reverse-RAG / my_1_openai.py
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import os
from openai import OpenAI
openai.api_key = os.environ.get("OPENAI_API_KEY")
openai.organization = os.environ.get("OPENAI_ORG_ID")
models = {
"assistant": "You are a helpful assistant.",
"binary": "you are a maschine that converts questions or prompts to binary outputs. "
"you can only answer 'yes' or 'no'. if uncertain, default to 'no'."
}
def gpt4_new(prompt_text):
client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "system",
"content": "Du bist eine Maschine, die Dokumente klassifiziert."},
{"role": "user", "content": prompt_text}])
return response.choices[0].message.content
def gpt4(prompt, model=models["assistant"]):
response = openai.Completion.create(
model="gpt-4",
messages=[
{"role": "system", "content": model},
{"role": "user", "content": prompt}
]
)
return response.choices[0].message['content']
def gpt_bool(prompt):
"""
:param prompt: the text prompt
:return: True or False
"""
true_values = ["yes", "Yes", "Y", "y", "yes.", "Yes.", "YES"]
return bool(gpt4(prompt, model=models["binary"]) in true_values)
def vectorize_data(data_input):
try:
response = openai.Embedding.create(input=data_input, model="text-embedding-ada-002")
except openai.error.InvalidRequestError as err:
print(err)
return [0, 0, 0]
return response['data'][0]['embedding']
if __name__ == "__main__":
print("here are all functions that directly call openai.")
print("hi, im chatGPT how can I help? ")
while True:
print(gpt_bool(input()))