Find out how to design persistent reminiscence and personalised agent AI methods with decay and self-evaluation?

import math, time, random
from typing import Checklist


class MemoryItem:
   def __init__(self, variety:str, content material:str, rating:float=1.0):
       self.variety = variety
       self.content material = content material
       self.rating = rating
       self.t = time.time()


class MemoryStore:
   def __init__(self, decay_half_life=1800):
       self.objects: Checklist[MemoryItem] = []
       self.decay_half_life = decay_half_life


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

 def add(self, variety:str, content material:str, rating:float=1.0):
       self.objects.append(MemoryItem(variety, content material, rating))


   def search(self, question:str, topk=3):
       scored = []
       for it in self.objects:
           decay = self._decay_factor(it)
           sim = len(set(question.decrease().cut up()) & set(it.content material.decrease().cut up()))
           last = (it.rating * decay) + sim
           scored.append((last, it))
       scored.type(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.objects:
           if it.rating * self._decay_factor(it) > min_score:
               new.append(it)
       self.objects = new

class Agent:
   def __init__(self, reminiscence:MemoryStore, title="PersonalAgent"):
       self.reminiscence = reminiscence
       self.title = title


   def _llm_sim(self, immediate:str, context:Checklist[str]):
       base = "OK. "
       if any("prefers quick" in c for c in context):
           base = ""
       reply = base + f"I thought-about {len(context)} previous notes. "
       if "summarize" in immediate.decrease():
           return reply + "Abstract: " + " | ".be part of(context[:2])
       if "suggest" in immediate.decrease():
           if any("cybersecurity" in c for c in context):
               return reply + "Really helpful: write extra cybersecurity articles."
           if any("rag" in c for c in context):
               return reply + "Really helpful: construct an agentic RAG demo subsequent."
           return reply + "Really helpful: proceed together with your final subject."
       return reply + "Here is my response to: " + immediate


   def understand(self, user_input:str):
       ui = user_input.decrease()
       if "i like" in ui or "i favor" in ui:
           self.reminiscence.add("desire", user_input, 1.5)
       if "subject:" in ui:
           self.reminiscence.add("subject", user_input, 1.2)
       if "venture" in ui:
           self.reminiscence.add("venture", user_input, 1.0)
   def act(self, user_input:str):
       mems = self.reminiscence.search(user_input, topk=4)
       ctx = [m.content for m in mems]
       reply = self._llm_sim(user_input, ctx)
       self.reminiscence.add("dialog", f"person mentioned: {user_input}", 0.6)
       self.reminiscence.cleanup()
       return reply, ctx

def evaluate_personalisation(agent:Agent):
   agent.reminiscence.add("desire", "Person likes cybersecurity articles", 1.6)
   q = "Suggest what to jot down subsequent"
   ans_personal, _ = agent.act(q)
   empty_mem = MemoryStore()
   cold_agent = Agent(empty_mem)
   ans_cold, _ = cold_agent.act(q)
   acquire = len(ans_personal) - len(ans_cold)
   return ans_personal, ans_cold, acquire

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


print("=== Demo: educating the agent about your self ===")
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.understand(inp)


print("n=== Now ask the agent one thing ===")
user_q = "Suggest what to jot down subsequent in my weblog"
ans, ctx = agent.act(user_q)
print("USER:", user_q)
print("AGENT:", ans)
print("USED MEMORY:", ctx)


print("n=== Consider personalisation profit ===")
p, c, g = evaluate_personalisation(agent)
print("With reminiscence :", p)
print("Chilly begin  :", c)
print("Personalisation acquire (chars):", g)


print("n=== Present reminiscence snapshot ===")
for it in agent.reminiscence.objects:
   print(f"- {it.variety} | {it.content material[:60]}... | rating~{spherical(it.rating,2)}")