> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Working with Nebius
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| input |
response |
| What is the capital of France? |
The capital of France is Paris. |
| Explain quantum computing in one sentence. |
Quantum computing is a type of computing that uses the principles of
quantum mechanics, such as superposition and entanglement, to process
information in ways that classical computers cannot, potentially solving
certain complex problems much faster. |
`, `
| input |
embedding |
| Nebius Token Factory provides language and embedding models. |
[ 0.001 0.004 -0.008 -0.036 0.012 -0.021 ... -0.003 0.004 0.014
-0.004 0.008 -0.015] |
`, `
| input |
sim |
| Another sentence for you to index. |
0.811 |
| Nebius Token Factory provides language and embedding models. |
0.573 |
`];
Pixeltable’s Nebius Token Factory integration enables you to access
Nebius language and embedding models via an OpenAI-compatible API.
### Prerequisites
* A Nebius Token Factory account with an API key
([https://tokenfactory.nebius.com/](https://tokenfactory.nebius.com/))
### Important notes
* Nebius usage may incur costs based on your Nebius plan.
* Be mindful of sensitive data and consider security measures when
integrating with external services.
First you’ll need to install the required libraries and enter a Nebius
API key. Nebius uses the OpenAI SDK as its Python API, so we need to
install it in addition to Pixeltable.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
%pip install -qU pixeltable openai
```
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
import getpass
import os
if 'NEBIUS_API_KEY' not in os.environ:
os.environ['NEBIUS_API_KEY'] = getpass.getpass('Nebius API Key:')
```
Now let’s create a Pixeltable directory to hold the tables for our demo.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
import pixeltable as pxt
# Remove the 'nebius_demo' directory and its contents, if it exists
pxt.drop_dir('nebius_demo', force=True)
pxt.create_dir('nebius_demo')
```
## Chat completions
Create a Table: In Pixeltable, create a table with columns to represent
your input data and the columns where you want to store the results from
Nebius.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
from pixeltable.functions import nebius
chat_t = pxt.create_table('nebius_demo/chat', {'input': pxt.String})
messages = [{'role': 'user', 'content': chat_t.input}]
chat_t.add_computed_column(
output=nebius.chat_completions(
messages=messages,
model='Qwen/Qwen3-30B-A3B-Instruct-2507',
model_kwargs={
# Optional dict with parameters for the Nebius API
'max_tokens': 300,
'temperature': 0.7,
},
)
)
chat_t.add_computed_column(
response=chat_t.output.choices[0].message.content
)
```
Created table 'chat'.
Added 0 column values with 0 errors in 0.01 s
Added 0 column values with 0 errors in 0.00 s
No rows affected.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
# Start a conversation
chat_t.insert(
[
{'input': 'What is the capital of France?'},
{'input': 'Explain quantum computing in one sentence.'},
]
)
chat_t.select(chat_t.input, chat_t.response).head()
```
Inserted 2 rows with 0 errors in 4.43 s (0.45 rows/s)
## Embeddings
Nebius currently serves the embedding model `Qwen/Qwen3-Embedding-8B`.
By default it produces 4096-dimensional embeddings, which exceed
Pixeltable’s embedding-index limit of 4000 dimensions. Request a smaller
size via `model_kwargs` when you need an indexable embedding.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
emb_t = pxt.create_table('nebius_demo/embeddings', {'input': pxt.String})
emb_t.add_computed_column(
embedding=nebius.embeddings(
input=emb_t.input, model='Qwen/Qwen3-Embedding-8B'
)
)
```
Created table 'embeddings'.
Added 0 column values with 0 errors in 0.00 s
No rows affected.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
emb_t.insert(
[
{
'input': 'Nebius Token Factory provides language and embedding models.'
}
]
)
```
Inserted 1 row with 0 errors in 8.15 s (0.12 rows/s)
1 row inserted.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
emb_t.head()
```
To build an embedding index, truncate to an indexable size (for example
1024\) with `model_kwargs`:
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
indexed = nebius.embeddings.using(
model='Qwen/Qwen3-Embedding-8B', model_kwargs={'dimensions': 1024}
)
emb_t.add_embedding_index('input', embedding=indexed)
```
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
emb_t.insert([{'input': 'Another sentence for you to index.'}])
```
Inserted 1 row with 0 errors in 3.62 s (0.28 rows/s)
1 row inserted.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
sim = emb_t.input.similarity(string='Indexing sentences is fun.')
emb_t.select(emb_t.input, sim=sim).order_by(sim, asc=False).collect()
```
### Learn more
To learn more about advanced techniques like RAG operations in
Pixeltable, check out the [RAG Operations in
Pixeltable](/howto/use-cases/rag-operations)
tutorial.
If you have any questions, don’t hesitate to reach out.