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deepseek

pixeltable.functions.deepseek

chat_completions async 

chat_completions(
    messages: Json,
    *,
    model: String,
    frequency_penalty: Optional[Float] = None,
    logprobs: Optional[Bool] = None,
    top_logprobs: Optional[Int] = None,
    max_tokens: Optional[Int] = None,
    presence_penalty: Optional[Float] = None,
    response_format: Optional[Json] = None,
    stop: Optional[Json] = None,
    temperature: Optional[Float] = None,
    tools: Optional[Json] = None,
    tool_choice: Optional[Json] = None,
    top_p: Optional[Float] = 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.

For details on the other parameters, see: https://api-docs.deepseek.com/api/create-chat-completion

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'))