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RichConsole: Prettify m1 CLI console using rich #4806 (#5123)

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Gerardo Moreno 2025-01-24 09:50:38 -08:00 committed by GitHub
parent 0de4fd83d1
commit 89631966cb
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6 changed files with 252 additions and 8 deletions
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2
python/packages/autogen-ext/pyproject.toml
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@ -105,6 +105,8 @@ semantic-kernel-all = [
"semantic-kernel[google,hugging_face,mistralai,ollama,onnx,anthropic,usearch,pandas,aws,dapr]>=1.17.1",
]
rich = ["rich>=13.9.4"]
[tool.hatch.build.targets.wheel]
packages = ["src/autogen_ext"]

7
python/packages/autogen-ext/src/autogen_ext/ui/__init__.py Normal file
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@ -0,0 +1,7 @@
"""
This module implements utility classes for formatting/printing agent messages.
"""
from ._rich_console import RichConsole
__all__ = ["RichConsole"]

219
python/packages/autogen-ext/src/autogen_ext/ui/_rich_console.py Normal file
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@ -0,0 +1,219 @@
import asyncio
import os
import sys
import time
from typing import (
AsyncGenerator,
Awaitable,
List,
Optional,
Tuple,
TypeVar,
cast,
)
from autogen_agentchat.base import Response, TaskResult
from autogen_agentchat.messages import (
AgentEvent,
ChatMessage,
MultiModalMessage,
UserInputRequestedEvent,
)
from autogen_agentchat.ui._console import UserInputManager
from autogen_core import Image
from autogen_core.models import RequestUsage
from rich.align import AlignMethod
from rich.console import Console
from rich.panel import Panel
AGENT_COLORS = {
"user": "bright_green",
"MagenticOneOrchestrator": "bright_blue",
"WebSurfer": "bright_yellow",
"FileSurfer": "bright_cyan",
"Coder": "bright_magenta",
"Executor": "bright_red",
}
DEFAULT_AGENT_COLOR = "white"
AGENT_ALIGNMENTS: dict[str, AlignMethod] = {"user": "right", "MagenticOneOrchestrator": "center"}
DEFAULT_AGENT_ALIGNMENT: AlignMethod = "left"
def _is_running_in_iterm() -> bool:
return os.getenv("TERM_PROGRAM") == "iTerm.app"
def _is_output_a_tty() -> bool:
return sys.stdout.isatty()
T = TypeVar("T", bound=TaskResult | Response)
def aprint(output: str, end: str = "\n") -> Awaitable[None]:
return asyncio.to_thread(print, output, end=end)
def _extract_message_content(message: AgentEvent | ChatMessage) -> Tuple[List[str], List[Image]]:
if isinstance(message, MultiModalMessage):
text_parts = [item for item in message.content if isinstance(item, str)]
image_parts = [item for item in message.content if isinstance(item, Image)]
else:
text_parts = [str(message.content)]
image_parts = []
return text_parts, image_parts
async def _aprint_panel(console: Console, text: str, title: str) -> None:
color = AGENT_COLORS.get(title, DEFAULT_AGENT_COLOR)
title_align = AGENT_ALIGNMENTS.get(title, DEFAULT_AGENT_ALIGNMENT)
await asyncio.to_thread(
console.print,
Panel(
text,
title=title,
title_align=title_align,
border_style=color,
),
)
async def _aprint_message_content(
console: Console,
text_parts: List[str],
image_parts: List[Image],
source: str,
*,
render_image_iterm: bool = False,
) -> None:
if text_parts:
await _aprint_panel(console, "\n".join(text_parts), source)
for img in image_parts:
if render_image_iterm:
await aprint(_image_to_iterm(img))
else:
await aprint("<image>\n")
async def RichConsole(
stream: AsyncGenerator[AgentEvent | ChatMessage | T, None],
*,
no_inline_images: bool = False,
output_stats: bool = False,
user_input_manager: UserInputManager | None = None,
) -> T:
"""
Consumes the message stream from :meth:`~autogen_agentchat.base.TaskRunner.run_stream`
or :meth:`~autogen_agentchat.base.ChatAgent.on_messages_stream` and renders the messages to the console.
Returns the last processed TaskResult or Response.
.. note::
`output_stats` is experimental and the stats may not be accurate.
It will be improved in future releases.
Args:
stream (AsyncGenerator[AgentEvent | ChatMessage | TaskResult, None] | AsyncGenerator[AgentEvent | ChatMessage | Response, None]): Message stream to render.
This can be from :meth:`~autogen_agentchat.base.TaskRunner.run_stream` or :meth:`~autogen_agentchat.base.ChatAgent.on_messages_stream`.
no_inline_images (bool, optional): If terminal is iTerm2 will render images inline. Use this to disable this behavior. Defaults to False.
output_stats (bool, optional): (Experimental) If True, will output a summary of the messages and inline token usage info. Defaults to False.
Returns:
last_processed: A :class:`~autogen_agentchat.base.TaskResult` if the stream is from :meth:`~autogen_agentchat.base.TaskRunner.run_stream`
or a :class:`~autogen_agentchat.base.Response` if the stream is from :meth:`~autogen_agentchat.base.ChatAgent.on_messages_stream`.
"""
render_image_iterm = _is_running_in_iterm() and _is_output_a_tty() and not no_inline_images
start_time = time.time()
total_usage = RequestUsage(prompt_tokens=0, completion_tokens=0)
rich_console = Console()
last_processed: Optional[T] = None
async for message in stream:
if isinstance(message, TaskResult):
duration = time.time() - start_time
if output_stats:
output = (
f"Number of messages: {len(message.messages)}\n"
f"Finish reason: {message.stop_reason}\n"
f"Total prompt tokens: {total_usage.prompt_tokens}\n"
f"Total completion tokens: {total_usage.completion_tokens}\n"
f"Duration: {duration:.2f} seconds\n"
)
await _aprint_panel(rich_console, output, "Summary")
last_processed = message # type: ignore
elif isinstance(message, Response):
duration = time.time() - start_time
# Print final response.
text_parts, image_parts = _extract_message_content(message.chat_message)
if message.chat_message.models_usage:
if output_stats:
text_parts.append(
f"[Prompt tokens: {message.chat_message.models_usage.prompt_tokens}, Completion tokens: {message.chat_message.models_usage.completion_tokens}]"
)
total_usage.completion_tokens += message.chat_message.models_usage.completion_tokens
total_usage.prompt_tokens += message.chat_message.models_usage.prompt_tokens
await _aprint_message_content(
rich_console,
text_parts,
image_parts,
message.chat_message.source,
render_image_iterm=render_image_iterm,
)
# Print summary.
if output_stats:
num_inner_messages = len(message.inner_messages) if message.inner_messages is not None else 0
output = (
f"Number of inner messages: {num_inner_messages}\n"
f"Total prompt tokens: {total_usage.prompt_tokens}\n"
f"Total completion tokens: {total_usage.completion_tokens}\n"
f"Duration: {duration:.2f} seconds\n"
)
await _aprint_panel(rich_console, output, "Summary")
# mypy ignore
last_processed = message # type: ignore
# We don't want to print UserInputRequestedEvent messages, we just use them to signal the user input event.
elif isinstance(message, UserInputRequestedEvent):
if user_input_manager is not None:
user_input_manager.notify_event_received(message.request_id)
else:
# Cast required for mypy to be happy
message = cast(AgentEvent | ChatMessage, message) # type: ignore
text_parts, image_parts = _extract_message_content(message)
# Add usage stats if needed
if message.models_usage:
if output_stats:
text_parts.append(
f"[Prompt tokens: {message.models_usage.prompt_tokens}, Completion tokens: {message.models_usage.completion_tokens}]"
)
total_usage.completion_tokens += message.models_usage.completion_tokens
total_usage.prompt_tokens += message.models_usage.prompt_tokens
await _aprint_message_content(
rich_console,
text_parts,
image_parts,
message.source,
render_image_iterm=render_image_iterm,
)
if last_processed is None:
raise ValueError("No TaskResult or Response was processed.")
return last_processed
# iTerm2 image rendering protocol: https://iterm2.com/documentation-images.html
def _image_to_iterm(image: Image) -> str:
image_data = image.to_base64()
return f"\033]1337;File=inline=1:{image_data}\a\n"

2
python/packages/magentic-one-cli/pyproject.toml
View File

@ -16,7 +16,7 @@ classifiers = [
]
dependencies = [
"autogen-agentchat>=0.4.2,<0.5",
"autogen-ext[openai,magentic-one]>=0.4.2,<0.5",
"autogen-ext[openai,magentic-one,rich]>=0.4.2,<0.5",
]
[project.scripts]

22
python/packages/magentic-one-cli/src/magentic_one_cli/_m1.py
View File

@ -7,6 +7,7 @@ from autogen_agentchat.ui import Console, UserInputManager
from autogen_core import CancellationToken
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.teams.magentic_one import MagenticOne
from autogen_ext.ui import RichConsole
# Suppress warnings about the requests.Session() not being closed
warnings.filterwarnings(action="ignore", message="unclosed", category=ResourceWarning)
@ -24,16 +25,18 @@ def main() -> None:
Command-line interface for running a complex task using MagenticOne.
This script accepts a single task string and an optional flag to disable
human-in-the-loop mode. It initializes the necessary clients and runs the
task using the MagenticOne class.
human-in-the-loop mode and enable rich console output. It initializes the
necessary clients and runs the task using the MagenticOne class.
Arguments:
task (str): The task to be executed by MagenticOne.
--no-hil: Optional flag to disable human-in-the-loop mode.
--rich: Optional flag to enable rich console output.
Example usage:
python magentic_one_cli.py "example task"
python magentic_one_cli.py --no-hil "example task"
python magentic_one_cli.py --rich "example task"
"""
parser = argparse.ArgumentParser(
description=(
@ -43,16 +46,25 @@ def main() -> None:
)
parser.add_argument("task", type=str, nargs=1, help="The task to be executed by MagenticOne.")
parser.add_argument("--no-hil", action="store_true", help="Disable human-in-the-loop mode.")
parser.add_argument(
"--rich",
action="store_true",
help="Enable rich console output",
)
args = parser.parse_args()
async def run_task(task: str, hil_mode: bool) -> None:
async def run_task(task: str, hil_mode: bool, use_rich_console: bool) -> None:
input_manager = UserInputManager(callback=cancellable_input)
client = OpenAIChatCompletionClient(model="gpt-4o")
m1 = MagenticOne(client=client, hil_mode=hil_mode, input_func=input_manager.get_wrapped_callback())
await Console(m1.run_stream(task=task), output_stats=False, user_input_manager=input_manager)
if use_rich_console:
await RichConsole(m1.run_stream(task=task), output_stats=False, user_input_manager=input_manager)
else:
await Console(m1.run_stream(task=task), output_stats=False, user_input_manager=input_manager)
task = args.task[0]
asyncio.run(run_task(task, not args.no_hil))
asyncio.run(run_task(task, not args.no_hil, args.rich))
if __name__ == "__main__":

8
python/uv.lock
View File

@ -601,6 +601,9 @@ openai = [
redis = [
{ name = "redis" },
]
rich = [
{ name = "rich" },
]
semantic-kernel-all = [
{ name = "semantic-kernel", extra = ["anthropic", "aws", "dapr", "google", "hugging-face", "mistralai", "ollama", "onnx", "pandas", "usearch"] },
]
@ -683,6 +686,7 @@ requires-dist = [
{ name = "playwright", marker = "extra == 'magentic-one'", specifier = ">=1.48.0" },
{ name = "playwright", marker = "extra == 'web-surfer'", specifier = ">=1.48.0" },
{ name = "redis", marker = "extra == 'redis'", specifier = ">=5.2.1" },
{ name = "rich", marker = "extra == 'rich'", specifier = ">=13.9.4" },
{ name = "semantic-kernel", marker = "extra == 'semantic-kernel-core'", specifier = ">=1.17.1" },
{ name = "semantic-kernel", extras = ["anthropic"], marker = "extra == 'semantic-kernel-anthropic'", specifier = ">=1.17.1" },
{ name = "semantic-kernel", extras = ["aws"], marker = "extra == 'semantic-kernel-aws'", specifier = ">=1.17.1" },
@ -3570,13 +3574,13 @@ version = "0.2.1"
source = { editable = "packages/magentic-one-cli" }
dependencies = [
{ name = "autogen-agentchat" },
{ name = "autogen-ext", extra = ["magentic-one", "openai"] },
{ name = "autogen-ext", extra = ["magentic-one", "openai", "rich"] },
]
[package.metadata]
requires-dist = [
{ name = "autogen-agentchat", editable = "packages/autogen-agentchat" },
{ name = "autogen-ext", extras = ["openai", "magentic-one"], editable = "packages/autogen-ext" },
{ name = "autogen-ext", extras = ["openai", "magentic-one", "rich"], editable = "packages/autogen-ext" },
]
[package.metadata.requires-dev]