@memofs/adapter-transformers

@memofs/adapter-transformers

         

Local Transformers.js ONNX embedder adapter for MemoFS with no API key or cloud dependency.

What is this?

Zero-config local embedder adapter for MemoFS. Runs a small sentence-embedding model in process via Transformers.js (ONNX runtime) — no API key, no cloud, and no network after the first model download.

This is what powers MemoFS's zero-API-key hybrid recall: install it (or let the runtime lazy-load it) and recall() gains a semantic vector path on top of the default lexical (BM25 + fuzzy) path, with nothing leaving your machine.

Installation

npm install @memofs/adapter-transformers @memofs/core

Requires Node.js >= 22.

This pulls in @huggingface/transformers and ships an ONNX-compatible embedder that implements MemoFS's provider-neutral MemoryEmbedder contract.

Quick Start

Standalone

import { createTransformersEmbedder } from "@memofs/adapter-transformers";

const embedder = createTransformersEmbedder({
  model: "Xenova/all-MiniLM-L6-v2",
});

// Embed a batch of texts (mean-pooled + L2-normalized vectors)
const { embeddings } = await embedder.embedTexts({
  texts: ["MemoFS gives agents durable memory.", "All-MiniLM-L6-v2 is a small model."],
});

console.log(embeddings[0].embedding.length); // 384
console.log(embeddings[0].model);            // "Xenova/all-MiniLM-L6-v2"

The first call downloads the ONNX weights once and caches them; subsequent calls are fully offline.

With MemoFS core

Plug the adapter straight into MemoFS for hybrid recall with your own embedder:

import { MemoFS } from "@memofs/core";
import { createNodeFsMemoryStore } from "@memofs/core/node-fs";
import { createTransformersEmbedder } from "@memofs/adapter-transformers";

const store = createNodeFsMemoryStore({ rootDir: "." });

const memo = new MemoFS({
  store,
  projectId: "my-app",
  embedder: createTransformersEmbedder(),
  recall: { engine: "auto" }, // upgrades to hybrid since an embedder is present
});

await memo.notes.record({ content: "User prefers TypeScript and strict mode." });
const hits = await memo.recall("coding language preference"); // semantic match

Zero-config (no code)

You usually do not need to touch this package directly. The MemoFS runtime can lazy-load it for you — just enable local embeddings:

export MEMOFS_LOCAL_EMBEDDINGS=true
export MEMOFS_RECALL_ENGINE=auto

or in .memofs/config.json:

{
  "$schema": "./node_modules/@memofs/cli/schema/config.json",
  "runtime": "local",
  "recall": { "engine": "auto", "localEmbeddings": true }
}

The runtime imports @memofs/adapter-transformers only when the first embedding is actually requested, so boot stays fast. If the adapter is missing or fails to load, MemoFS falls back to lexical (BM25 + fuzzy) recall — memory stays discoverable and writes are never broken.

Configuration

Embedder options

Option	Type	Default	Description
model	string	"Xenova/all-MiniLM-L6-v2"	Hugging Face model id (or local path under cacheDir) supported by Transformers.js.
cacheDir	string	Transformers.js default	Directory used to cache downloaded ONNX weights.
device	"cpu" | "gpu" | "wasm"	"cpu"	Inference device. Use "gpu" or "wasm" when the runtime supports it.
dtype	"fp32" | "fp16" | "q8" | "int8"	"fp32"	ONNX runtime data type. "fp32" is the safest across platforms.
batchSize	number	32	Maximum texts per inference batch. Larger batches trade memory for throughput.
retries	number	2	Maximum retry attempts when the initial model download/load fails with a transient network error. Set to 0 to disable.
onProgress	(info) => void	—	Callback for model download/load progress. Use it to show a one-time "warming up" notice.

Default model

Xenova/all-MiniLM-L6-v2 is a 384-dimensional sentence-embedding model: small, fast, and good enough for local agent memory. To use a different Transformers.js model, pass its id:

createTransformersEmbedder({ model: "Xenova/all-MiniLM-L12-v2" });

When to use this vs. a provider adapter

Need	Use
Offline / private / zero-cost semantic recall	This package (local ONNX)
Highest-quality embeddings, can call an API	@memofs/adapter-openai or @memofs/adapter-voyage
Persistent local vector recall	pair any embedder with createFsRecallStore from @memofs/core

The local embedder pairs with MemoFS's built-in filesystem recall store (createFsRecallStore, backed by .memofs/indexes/embeddings.jsonl) for a fully local vector memory. For large shared indices, prefer a provider embedder plus a managed vector store.

Testing

The embedder accepts an injectable pipelineFactory (an internal type), so tests never need to download weights or run real inference. Use the bundled fake factory from the public ./testing subpath:

import { createFakePipelineFactory } from "@memofs/adapter-transformers/testing";
import { createTransformersEmbedder } from "@memofs/adapter-transformers";

const embedder = createTransformersEmbedder({
  pipelineFactory: createFakePipelineFactory({ dimensions: 384 }),
});

const { embeddings } = await embedder.embedTexts({ texts: ["hello"] });
console.log(embeddings[0].embedding.length); // 384

The fake produces deterministic, hash-derived vectors — identical strings yield identical vectors, token-overlapping strings are partially similar — enough to exercise recall merging without the real model.

Boundary

This package owns the Transformers.js local embedder adapter. It does not own the MemoFS core MemoryEmbedder contract, other provider adapters, or the Transformers.js runtime itself.

Contributing

See our central Contributing Guide and development scripts for details on formatting, linting, and testing within the monorepo.

License

MIT