b2e9a24671
* First commit of memgpt client and some messy test code * rolled back unnecessary changes to abstract interface; switched client to always use Queueing Interface * Added missing interface clear() in run_command; added convenience method for checking if an agent exists, used that in create_agent * Formatting fixes * Fixed incorrect naming of get_agent_memory in rest server * Removed erroneous clear from client save method; Replaced print statements with appropriate logger calls in server * Updated readme with client usage instructions * added tests for Client * make printing to terminal togglable on queininginterface (should probably refactor this to a logger) * turn off printing to stdout via interface by default * allow importing the python client in a similar fashion to openai-python (see https://github.com/openai/openai-python) * Allowed quickstart on init of client; updated readme and test_client accordingly * oops, fixed name of openai_api_key config key * Fixed small typo * Fixed broken test by adding memgpt hosted model details to agent config * silence llamaindex 'LLM is explicitly disabled. Using MockLLM.' on server * default to openai if user's memgpt directory is empty (first time) * correct type hint * updated section on client in readme * added comment about how MemGPT config != Agent config * patch unrelated test * update wording on readme * patch another unrelated test * added python client to readme docs * Changed 'user' to 'human' in example; Defaulted AgentConfig.model to 'None'; Fixed issue in create_agent (accounting for dict config); matched test code to example * Fixed advanced example * patch test * patch --------- Co-authored-by: cpacker <packercharles@gmail.com>
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2.9 KiB
title, excerpt, category
| title | excerpt | category |
|---|---|---|
| Python client | Developing using the MemGPT Python client | 6580dab16cade8003f996d17 |
The fastest way to integrate MemGPT with your own Python projects is through the MemGPT client class:
from memgpt import MemGPT
# Create a MemGPT client object (sets up the persistent state)
client = MemGPT(
quickstart="openai",
config={
"openai_api_key": "YOUR_API_KEY"
}
)
# You can set many more parameters, this is just a basic example
agent_id = client.create_agent(
agent_config={
"persona": "sam_pov",
"user": "cs_phd",
}
)
# Now that we have an agent_name identifier, we can send it a message!
# The response will have data from the MemGPT agent
my_message = "Hi MemGPT! How's it going?"
response = client.user_message(agent_id=agent_id, message=my_message)
More in-depth example of using the MemGPT Python client
from memgpt.config import AgentConfig
from memgpt import MemGPT
from memgpt import constants
from memgpt.cli.cli import QuickstartChoice
client = MemGPT(
# When auto_save is 'True' then the agent(s) will be saved after every
# user message. This may have performance implications, so you
# can otherwise choose when to save explicitly using client.save().
auto_save=True,
# Quickstart will automatically configure MemGPT (without having to run `memgpt configure`
# If you choose 'openai' then you must set the api key (env or in config)
quickstart=QuickstartChoice.memgpt_hosted,
# Allows you to override default config generated by quickstart or `memgpt configure`
config={}
)
# Create an AgentConfig with default persona and human txt
# In this case, assume we wrote a custom persona file "my_persona.txt", located at ~/.memgpt/personas/my_persona.txt
# Same for a custom user file "my_user.txt", located at ~/.memgpt/humans/my_user.txt
agent_config = AgentConfig(
name="CustomAgent",
persona="my_persona",
human="my_user",
preset="memgpt_chat",
model="gpt-4",
)
# Create the agent according to AgentConfig we set up. If an agent with
# the same name already exists it will simply return, unless you set
# throw_if_exists to 'True'
agent_id = client.create_agent(agent_config=agent_config)
# Create a helper that sends a message and prints the assistant response only
def send_message(message: str):
"""
sends a message and prints the assistant output only.
:param message: the message to send
"""
response = client.user_message(agent_id=agent_id, message=message)
for r in response:
# Can also handle other types "function_call", "function_return", "function_message"
if "assistant_message" in r:
print("ASSISTANT:", r["assistant_message"])
elif "thoughts" in r:
print("THOUGHTS:", r["internal_monologue"])
# Send a message and see the response
send_message("Please introduce yourself and tell me about your abilities!")