moca-reader

The MOCA Reader for Python: open a package from a directory, a .moca archive or a host-supplied source; check it; compute its digest; verify attestations and evidence; resolve members; answer structure operations; produce citation records; and search with one entry point over lexical, dense-sidecar, store or hybrid backends.

It implements the Reader class of the Reader contract, follows the Reader interface with Python names, passes the whole conformance corpus, and returns the same ranked results as the reference Reader, moca-reader-typescript.

Install

pip install moca-reader

Python 3.11 or later. Getting started takes you from install to a cited search result and a conformance run in five steps.

from moca_reader import Library, read_package

pkg = read_package("support-kb.moca", trust_root="trust-root.json")
print(pkg.valid, pkg.digest, pkg.capabilities)

library = Library().add(pkg)
for record in library.search("refund window", audiences=["public"]):
    print(record["node"]["title"], record["node"]["ref"], record["score"])

A malformed package never raises: pkg.valid is False and pkg.diagnostics says why. The Reader never reaches the network, or any file outside the package, because a package names it. Citation records are plain dicts, the same JSON every MOCA Reader produces (citation-record.schema.json).

The API

The API is synchronous.

Framework bindings

Separate packages, each a thin adapter over the Reader (ADR-0002):

Both wrap a Search you build, so the gate and your audience filter always apply, and carry the whole citation record and the consuming guide's flat moca_* fields in each result's metadata. flat_metadata(record) gives you the same fields for your own vector store.

Search by meaning with a standard model

With the onnx extra, moca_reader.embedders runs MOCA's four standard embedding models locally, the way every MOCA tool does, so a query embedded here matches an index built with moca-index --model:

pip install "moca-reader[onnx]"
npx @openmoca/moca-embed models add bge-small-en-v1.5   # once: download and check the model

from moca_reader import DenseBackend, HybridBackend, LexicalBackend
from moca_reader.embedders import standard_embedder

embedder = standard_embedder("bge-small-en-v1.5")  # files checked against their pinned SHA-256
hybrid = HybridBackend(LexicalBackend(library), DenseBackend(library, embedder))

Models are read from ~/.moca/models (or MOCA_MODELS). The Reader never downloads them.

Lexical search

The built-in lexical backend is BM25 (k1 1.2, b 0.75) over node text, or over sidecar chunks when a sidecar is bound. Every MOCA Reader tokenises the same way, so they return the same results:

• NFKC-normalise, then lower-case each code point (simple case mapping).
• Letters, digits and combining marks make words; anything else separates them. Words in scripts with vowel signs, such as Devanagari, stay whole.
• Chinese, Japanese, Thai, Lao, Khmer and Myanmar text is split into each character (with its marks) and each pair of neighbouring characters.
• No stemming. A host that needs it supplies its own lexical backend.
• Without a locale, every language of a node is searched and its best match returned; with one, the representation package spec §5 selects.

Characters added in very recent Unicode versions can tokenise differently across Readers, because each language runtime ships its own Unicode data.

Conformance

As Reader contract §14 asks:

The conformance adapter ships with the package. From a moca-spec checkout:

node scripts/run-conformance.mjs --reader "python -m moca_reader.conformance"

Develop

You need uv. The tests read moca-spec's examples and run its conformance corpus. Check out moca-spec beside this repository, or set MOCA_SPEC_DIR. CI uses the tags pinned at the top of .github/workflows/test.yml (MOCA_SPEC_REF and the like); check out the same tags to match it.

uv sync
uv run pytest
uv run ruff check && uv run ruff format --check
uv run mypy

tests/parity/parity.mjs runs 18 search scenarios (87 queries) over moca-spec's examples through this Reader and the reference Reader, and fails on any difference in records, order, spans, text or scores. Dense sidecars use a deterministic hashing embedder, so no model is needed. With moca-reader-typescript and moca-tools (after npm ci) beside moca-spec:

node tests/parity/parity.mjs

Release

1. Add a dated section to CHANGELOG.md and update version in pyproject.toml and __version__ in src/moca_reader/__init__.py.
2. Point the MOCA_*_REF pins in .github/workflows/test.yml at the release tags (moca-spec docs/releasing.md).
3. The tests, corpus and parity pass.
4. Tag vX.Y.Z and publish a GitHub release whose notes are the changelog section.
5. uv build --all-packages and uv publish from the tagged commit: moca-reader first, then its bindings, which release at the same version.

Licence

Apache-2.0. See LICENSE and NOTICE.

OpenMOCA™ is a trade mark of PI & Mash Solutions Ltd. The licence covers the code, not the name: see the trade mark policy.