> For the complete documentation index, see [llms.txt](https://docs.ncsa.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ncsa.ai/experimental-embeddings-via-text-embedding-inference.md).

# Experimental: Embeddings via Text Embedding Inference

We deploy Text Embedding Inference on top of Ray, so we can auto-scale and deploy whatever model you request. There are **cold start times**. Models are "kept hot" for 60 minutes after the last usage before being purged.

* API reference: <https://huggingface.github.io/text-embeddings-inference/>
* Background information: <https://github.com/huggingface/text-embeddings-inference>

### Recommended model

My personal favorite open embeddings model (as of Apr 4, 2024) is [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)

### Usage

The base endpoint for HuggingFace embedding is `https://api.ncsa.ai/llm/v1/embeddings`

```
curl https://api.ncsa.ai/llm/v1/embeddings \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{
     "model": "nomic-ai/nomic-embed-text-v1.5",
     "input": "What is Deep Learning?"
   }'
```

### 🐍 Python

<pre class="language-python"><code class="lang-python">from openai import OpenAI
client = OpenAI(api_key="empty",base_url="https://api.ncsa.ai/llm/v1/")

response = client.embeddings.create(
<strong>    model="nomic-ai/nomic-embed-text-v1.5",
</strong>    input="What is Deep Learning?"
)

print(response.data[0].embedding)
</code></pre>


---

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