## Usage

To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.ai/settings/projects/~current/api-keys).

The `embedding_model_dims` parameter for `vector_store` should be set to `1024` for Together embedder.

**Breaking default change.** The default Together embedding model is now `intfloat/multilingual-e5-large-instruct` ( **1024-dim**), replacing the previous default `togethercomputer/m2-bert-80M-8k-retrieval` ( **768-dim**). If you created a self-hosted vector store with the old default, its collection is 768-dim and will reject the new 1024-dim vectors **recreate/reindex the collection at 1024 dimensions** after upgrading. To defer the change, pin the previous values explicitly (`model="togethercomputer/m2-bert-80M-8k-retrieval"`, `embedding_dims=768`) note Together no longer lists this model among its recommended embeddings, so reindexing at 1024 is the durable path.

### Config

Here are the parameters available for configuring Together embedder:

| Parameter         | Description                                           | Default Value                                           |
|-------------------|-------------------------------------------------------|-------------------------------------------------------|
| `model`           | The name of the embedding model to use                 | `intfloat/multilingual-e5-large-instruct`            |
| `embedding_dims`  | Dimensions of the embedding model                      | `1024`                                               |
| `api_key`        | The Together API key                                  | `None`                                               |

| Parameter         | Description                                           | Default Value                                           |
|-------------------|-------------------------------------------------------|-------------------------------------------------------|
| `model`           | The name of the embedding model to use                 | `intfloat/multilingual-e5-large-instruct`            |
| `embeddingDims`   | Dimensions of the embedding model for vector store configuration | `1024`                                               |
| `apiKey`         | The Together API key                                  | `TOGETHER_API_KEY`                                   |
| `baseURL`        | Base URL for an OpenAI-compatible Together endpoint   | `https://api.together.ai/v1`                          |

```python
import os
from mem0 import Memory

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

config = {
    "embedder": {
        "provider": "together",
        "config": {
            "model": "intfloat/multilingual-e5-large-instruct"
        }
    }
}

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")
```

```typescript
import { Memory } from 'mem0ai/oss';

const config = {
  embedder: {
    provider: 'together',
    config: {
      apiKey: process.env.TOGETHER_API_KEY || '',
      model: 'intfloat/multilingual-e5-large-instruct',
      embeddingDims: 1024,
    },
  },
};

const memory = new Memory(config);
await memory.add("I'm visiting Paris", { userId: "john" });
```