
+//! fails with it — the numbers are the issue-comment evidence either
+//! way.
+
+use std::path::PathBuf;
+
+use atlas_embed::Embedder;
+use serde::Deserialize;
+
+#[derive(Deserialize)]
+struct Fixture {
+ probes: Vec,
+}
+
+#[derive(Deserialize)]
+struct Probe {
+ name: String,
+ text: Option,
+ token_ids: Vec,
+ single_bits: Vec,
+ batch_bits: Vec,
+}
+
+fn delta_stats(got: &[f32; 768], want_bits: &[u32]) -> (f32, usize) {
+ let mut max_delta = 0.0f32;
+ let mut mismatches = 0usize;
+ for (k, &g) in got.iter().enumerate() {
+ let w = f32::from_bits(want_bits[k]);
+ if g.to_bits() != want_bits[k] {
+ mismatches += 1;
+ }
+ let d = (g - w).abs();
+ if d > max_delta {
+ max_delta = d;
+ }
+ }
+ (max_delta, mismatches)
+}
+
+#[test]
+#[ignore = "requires ATLAS_ORT_LIB and the pinned model snapshot; see file docs"]
+fn gate_a_bit_identity() {
+ let fixtures_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
+ .join("..")
+ .join("..")
+ .join("fixtures")
+ .join("embed-probes.json");
+ let raw = std::fs::read_to_string(&fixtures_path)
+ .unwrap_or_else(|e| panic!("read {}: {e}", fixtures_path.display()));
+ let fixture: Fixture = serde_json::from_str(&raw).expect("parse embed-probes.json");
+
+ let model_dir = std::env::var_os("ATLAS_MODEL_DIR").map(PathBuf::from).unwrap_or_else(|| {
+ let home = std::env::var_os("HOME").expect("HOME unset");
+ PathBuf::from(home)
+ .join(".cache/huggingface/hub/models--Snowflake--snowflake-arctic-embed-m-v2.0/snapshots/95c2741480856aa9666782eb4afe11959938017f")
+ });
+ let ort_lib = std::env::var_os("ATLAS_ORT_LIB")
+ .map(PathBuf::from)
+ .expect("ATLAS_ORT_LIB must point at a libonnxruntime.so (Gate A environment)");
+
+ let mut embedder = Embedder::load(&model_dir, &ort_lib).expect("load embedder");
+
+ // Resolve each probe's ids: Rust-tokenize text probes (asserting
+ // id equality), corpus probes use their recorded ids (their text
+ // is personal corpus content, referenced by chunk id — see the
+ // fixtures README).
+ let mut id_lists: Vec> = Vec::with_capacity(fixture.probes.len());
+ let mut ids_match: Vec> = Vec::with_capacity(fixture.probes.len());
+ let mut ids_ok = true;
+ for probe in &fixture.probes {
+ if let Some(text) = &probe.text {
+ let got = embedder.tokenize(text).expect("tokenize");
+ let eq = got == probe.token_ids;
+ if !eq {
+ ids_ok = false;
+ eprintln!(
+ "TOKEN IDS MISMATCH {}: rust {} ids vs fixture {} ids",
+ probe.name,
+ got.len(),
+ probe.token_ids.len()
+ );
+ }
+ ids_match.push(Some(eq));
+ id_lists.push(got);
+ } else {
+ ids_match.push(None);
+ id_lists.push(probe.token_ids.clone());
+ }
+ }
+
+ let batch = embedder.embed_ids(&id_lists).expect("embed batch");
+
+ let mut report = String::from(
+ "probe tokens ids single max|d| (bitdiff) batch max|d| (bitdiff)\n",
+ );
+ let mut all_ok = ids_ok;
+ for (i, probe) in fixture.probes.iter().enumerate() {
+ let single = embedder
+ .embed_ids(std::slice::from_ref(&id_lists[i]))
+ .expect("embed single")[0];
+ let (sd, sb) = delta_stats(&single, &probe.single_bits);
+ let (bd, bb) = delta_stats(&batch[i], &probe.batch_bits);
+ if sd != 0.0 || bd != 0.0 {
+ all_ok = false;
+ }
+ report.push_str(&format!(
+ "{:<22} {:>6} {:<5} {:.3e} ({}) {:.3e} ({})\n",
+ probe.name,
+ probe.token_ids.len(),
+ match ids_match[i] {
+ Some(true) => "ok",
+ Some(false) => "FAIL",
+ None => "n/a",
+ },
+ sd,
+ sb,
+ bd,
+ bb
+ ));
+ }
+ println!("{report}");
+ assert!(all_ok, "Gate A FAILED — per-probe deltas:\n{report}");
+}
diff --git a/pkgs/atlas-search/fixtures/README.md b/pkgs/atlas-search/fixtures/README.md
new file mode 100644
index 000000000..841dbd45e
--- /dev/null
+++ b/pkgs/atlas-search/fixtures/README.md
@@ -0,0 +1,39 @@
+# Fixtures — Gate A reference data
+
+`embed-probes.json` is the recorded output of the production Python
+corpus pipeline for a fixed probe set. The `atlas-embed` crate's
+Gate A test (`crates/atlas-embed/tests/gate_a.rs`) must reproduce it
+**bit-identically**: token ids equal, output vectors delta 0.0,
+embedded singly and in one fixed mixed batch. Provenance (model
+revision, file hashes, runtime version, thread counts, padding and
+pooling semantics) is recorded inside the JSON itself.
+
+## Regenerating
+
+`gen_fixtures.py` is the recorded, rerunnable generator — run it on
+the laptop (amd-6), never the sandbox:
+
+```sh
+LD_LIBRARY_PATH=: \
+ ~/atlas/v3-venv/bin/python gen_fixtures.py
+```
+
+It replicates `~/atlas/v3/embed_onnx.py` (the pipeline that embedded
+the production corpus) exactly: fp32 `onnx/model.onnx`, CLS pooling,
+L2 normalize, right-padding with token id 0 / attention mask 0. It
+also prints a padding diagnostic — on this fp32 graph, pad id 0 vs
+the fork sidecar's pad id 1 are bit-identical (masked positions
+never reach the CLS row); the corpus pipeline's 0 is what's
+recorded. Regenerate on any model revision bump, through this same
+ceremony, and commit the result with the bump.
+
+## Why corpus probes have no text here
+
+Three probes (and the >512-token long probe) are real corpus chunks.
+Their text is personal conversation content, so the fixture
+references them by `corpus_chunk_id` (in `~/atlas/v3/corpus.db`) and
+records the gate data — token ids + vectors — without copying the
+text into this repo. The generator re-derives the text from
+corpus.db on every run. The synthetic probes (the opencode#1554
+spike texts, an empty string, a query-shaped sentence) carry their
+text inline.
diff --git a/pkgs/atlas-search/fixtures/embed-probes.json b/pkgs/atlas-search/fixtures/embed-probes.json
new file mode 100644
index 000000000..60348a19f
--- /dev/null
+++ b/pkgs/atlas-search/fixtures/embed-probes.json
@@ -0,0 +1,19501 @@
+{
+ "provenance": {
+ "generated_by": "pkgs/atlas-search/fixtures/gen_fixtures.py",
+ "reference_pipeline": "~/atlas/v3/embed_onnx.py (production corpus pipeline)",
+ "model": "Snowflake/snowflake-arctic-embed-m-v2.0",
+ "model_rev": "95c2741480856aa9666782eb4afe11959938017f",
+ "onnx_file": "onnx/model.onnx (fp32 graph)",
+ "model_onnx_sha256": "c0c53d7f49a2db60761b92b7bbf5be87a7b3cf5d92dbbd7f1b5028bd5a40aa39",
+ "tokenizer_json_sha256": "f1cc44ad7faaeec47241864835473fd5403f2da94673f3f764a77ebcb0a803ec",
+ "onnxruntime_version": "1.31.1",
+ "transformers_version": "5.21.0",
+ "python_tokenizers_version": "0.23.3",
+ "intra_op_threads": 8,
+ "inter_op_threads": 2,
+ "padding": "right, pad token id 0, attention mask 0 (embed_onnx.py semantics)",
+ "pooling": "CLS (hidden[:, 0, :]), L2 normalize, f32",
+ "truncation": "off — model context is 8192; >8192 tokens is a loud error in the crate",
+ "batch_composition": "single mixed batch, every probe in listed order, padded to batch max length",
+ "generated_at": "2026-11-04T08:49:12Z",
+ "generator_host": "amd"
+ },
+ "probes": [
+ {
+ "name": "spike_fox",
+ "source": "opencode#1554 spike text",
+ "token_ids": [
+ 0,
+ 581,
+ 63773,
+ 119455,
+ 6,
+ 147797,
+ 88203,
+ 7,
+ 645,
+ 70,
+ 21,
+ 3285,
+ 10269,
+ 5,
+ 2
+ ],
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+ "text": "Snowflake Arctic Embed is a family of text embedding models optimized for retrieval workloads across many languages — 日本語のテキストも含まれます。"
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+ ],
+ "text": "how do I rebuild the atlas vector index?"
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pkgs/atlas-search/fixtures/gen_fixtures.py b/pkgs/atlas-search/fixtures/gen_fixtures.py
new file mode 100644
index 000000000..025769489
--- /dev/null
+++ b/pkgs/atlas-search/fixtures/gen_fixtures.py
@@ -0,0 +1,229 @@
+#!/usr/bin/env python3
+"""Generate fixtures/embed-probes.json — the Gate A reference data for the
+atlas-embed crate (developing-today/code#5351, design §5 Gate A).
+
+The reference pipeline is the one that produced the production Atlas
+corpus: ~/atlas/v3/embed_onnx.py — onnxruntime + AutoTokenizer over the
+Snowflake arctic-embed-m-v2.0 snapshot's onnx/model.onnx (fp32 graph),
+CLS pooling, L2 normalize, manual right-padding with 0 and attention
+mask 0. This script replicates that session construction and per-batch
+math exactly (same SessionOptions thread counts as the recorded pass:
+intra=8, inter=2) and records, per probe:
+
+ token_ids — tokenizer output (add_special_tokens=True)
+ single_bits — embedding of the probe alone (batch of 1, unpadded),
+ as IEEE-754 f32 bit patterns (u32)
+ batch_bits — embedding of the probe inside one fixed mixed batch
+ containing every probe in listed order, padded to the
+ batch max length with 0 (embed_onnx.py's per-batch math)
+
+Probe set:
+ spike_* — the five fixed texts from the fork's native-embeddings
+ spike (developing-today/opencode#1554), plus an empty
+ string edge probe
+ corpus_* — real corpus chunks, referenced by chunk id from
+ ~/atlas/v3/corpus.db; the chunk TEXT is deliberately
+ not copied into the repo (it is personal corpus
+ content). The generator re-derives it from corpus.db
+ on every run; the gate data is ids + vectors.
+ query_style — a natural query-shaped sentence embedded prompt-free
+ (document mode; the "query: " prompt path is step 2)
+
+Rerun (on the laptop, amd-6 — never the sandbox):
+ LD_LIBRARY_PATH=: ~/atlas/v3-venv/bin/python gen_fixtures.py
+with ATLAS_MODEL_DIR pointing at the pinned HF snapshot (or the HF
+cache default below). The script fails loudly if any invariant breaks
+(long probe not >512 tokens, tokenizer disagreement between batched
+and per-probe calls, runtime nondeterminism on a repeat embed).
+"""
+import hashlib
+import json
+import os
+import sqlite3
+import struct
+import subprocess
+import sys
+import time
+
+MODEL_REV = "95c2741480856aa9666782eb4afe11959938017f"
+DEFAULT_MODEL_DIR = os.path.expanduser(
+ "~/.cache/huggingface/hub/models--Snowflake--snowflake-arctic-embed-m-v2.0"
+ f"/snapshots/{MODEL_REV}"
+)
+CORPUS_DB = os.path.expanduser("~/atlas/v3/corpus.db")
+INTRA_THREADS = 8
+INTER_THREADS = 2
+
+SPIKE_TEXTS = [
+ ("spike_fox", "The quick brown fox jumps over the lazy dog."),
+ ("spike_query_prefix", "query: what is the capital of France?"),
+ (
+ "spike_semantic",
+ "Embeddings turn text into vectors that capture semantic meaning, "
+ "so similar sentences land close together in a high-dimensional space.",
+ ),
+ ("spike_single_char", "a"),
+ (
+ "spike_japanese",
+ "Snowflake Arctic Embed is a family of text embedding models "
+ "optimized for retrieval workloads across many languages — "
+ "日本語のテキストも含まれます。",
+ ),
+ ("spike_empty", ""),
+]
+GATE_CHUNK_IDS = [6359, 15202, 39430]
+QUERY_STYLE_TEXT = "how do I rebuild the atlas vector index?"
+
+
+def sha256_file(path):
+ h = hashlib.sha256()
+ with open(path, "rb") as f:
+ for block in iter(lambda: f.read(1 << 20), b""):
+ h.update(block)
+ return h.hexdigest()
+
+
+def bits(vec):
+ return [struct.unpack(" 512 and (fallback is None or n < fallback[2]):
+ fallback = (cid, text, n)
+ if scanned >= 400 and fallback is not None:
+ break
+ long_pick = long_pick or fallback
+ assert long_pick, "no corpus chunk with >512 tokens found"
+ print(f"long probe: chunk {long_pick[0]} = {long_pick[2]} tokens (scanned {scanned})")
+ probes.append(("corpus_long", "atlas v3 chunk, >512 tokens", long_pick[0], long_pick[1]))
+ con.close()
+ probes.append(("query_style", "synthetic query-shaped text", None, QUERY_STYLE_TEXT))
+
+ # ---- tokenize ---------------------------------------------------------
+ all_texts = [p[3] for p in probes]
+ bulk = tok(all_texts, add_special_tokens=True)["input_ids"]
+ id_lists = []
+ for i, p in enumerate(probes):
+ ids = tokenize(p[3])
+ assert ids == bulk[i], f"tokenizer disagreement on probe {p[0]}"
+ id_lists.append(ids)
+
+ # ---- embed: singles, one mixed batch (pad 0), diagnostic batch (pad 1)
+ singles = [run_batch([ids])[0] for ids in id_lists]
+ again = run_batch([id_lists[0]])[0]
+ assert bits(again) == bits(singles[0]), "runtime nondeterminism on repeat embed"
+ batch0 = run_batch(id_lists, pad_id=0)
+ batch1 = run_batch(id_lists, pad_id=1)
+
+ out_probes = []
+ print(f"{'probe':<20} {'tokens':>6} single-vs-batch0 max|d| batch pad0-vs-pad1 max|d|")
+ for i, (name, source, cid, text) in enumerate(probes):
+ d_sb = float(np.max(np.abs(singles[i] - batch0[i])))
+ d_pp = float(np.max(np.abs(batch0[i] - batch1[i])))
+ print(f"{name:<20} {len(id_lists[i]):>6} {d_sb:.3e} {d_pp:.3e}")
+ rec = {
+ "name": name,
+ "source": source,
+ "token_ids": [int(x) for x in id_lists[i]],
+ "single_bits": bits(singles[i]),
+ "batch_bits": bits(batch0[i]),
+ }
+ if cid is not None:
+ rec["corpus_chunk_id"] = cid
+ else:
+ rec["text"] = text
+ out_probes.append(rec)
+
+ long_rec = next(p for p in out_probes if p["name"] == "corpus_long")
+ assert len(long_rec["token_ids"]) > 512, "long probe is not >512 tokens"
+
+ fixture = {
+ "provenance": {
+ "generated_by": "pkgs/atlas-search/fixtures/gen_fixtures.py",
+ "reference_pipeline": "~/atlas/v3/embed_onnx.py (production corpus pipeline)",
+ "model": "Snowflake/snowflake-arctic-embed-m-v2.0",
+ "model_rev": MODEL_REV,
+ "onnx_file": "onnx/model.onnx (fp32 graph)",
+ "model_onnx_sha256": sha256_file(os.path.join(model_dir, "onnx", "model.onnx")),
+ "tokenizer_json_sha256": sha256_file(os.path.join(model_dir, "tokenizer.json")),
+ "onnxruntime_version": ort.__version__,
+ "transformers_version": transformers.__version__,
+ "python_tokenizers_version": tokenizers_rs.__version__,
+ "intra_op_threads": INTRA_THREADS,
+ "inter_op_threads": INTER_THREADS,
+ "padding": "right, pad token id 0, attention mask 0 (embed_onnx.py semantics)",
+ "pooling": "CLS (hidden[:, 0, :]), L2 normalize, f32",
+ "truncation": "off — model context is 8192; >8192 tokens is a loud error in the crate",
+ "batch_composition": "single mixed batch, every probe in listed order, padded to batch max length",
+ "generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+ "generator_host": subprocess.run(
+ ["hostname"], capture_output=True, text=True
+ ).stdout.strip(),
+ },
+ "probes": out_probes,
+ }
+ out_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "embed-probes.json")
+ with open(out_path, "w") as f:
+ json.dump(fixture, f, indent=1, ensure_ascii=False)
+ print(f"wrote {out_path} ({os.path.getsize(out_path)} bytes, {len(out_probes)} probes)")
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/pkgs/atlas-search/rust-toolchain.toml b/pkgs/atlas-search/rust-toolchain.toml
new file mode 100644
index 000000000..e2ae97754
--- /dev/null
+++ b/pkgs/atlas-search/rust-toolchain.toml
@@ -0,0 +1,16 @@
+# Rust toolchain configuration for atlas-search.
+#
+# IMPORTANT: DO NOT DELETE THIS FILE - it is required for Nix builds.
+# The flake.nix (step 5, #5355) uses rust-overlay which reads this file
+# to determine the Rust version. Deleting it will break `nix develop`
+# and `nix build`. Mirrors the pkgs/id pin.
+#
+# Components:
+# - rustfmt: Code formatting
+# - clippy: Linting
+
+[toolchain]
+# WARNING: this may cause rustc to build, ensure nixpkg has cached before committing this change.
+channel = "1.99.0" # WARNING: this may cause rustc to build, ensure nixpkg has cached before committing this change.
+components = ["rustfmt", "clippy"]
+profile = "default"