from lyzr import Cognis, CognisMessagecog = Cognis()cog.add( messages=[ CognisMessage(role="user", content="My name is Alice. I love hiking and I'm vegetarian."), CognisMessage(role="assistant", content="Nice to meet you, Alice!"), ], owner_id="user_alice",)
from cognis import Cognism = Cognis(owner_id="user_alice")result = m.add([ {"role": "user", "content": "My name is Alice. I love hiking and I'm vegetarian."}, {"role": "assistant", "content": "Nice to meet you, Alice!"},])print(result["message"])# "Extracted 3 memories from 2 messages"
Cognis automatically extracts discrete facts from the conversation and stores them as searchable memory records.
results = cog.search(query="What does Alice eat?", owner_id="user_alice", limit=5)for r in results: print(f" {r.content} (score: {r.score})")# → Alice is vegetarian (score: 0.89)
resp = m.search("What does Alice eat?", limit=5)for r in resp["results"]: print(f" {r['content']} (score: {r['score']})")# → Alice is vegetarian (score: 0.8712)
context = cog.context( current_messages=[CognisMessage(role="user", content="Recommend a restaurant")], owner_id="user_alice",)# Use context in your LLM system prompt
ctx = m.get_context( messages=[{"role": "user", "content": "Recommend a restaurant"}])print(ctx["context_string"])# "Relevant memories:\n- Alice is vegetarian\n- ..."m.close() # Required for OSS
