Kaip sukurti nuolatinę atmintį ir suasmenintą Agentišką AI sistemą su nykimu ir savęs vertinimu?

import math, time, random
from typing import List


class MemoryItem:
   def __init__(self, kind:str, content:str, score:float=1.0):
       self.kind = kind
       self.content = content
       self.score = score
       self.t = time.time()


class MemoryStore:
   def __init__(self, decay_half_life=1800):
       self.items: List(MemoryItem) = ()
       self.decay_half_life = decay_half_life


   def _decay_factor(self, item:MemoryItem):
       dt = time.time() - item.t
       return 0.5 ** (dt / self.decay_half_life)

 def add(self, kind:str, content:str, score:float=1.0):
       self.items.append(MemoryItem(kind, content, score))


   def search(self, query:str, topk=3):
       scored = ()
       for it in self.items:
           decay = self._decay_factor(it)
           sim = len(set(query.lower().split()) & set(it.content.lower().split()))
           final = (it.score * decay) + sim
           scored.append((final, it))
       scored.sort(key=lambda x: x(0), reverse=True)
       return (it for _, it in scored(:topk) if _ > 0)


   def cleanup(self, min_score=0.1):
       new = ()
       for it in self.items:
           if it.score * self._decay_factor(it) > min_score:
               new.append(it)
       self.items = new

class Agent:
   def __init__(self, memory:MemoryStore, name="PersonalAgent"):
       self.memory = memory
       self.name = name


   def _llm_sim(self, prompt:str, context:List(str)):
       base = "OK. "
       if any("prefers short" in c for c in context):
           base = ""
       reply = base + f"I considered {len(context)} past notes. "
       if "summarize" in prompt.lower():
           return reply + "Summary: " + " | ".join(context(:2))
       if "recommend" in prompt.lower():
           if any("cybersecurity" in c for c in context):
               return reply + "Recommended: write more cybersecurity articles."
           if any("rag" in c for c in context):
               return reply + "Recommended: build an agentic RAG demo next."
           return reply + "Recommended: continue with your last topic."
       return reply + "Here's my response to: " + prompt


   def perceive(self, user_input:str):
       ui = user_input.lower()
       if "i like" in ui or "i prefer" in ui:
           self.memory.add("preference", user_input, 1.5)
       if "topic:" in ui:
           self.memory.add("topic", user_input, 1.2)
       if "project" in ui:
           self.memory.add("project", user_input, 1.0)
   def act(self, user_input:str):
       mems = self.memory.search(user_input, topk=4)
       ctx = (m.content for m in mems)
       answer = self._llm_sim(user_input, ctx)
       self.memory.add("dialog", f"user said: {user_input}", 0.6)
       self.memory.cleanup()
       return answer, ctx

def evaluate_personalisation(agent:Agent):
   agent.memory.add("preference", "User likes cybersecurity articles", 1.6)
   q = "Recommend what to write next"
   ans_personal, _ = agent.act(q)
   empty_mem = MemoryStore()
   cold_agent = Agent(empty_mem)
   ans_cold, _ = cold_agent.act(q)
   gain = len(ans_personal) - len(ans_cold)
   return ans_personal, ans_cold, gain

mem = MemoryStore(decay_half_life=60)
agent = Agent(mem)


print("=== Demo: teaching the agent about yourself ===")
inputs = (
   "I prefer short answers.",
   "I like writing about RAG and agentic AI.",
   "Topic: cybersecurity, phishing, APTs.",
   "My current project is to build an agentic RAG Q&A system."
)
for inp in inputs:
   agent.perceive(inp)


print("\n=== Now ask the agent something ===")
user_q = "Recommend what to write next in my blog"
ans, ctx = agent.act(user_q)
print("USER:", user_q)
print("AGENT:", ans)
print("USED MEMORY:", ctx)


print("\n=== Evaluate personalisation benefit ===")
p, c, g = evaluate_personalisation(agent)
print("With memory :", p)
print("Cold start  :", c)
print("Personalisation gain (chars):", g)


print("\n=== Current memory snapshot ===")
for it in agent.memory.items:
   print(f"- {it.kind} | {it.content(:60)}... | score~{round(it.score,2)}")