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Replace actual processor by the pipeline - part1
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src/main/askai/core/processors/splitter/splitter_actions.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| #!/usr/bin/env python3 | ||
| # -*- coding: utf-8 -*- | ||
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| """ | ||
| @project: HsPyLib-AskAI | ||
| @package: askai.core.processors.splitter.splitter_actions | ||
| @file: splitter_actions.py | ||
| @created: Mon, 21 Oct 2024 | ||
| @author: <B>H</B>ugo <B>S</B>aporetti <B>J</B>unior | ||
| @site: https://github.com/yorevs/askai | ||
| @license: MIT - Please refer to <https://opensource.org/licenses/MIT> | ||
| Copyright (c) 2024, HomeSetup | ||
| """ | ||
| import logging as log | ||
| from pathlib import Path | ||
| from types import SimpleNamespace | ||
| from typing import Optional | ||
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| from hspylib.core.metaclass.singleton import Singleton | ||
| from langchain_core.messages import AIMessage | ||
| from langchain_core.prompts import ChatPromptTemplate, PromptTemplate, MessagesPlaceholder | ||
| from langchain_core.runnables import RunnableWithMessageHistory | ||
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| from askai.core.askai_configs import configs | ||
| from askai.core.askai_prompt import prompt | ||
| from askai.core.component.geo_location import geo_location | ||
| from askai.core.component.rag_provider import RAGProvider | ||
| from askai.core.engine.openai.temperature import Temperature | ||
| from askai.core.enums.response_model import ResponseModel | ||
| from askai.core.model.acc_response import AccResponse | ||
| from askai.core.model.action_plan import ActionPlan | ||
| from askai.core.model.model_result import ModelResult | ||
| from askai.core.router.agent_tools import features | ||
| from askai.core.router.task_agent import agent | ||
| from askai.core.router.tools.general import final_answer | ||
| from askai.core.support.langchain_support import lc_llm | ||
| from askai.core.support.shared_instances import shared | ||
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| class SplitterActions(metaclass=Singleton): | ||
| """TODO""" | ||
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| INSTANCE: 'SplitterActions' | ||
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| @staticmethod | ||
| def wrap_answer(question: str, answer: str, model_result: ModelResult = ModelResult.default()) -> str: | ||
| """Provide a final answer to the user by wrapping the AI response with additional context. | ||
| :param question: The user's question. | ||
| :param answer: The AI's response to the question. | ||
| :param model_result: The result from the selected routing model (default is ModelResult.default()). | ||
| :return: A formatted string containing the final answer. | ||
| """ | ||
| output: str = answer | ||
| args = {"user": prompt.user.title(), "idiom": shared.idiom, "context": answer, "question": question} | ||
| prompt_args: list[str] = [k for k in args.keys()] | ||
| model: ResponseModel = ResponseModel.of_model(model_result.mid) | ||
| # events.reply.emit(reply=AIReply.full(msg.model_select(model))) | ||
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| match model, configs.is_speak: | ||
| case ResponseModel.TERMINAL_COMMAND, True: | ||
| output = final_answer("taius-stt", prompt_args, **args) | ||
| case ResponseModel.ASSISTIVE_TECH_HELPER, _: | ||
| output = final_answer("taius-stt", prompt_args, **args) | ||
| case ResponseModel.CHAT_MASTER, _: | ||
| output = final_answer("taius-jarvis", prompt_args, **args) | ||
| case _: | ||
| pass # Default is to leave the last AI response as is | ||
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| # Save the conversation to use with the task agent executor. | ||
| shared.memory.save_context({"input": question}, {"output": output}) | ||
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| return output | ||
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| @staticmethod | ||
| def refine_answer(question: str, answer: str, acc_response: AccResponse | None = None) -> str: | ||
| """TODO | ||
| :param question: The user's question. | ||
| :param answer: The AI's response to the question. | ||
| :param acc_response: The final accuracy response, if available. | ||
| """ | ||
| if acc_response and acc_response.reasoning: | ||
| ctx: str = str(shared.context.flat("HISTORY")) | ||
| args = { | ||
| "improvements": acc_response.details, | ||
| "context": ctx, | ||
| "response": answer, | ||
| "question": question, | ||
| } | ||
| prompt_args = [k for k in args.keys()] | ||
| # events.reply.emit(reply=AIReply.debug(msg.refine_answer(answer))) | ||
| return final_answer("taius-refiner", prompt_args, **args) | ||
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| return answer | ||
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| @staticmethod | ||
| def process_action(action: SimpleNamespace) -> str: | ||
| """TODO""" | ||
| path_str: str | None = ( | ||
| "Path: " + action.path | ||
| if hasattr(action, "path") and action.path.upper() not in ["N/A", "NONE", ""] | ||
| else None | ||
| ) | ||
| task: str = f"{action.task} {path_str or ''}" | ||
| return agent.invoke(task) | ||
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| def __init__(self): | ||
| self._rag: RAGProvider = RAGProvider("task-splitter.csv") | ||
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| def splitter_template(self, query: str) -> ChatPromptTemplate: | ||
| """Retrieve the processor Template.""" | ||
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| evaluation: str = str(shared.context.flat("EVALUATION")) | ||
| template = PromptTemplate( | ||
| input_variables=["os_type", "shell", "datetime", "home", "agent_tools", "rag"], | ||
| template=prompt.read_prompt("task-splitter.txt"), | ||
| ) | ||
| return ChatPromptTemplate.from_messages( | ||
| [ | ||
| ( | ||
| "system", | ||
| template.format( | ||
| os_type=prompt.os_type, | ||
| shell=prompt.shell, | ||
| datetime=geo_location.datetime, | ||
| home=Path.home(), | ||
| agent_tools=features.available_tools, | ||
| rag=self._rag.get_rag_examples(query), | ||
| ), | ||
| ), | ||
| MessagesPlaceholder("chat_history"), | ||
| ("assistant", evaluation), | ||
| ("human", "Human Question: '{input}'"), | ||
| ] | ||
| ) | ||
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| def split(self, question: str, model: ModelResult = ModelResult.default()) -> Optional[ActionPlan]: | ||
| """Invoke the LLM to split the tasks and create an action plan.""" | ||
| runnable = self.splitter_template(question) | lc_llm.create_chat_model(Temperature.COLDEST.temp) | ||
| runnable = RunnableWithMessageHistory( | ||
| runnable, shared.context.flat, input_messages_key="input", history_messages_key="chat_history" | ||
| ) | ||
| response: AIMessage | ||
| if response := runnable.invoke({"input": question}, config={"configurable": {"session_id": "HISTORY"}}): | ||
| answer: str = str(response.content) | ||
| log.info("Router::[RESPONSE] Received from AI: \n%s.", answer) | ||
| return ActionPlan.create(question, answer, model) | ||
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| return None | ||
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| assert (actions := SplitterActions().INSTANCE) is not None |
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