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from abc import abstractclassmethod
import openai
import os
import time
from Memory import Memory
from utils import save_logs
class LLM:
def __init__(self) -> None:
pass
@abstractclassmethod
def get_response():
pass
class OpenAILLM(LLM):
def __init__(self,**kwargs) -> None:
super().__init__()
self.MAX_CHAT_HISTORY = eval(
os.environ["MAX_CHAT_HISTORY"]) if "MAX_CHAT_HISTORY" in os.environ else 10
self.model = kwargs["model"] if "model" in kwargs else "gpt-3.5-turbo-16k-0613"
self.temperature = kwargs["temperature"] if "temperature" in kwargs else 0.3
self.log_path = kwargs["log_path"].replace("/",os.sep) if "log_path" in kwargs else "logs"
def get_stream(self,response, log_path, messages):
ans = ""
for res in response:
if res:
r = (res.choices[0]["delta"].get("content")
if res.choices[0]["delta"].get("content") else "")
ans += r
yield r
save_logs(log_path, messages, ans)
def get_response(self,
chat_history,
system_prompt,
last_prompt=None,
stream=False,
functions=None,
function_call="auto",
WAIT_TIME=20,
**kwargs):
"""
return LLM's response
"""
openai.api_key = os.environ["API_KEY"]
if "PROXY" in os.environ:
assert "http:" in os.environ["PROXY"] or "socks" in os.environ["PROXY"],"PROXY error,PROXY must be http or socks"
openai.proxy = os.environ["PROXY"]
if "API_BASE" in os.environ:
openai.api_base = os.environ["API_BASE"]
active_mode = True if ("ACTIVE_MODE" in os.environ and os.environ["ACTIVE_MODE"] == "0") else False
model = self.model
temperature = self.temperature
if active_mode:
system_prompt = system_prompt + "Please keep your reply as concise as possible."
messages = [{
"role": "system",
"content": system_prompt
}] if system_prompt else []
if chat_history:
if len(chat_history) > self.MAX_CHAT_HISTORY:
chat_history = chat_history[- self.MAX_CHAT_HISTORY:]
if isinstance(chat_history[0],dict):
messages += chat_history
elif isinstance(chat_history[0],Memory):
messages += [memory.get_gpt_message("user") for memory in chat_history]
if last_prompt:
if active_mode:
last_prompt = last_prompt + "Please keep your reply as concise as possible."
# messages += [{"role": "system", "content": f"{last_prompt}"}]
messages[-1]["content"] += last_prompt
while True:
try:
if functions:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
functions=functions,
function_call=function_call,
temperature=temperature,
)
else:
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
stream=stream)
break
except Exception as e:
print(e)
if "maximum context length is" in str(e):
if len(messages)>1:
del messages[1]
else:
assert False, "exceed max length"
else:
print(f"Please wait {WAIT_TIME} seconds and resend later ...")
time.sleep(WAIT_TIME)
if functions:
save_logs(self.log_path, messages, response)
return response.choices[0].message
elif stream:
return self.get_stream(response, self.log_path, messages)
else:
save_logs(self.log_path, messages, response)
return response.choices[0].message["content"]
def init_LLM(default_log_path,**kwargs):
LLM_type = kwargs["LLM_type"] if "LLM_type" in kwargs else "OpenAI"
log_path = kwargs["log_path"].replace("/",os.sep) if "log_path" in kwargs else default_log_path
if LLM_type == "OpenAI":
LLM = (
OpenAILLM(**kwargs["LLM"])
if "LLM" in kwargs
else OpenAILLM(model = "gpt-3.5-turbo-16k-0613",temperature=0.3,log_path=log_path)
)
return LLM
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