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@@ -1,511 +1,130 @@ |
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""" |
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A simple wrapper for the official ChatGPT API |
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""" |
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import argparse |
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import json |
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import os |
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import sys |
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from datetime import date |
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import openai |
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import tiktoken |
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# encoding:utf-8 |
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from bot.bot import Bot |
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from config import conf |
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from common.log import logger |
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import openai |
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import time |
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ENGINE = os.environ.get("GPT_ENGINE") or "text-chat-davinci-002-20221122" |
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ENCODER = tiktoken.get_encoding("gpt2") |
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def get_max_tokens(prompt: str) -> int: |
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""" |
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Get the max tokens for a prompt |
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""" |
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return 4000 - len(ENCODER.encode(prompt)) |
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# ['text-chat-davinci-002-20221122'] |
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class Chatbot: |
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""" |
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Official ChatGPT API |
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""" |
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def __init__(self, api_key: str, buffer: int = None) -> None: |
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""" |
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Initialize Chatbot with API key (from https://platform.openai.com/account/api-keys) |
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""" |
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openai.api_key = api_key or os.environ.get("OPENAI_API_KEY") |
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self.conversations = Conversation() |
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self.prompt = Prompt(buffer=buffer) |
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def _get_completion( |
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self, |
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prompt: str, |
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temperature: float = 0.5, |
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stream: bool = False, |
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): |
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""" |
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Get the completion function |
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""" |
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return openai.Completion.create( |
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engine=ENGINE, |
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prompt=prompt, |
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temperature=temperature, |
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max_tokens=get_max_tokens(prompt), |
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stop=["\n\n\n"], |
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stream=stream, |
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) |
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def _process_completion( |
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self, |
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user_request: str, |
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completion: dict, |
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conversation_id: str = None, |
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user: str = "User", |
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) -> dict: |
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if completion.get("choices") is None: |
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raise Exception("ChatGPT API returned no choices") |
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if len(completion["choices"]) == 0: |
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raise Exception("ChatGPT API returned no choices") |
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if completion["choices"][0].get("text") is None: |
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raise Exception("ChatGPT API returned no text") |
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completion["choices"][0]["text"] = completion["choices"][0]["text"].rstrip( |
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"<|im_end|>", |
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) |
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# Add to chat history |
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self.prompt.add_to_history( |
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user_request, |
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completion["choices"][0]["text"], |
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user=user, |
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) |
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if conversation_id is not None: |
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self.save_conversation(conversation_id) |
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return completion |
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def _process_completion_stream( |
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self, |
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user_request: str, |
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completion: dict, |
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conversation_id: str = None, |
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user: str = "User", |
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) -> str: |
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full_response = "" |
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for response in completion: |
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if response.get("choices") is None: |
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raise Exception("ChatGPT API returned no choices") |
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if len(response["choices"]) == 0: |
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raise Exception("ChatGPT API returned no choices") |
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if response["choices"][0].get("finish_details") is not None: |
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break |
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if response["choices"][0].get("text") is None: |
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raise Exception("ChatGPT API returned no text") |
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if response["choices"][0]["text"] == "<|im_end|>": |
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break |
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yield response["choices"][0]["text"] |
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full_response += response["choices"][0]["text"] |
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# Add to chat history |
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self.prompt.add_to_history(user_request, full_response, user) |
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if conversation_id is not None: |
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self.save_conversation(conversation_id) |
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def ask( |
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self, |
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user_request: str, |
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temperature: float = 0.5, |
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conversation_id: str = None, |
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user: str = "User", |
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) -> dict: |
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""" |
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Send a request to ChatGPT and return the response |
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""" |
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if conversation_id is not None: |
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self.load_conversation(conversation_id) |
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completion = self._get_completion( |
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self.prompt.construct_prompt(user_request, user=user), |
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temperature, |
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) |
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return self._process_completion(user_request, completion, user=user) |
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def ask_stream( |
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self, |
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user_request: str, |
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temperature: float = 0.5, |
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conversation_id: str = None, |
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user: str = "User", |
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) -> str: |
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""" |
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Send a request to ChatGPT and yield the response |
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""" |
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if conversation_id is not None: |
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self.load_conversation(conversation_id) |
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prompt = self.prompt.construct_prompt(user_request, user=user) |
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return self._process_completion_stream( |
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user_request=user_request, |
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completion=self._get_completion(prompt, temperature, stream=True), |
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user=user, |
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) |
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def make_conversation(self, conversation_id: str) -> None: |
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""" |
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Make a conversation |
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""" |
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self.conversations.add_conversation(conversation_id, []) |
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def rollback(self, num: int) -> None: |
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""" |
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Rollback chat history num times |
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""" |
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for _ in range(num): |
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self.prompt.chat_history.pop() |
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def reset(self) -> None: |
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""" |
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Reset chat history |
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""" |
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self.prompt.chat_history = [] |
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def load_conversation(self, conversation_id) -> None: |
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""" |
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Load a conversation from the conversation history |
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""" |
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if conversation_id not in self.conversations.conversations: |
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# Create a new conversation |
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self.make_conversation(conversation_id) |
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self.prompt.chat_history = self.conversations.get_conversation(conversation_id) |
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def save_conversation(self, conversation_id) -> None: |
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""" |
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Save a conversation to the conversation history |
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""" |
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self.conversations.add_conversation(conversation_id, self.prompt.chat_history) |
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class AsyncChatbot(Chatbot): |
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""" |
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Official ChatGPT API (async) |
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""" |
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async def _get_completion( |
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self, |
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prompt: str, |
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temperature: float = 0.5, |
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stream: bool = False, |
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): |
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""" |
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Get the completion function |
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""" |
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return openai.Completion.acreate( |
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engine=ENGINE, |
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prompt=prompt, |
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temperature=temperature, |
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max_tokens=get_max_tokens(prompt), |
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stop=["\n\n\n"], |
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stream=stream, |
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) |
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async def ask( |
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self, |
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user_request: str, |
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temperature: float = 0.5, |
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user: str = "User", |
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) -> dict: |
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""" |
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Same as Chatbot.ask but async |
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} |
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""" |
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completion = await self._get_completion( |
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self.prompt.construct_prompt(user_request, user=user), |
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temperature, |
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) |
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return self._process_completion(user_request, completion, user=user) |
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async def ask_stream( |
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self, |
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user_request: str, |
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temperature: float = 0.5, |
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user: str = "User", |
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) -> str: |
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""" |
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Same as Chatbot.ask_stream but async |
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""" |
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prompt = self.prompt.construct_prompt(user_request, user=user) |
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return self._process_completion_stream( |
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user_request=user_request, |
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completion=await self._get_completion(prompt, temperature, stream=True), |
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user=user, |
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) |
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class Prompt: |
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""" |
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Prompt class with methods to construct prompt |
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""" |
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def __init__(self, buffer: int = None) -> None: |
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""" |
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Initialize prompt with base prompt |
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""" |
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self.base_prompt = ( |
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os.environ.get("CUSTOM_BASE_PROMPT") |
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or "You are ChatGPT, a large language model trained by OpenAI. Respond conversationally. Do not answer as the user. Current date: " |
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+ str(date.today()) |
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+ "\n\n" |
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+ "User: Hello\n" |
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+ "ChatGPT: Hello! How can I help you today? <|im_end|>\n\n\n" |
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) |
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# Track chat history |
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self.chat_history: list = [] |
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self.buffer = buffer |
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def add_to_chat_history(self, chat: str) -> None: |
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""" |
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Add chat to chat history for next prompt |
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""" |
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self.chat_history.append(chat) |
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def add_to_history( |
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self, |
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user_request: str, |
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response: str, |
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user: str = "User", |
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) -> None: |
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""" |
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Add request/response to chat history for next prompt |
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""" |
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self.add_to_chat_history( |
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user |
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+ ": " |
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+ user_request |
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+ "\n\n\n" |
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+ "ChatGPT: " |
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+ response |
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+ "<|im_end|>\n", |
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) |
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def history(self, custom_history: list = None) -> str: |
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""" |
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Return chat history |
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""" |
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return "\n".join(custom_history or self.chat_history) |
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def construct_prompt( |
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self, |
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new_prompt: str, |
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custom_history: list = None, |
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user: str = "User", |
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) -> str: |
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""" |
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Construct prompt based on chat history and request |
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""" |
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prompt = ( |
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self.base_prompt |
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+ self.history(custom_history=custom_history) |
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+ user |
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+ ": " |
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+ new_prompt |
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+ "\nChatGPT:" |
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) |
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# Check if prompt over 4000*4 characters |
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if self.buffer is not None: |
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max_tokens = 4000 - self.buffer |
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else: |
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max_tokens = 3200 |
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if len(ENCODER.encode(prompt)) > max_tokens: |
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# Remove oldest chat |
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if len(self.chat_history) == 0: |
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return prompt |
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self.chat_history.pop(0) |
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# Construct prompt again |
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prompt = self.construct_prompt(new_prompt, custom_history, user) |
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return prompt |
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class Conversation: |
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""" |
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For handling multiple conversations |
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""" |
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def __init__(self) -> None: |
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self.conversations = {} |
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def add_conversation(self, key: str, history: list) -> None: |
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""" |
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Adds a history list to the conversations dict with the id as the key |
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""" |
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self.conversations[key] = history |
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def get_conversation(self, key: str) -> list: |
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""" |
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Retrieves the history list from the conversations dict with the id as the key |
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""" |
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return self.conversations[key] |
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def remove_conversation(self, key: str) -> None: |
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""" |
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Removes the history list from the conversations dict with the id as the key |
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""" |
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del self.conversations[key] |
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def __str__(self) -> str: |
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""" |
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Creates a JSON string of the conversations |
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""" |
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return json.dumps(self.conversations) |
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def save(self, file: str) -> None: |
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""" |
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Saves the conversations to a JSON file |
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""" |
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with open(file, "w", encoding="utf-8") as f: |
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f.write(str(self)) |
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def load(self, file: str) -> None: |
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""" |
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Loads the conversations from a JSON file |
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""" |
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with open(file, encoding="utf-8") as f: |
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self.conversations = json.loads(f.read()) |
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user_session = dict() |
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def main(): |
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print( |
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""" |
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ChatGPT - A command-line interface to OpenAI's ChatGPT (https://chat.openai.com/chat) |
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Repo: github.com/acheong08/ChatGPT |
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""", |
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) |
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print("Type '!help' to show a full list of commands") |
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print("Press enter twice to submit your question.\n") |
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# OpenAI对话模型API (可用) |
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class ChatGPTBot(Bot): |
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def __init__(self): |
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openai.api_key = conf().get('open_ai_api_key') |
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def get_input(prompt): |
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""" |
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Multi-line input function |
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""" |
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# Display the prompt |
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print(prompt, end="") |
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def reply(self, query, context=None): |
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# acquire reply content |
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if not context or not context.get('type') or context.get('type') == 'TEXT': |
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logger.info("[OPEN_AI] query={}".format(query)) |
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from_user_id = context['from_user_id'] |
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if query == '#清除记忆': |
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Session.clear_session(from_user_id) |
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return '记忆已清除' |
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# Initialize an empty list to store the input lines |
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lines = [] |
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new_query = Session.build_session_query(query, from_user_id) |
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logger.debug("[OPEN_AI] session query={}".format(new_query)) |
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# Read lines of input until the user enters an empty line |
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while True: |
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line = input() |
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if line == "": |
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break |
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lines.append(line) |
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# if context.get('stream'): |
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# # reply in stream |
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# return self.reply_text_stream(query, new_query, from_user_id) |
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# Join the lines, separated by newlines, and store the result |
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user_input = "\n".join(lines) |
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reply_content = self.reply_text(new_query, from_user_id, 0) |
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logger.debug("[OPEN_AI] new_query={}, user={}, reply_cont={}".format(new_query, from_user_id, reply_content)) |
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if reply_content: |
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Session.save_session(query, reply_content, from_user_id) |
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return reply_content |
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# Return the input |
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return user_input |
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elif context.get('type', None) == 'IMAGE_CREATE': |
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return self.create_img(query, 0) |
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def chatbot_commands(cmd: str) -> bool: |
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""" |
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Handle chatbot commands |
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""" |
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if cmd == "!help": |
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print( |
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""" |
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!help - Display this message |
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!rollback - Rollback chat history |
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!reset - Reset chat history |
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!prompt - Show current prompt |
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!save_c <conversation_name> - Save history to a conversation |
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!load_c <conversation_name> - Load history from a conversation |
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!save_f <file_name> - Save all conversations to a file |
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!load_f <file_name> - Load all conversations from a file |
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!exit - Quit chat |
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""", |
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def reply_text(self, query, user_id, retry_count=0): |
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try: |
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response = openai.ChatCompletion.create( |
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model="gpt-3.5-turbo", # 对话模型的名称 |
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messages=query, |
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temperature=0.9, # 值在[0,1]之间，越大表示回复越具有不确定性 |
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max_tokens=1200, # 回复最大的字符数 |
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top_p=1, |
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frequency_penalty=0.0, # [-2,2]之间，该值越大则更倾向于产生不同的内容 |
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presence_penalty=0.0, # [-2,2]之间，该值越大则更倾向于产生不同的内容 |
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) |
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elif cmd == "!exit": |
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exit() |
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elif cmd == "!rollback": |
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chatbot.rollback(1) |
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elif cmd == "!reset": |
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chatbot.reset() |
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elif cmd == "!prompt": |
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print(chatbot.prompt.construct_prompt("")) |
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elif cmd.startswith("!save_c"): |
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chatbot.save_conversation(cmd.split(" ")[1]) |
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elif cmd.startswith("!load_c"): |
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chatbot.load_conversation(cmd.split(" ")[1]) |
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elif cmd.startswith("!save_f"): |
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chatbot.conversations.save(cmd.split(" ")[1]) |
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elif cmd.startswith("!load_f"): |
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chatbot.conversations.load(cmd.split(" ")[1]) |
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else: |
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return False |
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return True |
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# Get API key from command line |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--api_key", |
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type=str, |
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required=True, |
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help="OpenAI API key", |
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) |
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parser.add_argument( |
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"--stream", |
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action="store_true", |
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help="Stream response", |
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) |
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parser.add_argument( |
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"--temperature", |
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type=float, |
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default=0.5, |
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help="Temperature for response", |
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) |
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args = parser.parse_args() |
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# Initialize chatbot |
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chatbot = Chatbot(api_key=args.api_key) |
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# Start chat |
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while True: |
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# res_content = response.choices[0]['text'].strip().replace('<|endoftext|>', '') |
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logger.info(response.choices[0]['message']['content']) |
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# log.info("[OPEN_AI] reply={}".format(res_content)) |
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return response.choices[0]['message']['content'] |
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except openai.error.RateLimitError as e: |
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# rate limit exception |
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logger.warn(e) |
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if retry_count < 1: |
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time.sleep(5) |
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logger.warn("[OPEN_AI] RateLimit exceed, 第{}次重试".format(retry_count+1)) |
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return self.reply_text(query, user_id, retry_count+1) |
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else: |
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return "提问太快啦，请休息一下再问我吧" |
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except Exception as e: |
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# unknown exception |
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logger.exception(e) |
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Session.clear_session(user_id) |
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return "请再问我一次吧" |
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def create_img(self, query, retry_count=0): |
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try: |
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prompt = get_input("\nUser:\n") |
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except KeyboardInterrupt: |
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print("\nExiting...") |
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sys.exit() |
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if prompt.startswith("!"): |
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if chatbot_commands(prompt): |
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continue |
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if not args.stream: |
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response = chatbot.ask(prompt, temperature=args.temperature) |
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print("ChatGPT: " + response["choices"][0]["text"]) |
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else: |
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print("ChatGPT: ") |
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sys.stdout.flush() |
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for response in chatbot.ask_stream(prompt, temperature=args.temperature): |
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print(response, end="") |
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sys.stdout.flush() |
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print() |
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def Singleton(cls): |
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instance = {} |
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def _singleton_wrapper(*args, **kargs): |
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if cls not in instance: |
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instance[cls] = cls(*args, **kargs) |
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return instance[cls] |
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return _singleton_wrapper |
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@Singleton |
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class ChatGPTBot(Bot): |
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def __init__(self): |
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print("create") |
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self.bot = Chatbot(conf().get('open_ai_api_key')) |
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def reply(self, query, context=None): |
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if not context or not context.get('type') or context.get('type') == 'TEXT': |
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if len(query) < 10 and "reset" in query: |
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self.bot.reset() |
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return "reset OK" |
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return self.bot.ask(query)["choices"][0]["text"] |
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logger.info("[OPEN_AI] image_query={}".format(query)) |
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|
response = openai.Image.create( |
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prompt=query, #图片描述 |
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n=1, #每次生成图片的数量 |
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size="256x256" #图片大小,可选有 256x256, 512x512, 1024x1024 |
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) |
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image_url = response['data'][0]['url'] |
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|
logger.info("[OPEN_AI] image_url={}".format(image_url)) |
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return image_url |
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|
except openai.error.RateLimitError as e: |
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|
logger.warn(e) |
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|
if retry_count < 1: |
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|
time.sleep(5) |
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logger.warn("[OPEN_AI] ImgCreate RateLimit exceed, 第{}次重试".format(retry_count+1)) |
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|
return self.reply_text(query, retry_count+1) |
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else: |
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return "提问太快啦，请休息一下再问我吧" |
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|
except Exception as e: |
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|
logger.exception(e) |
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return None |
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|
|
|
class Session(object): |
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|
@staticmethod |
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|
|
def build_session_query(query, user_id): |
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|
|
''' |
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|
|
build query with conversation history |
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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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|
|
:param query: query content |
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|
|
:param user_id: from user id |
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|
|
:return: query content with conversaction |
|
|
|
''' |
|
|
|
session = user_session.get(user_id, []) |
|
|
|
if len(session) == 0: |
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|
|
system_prompt = conf().get("character_desc", "") |
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|
system_item = {'role': 'system', 'content': system_prompt} |
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|
|
session.append(system_item) |
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|
user_session[user_id] = session |
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|
|
user_item = {'role': 'user', 'content': query} |
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|
session.append(user_item) |
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|
return session |
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|
|
@staticmethod |
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|
|
def save_session(query, answer, user_id): |
|
|
|
session = user_session.get(user_id) |
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|
|
if session: |
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|
|
# append conversation |
|
|
|
gpt_item = {'role': 'assistant', 'content': answer} |
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|
|
session.append(gpt_item) |
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|
@staticmethod |
|
|
|
def clear_session(user_id): |
|
|
|
user_session[user_id] = [] |
|
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ubuntu
1 年之前