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missed a return in _execute_action_eval_mode, resulting none as stepo…
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…utput
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@liyin2015
liyin2015 committed Dec 27, 2024
1 parent 8ac80e4 commit 86665d9
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Showing 3 changed files with 73 additions and 51 deletions.
121 changes: 72 additions & 49 deletions adalflow/adalflow/components/agent/react.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
from adalflow.core.base_data_class import DataClass
from copy import deepcopy
import logging
import traceback


from adalflow.core.generator import Generator
Expand Down Expand Up @@ -231,6 +232,7 @@ def __init__(
# template for the planner
template: Optional[str] = None, # allow users to customize the template
context_variables: Optional[Dict] = None, # context variables
debug: bool = False,
):
super().__init__()
template = template or DEFAULT_REACT_AGENT_SYSTEM_PROMPT
Expand All @@ -239,6 +241,7 @@ def __init__(

self.add_llm_as_fallback = add_llm_as_fallback
self.context_variables = context_variables
self.debug = debug

tools = self._init_tools(tools, model_client, model_kwargs)
self.tool_manager: ToolManager = ToolManager(
Expand Down Expand Up @@ -311,8 +314,8 @@ def llm_tool(input: str, **kwargs) -> str:
response = output.data if output else None
return response
except Exception as e:
log.error(f"Error using the generator: {e}")
print(f"Error using the generator: {e}")
log.error(f"Error using the llm_tool: {e}")
print(f"Error using the llm_tool: {e}")

return None

Expand Down Expand Up @@ -406,6 +409,7 @@ def _execute_action_eval_mode(
step_output.observation = error_msg
step_output.action = None
log.error(error_msg)
return step_output
else:
try:
fun_expr: FunctionExpression = x.data
Expand All @@ -426,17 +430,21 @@ def _execute_action_eval_mode(
step_output.observation = result.output

# step_output = execute_action(step_output, id)
printc(f"Step {step}: \n{step_output}\n_______\n", color="blue")
if self.debug:
printc(f"Step {step}: \n{step_output}\n_______\n", color="blue")
return step_output
else:
printc(f"Failed to parse response for step {step}", color="red")
if self.debug:

printc(f"Failed to parse response for step {step}", color="red")
log.error(f"Failed to parse response for step {step}")
return step_output
except Exception as e:
error_msg = f"Error parsing response for step {step}: {e}"
step_output.observation = error_msg
log.error(error_msg)
printc(error_msg, color="red")
if self.debug:
printc(error_msg, color="red")
return step_output

def _run_one_step(
Expand All @@ -450,7 +458,8 @@ def _run_one_step(
"""Run one step of the agent. Plan and execute the action for the step.
Need to deal with both train and eval mode on the self.planner.
"""
printc(f"step: {step}", color="yellow")
if self.debug:
printc(f"step: {step}", color="yellow")

prompt_kwargs["step_history"] = step_history
step_history_value = (
Expand All @@ -465,14 +474,6 @@ def _run_one_step(
raise ValueError(
f"Expected StepOutput, but got {type(step)}, all steps: {step_history_value}"
)
# printc(
# f"prompt_kwargs 1: {prompt_kwargs}, training: {self.planner.training}",
# color="yellow",
# )

# prompt_str = self.planner.get_prompt(**prompt_kwargs)
# printc(f"prompt_str: {prompt_str}", color="red")
# return [StepOutput(step=step, action=None, observation="test")]

log.debug(
f"Running step {step} with prompt: {self.planner.prompt(**prompt_kwargs)}"
Expand All @@ -482,51 +483,71 @@ def _run_one_step(
response: Union[GeneratorOutput, Parameter] = self.planner(
prompt_kwargs=prompt_kwargs, model_kwargs=model_kwargs, id=id
)
# except Exception as e:
# error_msg = f"Error happened in planner response: {e}. Training mode: {self.planner.training}"
# raise ValueError(
# error_msg
# ) # raise the error for debugging as this should not happen in normal cases.

except Exception as e:
error_msg = f"Error happened in planner response: {e}. Training mode: {self.planner.training}"
raise ValueError(
error_msg
) # raise the error for debugging as this should not happen in normal cases.
error_msg = f"Error happened in planner response at step {step}: {e}.\n"
error_msg += (
f"Prompt kwargs: {prompt_kwargs}\nModel kwargs: {model_kwargs}\n"
)
error_msg += f"Traceback:\n{traceback.format_exc()}"
raise RuntimeError(error_msg)

# create a new step output
step_output: StepOutput = StepOutput(step=step)

# connecting two generators in the computation graph, it will set up self.step_history
if self.training and isinstance(response, Parameter):
# printc(f"response: {response}", color="yellow")
try:

if self.training and isinstance(response, Parameter):
# printc(f"response: {response}", color="yellow")

step_output: Parameter = self._execute_action(step_output, response, id)
step_output: Parameter = self._execute_action(step_output, response, id)

# printc(f"step_output: {step_output}", color="red")
if not isinstance(step_output, Parameter):
raise ValueError(
f"Ensure step_output to be Parameter at training mode. Got {type(step_output)}.\n\
Please check the observation for error details: {step_output}"
# printc(f"step_output: {step_output}", color="red")
if not isinstance(step_output, Parameter):
raise ValueError(
f"Ensure step_output to be Parameter at training mode. Got {type(step_output)}.\n\
Please check the observation for error details: {step_output}"
)
step_output.add_successor_map_fn(
successor=self.append_step_history, map_fn=lambda x: x.data
)
step_history.add_successor_map_fn(
successor=self.append_step_history, map_fn=lambda x: x.data
)
step_output.add_successor_map_fn(
successor=self.append_step_history, map_fn=lambda x: x.data
)
step_history.add_successor_map_fn(
successor=self.append_step_history, map_fn=lambda x: x.data
)

step_history = self.append_step_history.forward(step_output, step_history)
# connect step_history to the next planner
step_history.add_successor_map_fn(
successor=self.planner, map_fn=lambda x: x.data
)
# convert step history back to data
# printc(f"step_history: {step_history.data}", color="yellow")
return step_history
step_history = self.append_step_history.forward(
step_output, step_history
)
# connect step_history to the next planner
step_history.add_successor_map_fn(
successor=self.planner, map_fn=lambda x: x.data
)
return step_history

else:
else:

step_output: StepOutput = self._execute_action(
action_step=step_output, response=response, id=id
)
printc(f"step_output: {step_output}", color="red")
step_history.append(step_output)
return step_history
step_output: StepOutput = self._execute_action(
action_step=step_output, response=response, id=id
)
if not step_output:
raise RuntimeError(
f"Error executing action at step {step}: {step_output}"
)

if self.debug:
printc(f"step_output: {step_output}", color="red")
step_history.append(step_output)
return step_history
except Exception as e:
error_msg = f"Error during execution at step {step}: {e}.\n"
error_msg += f"Step output: {step_output}\nResponse: {response}\n"
error_msg += f"Traceback:\n{traceback.format_exc()}"
raise RuntimeError(error_msg)

def _check_last_step(
self, step_history: Union["Parameter", List[str]] = None
Expand Down Expand Up @@ -630,14 +651,16 @@ def bicall(
break
except Exception as e:
log.error(f"Error running step {step}: {e}")
printc(f"Error running step {step}: {e}", color="red")
raise e # the only place to raise the error for debugging. In normal cases, the agent should not raise an error.

answer = self._get_answer(step_history)
if self.training:
return answer
# wrap the output
output = ReActOutput(step_history=step_history, id=id, answer=answer)
printc(f"answer: {output}", color="yellow")
if self.debug:
printc(f"answer: {output}", color="yellow")

return output

Expand Down
1 change: 0 additions & 1 deletion adalflow/adalflow/optim/parameter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,6 @@ class ComponentTrace(DataClass):

def to_context_str(self):
output = f"""<INPUT>: {self.input_args}. <OUTPUT>: {self.full_response}"""
printc(f"output: {output}")
return output


Expand Down
2 changes: 1 addition & 1 deletion benchmarks/hotpot_qa/adal_exp/train_agent_rag.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,7 +161,7 @@ def train(
# )

train(
debug=True,
debug=False,
max_steps=2,
)
# 0.68 on val without training, 0.74on the second step. 0.84 test
Expand Down

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