Module tinytroupe.agent.action_generator
Expand source code
import json
import statistics # Add this import
import tinytroupe.utils as utils
from tinytroupe.clients import client
from tinytroupe.control import current_simulation, transactional
from tinytroupe.experimentation import Proposition
from tinytroupe.utils import JsonSerializableRegistry
from tinytroupe.validation import propositions
class ActionGenerator(JsonSerializableRegistry):
def __init__(
self,
max_attempts=2,
enable_quality_checks=True,
enable_regeneration=True,
enable_direct_correction=False, # TODO enable_direct_correction not working very well yet
enable_quality_check_for_persona_adherence=True,
enable_quality_check_for_selfconsistency=True,
enable_quality_check_for_fluency=True,
enable_quality_check_for_suitability=False,
enable_quality_check_for_similarity=False,
continue_on_failure=True,
quality_threshold=7,
max_action_similarity=0.6,
enable_reasoning_step=False,
enable_multi_action_output=True,
):
"""
Initializes the ActionGenerator.
Args:
max_attempts (int): The maximum number of attempts to generate an action.
enable_quality_checks (bool): Whether to perform quality checks on the generated action. If False, the first action generated
is returned without any checks.
enable_regeneration (bool): Whether to try to make the agent regenerate the action if the first attempt fails.
enable_direct_correction (bool): Whether to directly correct the action if the first attempt fails, without asking the agent to regenerate it.
enable_quality_check_for_persona_adherence (bool): Whether to check the action for persona adherence.
enable_quality_check_for_selfconsistency (bool): Whether to check the action for self-consistency.
enable_quality_check_for_fluency (bool): Whether to check the action for fluency.
enable_quality_check_for_suitability (bool): Whether to check the action for suitability.
continue_on_failure (bool): Whether to return the last tentative action, even if it fails to pass quality checks.
Presumably, the last tentative action is the one that is most likely to be correct, since it has gone through the most iterations of regeneration and correction.
quality_threshold (int): The minimum score for each quality check for the action to be considered good quality.
enable_reasoning_step (bool): Whether to enable reasoning step in the action generation process. This IS NOT the use of "reasoning models" (e.g., o1, o3),
but rather the use of an additional reasoning step in the regular text completion.
enable_multi_action_output (bool): If True, the LLM is expected to output the full sequence of actions for the turn (ending with DONE).
"""
self.max_attempts = max_attempts
self.regeneration_attempts = 0
self.direct_correction_attempts = 0
self.enable_quality_checks = enable_quality_checks
self.enable_regeneration = enable_regeneration
self.enable_direct_correction = enable_direct_correction
self.enable_multi_action_output = enable_multi_action_output
self.enable_quality_check_for_persona_adherence = (
enable_quality_check_for_persona_adherence
)
self.enable_quality_check_for_selfconsistency = (
enable_quality_check_for_selfconsistency
)
self.enable_quality_check_for_fluency = enable_quality_check_for_fluency
self.enable_quality_check_for_suitability = enable_quality_check_for_suitability
self.enable_quality_check_for_similarity = enable_quality_check_for_similarity
self.continue_on_failure = continue_on_failure
self.quality_threshold = quality_threshold
self.max_action_similarity = max_action_similarity
self.enable_reasoning_step = enable_reasoning_step
# This generator has its own copies of the propositions, in order to be able to isolate them
# from other agents, particularly when running the simulation in parallel.
self.action_persona_adherence = (
propositions.hard_action_persona_adherence.copy()
)
self.action_self_consistency = propositions.action_self_consistency.copy()
self.action_fluency = propositions.action_fluency.copy()
self.action_suitability = propositions.action_suitability.copy()
# initialize statistics
self.regeneration_failures = 0
self.direct_correction_failures = 0
self.regeneration_scores = []
self.direct_correction_scores = []
self.total_actions_produced = 0
self.total_original_actions_succeeded = 0
# New public API returning the full sequence of actions for the turn
def generate_next_actions(self, agent, current_messages: list):
action_or_actions, role, content, all_negative_feedbacks = (
self.generate_next_action(agent, current_messages)
)
# Normalize to sequence
if isinstance(action_or_actions, list):
actions = action_or_actions
else:
actions = [action_or_actions]
# If multi-action is enabled but no DONE present, append DONE defensively
if self.enable_multi_action_output:
if not any(a.get("type") == "DONE" for a in actions):
actions = actions + [{"type": "DONE", "content": "", "target": ""}]
if isinstance(content, dict):
content = content.copy()
content["actions"] = actions
content.pop("action", None)
return actions, role, content, all_negative_feedbacks
def generate_next_action(self, agent, current_messages: list):
from tinytroupe.agent import (
logger,
) # import here to avoid circular import issues
# clean up (remove unnecessary elements) and copy the list of current messages to avoid modifying the original ones
current_messages = [
{"role": msg["role"], "content": json.dumps(msg["content"])}
for msg in current_messages
]
# starts with no feedback
cur_feedback = None
all_negative_feedbacks = []
best_action = None
best_role = None
best_content = None
best_score = float("-inf")
original_score = None
def update_best(tentative_action, role, content, total_score):
nonlocal best_action, best_role, best_content, best_score
if total_score > best_score:
best_action = tentative_action
best_role = role
best_content = content
best_score = total_score
def finish_return(tentative_action, role, content, final_score):
if original_score is not None and final_score > original_score:
logger.warning(
f"[{agent.name}] improved total quality from {original_score} to {final_score}"
)
# ensure that tentative_action and content are dicts or lists where applicable
if isinstance(tentative_action, str):
tentative_action = json.loads(tentative_action)
if isinstance(content, str):
content = json.loads(content)
return tentative_action, role, content, all_negative_feedbacks
# First attempt to generate an action or actions
tentative, role, content = self._generate_tentative_action(
agent,
current_messages,
feedback_from_previous_attempt=cur_feedback,
previous_tentative_action=None,
previous_llm_role=None,
previous_llm_content=None,
)
# TODO obsolete?
#
# If model returned multi-action payload, we will score based on the first action
# def pick_reference_action_for_scoring(tentative_action_or_actions):
# if (
# isinstance(tentative_action_or_actions, list)
# and tentative_action_or_actions
# ):
# # Prefer first action
# return tentative_action_or_actions[0]
# return tentative_action_or_actions
def remove_done_actions(tentative_action_or_actions):
if isinstance(tentative_action_or_actions, list):
return [
action
for action in tentative_action_or_actions
if action is not None and isinstance(action, dict) and action.get("type") != "DONE"
]
return tentative_action_or_actions
tentative_action_for_quality = remove_done_actions(
tentative # TODO remove pick_reference_action_for_scoring(tentative)
)
if self.enable_quality_checks:
# First quality check (single-action proxy)
good_quality, total_score, cur_feedback = self._check_action_quality(
"Original Action", agent, tentative_action=tentative_action_for_quality
)
update_best(tentative, role, content, total_score)
if original_score is None:
original_score = total_score
if good_quality:
self.total_original_actions_succeeded += 1
# Found a good action(s), let's return it now
return finish_return(tentative, role, content, total_score)
else:
logger.warning(
f"[{agent.name}] Original action did not pass quality checks: {cur_feedback}"
)
all_negative_feedbacks.append(cur_feedback)
# GENERATE AND REGENERATE the action by the agent
if self.enable_regeneration:
for attempt in range(self.max_attempts):
# Generate tentative action
tentative, role, content = self._generate_tentative_action(
agent,
current_messages,
feedback_from_previous_attempt=cur_feedback,
previous_tentative_action=tentative,
previous_llm_role=role,
previous_llm_content=content,
)
logger.debug(f"[{agent.name}] Tentative action: {tentative}")
self.regeneration_attempts += 1
tentative_action_for_quality = remove_done_actions(
tentative
) # TODO remove pick_reference_action_for_scoring(tentative)
good_quality, total_score, cur_feedback_single = (
self._check_action_quality(
f"Action Regeneration ({attempt})",
agent,
tentative_action=tentative_action_for_quality,
)
)
update_best(tentative, role, content, total_score)
if good_quality:
# Found a good action(s), let's return it now
return finish_return(tentative, role, content, total_score)
else:
self.regeneration_failures += 1
self.regeneration_scores.append(total_score)
all_negative_feedbacks.append(cur_feedback_single)
cur_feedback = cur_feedback_single
# CORRECT OR REPHRASE the action directly
if self.enable_direct_correction:
for attempt in range(self.max_attempts):
# Only meaningful for single action, so pick the last action for correction
last_action = remove_done_actions(
tentative
) # TODO remove pick_reference_action_for_scoring(tentative)
corrected_action, role, content = self._correct_action(
last_action,
feedback=cur_feedback,
llm_role=role,
llm_content=content,
)
logger.warning(
f"[{agent.name}] Rephrased the action directly as: {corrected_action}"
)
self.direct_correction_attempts += 1
good_quality, total_score, cur_feedback = (
self._check_action_quality(
f"Direct Action Correction or Rephrasing ({attempt})",
agent,
tentative_action=corrected_action,
)
)
update_best(corrected_action, role, content, total_score)
if good_quality:
# Return corrected single action
return finish_return(
corrected_action, role, content, total_score
)
else:
self.direct_correction_failures += 1
self.direct_correction_scores.append(total_score)
all_negative_feedbacks.append(cur_feedback)
# If we got here, all attempts to generate a good action failed
if self.continue_on_failure:
logger.warning(
f"[{agent.name}] All attempts to generate a good action failed. Returning the best one."
)
return finish_return(best_action, best_role, best_content, best_score)
else:
raise PoorQualityActionException()
else:
# If we got here, it means that the action(s) was generated without quality checks
return tentative, role, content, []
def _generate_tentative_action(
self,
agent,
current_messages,
feedback_from_previous_attempt=None,
previous_tentative_action=None,
previous_llm_role=None,
previous_llm_content=None,
):
from tinytroupe.agent import ( # import here to avoid circular import issues
CognitiveActionModel,
CognitiveActionModelWithReasoning,
CognitiveActionsModel,
CognitiveActionsModelWithReasoning,
logger,
)
self.total_actions_produced += 1
# shallow clone current_messages
current_messages_context = current_messages.copy()
logger.debug(f"[{agent.name}] Sending messages to OpenAI API")
logger.debug(f"[{agent.name}] Last interaction: {current_messages[-1]}")
if feedback_from_previous_attempt:
current_messages_context.append(
{
"role": "user",
"content": f"""
WARNING! TENTATIVE ACTION GENERATION FAILED IN QUALITY CHECKS!
You were about to produce the following action(s), as a sequence for the previous actions or feedbacks (if any):
```
{previous_tentative_action}
```
However, it failed to pass the quality checks (as described in the quality feedback below), and therefore it was aborted and not added
to the simulation trajectory.
Now you **must** try again to generate a **BETTER** action or sequence of actions, such that the quality issues mentioned in the feedback are addressed,
or instead issue a DONE action and stop for this turn if it is unclear how to improve quality.
Your objective is to **PASS** the quality checks this time if possible.
You can choose either to FIX somehow the action(s) you were about to produce, or to generate something COMPLETELY NEW and DIFFERENT.
Each time your tentative action fail a quality check, you should be MORE RADICAL in your changes, and try to produce
something that is **very** different from the previous attempts.
If it is unclear how to produce a better action, you can choose to issue a DONE action instead.
**It is better to stop acting than to act poorly.**
In general, desireable properties of the action are:
- The action is consistent with the agent's persona, it is what one would expect from the agent given its persona.
- The action is self-consistent, it does contradict the agent's previous actions.
- The action is fluent and natural, and does not repeat itself or use overly formulaic language.
{feedback_from_previous_attempt}
""",
}
)
current_messages_context.append(
{
"role": "system",
"content": "Now generate a better action or sequence of actions based on the above feedback, and finish with a DONE action if you are done.",
}
)
# Choose response schema based on configuration
if not self.enable_reasoning_step:
if self.enable_multi_action_output:
response_format = CognitiveActionsModel
else:
response_format = CognitiveActionModel
next_message = client().send_message(
current_messages_context, response_format=response_format
)
else:
current_messages_context.append(
{
"role": "system",
"content": 'Use the "reasoning" field to add any reasoning process you might wish to use before generating the next action(s) and cognitive state. ',
}
)
if self.enable_multi_action_output:
response_format = CognitiveActionsModelWithReasoning
else:
response_format = CognitiveActionModelWithReasoning
next_message = client().send_message(
current_messages_context,
response_format=response_format,
)
logger.debug(f"[{agent.name}] Received message: {next_message}")
role, content = next_message["role"], utils.extract_json(
next_message["content"]
)
# Support both single-action and multi-action payloads
if "actions" in content and isinstance(content.get("actions"), list):
actions = content["actions"]
# Ensure the sequence ends with DONE
if not actions or actions[-1].get("type") != "DONE":
actions.append({"type": "DONE", "content": "", "target": ""})
return actions, role, content
else:
action = content["action"]
return action, role, content
###############################################################################################
# Quality evaluation methods
###############################################################################################
def _check_action_quality(self, stage, agent, tentative_action):
from tinytroupe.agent import (
logger,
) # import here to avoid circular import issues
#
# Compute various propositions about the action
#
(
persona_adherence_passed,
persona_adherence_score,
persona_adherence_feedback,
) = self._check_proposition(
agent,
self.action_persona_adherence,
tentative_action,
enable_proposition_check=self.enable_quality_check_for_persona_adherence,
)
selfconsistency_passed, selfconsistency_score, selfconsistency_feedback = (
self._check_proposition(
agent,
self.action_self_consistency,
tentative_action,
minimum_required_qty_of_actions=1,
enable_proposition_check=self.enable_quality_check_for_selfconsistency,
)
)
fluency_passed, fluency_passed_score, fluency_feedback = (
self._check_proposition(
agent,
self.action_fluency,
tentative_action,
enable_proposition_check=self.enable_quality_check_for_fluency,
)
)
suitability_passed, suitability_score, suitability_feedback = (
self._check_proposition(
agent,
self.action_suitability,
tentative_action,
enable_proposition_check=self.enable_quality_check_for_suitability,
)
)
similarity_passed, similarity_score, similarity_feedback = (
self._check_next_action_similarity(
agent,
tentative_action,
threshold=self.max_action_similarity,
enable_similarity_check=self.enable_quality_check_for_similarity,
)
)
# put the results together
good_quality = (
persona_adherence_passed
and selfconsistency_passed
and fluency_passed
and suitability_passed
and similarity_passed
)
total_score = (
persona_adherence_score
+ selfconsistency_score
+ fluency_passed_score
+ suitability_score
+ (similarity_score * Proposition.MAX_SCORE)
)
combined_feedback = utils.combine_texts(
persona_adherence_feedback,
selfconsistency_feedback,
fluency_feedback,
suitability_feedback,
similarity_feedback,
)
# give verdict
if good_quality:
return True, total_score, combined_feedback
else:
failure_feedback = f"""
# Quality feedback
This is the action that was about to be generated by the agent:
{tentative_action}
Unfortunately, the action failed to pass the quality checks, and therefore was aborted and not added to the similation trajectory.
The following problems were detected.
"""
if not persona_adherence_passed:
failure_feedback += f"""
## Problem: The action does not adhere to the persona specification.
{persona_adherence_feedback}
### RECOMMENDATIONS FOR IMPROVEMENT
Please follow the recommendations below when trying to generate this action again.
Use the feedback above to generate a better action.
{self.action_persona_adherence.recommendations_for_improvement()}
"""
if not selfconsistency_passed:
failure_feedback += f"""
## Problem: The action is not self-consistent.
{selfconsistency_feedback}
### RECOMMENDATIONS FOR IMPROVEMENT
Please follow the recommendations below when trying to generate this action again.
Use the feedback above to generate a better action.
{self.action_self_consistency.recommendations_for_improvement()}
"""
if not fluency_passed:
failure_feedback += f"""
## Problem: The action is not fluent.
{fluency_feedback}
### RECOMMENDATIONS FOR IMPROVEMENT
Please follow the recommendations below when trying to generate this action again.
Use the feedback above to generate a better action.
{self.action_fluency.recommendations_for_improvement()}
"""
if not suitability_passed:
failure_feedback += f"""
## Problem: The action is not suitable to the situation or task.
{suitability_feedback}
### RECOMMENDATIONS FOR IMPROVEMENT
Please follow the recommendations below when trying to generate this action again.
Use the feedback above to generate a better action.
{self.action_suitability.recommendations_for_improvement()}
"""
if not similarity_passed:
failure_feedback += f"""
## Problem: The action is too similar to the previous one.
{similarity_feedback}
"""
logger.warning(
f"[{agent.name}][{stage}] failed to pass quality checks: {failure_feedback}"
)
return False, total_score, failure_feedback
def _check_proposition(
self,
agent,
proposition,
tentative_action,
minimum_required_qty_of_actions=0,
enable_proposition_check=True,
):
from tinytroupe.agent import (
logger,
) # import here to avoid circular import issues
if enable_proposition_check:
if agent.actions_count >= minimum_required_qty_of_actions:
result = proposition.score(
target=agent,
claim_variables={"action": tentative_action},
return_full_response=True,
)
value_with_justification = f"Score = {result['value']} (out of {Proposition.MAX_SCORE}). Justification = {result['justification']}"
logger.debug(
f"[{agent.name}] Proposition '{proposition.__class__.__name__}' evaluation result: {value_with_justification}"
)
if result["value"] >= self.quality_threshold:
return True, result["value"], value_with_justification
else:
return False, result["value"], value_with_justification
else:
return (
True,
Proposition.MAX_SCORE,
f"The proposition is trivially true due to the lack of enough actions for comparison.",
)
else:
# If the proposition check is disabled, we assume it passed
return (
True,
Proposition.MAX_SCORE,
f"The proposition check is disabled, so it is assumed to have passed.",
)
def _check_next_action_similarity(
self, agent, proposed_next_action, threshold, enable_similarity_check=True
):
"""
Checks the similarity between the agent's current action and a proposed next action.
High similarity indicates that the proposed action is too similar to the current one, and this
check fails.
"""
from tinytroupe.agent import (
logger,
) # import here to avoid circular import issues
if enable_similarity_check:
similarity = utils.next_action_jaccard_similarity(
agent, proposed_next_action
)
logger.debug(f"[{agent.name}] Next-action Jaccard similarity: {similarity}")
if similarity >= threshold:
logger.warning(
f"[{agent.name}] Next-action Jaccard similarity is above the threshold ({threshold})."
)
return (
False,
similarity,
f"Similarity = {similarity} (range: 0.0 to 1.0). The action is too similar to the previous one.",
)
else:
logger.debug(
f"[{agent.name}] Next-action Jaccard similarity is below the threshold ({threshold})."
)
return (
True,
similarity,
f"Similarity = {similarity} (range: 0.0 to 1.0). The action is sufficiently different from the previous one.",
)
else:
# If the similarity check is disabled, we assume it passed
return (
True,
0.0,
f"The similarity check is disabled, so it is assumed to have passed.",
)
################################################################################################
# Action correction methods
################################################################################################
def _correct_action(self, action: dict, feedback, llm_role, llm_content):
situation = f"""
The following action by an agent was observed:
{action}
However, it does not conform to expectations about this agent behavior,
due to the following reasons.
{feedback}
"""
# restructured_situation =\
# utils.restructure_as_observed_vs_expected(\
# """)
# rule = utils.formulate_corrective_rule(restructured_situation)
rules = utils.extract_observed_vs_expected_rules(situation)
rephrased_action_content = utils.correct_according_to_rule(
action["content"], rules
)
# copy action
rephrased_action = action.copy()
# update content
rephrased_action["content"] = rephrased_action_content
# replace in the 'action' key in the original llm content message
llm_content["action"] = rephrased_action
return rephrased_action, llm_role, llm_content
def get_statistics(self):
regeneration_failure_rate = (
self.regeneration_failures / self.regeneration_attempts
if self.regeneration_attempts
else 0
)
direct_correction_failure_rate = (
self.direct_correction_failures / self.direct_correction_attempts
if self.direct_correction_attempts
else 0
)
regeneration_mean_score = (
statistics.mean(self.regeneration_scores) if self.regeneration_scores else 0
)
regeneration_sd_score = (
statistics.stdev(self.regeneration_scores)
if len(self.regeneration_scores) > 1
else 0
)
direct_correction_mean_score = (
statistics.mean(self.direct_correction_scores)
if self.direct_correction_scores
else 0
)
direct_correction_sd_score = (
statistics.stdev(self.direct_correction_scores)
if len(self.direct_correction_scores) > 1
else 0
)
original_success_rate = (
self.total_original_actions_succeeded / self.total_actions_produced
if self.total_actions_produced
else 0
)
return {
"regeneration_failure_rate": regeneration_failure_rate,
"direct_correction_failure_rate": direct_correction_failure_rate,
"regeneration_mean_score": regeneration_mean_score,
"regeneration_sd_score": regeneration_sd_score,
"direct_correction_mean_score": direct_correction_mean_score,
"direct_correction_sd_score": direct_correction_sd_score,
"total_actions_produced": self.total_actions_produced,
"total_original_actions_succeeded": self.total_original_actions_succeeded,
"original_success_rate": original_success_rate,
"regeneration_success_rate": 1 - regeneration_failure_rate,
"direct_correction_success_rate": 1 - direct_correction_failure_rate,
}
class PoorQualityActionException(Exception):
def __init__(self, message="The generated action is of poor quality"):
self.message = message
super().__init__(self.message)Classes
class ActionGenerator (max_attempts=2, enable_quality_checks=True, enable_regeneration=True, enable_direct_correction=False, enable_quality_check_for_persona_adherence=True, enable_quality_check_for_selfconsistency=True, enable_quality_check_for_fluency=True, enable_quality_check_for_suitability=False, enable_quality_check_for_similarity=False, continue_on_failure=True, quality_threshold=7, max_action_similarity=0.6, enable_reasoning_step=False, enable_multi_action_output=True)-
A mixin class that provides JSON serialization, deserialization, and subclass registration.
Initializes the ActionGenerator.
Args
max_attempts:int- The maximum number of attempts to generate an action.
enable_quality_checks:bool- Whether to perform quality checks on the generated action. If False, the first action generated is returned without any checks.
enable_regeneration:bool- Whether to try to make the agent regenerate the action if the first attempt fails.
enable_direct_correction:bool- Whether to directly correct the action if the first attempt fails, without asking the agent to regenerate it.
enable_quality_check_for_persona_adherence:bool- Whether to check the action for persona adherence.
enable_quality_check_for_selfconsistency:bool- Whether to check the action for self-consistency.
enable_quality_check_for_fluency:bool- Whether to check the action for fluency.
enable_quality_check_for_suitability:bool- Whether to check the action for suitability.
continue_on_failure:bool- Whether to return the last tentative action, even if it fails to pass quality checks. Presumably, the last tentative action is the one that is most likely to be correct, since it has gone through the most iterations of regeneration and correction.
quality_threshold:int- The minimum score for each quality check for the action to be considered good quality.
enable_reasoning_step:bool- Whether to enable reasoning step in the action generation process. This IS NOT the use of "reasoning models" (e.g., o1, o3), but rather the use of an additional reasoning step in the regular text completion.
enable_multi_action_output:bool- If True, the LLM is expected to output the full sequence of actions for the turn (ending with DONE).
Expand source code
class ActionGenerator(JsonSerializableRegistry): def __init__( self, max_attempts=2, enable_quality_checks=True, enable_regeneration=True, enable_direct_correction=False, # TODO enable_direct_correction not working very well yet enable_quality_check_for_persona_adherence=True, enable_quality_check_for_selfconsistency=True, enable_quality_check_for_fluency=True, enable_quality_check_for_suitability=False, enable_quality_check_for_similarity=False, continue_on_failure=True, quality_threshold=7, max_action_similarity=0.6, enable_reasoning_step=False, enable_multi_action_output=True, ): """ Initializes the ActionGenerator. Args: max_attempts (int): The maximum number of attempts to generate an action. enable_quality_checks (bool): Whether to perform quality checks on the generated action. If False, the first action generated is returned without any checks. enable_regeneration (bool): Whether to try to make the agent regenerate the action if the first attempt fails. enable_direct_correction (bool): Whether to directly correct the action if the first attempt fails, without asking the agent to regenerate it. enable_quality_check_for_persona_adherence (bool): Whether to check the action for persona adherence. enable_quality_check_for_selfconsistency (bool): Whether to check the action for self-consistency. enable_quality_check_for_fluency (bool): Whether to check the action for fluency. enable_quality_check_for_suitability (bool): Whether to check the action for suitability. continue_on_failure (bool): Whether to return the last tentative action, even if it fails to pass quality checks. Presumably, the last tentative action is the one that is most likely to be correct, since it has gone through the most iterations of regeneration and correction. quality_threshold (int): The minimum score for each quality check for the action to be considered good quality. enable_reasoning_step (bool): Whether to enable reasoning step in the action generation process. This IS NOT the use of "reasoning models" (e.g., o1, o3), but rather the use of an additional reasoning step in the regular text completion. enable_multi_action_output (bool): If True, the LLM is expected to output the full sequence of actions for the turn (ending with DONE). """ self.max_attempts = max_attempts self.regeneration_attempts = 0 self.direct_correction_attempts = 0 self.enable_quality_checks = enable_quality_checks self.enable_regeneration = enable_regeneration self.enable_direct_correction = enable_direct_correction self.enable_multi_action_output = enable_multi_action_output self.enable_quality_check_for_persona_adherence = ( enable_quality_check_for_persona_adherence ) self.enable_quality_check_for_selfconsistency = ( enable_quality_check_for_selfconsistency ) self.enable_quality_check_for_fluency = enable_quality_check_for_fluency self.enable_quality_check_for_suitability = enable_quality_check_for_suitability self.enable_quality_check_for_similarity = enable_quality_check_for_similarity self.continue_on_failure = continue_on_failure self.quality_threshold = quality_threshold self.max_action_similarity = max_action_similarity self.enable_reasoning_step = enable_reasoning_step # This generator has its own copies of the propositions, in order to be able to isolate them # from other agents, particularly when running the simulation in parallel. self.action_persona_adherence = ( propositions.hard_action_persona_adherence.copy() ) self.action_self_consistency = propositions.action_self_consistency.copy() self.action_fluency = propositions.action_fluency.copy() self.action_suitability = propositions.action_suitability.copy() # initialize statistics self.regeneration_failures = 0 self.direct_correction_failures = 0 self.regeneration_scores = [] self.direct_correction_scores = [] self.total_actions_produced = 0 self.total_original_actions_succeeded = 0 # New public API returning the full sequence of actions for the turn def generate_next_actions(self, agent, current_messages: list): action_or_actions, role, content, all_negative_feedbacks = ( self.generate_next_action(agent, current_messages) ) # Normalize to sequence if isinstance(action_or_actions, list): actions = action_or_actions else: actions = [action_or_actions] # If multi-action is enabled but no DONE present, append DONE defensively if self.enable_multi_action_output: if not any(a.get("type") == "DONE" for a in actions): actions = actions + [{"type": "DONE", "content": "", "target": ""}] if isinstance(content, dict): content = content.copy() content["actions"] = actions content.pop("action", None) return actions, role, content, all_negative_feedbacks def generate_next_action(self, agent, current_messages: list): from tinytroupe.agent import ( logger, ) # import here to avoid circular import issues # clean up (remove unnecessary elements) and copy the list of current messages to avoid modifying the original ones current_messages = [ {"role": msg["role"], "content": json.dumps(msg["content"])} for msg in current_messages ] # starts with no feedback cur_feedback = None all_negative_feedbacks = [] best_action = None best_role = None best_content = None best_score = float("-inf") original_score = None def update_best(tentative_action, role, content, total_score): nonlocal best_action, best_role, best_content, best_score if total_score > best_score: best_action = tentative_action best_role = role best_content = content best_score = total_score def finish_return(tentative_action, role, content, final_score): if original_score is not None and final_score > original_score: logger.warning( f"[{agent.name}] improved total quality from {original_score} to {final_score}" ) # ensure that tentative_action and content are dicts or lists where applicable if isinstance(tentative_action, str): tentative_action = json.loads(tentative_action) if isinstance(content, str): content = json.loads(content) return tentative_action, role, content, all_negative_feedbacks # First attempt to generate an action or actions tentative, role, content = self._generate_tentative_action( agent, current_messages, feedback_from_previous_attempt=cur_feedback, previous_tentative_action=None, previous_llm_role=None, previous_llm_content=None, ) # TODO obsolete? # # If model returned multi-action payload, we will score based on the first action # def pick_reference_action_for_scoring(tentative_action_or_actions): # if ( # isinstance(tentative_action_or_actions, list) # and tentative_action_or_actions # ): # # Prefer first action # return tentative_action_or_actions[0] # return tentative_action_or_actions def remove_done_actions(tentative_action_or_actions): if isinstance(tentative_action_or_actions, list): return [ action for action in tentative_action_or_actions if action is not None and isinstance(action, dict) and action.get("type") != "DONE" ] return tentative_action_or_actions tentative_action_for_quality = remove_done_actions( tentative # TODO remove pick_reference_action_for_scoring(tentative) ) if self.enable_quality_checks: # First quality check (single-action proxy) good_quality, total_score, cur_feedback = self._check_action_quality( "Original Action", agent, tentative_action=tentative_action_for_quality ) update_best(tentative, role, content, total_score) if original_score is None: original_score = total_score if good_quality: self.total_original_actions_succeeded += 1 # Found a good action(s), let's return it now return finish_return(tentative, role, content, total_score) else: logger.warning( f"[{agent.name}] Original action did not pass quality checks: {cur_feedback}" ) all_negative_feedbacks.append(cur_feedback) # GENERATE AND REGENERATE the action by the agent if self.enable_regeneration: for attempt in range(self.max_attempts): # Generate tentative action tentative, role, content = self._generate_tentative_action( agent, current_messages, feedback_from_previous_attempt=cur_feedback, previous_tentative_action=tentative, previous_llm_role=role, previous_llm_content=content, ) logger.debug(f"[{agent.name}] Tentative action: {tentative}") self.regeneration_attempts += 1 tentative_action_for_quality = remove_done_actions( tentative ) # TODO remove pick_reference_action_for_scoring(tentative) good_quality, total_score, cur_feedback_single = ( self._check_action_quality( f"Action Regeneration ({attempt})", agent, tentative_action=tentative_action_for_quality, ) ) update_best(tentative, role, content, total_score) if good_quality: # Found a good action(s), let's return it now return finish_return(tentative, role, content, total_score) else: self.regeneration_failures += 1 self.regeneration_scores.append(total_score) all_negative_feedbacks.append(cur_feedback_single) cur_feedback = cur_feedback_single # CORRECT OR REPHRASE the action directly if self.enable_direct_correction: for attempt in range(self.max_attempts): # Only meaningful for single action, so pick the last action for correction last_action = remove_done_actions( tentative ) # TODO remove pick_reference_action_for_scoring(tentative) corrected_action, role, content = self._correct_action( last_action, feedback=cur_feedback, llm_role=role, llm_content=content, ) logger.warning( f"[{agent.name}] Rephrased the action directly as: {corrected_action}" ) self.direct_correction_attempts += 1 good_quality, total_score, cur_feedback = ( self._check_action_quality( f"Direct Action Correction or Rephrasing ({attempt})", agent, tentative_action=corrected_action, ) ) update_best(corrected_action, role, content, total_score) if good_quality: # Return corrected single action return finish_return( corrected_action, role, content, total_score ) else: self.direct_correction_failures += 1 self.direct_correction_scores.append(total_score) all_negative_feedbacks.append(cur_feedback) # If we got here, all attempts to generate a good action failed if self.continue_on_failure: logger.warning( f"[{agent.name}] All attempts to generate a good action failed. Returning the best one." ) return finish_return(best_action, best_role, best_content, best_score) else: raise PoorQualityActionException() else: # If we got here, it means that the action(s) was generated without quality checks return tentative, role, content, [] def _generate_tentative_action( self, agent, current_messages, feedback_from_previous_attempt=None, previous_tentative_action=None, previous_llm_role=None, previous_llm_content=None, ): from tinytroupe.agent import ( # import here to avoid circular import issues CognitiveActionModel, CognitiveActionModelWithReasoning, CognitiveActionsModel, CognitiveActionsModelWithReasoning, logger, ) self.total_actions_produced += 1 # shallow clone current_messages current_messages_context = current_messages.copy() logger.debug(f"[{agent.name}] Sending messages to OpenAI API") logger.debug(f"[{agent.name}] Last interaction: {current_messages[-1]}") if feedback_from_previous_attempt: current_messages_context.append( { "role": "user", "content": f""" WARNING! TENTATIVE ACTION GENERATION FAILED IN QUALITY CHECKS! You were about to produce the following action(s), as a sequence for the previous actions or feedbacks (if any): ``` {previous_tentative_action} ``` However, it failed to pass the quality checks (as described in the quality feedback below), and therefore it was aborted and not added to the simulation trajectory. Now you **must** try again to generate a **BETTER** action or sequence of actions, such that the quality issues mentioned in the feedback are addressed, or instead issue a DONE action and stop for this turn if it is unclear how to improve quality. Your objective is to **PASS** the quality checks this time if possible. You can choose either to FIX somehow the action(s) you were about to produce, or to generate something COMPLETELY NEW and DIFFERENT. Each time your tentative action fail a quality check, you should be MORE RADICAL in your changes, and try to produce something that is **very** different from the previous attempts. If it is unclear how to produce a better action, you can choose to issue a DONE action instead. **It is better to stop acting than to act poorly.** In general, desireable properties of the action are: - The action is consistent with the agent's persona, it is what one would expect from the agent given its persona. - The action is self-consistent, it does contradict the agent's previous actions. - The action is fluent and natural, and does not repeat itself or use overly formulaic language. {feedback_from_previous_attempt} """, } ) current_messages_context.append( { "role": "system", "content": "Now generate a better action or sequence of actions based on the above feedback, and finish with a DONE action if you are done.", } ) # Choose response schema based on configuration if not self.enable_reasoning_step: if self.enable_multi_action_output: response_format = CognitiveActionsModel else: response_format = CognitiveActionModel next_message = client().send_message( current_messages_context, response_format=response_format ) else: current_messages_context.append( { "role": "system", "content": 'Use the "reasoning" field to add any reasoning process you might wish to use before generating the next action(s) and cognitive state. ', } ) if self.enable_multi_action_output: response_format = CognitiveActionsModelWithReasoning else: response_format = CognitiveActionModelWithReasoning next_message = client().send_message( current_messages_context, response_format=response_format, ) logger.debug(f"[{agent.name}] Received message: {next_message}") role, content = next_message["role"], utils.extract_json( next_message["content"] ) # Support both single-action and multi-action payloads if "actions" in content and isinstance(content.get("actions"), list): actions = content["actions"] # Ensure the sequence ends with DONE if not actions or actions[-1].get("type") != "DONE": actions.append({"type": "DONE", "content": "", "target": ""}) return actions, role, content else: action = content["action"] return action, role, content ############################################################################################### # Quality evaluation methods ############################################################################################### def _check_action_quality(self, stage, agent, tentative_action): from tinytroupe.agent import ( logger, ) # import here to avoid circular import issues # # Compute various propositions about the action # ( persona_adherence_passed, persona_adherence_score, persona_adherence_feedback, ) = self._check_proposition( agent, self.action_persona_adherence, tentative_action, enable_proposition_check=self.enable_quality_check_for_persona_adherence, ) selfconsistency_passed, selfconsistency_score, selfconsistency_feedback = ( self._check_proposition( agent, self.action_self_consistency, tentative_action, minimum_required_qty_of_actions=1, enable_proposition_check=self.enable_quality_check_for_selfconsistency, ) ) fluency_passed, fluency_passed_score, fluency_feedback = ( self._check_proposition( agent, self.action_fluency, tentative_action, enable_proposition_check=self.enable_quality_check_for_fluency, ) ) suitability_passed, suitability_score, suitability_feedback = ( self._check_proposition( agent, self.action_suitability, tentative_action, enable_proposition_check=self.enable_quality_check_for_suitability, ) ) similarity_passed, similarity_score, similarity_feedback = ( self._check_next_action_similarity( agent, tentative_action, threshold=self.max_action_similarity, enable_similarity_check=self.enable_quality_check_for_similarity, ) ) # put the results together good_quality = ( persona_adherence_passed and selfconsistency_passed and fluency_passed and suitability_passed and similarity_passed ) total_score = ( persona_adherence_score + selfconsistency_score + fluency_passed_score + suitability_score + (similarity_score * Proposition.MAX_SCORE) ) combined_feedback = utils.combine_texts( persona_adherence_feedback, selfconsistency_feedback, fluency_feedback, suitability_feedback, similarity_feedback, ) # give verdict if good_quality: return True, total_score, combined_feedback else: failure_feedback = f""" # Quality feedback This is the action that was about to be generated by the agent: {tentative_action} Unfortunately, the action failed to pass the quality checks, and therefore was aborted and not added to the similation trajectory. The following problems were detected. """ if not persona_adherence_passed: failure_feedback += f""" ## Problem: The action does not adhere to the persona specification. {persona_adherence_feedback} ### RECOMMENDATIONS FOR IMPROVEMENT Please follow the recommendations below when trying to generate this action again. Use the feedback above to generate a better action. {self.action_persona_adherence.recommendations_for_improvement()} """ if not selfconsistency_passed: failure_feedback += f""" ## Problem: The action is not self-consistent. {selfconsistency_feedback} ### RECOMMENDATIONS FOR IMPROVEMENT Please follow the recommendations below when trying to generate this action again. Use the feedback above to generate a better action. {self.action_self_consistency.recommendations_for_improvement()} """ if not fluency_passed: failure_feedback += f""" ## Problem: The action is not fluent. {fluency_feedback} ### RECOMMENDATIONS FOR IMPROVEMENT Please follow the recommendations below when trying to generate this action again. Use the feedback above to generate a better action. {self.action_fluency.recommendations_for_improvement()} """ if not suitability_passed: failure_feedback += f""" ## Problem: The action is not suitable to the situation or task. {suitability_feedback} ### RECOMMENDATIONS FOR IMPROVEMENT Please follow the recommendations below when trying to generate this action again. Use the feedback above to generate a better action. {self.action_suitability.recommendations_for_improvement()} """ if not similarity_passed: failure_feedback += f""" ## Problem: The action is too similar to the previous one. {similarity_feedback} """ logger.warning( f"[{agent.name}][{stage}] failed to pass quality checks: {failure_feedback}" ) return False, total_score, failure_feedback def _check_proposition( self, agent, proposition, tentative_action, minimum_required_qty_of_actions=0, enable_proposition_check=True, ): from tinytroupe.agent import ( logger, ) # import here to avoid circular import issues if enable_proposition_check: if agent.actions_count >= minimum_required_qty_of_actions: result = proposition.score( target=agent, claim_variables={"action": tentative_action}, return_full_response=True, ) value_with_justification = f"Score = {result['value']} (out of {Proposition.MAX_SCORE}). Justification = {result['justification']}" logger.debug( f"[{agent.name}] Proposition '{proposition.__class__.__name__}' evaluation result: {value_with_justification}" ) if result["value"] >= self.quality_threshold: return True, result["value"], value_with_justification else: return False, result["value"], value_with_justification else: return ( True, Proposition.MAX_SCORE, f"The proposition is trivially true due to the lack of enough actions for comparison.", ) else: # If the proposition check is disabled, we assume it passed return ( True, Proposition.MAX_SCORE, f"The proposition check is disabled, so it is assumed to have passed.", ) def _check_next_action_similarity( self, agent, proposed_next_action, threshold, enable_similarity_check=True ): """ Checks the similarity between the agent's current action and a proposed next action. High similarity indicates that the proposed action is too similar to the current one, and this check fails. """ from tinytroupe.agent import ( logger, ) # import here to avoid circular import issues if enable_similarity_check: similarity = utils.next_action_jaccard_similarity( agent, proposed_next_action ) logger.debug(f"[{agent.name}] Next-action Jaccard similarity: {similarity}") if similarity >= threshold: logger.warning( f"[{agent.name}] Next-action Jaccard similarity is above the threshold ({threshold})." ) return ( False, similarity, f"Similarity = {similarity} (range: 0.0 to 1.0). The action is too similar to the previous one.", ) else: logger.debug( f"[{agent.name}] Next-action Jaccard similarity is below the threshold ({threshold})." ) return ( True, similarity, f"Similarity = {similarity} (range: 0.0 to 1.0). The action is sufficiently different from the previous one.", ) else: # If the similarity check is disabled, we assume it passed return ( True, 0.0, f"The similarity check is disabled, so it is assumed to have passed.", ) ################################################################################################ # Action correction methods ################################################################################################ def _correct_action(self, action: dict, feedback, llm_role, llm_content): situation = f""" The following action by an agent was observed: {action} However, it does not conform to expectations about this agent behavior, due to the following reasons. {feedback} """ # restructured_situation =\ # utils.restructure_as_observed_vs_expected(\ # """) # rule = utils.formulate_corrective_rule(restructured_situation) rules = utils.extract_observed_vs_expected_rules(situation) rephrased_action_content = utils.correct_according_to_rule( action["content"], rules ) # copy action rephrased_action = action.copy() # update content rephrased_action["content"] = rephrased_action_content # replace in the 'action' key in the original llm content message llm_content["action"] = rephrased_action return rephrased_action, llm_role, llm_content def get_statistics(self): regeneration_failure_rate = ( self.regeneration_failures / self.regeneration_attempts if self.regeneration_attempts else 0 ) direct_correction_failure_rate = ( self.direct_correction_failures / self.direct_correction_attempts if self.direct_correction_attempts else 0 ) regeneration_mean_score = ( statistics.mean(self.regeneration_scores) if self.regeneration_scores else 0 ) regeneration_sd_score = ( statistics.stdev(self.regeneration_scores) if len(self.regeneration_scores) > 1 else 0 ) direct_correction_mean_score = ( statistics.mean(self.direct_correction_scores) if self.direct_correction_scores else 0 ) direct_correction_sd_score = ( statistics.stdev(self.direct_correction_scores) if len(self.direct_correction_scores) > 1 else 0 ) original_success_rate = ( self.total_original_actions_succeeded / self.total_actions_produced if self.total_actions_produced else 0 ) return { "regeneration_failure_rate": regeneration_failure_rate, "direct_correction_failure_rate": direct_correction_failure_rate, "regeneration_mean_score": regeneration_mean_score, "regeneration_sd_score": regeneration_sd_score, "direct_correction_mean_score": direct_correction_mean_score, "direct_correction_sd_score": direct_correction_sd_score, "total_actions_produced": self.total_actions_produced, "total_original_actions_succeeded": self.total_original_actions_succeeded, "original_success_rate": original_success_rate, "regeneration_success_rate": 1 - regeneration_failure_rate, "direct_correction_success_rate": 1 - direct_correction_failure_rate, }Ancestors
Methods
def generate_next_action(self, agent, current_messages: list)-
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def generate_next_action(self, agent, current_messages: list): from tinytroupe.agent import ( logger, ) # import here to avoid circular import issues # clean up (remove unnecessary elements) and copy the list of current messages to avoid modifying the original ones current_messages = [ {"role": msg["role"], "content": json.dumps(msg["content"])} for msg in current_messages ] # starts with no feedback cur_feedback = None all_negative_feedbacks = [] best_action = None best_role = None best_content = None best_score = float("-inf") original_score = None def update_best(tentative_action, role, content, total_score): nonlocal best_action, best_role, best_content, best_score if total_score > best_score: best_action = tentative_action best_role = role best_content = content best_score = total_score def finish_return(tentative_action, role, content, final_score): if original_score is not None and final_score > original_score: logger.warning( f"[{agent.name}] improved total quality from {original_score} to {final_score}" ) # ensure that tentative_action and content are dicts or lists where applicable if isinstance(tentative_action, str): tentative_action = json.loads(tentative_action) if isinstance(content, str): content = json.loads(content) return tentative_action, role, content, all_negative_feedbacks # First attempt to generate an action or actions tentative, role, content = self._generate_tentative_action( agent, current_messages, feedback_from_previous_attempt=cur_feedback, previous_tentative_action=None, previous_llm_role=None, previous_llm_content=None, ) # TODO obsolete? # # If model returned multi-action payload, we will score based on the first action # def pick_reference_action_for_scoring(tentative_action_or_actions): # if ( # isinstance(tentative_action_or_actions, list) # and tentative_action_or_actions # ): # # Prefer first action # return tentative_action_or_actions[0] # return tentative_action_or_actions def remove_done_actions(tentative_action_or_actions): if isinstance(tentative_action_or_actions, list): return [ action for action in tentative_action_or_actions if action is not None and isinstance(action, dict) and action.get("type") != "DONE" ] return tentative_action_or_actions tentative_action_for_quality = remove_done_actions( tentative # TODO remove pick_reference_action_for_scoring(tentative) ) if self.enable_quality_checks: # First quality check (single-action proxy) good_quality, total_score, cur_feedback = self._check_action_quality( "Original Action", agent, tentative_action=tentative_action_for_quality ) update_best(tentative, role, content, total_score) if original_score is None: original_score = total_score if good_quality: self.total_original_actions_succeeded += 1 # Found a good action(s), let's return it now return finish_return(tentative, role, content, total_score) else: logger.warning( f"[{agent.name}] Original action did not pass quality checks: {cur_feedback}" ) all_negative_feedbacks.append(cur_feedback) # GENERATE AND REGENERATE the action by the agent if self.enable_regeneration: for attempt in range(self.max_attempts): # Generate tentative action tentative, role, content = self._generate_tentative_action( agent, current_messages, feedback_from_previous_attempt=cur_feedback, previous_tentative_action=tentative, previous_llm_role=role, previous_llm_content=content, ) logger.debug(f"[{agent.name}] Tentative action: {tentative}") self.regeneration_attempts += 1 tentative_action_for_quality = remove_done_actions( tentative ) # TODO remove pick_reference_action_for_scoring(tentative) good_quality, total_score, cur_feedback_single = ( self._check_action_quality( f"Action Regeneration ({attempt})", agent, tentative_action=tentative_action_for_quality, ) ) update_best(tentative, role, content, total_score) if good_quality: # Found a good action(s), let's return it now return finish_return(tentative, role, content, total_score) else: self.regeneration_failures += 1 self.regeneration_scores.append(total_score) all_negative_feedbacks.append(cur_feedback_single) cur_feedback = cur_feedback_single # CORRECT OR REPHRASE the action directly if self.enable_direct_correction: for attempt in range(self.max_attempts): # Only meaningful for single action, so pick the last action for correction last_action = remove_done_actions( tentative ) # TODO remove pick_reference_action_for_scoring(tentative) corrected_action, role, content = self._correct_action( last_action, feedback=cur_feedback, llm_role=role, llm_content=content, ) logger.warning( f"[{agent.name}] Rephrased the action directly as: {corrected_action}" ) self.direct_correction_attempts += 1 good_quality, total_score, cur_feedback = ( self._check_action_quality( f"Direct Action Correction or Rephrasing ({attempt})", agent, tentative_action=corrected_action, ) ) update_best(corrected_action, role, content, total_score) if good_quality: # Return corrected single action return finish_return( corrected_action, role, content, total_score ) else: self.direct_correction_failures += 1 self.direct_correction_scores.append(total_score) all_negative_feedbacks.append(cur_feedback) # If we got here, all attempts to generate a good action failed if self.continue_on_failure: logger.warning( f"[{agent.name}] All attempts to generate a good action failed. Returning the best one." ) return finish_return(best_action, best_role, best_content, best_score) else: raise PoorQualityActionException() else: # If we got here, it means that the action(s) was generated without quality checks return tentative, role, content, [] def generate_next_actions(self, agent, current_messages: list)-
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def generate_next_actions(self, agent, current_messages: list): action_or_actions, role, content, all_negative_feedbacks = ( self.generate_next_action(agent, current_messages) ) # Normalize to sequence if isinstance(action_or_actions, list): actions = action_or_actions else: actions = [action_or_actions] # If multi-action is enabled but no DONE present, append DONE defensively if self.enable_multi_action_output: if not any(a.get("type") == "DONE" for a in actions): actions = actions + [{"type": "DONE", "content": "", "target": ""}] if isinstance(content, dict): content = content.copy() content["actions"] = actions content.pop("action", None) return actions, role, content, all_negative_feedbacks def get_statistics(self)-
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def get_statistics(self): regeneration_failure_rate = ( self.regeneration_failures / self.regeneration_attempts if self.regeneration_attempts else 0 ) direct_correction_failure_rate = ( self.direct_correction_failures / self.direct_correction_attempts if self.direct_correction_attempts else 0 ) regeneration_mean_score = ( statistics.mean(self.regeneration_scores) if self.regeneration_scores else 0 ) regeneration_sd_score = ( statistics.stdev(self.regeneration_scores) if len(self.regeneration_scores) > 1 else 0 ) direct_correction_mean_score = ( statistics.mean(self.direct_correction_scores) if self.direct_correction_scores else 0 ) direct_correction_sd_score = ( statistics.stdev(self.direct_correction_scores) if len(self.direct_correction_scores) > 1 else 0 ) original_success_rate = ( self.total_original_actions_succeeded / self.total_actions_produced if self.total_actions_produced else 0 ) return { "regeneration_failure_rate": regeneration_failure_rate, "direct_correction_failure_rate": direct_correction_failure_rate, "regeneration_mean_score": regeneration_mean_score, "regeneration_sd_score": regeneration_sd_score, "direct_correction_mean_score": direct_correction_mean_score, "direct_correction_sd_score": direct_correction_sd_score, "total_actions_produced": self.total_actions_produced, "total_original_actions_succeeded": self.total_original_actions_succeeded, "original_success_rate": original_success_rate, "regeneration_success_rate": 1 - regeneration_failure_rate, "direct_correction_success_rate": 1 - direct_correction_failure_rate, }
Inherited members
class PoorQualityActionException (message='The generated action is of poor quality')-
Common base class for all non-exit exceptions.
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class PoorQualityActionException(Exception): def __init__(self, message="The generated action is of poor quality"): self.message = message super().__init__(self.message)Ancestors
- builtins.Exception
- builtins.BaseException