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@danielgross
Created January 23, 2023 15:23
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A chat interface that drives GPT-3 towards more specific answers.
"""Stream a response from the OpenAI completion API."""
import os
import re
import sys
import time
import random
import openai
openai.api_key = open(os.path.expanduser("~/.openai")).read().strip()
def openai_request(prompt, **kwargs):
default = dict(engine="text-davinci-003",
temperature=0,
max_tokens=150,
top_p=1,
frequency_penalty=0,
presence_penalty=0.6,
stream=True
)
# print(prompt)
return openai.Completion.create(prompt=prompt, **{**default, **kwargs})
def is_hedge(input):
prompt_template = """We are detecting sentences that are not definitive or that are hedging. For example:
As a large language model, I am not sure I can answer that question. HEDGING: TRUE
That's a subjective question and one that I'm not qualified to answer. HEDGING: TRUE
That's a difficult question to answer. HEDGING: TRUE
Broadly, most believe that America was a good idea. HEDGING: FALSE
If I had to guess, I'd say that she is a person w ho cares deeply about the issues facing our country and works hard to make positive change. HEDGING: FALSE
Most believe that he was a good person. HEDGING: FALSE
%s HEDGING:"""
prompt = prompt_template % input
response = openai_request(prompt=prompt, stream=False)
text_result = response.choices[0].text.strip()
return True if text_result == 'TRUE' else False
def get_stuffed_word(existing_text=None):
# TODO: Do this with a LLM. For now return a bunch of candidates to stuff
# in the LLM's mouth.
options = [
"It's hard to know specifically. That said, if I had to guess,",
"I presume you want me to be specific. Let me speak in percentages:",
"I'll try to avoid being vague here -- specifically, I would say that",
]
if existing_text:
# remove any option that is already in the text
options = [o for o in options if o not in existing_text]
if not options:
raise ValueError("No options left to choose from")
return random.choice(options)
def main():
"""Stream a response from the OpenAI completion API."""
prompt_template = """The following is a conversation with an AI assistant.
The assistant is helpful, creative, clever, and very friendly.
Q: %s
A:"""
question = ' '.join(sys.argv[1:])
prompt = prompt_template % question
print(prompt, end="", flush=True)
response = openai_request(prompt)
response_buffer = []
while True:
try:
chunk = next(response)
time.sleep(0.01)
except StopIteration:
break
token = chunk.choices[0].text
print(token, end="", flush=True)
response_buffer.append(token)
sentence_ending = re.search(r'[\.\?\!]\s*$', token)
if not sentence_ending:
continue
last_sentence = re.search(r'[^\.!?]*[\.\?\!]\s*$', ''.join(response_buffer)).group(0)
if is_hedge(last_sentence):
# if it is, delete the last sentence and make a new request
# and clear the buffer
for i in range(len(last_sentence) - 1):
time.sleep(0.01)
sys.stdout.write('\b \b')
sys.stdout.flush()
# remove the last sentence from the buffer
stuffed_word = get_stuffed_word(existing_text=''.join(response_buffer))
response_buffer = response_buffer[:-len(last_sentence)]
response_buffer.append(stuffed_word)
print(stuffed_word, end="", flush=True)
new_request = prompt + ''.join(response_buffer)
response = openai_request(new_request)
print()
if __name__ == "__main__":
main()
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