# coding: utf-8
"""
Arize REST API
API specification for the backend data server. The API is hosted globally at https://api.arize.com/v2 or in your own environment.
The version of the OpenAPI document: 2.0.0
Generated by OpenAPI Generator (https://openapi-generator.tech)
Do not edit the class manually.
""" # noqa: E501
from __future__ import annotations
import pprint
import re # noqa: F401
import json
from pydantic import BaseModel, ConfigDict, Field, StrictFloat, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional, Union
from arize._generated.api_client.models.response_format import ResponseFormat
from arize._generated.api_client.models.tool_config import ToolConfig
from typing import Optional, Set
from typing_extensions import Self
[docs]
class InvocationParams(BaseModel):
"""
Parameters for the LLM invocation
""" # noqa: E501
temperature: Optional[Union[StrictFloat, StrictInt]] = Field(default=None, description="Sampling temperature (higher = more random)")
max_tokens: Optional[StrictInt] = Field(default=None, description="Maximum number of tokens to generate")
max_completion_tokens: Optional[StrictInt] = Field(default=None, description="Maximum number of completion tokens to generate")
top_p: Optional[Union[StrictFloat, StrictInt]] = Field(default=None, description="Nucleus sampling parameter")
frequency_penalty: Optional[Union[StrictFloat, StrictInt]] = Field(default=None, description="Frequency penalty (-2.0 to 2.0)")
presence_penalty: Optional[Union[StrictFloat, StrictInt]] = Field(default=None, description="Presence penalty (-2.0 to 2.0)")
stop: Optional[List[StrictStr]] = Field(default=None, description="Stop sequences")
response_format: Optional[ResponseFormat] = Field(default=None, description="Response format configuration. Optional. When omitted, no structured output constraint is applied (the provider's default plain-text behavior is used).")
tool_config: Optional[ToolConfig] = Field(default=None, description="Tool configuration for the LLM invocation. Optional. When omitted, no tools are made available to the model.")
top_k: Optional[StrictInt] = Field(default=None, description="Top-K sampling parameter. A top-K of 1 means the next selected token is the most probable (greedy decoding).")
thinking_level: Optional[StrictStr] = Field(default=None, description="Controls how much reasoning the model performs before responding. Supported by Gemini 3.x models. Accepted values: 'low', 'high'.")
thinking_budget: Optional[StrictInt] = Field(default=None, description="Maximum tokens the model may use for internal reasoning. Supported by Gemini 2.5 models. Range: 0-24576 (Flash/Flash-Lite) or 128-32768 (Pro). Set 0 to disable thinking on Flash models.")
reasoning_effort: Optional[StrictStr] = Field(default=None, description="Controls how much reasoning the model performs before responding. Supported by OpenAI o-series and GPT-5 models. o-series: 'low' | 'medium' | 'high'. GPT-5: 'none' | 'low' | 'medium' | 'high' | 'xhigh'.")
verbosity: Optional[StrictStr] = Field(default=None, description="Controls the verbosity of model output. Supported by OpenAI GPT-5 series. Accepted values: 'low' | 'medium' | 'high'.")
additional_properties: Dict[str, Any] = {}
__properties: ClassVar[List[str]] = ["temperature", "max_tokens", "max_completion_tokens", "top_p", "frequency_penalty", "presence_penalty", "stop", "response_format", "tool_config", "top_k", "thinking_level", "thinking_budget", "reasoning_effort", "verbosity"]
model_config = ConfigDict(
populate_by_name=True,
validate_assignment=True,
protected_namespaces=(),
)
[docs]
def to_str(self) -> str:
"""Returns the string representation of the model using alias"""
return pprint.pformat(self.model_dump(by_alias=True))
[docs]
def to_json(self) -> str:
"""Returns the JSON representation of the model using alias"""
# TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
return json.dumps(self.to_dict())
[docs]
@classmethod
def from_json(cls, json_str: str) -> Optional[Self]:
"""Create an instance of InvocationParams from a JSON string"""
return cls.from_dict(json.loads(json_str))
[docs]
def to_dict(self) -> Dict[str, Any]:
"""Return the dictionary representation of the model using alias.
This has the following differences from calling pydantic's
`self.model_dump(by_alias=True)`:
* `None` is only added to the output dict for nullable fields that
were set at model initialization. Other fields with value `None`
are ignored.
* Fields in `self.additional_properties` are added to the output dict.
"""
excluded_fields: Set[str] = set([
"additional_properties",
])
_dict = self.model_dump(
by_alias=True,
exclude=excluded_fields,
exclude_none=True,
)
# override the default output from pydantic by calling `to_dict()` of response_format
if self.response_format:
_dict['response_format'] = self.response_format.to_dict()
# override the default output from pydantic by calling `to_dict()` of tool_config
if self.tool_config:
_dict['tool_config'] = self.tool_config.to_dict()
# puts key-value pairs in additional_properties in the top level
if self.additional_properties is not None:
for _key, _value in self.additional_properties.items():
_dict[_key] = _value
return _dict
[docs]
@classmethod
def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
"""Create an instance of InvocationParams from a dict"""
if obj is None:
return None
if not isinstance(obj, dict):
return cls.model_validate(obj)
_obj = cls.model_validate({
"temperature": obj.get("temperature"),
"max_tokens": obj.get("max_tokens"),
"max_completion_tokens": obj.get("max_completion_tokens"),
"top_p": obj.get("top_p"),
"frequency_penalty": obj.get("frequency_penalty"),
"presence_penalty": obj.get("presence_penalty"),
"stop": obj.get("stop"),
"response_format": ResponseFormat.from_dict(obj["response_format"]) if obj.get("response_format") is not None else None,
"tool_config": ToolConfig.from_dict(obj["tool_config"]) if obj.get("tool_config") is not None else None,
"top_k": obj.get("top_k"),
"thinking_level": obj.get("thinking_level"),
"thinking_budget": obj.get("thinking_budget"),
"reasoning_effort": obj.get("reasoning_effort"),
"verbosity": obj.get("verbosity")
})
# store additional fields in additional_properties
for _key in obj.keys():
if _key not in cls.__properties:
_obj.additional_properties[_key] = obj.get(_key)
return _obj
