kaos-nlp-transformers 0.1.0rc1

Dense embeddings and small-model inference for the Kelvin Agentic OS — Rust-native ONNX backend, model2vec static lookup, optional GPU

kaos-nlp-transformers

Part of Kelvin Agentic OS (KAOS) — open agentic infrastructure for legal work, built by 273 Ventures. See the full KAOS package map for the rest of the stack.

kaos-nlp-transformers is the dense-embedding and small-model inference layer for KAOS — a typed Python API over an in-tree Rust cdylib that calls ort (libonnxruntime via Rust) to turn text into float32 vectors and back. It ships a license-vetted model registry, an optional cross-encoder reranker, and a semantic-dedup level that plugs into kaos-content's deduplication framework.

It is dependency-light at the BASE: the install pulls in only numpy, huggingface_hub, and the core KAOS runtime (kaos-core, kaos-content, kaos-nlp-core). No PyTorch, no Python fastembed, no Python onnxruntime — the inference path is a Rust cdylib (kaos_nlp_transformers._rust) shipped inside the wheel; libonnxruntime is statically linked. Both embedding (EmbeddingModel) and cross-encoder reranking (CrossEncoderReranker) run through the same backend on CPU out of the box. Optional extras layer in adjacencies — [gpu] for the GPU companion wheel (ort/cuda EP, NVIDIA), [openvino] for Intel OpenVINO acceleration, [model2vec] for the static-numpy lookup backend (~500x CPU speedup), [clustering] for SciPy-backed semantic dedup, and [mcp] for the MCP tool surface. Free-threaded Python (3.13t / 3.14t) is supported.

Use, data handling, and authorship disclosure

kaos-nlp-transformers runs inference locally through the bundled Rust cdylib (ONNX Runtime + Rust tokenizers). Once a model is downloaded into the local Hugging Face cache, every subsequent .embed(...) / .rerank(...) call stays in-process — no network, no provider-side data transmission. Model downloads themselves go to the Hugging Face Hub once per (model_id, revision) pair; pin KAOS_NLP_TRANSFORMERS_OFFLINE=1 or HF_HUB_OFFLINE=1 to forbid downloads in production. The SemanticChunker and ExtractiveRanker Programs in this package inherit the same local-only data path. Downstream consumers (notably kaos-llm-core Programs) may transmit text to LLM providers, so callers handling sensitive data should check the consuming package's data-handling disclosure.

This codebase is AI-assisted: substantial portions were generated with Claude (Anthropic) and human-reviewed before commit. Public behavior is covered by the test suite under tests/; live model downloads + GPU paths are opt-in (pytest -m integration and pytest -m gpu). Bug reports welcome via GitHub Issues; security reports follow SECURITY.md.

Install

uv add kaos-nlp-transformers
# or
pip install kaos-nlp-transformers

kaos-nlp-transformers requires Python 3.13 or newer (free-threaded 3.13t / 3.14t supported). The default install is CPU-only via the Rust ort backend. Add the extras you need:

uv add "kaos-nlp-transformers[gpu]"          # NVIDIA CUDA companion wheel (0.2.0a2)
uv add "kaos-nlp-transformers[openvino]"     # Intel CPU / GPU acceleration (0.2.0a2)
uv add "kaos-nlp-transformers[model2vec]"    # Static-numpy backend (~500x CPU)
uv add "kaos-nlp-transformers[clustering]"   # SemanticDedupLevel (scipy)
uv add "kaos-nlp-transformers[mcp]"          # MCP tool surface

0.2.0 migration note (KNT-601). Audit KNT-601 retired the Python fastembed wrapper. Inference now goes through a Rust cdylib (ort + libonnxruntime, statically linked). Same models, same outputs (per-row cosine ≥ 0.9999 vs the prior backend). The EmbeddingModel.load / EmbeddingModel.embed / CrossEncoderReranker public API is unchanged. The [gpu] / [openvino] extras are no-op stubs in 0.2.0a1; the GPU companion wheel ships in 0.2.0a2. The [torch] no-op alias from KNT-501 is still preserved for one more cycle; removed in 0.3.0. The EmbeddingRetriever text-only retriever is deprecated in favor of kaos_content.indexing.SearchableDocument and kaos_content.indexing.SearchableCorpus; removal scheduled for 0.3.0.

Platform coverage: per-platform cp313-abi3 wheels for Linux x86_64 + aarch64 (manylinux + musllinux), macOS aarch64, Windows x86_64 + aarch64. Free-threaded Python (3.13t / 3.14t) loads cleanly — no _check_gil_enabled guard, no py_rust_stemmers SIGSEGV path.

Quick start

import numpy as np
from kaos_nlp_transformers import EmbeddingModel

# Load the v0 default model (BAAI/bge-small-en-v1.5, 33M params, MIT).
# First call downloads and caches; subsequent calls are O(1).
model = EmbeddingModel.load("BAAI/bge-small-en-v1.5")

# Embed a small batch. Returns a float32 numpy array of shape (N, dim).
texts = [
    "Force majeure clauses excuse performance.",
    "Indemnity caps the liability of the seller.",
]
vecs = model.embed(texts)
assert vecs.shape == (2, 384) and vecs.dtype == np.float32

# Cosine similarity over the L2-normalized rows.
def cosine(a, b):
    return float(np.dot(a / np.linalg.norm(a), b / np.linalg.norm(b)))

print(f"sim: {cosine(vecs[0], vecs[1]):.3f}")
# sim: 0.637   (similar legal-contract topic, distinct concepts)

For retrieval over a corpus, build an EmbeddingRetriever:

import asyncio

from kaos_nlp_transformers import EmbeddingRetriever

retriever = EmbeddingRetriever.from_texts(
    texts=[
        "The buyer agrees to mediation in Delaware.",
        "All disputes shall be resolved by arbitration in New York.",
        "Force majeure clauses excuse performance.",
    ],
    doc_ids=[0, 1, 2],
)
hits = asyncio.run(retriever.retrieve("where do contract disputes go?", top_k=2))
for h in hits:
    print(f"{h.score:.3f}  {h.text}")

Phase-8: NLI, NER, and PII

In addition to embedding + reranking, the package ships three small-model inference surfaces for legal-tech and financial document workflows. All three run through the same in-tree Rust ort cdylib — no PyTorch — and emit byte-stable char-offset spans where applicable.

from kaos_nlp_transformers import NliModel, GLiNERExtractor, PiiDetector

# 1) NLI — zero-shot classification via entailment
#    Default: Xenova/nli-deberta-v3-base (Apache-2.0, 184M params)
nli = NliModel.load()
scores = nli.score(
    "Acme Corp shall pay rent of $5,000/month for the leased premises.",
    [
        "This text is about a lease agreement.",
        "This text is about employment.",
    ],
)
for s in scores:
    print(f"  entail={s.entailment:.2f}  neutral={s.neutral:.2f}  contradict={s.contradiction:.2f}")

# 2) GLiNER — zero-shot NER over caller-supplied labels
#    Default: onnx-community/gliner_medium-v2.1 (Apache-2.0, 195M params, 746 MB fp32)
gliner = GLiNERExtractor.load()
[entities] = gliner.extract(
    ["Barack Obama signed the bill on January 1, 2025."],
    labels=["person", "date"],
)
for e in entities:
    print(f"  [{e.score:.2f}] {e.label:<10} {e.text!r}")

# 3) PII — closed-label BERT-small detector (27 MB int8)
#    Default: onnx-community/bert-small-pii-detection-ONNX (Apache-2.0)
#    24 categories: PERSON, EMAIL_ADDRESS, PHONE_NUMBER, US_SSN,
#    CREDIT_CARD, IBAN_CODE, FINANCIAL, LOCATION, ORGANIZATION, ...
pii = PiiDetector.load()
[spans] = pii.detect(["Contact Jennifer Stacey at [e-mail] today."])
for e in spans:
    print(f"  [{e.score:.2f}] {e.label:<15} {e.text!r}")

When to pick which:

• PiiDetector — fastest (~17× per doc vs GLiNER), use whenever the 24 built-in PII categories cover your need. Redaction, compliance, intake screening.
• GLiNERExtractor — zero-shot over any label set you supply. Slower but flexible — use for domain-specific entities ("indemnification party", "termination notice period") and when accuracy matters more than throughput.
• NliModel — entailment-style classification. Pairs with ZeroShotNLIClassifier in kaos-llm-core for label-set classification without LLM cost.

Concepts

The package is built around a small set of typed primitives.

Concept	What it is
EmbeddingModel	The single entry point for inference. EmbeddingModel.load(model_id, *, device=None, backend=None, settings=None) resolves the registry entry, picks a backend (fastembed for ONNX models on CPU/GPU, model2vec for static lookup models), and returns an instance with an .embed(texts, *, batch_size=32) -> np.ndarray method. Backends are process-cached by (model_id, revision, device, cache_dir) so repeated load() calls are O(1).
RegisteredModel / REGISTRY / EXCLUDED	Curated, license-vetted model catalog. Each entry pins a HuggingFace Hub commit SHA (audit-01 KNT-003: revisions thread through the loader cache key). The EXCLUDED map names models intentionally rejected with their licensing reason — jina-v3 (CC-BY-NC), NV-Embed (CC-BY-NC), Qwen3-Embedding (MS MARCO ambiguity). v0 ships BAAI/bge-small-en-v1.5 (33M, MIT, fastembed) plus three model2vec entries (potion-base-8M, potion-base-32M, potion-retrieval-32M). potion-base-8M is vendored inside the wheel (~28 MB), so it loads offline with no network.
EmbeddingRetriever	Brute-force cosine similarity search over a numpy matrix. from_texts(...) and from_corpus(...) factories. For corpora up to ~50K documents this is faster than FAISS overhead. Implements the kaos_nlp_core.search.SearchHit protocol.
CrossEncoderReranker	Optional second-pass reranker via the in-tree Rust ort backend (default BAAI/bge-reranker-base, MIT). No extra required for CPU; [gpu] accelerates on CUDA. Use to refine EmbeddingRetriever top-50 → top-10. Sigmoid-normalized scores in [0, 1].
NliModel	Natural-language-inference cross-encoder for zero-shot classification. .score(premise, hypotheses) returns one (entailment, neutral, contradiction) triple per hypothesis (softmax-normalized, canonical order). Default Xenova/nli-deberta-v3-base (Apache-2.0 chain, 184M params, 244 MB int8). Satisfies the NLIScorer Protocol in kaos-llm-core — drop-in for ZeroShotNLIClassifier.
GLiNERExtractor	Zero-shot named-entity extraction via prompt-based span scoring. .extract(texts, labels=[...]) returns list[list[Entity]] with char-offset spans. Default onnx-community/gliner_medium-v2.1 (Apache-2.0 chain, 195M params, 746 MB fp32 — the int8 quantized export underperforms and was deliberately rejected). Multilingual sibling onnx-community/gliner_multi-v2.1 also registered.
PiiDetector	Closed-label BERT-small token classifier covering 24 PII categories (PERSON, EMAIL_ADDRESS, US_SSN, CREDIT_CARD, IBAN_CODE, FINANCIAL, …). Default onnx-community/bert-small-pii-detection-ONNX (Apache-2.0 chain, 28M params, 27 MB int8). Roughly 17× faster than GLiNERExtractor at the closed-label task; output Entity shape is shared so redaction pipelines consume both interchangeably.
SemanticDedupLevel	Plug-in for kaos-content's deduplication framework. Embeds documents, computes pairwise cosine distance with scipy.spatial.distance.pdist, and clusters with scipy.cluster.hierarchy.fcluster. Requires the [clustering] extra.
KaosNLPTransformersSettings	Typed settings (env prefix KAOS_NLP_TRANSFORMERS_): default_model, default_reranker_model, default_nli_model, default_ner_model, default_pii_model, cache_dir, offline, allow_unregistered, device, backend, profile. Honors legacy HF_HUB_OFFLINE and HF_HOME. When offline=True, the load path sets HF_HUB_OFFLINE=1 and TRANSFORMERS_OFFLINE=1.
Device detection	detect_devices() returns a SystemDevices snapshot (reachable accelerators + ONNX execution providers + latent GPUs the OS sees but the install can't drive). EmbeddingModel.load(device="auto") picks the best available; explicit "cpu" / "cuda" / "cuda:0" / "openvino" are honored. Audit-06 KNT-501 retired mps and xla alongside the torch backend.

CLI

kaos-nlp-transformers ships a kaos-nlp-transformers administrative CLI plus a kaos-nlp-transformers-serve MCP server launcher that requires the [mcp] extra:

# Diagnostic envelope: version, registry, device, ONNX providers
kaos-nlp-transformers info --json

# Pre-warm the HF Hub cache with every registered model — useful in
# Dockerfile builds, CI cache-warming, air-gapped image prep.
kaos-nlp-transformers prefetch                  # all 5 families
kaos-nlp-transformers prefetch --include pii    # one family
kaos-nlp-transformers prefetch --model onnx-community/bert-small-pii-detection-ONNX
kaos-nlp-transformers prefetch --dry-run        # show what would be fetched
kaos-nlp-transformers prefetch --quiet --json   # CI-friendly

# stdio MCP server
kaos-nlp-transformers-serve                     # requires [mcp]

Prefetch honors HF_HOME and KAOS_NLP_TRANSFORMERS_CACHE_DIR and exits non-zero on any model failure (continuing through the rest of the batch so one bad row doesn't sink the whole prefetch).

Compatibility & status

Aspect
Python	3.13, 3.14 — GIL builds only. Free-threaded builds (3.13t / 3.14t / Py_GIL_DISABLED) are not supported: EmbeddingModel.load / CrossEncoderReranker.load raise BackendNotInstalledError because fastembed's transitive py_rust_stemmers and tokenizers C extensions segfault during module init without the GIL. Pending upstream Py_GIL_DISABLED declarations from those extensions; the guard is removed once that lands. Pure-Python py3-none-any wheel.
OS	Any platform with a CPython 3.13+ wheel and ONNX Runtime support — Linux x86_64 + aarch64 (manylinux), macOS x86_64 + arm64, Windows x86_64.
Maturity	Alpha. The public API is documented in kaos_nlp_transformers.__all__.
Stability policy	Pre-1.0: minor bumps may change behaviour. Every change is documented in CHANGELOG.md.
Test coverage	138 unit tests + 24 integration tests (162 total, 77% line coverage). Integration suite hits real fastembed embedding + cross-encoder reranker downloads — no mocks. GPU tests gated on the gpu marker; reranker live tests on live.
Type checker	Validated with ty, Astral's Python type checker.

Companion packages

kaos-nlp-transformers is one of the packages in the Kelvin Agentic OS. The broader stack:

Package	Layer	What it does
kaos-core	Core	Foundational runtime, MCP-native types, registries, execution engine, VFS
kaos-content	Core	Typed document AST: Block/Inline, provenance, views
kaos-mcp	Bridge	FastMCP server, kaos management CLI, MCP resource templates
kaos-pdf	Extraction	PDF → AST with provenance
kaos-web	Extraction	Web extraction, browser automation, search, domain intelligence
kaos-office	Extraction	DOCX / PPTX / XLSX readers + writers to AST
kaos-tabular	Extraction	DuckDB-powered SQL analytics
kaos-source	Data	Government + financial data connectors (Federal Register, eCFR, EDGAR, GovInfo, PACER, GLEIF)
kaos-llm-client	LLM	Multi-provider LLM transport
kaos-llm-core	LLM	Typed LLM programming (Signatures, Programs, Optimizers)
kaos-nlp-core	Primitives (Rust)	High-performance NLP primitives
kaos-nlp-transformers	ML	Dense embeddings + retrieval
kaos-graph	Primitives (Rust)	Graph algorithms + RDF/SPARQL
kaos-ml-core	Primitives (Rust)	Classical ML on the document AST
kaos-citations	Legal	Legal citation extraction, resolution, verification
kaos-agents	Agentic	Agent runtime, memory, recipes
kaos-reference	Sample	Reference module for module authors

Packages depend on kaos-core; everything else is opt-in. Mix and match the ones you need.

Development

git clone https://github.com/273v/kaos-nlp-transformers
cd kaos-nlp-transformers
uv sync --group dev --extra clustering

Install pre-commit hooks (recommended — they run the same checks as CI on every commit, scoped to staged files):

uvx pre-commit install
uvx pre-commit run --all-files     # one-time full sweep

Manual QA commands (the same set CI runs):

uv run ruff format --check kaos_nlp_transformers tests
uv run ruff check kaos_nlp_transformers tests
uv run ty check kaos_nlp_transformers tests
uv run pytest tests/unit -q

Build from source

uv build
uv pip install dist/*.whl

Contributing

Issues and pull requests are welcome. See CONTRIBUTING.md for setup, quality gates, pull request expectations, and engineering standards. By contributing you agree to follow the project conduct expectations and certify the Developer Certificate of Origin v1.1 — sign every commit with git commit -s. Please open an issue before starting on a non-trivial change so we can align on scope.

Security

For security issues, please do not file a public issue. Report privately via GitHub Private Vulnerability Reporting or email [e-mail]. See SECURITY.md for the full disclosure policy.

License

Apache License 2.0 — see LICENSE and NOTICE.

Copyright 2026 273 Ventures LLC. Built for kelvin.legal.