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"""
Functions operating on the verbalisation of an ontology, providing support for
generating documentation, extracting competency questions, and preliminarly
testing an ontology via competency questions.
"""
import config
from openai import OpenAI
cqe_prompt_a = "You are asked to provide a comprehensive list of competency "\
"questions describing all the possible requirements that can be "\
"addressed by the ontology described before."
cqt_prompt_a = "You are asked to infer if the ontology described before can "\
"address the following competency question: \"{}\" "\
"Valid answers are: Yes, No."
class ChatInterface:
def __init__(self,
api_key: str,
model_name: str = config.DEFAULT_MODEL,
sampling_seed: int = config.DEFAULT_SEED,
temperature: int = config.DEFAULT_TEMPERATURE):
# Save client configuration for all calls
self.client = OpenAI(api_key=api_key)
self.model_name = model_name
self.sampling_seed = sampling_seed
self.temperature = temperature
def chat_completion(self, messages, **kwargs):
model = kwargs["model"] if "model" in kwargs else self.model_name
temperature = kwargs["temperature"] if "temperature" in kwargs \
else self.temperature # this do not alter the class defaults
completion = self.client.chat.completions.create(
model=model,
messages=messages,
seed=self.sampling_seed,
temperature=temperature,
)
return completion.choices[0].message.content
def extract_competency_questions(onto_verbalisation: str,
chat_interface: ChatInterface,
prompt: str = cqe_prompt_a):
"""
Extract competency questions from the verbalisation of an ontology.
Parameters
----------
onto_verbalisation : str
A string expressing the ontology verbalisation as output from a
supported method in the `verbaliser` module.
chat_interface : ChatInterface
An instance of a chat interface holding the API session.
prompt : str, optional
CQ extraction prompt, by default cqe_prompt_a
Returns
-------
competency_questions : str
A list of competency questions induced from the verbalisation.
"""
full_prompt = onto_verbalisation + "\n" + prompt
conversation_history = [
{"role": "system", "content": "You are an ontology expert."},
{"role": "user", "content": full_prompt}
]
competency_questions = chat_interface.chat_completion(
conversation_history, model="gpt-3.5-turbo-16k")
return competency_questions
def test_competency_questions(onto_verbalisation: str,
competency_questions: list[str],
chat_interface: ChatInterface,
cq_prompt: str = cqt_prompt_a):
"""
Performs a preliminary test of the ontology to assess whether its
verbalisation allows for addressing each competency questions given.
Parameters
----------
onto_verbalisation : str
A string expressing the ontology verbalisation as output from a
supported method in the `verbaliser` module.
competency_questions: list[str]
A list of competency questions to use for preliminary testing.
chat_interface : ChatInterface
An instance of a chat interface holding the API session.
cq_prompt : str, optional
CQ test prompt, by default cqt_prompt_a
Returns
-------
cq_test_dict : dict
A dictionary holding an outcome (yes/no) as a preliminary test of each
competency question. Keys correspond to CQs.
"""
cq_test_dict = {}
for cq in competency_questions:
full_prompt = onto_verbalisation + "\n" + cq_prompt.format(cq)
conversation_history = [
{"role": "system", "content": "You are an ontology engineer."},
{"role": "user", "content": full_prompt}
]
outcome = chat_interface.chat_completion(
conversation_history, model="gpt-3.5-turbo-16k")
cq_test_dict[cq] = outcome
return cq_test_dict
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