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GPT3Discord
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Format Python code with psf/black push

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github-actions 2 years ago
parent 2a576c07e4
commit 0ed400a505
2 changed files with 86 additions and 63 deletions
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128
cogs/gpt_3_commands_and_converser.py
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@ -29,14 +29,14 @@ original_message = {}
class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
def __init__(
self,
bot,
usage_service,
model,
message_queue,
deletion_queue,
DEBUG_GUILD,
DEBUG_CHANNEL,
self,
bot,
usage_service,
model,
message_queue,
deletion_queue,
DEBUG_GUILD,
DEBUG_CHANNEL,
):
self.debug_channel = None
self.bot = bot
@ -134,13 +134,13 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
def check_conversing(self, message):
cond1 = (
message.author.id in self.conversating_users
and message.channel.name in ["gpt3", "general-bot", "bot"]
message.author.id in self.conversating_users
and message.channel.name in ["gpt3", "general-bot", "bot"]
)
cond2 = (
message.author.id in self.conversating_users
and message.author.id in self.conversation_threads
and message.channel.id == self.conversation_threads[message.author.id]
message.author.id in self.conversating_users
and message.author.id in self.conversation_threads
and message.channel.id == self.conversation_threads[message.author.id]
)
# If the trimmed message starts with a Tilde, then we want to not contribute this to the conversation
@ -286,7 +286,7 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
async def paginate_and_send(self, response_text, message):
response_text = [
response_text[i: i + self.TEXT_CUTOFF]
response_text[i : i + self.TEXT_CUTOFF]
for i in range(0, len(response_text), self.TEXT_CUTOFF)
]
# Send each chunk as a message
@ -303,7 +303,7 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
async def queue_debug_chunks(self, debug_message, message, debug_channel):
debug_message_chunks = [
debug_message[i: i + self.TEXT_CUTOFF]
debug_message[i : i + self.TEXT_CUTOFF]
for i in range(0, len(debug_message), self.TEXT_CUTOFF)
]
@ -346,8 +346,8 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
if message.author.id in self.conversating_users:
# If the user has reached the max conversation length, end the conversation
if (
self.conversating_users[message.author.id].count
>= self.model.max_conversation_length
self.conversating_users[message.author.id].count
>= self.model.max_conversation_length
):
await message.reply(
"You have reached the maximum conversation length. You have ended the conversation with GPT3, and it has ended."
@ -360,14 +360,19 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
new_conversation_history = []
new_conversation_history.append(self.CONVERSATION_STARTER_TEXT)
new_conversation_history.append("\nThis conversation has some context from earlier, which has been summarized as follows: ")
new_conversation_history.append(
"\nThis conversation has some context from earlier, which has been summarized as follows: "
)
new_conversation_history.append(summarized_text)
new_conversation_history.append("\nContinue the conversation, paying very close attention to things Human told you, such as their name, and personal details.\n")
new_conversation_history.append(
"\nContinue the conversation, paying very close attention to things Human told you, such as their name, and personal details.\n"
)
# Get the last entry from the user's conversation history
new_conversation_history.append(self.conversating_users[message.author.id].history[-1]+"\n")
new_conversation_history.append(
self.conversating_users[message.author.id].history[-1] + "\n"
)
self.conversating_users[message.author.id].history = new_conversation_history
async def encapsulated_send(self, message, prompt, response_message=None):
# Append a newline, and GPTie: to the prompt
@ -381,33 +386,42 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
tokens = self.usage_service.count_tokens(new_prompt)
if tokens > self.model.summarize_threshold: # 250 is a buffer
if self.model.summarize_conversations:
await message.reply(
"I'm currently summarizing our current conversation so we can keep chatting, "
"give me one moment!")
if self.model.summarize_conversations:
await message.reply(
"I'm currently summarizing our current conversation so we can keep chatting, "
"give me one moment!"
)
self.summarize_conversation(message, new_prompt)
self.summarize_conversation(message, new_prompt)
# Check again if the prompt is about to go past the token limit
new_prompt = "".join(self.conversating_users[message.author.id].history) + "\nGPTie: "
# Check again if the prompt is about to go past the token limit
new_prompt = (
"".join(self.conversating_users[message.author.id].history)
+ "\nGPTie: "
)
tokens = self.usage_service.count_tokens(new_prompt)
tokens = self.usage_service.count_tokens(new_prompt)
if tokens > self.model.summarize_threshold - 150: # 150 is a buffer for the second stage
await message.reply("I tried to summarize our current conversation so we could keep chatting, "
"but it still went over the token "
"limit. Please try again later.")
if (
tokens > self.model.summarize_threshold - 150
): # 150 is a buffer for the second stage
await message.reply(
"I tried to summarize our current conversation so we could keep chatting, "
"but it still went over the token "
"limit. Please try again later."
)
await self.end_conversation(message)
return
else:
await message.reply("The conversation context limit has been reached.")
await self.end_conversation(message)
return
else:
await message.reply(
"The conversation context limit has been reached."
)
await self.end_conversation(message)
return
response = self.model.send_request(new_prompt, message)
response_text = response["choices"][0]["text"]
if re.search(r"<@!?\d+>|<@&\d+>|<#\d+>", response_text):
@ -444,8 +458,9 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
original_message[message.author.id] = message.id
else:
# We have response_text available, this is the original message that we want to edit
await response_message.edit(content=response_text.replace("<|endofstatement|>", ""))
await response_message.edit(
content=response_text.replace("<|endofstatement|>", "")
)
# After each response, check if the user has reached the conversation limit in terms of messages or time.
await self.check_conversation_limit(message)
@ -484,17 +499,18 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
if after.author.id in self.conversating_users:
# Remove the last two elements from the history array and add the new Human: prompt
self.conversating_users[after.author.id].history = self.conversating_users[after.author.id].history[
:-2]
self.conversating_users[
after.author.id
].history = self.conversating_users[after.author.id].history[:-2]
self.conversating_users[after.author.id].history.append(
f"\nHuman: {after.content}<|endofstatement|>\n")
edited_content = "".join(self.conversating_users[after.author.id].history)
f"\nHuman: {after.content}<|endofstatement|>\n"
)
edited_content = "".join(
self.conversating_users[after.author.id].history
)
self.conversating_users[after.author.id].count += 1
await self.encapsulated_send(
message,
edited_content, response_message
)
await self.encapsulated_send(message, edited_content, response_message)
redo_users[after.author.id].prompt = after.content
@ -532,7 +548,7 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
# A global GLOBAL_COOLDOWN_TIME timer for all users
if (message.author.id in self.last_used) and (
time.time() - self.last_used[message.author.id] < self.GLOBAL_COOLDOWN_TIME
time.time() - self.last_used[message.author.id] < self.GLOBAL_COOLDOWN_TIME
):
await message.reply(
"You must wait "
@ -577,9 +593,9 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
# If the user is not already conversating, start a conversation with GPT3
self.conversating_users[message.author.id] = User(message.author.id)
# Append the starter text for gpt3 to the user's history so it gets concatenated with the prompt later
self.conversating_users[
message.author.id
].history.append(self.CONVERSATION_STARTER_TEXT)
self.conversating_users[message.author.id].history.append(
self.CONVERSATION_STARTER_TEXT
)
# Create a new discord thread, and then send the conversation starting message inside of that thread
if not ("nothread" in prompt):
@ -621,16 +637,16 @@ class GPT3ComCon(commands.Cog, name="GPT3ComCon"):
# we can append their history to the prompt.
if message.author.id in self.conversating_users:
self.conversating_users[message.author.id].history.append(
"\nHuman: "
+ prompt
+ "<|endofstatement|>\n"
"\nHuman: " + prompt + "<|endofstatement|>\n"
)
# increment the conversation counter for the user
self.conversating_users[message.author.id].count += 1
# Send the request to the model
await self.encapsulated_send(message, "".join(self.conversating_users[message.author.id].history))
await self.encapsulated_send(
message, "".join(self.conversating_users[message.author.id].history)
)
class RedoView(discord.ui.View):

21
models/openai_model.py
Unescape View File

@ -82,7 +82,9 @@ class Model:
def summarize_threshold(self, value):
value = int(value)
if value < 800 or value > 4000:
raise ValueError("Summarize threshold cannot be greater than 4000 or less than 800!")
raise ValueError(
"Summarize threshold cannot be greater than 4000 or less than 800!"
)
self._summarize_threshold = value
@property
@ -292,9 +294,11 @@ class Model:
Sends a summary request to the OpenAI API
"""
summary_request_text = []
summary_request_text.append("The following is a conversation instruction set and a conversation"
" between two people named Human, and GPTie. Do not summarize the instructions for GPTie, only the conversation. Summarize the conversation in a detailed fashion. If Human mentioned their name, be sure to mention it in the summary. Pay close attention to things the Human has told you, such as personal details.")
summary_request_text.append(prompt+"\nDetailed summary of conversation: \n")
summary_request_text.append(
"The following is a conversation instruction set and a conversation"
" between two people named Human, and GPTie. Do not summarize the instructions for GPTie, only the conversation. Summarize the conversation in a detailed fashion. If Human mentioned their name, be sure to mention it in the summary. Pay close attention to things the Human has told you, such as personal details."
)
summary_request_text.append(prompt + "\nDetailed summary of conversation: \n")
summary_request_text = "".join(summary_request_text)
@ -327,7 +331,10 @@ class Model:
frequency_penalty_override=None,
presence_penalty_override=None,
max_tokens_override=None,
) -> (dict, bool): # The response, and a boolean indicating whether or not the context limit was reached.
) -> (
dict,
bool,
): # The response, and a boolean indicating whether or not the context limit was reached.
# Validate that all the parameters are in a good state before we send the request
if len(prompt) < self.prompt_min_length:
@ -361,7 +368,7 @@ class Model:
else frequency_penalty_override,
best_of=self.best_of if not best_of_override else best_of_override,
)
#print(response.__dict__)
# print(response.__dict__)
# Parse the total tokens used for this request and response pair from the response
tokens_used = int(response["usage"]["total_tokens"])
@ -378,7 +385,7 @@ class Model:
+ str(words)
)
#print("The prompt about to be sent is " + prompt)
# print("The prompt about to be sent is " + prompt)
self.usage_service.update_usage_image(self.image_size)
if not vary:

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