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import json
from typing import Any, Union, cast
from typing_extensions import Annotated
import httpx
import pytest
import pydantic
from openai import OpenAI, BaseModel
from openai._streaming import Stream
from openai._base_client import FinalRequestOptions
from openai._legacy_response import LegacyAPIResponse
from .utils import rich_print_str
class PydanticModel(pydantic.BaseModel): ...
def test_response_parse_mismatched_basemodel(client: OpenAI) -> None:
response = LegacyAPIResponse(
raw=httpx.Response(200, content=b"foo"),
client=client,
stream=False,
stream_cls=None,
cast_to=str,
options=FinalRequestOptions.construct(method="get", url="/foo"),
)
with pytest.raises(
TypeError,
match="Pydantic models must subclass our base model type, e.g. `from openai import BaseModel`",
):
response.parse(to=PydanticModel)
@pytest.mark.parametrize(
"content, expected",
[
("false", False),
("true", True),
("False", False),
("True", True),
("TrUe", True),
("FalSe", False),
],
)
def test_response_parse_bool(client: OpenAI, content: str, expected: bool) -> None:
response = LegacyAPIResponse(
raw=httpx.Response(200, content=content),
client=client,
stream=False,
stream_cls=None,
cast_to=str,
options=FinalRequestOptions.construct(method="get", url="/foo"),
)
result = response.parse(to=bool)
assert result is expected
def test_response_parse_custom_stream(client: OpenAI) -> None:
response = LegacyAPIResponse(
raw=httpx.Response(200, content=b"foo"),
client=client,
stream=True,
stream_cls=None,
cast_to=str,
options=FinalRequestOptions.construct(method="get", url="/foo"),
)
stream = response.parse(to=Stream[int])
assert stream._cast_to == int
class CustomModel(BaseModel):
foo: str
bar: int
def test_response_parse_custom_model(client: OpenAI) -> None:
response = LegacyAPIResponse(
raw=httpx.Response(200, content=json.dumps({"foo": "hello!", "bar": 2})),
client=client,
stream=False,
stream_cls=None,
cast_to=str,
options=FinalRequestOptions.construct(method="get", url="/foo"),
)
obj = response.parse(to=CustomModel)
assert obj.foo == "hello!"
assert obj.bar == 2
def test_response_basemodel_request_id(client: OpenAI) -> None:
response = LegacyAPIResponse(
raw=httpx.Response(
200,
headers={"x-request-id": "my-req-id"},
content=json.dumps({"foo": "hello!", "bar": 2}),
),
client=client,
stream=False,
stream_cls=None,
cast_to=str,
options=FinalRequestOptions.construct(method="get", url="/foo"),
)
obj = response.parse(to=CustomModel)
assert obj._request_id == "my-req-id"
assert obj.foo == "hello!"
assert obj.bar == 2
assert obj.to_dict() == {"foo": "hello!", "bar": 2}
assert "_request_id" not in rich_print_str(obj)
assert "__exclude_fields__" not in rich_print_str(obj)
def test_response_parse_annotated_type(client: OpenAI) -> None:
response = LegacyAPIResponse(
raw=httpx.Response(200, content=json.dumps({"foo": "hello!", "bar": 2})),
client=client,
stream=False,
stream_cls=None,
cast_to=str,
options=FinalRequestOptions.construct(method="get", url="/foo"),
)
obj = response.parse(
to=cast("type[CustomModel]", Annotated[CustomModel, "random metadata"]),
)
assert obj.foo == "hello!"
assert obj.bar == 2
class OtherModel(pydantic.BaseModel):
a: str
@pytest.mark.parametrize("client", [False], indirect=True) # loose validation
def test_response_parse_expect_model_union_non_json_content(client: OpenAI) -> None:
response = LegacyAPIResponse(
raw=httpx.Response(200, content=b"foo", headers={"Content-Type": "application/text"}),
client=client,
stream=False,
stream_cls=None,
cast_to=str,
options=FinalRequestOptions.construct(method="get", url="/foo"),
)
obj = response.parse(to=cast(Any, Union[CustomModel, OtherModel]))
assert isinstance(obj, str)
assert obj == "foo"
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