ai-content-maker/.venv/Lib/site-packages/openai/cli/_api/chat/completions.py

157 lines
5.2 KiB
Python

from __future__ import annotations
import sys
from typing import TYPE_CHECKING, List, Optional, cast
from argparse import ArgumentParser
from typing_extensions import Literal, NamedTuple
from ..._utils import get_client
from ..._models import BaseModel
from ...._streaming import Stream
from ....types.chat import (
ChatCompletionRole,
ChatCompletionChunk,
CompletionCreateParams,
)
from ....types.chat.completion_create_params import (
CompletionCreateParamsStreaming,
CompletionCreateParamsNonStreaming,
)
if TYPE_CHECKING:
from argparse import _SubParsersAction
def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
sub = subparser.add_parser("chat.completions.create")
sub._action_groups.pop()
req = sub.add_argument_group("required arguments")
opt = sub.add_argument_group("optional arguments")
req.add_argument(
"-g",
"--message",
action="append",
nargs=2,
metavar=("ROLE", "CONTENT"),
help="A message in `{role} {content}` format. Use this argument multiple times to add multiple messages.",
required=True,
)
req.add_argument(
"-m",
"--model",
help="The model to use.",
required=True,
)
opt.add_argument(
"-n",
"--n",
help="How many completions to generate for the conversation.",
type=int,
)
opt.add_argument("-M", "--max-tokens", help="The maximum number of tokens to generate.", type=int)
opt.add_argument(
"-t",
"--temperature",
help="""What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
Mutually exclusive with `top_p`.""",
type=float,
)
opt.add_argument(
"-P",
"--top_p",
help="""An alternative to sampling with temperature, called nucleus sampling, where the 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.
Mutually exclusive with `temperature`.""",
type=float,
)
opt.add_argument(
"--stop",
help="A stop sequence at which to stop generating tokens for the message.",
)
opt.add_argument("--stream", help="Stream messages as they're ready.", action="store_true")
sub.set_defaults(func=CLIChatCompletion.create, args_model=CLIChatCompletionCreateArgs)
class CLIMessage(NamedTuple):
role: ChatCompletionRole
content: str
class CLIChatCompletionCreateArgs(BaseModel):
message: List[CLIMessage]
model: str
n: Optional[int] = None
max_tokens: Optional[int] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
stop: Optional[str] = None
stream: bool = False
class CLIChatCompletion:
@staticmethod
def create(args: CLIChatCompletionCreateArgs) -> None:
params: CompletionCreateParams = {
"model": args.model,
"messages": [
{"role": cast(Literal["user"], message.role), "content": message.content} for message in args.message
],
"n": args.n,
"temperature": args.temperature,
"top_p": args.top_p,
"stop": args.stop,
# type checkers are not good at inferring union types so we have to set stream afterwards
"stream": False,
}
if args.stream:
params["stream"] = args.stream # type: ignore
if args.max_tokens is not None:
params["max_tokens"] = args.max_tokens
if args.stream:
return CLIChatCompletion._stream_create(cast(CompletionCreateParamsStreaming, params))
return CLIChatCompletion._create(cast(CompletionCreateParamsNonStreaming, params))
@staticmethod
def _create(params: CompletionCreateParamsNonStreaming) -> None:
completion = get_client().chat.completions.create(**params)
should_print_header = len(completion.choices) > 1
for choice in completion.choices:
if should_print_header:
sys.stdout.write("===== Chat Completion {} =====\n".format(choice.index))
content = choice.message.content if choice.message.content is not None else "None"
sys.stdout.write(content)
if should_print_header or not content.endswith("\n"):
sys.stdout.write("\n")
sys.stdout.flush()
@staticmethod
def _stream_create(params: CompletionCreateParamsStreaming) -> None:
# cast is required for mypy
stream = cast( # pyright: ignore[reportUnnecessaryCast]
Stream[ChatCompletionChunk], get_client().chat.completions.create(**params)
)
for chunk in stream:
should_print_header = len(chunk.choices) > 1
for choice in chunk.choices:
if should_print_header:
sys.stdout.write("===== Chat Completion {} =====\n".format(choice.index))
content = choice.delta.content or ""
sys.stdout.write(content)
if should_print_header:
sys.stdout.write("\n")
sys.stdout.flush()
sys.stdout.write("\n")