96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
import pytest
|
|
from openai import OpenAI
|
|
from utils import *
|
|
|
|
server: ServerProcess
|
|
|
|
@pytest.fixture(autouse=True)
|
|
def create_server():
|
|
global server
|
|
server = ServerPreset.tinyllama2()
|
|
|
|
def test_responses_with_openai_library():
|
|
global server
|
|
server.start()
|
|
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
|
|
res = client.responses.create(
|
|
model="gpt-4.1",
|
|
input=[
|
|
{"role": "system", "content": "Book"},
|
|
{"role": "user", "content": "What is the best book"},
|
|
],
|
|
max_output_tokens=8,
|
|
temperature=0.8,
|
|
)
|
|
assert res.id.startswith("resp_")
|
|
assert res.output[0].id is not None
|
|
assert res.output[0].id.startswith("msg_")
|
|
assert match_regex("(Suddenly)+", res.output_text)
|
|
|
|
def test_responses_stream_with_openai_library():
|
|
global server
|
|
server.start()
|
|
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
|
|
stream = client.responses.create(
|
|
model="gpt-4.1",
|
|
input=[
|
|
{"role": "system", "content": "Book"},
|
|
{"role": "user", "content": "What is the best book"},
|
|
],
|
|
max_output_tokens=8,
|
|
temperature=0.8,
|
|
stream=True,
|
|
)
|
|
|
|
gathered_text = ''
|
|
resp_id = ''
|
|
msg_id = ''
|
|
for r in stream:
|
|
if r.type == "response.created":
|
|
assert r.response.id.startswith("resp_")
|
|
resp_id = r.response.id
|
|
if r.type == "response.in_progress":
|
|
assert r.response.id == resp_id
|
|
if r.type == "response.output_item.added":
|
|
assert r.item.id is not None
|
|
assert r.item.id.startswith("msg_")
|
|
msg_id = r.item.id
|
|
if (r.type == "response.content_part.added" or
|
|
r.type == "response.output_text.delta" or
|
|
r.type == "response.output_text.done" or
|
|
r.type == "response.content_part.done"):
|
|
assert r.item_id == msg_id
|
|
if r.type == "response.output_item.done":
|
|
assert r.item.id == msg_id
|
|
|
|
if r.type == "response.output_text.delta":
|
|
gathered_text += r.delta
|
|
if r.type == "response.completed":
|
|
assert r.response.id.startswith("resp_")
|
|
assert r.response.output[0].id is not None
|
|
assert r.response.output[0].id.startswith("msg_")
|
|
assert gathered_text == r.response.output_text
|
|
assert match_regex("(Suddenly)+", r.response.output_text)
|
|
|
|
@pytest.mark.parametrize("text_format,n_predicted,re_content", [
|
|
({"type": "json_schema", "name": "test_schema", "schema": {"const": "foooooo"}}, 10, "\"foooooo\""),
|
|
({"type": "json_object"}, 10, "(\\{|John)+"),
|
|
])
|
|
def test_responses_text_format(text_format: dict, n_predicted: int, re_content: str | None):
|
|
global server
|
|
server.start()
|
|
res = server.make_request("POST", "/v1/responses", data={
|
|
"model": "gpt-4.1",
|
|
"max_output_tokens": n_predicted,
|
|
"input": [
|
|
{"role": "system", "content": "You are a coding assistant."},
|
|
{"role": "user", "content": "Write an example"},
|
|
],
|
|
"text": {"format": text_format},
|
|
})
|
|
|
|
|
|
assert res.status_code == 200
|
|
output_text = res.body["output"][0]["content"][0]["text"]
|
|
assert match_regex(re_content, output_text)
|