kaos-nlp-transformers 0.1.0a1

Dense embeddings and small-model inference for the Kelvin Agentic OS — fastembed-first ONNX, optional torch / GPU / OpenVINO backends

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 fastembed (ONNX-only, no PyTorch in the BASE install) that turns text into float32 vectors and back. It ships a license-vetted model registry, a shared EmbeddingRetriever for cosine similarity search, 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 fastembed (Apache-2.0) and the core KAOS runtime (kaos-core, kaos-content, kaos-nlp-core) plus numpy. Optional extras layer in the rest of the inference stack — [torch] for sentence-transformers

• PyTorch, [gpu] for ONNX Runtime CUDA, [openvino] for Intel OpenVINO, [clustering] for SciPy-backed semantic dedup, and [mcp] for the forthcoming MCP tool surface.

Install

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

kaos-nlp-transformers requires Python 3.13 or newer. The default install is fastembed-only (CPU + ONNX). Add the extras you need:

uv add "kaos-nlp-transformers[torch]"        # GPU inference / non-ONNX models
uv add "kaos-nlp-transformers[gpu]"          # NVIDIA CUDA via onnxruntime-gpu
uv add "kaos-nlp-transformers[openvino]"     # Intel CPU / GPU acceleration
uv add "kaos-nlp-transformers[clustering]"   # SemanticDedupLevel (scipy)
uv add "kaos-nlp-transformers[mcp]"          # MCP tool surface (planned 0.1.0a2+)

Platform coverage: any platform with a CPython 3.13+ wheel and ONNX Runtime support — the fastembed wheel matrix covers Linux x86_64 + aarch64 (manylinux), macOS x86_64 + arm64, and Windows x86_64.

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}")

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 by default, sentence-transformers for GPU / non-ONNX 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 one entry: BAAI/bge-small-en-v1.5 (33M, MIT).
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 (cross-encoder / pair-scoring model, [torch] extra). Use to refine EmbeddingRetriever top-50 → top-10.
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, 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 (audit-01 KNT-005).
Device detection	detect_devices() returns a SystemDevices snapshot (CUDA visible? MPS? OpenVINO? counts and names) so callers can route work appropriately. EmbeddingModel.load(device="auto") picks the best available; explicit "cpu" / "cuda" / "cuda:0" / "mps" / "openvino" are honored.

CLI

kaos-nlp-transformers ships a kaos-nlp-transformers administrative CLI (info subcommand only in 0.1.0a1) plus a placeholder kaos-nlp-transformers-serve for the future MCP server (the [mcp] extra will wire it up):

kaos-nlp-transformers info --json    # version + registry + device snapshot
kaos-nlp-transformers-serve          # placeholder; MCP wiring in 0.1.0a2+

Compatibility & status

Aspect
Python	3.13, 3.14 (informational matrix entries for 3.14t free-threaded and 3.15-dev). 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	56 Python unit tests + 7 audit-01 regression tests (63 total). Live integration tests exercise real fastembed model downloads and a sentence-transformers GPU path; gated on the live and gpu markers respectively.
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. By contributing you 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.