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Docmentation for agents (#1057)
* add agent notebook and documentation * fix bug * set flush to True when printing msg in agent * add a math problem in agent notebook * remove * header * improve notebook doc * notebook update * improve notebook example * improve doc * improve notebook doc * improve print * doc * human_input_mode * human_input_mode str * indent * indent * Update flaml/autogen/agent/user_proxy_agent.py Co-authored-by: Chi Wang <wang.chi@microsoft.com> * Update notebook/autogen_agent.ipynb Co-authored-by: Chi Wang <wang.chi@microsoft.com> * Update notebook/autogen_agent.ipynb Co-authored-by: Chi Wang <wang.chi@microsoft.com> * Update notebook/autogen_agent.ipynb Co-authored-by: Chi Wang <wang.chi@microsoft.com> * add agent doc * del old files * remove chat * agent doc * remove chat_agent * naming * improve documentation * wording * improve agent doc * wording * general auto reply * update agent doc * human input mode * add agent figure * update agent figure * update agent example figure * update code example * extensibility of UserProxyAgent --------- Co-authored-by: Chi Wang <wang.chi@microsoft.com>
This commit is contained in:
Qingyun Wu
GitHub
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from .agent import Agent
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from flaml.autogen.code_utils import DEFAULT_MODEL
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from flaml import oai
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class ChatAgent(Agent):
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DEFAULT_SYSTEM_MESSAGE = """You are a chat agent.
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"""
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DEFAULT_CONFIG = {
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"model": DEFAULT_MODEL,
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}
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def __init__(self, name, system_message=DEFAULT_SYSTEM_MESSAGE, **config):
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super().__init__(name, system_message)
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self._config = self.DEFAULT_CONFIG.copy()
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self._config.update(config)
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self._sender_dict = {}
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def receive(self, message, sender):
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super().receive(message, sender)
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responses = oai.ChatCompletion.create(messages=self._conversations[sender.name], **self._config)
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# cost = oai.ChatCompletion.cost(responses)
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response = oai.ChatCompletion.extract_text(responses)[0]
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self._send(response, sender)
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from flaml import oai
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KEY_LOC = "test/autogen"
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def test_user_proxy_agent():
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try:
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import openai
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except ImportError:
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return
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from flaml.autogen.agent.chat_agent import ChatAgent
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from flaml.autogen.agent.user_proxy_agent import UserProxyAgent
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conversations = {}
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oai.ChatCompletion.start_logging(conversations)
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agent = ChatAgent("chat_agent", config_list=oai.config_list_gpt4_gpt35(key_file_path=KEY_LOC))
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user = UserProxyAgent("human_user", human_input_mode="NEVER", max_consecutive_auto_reply=2)
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agent.receive(
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"""Write python code to solve the equation x^3=125. You must write code in the following format. You must always print the result.
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Wait for me to return the result.
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```python
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# your code
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print(your_result)
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```
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""",
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user,
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)
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print(conversations)
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if __name__ == "__main__":
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test_user_proxy_agent()
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@ -382,8 +382,59 @@ The compact history is more efficient and the individual API call history contai
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[`flaml.autogen.agents`](../reference/autogen/agent/agent) contains an experimental implementation of interactive agents which can adapt to human or simulated feedback. This subpackage is under active development.
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[`flaml.autogen.agents`](../reference/autogen/agent/agent) contains an experimental implementation of interactive agents which can adapt to human or simulated feedback. This subpackage is under active development.
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*Interested in trying it yourself? Please check the following notebook example:*
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We have designed different classes of Agents that are capable of communicating with each other through the exchange of messages to collaboratively finish a task. An agent can communicate with other agents and perform actions. Different agents can differ in what actions they perform in the `receive` method.
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* [Use agents in FLAML to perform tasks with code](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agent_auto_feedback_from_code_execution.ipynb)
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### `AssistantAgent`
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`AssistantAgent` is an Agent class designed to act as an assistant by responding to user requests. It could write Python code (in a Python coding block) for a user to execute when a message (typically a description of a task that needs to be solved) is received. Under the hood, the Python code is written by LLM (e.g., GPT-4).
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### `UserProxyAgent`
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`UserProxyAgent` is an Agent class that serves as a proxy for the human user. Upon receiving a message, the UserProxyAgent will either solicit the human user's input or prepare an automatically generated reply. The chosen action depends on the settings of the `human_input_mode` and `max_consecutive_auto_reply` when the `UserProxyAgent` instance is constructed, and whether a human user input is available.
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Currently, the automatically generated reply is crafted based on automatic code execution. The `UserProxyAgent` triggers code execution automatically when it detects an executable code block in the received message and no human user input is provided. We plan to add more capabilities in `UserProxyAgent` beyond code execution. One can also easily extend it by overriding the `auto_reply` function of the `UserProxyAgent` to add or modify responses to the `AssistantAgent`'s specific type of message. For example, one can easily extend it to execute function calls to external API, which is especially useful with the newly added [function calling capability of OpenAI's Chat Completions API](https://openai.com/blog/function-calling-and-other-api-updates?ref=upstract.com). This auto-reply capability allows for more autonomous user-agent communication while retaining the possibility of human intervention.
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Example usage of the agents to solve a task with code:
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```python
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from flaml.autogen.agent import AssistantAgent, UserProxyAgent
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# create an AssistantAgent instance named "assistant"
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assistant = AssistantAgent(name="assistant")
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# create a UserProxyAgent instance named "user_proxy"
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user_proxy = UserProxyAgent(
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name="user_proxy",
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human_input_mode="NEVER", # in this mode, the agent will never solicit human input but always auto reply
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max_consecutive_auto_reply=10, # the maximum number of consecutive auto replies
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is_termination_msg=lambda x: x.rstrip().endswith("TERMINATE") or x.rstrip().endswith('"TERMINATE".'), # the function to determine whether a message is a termination message
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work_dir=".",
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)
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# the assistant receives a message from the user, which contains the task description
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assistant.receive(
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"""What date is today? Which big tech stock has the largest year-to-date gain this year? How much is the gain?""",
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user_proxy,
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)
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```
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In the example above, we create an AssistantAgent named "assistant" to serve as the assistant and a UserProxyAgent named "user_proxy" to serve as a proxy for the human user.
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1. The assistant receives a message from the user_proxy, which contains the task description.
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2. The assistant then tries to write Python code to solve the task and sends the response to the user_proxy.
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3. Once the user_proxy receives a response from the assistant, it tries to reply by either soliciting human input or preparing an automatically generated reply. In this specific example, since `human_input_mode` is set to `"NEVER"`, the user_proxy will not solicit human input but prepare an automatically generated reply (auto reply). More specifically, the user_proxy executes the code and uses the result as the auto-reply.
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4. The assistant then generates a further response for the user_proxy. The user_proxy can then decide whether to terminate the conversation. If not, steps 3 and 4 are repeated.
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Please find a visual illustration of how UserProxyAgent and AssistantAgent collaboratively solve the above task below:
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Notes:
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- Under the mode `human_input_mode="NEVER"`, the multi-turn conversation between the assistant and the user_proxy stops when the number of auto-reply reaches the upper limit specified by `max_consecutive_auto_reply` or the received message is a termination message according to `is_termination_msg`.
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- When `human_input_mode` is set to `"ALWAYS"`, the user proxy agent solicits human input every time a message is received; and the conversation stops when the human input is "exit", or when the received message is a termination message and no human input is provided.
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- When `human_input_mode` is set to `"TERMINATE"`, the user proxy agent solicits human input only when a termination message is received or the number of auto reply reaches `max_consecutive_auto_reply`.
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*Interested in trying it yourself? Please check the following notebook examples:*
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* [Interactive LLM Agent with Auto Feedback from Code Execution](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agent_auto_feedback_from_code_execution.ipynb)
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* [Interactive LLM Agent with Human Feedback](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agent_human_feedback.ipynb)
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* [Interactive LLM Agent Dealing with Web Info](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agent_web_info.ipynb)
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## Utilities for Applications
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## Utilities for Applications
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website/docs/Use-Cases/images/agent_example.png
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