feat: restructure project as UV workspace with crewai in lib/

tests/utilities/test_knowledge_planning.py

@@ -1,92 +0,0 @@
-"""
-Tests for verifying the integration of knowledge sources in the planning process.
-This module ensures that agent knowledge is properly included during task planning.
-"""
-
-from unittest.mock import patch
-
-import pytest
-
-from crewai.agent import Agent
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-from crewai.task import Task
-from crewai.utilities.planning_handler import CrewPlanner
-
-
-@pytest.fixture
-def mock_knowledge_source():
-    """
-    Create a mock knowledge source with test content.
-    Returns:
-        StringKnowledgeSource:
-            A knowledge source containing AI-related test content
-    """
-    content = """
-    Important context about AI:
-    1. AI systems use machine learning algorithms
-    2. Neural networks are a key component
-    3. Training data is essential for good performance
-    """
-    return StringKnowledgeSource(content=content)
-
-
-@patch("crewai.rag.config.utils.get_rag_client")
-def test_knowledge_included_in_planning(mock_get_client):
-    """Test that verifies knowledge sources are properly included in planning."""
-    # Mock RAG client
-    mock_client = mock_get_client.return_value
-    mock_client.get_or_create_collection.return_value = None
-    mock_client.add_documents.return_value = None
-
-    # Create an agent with knowledge
-    agent = Agent(
-        role="AI Researcher",
-        goal="Research and explain AI concepts",
-        backstory="Expert in artificial intelligence",
-        knowledge_sources=[
-            StringKnowledgeSource(
-                content="AI systems require careful training and validation."
-            )
-        ],
-    )
-
-    # Create a task for the agent
-    task = Task(
-        description="Explain the basics of AI systems",
-        expected_output="A clear explanation of AI fundamentals",
-        agent=agent,
-    )
-
-    # Create a crew planner
-    planner = CrewPlanner([task], None)
-
-    # Get the task summary
-    task_summary = planner._create_tasks_summary()
-
-    # Verify that knowledge is included in planning when present
-    assert "AI systems require careful training" in task_summary, (
-        "Knowledge content should be present in task summary when knowledge exists"
-    )
-    assert '"agent_knowledge"' in task_summary, (
-        "agent_knowledge field should be present in task summary when knowledge exists"
-    )
-
-    # Verify that knowledge is properly formatted
-    assert isinstance(task.agent.knowledge_sources, list), (
-        "Knowledge sources should be stored in a list"
-    )
-    assert len(task.agent.knowledge_sources) > 0, (
-        "At least one knowledge source should be present"
-    )
-    assert task.agent.knowledge_sources[0].content in task_summary, (
-        "Knowledge source content should be included in task summary"
-    )
-
-    # Verify that other expected components are still present
-    assert task.description in task_summary, (
-        "Task description should be present in task summary"
-    )
-    assert task.expected_output in task_summary, (
-        "Expected output should be present in task summary"
-    )
-    assert agent.role in task_summary, "Agent role should be present in task summary"