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- import os
- import openai
- from plugins.common import settings
- def chat_init(history):
- return history
- def chat_one(prompt, history_formatted, max_length, top_p, temperature, data):
- history_data = [ {"role": "system", "content": "You are a helpful assistant."}]
- if history_formatted is not None:
- for i, old_chat in enumerate(history_formatted):
- if old_chat['role'] == "user":
- history_data.append(
- {"role": "user", "content": old_chat['content']},)
- elif old_chat['role'] == "AI" or old_chat['role'] == 'assistant':
- history_data.append(
- {"role": "assistant", "content": old_chat['content']},)
- history_data.append({"role": "user", "content": prompt},)
- response = openai.ChatCompletion.create(
- model="gpt-3.5-turbo",
- messages=history_data,
- stream=True
- )
- resTemp=""
- for chunk in response:
- #print(chunk)
- if chunk['choices'][0]["finish_reason"]!="stop":
- if hasattr(chunk['choices'][0]['delta'], 'content'):
- resTemp+=chunk['choices'][0]['delta']['content']
- yield resTemp
- chatCompletion = None
- def load_model():
- openai.api_key = os.getenv("OPENAI_API_KEY")
- openai.api_base = settings.llm.api_host
- class Lock:
- def __init__(self):
- pass
- def get_waiting_threads(self):
- return 0
- def __enter__(self):
- pass
- def __exit__(self, exc_type, exc_val, exc_tb):
- pass
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