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Revamp max iteration Logic (#111)

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This now will allow to add a max_inter option to agents while also making sure to force the agent to give it's best final answer before running out of it's max_inter.
This commit is contained in:
João Moura
2024-01-11 12:32:54 -03:00
committed by GitHub
parent 8cc51d5e9e
commit ea7759b322
6 changed files with 1035 additions and 17 deletions
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12
poetry.lock generated
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@@ -762,13 +762,13 @@ colors = ["colorama (>=0.4.6)"]
[[package]]
name = "jinja2"
version = "3.1.2"
version = "3.1.3"
description = "A very fast and expressive template engine."
optional = false
python-versions = ">=3.7"
files = [
{file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
{file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
{file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
{file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
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[package.dependencies]
@@ -926,13 +926,13 @@ requests = ">=2,<3"
[[package]]
name = "markdown"
version = "3.5.1"
version = "3.5.2"
description = "Python implementation of John Gruber's Markdown."
optional = false
python-versions = ">=3.8"
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{file = "Markdown-3.5.1.tar.gz", hash = "sha256:b65d7beb248dc22f2e8a31fb706d93798093c308dc1aba295aedeb9d41a813bd"},
{file = "Markdown-3.5.2-py3-none-any.whl", hash = "sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd"},
{file = "Markdown-3.5.2.tar.gz", hash = "sha256:e1ac7b3dc550ee80e602e71c1d168002f062e49f1b11e26a36264dafd4df2ef8"},
]
[package.dependencies]

5
src/crewai/agent.py
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@@ -38,6 +38,7 @@ class Agent(BaseModel):
goal: The objective of the agent.
backstory: The backstory of the agent.
llm: The language model that will run the agent.
max_iter: Maximum number of iterations for an agent to execute a task.
memory: Whether the agent should have memory or not.
verbose: Whether the agent execution should be in verbose mode.
allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
@@ -72,6 +73,9 @@ class Agent(BaseModel):
tools: List[Any] = Field(
default_factory=list, description="Tools at agents disposal"
)
max_iter: Optional[int] = Field(
default=15, description="Maximum iterations for an agent to execute a task"
)
agent_executor: Optional[InstanceOf[CrewAgentExecutor]] = Field(
default=None, description="An instance of the CrewAgentExecutor class."
)
@@ -147,6 +151,7 @@ class Agent(BaseModel):
"tools": self.tools,
"verbose": self.verbose,
"handle_parsing_errors": True,
"max_iterations": self.max_iter,
}
if self.memory:

89
src/crewai/agents/executor.py
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@@ -1,4 +1,6 @@
from typing import Dict, Iterator, List, Optional, Tuple, Union
import time
from textwrap import dedent
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from langchain.agents import AgentExecutor
from langchain.agents.agent import ExceptionTool
@@ -6,13 +8,85 @@ from langchain.agents.tools import InvalidTool
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain_core.agents import AgentAction, AgentFinish, AgentStep
from langchain_core.exceptions import OutputParserException
from langchain_core.pydantic_v1 import root_validator
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from ..tools.cache_tools import CacheTools
from .cache.cache_hit import CacheHit
class CrewAgentExecutor(AgentExecutor):
iterations: int = 0
max_iterations: Optional[int] = 15
force_answer_max_iterations: Optional[int] = None
@root_validator()
def set_force_answer_max_iterations(cls, values: Dict) -> Dict:
values["force_answer_max_iterations"] = values["max_iterations"] - 2
return values
def _should_force_answer(self) -> bool:
return True if self.iterations == self.force_answer_max_iterations else False
def _force_answer(self, output: AgentAction):
return AgentStep(
action=output,
observation=dedent(
"""\
I've used too many tools for this task.
I'm going to give you my absolute BEST Final answer now and
not use any more tools."""
),
)
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run text through and get agent response."""
# Construct a mapping of tool name to tool for easy lookup
name_to_tool_map = {tool.name: tool for tool in self.tools}
# We construct a mapping from each tool to a color, used for logging.
color_mapping = get_color_mapping(
[tool.name for tool in self.tools], excluded_colors=["green", "red"]
)
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Let's start tracking the number of iterations and time elapsed
self.iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
while self._should_continue(self.iterations, time_elapsed):
next_step_output = self._take_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return self._return(
next_step_output, intermediate_steps, run_manager=run_manager
)
intermediate_steps.extend(next_step_output)
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(
tool_return, intermediate_steps, run_manager=run_manager
)
self.iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return self._return(output, intermediate_steps, run_manager=run_manager)
def _iter_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
@@ -34,6 +108,14 @@ class CrewAgentExecutor(AgentExecutor):
callbacks=run_manager.get_child() if run_manager else None,
**inputs,
)
if self._should_force_answer():
if isinstance(output, AgentAction):
output = output
else:
output = output.action
yield self._force_answer(output)
return
except OutputParserException as e:
if isinstance(self.handle_parsing_errors, bool):
raise_error = not self.handle_parsing_errors
@@ -70,6 +152,11 @@ class CrewAgentExecutor(AgentExecutor):
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
if self._should_force_answer():
yield self._force_answer(output)
return
yield AgentStep(action=output, observation=observation)
return

65
tests/agent_test.py
View File

@@ -1,10 +1,14 @@
"""Test Agent creation and execution basic functionality."""
from unittest.mock import patch
import pytest
from langchain.tools import tool
from langchain_openai import ChatOpenAI as OpenAI
from crewai.agent import Agent
from crewai.agents.cache import CacheHandler
from crewai.agents.executor import CrewAgentExecutor
def test_agent_creation():
@@ -79,8 +83,6 @@ def test_agent_execution():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execution_with_tools():
from langchain.tools import tool
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
@@ -104,8 +106,6 @@ def test_agent_execution_with_tools():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_logging_tool_usage():
from langchain.tools import tool
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
@@ -137,10 +137,6 @@ def test_logging_tool_usage():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_cache_hitting():
from unittest.mock import patch
from langchain.tools import tool
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
@@ -182,8 +178,6 @@ def test_cache_hitting():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execution_with_specific_tools():
from langchain.tools import tool
@tool
def multiplier(numbers) -> float:
"""Useful for when you need to multiply two numbers together.
@@ -202,3 +196,54 @@ def test_agent_execution_with_specific_tools():
output = agent.execute_task(task="What is 3 times 4", tools=[multiplier])
assert output == "3 times 4 is 12."
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_custom_max_iterations():
@tool
def get_final_answer(numbers) -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
max_iter=1,
allow_delegation=False,
)
with patch.object(
CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
) as private_mock:
agent.execute_task(
task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
tools=[get_final_answer],
)
private_mock.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_moved_on_after_max_iterations():
@tool
def get_final_answer(numbers) -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
agent = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
max_iter=3,
allow_delegation=False,
)
output = agent.execute_task(
task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
tools=[get_final_answer],
)
assert (
output == "I have used the tool multiple times and the final answer remains 42."
)

442
tests/cassettes/test_agent_custom_max_iterations.yaml Normal file
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