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from pathlib import Path
from typing import Iterator, List
from omagent_core.advanced_components.workflow.dnc.schemas.dnc_structure import \
TaskTree
from omagent_core.engine.worker.base import BaseWorker
from omagent_core.memories.ltms.ltm import LTM
from omagent_core.models.llms.base import BaseLLMBackend
from omagent_core.models.llms.prompt import PromptTemplate
from omagent_core.utils.logger import logging
from omagent_core.utils.registry import registry
from openai import Stream
from pydantic import Field
CURRENT_PATH = root_path = Path(__file__).parents[0]
@registry.register_worker()
class WebpageConclude(BaseLLMBackend, BaseWorker):
prompts: List[PromptTemplate] = Field(
default=[
PromptTemplate.from_file(
CURRENT_PATH.joinpath("sys_prompt.prompt"), role="system"
),
PromptTemplate.from_file(
CURRENT_PATH.joinpath("user_prompt.prompt"), role="user"
),
]
)
def _run(self, dnc_structure: dict, last_output: str, *args, **kwargs):
"""A conclude node that summarizes and completes the root task.
This component acts as the final node that:
- Takes the root task and its execution results
- Generates a final conclusion/summary of the entire task execution
- Formats and presents the final output in a clear way
- Cleans up any temporary state/memory used during execution
The conclude node is responsible for providing a coherent final response that
addresses the original root task objective based on all the work done by
previous nodes.
Args:
agent_task (dict): The task tree containing the root task and results
last_output (str): The final output from previous task execution
*args: Additional arguments
**kwargs: Additional keyword arguments
Returns:
dict: Final response containing the conclusion/summary
"""
task = TaskTree(**dnc_structure)
self.callback.info(
agent_id=self.workflow_instance_id,
progress=f"Conclude",
message=f"{task.get_current_node().task}",
)
chat_complete_res = self.simple_infer(
task=task.get_root().task,
result=str(last_output),
img_placeholders="".join(
list(self.stm(self.workflow_instance_id).get("image_cache", {}).keys())
),
)
if isinstance(chat_complete_res, Iterator):
last_output = "Answer: "
for chunk in chat_complete_res:
if len(chunk.choices) > 0:
current_msg = chunk.choices[0].delta.content if chunk.choices[0].delta.content is not None else ''
last_output += current_msg
self.callback.send_answer(agent_id=self.workflow_instance_id, msg=last_output)
else:
last_output = chat_complete_res["choices"][0]["message"]["content"]
self.callback.send_answer(
agent_id=self.workflow_instance_id,
msg=f'Answer: {chat_complete_res["choices"][0]["message"]["content"]}',
)
# self.callback.send_answer(agent_id=self.workflow_instance_id, msg=f"Token usage: {self.token_usage}")
self.stm(self.workflow_instance_id).clear()
return {"last_output": last_output}
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