GoodAI-LTM equips agents with text-based long-term memory by combining essential components such as text embedding models, reranking, vector databases, memory and query rewriting, automatic chunking, chunk metadata, and chunk expansion. This package is specifically designed to offer a dialog-centric memory stream for social agents.
Additionally, GoodAI-LTM includes a conversational agent component (LTMAgent) for seamless integration into Python-based apps.
pip install goodai-ltm
Call the reply method of an LTMAgent instance to get a response from the agent.
from goodai.ltm.agent import LTMAgent
agent = LTMAgent(model="gpt-3.5-turbo")
response = agent.reply("What can you tell me about yourself?")
print(response)
The model parameter can be the name of any model supported by the litellm library.
A session history is maintained automatically by the agent. If you want to start a
new session, call the new_session method.
agent.new_session()
print(f"Number of messages in session: {len(agent.session.message_history)}")
The agent has a conversational memory and also a knowledge base. You can tell the agent
to store knowledge by invoking the add_knowledge method.
agent.clear_knowledge()
agent.add_knowledge("The user's birthday is February 10.")
agent.add_knowledge("Refer to the user as 'boss'.")
response = agent.reply("Today is February 10. I think this is an important date. Can you remind me?")
print(response)
LTMAgent is a seamless RAG system. The ltm_agent_with_wiki example
shows how to add Wikipedia articles to the agent's knowledge base.
You can persist the agent's configuration and its memories/knowledge by obtaining
its state as a string via the state_as_text method.
state_text = agent.state_as_text()
# Persist state_text to secondary storage
To build an agent from state text, call the from_state_text method.
agent2 = LTMAgent.from_state_text(state_text)
Note that this does not restore the conversation session. The persist the conversation session
call the state_as_text method of the session.
from goodai.ltm.agent import LTMAgentSession
session_state_text = agent.session.state_as_text()
# session_state_text can be persisted in secondary storage
# The session.session_id field can serve as an identifier of the persisted session
# Now let's restore the session in agent2
p_session = LTMAgentSession.from_state_text(session_state_text)
agent2.use_session(p_session)
Visit the Github page: https://github.com/GoodAI/goodai-ltm