grepai-embeddings-lmstudio

Configure GrepAI to use LM Studio for local embedding generation.

18|2|Updated Jan 28, 2026

Search Tags:#local #gui #embedding #model-management #grepai #lmstudio

Ready to run

npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-embeddings-lmstudio

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: grepai-embeddings-lmstudio
Source: https://github.com/yoanbernabeu/grepai-skills/tree/main/skills/embeddings/grepai-embeddings-lmstudio
Command: npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-embeddings-lmstudio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GrepAI can leverage LM Studio as a local, private embedding provider with a graphical user interface to manage models and endpoints, enabling on-device text representations without exposing data externally.

Core Features & Use Cases

GUI-based embedding management: Install, configure, and switch embedding models directly from LM Studio and reflect changes in GrepAI.
Local, private embeddings: Perform embedding generation entirely on the user's machine for privacy-sensitive projects.
Easy model switching: Seamlessly swap between embedding models (e.g., nomic-embed-text-v1.5, bge-small-en-v1.5) without code changes.
Use Case: Ideal for a secure team indexing a codebase with sensitive data while maintaining full data control.

Quick Start

Launch LM Studio and download a compatible embedding model (e.g., nomic-embed-text-v1.5).
Update GrepAI config to point to LM Studio: embedder: provider: lmstudio model: nomic-embed-text-v1.5 endpoint: http://localhost:1234
Run GrepAI indexing or watch to begin embedding generation against the local server.

Frequently Asked Questions about grepai-embeddings-lmstudio

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I use local embeddings for code search without sending data externally?▼

You can generate local embeddings by configuring GrepAI to use LM Studio as the provider in your .grepai/config.yaml file. This setup ensures on-device text representation for code search without exposing your data externally.

Can I switch embedding models in LM Studio without changing my code?▼

Yes, you can switch embedding models directly from the LM Studio GUI. Seamless model swapping allows you to transition between models like nomic-embed-text-v1.5 and bge-small-en-v1.5 without modifying your project code.

How do I configure GrepAI to connect to an LM Studio embedding server?▼

To connect GrepAI to LM Studio, update your .grepai/config.yaml file. Set the embedder provider to lmstudio, specify your downloaded model name, and point the endpoint to your local LM Studio server address.

Do I need to install any specific dependencies to use GUI-managed local embeddings?▼

You need LM Studio installed and a downloaded embedding model. You must also configure your .grepai/config.yaml file with the correct provider, model, and endpoint settings to enable GUI-managed local embeddings.

When should I use a local embedding provider for codebase indexing?▼

Use a local embedding provider for privacy-sensitive projects requiring full data control. It is ideal for secure teams indexing a codebase with sensitive data while maintaining strict on-device privacy and local model management.