mirror of
https://github.com/aljazceru/Auto-GPT.git
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bfbe613960
Additional changes: * Improve typing * Modularize message history memory & fix/refactor lots of things * Fix summarization * Move memory relevance calculation to MemoryItem & improve test * Fix import warnings in web_selenium.py * Remove `memory_add` ghost command * Implement overlap in `split_text` * Move memory tests into subdirectory * Remove deprecated `get_ada_embedding()` and helpers * Fix used token calculation in `chat_with_ai` * Replace Message TypedDict by dataclass * Fix AgentManager singleton issues in tests --------- Co-authored-by: Auto-GPT-Bot <github-bot@agpt.co>
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
from datetime import datetime
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from autogpt.agent.agent import Agent
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from autogpt.config import AIConfig
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from autogpt.llm.chat import create_chat_completion
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from autogpt.log_cycle.log_cycle import LogCycleHandler
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def test_get_self_feedback(mocker):
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# Define a sample thoughts dictionary
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thoughts = {
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"reasoning": "Sample reasoning.",
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"plan": "Sample plan.",
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"thoughts": "Sample thoughts.",
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}
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# Define a fake response for the create_chat_completion function
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fake_response = (
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"The AI Agent has demonstrated a reasonable thought process, but there is room for improvement. "
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"For example, the reasoning could be elaborated to better justify the plan, and the plan itself "
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"could be more detailed to ensure its effectiveness. In addition, the AI Agent should focus more "
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"on its core role and prioritize thoughts that align with that role."
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)
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# Mock the create_chat_completion function
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mock_create_chat_completion = mocker.patch(
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"autogpt.agent.agent.create_chat_completion", wraps=create_chat_completion
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)
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mock_create_chat_completion.return_value = fake_response
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# Create a MagicMock object to replace the Agent instance
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agent_mock = mocker.MagicMock(spec=Agent)
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# Mock the config attribute of the Agent instance
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agent_mock.config = AIConfig()
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# Mock the log_cycle_handler attribute of the Agent instance
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agent_mock.log_cycle_handler = LogCycleHandler()
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# Mock the create_nested_directory method of the LogCycleHandler instance
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agent_mock.created_at = datetime.now().strftime("%Y%m%d_%H%M%S")
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# Mock the cycle_count attribute of the Agent instance
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agent_mock.cycle_count = 0
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# Call the get_self_feedback method
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feedback = Agent.get_self_feedback(
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agent_mock,
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thoughts,
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"gpt-3.5-turbo",
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)
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# Check if the response is a non-empty string
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assert isinstance(feedback, str) and len(feedback) > 0
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# Check if certain keywords from input thoughts are present in the feedback response
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for keyword in ["reasoning", "plan", "thoughts"]:
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assert keyword in feedback
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