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PromptAgent Module - Core agent class implementing the thought-action-observation loop.
This module implements the main agent logic that:
- Manages conversation history with the LLM
- Parses actions from LLM responses
- Executes actions in the Docker environment
- Tracks the agent's trajectory (thoughts, actions, observations)
Reference: https://github.com/yiyihum/da-code/tree/main/da_agent/agent/agents.py
"""
import logging
import re
import time
from typing import Dict, List
from da_agent.agent.prompts import SYS_PROMPT_IN_OUR_CODE
from da_agent.agent.action import Bash, Action, Terminate, Python, SQL
from da_agent.envs.da_agent import DA_Agent_Env
from typing import Dict, List
from pathlib import Path
import sys
project_root = Path(__file__).resolve().parents[3]
sys.path.append(str(project_root))
from utils.llm_client import QwenClient
from da_agent.agent.base import BaseAgent
logger = logging.getLogger("da_agent")
class PromptAgent(BaseAgent):
def __init__(
self,
model,
max_tokens,
top_p,
temperature,
max_memory_length,
max_steps,
):
super().__init__(
model=model,
max_tokens=max_tokens,
top_p=top_p,
temperature=temperature,
max_memory_length=max_memory_length,
max_steps=max_steps,
)
# Cross-task state (reused across tasks); per-task state is set in
# set_env_and_task.
self._AVAILABLE_ACTION_CLASSES = [Bash, Python, SQL, Terminate]
self.client = QwenClient()
def set_env_and_task(self, env: DA_Agent_Env):
self.env = env
self.instruction = self.env.task_config['question']
self.thoughts = []
self.responses = []
self.actions = []
self.observations = []
self.usages = []
self.timings = []
self.codes = []
self.history_messages = []
action_space = "".join([action_cls.get_action_description() for action_cls in self._AVAILABLE_ACTION_CLASSES])
self.system_message = SYS_PROMPT_IN_OUR_CODE.format(work_dir=self.work_dir, action_space=action_space, task=self.instruction, max_steps=self.max_steps)
self.history_messages.append({
"role": "system",
"content": [
{
"type": "text",
"text": self.system_message
},
]
})
def predict(self, obs: Dict=None) -> List:
"""
Predict the next action(s) based on the current observation.
"""
assert len(self.observations) == len(self.actions) and len(self.actions) == len(self.thoughts) \
, "The number of observations and actions should be the same."
start_time = time.time()
status = False
while not status:
messages = self.history_messages.copy()
messages.append({
"role": "user",
"content": [
{
"type": "text",
"text": "Observation: {}\n".format(str(obs))
}
]
})
try:
_, response, usage = self.client.generate(
messages=messages,
model=self.model,
# max_tokens=self.max_tokens,
# temperature=self.temperature,
# top_p=self.top_p,
enable_thinking=True
)
status = True
except Exception as e:
logging.getLogger("api-llms").error("Failed to call LLM: " + str(e))
error_info = e.response.json()
code_value = error_info['error']['code']
response = code_value
status = False
response = response.strip()
if not status:
if response in ["context_length_exceeded","rate_limit_exceeded","max_tokens"]:
self.history_messages = [self.history_messages[0]] + self.history_messages[3:]
else:
raise Exception(f"Failed to call LLM, response: {response}")
try:
action = self.parse_action(response)
thought = re.search(r'Thought:(.*?)Action', response, flags=re.DOTALL)
if thought:
thought = thought.group(1).strip()
else:
thought = response
except ValueError as e:
print("Failed to parse action from response", e)
action = None
logger.info("Observation: %s", obs)
logger.info("Response: %s", response)
self._add_message(obs, thought, action)
self.observations.append(obs)
self.thoughts.append(thought)
self.responses.append(response)
self.actions.append(action)
self.usages.append(dict(usage))
end_time = time.time()
self.timings.append({'start_time': start_time, 'end_time': end_time, 'duration': end_time - start_time})
if action is not None:
self.codes.append(action.code)
else:
self.codes.append(None)
return response, action
def _add_message(self, observations: str, thought: str, action: Action):
self.history_messages.append({
"role": "user",
"content": [
{
"type": "text",
"text": "Observation: {}".format(observations)
}
]
})
self.history_messages.append({
"role": "assistant",
"content": [
{
"type": "text",
"text": "Thought: {}\n\nAction: {}".format(thought, str(action))
}
]
})
if len(self.history_messages) > self.max_memory_length*2+1:
self.history_messages = [self.history_messages[0]] + self.history_messages[-self.max_memory_length*2:]
def parse_action(self, output: str) -> Action:
""" Parse action from text """
if output is None or len(output) == 0:
pass
action_string = ""
patterns = [r'["\']?Action["\']?:? (.*?)Observation',r'["\']?Action["\']?:? (.*?)Thought', r'["\']?Action["\']?:? (.*?)$', r'^(.*?)Observation']
for p in patterns:
match = re.search(p, output, flags=re.DOTALL)
if match:
action_string = match.group(1).strip()
break
if action_string == "":
action_string = output.strip()
output_action = None
for action_cls in self._AVAILABLE_ACTION_CLASSES:
action = action_cls.parse_action_from_text(action_string)
if action is not None:
output_action = action
break
if output_action is None:
action_string = action_string.replace("\_", "_").replace("'''","```")
for action_cls in self._AVAILABLE_ACTION_CLASSES:
action = action_cls.parse_action_from_text(action_string)
if action is not None:
output_action = action
break
return output_action
def run(self):
assert self.env is not None, "Environment is not set."
result = ""
done = False
step_idx = 0
obs = "You are in the folder now."
retry_count = 0
last_action = None
repeat_action = False
while not done and step_idx < self.max_steps:
_, action = self.predict(
obs
)
if action is None:
logger.info("Failed to parse action from response, try again.")
retry_count += 1
if retry_count > 3:
logger.info("Failed to parse action from response, stop.")
break
obs = "Failed to parse action from your response, make sure you provide a valid action."
else:
logger.info("Step %d: %s", step_idx + 1, action)
if last_action is not None and last_action == action:
if repeat_action:
return False, "ERROR: Repeated action"
else:
obs = "The action is the same as the last one, please provide a different action."
repeat_action = True
else:
obs, done = self.env.step(action)
last_action = action
repeat_action = False
if done:
if isinstance(action, Terminate):
result = action.output
logger.info("The task is done.")
break
step_idx += 1
return done, result
def get_trajectory(self):
trajectory = []
for i in range(len(self.observations)):
trajectory.append({
"observation": self.observations[i],
"thought": self.thoughts[i],
"action": str(self.actions[i]),
"code": self.codes[i],
"response": self.responses[i],
"usage": self.usages[i],
"timing": self.timings[i]
})
trajectory_log = {
"task": self.instruction,
"system_message": self.system_message,
"trajectory": trajectory
}
return trajectory_log
|