deepseek

pixeltable.functions.deepseek

chat_completions async

chat_completions(
    messages: Json,
    *,
    model: String,
    model_kwargs: Optional[Json] = None,
    tools: Optional[Json] = None,
    tool_choice: Optional[Json] = None
) -> 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

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

Requirements:

• pip install openai

Parameters:

• messages (Json) –

  A list of messages to use for chat completion, as described in the Deepseek API documentation.

• model (String) –

  The model to use for chat completion.

• model_kwargs (Optional[Json], default: None ) –

  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

• tools (Optional[Json], default: None ) –

  An optional list of Pixeltable tools to use for the request.

• tool_choice (Optional[Json], default: None ) –

  An optional tool choice configuration.

Returns:

• Json –

  A dictionary containing the response and other metadata.

Examples:

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

>>> messages = [
        {'role': 'system', 'content': 'You are a helpful assistant.'},
        {'role': 'user', 'content': tbl.prompt}
    ]
    tbl.add_computed_column(response=chat_completions(messages, model='deepseek-chat'))