mirror of
https://github.com/crewAIInc/crewAI.git
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78a68c677c
- Merged upstream changes from crewAI main branch - Resolved conflicts in pyproject.toml - Fixed mypy import issue in qdrant factory - Updated pre-commit mypy configuration for monorepo structure
93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
"""
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Tests for verifying the integration of knowledge sources in the planning process.
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This module ensures that agent knowledge is properly included during task planning.
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"""
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from unittest.mock import patch
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import pytest
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from crewai.agent import Agent
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from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
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from crewai.task import Task
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from crewai.utilities.planning_handler import CrewPlanner
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@pytest.fixture
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def mock_knowledge_source():
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"""
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Create a mock knowledge source with test content.
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Returns:
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StringKnowledgeSource:
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A knowledge source containing AI-related test content
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"""
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content = """
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Important context about AI:
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1. AI systems use machine learning algorithms
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2. Neural networks are a key component
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3. Training data is essential for good performance
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"""
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return StringKnowledgeSource(content=content)
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@patch("crewai.rag.config.utils.get_rag_client")
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def test_knowledge_included_in_planning(mock_get_client):
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"""Test that verifies knowledge sources are properly included in planning."""
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# Mock RAG client
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mock_client = mock_get_client.return_value
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mock_client.get_or_create_collection.return_value = None
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mock_client.add_documents.return_value = None
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# Create an agent with knowledge
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agent = Agent(
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role="AI Researcher",
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goal="Research and explain AI concepts",
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backstory="Expert in artificial intelligence",
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knowledge_sources=[
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StringKnowledgeSource(
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content="AI systems require careful training and validation."
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)
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],
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)
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# Create a task for the agent
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task = Task(
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description="Explain the basics of AI systems",
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expected_output="A clear explanation of AI fundamentals",
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agent=agent,
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)
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# Create a crew planner
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planner = CrewPlanner([task], None)
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# Get the task summary
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task_summary = planner._create_tasks_summary()
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# Verify that knowledge is included in planning when present
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assert "AI systems require careful training" in task_summary, (
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"Knowledge content should be present in task summary when knowledge exists"
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)
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assert '"agent_knowledge"' in task_summary, (
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"agent_knowledge field should be present in task summary when knowledge exists"
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)
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# Verify that knowledge is properly formatted
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assert isinstance(task.agent.knowledge_sources, list), (
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"Knowledge sources should be stored in a list"
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)
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assert len(task.agent.knowledge_sources) > 0, (
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"At least one knowledge source should be present"
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)
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assert task.agent.knowledge_sources[0].content in task_summary, (
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"Knowledge source content should be included in task summary"
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)
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# Verify that other expected components are still present
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assert task.description in task_summary, (
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"Task description should be present in task summary"
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)
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assert task.expected_output in task_summary, (
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"Expected output should be present in task summary"
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)
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assert agent.role in task_summary, "Agent role should be present in task summary"
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