Find out how to Construct an Superior AI Agent with Summarized Quick-Time period and Vector-Based mostly Lengthy-Time period Reminiscence

!pip -q set up transformers speed up bitsandbytes sentence-transformers faiss-cpu


import os, json, time, uuid, math, re
from datetime import datetime
import torch, faiss
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
from sentence_transformers import SentenceTransformer
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

def load_llm(model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0"):
   attempt:
       if DEVICE=="cuda":
           bnb=BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_compute_dtype=torch.bfloat16,bnb_4bit_quant_type="nf4")
           tok=AutoTokenizer.from_pretrained(model_name, use_fast=True)
           mdl=AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb, device_map="auto")
       else:
           tok=AutoTokenizer.from_pretrained(model_name, use_fast=True)
           mdl=AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, low_cpu_mem_usage=True)
       return pipeline("text-generation", mannequin=mdl, tokenizer=tok, system=0 if DEVICE=="cuda" else -1, do_sample=True)
   besides Exception as e:
       increase RuntimeError(f"Didn't load LLM: {e}")

class VectorMemory:
   def __init__(self, path="/content material/agent_memory.json", dim=384):
       self.path=path; self.dim=dim; self.gadgets=[]
       self.embedder=SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", system=DEVICE)
       self.index=faiss.IndexFlatIP(dim)
       if os.path.exists(path):
           knowledge=json.load(open(path))
           self.gadgets=knowledge.get("gadgets",[])
           if self.gadgets:
               X=torch.tensor([x["emb"] for x in self.gadgets], dtype=torch.float32).numpy()
               self.index.add(X)
   def _emb(self, textual content):
       v=self.embedder.encode([text], normalize_embeddings=True)[0]
       return v.tolist()
   def add(self, textual content, meta=None):
       e=self._emb(textual content); self.index.add(torch.tensor([e]).numpy())
       rec={"id":str(uuid.uuid4()),"textual content":textual content,"meta":meta or {}, "emb":e}
       self.gadgets.append(rec); self._save(); return rec["id"]
   def search(self, question, ok=5, thresh=0.25):
       if len(self.gadgets)==0: return []
       q=self.embedder.encode([query], normalize_embeddings=True)
       D,I=self.index.search(q, min(ok, len(self.gadgets)))
       out=[]
       for d,i in zip(D[0],I[0]):
           if i==-1: proceed
           if d>=thresh: out.append((d,self.gadgets[i]))
       return out
   def _save(self):
       slim=[{k:v for k,v in it.items()} for it in self.items]
       json.dump({"gadgets":slim}, open(self.path,"w"), indent=2)

def now_iso(): return datetime.now().isoformat(timespec="seconds")
def clamp(txt, n=1600): return txt if len(txt)<=n else txt[:n]+" …"
def strip_json(s):
   m=re.search(r"{.*}", s, flags=re.S);
   return m.group(0) if m else None


SYS_GUIDE = (
"You're a useful, concise assistant with reminiscence. Use offered MEMORY when related. "
"Favor information from MEMORY over guesses. Reply straight; hold code blocks tight. If not sure, say so."
)


SUMMARIZE_PROMPT = lambda convo: f"Summarize the dialog beneath in 4-6 bullet factors specializing in secure information and duties:nn{convo}nnSummary:"
DISTILL_PROMPT = lambda consumer: (
f"""Determine if the USER textual content comprises sturdy information value long-term reminiscence (preferences, id, initiatives, deadlines, information).
Return compact JSON solely: {{"save": true/false, "reminiscence": "one-sentence reminiscence"}}.
USER: {consumer}""")


class MemoryAgent:
   def __init__(self):
       self.llm=load_llm()
       self.mem=VectorMemory()
       self.turns=[]    
       self.abstract=""   
       self.max_turns=10
   def _gen(self, immediate, max_new_tokens=256, temp=0.7):
       out=self.llm(immediate, max_new_tokens=max_new_tokens, temperature=temp, top_p=0.95, num_return_sequences=1, pad_token_id=self.llm.tokenizer.eos_token_id)[0]["generated_text"]
       return out[len(prompt):].strip() if out.startswith(immediate) else out.strip()
   def _chat_prompt(self, consumer, memory_context):
       convo="n".be part of([f"{r.upper()}: {t}" for r,t in self.turns[-8:]])
       sys=f"System: {SYS_GUIDE}nTime: {now_iso()}nn"
       mem = f"MEMORY (related excerpts):n{memory_context}nn" if memory_context else ""
       summ=f"CONTEXT SUMMARY:n{self.abstract}nn" if self.abstract else ""
       return sys+mem+summ+convo+f"nUSER: {consumer}nASSISTANT:"
   def _distill_and_store(self, consumer):
       attempt:
           uncooked=self._gen(DISTILL_PROMPT(consumer), max_new_tokens=120, temp=0.1)
           js=strip_json(uncooked)
           if js:
               obj=json.masses(js)
               if obj.get("save") and obj.get("reminiscence"):
                   self.mem.add(obj["memory"], {"ts":now_iso(),"supply":"distilled"})
                   return True, obj["memory"]
       besides Exception: go
       if re.search(r"b(my identify is|name me|I like|deadline|due|e-mail|telephone|engaged on|want|timezone|birthday|aim|examination)b", consumer, flags=re.I):
           m=f"Person stated: {clamp(consumer,120)}"
           self.mem.add(m, {"ts":now_iso(),"supply":"heuristic"})
           return True, m
       return False, ""
   def _maybe_summarize(self):
       if len(self.turns)>self.max_turns:
           convo="n".be part of([f"{r}: {t}" for r,t in self.turns])
           s=self._gen(SUMMARIZE_PROMPT(clamp(convo, 3500)), max_new_tokens=180, temp=0.2)
           self.abstract=s; self.turns=self.turns[-4:]
   def recall(self, question, ok=5):
       hits=self.mem.search(question, ok=ok)
       return "n".be part of([f"- ({d:.2f}) {h['text']} [meta={h['meta']}]" for d,h in hits])
   def ask(self, consumer):
       self.turns.append(("consumer", consumer))
       saved, memline = self._distill_and_store(consumer)
       mem_ctx=self.recall(consumer, ok=6)
       immediate=self._chat_prompt(consumer, mem_ctx)
       reply=self._gen(immediate)
       self.turns.append(("assistant", reply))
       self._maybe_summarize()
       standing=f"💾 memory_saved: {saved}; " + (f"notice: {memline}" if saved else "notice: -")
       print(f"nUSER: {consumer}nASSISTANT: {reply}n{standing}")
       return reply

agent=MemoryAgent()


print("✅ Agent prepared. Attempt these:n")
agent.ask("Hello! My identify is Nicolaus, I want being known as Nik. I am getting ready for UPSC in 2027.")
agent.ask("Additionally, I work at  Visa in analytics and love concise solutions.")
agent.ask("What's my examination yr and the way do you have to handle me subsequent time?")
agent.ask("Reminder: I like agentic RAG tutorials with single-file Colab code.")
agent.ask("Given my prefs, recommend a examine focus for this week in a single paragraph.")