Dr VegaPunk

.gitignore

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+.env

punkrecord/models.py

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+
+
+
+
+
+for i in range(0, 100):
+    print(i)
+    if i == 10:
+        break

req.txt

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+langchain>=0.0.100
+openai>=0.27.0
+python-dotenv>=0.19.0

satellites/atlas/agent.py

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+from satellites.base_satellite import BaseSatellite
+
+class Atlas

satellites/base_satellite.py

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+from langchain import OpenAI, LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+
+from abc import ABC, abstractmethod
+from typing import List,Dict,Any
+
+
+class  BaseSatellite(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]:
+        # Recupere la prochaine tache a traiter
+        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_punkrocord(self) -> None:
+        # Methode pour mettre a jour de la base de connaissance local du satellite depuis punkrecord
+        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)