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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 99e8360e89
commit f8d797f3a8
4 changed files with 148 additions and 54 deletions
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50
cogs/text_service_cog.py
Unescape View File

@ -722,7 +722,7 @@ class GPT3ComCon(discord.Cog, name="GPT3ComCon"):
await ctx.defer()
overrides = Override(temperature,top_p,frequency_penalty,presence_penalty)
overrides = Override(temperature, top_p, frequency_penalty, presence_penalty)
await TextService.encapsulated_send(
self,
@ -768,7 +768,7 @@ class GPT3ComCon(discord.Cog, name="GPT3ComCon"):
await ctx.defer()
overrides = Override(temperature,top_p,0,0)
overrides = Override(temperature, top_p, 0, 0)
await TextService.encapsulated_send(
self,
@ -966,7 +966,12 @@ class GPT3ComCon(discord.Cog, name="GPT3ComCon"):
self.conversation_threads[thread.id].count += 1
overrides = Override(overrides['temperature'], overrides['top_p'], overrides['frequency_penalty'], overrides['presence_penalty'])
overrides = Override(
overrides["temperature"],
overrides["top_p"],
overrides["frequency_penalty"],
overrides["presence_penalty"],
)
await TextService.encapsulated_send(
self,
@ -1058,61 +1063,74 @@ class GPT3ComCon(discord.Cog, name="GPT3ComCon"):
async def ask_gpt_action(self, ctx, message: discord.Message):
"""Message command. Return the message"""
prompt = await self.mention_to_username(ctx, message.content)
await self.ask_command(ctx, prompt, None, None, None, None, from_ask_action=prompt)
await self.ask_command(
ctx, prompt, None, None, None, None, from_ask_action=prompt
)
async def paraphrase_action(self, ctx, message: discord.Message):
"""Message command. paraphrase the current message content"""
user = ctx.user
prompt = await self.mention_to_username(ctx, message.content)
from_other_action = prompt+"\nParaphrased:"
from_other_action = prompt + "\nParaphrased:"
# Construct the paraphrase prompt
prompt = f"Paraphrase the following text. Maintain roughly the same text length after paraphrasing and the same tone of voice: {prompt} \nParaphrased:"
tokens = self.model.usage_service.count_tokens(prompt)
if tokens > self.model.max_tokens-1000:
if tokens > self.model.max_tokens - 1000:
await ctx.respond(
f"This message is too long to paraphrase.",
ephemeral=True, delete_after=10,
ephemeral=True,
delete_after=10,
)
return
await self.ask_command(ctx, prompt, None, None, None, None, from_other_action=from_other_action)
await self.ask_command(
ctx, prompt, None, None, None, None, from_other_action=from_other_action
)
async def elaborate_action(self, ctx, message: discord.Message):
"""Message command. elaborate on the subject of the current message content"""
user = ctx.user
prompt = await self.mention_to_username(ctx, message.content)
from_other_action = prompt+"\nElaboration:"
from_other_action = prompt + "\nElaboration:"
# Construct the paraphrase prompt
prompt = f"Elaborate with more information about the subject of the following message. Be objective and detailed and respond with elaborations only about the subject(s) of the message: {prompt} \n\nElaboration:"
tokens = self.model.usage_service.count_tokens(prompt)
if tokens > self.model.max_tokens-500:
if tokens > self.model.max_tokens - 500:
await ctx.respond(
f"This message is too long to elaborate on.",
ephemeral=True, delete_after=10,
ephemeral=True,
delete_after=10,
)
return
await self.ask_command(ctx, prompt, None, None, None, None, from_other_action=from_other_action)
await self.ask_command(
ctx, prompt, None, None, None, None, from_other_action=from_other_action
)
async def summarize_action(self, ctx, message: discord.Message):
"""Message command. elaborate on the subject of the current message content"""
user = ctx.user
prompt = await self.mention_to_username(ctx, message.content)
from_other_action = "Message at message link: " + message.jump_url + "\nSummarized:"
from_other_action = (
"Message at message link: " + message.jump_url + "\nSummarized:"
)
# Construct the paraphrase prompt
prompt = f"Summarize the following message, be as short and concise as possible: {prompt} \n\nSummary:"
tokens = self.model.usage_service.count_tokens(prompt)
if tokens > self.model.max_tokens-300:
if tokens > self.model.max_tokens - 300:
await ctx.respond(
f"Your prompt is too long. It has {tokens} tokens, but the maximum is {self.model.max_tokens-300}.",
ephemeral=True, delete_after=10,
ephemeral=True,
delete_after=10,
)
return
await self.ask_command(ctx, prompt, None, None, None, None, from_other_action=from_other_action)
await self.ask_command(
ctx, prompt, None, None, None, None, from_other_action=from_other_action
)

4
gpt3discord.py
Unescape View File

@ -237,7 +237,9 @@ def init():
signal.signal(signal.SIGTERM, cleanup_pid_file)
if check_process_file(PID_FILE):
print("Process ID file already exists. Remove the file if you're sure another instance isn't running with the command: rm bot.pid")
print(
"Process ID file already exists. Remove the file if you're sure another instance isn't running with the command: rm bot.pid"
)
sys.exit(1)
else:
with PID_FILE.open("w") as f:

140
models/openai_model.py
Unescape View File

@ -25,12 +25,14 @@ except Exception as e:
print("Failed to retrieve the settings DB. The bot is terminating.")
raise e
class Mode:
TEMPERATURE = "temperature"
TOP_P = "top_p"
ALL_MODES = [TEMPERATURE, TOP_P]
class Override:
def __init__(self, temp=None, top_p=None, frequency=None, presence=None):
self.temperature = temp
@ -126,33 +128,101 @@ class ModelLimits:
class Model:
def set_initial_state(self, usage_service):
self.mode = Mode.TEMPERATURE
self.temp = SETTINGS_DB['temp'] if 'temp' in SETTINGS_DB else 0.8 # Higher value means more random, lower value means more likely to be a coherent sentence
self.top_p = SETTINGS_DB['top_p'] if 'top_p' in SETTINGS_DB else 1 # 1 is equivalent to greedy sampling, 0.1 means that the model will only consider the top 10% of the probability distribution
self.max_tokens = SETTINGS_DB['max_tokens'] if 'max_tokens' in SETTINGS_DB else 4000 # The maximum number of tokens the model can generate
self.presence_penalty = SETTINGS_DB['presence_penalty'] if 'presence_penalty' in SETTINGS_DB else 0.0 # The presence penalty is a number between -2 and 2 that determines how much the model should avoid repeating the same text
self.temp = (
SETTINGS_DB["temp"] if "temp" in SETTINGS_DB else 0.8
) # Higher value means more random, lower value means more likely to be a coherent sentence
self.top_p = (
SETTINGS_DB["top_p"] if "top_p" in SETTINGS_DB else 1
) # 1 is equivalent to greedy sampling, 0.1 means that the model will only consider the top 10% of the probability distribution
self.max_tokens = (
SETTINGS_DB["max_tokens"] if "max_tokens" in SETTINGS_DB else 4000
) # The maximum number of tokens the model can generate
self.presence_penalty = (
SETTINGS_DB["presence_penalty"]
if "presence_penalty" in SETTINGS_DB
else 0.0
) # The presence penalty is a number between -2 and 2 that determines how much the model should avoid repeating the same text
# Penalize new tokens based on their existing frequency in the text so far. (Higher frequency = lower probability of being chosen.)
self.frequency_penalty = SETTINGS_DB['frequency_penalty'] if 'frequency_penalty' in SETTINGS_DB else 0.0
self.best_of = SETTINGS_DB['best_of'] if 'best_of' in SETTINGS_DB else 1 # Number of responses to compare the loglikelihoods of
self.prompt_min_length = SETTINGS_DB['prompt_min_length'] if 'prompt_min_length' in SETTINGS_DB else 6 # The minimum length of the prompt
self.max_conversation_length = SETTINGS_DB['max_conversation_length'] if 'max_conversation_length' in SETTINGS_DB else 100 # The maximum number of conversation items to keep in memory
self.model = SETTINGS_DB['model'] if 'model' in SETTINGS_DB else Models.DEFAULT # The model to use
self.frequency_penalty = (
SETTINGS_DB["frequency_penalty"]
if "frequency_penalty" in SETTINGS_DB
else 0.0
)
self.best_of = (
SETTINGS_DB["best_of"] if "best_of" in SETTINGS_DB else 1
) # Number of responses to compare the loglikelihoods of
self.prompt_min_length = (
SETTINGS_DB["prompt_min_length"]
if "prompt_min_length" in SETTINGS_DB
else 6
) # The minimum length of the prompt
self.max_conversation_length = (
SETTINGS_DB["max_conversation_length"]
if "max_conversation_length" in SETTINGS_DB
else 100
) # The maximum number of conversation items to keep in memory
self.model = (
SETTINGS_DB["model"] if "model" in SETTINGS_DB else Models.DEFAULT
) # The model to use
self._low_usage_mode = False
self.usage_service = usage_service
self.DAVINCI_ROLES = ["admin", "Admin", "GPT", "gpt"]
self.image_size = SETTINGS_DB['image_size'] if 'image_size' in SETTINGS_DB else ImageSize.MEDIUM
self.num_images = SETTINGS_DB['num_images'] if 'num_images' in SETTINGS_DB else 2
self.summarize_conversations = bool(SETTINGS_DB['summarize_conversations']) if 'summarize_conversations' in SETTINGS_DB else True
self.summarize_threshold = SETTINGS_DB['summarize_threshold'] if 'summarize_threshold' in SETTINGS_DB else 3000
self.image_size = (
SETTINGS_DB["image_size"]
if "image_size" in SETTINGS_DB
else ImageSize.MEDIUM
)
self.num_images = (
SETTINGS_DB["num_images"] if "num_images" in SETTINGS_DB else 2
)
self.summarize_conversations = (
bool(SETTINGS_DB["summarize_conversations"])
if "summarize_conversations" in SETTINGS_DB
else True
)
self.summarize_threshold = (
SETTINGS_DB["summarize_threshold"]
if "summarize_threshold" in SETTINGS_DB
else 3000
)
self.model_max_tokens = 4024
self.welcome_message_enabled = bool(SETTINGS_DB['welcome_message_enabled']) if 'welcome_message_enabled' in SETTINGS_DB else False
self.num_static_conversation_items = SETTINGS_DB['num_static_conversation_items'] if 'num_static_conversation_items' in SETTINGS_DB else 10
self.num_conversation_lookback = SETTINGS_DB['num_conversation_lookback'] if 'num_conversation_lookback' in SETTINGS_DB else 5
self.welcome_message_enabled = (
bool(SETTINGS_DB["welcome_message_enabled"])
if "welcome_message_enabled" in SETTINGS_DB
else False
)
self.num_static_conversation_items = (
SETTINGS_DB["num_static_conversation_items"]
if "num_static_conversation_items" in SETTINGS_DB
else 10
)
self.num_conversation_lookback = (
SETTINGS_DB["num_conversation_lookback"]
if "num_conversation_lookback" in SETTINGS_DB
else 5
)
def reset_settings(self):
keys = ['temp', 'top_p', 'max_tokens', 'presence_penalty', 'frequency_penalty', 'best_of', 'prompt_min_length', 'max_conversation_length', 'model', 'image_size', 'num_images', 'summarize_conversations', 'summarize_threshold', 'welcome_message_enabled', 'num_static_conversation_items', 'num_conversation_lookback']
keys = [
"temp",
"top_p",
"max_tokens",
"presence_penalty",
"frequency_penalty",
"best_of",
"prompt_min_length",
"max_conversation_length",
"model",
"image_size",
"num_images",
"summarize_conversations",
"summarize_threshold",
"welcome_message_enabled",
"num_static_conversation_items",
"num_conversation_lookback",
]
for key in keys:
try:
del SETTINGS_DB[key]
@ -224,7 +294,7 @@ class Model:
f"Number of static conversation items must be <= {ModelLimits.MAX_NUM_STATIC_CONVERSATION_ITEMS}, this is to ensure reliability and reduce token wastage!"
)
self._num_static_conversation_items = value
SETTINGS_DB['num_static_conversation_items'] = value
SETTINGS_DB["num_static_conversation_items"] = value
@property
def num_conversation_lookback(self):
@ -242,7 +312,7 @@ class Model:
f"Number of conversations to look back on must be <= {ModelLimits.MIN_NUM_CONVERSATION_LOOKBACK}, this is to ensure reliability and reduce token wastage!"
)
self._num_conversation_lookback = value
SETTINGS_DB['num_conversation_lookback'] = value
SETTINGS_DB["num_conversation_lookback"] = value
@property
def welcome_message_enabled(self):
@ -258,7 +328,7 @@ class Model:
else:
raise ValueError("Value must be either `true` or `false`!")
self._welcome_message_enabled = value
SETTINGS_DB['welcome_message_enabled'] = self._welcome_message_enabled
SETTINGS_DB["welcome_message_enabled"] = self._welcome_message_enabled
@property
def summarize_threshold(self):
@ -275,7 +345,7 @@ class Model:
f"Summarize threshold should be a number between {ModelLimits.MIN_SUMMARIZE_THRESHOLD} and {ModelLimits.MAX_SUMMARIZE_THRESHOLD}!"
)
self._summarize_threshold = value
SETTINGS_DB['summarize_threshold'] = value
SETTINGS_DB["summarize_threshold"] = value
@property
def summarize_conversations(self):
@ -292,7 +362,7 @@ class Model:
else:
raise ValueError("Value must be either `true` or `false`!")
self._summarize_conversations = value
SETTINGS_DB['summarize_conversations'] = value
SETTINGS_DB["summarize_conversations"] = value
@property
def image_size(self):
@ -302,7 +372,7 @@ class Model:
def image_size(self, value):
if value in ImageSize.ALL_SIZES:
self._image_size = value
SETTINGS_DB['image_size'] = value
SETTINGS_DB["image_size"] = value
else:
raise ValueError(
f"Image size must be one of the following: {ImageSize.ALL_SIZES}"
@ -320,7 +390,7 @@ class Model:
f"Number of images to generate should be a number between {ModelLimits.MIN_NUM_IMAGES} and {ModelLimits.MAX_NUM_IMAGES}!"
)
self._num_images = value
SETTINGS_DB['num_images'] = value
SETTINGS_DB["num_images"] = value
@property
def low_usage_mode(self):
@ -357,7 +427,7 @@ class Model:
# Set the token count
self._max_tokens = Models.get_max_tokens(self._model)
SETTINGS_DB['model'] = model
SETTINGS_DB["model"] = model
@property
def max_conversation_length(self):
@ -375,7 +445,7 @@ class Model:
f"Max conversation length must be less than {ModelLimits.MIN_CONVERSATION_LENGTH}, this will start using credits quick."
)
self._max_conversation_length = value
SETTINGS_DB['max_conversation_length'] = value
SETTINGS_DB["max_conversation_length"] = value
@property
def mode(self):
@ -396,7 +466,7 @@ class Model:
raise ValueError(f"Unknown mode: {value}")
self._mode = value
SETTINGS_DB['mode'] = value
SETTINGS_DB["mode"] = value
@property
def temp(self):
@ -411,7 +481,7 @@ class Model:
)
self._temp = value
SETTINGS_DB['temp'] = value
SETTINGS_DB["temp"] = value
@property
def top_p(self):
@ -425,7 +495,7 @@ class Model:
f"Top P must be between {ModelLimits.MIN_TOP_P} and {ModelLimits.MAX_TOP_P}, it is currently: {value}"
)
self._top_p = value
SETTINGS_DB['top_p'] = value
SETTINGS_DB["top_p"] = value
@property
def max_tokens(self):
@ -439,7 +509,7 @@ class Model:
f"Max tokens must be between {ModelLimits.MIN_TOKENS} and {ModelLimits.MAX_TOKENS}, it is currently: {value}"
)
self._max_tokens = value
SETTINGS_DB['max_tokens'] = value
SETTINGS_DB["max_tokens"] = value
@property
def presence_penalty(self):
@ -456,7 +526,7 @@ class Model:
f"Presence penalty must be between {ModelLimits.MIN_PRESENCE_PENALTY} and {ModelLimits.MAX_PRESENCE_PENALTY}, it is currently: {value}"
)
self._presence_penalty = value
SETTINGS_DB['presence_penalty'] = value
SETTINGS_DB["presence_penalty"] = value
@property
def frequency_penalty(self):
@ -473,7 +543,7 @@ class Model:
f"Frequency penalty must be greater between {ModelLimits.MIN_FREQUENCY_PENALTY} and {ModelLimits.MAX_FREQUENCY_PENALTY}, it is currently: {value}"
)
self._frequency_penalty = value
SETTINGS_DB['frequency_penalty'] = value
SETTINGS_DB["frequency_penalty"] = value
@property
def best_of(self):
@ -487,7 +557,7 @@ class Model:
f"Best of must be between {ModelLimits.MIN_BEST_OF} and {ModelLimits.MAX_BEST_OF}, it is currently: {value}\nNote that increasing the value of this parameter will act as a multiplier on the number of tokens requested!"
)
self._best_of = value
SETTINGS_DB['best_of'] = value
SETTINGS_DB["best_of"] = value
@property
def prompt_min_length(self):
@ -504,7 +574,7 @@ class Model:
f"Minimal prompt length must be between {ModelLimits.MIN_PROMPT_MIN_LENGTH} and {ModelLimits.MAX_PROMPT_MIN_LENGTH}, it is currently: {value}"
)
self._prompt_min_length = value
SETTINGS_DB['prompt_min_length'] = value
SETTINGS_DB["prompt_min_length"] = value
def backoff_handler(details):
print(

8
services/text_service.py
Unescape View File

@ -625,8 +625,12 @@ class TextService:
conversation_overrides = converser_cog.conversation_threads[
message.channel.id
].get_overrides()
overrides = Override(conversation_overrides['temperature'],conversation_overrides['top_p'],conversation_overrides['frequency_penalty'],conversation_overrides['presence_penalty'])
overrides = Override(
conversation_overrides["temperature"],
conversation_overrides["top_p"],
conversation_overrides["frequency_penalty"],
conversation_overrides["presence_penalty"],
)
await TextService.encapsulated_send(
converser_cog,

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