mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-01-19 01:21:01 +08:00
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
from bot.session_manager import Session
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from common.log import logger
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"""
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e.g. [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Who won the world series in 2020?"},
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{"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
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{"role": "user", "content": "Where was it played?"}
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]
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"""
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class ChatGPTSession(Session):
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def __init__(self, session_id, system_prompt=None, model="gpt-3.5-turbo"):
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super().__init__(session_id, system_prompt)
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self.model = model
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self.reset()
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def discard_exceeding(self, max_tokens, cur_tokens=None):
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precise = True
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try:
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cur_tokens = self.calc_tokens()
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except Exception as e:
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precise = False
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if cur_tokens is None:
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raise e
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logger.debug(
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"Exception when counting tokens precisely for query: {}".format(e)
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)
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while cur_tokens > max_tokens:
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if len(self.messages) > 2:
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self.messages.pop(1)
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elif len(self.messages) == 2 and self.messages[1]["role"] == "assistant":
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self.messages.pop(1)
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if precise:
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cur_tokens = self.calc_tokens()
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else:
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cur_tokens = cur_tokens - max_tokens
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break
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elif len(self.messages) == 2 and self.messages[1]["role"] == "user":
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logger.warn(
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"user message exceed max_tokens. total_tokens={}".format(cur_tokens)
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)
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break
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else:
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logger.debug(
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"max_tokens={}, total_tokens={}, len(messages)={}".format(
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max_tokens, cur_tokens, len(self.messages)
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)
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)
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break
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if precise:
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cur_tokens = self.calc_tokens()
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else:
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cur_tokens = cur_tokens - max_tokens
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return cur_tokens
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def calc_tokens(self):
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return num_tokens_from_messages(self.messages, self.model)
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# refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
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def num_tokens_from_messages(messages, model):
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"""Returns the number of tokens used by a list of messages."""
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import tiktoken
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try:
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encoding = tiktoken.encoding_for_model(model)
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except KeyError:
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logger.debug("Warning: model not found. Using cl100k_base encoding.")
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encoding = tiktoken.get_encoding("cl100k_base")
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if model == "gpt-3.5-turbo" or model == "gpt-35-turbo":
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return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
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elif model == "gpt-4":
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return num_tokens_from_messages(messages, model="gpt-4-0314")
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elif model == "gpt-3.5-turbo-0301":
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tokens_per_message = (
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4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
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)
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tokens_per_name = -1 # if there's a name, the role is omitted
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elif model == "gpt-4-0314":
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tokens_per_message = 3
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tokens_per_name = 1
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else:
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logger.warn(
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f"num_tokens_from_messages() is not implemented for model {model}. Returning num tokens assuming gpt-3.5-turbo-0301."
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)
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return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
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num_tokens = 0
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for message in messages:
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num_tokens += tokens_per_message
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for key, value in message.items():
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num_tokens += len(encoding.encode(value))
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if key == "name":
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num_tokens += tokens_per_name
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num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
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return num_tokens
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