---
title: Text Embedding Inference
 | Developer Documentation
---

This notebook demonstrates how to configure `TextEmbeddingInference` embeddings.

The first step is to deploy the embeddings server. For detailed instructions, see the [official repository for Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference). Or [tei-gaudi repository](https://github.com/huggingface/tei-gaudi) if you are deploying on Habana Gaudi/Gaudi 2.

Once deployed, the code below will connect to and submit embeddings for inference.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-embeddings-text-embeddings-inference
```

```
!pip install llama-index
```

```
from llama_index.embeddings.text_embeddings_inference import (
    TextEmbeddingsInference,
)




embed_model = TextEmbeddingsInference(
    model_name="BAAI/bge-large-en-v1.5",  # required for formatting inference text,
    timeout=60,  # timeout in seconds
    embed_batch_size=10,  # batch size for embedding
)
```

```
embeddings = embed_model.get_text_embedding("Hello World!")
print(len(embeddings))
print(embeddings[:5])
```

```
1024
[0.010597229, 0.05895996, 0.022445679, -0.012046814, -0.03164673]
```

```
embeddings = await embed_model.aget_text_embedding("Hello World!")
print(len(embeddings))
print(embeddings[:5])
```

```
1024
[0.010597229, 0.05895996, 0.022445679, -0.012046814, -0.03164673]
```