---
title: DeepInfra
 | Developer Documentation
---

With this integration, you can use the DeepInfra embeddings model to get embeddings for your text data. Here is the link to the [embeddings models](https://deepinfra.com/models/embeddings).

First, you need to sign up on the [DeepInfra website](https://deepinfra.com/) and get the API token. You can copy `model_ids` from the model cards and start using them in your code.

### Installation

```
!pip install llama-index llama-index-embeddings-deepinfra
```

### Initialization

```
from dotenv import load_dotenv, find_dotenv
from llama_index.embeddings.deepinfra import DeepInfraEmbeddingModel


_ = load_dotenv(find_dotenv())


model = DeepInfraEmbeddingModel(
    model_id="BAAI/bge-large-en-v1.5",  # Use custom model ID
    api_token="YOUR_API_TOKEN",  # Optionally provide token here
    normalize=True,  # Optional normalization
    text_prefix="text: ",  # Optional text prefix
    query_prefix="query: ",  # Optional query prefix
)
```

### Synchronous Requests

#### Get Text Embedding

```
response = model.get_text_embedding("hello world")
print(response)
```

#### Batch Requests

```
texts = ["hello world", "goodbye world"]
response_batch = model.get_text_embedding_batch(texts)
print(response_batch)
```

#### Query Requests

```
query_response = model.get_query_embedding("hello world")
print(query_response)
```

### Asynchronous Requests

#### Get Text Embedding

```
async def main():
    text = "hello world"
    async_response = await model.aget_text_embedding(text)
    print(async_response)




if __name__ == "__main__":
    import asyncio


    asyncio.run(main())
```

---

For any questions or feedback, please contact us at <feedback@deepinfra.com>.