okf-embed — Jina v5 text embeddings (Rust core, PyO3)

Exact port of EmbeddingEngine._encode: task prefix → tokenize (8192) → ONNX forward → last-token pooling → L2 → Matryoshka truncate → re-normalise. Pinned against a numpy/transformers replication of that pipeline by tests/test_parity.py (max abs diff ≤ 1e-5, cosine ≥ 0.999999).

The only embedding backend. There is no Python fallback stack, no embedding_backend selector, and no optimum/transformers in the runtime path — a mid-run stack switch would silently mix vector spaces in one index, so the design is fail-fast instead.

Install

Published to PyPI — okfgraph pulls it in automatically (platform wheels for Linux / Windows / macOS-arm64, Python 3.11–3.13):

uv sync          # editable path source, builds via maturin

From source (needs a Rust toolchain 1.85+ and maturin; maturin develop needs pip, which uv venvs lack — build the wheel and install it instead):

cd rust/okf-embed
maturin build --release
uv pip install --python <venv> target/wheels/okf_embed-*.whl --reinstall

Runtime: ONNX Runtime discovery

ort loads dynamically (load-dynamic, same pin as bobine: 2.0.0-rc.13). Resolution order: ORT_DYLIB_PATH first (user override always wins), else the OS loader path. OKFgraph's resolve_ort_dylib() points the var at the pip-installed onnxruntime build when unset, so both bobine and okf-embed share one ORT binary — no version/CUDA drift between ingest and import.

Failure policy

Level	Behaviour
Install	The wheel is a core dependency of OKFgraph; if it is missing or fails to import, the router raises a clear `RuntimeError` with the install hint — never an `ImportError` from deep inside, never a silent fallback.
Device	CUDA is opportunistic: `auto`/`cuda` use it when the loaded ORT registers the EP, else warn (stderr) + CPU. `used_cuda` reports the outcome. Never fatal.
Encode	Fail fast. No fallback at encode time — vectors must stay bit-comparable within one index.
Tokenizer	No transformers in the runtime path, anywhere: internal tokenize + `count_tokens()` (== `tokenizer.encode(t, add_special_tokens=False)`) feed the context-window guard.

Contract notes

Session IO is discovered at load (input_ids + attention_mask required, token_type_ids fed only if declared — v5's export doesn't declare it, which is where generic runners fail). Output prefers last_hidden_state.
truncate_dim validated like the router (32–1024, warning off the Matryoshka ladder). MAX_LENGTH (8192) is exposed for the window guard.
Batch encoding is sequential by design (padded batches waste attention compute on variable-length docs). GIL is released during encode.
input_ids/attention_mask feed as int64; pooling takes the last attended token (mask_sum - 1, clamped ≥ 0).

Testing

Rust unit tests (13, pure — no network, no dylib, no tokenizer file): device parsing, task-prefix idempotence, the L2 → truncate → re-normalise math, contract constants, and open() validation firing before I/O.
```
cd rust/okf-embed && cargo test --locked
```
Runs in CI (Ubuntu, --locked) alongside OKFgraph's pytest jobs.
Python parity (tests/test_parity.py, marked slow): Rust output vs a numpy/transformers replication across dims × tasks × texts, ≤ 1e-5. Needs the omni extra (transformers rides in via sentence-transformers).
Python e2e (tests/test_rust_backend.py, tests/test_rust_e2e.py): wheel import, count_tokens contract, encode against the real model.

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0.2.0

Sep 13, 2026 9 files

This release

0.1.1 This release

Sep 13, 2026 9 files

0.1.0

Sep 12, 2026 9 files

okf-embed 0.1.1

okf-embed — Jina v5 text embeddings (Rust core, PyO3)

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Runtime: ONNX Runtime discovery

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