moca-embed

MOCA’s four standard embedding models, run locally, for semantic and hybrid search over MOCA packages. An index built with one of them, and a question embedded with the same one, match whichever tool did each: moca-index, a MOCA Reader, or the MOCA MCP server.

Model	Size	Best for
bge-small-en-v1.5	33 MB	English, a good default
all-MiniLM-L6-v2	22 MB	English, the smallest and fastest
nomic-embed-text-v1.5	131 MB	English, long pages
multilingual-e5-small	117 MB	Many languages

Install a model

npx @openmoca/moca-embed models add bge-small-en-v1.5
npx @openmoca/moca-embed models list

Models go in a store folder: ~/.moca/models, or MOCA_MODELS, or --models <folder>. models add is the only command that downloads: the files you name, from Hugging Face at a pinned commit, each checked against its pinned SHA-256 before it is installed.

Search with a TypeScript Reader

import { Library, Search, LexicalBackend, DenseBackend, HybridBackend, readPackage, bindSidecar } from '@openmoca/moca-core';
import { standardEmbedder } from '@openmoca/moca-embed';

const pkg = await readPackage('acme-support.moca', { trustRoot: 'trust-root.json' });
const sidecar = bindSidecar('acme-support.moca.idx', pkg);
const library = new Library().add(pkg, sidecar);

// The same model the index was built with; its files are checked before loading.
const embedder = await standardEmbedder('bge-small-en-v1.5');
const backend = new HybridBackend({ lexical: new LexicalBackend(library), dense: new DenseBackend(library, { embedder }) });
const results = await new Search(library, { backend, audiences: ['public'] }).search('money back after buying');

Build the index with the same model: npx @openmoca/moca-index build acme-support.moca -o acme-support.moca.idx --chunk headings --model bge-small-en-v1.5.

How each model is run

The convention is part of MOCA’s sidecar index specification (§5.1), so every tool agrees: the role’s prefix, the model’s own tokenizer cut as Hugging Face’s Python tokenizer cuts, one text per run, then the model’s pooling and unit length. standardEmbedder() returns an embedder in the Reader’s shape: name, version (onnx-q8), dimensions, queryPrefix, documentPrefix and embed(texts, { role }).

Licence

Apache-2.0. Each model has its own licence: MIT (bge-small, multilingual-e5) or Apache-2.0 (all-MiniLM, nomic-embed).