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Update inner tool usage logic to support both regular and function calling

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This commit is contained in:
João Moura
2024-03-02 13:54:23 -03:00
parent c523fcdaab
commit 8468445e1d
10 changed files with 208 additions and 142 deletions
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45
src/crewai/agents/executor.py
View File

@@ -27,6 +27,7 @@ class CrewAgentExecutor(AgentExecutor):
request_within_rpm_limit: Any = None
tools_handler: InstanceOf[ToolsHandler] = None
max_iterations: Optional[int] = 15
have_forced_answer: bool = False
force_answer_max_iterations: Optional[int] = None
step_callback: Optional[Any] = None
@@ -36,7 +37,9 @@ class CrewAgentExecutor(AgentExecutor):
return values
def _should_force_answer(self) -> bool:
return True if self.iterations == self.force_answer_max_iterations else False
return (
self.iterations == self.force_answer_max_iterations
) and not self.have_forced_answer
def _call(
self,
@@ -103,6 +106,13 @@ class CrewAgentExecutor(AgentExecutor):
Override this to take control of how the agent makes and acts on choices.
"""
try:
if self._should_force_answer():
error = self._i18n.errors("force_final_answer")
output = AgentAction("_Exception", error, error)
self.have_forced_answer = True
yield AgentStep(action=output, observation=error)
return
intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
# Call the LLM to see what to do.
output = self.agent.plan(
@@ -111,23 +121,6 @@ class CrewAgentExecutor(AgentExecutor):
**inputs,
)
if self._should_force_answer():
if isinstance(output, AgentFinish):
yield output
return
if isinstance(output, AgentAction):
output = output
else:
raise ValueError(
f"Unexpected output type from agent: {type(output)}"
)
yield AgentStep(
action=output, observation=self._i18n.errors("force_final_answer")
)
return
except OutputParserException as e:
if isinstance(self.handle_parsing_errors, bool):
raise_error = not self.handle_parsing_errors
@@ -140,11 +133,11 @@ class CrewAgentExecutor(AgentExecutor):
"again, pass `handle_parsing_errors=True` to the AgentExecutor. "
f"This is the error: {str(e)}"
)
text = str(e)
str(e)
if isinstance(self.handle_parsing_errors, bool):
if e.send_to_llm:
observation = f"\n{str(e.observation)}"
text = str(e.llm_output)
str(e.llm_output)
else:
observation = ""
elif isinstance(self.handle_parsing_errors, str):
@@ -153,22 +146,22 @@ class CrewAgentExecutor(AgentExecutor):
observation = f"\n{self.handle_parsing_errors(e)}"
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, text)
output = AgentAction("_Exception", observation, "")
if run_manager:
run_manager.on_agent_action(output, color="green")
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = ExceptionTool().run(
output.tool_input,
verbose=self.verbose,
verbose=False,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
if self._should_force_answer():
yield AgentStep(
action=output, observation=self._i18n.errors("force_final_answer")
)
error = self._i18n.errors("force_final_answer")
output = AgentAction("_Exception", error, error)
yield AgentStep(action=output, observation=error)
return
yield AgentStep(action=output, observation=observation)
@@ -192,8 +185,8 @@ class CrewAgentExecutor(AgentExecutor):
tools_description=self.tools_description,
tools_names=self.tools_names,
function_calling_llm=self.function_calling_llm,
llm=self.llm,
task=self.task,
action=agent_action,
)
tool_calling = tool_usage.parse(agent_action.log)

73
src/crewai/agents/parser.py
View File

@@ -1,3 +1,4 @@
import re
from typing import Any, Union
from langchain.agents.output_parsers import ReActSingleInputOutputParser
@@ -6,13 +7,14 @@ from langchain_core.exceptions import OutputParserException
from crewai.utilities import I18N
TOOL_USAGE_SECTION = "Use Tool:"
FINAL_ANSWER_ACTION = "Final Answer:"
FINAL_ANSWER_AND_TOOL_ERROR_MESSAGE = "I tried to use a tool and give a final answer at the same time, I must choose only one."
MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE = "I did it wrong. Invalid Format: I missed the 'Action:' after 'Thought:'. I will do right next, and don't use a tool I have already used.\n"
MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE = "I did it wrong. Invalid Format: I missed the 'Action Input:' after 'Action:'. I will do right next, and don't use a tool I have already used.\n"
FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = "I did it wrong. Tried to both perform Action and give a Final Answer at the same time, I must do one or the other"
class CrewAgentParser(ReActSingleInputOutputParser):
"""Parses Crew-style LLM calls that have a single tool input.
"""Parses ReAct-style LLM calls that have a single tool input.
Expects output to be in one of two formats.
@@ -20,17 +22,16 @@ class CrewAgentParser(ReActSingleInputOutputParser):
should be in the below format. This will result in an AgentAction
being returned.
```
Use Tool: All context for using the tool here
```
Thought: agent thought here
Action: search
Action Input: what is the temperature in SF?
If the output signals that a final answer should be given,
should be in the below format. This will result in an AgentFinish
being returned.
```
Thought: agent thought here
Final Answer: The temperature is 100 degrees
```
"""
_i18n: I18N = I18N()
@@ -38,26 +39,52 @@ class CrewAgentParser(ReActSingleInputOutputParser):
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
includes_answer = FINAL_ANSWER_ACTION in text
includes_tool = TOOL_USAGE_SECTION in text
if includes_tool:
regex = (
r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
)
action_match = re.search(regex, text, re.DOTALL)
if action_match:
if includes_answer:
self.agent.increment_formatting_errors()
raise OutputParserException(f"{FINAL_ANSWER_AND_TOOL_ERROR_MESSAGE}")
raise OutputParserException(
f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
)
action = action_match.group(1).strip()
action_input = action_match.group(2)
tool_input = action_input.strip(" ")
tool_input = tool_input.strip('"')
return AgentAction("", "", text)
return AgentAction(action, tool_input, text)
elif includes_answer:
return AgentFinish(
{"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
)
format = self._i18n.slice("format_without_tools")
error = f"{format}"
self.agent.increment_formatting_errors()
raise OutputParserException(
error,
observation=error,
llm_output=text,
send_to_llm=True,
)
if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
self.agent.increment_formatting_errors()
raise OutputParserException(
f"Could not parse LLM output: `{text}`",
observation=f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
llm_output=text,
send_to_llm=True,
)
elif not re.search(
r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
):
self.agent.increment_formatting_errors()
raise OutputParserException(
f"Could not parse LLM output: `{text}`",
observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
llm_output=text,
send_to_llm=True,
)
else:
format = self._i18n.slice("format_without_tools")
error = f"{format}"
self.agent.increment_formatting_errors()
raise OutputParserException(
error,
observation=error,
llm_output=text,
send_to_llm=True,
)