1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
|
import argparse
import argcomplete
import os
from dataclasses import dataclass
from typing import Tuple, Optional
from enum import Enum
class AutoDetectedOption(Enum):
ON = 'on'
OFF = 'off'
AUTO = 'auto'
def __str__(self : "AutoDetectedOption"):
return self.value
######################
## PUBLIC INTERFACE ##
######################
@dataclass
class RawArguments:
args : argparse.Namespace
debug : bool
openai_key : Optional[str] = None
def parse_raw_args_or_complete() -> RawArguments:
parser, debug = _construct_parser()
argcomplete.autocomplete( parser )
args = parser.parse_args()
openai_key = os.getenv("OPENAI_KEY", os.getenv("OPENAI_API_KEY"))
return RawArguments(
args = args,
debug = debug,
openai_key = openai_key
)
#####################
## PRIVATE LOGIC ##
#####################
_GPT_CLI_ENV_PREFIX = "GPT_CLI_"
def _construct_parser() \
-> Tuple[argparse.ArgumentParser, bool]:
debug = os.getenv(f'{_GPT_CLI_ENV_PREFIX}DEBUG') is not None
parser = argparse.ArgumentParser()
parser.add_argument(
"-m",
"--model",
default=os.getenv(f'{_GPT_CLI_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'{_GPT_CLI_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'{_GPT_CLI_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'{_GPT_CLI_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'{_GPT_CLI_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'{_GPT_CLI_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'{_GPT_CLI_ENV_PREFIX}N_COMPLETIONS', 1),
help="How many chat completion choices to generate for each input message.",
)
parser.add_argument(
"--system-message",
type=str,
default=os.getenv(f'{_GPT_CLI_ENV_PREFIX}SYSTEM_MESSAGE'),
help="Specify an alternative system 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(
"--version",
action="store_true",
help="Print version and exit"
)
parser.add_argument(
"-l",
"--list-models",
action="store_true",
help="List models and exit"
)
parser.add_argument(
"-i",
"--interactive",
action="store_true",
help="Start an interactive session"
)
parser.add_argument(
"--interactive-editor",
type=str,
default=os.getenv(f'{_GPT_CLI_ENV_PREFIX}INTERACTIVE_EDITOR'),
help="The editor which is launched by default using the /edit command in interactive mode"
)
initial_prompt = parser.add_mutually_exclusive_group()
initial_prompt.add_argument(
'--prompt-from-fd',
type=int,
help="Obtain the initial prompt from the specified file descriptor",
)
initial_prompt.add_argument(
'--prompt-from-file',
type=str,
help="Obtain the initial prompt from the specified file",
)
parser.add_argument(
"message",
type=str,
nargs='?',
help=(
"The contents of the message. When in a interactive session, this is "
" the initial prompt 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",
)
return parser, debug
|
