kaos-nlp-core 0.1.0a3

High-performance NLP primitives for the Kelvin Agentic OS — SIMD string ops, multi-pattern matching, FST dictionaries, sentence segmentation, BM25 retrieval, fuzzy hashing

kaos-nlp-core

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-core is a high-performance NLP primitives library for KAOS — a pure-Rust core with Python bindings via PyO3/Maturin. It provides the text-processing building blocks the rest of the stack relies on: SIMD-accelerated string operations, multi-pattern matching, finite-state transducers, sentence segmentation, BM25 retrieval, fuzzy hashing, and typed Python wrappers throughout.

It is dependency-light: the BASE install pulls only kaos-nlp-core itself plus the bundled Punkt sentence-segmenter model (~12 MB). Optional extras layer in the rest of the KAOS ecosystem.

Install

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

kaos-nlp-core requires Python 3.13 or newer. The published wheels are cp313-abi3 — one wheel per OS/architecture covers every CPython 3.13+ minor (3.13, 3.14, 3.15, …). No re-release needed when 3.15 ships.

Platform coverage: Linux x86_64 (manylinux + musllinux), Linux aarch64 (manylinux + musllinux), macOS arm64, Windows x86_64, Windows arm64.

Quick start

from kaos_nlp_core import tokenizer, algorithms

# Two output shapes for tokenization:
#   tokenize_words → list[str]        — just the surface forms (fastest)
#   tokenize       → list[TokenSpan]  — .text / .start / .end when you
#                                       need character offsets back into
#                                       the source string
words = tokenizer.tokenize_words("kaos-nlp-core ships fast NLP primitives.")
print(words)
# ['kaos-nlp-core', 'ships', 'fast', 'NLP', 'primitives']

for s in tokenizer.tokenize("kaos-nlp-core ships fast NLP primitives.")[:3]:
    print(f"{s.start}-{s.end}: {s.text!r}")
# 0-13: 'kaos-nlp-core'
# 14-19: 'ships'
# 20-24: 'fast'

# Multi-byte safe (CJK + emoji) — offsets are CHARACTER offsets, not bytes
for s in tokenizer.tokenize("東京 emoji 😀 test"):
    print(f"{s.start}-{s.end}: {s.text!r}")
# 0-2: '東京'
# 3-8: 'emoji'
# 9-10: '😀'
# 11-15: 'test'

# Algorithms always return rich typed results
result = algorithms.levenshtein("kitten", "sitting")
print(f"distance={result.distance} similarity={result.similarity:.4f}")
# distance=3.0 similarity=0.5714

The _words shortcut exists wherever skipping offsets is meaningful work (tokenization). Everywhere else — segmentation (segment_sentences, segment_paragraphs, segment_lines), pattern matching, similarity algorithms — the API only ships the rich typed shape, because the metadata is the value.

Concepts

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

Concept	What it is
Algorithms	kaos_nlp_core.algorithms — Levenshtein, Hamming, Jaro-Winkler, longest common substring, edit-distance variants. SIMD fast paths via stringzilla; ASCII fast paths before Unicode fallbacks.
Tokenizer	kaos_nlp_core.tokenizer — Unicode-aware word/sentence tokenization with byte→char offset translation via build_byte_to_char_table(). Multi-byte safe (Latin diacritics, CJK, emoji).
Segmentation	kaos_nlp_core.segmentation — Punkt sentence segmenter (bundled model models/default.npkt.gz, ~12 MB Apache-2.0 NLTK port).
Matching	kaos_nlp_core.matching — Aho-Corasick multi-pattern matching, FST-backed fuzzy lookup via Levenshtein automata, regex.
Search	kaos_nlp_core.search — BM25 retrieval, Searcher, sentence/paragraph search; pickle-safe with KNC magic header for index files.
Structures	kaos_nlp_core.structures — Vocabulary, InvertedIndex, SparseTermMatrix, SimilarityMatrix. Compact, pickle-safe, bincode-2.0 backed.
Hashing	kaos_nlp_core.hashing — CTPH (context-triggered piecewise hashing) via blake3, MinHash, LSH index, near-duplicate grouping.
Lexicon	kaos_nlp_core.lexicon — query expansion, semantic graph traversal, gazetteer lookups.
Documents	kaos_nlp_core.documents — Document, DocumentCollection with JSONL / HuggingFace loaders.
Quality	kaos_nlp_core.quality — text-quality heuristics (token ratios, Unicode block distribution).

CLI

kaos-nlp-core ships a kaos-nlp administrative CLI plus an optional kaos-nlp-serve MCP server (loopback-only by default; --http requires KAOS_NLP_HTTP_TOKEN as an operator acknowledgement that a reverse proxy is fronting authentication):

kaos-nlp tokenize doc.txt --lowercase --json          # word tokenization with spans
kaos-nlp segment doc.txt --mode sentences             # sentence segmentation (Punkt)
kaos-nlp compare "Robert" "Rupert" --algorithm jaro-winkler
kaos-nlp find "pattern" doc.txt --case-insensitive    # SIMD substring search
kaos-nlp index build corpus.txt --output idx.kncidx   # native persisted index
kaos-nlp search --index idx.kncidx "query terms"      # ranked search (BM25 default)
kaos-nlp hash doc.txt --algorithm ctph                # fuzzy hash
kaos-nlp duplicates ./corpus/ --threshold 0.5         # near-duplicate detection
kaos-nlp encode "Robert" --algorithm soundex          # phonetic encoding
kaos-nlp vocab build doc.txt --type frequency         # build vocabulary
kaos-nlp analyze doc.txt --json                       # text statistics report

kaos-nlp-serve            # MCP server, stdio transport
kaos-nlp-serve --http     # MCP server, streamable HTTP (operator-token gated)

Every command supports --json for machine-readable output. CLI search reads both the native persisted index format (KNC) and legacy .json bundles.

Note: 17 MCP tools are registered by register_nlp_tools(). Until 0.1.0a2, the [mcp] extra is reserved but unpopulated — manually run pip install kaos-core kaos-mcp before using kaos-nlp-serve. Once siblings publish to PyPI, pip install kaos-nlp-core[mcp] will cover the full install. Until then kaos-nlp-serve exits with an actionable install hint if kaos-core or kaos-mcp are missing.

Compatibility & status

Aspect
Python	3.13, 3.14 (informational matrix entries for 3.14t free-threaded and 3.15-dev). One cp313-abi3 wheel per OS/arch covers all 3.13+ minors.
OS	Linux (manylinux + musllinux, x86_64 + aarch64), macOS arm64, Windows x86_64, Windows arm64. macOS x86_64 deliberately skipped (Apple ended Intel sales in 2023).
Maturity	Alpha. The public API is documented in kaos_nlp_core.__all__.
Stability policy	Pre-1.0: minor bumps may change behaviour. Every change is documented in CHANGELOG.md.
Test coverage	298 Rust unit tests + Python pytest suite. Round-trip offset tests cover ASCII, multi-byte Latin, CJK, and emoji.
Type checker	Validated with ty, Astral's Python type checker.

Companion packages

kaos-nlp-core 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-core
cd kaos-nlp-core
uv sync --group dev
uv run maturin develop --release

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):

cargo fmt --check
cargo clippy --no-default-features --all-targets -- -D warnings
cargo test --no-default-features --lib
uv run ruff format --check python/kaos_nlp_core tests
uv run ruff check python/kaos_nlp_core tests
uv run ty check python/kaos_nlp_core tests
uv run pytest tests/

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.