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3680197f70
* Partial implementation * Partial implementation * Fixes * update tests * cleanup * update tests * comments * logging * wording * underscore * Extend notebook for teachable GPTAssistantAgent * Notebook for teachable GPTAssistantAgents * Update notebook * Update notebook * Update notebook * Update notebook * revert file * Update blog post and other documentation. * pre-commit * Address reviewer feedback. * Add new nb link to examples page. --------- Co-authored-by: Chi Wang <wang.chi@microsoft.com>
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
from autogen import UserProxyAgent, config_list_from_json
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from autogen.agentchat.contrib.capabilities.teachability import Teachability
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from autogen import ConversableAgent
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import os
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import sys
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sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
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from test_assistant_agent import OAI_CONFIG_LIST, KEY_LOC # noqa: E402
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try:
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from termcolor import colored
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except ImportError:
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def colored(x, *args, **kwargs):
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return x
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# Specify the model to use. GPT-3.5 is less reliable than GPT-4 at learning from user input.
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filter_dict = {"model": ["gpt-4-1106-preview"]}
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# filter_dict = {"model": ["gpt-3.5-turbo-1106"]}
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# filter_dict = {"model": ["gpt-4-0613"]}
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# filter_dict = {"model": ["gpt-3.5-turbo-0613"]}
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# filter_dict = {"model": ["gpt-4"]}
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# filter_dict = {"model": ["gpt-35-turbo-16k", "gpt-3.5-turbo-16k"]}
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def create_teachable_agent(reset_db=False):
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"""Instantiates a teachable agent using the settings from the top of this file."""
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# Load LLM inference endpoints from an env variable or a file
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# See https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints
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# and OAI_CONFIG_LIST_sample
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config_list = config_list_from_json(env_or_file=OAI_CONFIG_LIST, filter_dict=filter_dict, file_location=KEY_LOC)
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# Start by instantiating any agent that inherits from ConversableAgent.
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teachable_agent = ConversableAgent(
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name="teachable_agent",
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llm_config={"config_list": config_list, "timeout": 120, "cache_seed": None}, # Disable caching.
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)
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# Instantiate the Teachability capability. Its parameters are all optional.
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teachability = Teachability(
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verbosity=0, # 0 for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists.
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reset_db=reset_db,
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path_to_db_dir="./tmp/interactive/teachability_db",
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recall_threshold=1.5, # Higher numbers allow more (but less relevant) memos to be recalled.
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)
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# Now add the Teachability capability to the agent.
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teachability.add_to_agent(teachable_agent)
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return teachable_agent
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def interact_freely_with_user():
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"""Starts a free-form chat between the user and a teachable agent."""
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# Create the agents.
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print(colored("\nLoading previous memory (if any) from disk.", "light_cyan"))
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teachable_agent = create_teachable_agent(reset_db=False)
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user = UserProxyAgent("user", human_input_mode="ALWAYS")
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# Start the chat.
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teachable_agent.initiate_chat(user, message="Greetings, I'm a teachable user assistant! What's on your mind today?")
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if __name__ == "__main__":
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"""Lets the user test a teachable agent interactively."""
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interact_freely_with_user()
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