-
Notifications
You must be signed in to change notification settings - Fork 5.9k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
doc & sample: Update documentation for human-in-the-loop and UserProx…
…yAgent; Add UserProxyAgent to ChainLit sample; (#5656) Resolves #5610 And address various questions regarding to how to use user proxy agent and human-in-the-loop.
- Loading branch information
Showing
5 changed files
with
196 additions
and
14 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
140 changes: 140 additions & 0 deletions
140
python/samples/agentchat_chainlit/app_team_user_proxy.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,140 @@ | ||
| from typing import List, cast | ||
|
|
||
| import chainlit as cl | ||
| import yaml | ||
| from autogen_agentchat.agents import AssistantAgent, UserProxyAgent | ||
| from autogen_agentchat.base import TaskResult | ||
| from autogen_agentchat.conditions import TextMentionTermination | ||
| from autogen_agentchat.messages import ModelClientStreamingChunkEvent, TextMessage | ||
| from autogen_agentchat.teams import RoundRobinGroupChat | ||
| from autogen_core import CancellationToken | ||
| from autogen_core.models import ChatCompletionClient | ||
|
|
||
|
|
||
| async def user_input_func(prompt: str, cancellation_token: CancellationToken | None = None) -> str: | ||
| """Get user input from the UI for the user proxy agent.""" | ||
| try: | ||
| response = await cl.AskUserMessage(content=prompt).send() | ||
| except TimeoutError: | ||
| return "User did not provide any input within the time limit." | ||
| if response: | ||
| return response["output"] # type: ignore | ||
| else: | ||
| return "User did not provide any input." | ||
|
|
||
|
|
||
| async def user_action_func(prompt: str, cancellation_token: CancellationToken | None = None) -> str: | ||
| """Get user action from the UI for the user proxy agent.""" | ||
| try: | ||
| response = await cl.AskActionMessage( | ||
| content="Pick an action", | ||
| actions=[ | ||
| cl.Action(name="approve", label="Approve", payload={"value": "approve"}), | ||
| cl.Action(name="reject", label="Reject", payload={"value": "reject"}), | ||
| ], | ||
| ).send() | ||
| except TimeoutError: | ||
| return "User did not provide any input within the time limit." | ||
| if response and response.get("payload"): # type: ignore | ||
| if response.get("payload").get("value") == "approve": # type: ignore | ||
| return "APPROVE." # This is the termination condition. | ||
| else: | ||
| return "REJECT." | ||
| else: | ||
| return "User did not provide any input." | ||
|
|
||
|
|
||
| @cl.on_chat_start # type: ignore | ||
| async def start_chat() -> None: | ||
| # Load model configuration and create the model client. | ||
| with open("model_config.yaml", "r") as f: | ||
| model_config = yaml.safe_load(f) | ||
| model_client = ChatCompletionClient.load_component(model_config) | ||
|
|
||
| # Create the assistant agent. | ||
| assistant = AssistantAgent( | ||
| name="assistant", | ||
| model_client=model_client, | ||
| system_message="You are a helpful assistant.", | ||
| model_client_stream=True, # Enable model client streaming. | ||
| ) | ||
|
|
||
| # Create the critic agent. | ||
| critic = AssistantAgent( | ||
| name="critic", | ||
| model_client=model_client, | ||
| system_message="You are a critic. Provide constructive feedback. " | ||
| "Respond with 'APPROVE' if your feedback has been addressed.", | ||
| model_client_stream=True, # Enable model client streaming. | ||
| ) | ||
|
|
||
| # Create the user proxy agent. | ||
| user = UserProxyAgent( | ||
| name="user", | ||
| # input_func=user_input_func, # Uncomment this line to use user input as text. | ||
| input_func=user_action_func, # Uncomment this line to use user input as action. | ||
| ) | ||
|
|
||
| # Termination condition. | ||
| termination = TextMentionTermination("APPROVE", sources=["user"]) | ||
|
|
||
| # Chain the assistant, critic and user agents using RoundRobinGroupChat. | ||
| group_chat = RoundRobinGroupChat([assistant, critic, user], termination_condition=termination) | ||
|
|
||
| # Set the assistant agent in the user session. | ||
| cl.user_session.set("prompt_history", "") # type: ignore | ||
| cl.user_session.set("team", group_chat) # type: ignore | ||
|
|
||
|
|
||
| @cl.set_starters # type: ignore | ||
| async def set_starts() -> List[cl.Starter]: | ||
| return [ | ||
| cl.Starter( | ||
| label="Poem Writing", | ||
| message="Write a poem about the ocean.", | ||
| ), | ||
| cl.Starter( | ||
| label="Story Writing", | ||
| message="Write a story about a detective solving a mystery.", | ||
| ), | ||
| cl.Starter( | ||
| label="Write Code", | ||
| message="Write a function that merge two list of numbers into single sorted list.", | ||
| ), | ||
| ] | ||
|
|
||
|
|
||
| @cl.on_message # type: ignore | ||
| async def chat(message: cl.Message) -> None: | ||
| # Get the team from the user session. | ||
| team = cast(RoundRobinGroupChat, cl.user_session.get("team")) # type: ignore | ||
| # Streaming response message. | ||
| streaming_response: cl.Message | None = None | ||
| # Stream the messages from the team. | ||
| async for msg in team.run_stream( | ||
| task=[TextMessage(content=message.content, source="user")], | ||
| cancellation_token=CancellationToken(), | ||
| ): | ||
| if isinstance(msg, ModelClientStreamingChunkEvent): | ||
| # Stream the model client response to the user. | ||
| if streaming_response is None: | ||
| # Start a new streaming response. | ||
| streaming_response = cl.Message(content="", author=msg.source) | ||
| await streaming_response.stream_token(msg.content) | ||
| elif streaming_response is not None: | ||
| # Done streaming the model client response. | ||
| # We can skip the current message as it is just the complete message | ||
| # of the streaming response. | ||
| await streaming_response.send() | ||
| # Reset the streaming response so we won't enter this block again | ||
| # until the next streaming response is complete. | ||
| streaming_response = None | ||
| elif isinstance(msg, TaskResult): | ||
| # Send the task termination message. | ||
| final_message = "Task terminated. " | ||
| if msg.stop_reason: | ||
| final_message += msg.stop_reason | ||
| await cl.Message(content=final_message).send() | ||
| else: | ||
| # Skip all other message types. | ||
| pass |
This file was deleted.
Oops, something went wrong.