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- 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.
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
"""Admin-Vorschau für Planungs-Prompt-Slugs."""
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from unittest.mock import MagicMock, patch
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import pytest
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from ai_prompt_planning_preview import (
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PLANNING_PROMPT_SLUGS,
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PlanningPromptPreviewInput,
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is_planning_prompt_slug,
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resolve_planning_prompt_preview_variables,
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)
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def test_is_planning_prompt_slug():
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assert is_planning_prompt_slug("planning_progression_roadmap")
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assert is_planning_prompt_slug("PLANNING_EXERCISE_PATH_QA")
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assert not is_planning_prompt_slug("exercise_summary")
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assert not is_planning_prompt_slug("")
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def test_resolve_roadmap_preview_variables():
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body = PlanningPromptPreviewInput(goal_query="Mae Geri Basics", max_steps=4)
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vars_map = resolve_planning_prompt_preview_variables(
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MagicMock(),
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"planning_progression_roadmap",
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body,
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)
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assert vars_map["goal_query"] == "Mae Geri Basics"
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assert vars_map["max_steps"] == "4"
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assert "goal_analysis_json" in vars_map
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assert "semantic_brief_json" in vars_map
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def test_resolve_stage_spec_includes_intent_context():
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body = PlanningPromptPreviewInput(user_notes="Breitensport")
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vars_map = resolve_planning_prompt_preview_variables(
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MagicMock(),
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"planning_progression_stage_spec",
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body,
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)
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assert "intent_context_json" in vars_map
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assert "major_steps_json" in vars_map
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@patch("ai_prompt_planning_preview._load_catalog_variables")
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def test_resolve_search_intent_includes_catalogs(mock_catalog):
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mock_catalog.return_value = {
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"skills_catalog_json": "[]",
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"focus_areas_catalog_json": "[]",
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"training_types_catalog_json": "[]",
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"style_directions_catalog_json": "[]",
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"target_groups_catalog_json": "[]",
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}
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body = PlanningPromptPreviewInput(search_query="Mae Geri nächster Schritt")
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vars_map = resolve_planning_prompt_preview_variables(
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MagicMock(),
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"planning_exercise_search_intent",
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body,
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)
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assert vars_map["search_query"] == "Mae Geri nächster Schritt"
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assert vars_map["skills_catalog_json"] == "[]"
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def test_non_planning_slug_raises():
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with pytest.raises(ValueError, match="Kein Planungs-Prompt-Slug"):
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resolve_planning_prompt_preview_variables(
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MagicMock(),
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"exercise_summary",
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PlanningPromptPreviewInput(),
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)
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def test_all_registered_slugs_resolve():
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for slug in PLANNING_PROMPT_SLUGS:
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with patch("ai_prompt_planning_preview._load_catalog_variables") as mock_catalog:
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mock_catalog.return_value = {
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"skills_catalog_json": "[]",
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"focus_areas_catalog_json": "[]",
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"training_types_catalog_json": "[]",
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"style_directions_catalog_json": "[]",
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"target_groups_catalog_json": "[]",
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}
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vars_map = resolve_planning_prompt_preview_variables(
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MagicMock(),
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slug,
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PlanningPromptPreviewInput(),
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)
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assert isinstance(vars_map, dict)
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assert len(vars_map) >= 1
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