from abc import ABC, abstractmethod | |
from typing import Dict, Any | |
class VegapunkSatellite(ABC): | |
def __init__(self, name: str, specialty: str): | |
self.name = name | |
self.specialty = specialty | |
self.knowledge_base = {} | |
self.task_queue = [] | |
def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]: | |
""" | |
Traite une tache specifique au satellite | |
a implementer dans chaque classe de satellite specifique | |
""" | |
pass | |
def add_to_knowledge_base(self, key: str, value: Any): | |
# Ajoute une information a la base de connaissance du satellite | |
self.knowledge_base[key] = value | |
def get_from_knowledge_base(self, key: str) -> Any: | |
# Recupere une information de la base de connaissance du satellite | |
return self.knowledge_base.get(key) | |
def add_task(self, task: Dict[str, Any]): | |
# Ajoute une tache a la file d'attente du satellite | |
self.task_queue.append(task) | |
def get_next_task(self) -> Dict[str, Any]: | |
"""Récupère et supprime la prochaine tâche de la file d'attente.""" | |
if self.task_queue: | |
return self.task_queue.pop(0) | |
return None | |
def report_status(self): | |
# Rapporte le status du satellite | |
return { | |
"name": self.name, | |
"specialty": self.specialty, | |
"knowledge_base": self.knowledge_base, | |
"task_queue": self.task_queue, | |
"task_pending": len(self.task_queue), | |
"Knowledge_base_size": len(self.knowledge_base), | |
} | |
def communicate_with_stellar(self, message: Dict[str, Any]) -> Dict[str, Any]: | |
""" | |
Méthode pour communiquer avec le satellite manager (Stellar). | |
À implémenter dans chaque classe de satellite spécifique. | |
""" | |
pass | |
def update_from_punkrecord(self) -> None: | |
# Methode pour mettre a jour de la base de connaissance local du satellite depuis punkrecord | |
pass | |
# def communicate_with_other_satellite(self, satellite: VegapunkSatellite, message: Dict[str, Any]) -> Dict[str, Any]: | |
# # Methode pour communiquer avec un autre satellite | |
# pass | |
# | |
# | |
# class Satellite: | |
# def __init__(self, name, specialty): | |
# self.name = name | |
# self.specialty = specialty | |
# self.llm = OpenAI(temperature=0.7) | |
# self.memory = ConversationBufferMemory(memory_key="chat_history") | |
# self.prompt = PromptTemplate( | |
# input_variables=["chat_history", "human_input", "specialty"], | |
# template="""You are {specialty}. | |
# Chat History: {chat_history} | |
# Human: {human_input} | |
# AI Assistant:""" | |
# ) | |
# self.chain = LLMChain( | |
# llm=self.llm, | |
# prompt=self.prompt, | |
# memory=self.memory, | |
# ) | |
# | |
# def process(self, input_text): | |
# return self.chain.run(human_input=input_text, specialty=self.specialty) | |
# | |
# | |
# # Exemple d'utilisation | |
# shaka = Satellite("Shaka", "an AI specializing in wisdom and general knowledge") | |
# response = shaka.process("Tell me about the importance of knowledge.") | |
# print(response) | |