feat: add psagentai agent with deployment and configuration
Initial project setup including the main agent script, configuration files, profile JSON state, systemd service unit, startup script, deployment documentation, and Python dependencies. Includes .env, venv/, and __pycache__/ which should likely be excluded via .gitignore to avoid committing secrets and artifacts.
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from __future__ import annotations
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import sys
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from typing import TYPE_CHECKING, Optional, cast
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from argparse import ArgumentParser
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from functools import partial
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from openai.types.completion import Completion
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from .._utils import get_client
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from ..._types import NOT_GIVEN, NotGivenOr
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from ..._utils import is_given
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from .._errors import CLIError
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from .._models import BaseModel
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from ..._streaming import Stream
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if TYPE_CHECKING:
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from argparse import _SubParsersAction
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def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
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sub = subparser.add_parser("completions.create")
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# Required
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sub.add_argument(
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"-m",
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"--model",
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help="The model to use",
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required=True,
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)
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# Optional
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sub.add_argument("-p", "--prompt", help="An optional prompt to complete from")
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sub.add_argument("--stream", help="Stream tokens as they're ready.", action="store_true")
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sub.add_argument("-M", "--max-tokens", help="The maximum number of tokens to generate", type=int)
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sub.add_argument(
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"-t",
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"--temperature",
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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.
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Mutually exclusive with `top_p`.""",
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type=float,
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)
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sub.add_argument(
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"-P",
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"--top_p",
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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.
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Mutually exclusive with `temperature`.""",
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type=float,
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)
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sub.add_argument(
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"-n",
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"--n",
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help="How many sub-completions to generate for each prompt.",
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type=int,
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)
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sub.add_argument(
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"--logprobs",
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help="Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. So for example, if `logprobs` is 10, the API will return a list of the 10 most likely tokens. If `logprobs` is 0, only the chosen tokens will have logprobs returned.",
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type=int,
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)
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sub.add_argument(
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"--best_of",
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help="Generates `best_of` completions server-side and returns the 'best' (the one with the highest log probability per token). Results cannot be streamed.",
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type=int,
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)
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sub.add_argument(
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"--echo",
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help="Echo back the prompt in addition to the completion",
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action="store_true",
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)
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sub.add_argument(
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"--frequency_penalty",
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help="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.",
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type=float,
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)
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sub.add_argument(
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"--presence_penalty",
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help="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.",
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type=float,
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)
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sub.add_argument("--suffix", help="The suffix that comes after a completion of inserted text.")
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sub.add_argument("--stop", help="A stop sequence at which to stop generating tokens.")
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sub.add_argument(
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"--user",
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help="A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.",
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)
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# TODO: add support for logit_bias
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sub.set_defaults(func=CLICompletions.create, args_model=CLICompletionCreateArgs)
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class CLICompletionCreateArgs(BaseModel):
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model: str
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stream: bool = False
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prompt: Optional[str] = None
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n: NotGivenOr[int] = NOT_GIVEN
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stop: NotGivenOr[str] = NOT_GIVEN
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user: NotGivenOr[str] = NOT_GIVEN
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echo: NotGivenOr[bool] = NOT_GIVEN
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suffix: NotGivenOr[str] = NOT_GIVEN
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best_of: NotGivenOr[int] = NOT_GIVEN
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top_p: NotGivenOr[float] = NOT_GIVEN
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logprobs: NotGivenOr[int] = NOT_GIVEN
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max_tokens: NotGivenOr[int] = NOT_GIVEN
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temperature: NotGivenOr[float] = NOT_GIVEN
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presence_penalty: NotGivenOr[float] = NOT_GIVEN
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frequency_penalty: NotGivenOr[float] = NOT_GIVEN
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class CLICompletions:
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@staticmethod
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def create(args: CLICompletionCreateArgs) -> None:
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if is_given(args.n) and args.n > 1 and args.stream:
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raise CLIError("Can't stream completions with n>1 with the current CLI")
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make_request = partial(
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get_client().completions.create,
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n=args.n,
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echo=args.echo,
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stop=args.stop,
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user=args.user,
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model=args.model,
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top_p=args.top_p,
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prompt=args.prompt,
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suffix=args.suffix,
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best_of=args.best_of,
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logprobs=args.logprobs,
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max_tokens=args.max_tokens,
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temperature=args.temperature,
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presence_penalty=args.presence_penalty,
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frequency_penalty=args.frequency_penalty,
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)
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if args.stream:
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return CLICompletions._stream_create(
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# mypy doesn't understand the `partial` function but pyright does
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cast(Stream[Completion], make_request(stream=True)) # pyright: ignore[reportUnnecessaryCast]
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)
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return CLICompletions._create(make_request())
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@staticmethod
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def _create(completion: Completion) -> None:
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should_print_header = len(completion.choices) > 1
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for choice in completion.choices:
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if should_print_header:
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sys.stdout.write("===== Completion {} =====\n".format(choice.index))
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sys.stdout.write(choice.text)
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if should_print_header or not choice.text.endswith("\n"):
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sys.stdout.write("\n")
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sys.stdout.flush()
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@staticmethod
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def _stream_create(stream: Stream[Completion]) -> None:
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for completion in stream:
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should_print_header = len(completion.choices) > 1
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for choice in sorted(completion.choices, key=lambda c: c.index):
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if should_print_header:
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sys.stdout.write("===== Chat Completion {} =====\n".format(choice.index))
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sys.stdout.write(choice.text)
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if should_print_header:
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sys.stdout.write("\n")
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sys.stdout.flush()
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sys.stdout.write("\n")
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