Dr-Vegapunk / satellites /base_satellite.py
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adding another prototype fonctionalite
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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 = []
@abstractmethod
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),
}
@abstractmethod
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)