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>
89 lines
2.9 KiB
Markdown
89 lines
2.9 KiB
Markdown
---
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title: Python client
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excerpt: Developing using the MemGPT Python client
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category: 6580dab16cade8003f996d17
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---
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The fastest way to integrate MemGPT with your own Python projects is through the `MemGPT` client class:
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```python
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from memgpt import MemGPT
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# Create a MemGPT client object (sets up the persistent state)
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client = MemGPT(
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quickstart="openai",
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config={
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"openai_api_key": "YOUR_API_KEY"
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}
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)
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# You can set many more parameters, this is just a basic example
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agent_id = client.create_agent(
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agent_config={
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"persona": "sam_pov",
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"user": "cs_phd",
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}
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)
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# Now that we have an agent_name identifier, we can send it a message!
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# The response will have data from the MemGPT agent
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my_message = "Hi MemGPT! How's it going?"
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response = client.user_message(agent_id=agent_id, message=my_message)
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```
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## More in-depth example of using the MemGPT Python client
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```python
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from memgpt.config import AgentConfig
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from memgpt import MemGPT
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from memgpt import constants
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from memgpt.cli.cli import QuickstartChoice
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client = MemGPT(
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# When auto_save is 'True' then the agent(s) will be saved after every
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# user message. This may have performance implications, so you
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# can otherwise choose when to save explicitly using client.save().
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auto_save=True,
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# Quickstart will automatically configure MemGPT (without having to run `memgpt configure`
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# If you choose 'openai' then you must set the api key (env or in config)
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quickstart=QuickstartChoice.memgpt_hosted,
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# Allows you to override default config generated by quickstart or `memgpt configure`
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config={}
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)
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# Create an AgentConfig with default persona and human txt
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# In this case, assume we wrote a custom persona file "my_persona.txt", located at ~/.memgpt/personas/my_persona.txt
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# Same for a custom user file "my_user.txt", located at ~/.memgpt/humans/my_user.txt
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agent_config = AgentConfig(
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name="CustomAgent",
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persona="my_persona",
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human="my_user",
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preset="memgpt_chat",
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model="gpt-4",
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)
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# Create the agent according to AgentConfig we set up. If an agent with
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# the same name already exists it will simply return, unless you set
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# throw_if_exists to 'True'
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agent_id = client.create_agent(agent_config=agent_config)
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# Create a helper that sends a message and prints the assistant response only
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def send_message(message: str):
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"""
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sends a message and prints the assistant output only.
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:param message: the message to send
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"""
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response = client.user_message(agent_id=agent_id, message=message)
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for r in response:
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# Can also handle other types "function_call", "function_return", "function_message"
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if "assistant_message" in r:
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print("ASSISTANT:", r["assistant_message"])
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elif "thoughts" in r:
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print("THOUGHTS:", r["internal_monologue"])
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# Send a message and see the response
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send_message("Please introduce yourself and tell me about your abilities!")
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```
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