A Coding Implementation to Construct Agent-Native Reminiscence Infrastructure with Memori for Persistent Multi-Person and Multi-Session LLM Functions

banner("Half 5 — Streaming")
mem.attribution(entity_id="[email protected]", process_id="personal-assistant")
stream = consumer.chat.completions.create(
   mannequin=MODEL,
   messages=[{"role": "user",
              "content": "In two sentences, what do you remember about me?"}],
   stream=True,
)
print("[stream] ", finish="")
for chunk in stream:
   d = chunk.decisions[0].delta.content material
   if d: print(d, finish="", flush=True)
print(); time.sleep(WRITE_DELAY)
banner("Half 6 — Async LLM calls")
async def async_demo():
   r = await async_client.chat.completions.create(
       mannequin=MODEL,
       messages=[{"role": "user",
                  "content": "What dietary restriction do I have? (asked async)"}],
   )
   return r.decisions[0].message.content material
print("[async]", asyncio.run(async_demo()))
banner("Half 7 — Mini help agent throughout a number of classes")
def help(user_id, immediate):
   mem.attribution(entity_id=user_id, process_id="support-bot")
   return ask(immediate, system=(
       "You're a calm, useful buyer help agent. "
       "Use what you keep in mind in regards to the person. If you do not know, say so."
   ))
USER = "[email protected]"
mem.attribution(entity_id=USER, process_id="support-bot")
mem.new_session()
print("[support T1]", help(USER,
   "Hello! I am Charlie, on the Professional plan. E-mail: [email protected]. "
   "Billing query for subsequent month."))
time.sleep(WRITE_DELAY)
mem.new_session()
print("[support T2]", help(USER,
   "Hey, me once more. What plan am I on and what's my e mail of report?"))
banner("Accomplished. Open https://app.memorilabs.ai to examine reminiscences, "
      "or use Memori BYODB to level at your individual Postgres.")