n8n-nodes-js-vector-store

An n8n community node to store, search and manage vector embeddings directly from your workflows — backed by js-vector-store, a zero-dependency pure-JS vector store.

No native binary, no external database: vectors persist to a directory on the n8n host. Great for semantic search / RAG over your own data (catalogs, docs, support tickets, spreadsheets migrated to structured data).

This node stores and searches vectors — it does not generate embeddings. Produce embeddings with an embeddings node (OpenAI, Cohere, Workers AI, etc.) and pass the vector in.

Installation · Operations · Credentials · AI / tool use · Notes

Installation

Follow the community nodes installation guide and install n8n-nodes-js-vector-store.

Self-hosted only. The node reads/writes a vector store directory on the host filesystem, so it requires a self-hosted / Docker n8n (n8n Cloud has no persistent local filesystem).

Operations

• Upsert — insert or update a vector by id, with optional metadata.
• Search — k-nearest neighbours for a query vector. Metric (cosine/euclidean/dotProduct/manhattan), limit, optional Mongo-style metadata filter, optional dimSlice (Matryoshka).
• Get — fetch a vector + metadata by id.
• Delete — remove a vector by id.
• Count — number of vectors in a collection.
• Drop Collection — delete a whole collection.
• List Collections — list collection names in the store.

Vectors and metadata are validated (dimension must match the credential; limit >= 1) and writes are flushed to disk automatically.

Credentials

A single credential Vector Store API:

• Store Directory — absolute path on the n8n host where the store persists (e.g. /data/vectors).
• Dimension — vector length (fixed per store; every vector must match it).

AI / tool use

The node is enabled as an AI tool (usableAsTool). Turn on Read-only to let an AI agent query the store (Search/Get/Count/List) while write operations (Upsert/Delete/Drop) are rejected — safe for RAG retrieval.

Ready-to-import example workflows (Index & Search, Filtered Search, RAG retrieval) live in examples/.

Notes

• Default search is exact (brute-force): fast up to tens of thousands of vectors. For larger sets, approximate indexing (IVF) and quantization are on the roadmap.
• Metadata filters are applied after scoring; with very selective filters raise limit.

License

MIT