fleet-memory/memora-clients/python/models/search_request.py
2025-11-13 13:51:42 +01:00

155 lines
4.7 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
from ..types import UNSET, Unset
T = TypeVar("T", bound="SearchRequest")
@_attrs_define
class SearchRequest:
"""Request model for search endpoint.
Example:
{'fact_type': ['world', 'agent'], 'max_tokens': 4096, 'query': 'What did Alice say about machine learning?',
'question_date': '2023-05-30T23:40:00', 'reranker': 'heuristic', 'thinking_budget': 100, 'trace': True}
Attributes:
query (str):
fact_type (list[str] | None | Unset):
thinking_budget (int | Unset): Default: 100.
max_tokens (int | Unset): Default: 4096.
reranker (str | Unset): Default: 'heuristic'.
trace (bool | Unset): Default: False.
question_date (None | str | Unset):
"""
query: str
fact_type: list[str] | None | Unset = UNSET
thinking_budget: int | Unset = 100
max_tokens: int | Unset = 4096
reranker: str | Unset = "heuristic"
trace: bool | Unset = False
question_date: None | str | Unset = UNSET
additional_properties: dict[str, Any] = _attrs_field(init=False, factory=dict)
def to_dict(self) -> dict[str, Any]:
query = self.query
fact_type: list[str] | None | Unset
if isinstance(self.fact_type, Unset):
fact_type = UNSET
elif isinstance(self.fact_type, list):
fact_type = self.fact_type
else:
fact_type = self.fact_type
thinking_budget = self.thinking_budget
max_tokens = self.max_tokens
reranker = self.reranker
trace = self.trace
question_date: None | str | Unset
if isinstance(self.question_date, Unset):
question_date = UNSET
else:
question_date = self.question_date
field_dict: dict[str, Any] = {}
field_dict.update(self.additional_properties)
field_dict.update(
{
"query": query,
}
)
if fact_type is not UNSET:
field_dict["fact_type"] = fact_type
if thinking_budget is not UNSET:
field_dict["thinking_budget"] = thinking_budget
if max_tokens is not UNSET:
field_dict["max_tokens"] = max_tokens
if reranker is not UNSET:
field_dict["reranker"] = reranker
if trace is not UNSET:
field_dict["trace"] = trace
if question_date is not UNSET:
field_dict["question_date"] = question_date
return field_dict
@classmethod
def from_dict(cls: type[T], src_dict: Mapping[str, Any]) -> T:
d = dict(src_dict)
query = d.pop("query")
def _parse_fact_type(data: object) -> list[str] | None | Unset:
if data is None:
return data
if isinstance(data, Unset):
return data
try:
if not isinstance(data, list):
raise TypeError()
fact_type_type_0 = cast(list[str], data)
return fact_type_type_0
except (TypeError, ValueError, AttributeError, KeyError):
pass
return cast(list[str] | None | Unset, data)
fact_type = _parse_fact_type(d.pop("fact_type", UNSET))
thinking_budget = d.pop("thinking_budget", UNSET)
max_tokens = d.pop("max_tokens", UNSET)
reranker = d.pop("reranker", UNSET)
trace = d.pop("trace", UNSET)
def _parse_question_date(data: object) -> None | str | Unset:
if data is None:
return data
if isinstance(data, Unset):
return data
return cast(None | str | Unset, data)
question_date = _parse_question_date(d.pop("question_date", UNSET))
search_request = cls(
query=query,
fact_type=fact_type,
thinking_budget=thinking_budget,
max_tokens=max_tokens,
reranker=reranker,
trace=trace,
question_date=question_date,
)
search_request.additional_properties = d
return search_request
@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