> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LM Studio

> Configure LM Studio as an embedding provider in Mem0 for local embedding generation with models like nomic-embed-text.

You can use embedding models from LM Studio to run Mem0 locally.

### Usage

```python theme={null}
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM

config = {
    "embedder": {
        "provider": "lmstudio",
        "config": {
            "model": "nomic-ai/nomic-embed-text-v1.5-GGUF"
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="john")
```

### Config

Here are the parameters available for configuring LM Studio embedder:

| Parameter           | Description                            | Default Value                         |
| ------------------- | -------------------------------------- | ------------------------------------- |
| `model`             | The name of the LM Studio model to use | `nomic-ai/nomic-embed-text-v1.5-GGUF` |
| `embedding_dims`    | Dimensions of the embedding model      | `1536`                                |
| `lmstudio_base_url` | Base URL for LM Studio connection      | `http://localhost:1234/v1`            |