mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-09 08:08:32 +00:00
Removing LangChain and Rebuilding Executor (#1322)
* rebuilding executor * removing langchain * Making all tests good * fixing types and adding ability for nor using system prompts * improving types * pleasing the types gods * pleasing the types gods * fixing parser, tools and executor * making sure all tests pass * final pass * fixing type * Updating Docs * preparing to cut new version
This commit is contained in:
João Moura
@@ -6,15 +6,14 @@ from unittest.mock import patch
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import pytest
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from crewai import Agent, Crew, Task
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from crewai.agents.cache import CacheHandler
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from crewai.agents.executor import CrewAgentExecutor
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from crewai.agents.parser import CrewAgentParser
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from crewai.agents.crew_agent_executor import CrewAgentExecutor
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from crewai.llm import LLM
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from crewai.agents.parser import CrewAgentParser, OutputParserException
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from crewai.tools.tool_calling import InstructorToolCalling
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from crewai.tools.tool_usage import ToolUsage
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from crewai.utilities import RPMController
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from langchain.schema import AgentAction
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from langchain.tools import tool
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from langchain_core.exceptions import OutputParserException
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from langchain_openai import ChatOpenAI
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from crewai_tools import tool
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from crewai.agents.parser import AgentAction
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def test_agent_creation():
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@@ -28,15 +27,20 @@ def test_agent_creation():
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def test_agent_default_values():
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agent = Agent(role="test role", goal="test goal", backstory="test backstory")
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assert isinstance(agent.llm, ChatOpenAI)
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assert agent.llm.model_name == "gpt-4o"
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assert agent.llm.temperature == 0.7
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assert agent.llm.verbose is False
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assert agent.allow_delegation is True
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assert agent.llm == "gpt-4o"
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assert agent.allow_delegation is False
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def test_custom_llm():
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agent = Agent(
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role="test role", goal="test goal", backstory="test backstory", llm="gpt-4"
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)
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assert agent.llm == "gpt-4"
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def test_custom_llm_with_langchain():
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from langchain_openai import ChatOpenAI
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agent = Agent(
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role="test role",
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goal="test goal",
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@@ -44,9 +48,7 @@ def test_custom_llm():
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llm=ChatOpenAI(temperature=0, model="gpt-4"),
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)
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assert isinstance(agent.llm, ChatOpenAI)
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assert agent.llm.model_name == "gpt-4"
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assert agent.llm.temperature == 0
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assert agent.llm == "gpt-4"
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@pytest.mark.vcr(filter_headers=["authorization"])
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@@ -89,7 +91,7 @@ def test_agent_execution_with_tools():
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task)
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assert output == "The result of 3 times 4 is 12."
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assert output == "The result of the multiplication is 12."
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@pytest.mark.vcr(filter_headers=["authorization"])
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@@ -104,7 +106,6 @@ def test_logging_tool_usage():
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goal="test goal",
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backstory="test backstory",
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tools=[multiplier],
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allow_delegation=False,
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verbose=True,
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)
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@@ -120,7 +121,7 @@ def test_logging_tool_usage():
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tool_usage = InstructorToolCalling(
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tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
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)
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assert output == "12"
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assert output == "The result of 3 times 4 is 12."
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assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
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assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
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@@ -274,13 +275,67 @@ def test_agent_execution_with_specific_tools():
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task=task, tools=[multiplier])
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assert output == "The result of the multiplication is 12."
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assert output == "The result of the multiplication of 3 times 4 is 12."
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_powered_by_new_o_model_family_that_allows_skipping_tool():
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@tool
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def multiplier(first_number: int, second_number: int) -> float:
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"""Useful for when you need to multiply two numbers together."""
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return first_number * second_number
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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llm="o1-preview",
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max_iter=3,
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use_system_prompt=False,
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allow_delegation=False,
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use_stop_words=False,
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)
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task = Task(
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description="What is 3 times 4?",
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agent=agent,
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expected_output="The result of the multiplication.",
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)
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output = agent.execute_task(task=task, tools=[multiplier])
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assert output == "12"
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_powered_by_new_o_model_family_that_uses_tool():
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@tool
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def comapny_customer_data() -> float:
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"""Useful for getting customer related data."""
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return "The company has 42 customers"
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agent = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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llm="o1-preview",
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max_iter=3,
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use_system_prompt=False,
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allow_delegation=False,
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use_stop_words=False,
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)
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task = Task(
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description="How many customers does the company have?",
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agent=agent,
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expected_output="The number of customers",
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)
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output = agent.execute_task(task=task, tools=[comapny_customer_data])
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assert output == "The company has 42 customers"
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_custom_max_iterations():
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@tool
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def get_final_answer(numbers) -> float:
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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@@ -294,7 +349,7 @@ def test_agent_custom_max_iterations():
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)
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with patch.object(
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CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
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LLM, "call", wraps=LLM("gpt-4o", stop=["\nObservation:"]).call
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) as private_mock:
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task = Task(
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description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
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@@ -304,13 +359,13 @@ def test_agent_custom_max_iterations():
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task=task,
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tools=[get_final_answer],
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)
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private_mock.assert_called_once()
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assert private_mock.call_count == 2
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_repeated_tool_usage(capsys):
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@tool
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def get_final_answer(anything: str) -> float:
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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@@ -320,7 +375,7 @@ def test_agent_repeated_tool_usage(capsys):
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goal="test goal",
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backstory="test backstory",
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max_iter=4,
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llm=ChatOpenAI(model="gpt-4"),
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llm="gpt-4",
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allow_delegation=False,
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verbose=True,
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)
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@@ -357,7 +412,7 @@ def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
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goal="test goal",
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backstory="test backstory",
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max_iter=4,
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llm=ChatOpenAI(model="gpt-4"),
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llm="gpt-4",
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allow_delegation=False,
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verbose=True,
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cache=False,
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@@ -383,7 +438,7 @@ def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_moved_on_after_max_iterations():
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@tool
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def get_final_answer(numbers) -> float:
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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@@ -404,13 +459,13 @@ def test_agent_moved_on_after_max_iterations():
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task=task,
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tools=[get_final_answer],
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)
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assert output == "42"
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assert output == "The final answer is 42."
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_respect_the_max_rpm_set(capsys):
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@tool
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def get_final_answer(anything: str) -> float:
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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@@ -444,11 +499,10 @@ def test_agent_respect_the_max_rpm_set(capsys):
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
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from unittest.mock import patch
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from langchain.tools import tool
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from crewai_tools import tool
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@tool
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def get_final_answer(numbers) -> float:
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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@@ -483,10 +537,10 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
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def test_agent_without_max_rpm_respet_crew_rpm(capsys):
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from unittest.mock import patch
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from langchain.tools import tool
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from crewai_tools import tool
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@tool
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def get_final_answer(numbers) -> float:
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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@@ -496,6 +550,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
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goal="test goal",
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backstory="test backstory",
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max_rpm=10,
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max_iter=2,
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verbose=True,
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allow_delegation=False,
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)
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@@ -504,7 +559,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
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role="test role2",
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goal="test goal2",
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backstory="test backstory2",
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max_iter=2,
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max_iter=1,
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verbose=True,
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allow_delegation=False,
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)
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@@ -514,7 +569,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
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description="Just say hi.", agent=agent1, expected_output="Your greeting."
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),
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Task(
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description="NEVER give a Final Answer, instead keep using the `get_final_answer` tool non-stop",
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description="NEVER give a Final Answer, unless you are told otherwise, instead keep using the `get_final_answer` tool non-stop, until you must give you best final answer",
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expected_output="The final answer",
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tools=[get_final_answer],
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agent=agent2,
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@@ -535,8 +590,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_error_on_parsing_tool(capsys):
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from unittest.mock import patch
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from langchain.tools import tool
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from crewai_tools import tool
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@tool
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def get_final_answer() -> float:
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@@ -548,6 +602,7 @@ def test_agent_error_on_parsing_tool(capsys):
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role="test role",
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goal="test goal",
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backstory="test backstory",
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max_iter=1,
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verbose=True,
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)
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tasks = [
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@@ -563,7 +618,7 @@ def test_agent_error_on_parsing_tool(capsys):
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agents=[agent1],
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tasks=tasks,
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verbose=True,
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function_calling_llm=ChatOpenAI(model="gpt-4-0125-preview"),
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function_calling_llm="gpt-4o",
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)
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with patch.object(ToolUsage, "_render") as force_exception:
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@@ -577,10 +632,10 @@ def test_agent_error_on_parsing_tool(capsys):
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def test_agent_remembers_output_format_after_using_tools_too_many_times():
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from unittest.mock import patch
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from langchain.tools import tool
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from crewai_tools import tool
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@tool
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def get_final_answer(anything: str) -> float:
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def get_final_answer() -> float:
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"""Get the final answer but don't give it yet, just re-use this
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tool non-stop."""
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return 42
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@@ -611,7 +666,6 @@ def test_agent_remembers_output_format_after_using_tools_too_many_times():
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_use_specific_tasks_output_as_context(capsys):
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agent1 = Agent(role="test role", goal="test goal", backstory="test backstory")
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agent2 = Agent(role="test role2", goal="test goal2", backstory="test backstory2")
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say_hi_task = Task(
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@@ -631,7 +685,7 @@ def test_agent_use_specific_tasks_output_as_context(capsys):
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crew = Crew(agents=[agent1, agent2], tasks=tasks)
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result = crew.kickoff()
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print("LOWER RESULT", result.raw)
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assert "bye" not in result.raw.lower()
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assert "hi" in result.raw.lower() or "hello" in result.raw.lower()
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@@ -640,12 +694,12 @@ def test_agent_use_specific_tasks_output_as_context(capsys):
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def test_agent_step_callback():
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class StepCallback:
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def callback(self, step):
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print(step)
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pass
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with patch.object(StepCallback, "callback") as callback:
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@tool
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def learn_about_AI(topic) -> str:
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def learn_about_AI() -> str:
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"""Useful for when you need to learn about AI to write an paragraph about it."""
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return "AI is a very broad field."
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@@ -672,36 +726,51 @@ def test_agent_step_callback():
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_function_calling_llm():
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from langchain_openai import ChatOpenAI
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llm = "gpt-4o"
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llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
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@tool
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def learn_about_AI() -> str:
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"""Useful for when you need to learn about AI to write an paragraph about it."""
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return "AI is a very broad field."
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with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
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agent1 = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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tools=[learn_about_AI],
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llm="gpt-4o",
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max_iter=2,
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function_calling_llm=llm,
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)
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@tool
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def learn_about_AI(topic) -> str:
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"""Useful for when you need to learn about AI to write an paragraph about it."""
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return "AI is a very broad field."
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essay = Task(
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description="Write and then review an small paragraph on AI until it's AMAZING",
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expected_output="The final paragraph.",
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agent=agent1,
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)
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tasks = [essay]
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crew = Crew(agents=[agent1], tasks=tasks)
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from unittest.mock import patch, Mock
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import instructor
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|
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agent1 = Agent(
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role="test role",
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goal="test goal",
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backstory="test backstory",
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tools=[learn_about_AI],
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llm=ChatOpenAI(model="gpt-4-0125-preview"),
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function_calling_llm=llm,
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)
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essay = Task(
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description="Write and then review an small paragraph on AI until it's AMAZING",
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expected_output="The final paragraph.",
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agent=agent1,
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)
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tasks = [essay]
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crew = Crew(agents=[agent1], tasks=tasks)
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with patch.object(instructor, "from_litellm") as mock_from_litellm:
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mock_client = Mock()
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mock_from_litellm.return_value = mock_client
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mock_chat = Mock()
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mock_client.chat = mock_chat
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mock_completions = Mock()
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mock_chat.completions = mock_completions
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mock_create = Mock()
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mock_completions.create = mock_create
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crew.kickoff()
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private_mock.assert_called()
|
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|
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mock_from_litellm.assert_called()
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mock_create.assert_called()
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calls = mock_create.call_args_list
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assert any(
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call.kwargs.get("model") == "gpt-4o" for call in calls
|
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), "Instructor was not created with the expected model"
|
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|
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|
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def test_agent_count_formatting_error():
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@@ -714,8 +783,7 @@ def test_agent_count_formatting_error():
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verbose=True,
|
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)
|
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|
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parser = CrewAgentParser()
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||||
parser.agent = agent1
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parser = CrewAgentParser(agent=agent1)
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|
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with patch.object(Agent, "increment_formatting_errors") as mock_count_errors:
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test_text = "This text does not match expected formats."
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@@ -751,7 +819,6 @@ def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
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crew = Crew(agents=[agent1], tasks=tasks)
|
||||
|
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result = crew.kickoff()
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print("RESULT: ", result.raw)
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assert result.raw == "Howdy!"
|
||||
|
||||
|
||||
@@ -792,22 +859,6 @@ def test_tool_usage_information_is_appended_to_agent():
|
||||
]
|
||||
|
||||
|
||||
def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
|
||||
agent1 = Agent(
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||||
role="test role",
|
||||
goal="test goal",
|
||||
backstory="test backstory",
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
assert len(agent1.llm.callbacks) == 1
|
||||
assert agent1.llm.callbacks[0].__class__.__name__ == "TokenCalcHandler"
|
||||
assert agent1.llm.callbacks[0].model_name == "gpt-4o"
|
||||
assert (
|
||||
agent1.llm.callbacks[0].token_cost_process.__class__.__name__ == "TokenProcess"
|
||||
)
|
||||
|
||||
|
||||
def test_agent_definition_based_on_dict():
|
||||
config = {
|
||||
"role": "test role",
|
||||
@@ -846,7 +897,7 @@ def test_agent_human_input():
|
||||
)
|
||||
|
||||
with patch.object(CrewAgentExecutor, "_ask_human_input") as mock_human_input:
|
||||
mock_human_input.return_value = "Hello"
|
||||
mock_human_input.return_value = "Don't say hi, say Hello instead!"
|
||||
output = agent.execute_task(task)
|
||||
mock_human_input.assert_called_once()
|
||||
assert output == "Hello"
|
||||
@@ -870,6 +921,31 @@ def test_interpolate_inputs():
|
||||
assert agent.backstory == "I am the master of nothing"
|
||||
|
||||
|
||||
def test_not_using_system_prompt():
|
||||
agent = Agent(
|
||||
role="{topic} specialist",
|
||||
goal="Figure {goal} out",
|
||||
backstory="I am the master of {role}",
|
||||
use_system_prompt=False,
|
||||
)
|
||||
|
||||
agent.create_agent_executor()
|
||||
assert not agent.agent_executor.prompt.get("user")
|
||||
assert not agent.agent_executor.prompt.get("system")
|
||||
|
||||
|
||||
def test_using_system_prompt():
|
||||
agent = Agent(
|
||||
role="{topic} specialist",
|
||||
goal="Figure {goal} out",
|
||||
backstory="I am the master of {role}",
|
||||
)
|
||||
|
||||
agent.create_agent_executor()
|
||||
assert agent.agent_executor.prompt.get("user")
|
||||
assert agent.agent_executor.prompt.get("system")
|
||||
|
||||
|
||||
def test_system_and_prompt_template():
|
||||
agent = Agent(
|
||||
role="{topic} specialist",
|
||||
@@ -886,13 +962,11 @@ def test_system_and_prompt_template():
|
||||
{{ .Response }}<|eot_id|>""",
|
||||
)
|
||||
|
||||
template = agent.agent_executor.agent.dict()["runnable"]["middle"][0]["template"]
|
||||
assert (
|
||||
template
|
||||
== """<|start_header_id|>system<|end_header_id|>
|
||||
expected_prompt = """<|start_header_id|>system<|end_header_id|>
|
||||
|
||||
You are {role}. {backstory}
|
||||
Your personal goal is: {goal}To give my best complete final answer to the task use the exact following format:
|
||||
Your personal goal is: {goal}
|
||||
To give my best complete final answer to the task use the exact following format:
|
||||
|
||||
Thought: I now can give a great answer
|
||||
Final Answer: my best complete final answer to the task.
|
||||
@@ -906,12 +980,22 @@ Current Task: {input}
|
||||
|
||||
Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!
|
||||
|
||||
Thought:
|
||||
{agent_scratchpad}<|eot_id|>
|
||||
Thought:<|eot_id|>
|
||||
<|start_header_id|>assistant<|end_header_id|>
|
||||
|
||||
"""
|
||||
)
|
||||
|
||||
with patch.object(CrewAgentExecutor, "_format_prompt") as mock_format_prompt:
|
||||
mock_format_prompt.return_value = expected_prompt
|
||||
|
||||
# Trigger the _format_prompt method
|
||||
agent.agent_executor._format_prompt("dummy_prompt", {})
|
||||
|
||||
# Assert that _format_prompt was called
|
||||
mock_format_prompt.assert_called_once()
|
||||
|
||||
# Assert that the returned prompt matches the expected prompt
|
||||
assert mock_format_prompt.return_value == expected_prompt
|
||||
|
||||
|
||||
@patch("crewai.agent.CrewTrainingHandler")
|
||||
@@ -1000,16 +1084,18 @@ def test_agent_max_retry_limit():
|
||||
[
|
||||
mock.call(
|
||||
{
|
||||
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi \n you MUST return the actual complete content as the final answer, not a summary.",
|
||||
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
|
||||
"tool_names": "",
|
||||
"tools": "",
|
||||
"ask_for_human_input": True,
|
||||
}
|
||||
),
|
||||
mock.call(
|
||||
{
|
||||
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi \n you MUST return the actual complete content as the final answer, not a summary.",
|
||||
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
|
||||
"tool_names": "",
|
||||
"tools": "",
|
||||
"ask_for_human_input": True,
|
||||
}
|
||||
),
|
||||
]
|
||||
@@ -1024,11 +1110,14 @@ def test_handle_context_length_exceeds_limit():
|
||||
backstory="test backstory",
|
||||
)
|
||||
original_action = AgentAction(
|
||||
tool="test_tool", tool_input="test_input", log="test_log"
|
||||
tool="test_tool",
|
||||
tool_input="test_input",
|
||||
text="test_log",
|
||||
thought="test_thought",
|
||||
)
|
||||
|
||||
with patch.object(
|
||||
CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
|
||||
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
|
||||
) as private_mock:
|
||||
task = Task(
|
||||
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
|
||||
@@ -1038,25 +1127,23 @@ def test_handle_context_length_exceeds_limit():
|
||||
task=task,
|
||||
)
|
||||
private_mock.assert_called_once()
|
||||
with patch("crewai.agents.executor.click") as mock_prompt:
|
||||
mock_prompt.return_value = "y"
|
||||
with patch.object(
|
||||
CrewAgentExecutor, "_handle_context_length"
|
||||
) as mock_handle_context:
|
||||
mock_handle_context.side_effect = ValueError(
|
||||
"Context length limit exceeded"
|
||||
with patch.object(
|
||||
CrewAgentExecutor, "_handle_context_length"
|
||||
) as mock_handle_context:
|
||||
mock_handle_context.side_effect = ValueError(
|
||||
"Context length limit exceeded"
|
||||
)
|
||||
|
||||
long_input = "This is a very long input. " * 10000
|
||||
|
||||
# Attempt to handle context length, expecting the mocked error
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
agent.agent_executor._handle_context_length(
|
||||
[(original_action, long_input)]
|
||||
)
|
||||
|
||||
long_input = "This is a very long input. " * 10000
|
||||
|
||||
# Attempt to handle context length, expecting the mocked error
|
||||
with pytest.raises(ValueError) as excinfo:
|
||||
agent.agent_executor._handle_context_length(
|
||||
[(original_action, long_input)]
|
||||
)
|
||||
|
||||
assert "Context length limit exceeded" in str(excinfo.value)
|
||||
mock_handle_context.assert_called_once()
|
||||
assert "Context length limit exceeded" in str(excinfo.value)
|
||||
mock_handle_context.assert_called_once()
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@@ -1065,11 +1152,12 @@ def test_handle_context_length_exceeds_limit_cli_no():
|
||||
role="test role",
|
||||
goal="test goal",
|
||||
backstory="test backstory",
|
||||
sliding_context_window=False,
|
||||
)
|
||||
task = Task(description="test task", agent=agent, expected_output="test output")
|
||||
|
||||
with patch.object(
|
||||
CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
|
||||
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
|
||||
) as private_mock:
|
||||
task = Task(
|
||||
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
|
||||
@@ -1079,10 +1167,8 @@ def test_handle_context_length_exceeds_limit_cli_no():
|
||||
task=task,
|
||||
)
|
||||
private_mock.assert_called_once()
|
||||
with patch("crewai.agents.executor.click") as mock_prompt:
|
||||
mock_prompt.return_value = "n"
|
||||
pytest.raises(SystemExit)
|
||||
with patch.object(
|
||||
CrewAgentExecutor, "_handle_context_length"
|
||||
) as mock_handle_context:
|
||||
mock_handle_context.assert_not_called()
|
||||
pytest.raises(SystemExit)
|
||||
with patch.object(
|
||||
CrewAgentExecutor, "_handle_context_length"
|
||||
) as mock_handle_context:
|
||||
mock_handle_context.assert_not_called()
|
||||
|
||||
@@ -24,7 +24,7 @@ def test_delegate_work():
|
||||
|
||||
assert (
|
||||
result
|
||||
== "As a researcher, my opinions are based on facts and extensive study. Regarding AI Agents, they are a fundamental part of the advancement in technology. AI agents are essentially the entities that perceive their environment and take actions to maximize their chances of success. They have a wide range of applications from self-driving cars to intelligent personal assistants like Siri and Alexa. They have the potential to greatly improve our lives by automating mundane tasks, helping us make better decisions, and even potentially solving complex problems. However, like any technology, they have their own set of challenges such as the risk of job displacement and the ethical implications of their use. My goal as a researcher is not to love or hate AI agents, but to understand them, their benefits, and their implications. It's about maintaining an objective view in order to provide the most accurate and comprehensive analysis."
|
||||
== "While it's a common perception that I might \"hate\" AI agents, my actual stance is much more nuanced and guided by an in-depth understanding of their potential and limitations. As an expert researcher in technology, I recognize that AI agents are a significant advancement in the field of computing and artificial intelligence, offering numerous benefits and applications across various sectors. Here's a detailed take on AI agents:\n\n**Advantages of AI Agents:**\n1. **Automation and Efficiency:** AI agents can automate repetitive tasks, thus freeing up human workers for more complex and creative work. This leads to significant efficiency gains in industries such as customer service (chatbots), data analysis, and even healthcare (AI diagnostic tools).\n\n2. **24/7 Availability:** Unlike human workers, AI agents can operate continuously without fatigue. This is particularly beneficial in customer service environments where support can be provided around the clock.\n\n3. **Data Handling and Analysis:** AI agents can process and analyze vast amounts of data more quickly and accurately than humans. This ability is invaluable in fields like finance, where AI can detect fraudulent activities, or in marketing, where consumer data can be analyzed to improve customer engagement strategies.\n\n4. **Personalization:** AI agents can provide personalized experiences by learning from user interactions. For example, recommendation systems on platforms like Netflix and Amazon use AI agents to suggest content or products tailored to individual preferences.\n\n5. **Scalability:** AI agents can be scaled up easily to handle increasing workloads, making them ideal for businesses experiencing growth or variable demand.\n\n**Challenges and Concerns:**\n1. **Ethical Implications:** The deployment of AI agents raises significant ethical questions, including issues of bias, privacy, and the potential for job displacement. It’s crucial to address these concerns by incorporating transparent, fair, and inclusive practices in AI development and deployment.\n\n2. **Dependability and Error Rates:** While AI agents are generally reliable, they are not infallible. Errors, especially in critical areas like healthcare or autonomous driving, can have severe consequences. Therefore, rigorous testing and validation are essential.\n\n3. **Lack of Understanding:** Many users and stakeholders may not fully understand how AI agents work, leading to mistrust or misuse. Improving AI literacy and transparency can help build trust in these systems.\n\n4. **Security Risks:** AI agents can be vulnerable to cyber-attacks. Ensuring robust cybersecurity measures are in place is vital to protect sensitive data and maintain the integrity of AI systems.\n\n5. **Regulation and Oversight:** The rapid development of AI technology often outpaces regulatory frameworks. Effective governance is needed to ensure AI is used responsibly and ethically.\n\nIn summary, while I thoroughly understand the transformative potential of AI agents and their numerous advantages, I also recognize the importance of addressing the associated challenges. It's not about hating AI agents, but rather advocating for their responsible and ethical use to ensure they benefit society as a whole. My critical perspective is rooted in a desire to see AI agents implemented in ways that maximize their benefits while minimizing potential harms."
|
||||
)
|
||||
|
||||
|
||||
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
|
||||
|
||||
assert (
|
||||
result
|
||||
== "AI Agents are essentially computer programs that are designed to perform tasks autonomously, with the ability to adapt and learn from their environment. These tasks range from simple ones such as setting alarms, to more complex ones like diagnosing diseases or driving cars. AI agents have the potential to revolutionize many industries, making processes more efficient and accurate. \n\nHowever, like any technology, AI agents have their downsides. They can be susceptible to biases based on the data they're trained on and they can also raise privacy concerns. Moreover, the widespread adoption of AI agents could result in significant job displacement in certain industries.\n\nDespite these concerns, it's important to note that the development and use of AI agents are heavily dependent on human decisions and policies. Therefore, the key to harnessing the benefits of AI agents while mitigating the risks lies in responsible and thoughtful development and implementation.\n\nWhether one 'loves' or 'hates' AI agents often comes down to individual perspectives and experiences. But as a researcher, it is my job to provide balanced and factual information, so I hope this explanation helps you understand better what AI Agents are and the implications they have."
|
||||
== "As an expert researcher in technology, particularly in the field of AI and AI agents, it is essential to clarify that my perspective is not one of hatred but rather critical analysis. My evaluation of AI agents is grounded in a balanced view of their advantages and the challenges they present. \n\nAI agents represent a significant leap in technological progress with a wide array of applications across industries. They can perform tasks ranging from customer service interactions, data analysis, complex simulations, to even personal assistance. Their ability to learn and adapt makes them powerful tools for enhancing productivity and innovation.\n\nHowever, there are considerable challenges and ethical concerns associated with their deployment. These include privacy issues, job displacement, and the potential for biased decision-making driven by flawed algorithms. Furthermore, the security risks posed by AI agents, such as how they can be manipulated or hacked, are critical concerns that cannot be ignored.\n\nIn essence, while I do recognize the transformative potential of AI agents, I remain vigilant about their implications. It is vital to ensure that their development is guided by robust ethical standards and stringent regulations to mitigate risks. My view is not rooted in hatred but in a deep commitment to responsible and thoughtful technological advancement. \n\nI hope this clarifies my stance on AI agents and underscores the importance of critical engagement with emerging technologies."
|
||||
)
|
||||
|
||||
|
||||
@@ -52,7 +52,7 @@ def test_ask_question():
|
||||
|
||||
assert (
|
||||
result
|
||||
== "As an AI researcher, I don't have personal feelings or emotions like love or hate. However, I recognize the importance of AI Agents in today's technological landscape. They have the potential to greatly enhance our lives and make tasks more efficient. At the same time, it is crucial to consider the ethical implications and societal impacts that come with their use. My role is to provide objective research and analysis on these topics."
|
||||
== "No, I do not hate AI agents; in fact, I find them incredibly fascinating and useful. As a researcher specializing in technology, particularly in AI and AI agents, I appreciate their potential to revolutionize various industries by automating tasks, providing deep insights through data analysis, and even enhancing decision-making processes. AI agents can streamline operations, improve efficiency, and contribute to advancements in fields like healthcare, finance, and cybersecurity. While they do present challenges, such as ethical considerations and the need for robust security measures, the benefits and potential for positive impact are immense. Therefore, my stance is one of strong support and enthusiasm for AI agents and their future developments."
|
||||
)
|
||||
|
||||
|
||||
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
|
||||
|
||||
assert (
|
||||
result
|
||||
== "No, I don't hate AI agents. In fact, I find them quite fascinating. They are powerful tools that can greatly assist in various tasks, including my research. As a technology researcher, AI and AI agents are subjects of interest to me due to their potential in advancing our understanding and capabilities in various fields. My supposed love for them stems from this professional interest and the potential they hold."
|
||||
== "I do not hate AI agents; in fact, I appreciate them for their immense potential and the numerous benefits they bring to various fields. My passion for AI agents stems from their ability to streamline processes, enhance decision-making, and provide innovative solutions to complex problems. They significantly contribute to advancements in healthcare, finance, education, and many other sectors, making tasks more efficient and freeing up human capacities for more creative and strategic endeavors. So, to answer your question, I love AI agents because of the positive impact they have on our world and their capability to drive technological progress."
|
||||
)
|
||||
|
||||
|
||||
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
|
||||
|
||||
assert (
|
||||
result
|
||||
== "AI Agents are software entities which operate in an environment to achieve a particular goal. They can perceive their environment, reason about it, and take actions to fulfill their objectives. This includes everything from chatbots to self-driving cars. They are designed to act autonomously to a certain extent and are capable of learning from their experiences to improve their performance over time.\n\nDespite some people's fears or dislikes, AI Agents are not inherently good or bad. They are tools, and like any tool, their value depends on how they are used. For instance, AI Agents can be used to automate repetitive tasks, provide customer support, or analyze vast amounts of data far more quickly and accurately than a human could. They can also be used in ways that invade privacy or replace jobs, which is often where the apprehension comes from.\n\nThe key is to create regulations and ethical guidelines for the use of AI Agents, and to continue researching and developing them in a way that maximizes their benefits and minimizes their potential harm. From a research perspective, there's a lot of potential in AI Agents, and it's a fascinating field to be a part of."
|
||||
== "AI agents have emerged as a revolutionary force in today's technological landscape, and my stance on them is not rooted in hatred but in a critical, analytical perspective. Let's delve deeper into what makes AI agents both a boon and a bane in various contexts.\n\n**Benefits of AI Agents:**\n\n1. **Automation and Efficiency:**\n AI agents excel at automating repetitive tasks, which frees up human resources for more complex and creative endeavors. They are capable of performing tasks rapidly and with high accuracy, leading to increased efficiency in operations.\n\n2. **Data Analysis and Decision Making:**\n These agents can process vast amounts of data at speeds far beyond human capability. They can identify patterns and insights that would otherwise be missed, aiding in informed decision-making processes across industries like finance, healthcare, and logistics.\n\n3. **Personalization and User Experience:**\n AI agents can personalize interactions on a scale that is impractical for humans. For example, recommendation engines in e-commerce or content platforms tailor suggestions to individual users, enhancing user experience and satisfaction.\n\n4. **24/7 Availability:**\n Unlike human employees, AI agents can operate round-the-clock without the need for breaks, sleep, or holidays. This makes them ideal for customer service roles, providing consistent and immediate responses any time of the day.\n\n**Challenges and Concerns:**\n\n1. **Job Displacement:**\n One of the major concerns is the displacement of jobs. As AI agents become more proficient at a variety of tasks, there is a legitimate fear of human workers being replaced, leading to unemployment and economic disruption.\n\n2. **Bias and Fairness:**\n AI agents are only as good as the data they are trained on. If the training data contains biases, the AI agents can perpetuate or even exacerbate these biases, leading to unfair and discriminatory outcomes.\n\n3. **Privacy and Security:**\n The use of AI agents often involves handling large amounts of personal data, raising significant privacy and security concerns. Unauthorized access or breaches could lead to severe consequences for individuals and organizations.\n\n4. **Accountability and Transparency:**\n The decision-making processes of AI agents can be opaque, making it difficult to hold them accountable. This lack of transparency can lead to mistrust and ethical dilemmas, particularly when AI decisions impact human lives.\n\n5. **Ethical Considerations:**\n The deployment of AI agents in sensitive areas, such as surveillance and law enforcement, raises ethical issues. The potential for misuse or overdependence on AI decision-making poses a threat to individual freedoms and societal norms.\n\nIn conclusion, while AI agents offer remarkable advantages in terms of efficiency, data handling, and user experience, they also bring significant challenges that need to be addressed carefully. My critical stance is driven by a desire to ensure that their integration into society is balanced, fair, and beneficial to all, without ignoring the potential downsides. Therefore, a nuanced approach is essential in leveraging the power of AI agents responsibly."
|
||||
)
|
||||
|
||||
|
||||
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
|
||||
|
||||
assert (
|
||||
result
|
||||
== "I don't hate or love AI agents. My passion lies in understanding them, researching about their capabilities, implications, and potential for development. As a researcher, my feelings toward AI are more of fascination and interest rather than personal love or hate."
|
||||
== "As a researcher specialized in technology, particularly in AI and AI agents, my feelings toward them are far more nuanced than simply loving or hating them. AI agents represent a remarkable advancement in technology and hold tremendous potential for improving various aspects of our lives and industries. They can automate tedious tasks, provide intelligent data analysis, support decision-making, and even enhance our creative processes. These capabilities can drive efficiency, innovation, and economic growth.\n\nHowever, it is also crucial to acknowledge the challenges and ethical considerations posed by AI agents. Issues such as data privacy, security, job displacement, and the need for proper regulation are significant concerns that must be carefully managed. Moreover, the development and deployment of AI should be guided by principles that ensure fairness, transparency, and accountability.\n\nIn essence, I appreciate the profound impact AI agents can have, but I also recognize the importance of approaching their integration into society with thoughtful consideration and responsibility. Balancing enthusiasm with caution and ethical oversight is key to harnessing the full potential of AI while mitigating its risks."
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -1,26 +1,18 @@
|
||||
interactions:
|
||||
- request:
|
||||
body: '{"messages": [{"role": "user", "content": "You are researcher.\nYou''re
|
||||
an expert researcher, specialized in technology\n\nYour personal goal is: make
|
||||
the best research and analysis on content about AI and AI agentsTOOLS:\n------\nYou
|
||||
have access to only the following tools:\n\n\n\nTo use a tool, please use the
|
||||
exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction:
|
||||
the tool you wanna use, should be one of [], just the name.\nAction Input: Any
|
||||
and all relevant information input and context for using the tool\nObservation:
|
||||
the result of using the tool\n```\n\nWhen you have a response for your task,
|
||||
or if you do not need to use a tool, you MUST use the format:\n\n```\nThought:
|
||||
Do I need to use a tool? No\nFinal Answer: [your response here]```This is the
|
||||
summary of your work so far:\nThe human asks the AI''s opinion on AI Agents,
|
||||
suggesting that the AI dislikes them. The AI, identifying as a researcher, clarifies
|
||||
that its opinions are based on research and study. It views AI Agents as a key
|
||||
part of technological advancement, with potential to improve lives through automation
|
||||
and decision-making assistance. However, it also acknowledges challenges, including
|
||||
job displacement risk and ethical implications. The AI aims to maintain an objective
|
||||
view for accurate analysis, rather than loving or hating AI Agents.Begin! This
|
||||
is VERY important to you, your job depends on it!\n\nCurrent Task: do you hate
|
||||
AI Agents?\nThis is the context you''re working with:\nI heard you LOVE them\n"}],
|
||||
"model": "gpt-4", "n": 1, "stop": ["\nObservation"], "stream": true, "temperature":
|
||||
0.7}'
|
||||
body: '{"messages": [{"role": "system", "content": "You are researcher. You''re
|
||||
an expert researcher, specialized in technology\nYour personal goal is: make
|
||||
the best research and analysis on content about AI and AI agents\nTo give my
|
||||
best complete final answer to the task use the exact following format:\n\nThought:
|
||||
I now can give a great answer\nFinal Answer: Your final answer must be the great
|
||||
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assert result.output == "The temperature is 100 degrees"
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def test_missing_action_error(parser):
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text = "Thought: Let's find the temperature\nAction Input: what is the temperature in SF?"
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parser.parse(text)
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||||
assert "Could not parse LLM output" in str(exc_info.value)
|
||||
assert "Invalid Format: I missed the 'Action:' after 'Thought:'." in str(
|
||||
exc_info.value
|
||||
)
|
||||
|
||||
|
||||
def test_missing_action_input_error(parser):
|
||||
text = "Thought: Let's find the temperature\nAction: search"
|
||||
with pytest.raises(OutputParserException) as exc_info:
|
||||
parser.parse(text)
|
||||
assert "Could not parse LLM output" in str(exc_info.value)
|
||||
assert "I missed the 'Action Input:' after 'Action:'." in str(exc_info.value)
|
||||
|
||||
|
||||
def test_action_and_final_answer_error(parser):
|
||||
@@ -240,7 +243,6 @@ def test_safe_repair_json(parser):
|
||||
def test_safe_repair_json_unrepairable(parser):
|
||||
invalid_json = "{invalid_json"
|
||||
result = parser._safe_repair_json(invalid_json)
|
||||
print("result:", invalid_json)
|
||||
assert result == invalid_json # Should return the original if unrepairable
|
||||
|
||||
|
||||
@@ -292,7 +294,6 @@ def test_safe_repair_json_unescaped_characters(parser):
|
||||
invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher\n"}'
|
||||
expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
|
||||
result = parser._safe_repair_json(invalid_json)
|
||||
print("result:", result)
|
||||
assert result == expected_repaired_json
|
||||
|
||||
|
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|
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