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"""
A simple wrapper for the official ChatGPT API
"""
import json
import os
import threading
import time
import requests
import tiktoken
from typing import Generator
from queue import PriorityQueue as PQ
import json
import os
import time
ENCODER = tiktoken.get_encoding("gpt2")
class chatPaper:
"""
Official ChatGPT API
"""
def __init__(
self,
api_keys: list,
proxy = None,
api_proxy = None,
max_tokens: int = 4000,
temperature: float = 0.5,
top_p: float = 1.0,
model_name: str = "gpt-3.5-turbo",
reply_count: int = 1,
system_prompt = "You are ChatPaper, A paper reading bot",
lastAPICallTime = time.time()-100,
apiTimeInterval = 20,
) -> None:
self.model_name = model_name
self.system_prompt = system_prompt
self.apiTimeInterval = apiTimeInterval
self.session = requests.Session()
self.api_keys = PQ()
for key in api_keys:
self.api_keys.put((lastAPICallTime,key))
self.proxy = proxy
if self.proxy:
proxies = {
"http": self.proxy,
"https": self.proxy,
}
self.session.proxies = proxies
self.max_tokens = max_tokens
self.temperature = temperature
self.top_p = top_p
self.reply_count = reply_count
self.decrease_step = 250
self.conversation = {}
if self.token_str(self.system_prompt) > self.max_tokens:
raise Exception("System prompt is too long")
self.lock = threading.Lock()
def get_api_key(self):
with self.lock:
apiKey = self.api_keys.get()
delay = self._calculate_delay(apiKey)
time.sleep(delay)
self.api_keys.put((time.time(), apiKey[1]))
return apiKey[1]
def _calculate_delay(self, apiKey):
elapsed_time = time.time() - apiKey[0]
if elapsed_time < self.apiTimeInterval:
return self.apiTimeInterval - elapsed_time
else:
return 0
def add_to_conversation(self, message: str, role: str, convo_id: str = "default"):
if(convo_id not in self.conversation):
self.reset(convo_id)
self.conversation[convo_id].append({"role": role, "content": message})
def __truncate_conversation(self, convo_id: str = "default"):
"""
Truncate the conversation
"""
last_dialog = self.conversation[convo_id][-1]
query = str(last_dialog['content'])
if(len(ENCODER.encode(str(query)))>self.max_tokens):
query = query[:int(1.5*self.max_tokens)]
while(len(ENCODER.encode(str(query)))>self.max_tokens):
query = query[:self.decrease_step]
self.conversation[convo_id] = self.conversation[convo_id][:-1]
full_conversation = "\n".join([str(x["content"]) for x in self.conversation[convo_id]],)
if len(ENCODER.encode(full_conversation)) > self.max_tokens:
self.conversation_summary(convo_id=convo_id)
full_conversation = ""
for x in self.conversation[convo_id]:
full_conversation = str(x["content"]) + "\n" + full_conversation
while True:
if (len(ENCODER.encode(full_conversation+query)) > self.max_tokens):
query = query[:self.decrease_step]
else:
break
last_dialog['content'] = str(query)
self.conversation[convo_id].append(last_dialog)
def ask_stream(
self,
prompt: str,
role: str = "user",
convo_id: str = "default",
**kwargs,
) -> Generator:
if convo_id not in self.conversation:
self.reset(convo_id=convo_id)
self.add_to_conversation(prompt, "user", convo_id=convo_id)
self.__truncate_conversation(convo_id=convo_id)
apiKey = self.get_api_key()
response = self.session.post(
"https://api.openai.com/v1/chat/completions",
headers={"Authorization": f"Bearer {kwargs.get('api_key', apiKey)}"},
json={
"model": self.model_name,
"messages": self.conversation[convo_id],
"stream": True,
# kwargs
"temperature": kwargs.get("temperature", self.temperature),
"top_p": kwargs.get("top_p", self.top_p),
"n": kwargs.get("n", self.reply_count),
"user": role,
},
stream=True,
)
if response.status_code != 200:
raise Exception(
f"Error: {response.status_code} {response.reason} {response.text}",
)
for line in response.iter_lines():
if not line:
continue
# Remove "data: "
line = line.decode("utf-8")[6:]
if line == "[DONE]":
break
resp: dict = json.loads(line)
choices = resp.get("choices")
if not choices:
continue
delta = choices[0].get("delta")
if not delta:
continue
if "content" in delta:
content = delta["content"]
yield content
def ask(self, prompt: str, role: str = "user", convo_id: str = "default", **kwargs):
"""
Non-streaming ask
"""
response = self.ask_stream(
prompt=prompt,
role=role,
convo_id=convo_id,
**kwargs,
)
full_response: str = "".join(response)
self.add_to_conversation(full_response, role, convo_id=convo_id)
usage_token = self.token_str(prompt)
com_token = self.token_str(full_response)
total_token = self.token_cost(convo_id=convo_id)
return full_response, usage_token, com_token, total_token
def check_api_available(self):
response = self.session.post(
"https://api.openai.com/v1/chat/completions",
headers={"Authorization": f"Bearer {self.get_api_key()}"},
json={
"model": self.model_name,
"messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "print A"}],
"stream": True,
# kwargs
"temperature": self.temperature,
"top_p": self.top_p,
"n": self.reply_count,
"user": "user",
},
stream=True,
)
if response.status_code == 200:
return True
else:
return False
def reset(self, convo_id: str = "default", system_prompt = None):
"""
Reset the conversation
"""
self.conversation[convo_id] = [
{"role": "system", "content": str(system_prompt or self.system_prompt)},
]
def conversation_summary(self, convo_id: str = "default"):
input = ""
role = ""
for conv in self.conversation[convo_id]:
if (conv["role"]=='user'):
role = 'User'
else:
role = 'ChatGpt'
input+=role+' : '+conv['content']+'\n'
prompt = "Your goal is to summarize the provided conversation in English. Your summary should be concise and focus on the key information to facilitate better dialogue for the large language model.Ensure that you include all necessary details and relevant information while still reducing the length of the conversation as much as possible. Your summary should be clear and easily understandable for the ChatGpt model providing a comprehensive and concise summary of the conversation."
if(self.token_str(str(input)+prompt)>self.max_tokens):
input = input[self.token_str(str(input))-self.max_tokens:]
while self.token_str(str(input)+prompt)>self.max_tokens:
input = input[self.decrease_step:]
prompt = prompt.replace("{conversation}", input)
self.reset(convo_id='conversationSummary')
response = self.ask(prompt,convo_id='conversationSummary')
while self.token_str(str(response))>self.max_tokens:
response = response[:-self.decrease_step]
self.reset(convo_id='conversationSummary',system_prompt='Summariaze our diaglog')
self.conversation[convo_id] = [
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": "Summariaze our diaglog"},
{"role": 'assistant', "content": response},
]
return self.conversation[convo_id]
def token_cost(self,convo_id: str = "default"):
return len(ENCODER.encode("\n".join([x["content"] for x in self.conversation[convo_id]])))
def token_str(self,content:str):
return len(ENCODER.encode(content))
def main():
return
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