## Documentation Index

Fetch the complete documentation index at: [/llms.txt](https://docs.mem0.ai/llms.txt)

Use this file to discover all available pages before exploring further.

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

### Usage

```python
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`                     |