fleet-memory/memora-clients/python/models/document_response.py
Nicolò Boschi c82fff949b add profiles
2025-11-13 13:22:27 +01:00

120 lines
3.2 KiB
Python

from __future__ import annotations
from collections.abc import Mapping
from typing import Any, TypeVar, cast
from attrs import define as _attrs_define
from attrs import field as _attrs_field
T = TypeVar("T", bound="DocumentResponse")
@_attrs_define
class DocumentResponse:
"""Response model for get document endpoint.
Example:
{'agent_id': 'user123', 'content_hash': 'abc123', 'created_at': '2024-01-15T10:30:00Z', 'id': 'session_1',
'memory_unit_count': 15, 'original_text': 'Full document text here...', 'updated_at': '2024-01-15T10:30:00Z'}
Attributes:
id (str):
agent_id (str):
original_text (str):
content_hash (None | str):
created_at (str):
updated_at (str):
memory_unit_count (int):
"""
id: str
agent_id: str
original_text: str
content_hash: None | str
created_at: str
updated_at: str
memory_unit_count: int
additional_properties: dict[str, Any] = _attrs_field(init=False, factory=dict)
def to_dict(self) -> dict[str, Any]:
id = self.id
agent_id = self.agent_id
original_text = self.original_text
content_hash: None | str
content_hash = self.content_hash
created_at = self.created_at
updated_at = self.updated_at
memory_unit_count = self.memory_unit_count
field_dict: dict[str, Any] = {}
field_dict.update(self.additional_properties)
field_dict.update(
{
"id": id,
"agent_id": agent_id,
"original_text": original_text,
"content_hash": content_hash,
"created_at": created_at,
"updated_at": updated_at,
"memory_unit_count": memory_unit_count,
}
)
return field_dict
@classmethod
def from_dict(cls: type[T], src_dict: Mapping[str, Any]) -> T:
d = dict(src_dict)
id = d.pop("id")
agent_id = d.pop("agent_id")
original_text = d.pop("original_text")
def _parse_content_hash(data: object) -> None | str:
if data is None:
return data
return cast(None | str, data)
content_hash = _parse_content_hash(d.pop("content_hash"))
created_at = d.pop("created_at")
updated_at = d.pop("updated_at")
memory_unit_count = d.pop("memory_unit_count")
document_response = cls(
id=id,
agent_id=agent_id,
original_text=original_text,
content_hash=content_hash,
created_at=created_at,
updated_at=updated_at,
memory_unit_count=memory_unit_count,
)
document_response.additional_properties = d
return document_response
@property
def additional_keys(self) -> list[str]:
return list(self.additional_properties.keys())
def __getitem__(self, key: str) -> Any:
return self.additional_properties[key]
def __setitem__(self, key: str, value: Any) -> None:
self.additional_properties[key] = value
def __delitem__(self, key: str) -> None:
del self.additional_properties[key]
def __contains__(self, key: str) -> bool:
return key in self.additional_properties