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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
View File

@@ -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())

130
python/benchmarks/HumanEval/Templates/TwoAgents/scenario.py
View File

@@ -7,7 +7,11 @@ from typing import Any, Dict, List, Tuple, Union
from agnext.application import SingleThreadedAgentRuntime
from agnext.components import FunctionCall, TypeRoutedAgent, message_handler
from agnext.components.code_executor import CodeBlock, CodeExecutor, LocalCommandLineCodeExecutor
from agnext.components.code_executor import (
CodeBlock,
CodeExecutor,
LocalCommandLineCodeExecutor,
)
from agnext.components.models import (
AssistantMessage,
AzureOpenAIChatCompletionClient,
@@ -23,30 +27,35 @@ from agnext.components.models import (
from agnext.components.tools import CodeExecutionResult, PythonCodeExecutionTool
from agnext.core import AgentId, CancellationToken
#from azure.identity import DefaultAzureCredential, get_bearer_token_provider
# from azure.identity import DefaultAzureCredential, get_bearer_token_provider
@dataclass
class TaskMessage:
content: str
@dataclass
class CodeExecutionRequestMessage:
session_id: str
execution_request: str
@dataclass
class CodeExecutionResultMessage:
session_id: str
output: str
exit_code: int
class Coder(TypeRoutedAgent):
"""An agent that uses tools to write, execute, and debug Python code."""
DEFAULT_DESCRIPTION = "A Python coder assistant."
DEFAULT_SYSTEM_MESSAGES = [
SystemMessage("""You are a helpful AI assistant. Solve tasks using your Python coding skills. The code you output must be formatted in Markdown code blocks demarcated by triple backticks (```). As an example:
SystemMessage(
"""You are a helpful AI assistant. Solve tasks using your Python coding skills. The code you output must be formatted in Markdown code blocks demarcated by triple backticks (```). As an example:
```python
@@ -62,7 +71,8 @@ The user cannot provide any feedback or perform any other action beyond executin
Check the execution result returned by the user. If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes -- code blocks must stand alone and be ready to execute without modification. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, and think of a different approach to try.
If the code has executed successfully, and the problem is stolved, reply "TERMINATE".
""")
"""
)
]
def __init__(
@@ -85,60 +95,105 @@ If the code has executed successfully, and the problem is stolved, reply "TERMIN
"""Handle a user message, execute the model and tools, and returns the response."""
# Create a new session.
session_id = str(uuid.uuid4())
self._session_memory.setdefault(session_id, []).append(UserMessage(content=message.content, source="user"))
self._session_memory.setdefault(session_id, []).append(
UserMessage(content=message.content, source="user")
)
# Make an inference to the model.
response = await self._model_client.create(self._system_messages + self._session_memory[session_id])
response = await self._model_client.create(
self._system_messages + self._session_memory[session_id]
)
assert isinstance(response.content, str)
self._session_memory[session_id].append(AssistantMessage(content=response.content, source=self.metadata["name"]))
self._session_memory[session_id].append(
AssistantMessage(content=response.content, source=self.metadata["name"])
)
await self.publish_message(CodeExecutionRequestMessage(execution_request=response.content, session_id=session_id), cancellation_token=cancellation_token)
await self.publish_message(
CodeExecutionRequestMessage(
execution_request=response.content, session_id=session_id
),
cancellation_token=cancellation_token,
)
@message_handler
async def handle_code_execution_result(self, message: CodeExecutionResultMessage, cancellation_token: CancellationToken) -> None:
async def handle_code_execution_result(
self, message: CodeExecutionResultMessage, cancellation_token: CancellationToken
) -> None:
execution_result = f"The script ran, then exited with Unix exit code: {message.exit_code}\nIts output was:\n{message.output}"
# Store the code execution output.
self._session_memory[message.session_id].append(UserMessage(content=execution_result, source="user"))
self._session_memory[message.session_id].append(
UserMessage(content=execution_result, source="user")
)
# Count the number of rounds so far
if self._max_turns is not None:
n_turns = sum(1 for message in self._session_memory[message.session_id] if isinstance(message, AssistantMessage))
n_turns = sum(
1
for message in self._session_memory[message.session_id]
if isinstance(message, AssistantMessage)
)
if n_turns >= self._max_turns:
return
# Make an inference to the model.
response = await self._model_client.create(self._system_messages + self._session_memory[message.session_id])
response = await self._model_client.create(
self._system_messages + self._session_memory[message.session_id]
)
assert isinstance(response.content, str)
self._session_memory[message.session_id].append(AssistantMessage(content=response.content, source=self.metadata["name"]))
self._session_memory[message.session_id].append(
AssistantMessage(content=response.content, source=self.metadata["name"])
)
if "TERMINATE" in response.content:
return
return
else:
await self.publish_message(CodeExecutionRequestMessage(execution_request=response.content, session_id=message.session_id), cancellation_token=cancellation_token)
await self.publish_message(
CodeExecutionRequestMessage(
execution_request=response.content, session_id=message.session_id
),
cancellation_token=cancellation_token,
)
class Executor(TypeRoutedAgent):
def __init__(self, description: str, executor: CodeExecutor) -> None:
super().__init__(description)
self._executor = executor
@message_handler
async def handle_code_execution(self, message: CodeExecutionRequestMessage, cancellation_token: CancellationToken) -> None:
async def handle_code_execution(
self,
message: CodeExecutionRequestMessage,
cancellation_token: CancellationToken,
) -> None:
# Extract code block from the message.
code = self._extract_execution_request(message.execution_request)
if code is not None:
execution_requests = [CodeBlock(code=code, language="python")]
future = asyncio.get_event_loop().run_in_executor(None, self._executor.execute_code_blocks, execution_requests)
future = asyncio.get_event_loop().run_in_executor(
None, self._executor.execute_code_blocks, execution_requests
)
cancellation_token.link_future(future)
result = await future
await self.publish_message(CodeExecutionResultMessage(output=result.output, exit_code=result.exit_code, session_id=message.session_id))
await self.publish_message(
CodeExecutionResultMessage(
output=result.output,
exit_code=result.exit_code,
session_id=message.session_id,
)
)
else:
await self.publish_message(CodeExecutionResultMessage(output="No code block detected. Please provide a markdown-encoded code block to execute.", exit_code=1, session_id=message.session_id))
await self.publish_message(
CodeExecutionResultMessage(
output="No code block detected. Please provide a markdown-encoded code block to execute.",
exit_code=1,
session_id=message.session_id,
)
)
def _extract_execution_request(self, markdown_text: str) -> Union[str, None]:
pattern = r"```(\w+)\n(.*?)\n```"
@@ -149,18 +204,21 @@ class Executor(TypeRoutedAgent):
return match.group(2)
return None
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.
@@ -170,16 +228,19 @@ async def main() -> None:
)
runtime.register(
"Executor",
lambda: Executor("A agent for executing code", executor=LocalCommandLineCodeExecutor())
lambda: Executor(
"A agent for executing code", executor=LocalCommandLineCodeExecutor()
),
)
prompt = ""
with open("prompt.txt", "rt") as fh:
prompt = fh.read()
entry_point = "__ENTRY_POINT__"
entry_point = "__ENTRY_POINT__"
task = TaskMessage(f"""
task = TaskMessage(
f"""
The following python code imports the `run_tests` function from unit_tests.py, and runs
it on the function `{entry_point}`. This will run a set of automated unit tests to verify the
correct implementation of `{entry_point}`. However, `{entry_point}` is only partially
@@ -196,13 +257,15 @@ from unit_tests import run_tests
# Run the unit tests
run_tests({entry_point})
```
""".strip())
# Send a task to the tool user.
await runtime.send_message(task, coder)
""".strip()
)
# Run the runtime until the task is completed.
await runtime.process_until_idle()
run_context = runtime.start()
# Send a task to the tool user.
await runtime.send_message(task, coder)
await run_context.stop_when_idle()
if __name__ == "__main__":
import logging
@@ -210,4 +273,3 @@ if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logging.getLogger("agnext").setLevel(logging.DEBUG)
asyncio.run(main())