snowflake.cortex.CompleteOptions¶

class snowflake.cortex.CompleteOptions(*args, **kwargs)¶

Bases: dict

Options configuring a snowflake.cortex.Complete call.

Methods

clear() → None.  Remove all items from D.¶

copy() → a shallow copy of D¶

fromkeys(value=None, /)¶

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)¶

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items¶

keys() → a set-like object providing a view on D's keys¶

pop(k[, d]) → v, remove specified key and return the corresponding value.¶

If key is not found, d is returned if given, otherwise KeyError is raised

popitem()¶

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)¶

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.¶

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values¶

Attributes

max_tokens: typing_extensions.NotRequired[int]¶

Sets the maximum number of output tokens in the response. Small values can result in truncated responses.

temperature: typing_extensions.NotRequired[float]¶

A value from 0 to 1 (inclusive) that controls the randomness of the output of the language model. A higher temperature (for example, 0.7) results in more diverse and random output, while a lower temperature (such as 0.2) makes the output more deterministic and focused.

top_p: typing_extensions.NotRequired[float]¶

A value from 0 to 1 (inclusive) that controls the randomness and diversity of the language model, generally used as an alternative to temperature. The difference is that top_p restricts the set of possible tokens that the model outputs, while temperature influences which tokens are chosen at each step.