plano/tests/archgw/test_prompt_gateway.py
Salman Paracha 88c2bd1851
removing model_server python module to brightstaff (function calling) (#615)
* adding function_calling functionality via rust

* fixed rendered YAML file

* removed model_server from envoy.template and forwarding traffic to bright_staff

* fixed bugs in function_calling.rs that were breaking tests. All good now

* updating e2e test to clean up disk usage

* removing Arch* models to be used as a default model if one is not specified

* if the user sets arch-function base_url we should honor it

* fixing demos as we needed to pin to a particular version of huggingface_hub else the chatbot ui wouldn't build

* adding a constant for Arch-Function model name

* fixing some edge cases with calls made to Arch-Function

* fixed JSON parsing issues in function_calling.rs

* fixed bug where the raw response from Arch-Function was re-encoded

* removed debug from supervisord.conf

* commenting out disk cleanup

* adding back disk space

---------

Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-288.local>
Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-342.local>
2025-11-22 12:55:00 -08:00

141 lines
4.6 KiB
Python

import json
import pytest
import requests
from deepdiff import DeepDiff
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
from pytest_httpserver import HTTPServer, RequestMatcher
@pytest.fixture(scope="session")
def httpserver_listen_address():
return ("0.0.0.0", 51001)
from common import (
PROMPT_GATEWAY_ENDPOINT,
TEST_CASE_FIXTURES,
get_arch_messages,
)
def normalize_tool_call_arguments(tool_call):
"""
Normalize tool call arguments to ensure they are always a dict.
According to OpenAI API spec, the 'arguments' field should be a JSON string,
but for easier testing we parse it into a dict here.
Args:
tool_call: A tool call dict that may have 'arguments' as either a string or dict
Returns:
A tool call dict with 'arguments' guaranteed to be a dict
"""
if "arguments" in tool_call and isinstance(tool_call["arguments"], str):
try:
tool_call["arguments"] = json.loads(tool_call["arguments"])
except (json.JSONDecodeError, TypeError):
# If parsing fails, keep it as is
pass
return tool_call
def test_prompt_gateway(httpserver: HTTPServer):
simple_fixture = TEST_CASE_FIXTURES["SIMPLE"]
input = simple_fixture["input"]
model_server_response = simple_fixture["model_server_response"]
api_server_response = simple_fixture["api_server_response"]
expected_tool_call = {
"name": "get_current_weather",
"arguments": {"location": "seattle, wa", "days": "2"},
}
# setup mock response from model_server
httpserver.expect_request("/function_calling").respond_with_data(
json.dumps(model_server_response)
)
# setup mock response from api_server
httpserver.expect_request("/weather").respond_with_data(
json.dumps(api_server_response)
)
response = requests.post(PROMPT_GATEWAY_ENDPOINT, json=input)
assert response.status_code == 200
httpserver.assert_request_made(
RequestMatcher(uri="/function_calling", method="POST")
)
httpserver.assert_request_made(RequestMatcher(uri="/weather", method="POST"))
response_json = response.json()
assert response_json.get("model").startswith("gpt-4o-mini")
choices = response_json.get("choices", [])
assert len(choices) > 0
assert "message" in choices[0]
assistant_message = choices[0]["message"]
assert "role" in assistant_message
assert assistant_message["role"] == "assistant"
assert "content" in assistant_message
assert "weather" in assistant_message["content"]
# now verify arch_messages (tool call and api response) that are sent as response metadata
arch_messages = get_arch_messages(response_json)
assert len(arch_messages) == 2
tool_calls_message = arch_messages[0]
tool_calls = tool_calls_message.get("tool_calls", [])
assert len(tool_calls) > 0
tool_call = normalize_tool_call_arguments(tool_calls[0]["function"])
diff = DeepDiff(tool_call, expected_tool_call, ignore_string_case=True)
assert not diff
def test_prompt_gateway_api_server_404(httpserver: HTTPServer):
simple_fixture = TEST_CASE_FIXTURES["SIMPLE"]
input = simple_fixture["input"]
model_server_response = simple_fixture["model_server_response"]
# setup mock response from model_server
httpserver.expect_request("/function_calling").respond_with_data(
json.dumps(model_server_response)
)
# setup mock response from model_server
httpserver.expect_request("/weather").respond_with_data(status=404)
response = requests.post(PROMPT_GATEWAY_ENDPOINT, json=input)
assert response.status_code == 404
httpserver.assert_request_made(
RequestMatcher(uri="/function_calling", method="POST")
)
httpserver.assert_request_made(RequestMatcher(uri="/weather", method="POST"))
assert (
response.text
== "upstream application error host=weather_forecast_service, path=/weather, status=404, body="
)
def test_prompt_gateway_model_server_500(httpserver: HTTPServer):
simple_fixture = TEST_CASE_FIXTURES["SIMPLE"]
input = simple_fixture["input"]
# setup mock response from model_server
httpserver.expect_request("/function_calling").respond_with_data(status=500)
response = requests.post(PROMPT_GATEWAY_ENDPOINT, json=input)
assert response.status_code == 500
httpserver.assert_request_made(
RequestMatcher(uri="/function_calling", method="POST")
)
assert (
response.text
== "upstream application error host=arch_internal, path=/function_calling, status=500, body="
)