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Numerous fixes for agbench (#170)

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* Shift to new runtime API

* Add pretty printing

* Reformat

* Fix linting errors
This commit is contained in:
gagb
2024-07-02 16:18:48 -07:00
committed by GitHub
parent ca42b560e9
commit bcec0502d7
5 changed files with 177 additions and 50 deletions
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54
python/benchmarks/HumanEval/Templates/TeamOne/scenario.py
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@@ -1,30 +1,36 @@
import asyncio
#from typing import Any, Dict, List, Tuple, Union
import logging
# from typing import Any, Dict, List, Tuple, Union
from agnext.application import SingleThreadedAgentRuntime
from agnext.components.models import (
AzureOpenAIChatCompletionClient,
LLMMessage,
ModelCapabilities,
UserMessage,
)
from agnext.components.code_executor import LocalCommandLineCodeExecutor
from agnext.application.logging import EVENT_LOGGER_NAME
from team_one.agents.coder import Coder, Executor
from team_one.agents.orchestrator import RoundRobinOrchestrator
from team_one.messages import BroadcastMessage
from team_one.messages import BroadcastMessage, OrchestrationEvent
async def main() -> None:
# Create the runtime.
runtime = SingleThreadedAgentRuntime()
# Create the AzureOpenAI client, with AAD auth
#token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
# token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
client = AzureOpenAIChatCompletionClient(
api_version="2024-02-15-preview",
azure_endpoint="https://aif-complex-tasks-west-us-3.openai.azure.com/",
model="gpt-4o-2024-05-13",
model_capabilities=ModelCapabilities(function_calling=True, json_output=True, vision=True),
#azure_ad_token_provider=token_provider
model_capabilities=ModelCapabilities(
function_calling=True, json_output=True, vision=True
),
# azure_ad_token_provider=token_provider
)
# Register agents.
@@ -34,7 +40,9 @@ async def main() -> None:
)
executor = runtime.register_and_get_proxy(
"Executor",
lambda: Executor("A agent for executing code", executor=LocalCommandLineCodeExecutor())
lambda: Executor(
"A agent for executing code", executor=LocalCommandLineCodeExecutor()
),
)
runtime.register("orchestrator", lambda: RoundRobinOrchestrator([coder, executor]))
@@ -43,7 +51,7 @@ async def main() -> None:
with open("prompt.txt", "rt") as fh:
prompt = fh.read()
entry_point = "__ENTRY_POINT__"
entry_point = "__ENTRY_POINT__"
task = f"""
The following python code imports the `run_tests` function from unit_tests.py, and runs
@@ -64,16 +72,32 @@ run_tests({entry_point})
```
""".strip()
run_context = runtime.start()
await runtime.publish_message(BroadcastMessage(content=UserMessage(content=task, source="human")), namespace="default")
await runtime.publish_message(
BroadcastMessage(content=UserMessage(content=task, source="human")),
namespace="default",
)
await run_context.stop_when_idle()
class MyHandler(logging.Handler):
def __init__(self) -> None:
super().__init__()
def emit(self, record: logging.LogRecord) -> None:
try:
if isinstance(record.msg, OrchestrationEvent):
print(f"[{record.msg.timestamp}]: {record.msg.message}", flush=True)
except Exception:
self.handleError(record)
# Run the runtime until the task is completed.
await runtime.process_until_idle()
if __name__ == "__main__":
import logging
logging.basicConfig(level=logging.WARNING)
logging.getLogger("agnext").setLevel(logging.DEBUG)
logger = logging.getLogger(EVENT_LOGGER_NAME)
logger.setLevel(logging.INFO)
my_handler = MyHandler()
logger.handlers = [my_handler]
asyncio.run(main())