---
title: Local Embeddings
description: Turn text into vectors on this device — free and private
---

Turn text into vectors on your own device, so a knowledge base can find passages by meaning without an API key or sending anything out.

## What it does

Local Embeddings plugs into the **Embeddings** slot of a [Local Knowledge](/nodes/builtin/ai/aidependencies/vectorstore/localknowledge/) node. It does not run on its own and has no output. When the knowledge base is filled or searched, an in-browser model turns the text into vectors (lists of numbers that capture meaning). It runs in the background, so the editor stays responsive.

* The default model is **all-MiniLM-L6-v2**. You can also pick the older **Universal Sentence Encoder** (to keep older knowledge bases readable) or any embeddings model you installed.
* A knowledge base remembers the model it was built with and always uses it. A new knowledge base uses the model connected on its first fill, or all-MiniLM-L6-v2 when nothing is connected. Connecting another model later does not re-embed it: the knowledge base keeps its own model and the run log shows a warning.
* The model loads on first use (downloading it if needed), then stays ready for the session.

## When to use it

* A private, offline knowledge base for the [RAG Agent](/nodes/builtin/ai/aiagents/ragnode/) or [Q\&A Agent](/nodes/builtin/ai/aiagents/qanode/).
* Indexing many documents without paying per token.
* You want to pick a specific on-device embeddings model instead of the default.

Since a new knowledge base already uses all-MiniLM-L6-v2 when nothing is connected, you only need this node to choose another on-device model.

## Inputs and settings

Pick the **Model** before the knowledge base's first fill. To switch an existing knowledge base to another model, re-embed it from its settings on the [knowledge bases page](/app/knowledge-bases/overview/). Install more models from the [Local Models](/app/local-models/) page, or download them from the picker.

## Outputs

This node has no output port. It plugs into a slot on another node instead of passing items along.

* **Embeddings** slot of [Local Knowledge](/nodes/builtin/ai/aidependencies/vectorstore/localknowledge/)

The vectors go to the knowledge base, not to the next node.

## Dependencies and credentials

None. This node needs no connection and has no slots for other nodes.

## Example workflow

1. A [Context Menu](/nodes/builtin/trigger/contextmenu/) trigger on the page.
2. [Get All Text](/nodes/extension/getalltext/) with **Output field name** `pageText`.
3. [Indexer](/nodes/builtin/ai/aiagents/indexernode/) with **Input** `{{ $input.pageText }}`, a [Recursive Character Text Splitter](/nodes/builtin/ai/aidependencies/textsplitter/recursivecharactertextsplitter/) in its **Text Splitter** slot, and a [Local Knowledge](/nodes/builtin/ai/aidependencies/vectorstore/localknowledge/) pointing to a new knowledge base in its **Knowledge** slot.
4. Local Embeddings in the Local Knowledge's **Embeddings** slot, with the model you want (for example `Xenova/all-MiniLM-L6-v2`).

Later, a [RAG Agent](/nodes/builtin/ai/aiagents/ragnode/) with the same Local Knowledge answers questions about the pages you saved.

## Common issues

* **"Embeddings model "…" isn't available. Install it from Local AI."** The model is not installed in this browser. Install it from [Local Models](/app/local-models/), or download it from the picker.
* **A "Model mismatch" warning in the run log.** The knowledge base was built with another model, so that model was used. Disconnect this node, or re-embed the knowledge base with this model.
* **The first run is slow.** The model is downloading and loading. Later runs in the same session are faster.

## Related nodes

* [Local Knowledge](/nodes/builtin/ai/aidependencies/vectorstore/localknowledge/): the knowledge base it plugs into.
* [Indexer](/nodes/builtin/ai/aiagents/indexernode/): fill a knowledge base from a workflow.
* [Ollama Embeddings](/nodes/builtin/ai/aidependencies/embeddings/ollamaembeddings/): on-device embeddings through the Ollama app.