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|
import argparse
import os
import logging
import openai
import sys
from enum import Enum
class AutoDetectedOption(Enum):
ON = 'on'
OFF = 'off'
AUTO = 'auto'
def die_validation_err(err : str):
print(err, file=sys.stderr)
sys.exit(1)
def validate_args(args: argparse.Namespace) -> None:
if not 0 <= args.temperature <= 2:
die_validation_err("Temperature must be between 0 and 2.")
if not -2 <= args.frequency_penalty <= 2:
die_validation_err("Frequency penalty must be between -2.0 and 2.0.")
if not -2 <= args.presence_penalty <= 2:
die_validation_err("Presence penalty must be between -2.0 and 2.0.")
if args.max_tokens < 1:
die_validation_err("Max tokens must be greater than or equal to 1.")
if not 0 <= args.top_p <= 1:
die_validation_err("Top_p must be between 0 and 1.")
if args.n_completions < 1:
die_validation_err("Number of completions must be greater than or equal to 1.")
def parse_args():
GCLI_ENV_PREFIX = "GCLI_"
debug = os.getenv(f'{GCLI_ENV_PREFIX}DEBUG') is not None
if debug:
logging.warning("Debugging mode and unstable features have been enabled.")
parser = argparse.ArgumentParser()
parser.add_argument(
"-m",
"--model",
default=os.getenv(f'{GCLI_ENV_PREFIX}MODEL', "gpt-3.5-turbo"),
help="ID of the model to use",
)
parser.add_argument(
"-t",
"--temperature",
type=float,
default=os.getenv(f'{GCLI_ENV_PREFIX}TEMPERATURE', 0.5),
help=(
"What sampling temperature to use, between 0 and 2. Higher values "
"like 0.8 will make the output more random, while lower values "
"like 0.2 will make it more focused and deterministic."
),
)
parser.add_argument(
"-f",
"--frequency-penalty",
type=float,
default=os.getenv(f'{GCLI_ENV_PREFIX}FREQUENCY_PENALTY', 0),
help=(
"Number between -2.0 and 2.0. Positive values penalize new tokens based "
"on their existing frequency in the text so far, decreasing the model's "
"likelihood to repeat the same line verbatim."
),
)
parser.add_argument(
"-p",
"--presence-penalty",
type=float,
default=os.getenv(f'{GCLI_ENV_PREFIX}PRESENCE_PENALTY', 0),
help=(
"Number between -2.0 and 2.0. Positive values penalize new tokens based "
"on whether they appear in the text so far, increasing the model's "
"likelihood to talk about new topics."
),
)
parser.add_argument(
"-k",
"--max-tokens",
type=int,
default=os.getenv(f'{GCLI_ENV_PREFIX}MAX_TOKENS', 2048),
help=(
"The maximum number of tokens to generate in the chat completion. "
"Defaults to 2048."
),
)
parser.add_argument(
"-s",
"--top-p",
type=float,
default=os.getenv(f'{GCLI_ENV_PREFIX}TOP_P', 1),
help=(
"An alternative to sampling with temperature, called nucleus sampling, "
"where the model considers the results of the tokens with top_p "
"probability mass. So 0.1 means only the tokens comprising the top 10%% "
"probability mass are considered."
),
)
parser.add_argument(
"-n",
"--n-completions",
type=int,
default=os.getenv('f{GCLI_ENV_PREFIX}N_COMPLETIONS', 1),
help="How many chat completion choices to generate for each input message.",
)
parser.add_argument(
"--adornments",
type=AutoDetectedOption,
choices=list(AutoDetectedOption),
default=AutoDetectedOption.AUTO,
help=(
"Show adornments to indicate the model and response."
" Can be set to 'on', 'off', or 'auto'."
)
)
parser.add_argument(
"--color",
type=AutoDetectedOption,
choices=list(AutoDetectedOption),
default=AutoDetectedOption.AUTO,
help="Set color to 'on', 'off', or 'auto'.",
)
parser.add_argument(
"message",
type=str,
help=(
"The contents of the message. When used in chat mode, this is the initial "
"message if provided."
),
)
if debug:
group = parser.add_mutually_exclusive_group()
group.add_argument(
'--save-response-to-file',
type=str,
help="UNSTABLE: save the response to a file. This can reply a response for debugging purposes",
)
group.add_argument(
'--load-response-from-file',
type=str,
help="UNSTABLE: load a response from a file. This can reply a response for debugging purposes",
)
openai_key = os.getenv("OPENAI_KEY", os.getenv("OPENAI_API_KEY"))
if not openai_key:
print("The OPENAI_API_KEY or OPENAI_KEY environment variable must be defined.", file=sys.stderr)
print("The OpenAI API uses API keys for authentication. Visit your (API Keys page)[https://platform.openai.com/account/api-keys] to retrieve the API key you'll use in your requests.", file=sys.stderr)
sys.exit(1)
openai.api_key = openai_key
args = parser.parse_args()
if debug and args.load_response_from_file:
logging.warning(f'Ignoring the provided arguments in favor of those provided when the response in {args.load_response_from_file} was generated')
if args.color == AutoDetectedOption.AUTO:
if os.getenv("NO_COLOR"):
args.color = AutoDetectedOption.OFF
else:
args.color = AutoDetectedOption.ON
if args.adornments == AutoDetectedOption.AUTO:
args.adornments = AutoDetectedOption.ON
if not debug:
args.load_response_from_file = None
args.save_response_to_file = None
validate_args(args)
return args
|
