Lars
9cee862c32
All checks were successful
Deploy Development / deploy (push) Successful in 47s
Test Suite / pytest-backend (push) Successful in 49s
Test Suite / lint-backend (push) Successful in 0s
Test Suite / build-frontend (push) Successful in 15s
Test Suite / k6 /health Baseline (push) Successful in 34s
Test Suite / playwright-tests (push) Successful in 1m26s
- Added new fields for goal query, user notes, max steps, and search query in the AiPromptPreviewBody to support planning prompts. - Integrated planning prompt handling in the preview_ai_prompt function, allowing for distinct processing of planning and exercise prompts. - Introduced LLM usage tracking in openrouter_chat_completion and planning_exercise_suggest functions to monitor AI call metrics. - Updated frontend components to accommodate new input fields for planning prompts, enhancing user experience and functionality.
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
"""LLM-Zählung für Planungs-APIs (P1-C2)."""
|
|
from unittest.mock import MagicMock, patch
|
|
|
|
import pytest
|
|
|
|
from planning_llm_usage import (
|
|
current_planning_llm_call_counter,
|
|
planning_llm_call_meter,
|
|
record_planning_llm_call,
|
|
)
|
|
|
|
|
|
def test_meter_inactive_by_default():
|
|
assert current_planning_llm_call_counter() is None
|
|
record_planning_llm_call(3)
|
|
assert current_planning_llm_call_counter() is None
|
|
|
|
|
|
def test_meter_counts_within_scope():
|
|
with planning_llm_call_meter() as meter:
|
|
record_planning_llm_call(1)
|
|
record_planning_llm_call(2)
|
|
assert meter.count == 3
|
|
|
|
|
|
def test_openrouter_increments_active_meter():
|
|
from openrouter_chat import openrouter_chat_completion
|
|
|
|
fake_resp = MagicMock()
|
|
fake_resp.status_code = 200
|
|
fake_resp.json.return_value = {
|
|
"choices": [{"message": {"content": "ok"}, "finish_reason": "stop"}],
|
|
}
|
|
|
|
with planning_llm_call_meter() as meter:
|
|
with patch("openrouter_chat.httpx.Client") as client_cls:
|
|
client = MagicMock()
|
|
client.__enter__.return_value = client
|
|
client.post.return_value = fake_resp
|
|
client_cls.return_value = client
|
|
out = openrouter_chat_completion(
|
|
api_key="test-key",
|
|
model="test/model",
|
|
user_content="hello",
|
|
)
|
|
assert out == "ok"
|
|
assert meter.count == 1
|
|
|
|
|
|
def test_openrouter_skips_meter_on_http_error():
|
|
from openrouter_chat import OpenRouterError, openrouter_chat_completion
|
|
|
|
fake_resp = MagicMock()
|
|
fake_resp.status_code = 500
|
|
fake_resp.json.return_value = {"error": {"message": "fail"}}
|
|
fake_resp.text = "fail"
|
|
|
|
with planning_llm_call_meter() as meter:
|
|
with patch("openrouter_chat.httpx.Client") as client_cls:
|
|
client = MagicMock()
|
|
client.__enter__.return_value = client
|
|
client.post.return_value = fake_resp
|
|
client_cls.return_value = client
|
|
with pytest.raises(OpenRouterError):
|
|
openrouter_chat_completion(
|
|
api_key="test-key",
|
|
model="test/model",
|
|
user_content="hello",
|
|
)
|
|
assert meter.count == 0
|
|
|
|
|
|
def test_uses_ai_gap_fill_not_counted_without_openrouter():
|
|
"""Regression: Gap-Fill-Flag allein löst keinen OpenRouter-Aufruf aus."""
|
|
from planning_exercise_path_builder import ProgressionPathSuggestRequest
|
|
|
|
body = ProgressionPathSuggestRequest(
|
|
query="Mae Geri Progression",
|
|
include_llm_intent=False,
|
|
include_llm_path_qa=False,
|
|
include_llm_roadmap=False,
|
|
include_llm_start_target=False,
|
|
include_ai_gap_fill=True,
|
|
evaluate_only=True,
|
|
evaluate_steps=[],
|
|
)
|
|
uses_ai = (
|
|
body.include_llm_intent
|
|
or body.include_llm_path_qa
|
|
or body.include_llm_roadmap
|
|
or body.include_llm_start_target
|
|
or (body.start_target_only and body.include_llm_start_target)
|
|
)
|
|
assert uses_ai is False
|