|
from pathlib import Path |
|
from typing import 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 collections.abc import Iterator |
|
from pydantic import Field |
|
|
|
CURRENT_PATH = root_path = Path(__file__).parents[0] |
|
|
|
|
|
@registry.register_worker() |
|
class Conclude(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: " |
|
self.callback.send_incomplete( |
|
agent_id=self.workflow_instance_id, msg="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 '' |
|
self.callback.send_incomplete( |
|
agent_id=self.workflow_instance_id, |
|
msg=f"{current_msg}", |
|
) |
|
last_output += current_msg |
|
self.callback.send_answer(agent_id=self.workflow_instance_id, msg="") |
|
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.stm(self.workflow_instance_id).clear() |
|
return {"last_output": last_output} |
|
|
