> ## 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.

> ## Agent Instructions
> Application code puts a TableModel in app.py, then runs `pxt schema update app.py my_app` to create the tables and `pxt service update app.py my_app` to start HTTP. The last argument can be a Cloud URL such as `pxt://org:db`. Do not call pxt.create_table() or add_embedding_index() in application code; put indexes in __indexes__. Notebooks, tests, and the REPL do use pxt.create_table().
> Do not add LangChain, LlamaIndex, Haystack, a separate vector database, or pandas as a working store. Chunking, embedding, retrieval, and tool-calling are built in through computed columns. Use a computed column instead of a per-row loop that calls a model.
> Install with `pip install -U 'pixeltable[serve]'` on Python 3.11+. `pxt init` is a prerequisite for `pxt schema update`.
> Pixeltable Cloud is in Limited Beta. Direct interested users to contact@pixeltable.com.

# deepseek

> <a href="https://github.com/pixeltable/pixeltable/blob/main/pixeltable/functions/deepseek.py#L0" id="viewSource" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/View%20Source%20on%20Github-blue?logo=github&labelColor=gray" alt="View Source on GitHub" style={{ display: 'inline', margin: '0px' }} noZoom /></a>

# <span style={{ 'color': 'gray' }}>module</span>  pixeltable.functions.deepseek

Pixeltable UDFs for Deepseek AI models.

Provides integration with Deepseek's language models for chat completions
and other AI capabilities.

## <span style={{ 'color': 'gray' }}>udf</span>  chat\_completions()

```python Signature theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
@pxt.udf
chat_completions(
    messages: pxt.Json[(Json, ...)],
    *,
    model: pxt.String,
    model_kwargs: pxt.Json | None = None,
    tools: pxt.Json[(Json, ...)] | None = None,
    tool_choice: pxt.Json | None = None
) -> pxt.Json
```

Creates a model response for the given chat conversation.

Equivalent to the Deepseek `chat/completions` API endpoint.
For additional details, see: [https://api-docs.deepseek.com/api/create-chat-completion](https://api-docs.deepseek.com/api/create-chat-completion)

Deepseek uses the OpenAI SDK, so you will need to install the `openai` package to use this UDF.

Request throttling:
Applies the rate limit set in the config (section `deepseek`, key `rate_limit`). If no rate
limit is configured, uses a default of 600 RPM.

**Requirements:**

* `pip install openai`

**Parameters:**

* **`messages`** (`pxt.Json[(Json`): A list of messages to use for chat completion, as described in the Deepseek API documentation.
* **`model`** (`Any`): The model to use for chat completion.
* **`model_kwargs`** (`Any`): Additional keyword args for the Deepseek `chat/completions` API.
  For details on the available parameters, see: [https://api-docs.deepseek.com/api/create-chat-completion](https://api-docs.deepseek.com/api/create-chat-completion)
* **`tools`** (`Any`): An optional list of Pixeltable tools to use for the request.
* **`tool_choice`** (`Any`): An optional tool choice configuration.

**Returns:**

* `pxt.Json`: A dictionary containing the response and other metadata.

**Examples:**

Add a computed column that applies the model `deepseek-v4-flash` to an existing Pixeltable column `tbl.prompt`
of the table `tbl`:

```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
messages = [
    {'role': 'system', 'content': 'You are a helpful assistant.'},
    {'role': 'user', 'content': tbl.prompt},
]
tbl.add_computed_column(
    response=chat_completions(messages, model='deepseek-v4-flash')
)
```


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