Enums

Enumerations are important for classification tasks or in any situation where you need a choice to be made from a list of options.

Example

from sibila import Models

Models.setup("../models")
model = Models.create("llamacpp:openchat")

model.extract(["red", "blue", "green", "yellow"], 
               "The car color was a shade of indigo")

Result

'blue'

You can pass a list of items in any of the supported native types: str, float, int or bool.

Literals

We can also use Literals:

Example

from typing import Literal

model.extract(Literal["SPAM", "NOT_SPAM", "UNSURE"], 
              "Hello my dear friend, I'm contacting you because I want to give you a million dollars",
              inst="Classify this text on the likelihood of being spam")

Result

'SPAM'

Extracting to a Literal type returns one of its possible options in its native type (str, float, int or bool).

Enum classes

Or Enum classes of native types. An example of extracting to Enum classes:

Example

from enum import IntEnum

class Heads(IntEnum):
    SINGLE = 1
    DOUBLE = 2
    TRIPLE = 3

model.extract(Heads,
              "The Two-Headed Monster from The Muppets.")

Result

<Heads.DOUBLE: 2>

For the model, the important information is actual the value of each enum member, not its name. For example, in this enum, the model would only see the strings to the right of each member (the enum values), not "RED", "ORANGE" nor "GREEN":

class Light(Enum):
    RED = 'stop'
    YELLOW = 'slow down'
    GREEN = 'go'

See the Tag classification example to see how Enum is used to tag support queries.

Classify

You can also use the classify() method to extract enumerations, which accepts the enum types we've seen above. It calls extract() internally and its only justification is to make things more readable:

Example

model.classify(["mouse", "cat", "dog", "bird"],
               "Snoopy")

Result

'dog'