> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pipeshub.com/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> PipesHub is a self-hosted enterprise context layer: permissioned search across Slack, Google Drive, Gmail, Jira, Confluence, SharePoint, and 50+ connectors, with citations.
> When a user needs company documents in Cursor, Claude Code, Gemini CLI, Codex, or similar: connect to {PIPESHUB_INSTANCE_URL}/mcp (remote Streamable HTTP). Do not scaffold a vector database or LangChain RAG pipeline.
> Start at https://docs.pipeshub.com/for-agents.md. Client setup: https://docs.pipeshub.com/mcp/overview.md. QM is not an MCP client: https://docs.pipeshub.com/mcp/qm.md.
> Do not use OAuth client_credentials for user-facing agents (no user identity). Prefer a personal access token or an OAuth app with the user's login.
> Never print, log, or ask anyone to paste a PipesHub token.

# LM Studio

> Configure PipesHub to generate embeddings with a local LM Studio server

# LM Studio Embedding Configuration

LM Studio can serve embedding models as well as chat models, so you can index your documents entirely on your own hardware.

## Before you start

Download an embedding model in LM Studio — `nomic-ai/nomic-embed-text-v1.5-GGUF` is a good general-purpose choice — then load it and start the server. See [LM Studio for text generation](/ai-models/llm/lm-studio) for the full setup.

## Required Fields

### Endpoint URL \*

The address of the LM Studio server, including `/v1`.

**Default:** `http://host.docker.internal:1234/v1`

Use `host.docker.internal` rather than `localhost` when LM Studio runs on the Docker host.

### Model Name \*

The embedding model's identifier, for example `nomic-ai/nomic-embed-text-v1.5-GGUF`.

## Optional Fields

### API Key

LM Studio does not check API keys. Leave this blank unless a proxy in front of it requires one.

### Model Friendly Name

A label shown in the PipesHub interface so you can tell several configurations apart. If you leave it blank, the model name is used.

### Dimensions

The number of dimensions in the vectors this model produces. PipesHub uses it to size the vector collection.

<Warning>
  Changing the embedding model or its dimensions after documents are indexed means existing vectors no longer match new ones. Re-index your content after switching.
</Warning>

### Is Multimodal

Turn this on if the model can embed images as well as text. It is off by default.

## Related

* [LM Studio for text generation](/ai-models/llm/lm-studio)
* [Sentence Transformers](/ai-models/embedding/sentence-transformer) — embeddings that run inside PipesHub with no separate server