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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import json
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from functools import lru_cache
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, Callable, Optional, Type, TypeVar, Union
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from pydantic.v1.parse import Protocol, load_file, load_str_bytes
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from pydantic.v1.types import StrBytes
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from pydantic.v1.typing import display_as_type
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__all__ = ('parse_file_as', 'parse_obj_as', 'parse_raw_as', 'schema_of', 'schema_json_of')
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NameFactory = Union[str, Callable[[Type[Any]], str]]
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if TYPE_CHECKING:
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from pydantic.v1.typing import DictStrAny
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def _generate_parsing_type_name(type_: Any) -> str:
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return f'ParsingModel[{display_as_type(type_)}]'
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@lru_cache(maxsize=2048)
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def _get_parsing_type(type_: Any, *, type_name: Optional[NameFactory] = None) -> Any:
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from pydantic.v1.main import create_model
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if type_name is None:
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type_name = _generate_parsing_type_name
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if not isinstance(type_name, str):
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type_name = type_name(type_)
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return create_model(type_name, __root__=(type_, ...))
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T = TypeVar('T')
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def parse_obj_as(type_: Type[T], obj: Any, *, type_name: Optional[NameFactory] = None) -> T:
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model_type = _get_parsing_type(type_, type_name=type_name) # type: ignore[arg-type]
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return model_type(__root__=obj).__root__
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def parse_file_as(
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type_: Type[T],
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path: Union[str, Path],
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*,
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content_type: str = None,
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encoding: str = 'utf8',
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proto: Protocol = None,
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allow_pickle: bool = False,
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json_loads: Callable[[str], Any] = json.loads,
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type_name: Optional[NameFactory] = None,
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) -> T:
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obj = load_file(
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path,
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proto=proto,
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content_type=content_type,
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encoding=encoding,
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allow_pickle=allow_pickle,
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json_loads=json_loads,
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)
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return parse_obj_as(type_, obj, type_name=type_name)
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def parse_raw_as(
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type_: Type[T],
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b: StrBytes,
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*,
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content_type: str = None,
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encoding: str = 'utf8',
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proto: Protocol = None,
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allow_pickle: bool = False,
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json_loads: Callable[[str], Any] = json.loads,
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type_name: Optional[NameFactory] = None,
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) -> T:
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obj = load_str_bytes(
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b,
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proto=proto,
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content_type=content_type,
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encoding=encoding,
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allow_pickle=allow_pickle,
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json_loads=json_loads,
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)
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return parse_obj_as(type_, obj, type_name=type_name)
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def schema_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_kwargs: Any) -> 'DictStrAny':
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"""Generate a JSON schema (as dict) for the passed model or dynamically generated one"""
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return _get_parsing_type(type_, type_name=title).schema(**schema_kwargs)
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def schema_json_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_json_kwargs: Any) -> str:
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"""Generate a JSON schema (as JSON) for the passed model or dynamically generated one"""
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return _get_parsing_type(type_, type_name=title).schema_json(**schema_json_kwargs)
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