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# fabric
> 
# module pixeltable.functions.fabric
Pixeltable UDFs that wrap Azure OpenAI endpoints via Microsoft Fabric.
These functions provide seamless access to Azure OpenAI models within Microsoft Fabric
notebook environments. Authentication and endpoint discovery are handled automatically
using Fabric's built-in service discovery and token utilities.
**Note:** These functions only work within Microsoft Fabric notebook environments.
For more information on Fabric AI services, see:
[https://learn.microsoft.com/en-us/fabric/data-science/ai-services/ai-services-overview](https://learn.microsoft.com/en-us/fabric/data-science/ai-services/ai-services-overview)
## udf chat\_completions()
```python Signature theme={null}
@pxt.udf
chat_completions(messages: pxt.Json[(Json, *, model: pxt.String, api_version: pxt.String | None = None, model_kwargs: pxt.Json | None = None) -> pxt.Json
```
Creates a model response for the given chat conversation using Azure OpenAI in Fabric.
Equivalent to the Azure OpenAI `chat/completions` API endpoint.
For additional details, see: [https://learn.microsoft.com/en-us/azure/ai-services/openai/reference](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference)
**Automatic authentication:** Authentication is handled automatically in Fabric notebooks using
token-based authentication. No API keys are required.
**Supported models in Fabric:**
* `gpt-5` (reasoning model)
* `gpt-4.1`
* `gpt-4.1-mini`
Request throttling:
Applies the rate limit set in the config (section `fabric.rate_limits`, key `chat`). If no rate
limit is configured, uses a default of 600 RPM.
**Requirements:**
* Microsoft Fabric notebook environment
* `synapse-ml-fabric` package (pre-installed in Fabric)
**Parameters:**
* **`messages`** (`pxt.Json[(Json`): A list of message dicts with 'role' and 'content' keys, as described in the
Azure OpenAI API documentation.
* **`model`** (`Any`): The deployment name to use (e.g., 'gpt-5', 'gpt-4.1', 'gpt-4.1-mini').
* **`api_version`** (`Any`): Optional API version override. If not specified, defaults to '2025-04-01-preview'
for reasoning models (gpt-5) and '2024-02-15-preview' for standard models.
* **`model_kwargs`** (`Any`): Additional keyword args for the Azure OpenAI `chat/completions` API.
For details on available parameters, see:
[https://learn.microsoft.com/en-us/azure/ai-services/openai/reference](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference)
**Note:** Reasoning models (gpt-5) use `max_completion_tokens` instead of `max_tokens`
and do not support the `temperature` parameter.
**Returns:**
* `pxt.Json`: A dictionary containing the response and other metadata.
**Examples:**
Add a computed column that applies the model `gpt-4.1` to an existing Pixeltable column `tbl.prompt` of the table `tbl`:
```python theme={null}
from pixeltable.functions import fabric
messages = [
{'role': 'system', 'content': 'You are a helpful assistant.'},
{'role': 'user', 'content': tbl.prompt},
]
tbl.add_computed_column(
response=fabric.chat_completions(messages, model='gpt-4.1')
)
```
Using a reasoning model (gpt-5):
```python theme={null}
tbl.add_computed_column(
reasoning_response=fabric.chat_completions(
messages,
model='gpt-5',
model_kwargs={'max_completion_tokens': 5000},
)
)
```
## udf embeddings()
```python Signature theme={null}
@pxt.udf
embeddings(
input: pxt.String,
*,
model: pxt.String = 'text-embedding-ada-002',
api_version: pxt.String = '2024-02-15-preview',
model_kwargs: pxt.Json | None = None
) -> pxt.Array[(None,), float32]
```
Creates an embedding vector representing the input text using Azure OpenAI in Fabric.
Equivalent to the Azure OpenAI `embeddings` API endpoint.
For additional details, see: [https://learn.microsoft.com/en-us/azure/ai-services/openai/reference](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference)
**Automatic authentication:** Authentication is handled automatically in Fabric notebooks using
token-based authentication. No API keys are required.
**Supported models in Fabric:**
* `text-embedding-ada-002`
* `text-embedding-3-small`
* `text-embedding-3-large`
Request throttling:
Applies the rate limit set in the config (section `fabric.rate_limits`, key `embeddings`). If no rate
limit is configured, uses a default of 600 RPM. Batches up to 32 inputs per request for efficiency.
**Requirements:**
* Microsoft Fabric notebook environment
* `synapse-ml-fabric` package (pre-installed in Fabric)
**Parameters:**
* **`input`** (`pxt.String`): The text to embed (automatically batched).
* **`model`** (`pxt.String`): The embedding model deployment name (default: 'text-embedding-ada-002').
* **`api_version`** (`pxt.String`): The API version to use (default: '2024-02-15-preview').
* **`model_kwargs`** (`pxt.Json | None`): Additional keyword args for the Azure OpenAI `embeddings` API.
For details on available parameters, see:
[https://learn.microsoft.com/en-us/azure/ai-services/openai/reference](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference)
**Returns:**
* `pxt.Array[(None,), float32]`: An array representing the embedding vector for the input text.
**Examples:**
Add a computed column that applies the model `text-embedding-ada-002` to an existing Pixeltable column `tbl.text` of the table `tbl`:
```python theme={null}
from pixeltable.functions import fabric
tbl.add_computed_column(embed=fabric.embeddings(tbl.text))
```
Add an embedding index to an existing column `text`:
```python theme={null}
tbl.add_embedding_index(
'text',
embedding=fabric.embeddings.using(model='text-embedding-ada-002'),
)
```
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