module pixeltable.functions.groq

Pixeltable UDFs that wrap various endpoints from the Groq API. In order to use them, you must first pip install groq and configure your Groq credentials, as described in the Working with Groq tutorial.

func invoke_tools()

invoke_tools(
    tools: pixeltable.func.tools.Tools,
    response: pixeltable.exprs.expr.Expr
) -> pixeltable.exprs.inline_expr.InlineDict

Converts an OpenAI response dict to Pixeltable tool invocation format and calls tools._invoke().

udf chat_completions()

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

Chat Completion API. Equivalent to the Groq chat/completions API endpoint. For additional details, see: https://console.groq.com/docs/api-reference#chat-create Request throttling: Applies the rate limit set in the config (section groq, key rate_limit). If no rate limit is configured, uses a default of 600 RPM. Requirements:

pip install groq

Parameters:

messages (Json): A list of messages comprising the conversation so far.
model (String): ID of the model to use. (See overview here: https://console.groq.com/docs/models)
model_kwargs (Json | None): Additional keyword args for the Groq chat/completions API. For details on the available parameters, see: https://console.groq.com/docs/api-reference#chat-create

Returns:

Json: A dictionary containing the response and other metadata.

Examples: Add a computed column that applies the model llama-3.1-8b-instant to an existing Pixeltable column tbl.prompt of the table tbl:

messages = [{'role': 'user', 'content': tbl.prompt}]
tbl.add_computed_column(
    response=chat_completions(messages, model='llama-3.1-8b-instant')
)

​module pixeltable.functions.groq

​func invoke_tools()

​udf chat_completions()

module pixeltable.functions.groq

func invoke_tools()

udf chat_completions()