Embedders

An embedder turns text into a dense vector so memory can do semantic (vector) search alongside the FTS5 keyword index. Embedders implement the EmbedderProvider contract from @graphorin/core/contracts, so the rest of the framework never depends on a specific backend.

Embedders are opt-in. With no embedder configured, vector search is disabled and memory falls back to keyword search only.

import { createMemory } from '@graphorin/memory';
import { createOllamaEmbedder } from '@graphorin/embedder-ollama';

const memory = createMemory({
  store,
  embeddings: store.embeddings,
  embedder: createOllamaEmbedder({ model: 'nomic-embed-text' }),
});

Choosing a backend

	@graphorin/embedder-ollama	@graphorin/embedder-transformersjs
Runs where	A local Ollama daemon over HTTP	In-process (@huggingface/transformers)
Default model	nomic-embed-text (768d)	Xenova/multilingual-e5-base (768d)
First-call cost	HTTP round-trip + /api/show digest probe	One-time model download, then cached
Best when	Ollama is already part of your stack	You want zero external services
Network	Localhost HTTP to the daemon	Model download on first use (then offline)

Both are first-class, MIT-licensed, and privacy-respecting: the only network traffic is to the local Ollama daemon or the one-time Hugging Face model download. See Privacy & no-phone-home.

Configuration

Ollama

createOllamaEmbedder({
  model: 'nomic-embed-text',
  baseUrl: 'http://127.0.0.1:11434', // default
  timeoutMs: 30_000,                 // per-request hard timeout (default)
});

timeoutMs aborts any /api/show, /api/embed, or legacy /api/embeddings call that the daemon does not answer in time, so a hung daemon never stalls a memory write. Set timeoutMs: 0 to disable. A per-call signal (passed by the runtime) is combined with the timeout.

Transformers.js

createTransformersEmbedder({
  model: 'Xenova/multilingual-e5-base',
  cacheDir: process.env.GRAPHORIN_CACHE_DIR, // honoured automatically
});

Set GRAPHORIN_CACHE_DIR to control where models are cached and to pre-stage them in offline / air-gapped deployments (download once on a networked machine, copy the cache, run offline thereafter).

Embedder identity & migrations

Each embedder reports a stable canonical id (embedderId()) derived from its model + config (for Ollama, including the resolved model digest). Memory keys vector rows by that id, so swapping models or upgrading the underlying model triggers the configured migration policy rather than silently mixing incompatible vectors:

• lock-on-first — the first embedder id wins; a mismatch refuses to start.
• multi-active — multiple embedder ids coexist (read across, write newest).
• auto-migrate — re-embed existing rows into the new id in the background.

See the Memory system guide for how vector search, the RRF fusion step, and rerankers fit together.