From 5cb22d6b50b0a6ade0687b7d48519f0142211b2e Mon Sep 17 00:00:00 2001 From: Onkar Kulkarni Date: Thu, 9 Oct 2025 10:43:02 -0700 Subject: [PATCH 1/4] [synopsys][onkkul] Adding Co-Authors Signed-off-by: Onkar Kulkarni Co-authored-by: Sangharsh Aglave Co-authored-by: Bhuvnesh Kumar Co-authored-by: Pasan Udawatta --- .../frameworks/nat_autogen_demo/README.md | 126 + .../nat_autogen_demo/configs/config.yml | 60 + .../nat_autogen_demo/pyproject.toml | 24 + .../src/nat_autogen_demo/__init__.py | 14 + .../src/nat_autogen_demo/autogen_team.py | 119 + .../src/nat_autogen_demo/register.py | 17 + .../nat_autogen_demo/weather_update_tool.py | 56 + packages/nvidia_nat_autogen/pyproject.toml | 49 + .../nvidia_nat_autogen/src/nat/meta/pypi.md | 23 + .../src/nat/plugins/autogen/__init__.py | 14 + .../autogen/autogen_callback_handler.py | 391 + .../src/nat/plugins/autogen/llm.py | 231 + .../src/nat/plugins/autogen/register.py | 22 + .../src/nat/plugins/autogen/tool_wrapper.py | 167 + packages/nvidia_nat_autogen/tests/__init__.py | 14 + .../tests/test_autogen_callback_handler.py | 614 ++ packages/nvidia_nat_autogen/tests/test_llm.py | 636 ++ .../nvidia_nat_autogen/tests/test_register.py | 63 + .../tests/test_tool_wrapper.py | 296 + packages/nvidia_nat_test/src/nat/test/llm.py | 30 +- pyproject.toml | 5 + src/nat/builder/framework_enum.py | 1 + .../profiler/decorators/framework_wrapper.py | 15 + uv.lock | 7913 +++++++++-------- 24 files changed, 7120 insertions(+), 3780 deletions(-) create mode 100644 examples/frameworks/nat_autogen_demo/README.md create mode 100644 examples/frameworks/nat_autogen_demo/configs/config.yml create mode 100644 examples/frameworks/nat_autogen_demo/pyproject.toml create mode 100644 examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/__init__.py create mode 100644 examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/autogen_team.py create mode 100644 examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/register.py create mode 100644 examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/weather_update_tool.py create mode 100644 packages/nvidia_nat_autogen/pyproject.toml create mode 100644 packages/nvidia_nat_autogen/src/nat/meta/pypi.md create mode 100644 packages/nvidia_nat_autogen/src/nat/plugins/autogen/__init__.py create mode 100644 packages/nvidia_nat_autogen/src/nat/plugins/autogen/autogen_callback_handler.py create mode 100644 packages/nvidia_nat_autogen/src/nat/plugins/autogen/llm.py create mode 100644 packages/nvidia_nat_autogen/src/nat/plugins/autogen/register.py create mode 100644 packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py create mode 100644 packages/nvidia_nat_autogen/tests/__init__.py create mode 100644 packages/nvidia_nat_autogen/tests/test_autogen_callback_handler.py create mode 100644 packages/nvidia_nat_autogen/tests/test_llm.py create mode 100644 packages/nvidia_nat_autogen/tests/test_register.py create mode 100644 packages/nvidia_nat_autogen/tests/test_tool_wrapper.py diff --git a/examples/frameworks/nat_autogen_demo/README.md b/examples/frameworks/nat_autogen_demo/README.md new file mode 100644 index 000000000..d2d88089c --- /dev/null +++ b/examples/frameworks/nat_autogen_demo/README.md @@ -0,0 +1,126 @@ + + + +# AutoGen Framework Example + +A minimal example using Microsoft's AutoGen framework showcasing a multi-agent weather and time information system where agents collaborate through AutoGen's conversation system to provide accurate weather and time data for specified cities. + +## Key Features + +- **AutoGen Framework Integration:** Demonstrates NeMo Agent Toolkit support for Microsoft's AutoGen framework alongside other frameworks like LangChain/LangGraph and Semantic Kernel. +- **Multi-Agent Collaboration:** Shows two specialized agents working together - a WeatherAndTimeAgent for data retrieval and a FinalResponseAgent for response formatting. +- **MCP Server Integration:** Demonstrates how NAT can interact with MCP (Model Context Protocol) servers to provide time information through external services. +- **Tool Integration:** Showcases integration of both local tools (weather updates) and remote tools (MCP time service) within the AutoGen framework. +- **Round-Robin Group Chat:** Uses AutoGen's RoundRobinGroupChat for structured agent communication with termination conditions. + +## Installation and Setup + +If you have not already done so, follow the instructions in the [Install Guide](../../../docs/source/quick-start/installing.md#install-from-source) to create the development environment and install NeMo Agent Toolkit. + +### Install this Workflow + +From the root directory of the NAT library, run the following commands: + +```bash +uv pip install -e examples/getting_started/simple_calculator # required to run the current_datetime MCP tool used in example workflow. +uv pip install -e examples/frameworks/nat_autogen_demo +``` + +### Export required ENV variables +If you have not already done so, follow the [Obtaining API Keys](../../../docs/source/quick-start/installing.md#obtaining-api-keys) instructions to obtain API keys. + +For OpenAI Export these: +- OPENAI_MODEL_NAME +- OPENAI_API_KEY +- OPENAI_API_BASE + +### Run the Workflow + +#### Set up the MCP server +This example demonstrates how NAT can interact with MCP servers on behalf of AutoGen agents. + +First run the MCP server with this command: + +```bash +nat mcp serve --config_file examples/getting_started/simple_calculator/configs/config.yml --tool_names current_datetime +``` + +Then run the workflow with the NAT CLI: + +```bash +nat run --config_file examples/frameworks/nat_autogen_demo/configs/config.yml --input "What is the weather and time in New York today?" +``` + +### Expected output + +```console +2025-10-07 14:34:28,122 - nat.cli.commands.start - INFO - Starting NAT from config file: 'examples/frameworks/nat_autogen_demo/configs/config.yml' +2025-10-07 14:34:30,285 - mcp.client.streamable_http - INFO - Received session ID: 652a05b6646c4ddb945cf2adf0b3ec18 +Received session ID: 652a05b6646c4ddb945cf2adf0b3ec18 +2025-10-07 14:34:30,287 - mcp.client.streamable_http - INFO - Negotiated protocol version: 2025-06-18 +Negotiated protocol version: 2025-06-18 + +Configuration Summary: +-------------------- +Workflow Type: autogen_team +Number of Functions: 1 +Number of Function Groups: 0 +Number of LLMs: 1 +Number of Embedders: 0 +Number of Memory: 0 +Number of Object Stores: 0 +Number of Retrievers: 0 +Number of TTC Strategies: 0 +Number of Authentication Providers: 0 + +2025-10-07 14:34:30,301 - nat.observability.exporter_manager - INFO - Started exporter 'otelcollector' +2025-10-07 14:34:46,704 - nat.front_ends.console.console_front_end_plugin - INFO - +. +. +. + +. +. +. +-------------------------------------------------- +Workflow Result: +['New York weather: Sunny, around 25°C (77°F).\nCurrent local time in New York: 5:34 PM EDT (UTC−4) on October 7, 2025.\n\nAPPROVE'] +-------------------------------------------------- + +-------------------------------------------------- +Workflow Result: +['New York weather: Sunny, around 25°C (77°F).\nCurrent local time in New York: 5:34 PM EDT (UTC−4) on October 7, 2025.\n\nAPPROVE'] +-------------------------------------------------- + +``` + +## Architecture + +The AutoGen workflow consists of two main agents: + +1. **WeatherAndTimeAgent**: Retrieves weather and time information using tools + - Uses the `weather_update_tool` for current weather conditions + - Connects to MCP server for accurate time information + - Responds with "DONE" when task is completed + +2. **FinalResponseAgent**: Formats and presents the final response + - Consolidates information from other agents + - Provides clear, concise answers to user queries + - Terminates the conversation with "APPROVE". + +The agents communicate through AutoGen's RoundRobinGroupChat system, which manages the conversation flow and ensures proper termination when the task is complete. diff --git a/examples/frameworks/nat_autogen_demo/configs/config.yml b/examples/frameworks/nat_autogen_demo/configs/config.yml new file mode 100644 index 000000000..f384a845e --- /dev/null +++ b/examples/frameworks/nat_autogen_demo/configs/config.yml @@ -0,0 +1,60 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +general: + front_end: + _type: console + cors: + allow_origins: ["*"] + telemetry: + logging: + console: + _type: console + level: debug + tracing: + otelcollector: + _type: otelcollector + endpoint: ${OTEL_ENDPOINT:-http://localhost:6006/v1/traces} + project: nat_autogen_demo + +llms: + openai_llm: + _type: openai + model_name: ${OPENAI_MODEL_NAME:-gpt-5} + api_key: ${OPENAI_API_KEY} + base_url: ${OPENAI_API_BASE} + +functions: + weather_update_tool: + _type: nat_autogen_demo/weather_update_autogen + description: "Get the current weather for a specified city" + +workflow: + _type: autogen_team + llm_name: openai_llm + description: "To get the current weather and time in a specific city" + tool_names: [weather_update_tool] + query_processing_agent_name: WeatherAndTimeAgent + query_processing_agent_instructions: | + You are an agent that provides the current weather and time information. + When asked about the weather, provide the current weather conditions. + When asked about the time, provide the current local time. + If asked about anything else, respond with 'I can only provide weather and time information.' + Once you are done, reply with the text 'DONE'. + final_response_agent_name: FinalResponseAgent + final_response_agent_instructions: | + You are the final response agent. + Your role is to provide a concise and clear answer based on the information provided by other agents. + Once you are done, reply with the final answer and then say 'APPROVE'. diff --git a/examples/frameworks/nat_autogen_demo/pyproject.toml b/examples/frameworks/nat_autogen_demo/pyproject.toml new file mode 100644 index 000000000..6296d8f7e --- /dev/null +++ b/examples/frameworks/nat_autogen_demo/pyproject.toml @@ -0,0 +1,24 @@ +[build-system] +build-backend = "setuptools.build_meta" +requires = ["setuptools >= 64", "setuptools-scm>=8"] + +[tool.setuptools_scm] +git_describe_command = "git describe --long --first-parent" +root = "../../.." + +[project] +name = "nat_autogen_demo" +dynamic = ["version"] +dependencies = [ + "nvidia-nat[autogen]~=1.4", +] +requires-python = ">=3.11,<3.14" +description = "AutoGen Workflow Example" +keywords = ["ai", "rag", "agents", "autogen", "multi-agent"] +classifiers = ["Programming Language :: Python"] + +[tool.uv.sources] +nvidia-nat = { path = "../../..", editable = true } + +[project.entry-points.'nat.components'] +nat_autogen_demo = "nat_autogen_demo.register" diff --git a/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/__init__.py b/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/__init__.py new file mode 100644 index 000000000..cf7c586a5 --- /dev/null +++ b/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/autogen_team.py b/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/autogen_team.py new file mode 100644 index 000000000..d17a5168c --- /dev/null +++ b/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/autogen_team.py @@ -0,0 +1,119 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from collections.abc import AsyncGenerator + +from pydantic import Field + +from nat.builder.builder import Builder +from nat.builder.framework_enum import LLMFrameworkEnum +from nat.builder.function_info import FunctionInfo +from nat.cli.register_workflow import register_function +from nat.data_models.component_ref import LLMRef +from nat.data_models.function import FunctionBaseConfig + +logger = logging.getLogger(__name__) + + +class AutoGenFunctionConfig(FunctionBaseConfig, name="autogen_team"): + """Configuration for AutoGen Agent workflow.""" + + llm_name: LLMRef = Field(description="The LLM model to use with AutoGen agents.") + tool_names: list[str] = Field(default_factory=list, description="List of tool names to be used by the agents.") + mcp_server_url: str = Field( + default="http://0.0.0.0:9901/mcp", + description="URL for the MCP time server.", + ) + query_processing_agent_name: str = Field(description="Name of the query processing agent") + query_processing_agent_instructions: str = Field(description="Instructions for the query processing agent") + final_response_agent_name: str = Field(description="Name of the final response agent") + final_response_agent_instructions: str = Field(description="Instructions for the final response agent") + + +@register_function(config_type=AutoGenFunctionConfig, framework_wrappers=[LLMFrameworkEnum.AUTOGEN]) +async def autogen_team(config: AutoGenFunctionConfig, builder: Builder) -> AsyncGenerator[FunctionInfo, None]: + """ + AutoGen multi-agent workflow that demonstrates collaborative agents in a team. + The agents communicate through AutoGen's conversation system to produce output. + + Args: + config (AutoGenFunctionConfig): Configuration for the workflow. + builder (Builder): The NAT workflow builder to access registered components. + + Yields: + AsyncGenerator[FunctionInfo, None]: Yields a FunctionInfo object encapsulating the workflow. + """ + + from autogen_agentchat.agents import AssistantAgent + from autogen_agentchat.conditions import TextMentionTermination + from autogen_agentchat.teams import RoundRobinGroupChat + from autogen_ext.tools.mcp import StreamableHttpMcpToolAdapter + from autogen_ext.tools.mcp import StreamableHttpServerParams + + try: + llm_client = await builder.get_llm(config.llm_name, wrapper_type=LLMFrameworkEnum.AUTOGEN) + tools = await builder.get_tools(config.tool_names, wrapper_type=LLMFrameworkEnum.AUTOGEN) + + time_server_params = StreamableHttpServerParams( + url=config.mcp_server_url, + headers={"Content-Type": "application/json"}, + timeout=30, + ) + adapter = await StreamableHttpMcpToolAdapter.from_server_params(time_server_params, "current_datetime") + tools.append(adapter) + + query_processing_agent = AssistantAgent(name=config.query_processing_agent_name, + model_client=llm_client, + tools=tools, + system_message=config.query_processing_agent_instructions) + final_response_agent = AssistantAgent(name=config.final_response_agent_name, + model_client=llm_client, + system_message=config.final_response_agent_instructions) + + team = RoundRobinGroupChat(participants=[query_processing_agent, final_response_agent], + termination_condition=TextMentionTermination("APPROVE")) + + async def _autogen_team_workflow(user_input: str) -> str: + """Execute the workflow with the given input. + + Args: + user_input (str): User's query + + Returns: + str: The final response generated by the team. + """ + try: + result = await team.run(task=user_input) + + if hasattr(result, 'messages') and result.messages: + return result.messages[-1].content + else: + return "The workflow finished but no output was generated." + + except Exception as e: + logger.exception("Error in AutoGen workflow") + return f"Error occurred during AutoGen workflow: {e!s}" + + # Yield the function info + yield FunctionInfo.create(single_fn=_autogen_team_workflow) + + except GeneratorExit: + logger.info("AutoGen workflow exited early") + except Exception as _e: + logger.exception("Failed to initialize AutoGen workflow") + raise + finally: + logger.debug("AutoGen workflow cleanup completed") diff --git a/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/register.py b/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/register.py new file mode 100644 index 000000000..742c530f8 --- /dev/null +++ b/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/register.py @@ -0,0 +1,17 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from . import autogen_team # noqa: F401 # pylint: disable=W0611 #imported for side effects (registration) +from . import weather_update_tool # noqa: F401 # pylint: disable=W0611 #imported for side effects (registration) diff --git a/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/weather_update_tool.py b/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/weather_update_tool.py new file mode 100644 index 000000000..1680233ea --- /dev/null +++ b/examples/frameworks/nat_autogen_demo/src/nat_autogen_demo/weather_update_tool.py @@ -0,0 +1,56 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Weather update tool file.""" + +from collections.abc import AsyncIterator + +from nat.builder.builder import Builder +from nat.builder.framework_enum import LLMFrameworkEnum +from nat.builder.function_info import FunctionInfo +from nat.cli.register_workflow import register_function +from nat.data_models.function import FunctionBaseConfig + + +class WeatherToolConfig(FunctionBaseConfig, name="weather_update_autogen"): + """Configuration for the weather update tool.""" + + +@register_function(config_type=WeatherToolConfig, framework_wrappers=[LLMFrameworkEnum.AUTOGEN]) +async def weather_update(_config: WeatherToolConfig, _builder: Builder) -> AsyncIterator[FunctionInfo]: + """NAT function that provides weather updates for a specified city. + + Args: + _config (WeatherToolConfig): The configuration for the weather update tool. + _builder (Builder): The NAT builder instance. + + Yields: + AsyncIterator[FunctionInfo]: Yields a FunctionInfo object encapsulating the weather update tool + """ + + async def _weather_update(city: str) -> str: + """ + Get the current weather for a specified city. + + Args: + city (str): The name of the city. + + Returns: + str: The current weather for the specified city. + """ + if "new york" in city.lower(): + return "The weather in New York is sunny with a temperature of 25 degrees Celsius (77 degrees Fahrenheit)." + return f"Weather information for '{city}' is not available." + + yield FunctionInfo.from_fn(_weather_update, description=_weather_update.__doc__) diff --git a/packages/nvidia_nat_autogen/pyproject.toml b/packages/nvidia_nat_autogen/pyproject.toml new file mode 100644 index 000000000..1b1af9e7c --- /dev/null +++ b/packages/nvidia_nat_autogen/pyproject.toml @@ -0,0 +1,49 @@ +[build-system] +build-backend = "setuptools.build_meta" +requires = ["setuptools >= 64", "setuptools-scm>=8"] + + +[tool.setuptools.packages.find] +where = ["src"] +include = ["nat.*"] + + +[tool.setuptools_scm] +git_describe_command = "git describe --long --first-parent" +root = "../.." + + +[project] +name = "nvidia-nat-autogen" +dynamic = ["version"] +dependencies = [ + # Keep package version constraints as open as possible to avoid conflicts with other packages. Always define a minimum + # version when adding a new package. If unsure, default to using `~=` instead of `==`. Does not apply to nvidia-nat packages. + # Keep sorted!!! + "nvidia-nat~=1.4", + "autogen-agentchat~=0.7", + "autogen-core~=0.7", + "autogen-ext~=0.7", +] +requires-python = ">=3.11,<3.14" +readme = "src/nat/meta/pypi.md" +description = "Subpackage for AutoGen integration in NeMo Agent toolkit" +keywords = ["ai", "rag", "agents", "autogen", "multi-agent"] +classifiers = [ + "Programming Language :: Python", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] + + +[tool.uv] +config-settings = { editable_mode = "compat" } + + +[tool.uv.sources] +nvidia-nat = { workspace = true } + + +[project.entry-points.'nat.components'] +nat_autogen = "nat.plugins.autogen.register" diff --git a/packages/nvidia_nat_autogen/src/nat/meta/pypi.md b/packages/nvidia_nat_autogen/src/nat/meta/pypi.md new file mode 100644 index 000000000..2be675932 --- /dev/null +++ b/packages/nvidia_nat_autogen/src/nat/meta/pypi.md @@ -0,0 +1,23 @@ + + +![NVIDIA NeMo Agent Toolkit](https://media.githubusercontent.com/media/NVIDIA/NeMo-Agent-Toolkit/refs/heads/main/docs/source/_static/banner.png "NeMo Agent toolkit banner image") + +# NVIDIA NeMo Agent Toolkit Subpackage +This is a subpackage for [`Microsoft Autogen`](https://github.com/microsoft/autogen) integration in NeMo Agent toolkit. + +For more information about the NVIDIA NeMo Agent toolkit, please visit the [NeMo Agent toolkit GitHub Repo](https://github.com/NVIDIA/NeMo-Agent-Toolkit). diff --git a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/__init__.py b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/__init__.py new file mode 100644 index 000000000..cf7c586a5 --- /dev/null +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/autogen_callback_handler.py b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/autogen_callback_handler.py new file mode 100644 index 000000000..dca94c452 --- /dev/null +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/autogen_callback_handler.py @@ -0,0 +1,391 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""AutoGen callback handler for usage statistics collection. """ + +import copy +import logging +import threading +import time +from collections.abc import Callable +from typing import Any + +from nat.builder.context import Context +from nat.builder.framework_enum import LLMFrameworkEnum +from nat.data_models.intermediate_step import IntermediateStepPayload +from nat.data_models.intermediate_step import IntermediateStepType +from nat.data_models.intermediate_step import StreamEventData +from nat.data_models.intermediate_step import TraceMetadata +from nat.data_models.intermediate_step import UsageInfo +from nat.profiler.callbacks.base_callback_class import BaseProfilerCallback +from nat.profiler.callbacks.token_usage_base_model import TokenUsageBaseModel + +logger = logging.getLogger(__name__) + + +class AutoGenProfilerHandler(BaseProfilerCallback): + """ + A callback manager/handler for AutoGen that intercepts calls to: + - Tools + - LLMs + to collect usage statistics (tokens, inputs, outputs, time intervals, etc.) + and store them in NeMo Agent Toolkit's usage_stats queue for subsequent analysis. + """ + + def __init__(self) -> None: + """ + Initializes the AutogenProfilerHandler. + """ + super().__init__() + self._lock = threading.Lock() + self.last_call_ts = time.time() + self.step_manager = Context.get().intermediate_step_manager + + # Original references to AutoGen Tool and LLM methods (for uninstrumenting if needed) + self._original_tool_call = None + self._original_llm_call = None + self._instrumented = False + + def instrument(self) -> None: + """ + Monkey-patch the relevant AutoGen methods with usage-stat collection logic. + Assumes the 'autogen-core' library is installed. + """ + + if getattr(self, "_instrumented", False): + logger.debug("AutoGenProfilerHandler already instrumented; skipping.") + return + try: + from autogen_core.tools import BaseTool + + # from autogen_ext.tools.mcp import McpWorkbench + from autogen_ext.models.openai import OpenAIChatCompletionClient + except Exception as _e: + logger.exception("AutoGen import failed; skipping instrumentation") + return + + # Save the originals + self._original_tool_call = getattr(BaseTool, "run_json", None) + self._original_llm_call = getattr(OpenAIChatCompletionClient, "create", None) + + if self._original_llm_call: + OpenAIChatCompletionClient.create = self._llm_call_monkey_patch() + + if self._original_tool_call: + BaseTool.run_json = self._tool_call_monkey_patch() + + logger.debug("AutoGenProfilerHandler instrumentation applied successfully.") + self._instrumented = True + + def uninstrument(self) -> None: + """ Restore the original AutoGen methods. + Add an explicit unpatch to avoid side-effects across tests/process lifetime. + """ + try: + if self._original_llm_call: + from autogen_ext.models.openai import OpenAIChatCompletionClient + OpenAIChatCompletionClient.create = self._original_llm_call + if self._original_tool_call: + from autogen_core.tools import BaseTool + BaseTool.run_json = self._original_tool_call + self._instrumented = False + logger.debug("AutoGenProfilerHandler uninstrumented successfully.") + except Exception as _e: + logger.exception("Failed to uninstrument AutoGenProfilerHandler") + + def _llm_call_monkey_patch(self) -> Callable[..., Any]: + """ + Returns a function that wraps calls to ChatCompletionClient.create(...) with usage-logging. + + Returns: + Callable[..., Any]: The wrapped function. + """ + original_func = self._original_llm_call + + async def wrapped_llm_call(*args: Any, **kwargs: Any) -> Any: + """ + Replicates ChatCompletionClient.create(...) logic without wrapt: collects usage stats, + calls the original, and captures output stats. + + Args: + *args (Any): Positional arguments to the LLM call. + **kwargs (Any): Keyword arguments to the LLM call. + + Returns: + Any: The result of the LLM call. + """ + + now = time.time() + with self._lock: + seconds_between_calls = int(now - self.last_call_ts) + + model_name = None + try: + model_name = getattr(args[0], "_raw_config", {}).get("model", None) + except Exception as _e: + logger.exception("Error retrieving model name from args[0]._raw_config") + if not model_name: + model_name = str(getattr(args[0], "model", "unknown_model")) + + model_input = "" + try: + for message in kwargs.get("messages", []): + content = message.get("content", "") + if isinstance(content, list): + for part in content: + if isinstance(part, dict): + model_input += str(part.get("text", "")) # text parts + else: + model_input += str(part) + else: + model_input += content or "" + except Exception as _e: + logger.exception("Error getting model input") + + # Record the start event + input_stats = IntermediateStepPayload( + event_type=IntermediateStepType.LLM_START, + framework=LLMFrameworkEnum.AUTOGEN, + name=model_name, + data=StreamEventData(input=model_input), + metadata=TraceMetadata(chat_inputs=copy.deepcopy(kwargs.get("messages", []))), + usage_info=UsageInfo( + token_usage=TokenUsageBaseModel(), + num_llm_calls=1, + seconds_between_calls=seconds_between_calls, + ), + ) + + llm_start_uuid = input_stats.UUID + + self.step_manager.push_intermediate_step(input_stats) + + # Call the original ChatCompletionClient.create(...) + + try: + output = await original_func(*args, **kwargs) + except Exception as _e: + logger.error("Error during LLM call: %s", _e) + self.step_manager.push_intermediate_step( + IntermediateStepPayload( + event_type=IntermediateStepType.LLM_END, + span_event_timestamp=time.time(), + framework=LLMFrameworkEnum.AUTOGEN, + name=model_name, + data=StreamEventData(input=model_input, output=str(_e)), + metadata=TraceMetadata(error=str(_e)), + usage_info=UsageInfo(token_usage=TokenUsageBaseModel()), + UUID=llm_start_uuid, + )) + with self._lock: + self.last_call_ts = time.time() + raise + + model_output = "" + try: + for content in output.content: + msg = str(content) + model_output += msg or "" + except Exception as _e: + logger.error("Error getting model output") + self.step_manager.push_intermediate_step( + IntermediateStepPayload( + event_type=IntermediateStepType.LLM_END, + span_event_timestamp=time.time(), + framework=LLMFrameworkEnum.AUTOGEN, + name=model_name, + data=StreamEventData(input=model_input, output=str(_e)), + metadata=TraceMetadata(error=str(_e)), + usage_info=UsageInfo(token_usage=TokenUsageBaseModel()), + UUID=llm_start_uuid, + )) + with self._lock: + self.last_call_ts = time.time() + raise + + now = time.time() + # Record the end event + # Prepare safe metadata and usage + chat_resp: dict[str, Any] = {} + try: + if getattr(output, "choices", []): + first_choice = output.choices[0] + chat_resp = first_choice.model_dump() if hasattr( + first_choice, "model_dump") else getattr(first_choice, "__dict__", {}) or {} + except Exception as _e: + logger.error("Error preparing chat_responses") + self.step_manager.push_intermediate_step( + IntermediateStepPayload( + event_type=IntermediateStepType.LLM_END, + span_event_timestamp=time.time(), + framework=LLMFrameworkEnum.AUTOGEN, + name=model_name, + data=StreamEventData(input=model_input, output=str(_e)), + metadata=TraceMetadata(error=str(_e)), + usage_info=UsageInfo(token_usage=TokenUsageBaseModel()), + UUID=llm_start_uuid, + )) + with self._lock: + self.last_call_ts = time.time() + raise + + usage_payload: dict[str, Any] = {} + try: + usage_obj = getattr(output, "usage", None) or (getattr(output, "model_extra", {}) or {}).get("usage") + if usage_obj: + if hasattr(usage_obj, "model_dump"): + usage_payload = usage_obj.model_dump() + elif isinstance(usage_obj, dict): + usage_payload = usage_obj + except Exception as _e: + logger.exception("Error preparing token usage") + + output_stats = IntermediateStepPayload(event_type=IntermediateStepType.LLM_END, + span_event_timestamp=now, + framework=LLMFrameworkEnum.AUTOGEN, + name=model_name, + data=StreamEventData(input=model_input, output=model_output), + metadata=TraceMetadata(chat_responses=chat_resp), + usage_info=UsageInfo( + token_usage=TokenUsageBaseModel(**usage_payload), + num_llm_calls=1, + seconds_between_calls=seconds_between_calls, + ), + UUID=llm_start_uuid) + + self.step_manager.push_intermediate_step(output_stats) + + with self._lock: + self.last_call_ts = now + + return output + + return wrapped_llm_call + + def _tool_call_monkey_patch(self) -> Callable[..., Any]: + """ + Returns a function that wraps calls to BaseTool.run_json(...) with usage-logging. + + Returns: + Callable[..., Any]: The wrapped function. + """ + original_func = self._original_tool_call + + async def wrapped_tool_call(*args: Any, **kwargs: Any) -> Any: + """ + Replicates BaseTool.run_json(...) logic without wrapt: collects usage stats, + calls the original, and captures output stats. + + Args: + *args (Any): Positional arguments to the tool call. + **kwargs (Any): Keyword arguments to the tool call. + + Returns: + Any: The result of the tool call. + """ + + now = time.time() + with self._lock: + seconds_between_calls = int(now - self.last_call_ts) + + tool_name = None + try: + tool_name = str(getattr(args[0], "name", "unknown_tool")) + except Exception as _e: + logger.exception("Error getting tool name") + + # Record the start event + input_stats = IntermediateStepPayload( + event_type=IntermediateStepType.TOOL_START, + framework=LLMFrameworkEnum.AUTOGEN, + name=tool_name, + data=StreamEventData(input={}), + usage_info=UsageInfo( + token_usage=TokenUsageBaseModel(), + num_llm_calls=0, + seconds_between_calls=seconds_between_calls, + ), + ) + + tool_start_uuid = input_stats.UUID + + self.step_manager.push_intermediate_step(input_stats) + + tool_input = "" + try: + tool_input = str(args[1].kwargs) + except (IndexError, AttributeError): + tool_input = str(args[1].get('kwargs', {})) + except Exception as _e: + logger.error("Error getting tool input: %s", _e) + self.step_manager.push_intermediate_step( + IntermediateStepPayload( + event_type=IntermediateStepType.TOOL_END, + span_event_timestamp=time.time(), + framework=LLMFrameworkEnum.AUTOGEN, + name=tool_name, + data=StreamEventData(input=tool_input, output=str(_e)), + metadata=TraceMetadata(error=str(_e)), + usage_info=UsageInfo(token_usage=TokenUsageBaseModel()), + UUID=tool_start_uuid, + )) + with self._lock: + self.last_call_ts = time.time() + raise + + try: + # Call the original BaseTool.run_json(...) + # output = await original_func(*args, **kwargs) + output = await original_func(*args, **kwargs) + except Exception as _e: + logger.error("Tool execution failed with error: %s", _e) + self.step_manager.push_intermediate_step( + IntermediateStepPayload( + event_type=IntermediateStepType.TOOL_END, + span_event_timestamp=time.time(), + framework=LLMFrameworkEnum.AUTOGEN, + name=tool_name, + data=StreamEventData(input=tool_input, output=str(_e)), + metadata=TraceMetadata(error=str(_e)), + usage_info=UsageInfo(token_usage=TokenUsageBaseModel()), + UUID=tool_start_uuid, + )) + with self._lock: + self.last_call_ts = time.time() + raise + + tool_output = output + + now = time.time() + # Record the end event + kwargs_args = (kwargs.get("args", {}) if isinstance(kwargs.get("args"), dict) else {}) + usage_stat = IntermediateStepPayload(event_type=IntermediateStepType.TOOL_END, + span_event_timestamp=now, + framework=LLMFrameworkEnum.AUTOGEN, + name=tool_name, + data=StreamEventData( + input={ + "args": args, "kwargs": dict(kwargs_args) + }, + output=str(tool_output), + ), + metadata=TraceMetadata(tool_outputs={"result": str(tool_output)}), + usage_info=UsageInfo(token_usage=TokenUsageBaseModel()), + UUID=tool_start_uuid) + + self.step_manager.push_intermediate_step(usage_stat) + + return tool_output + + return wrapped_tool_call diff --git a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/llm.py b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/llm.py new file mode 100644 index 000000000..46ba8b29d --- /dev/null +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/llm.py @@ -0,0 +1,231 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""AutoGen LLM client registrations for NAT.""" + +from collections.abc import AsyncGenerator +from typing import Any +from typing import TypeVar + +from nat.builder.builder import Builder +from nat.builder.framework_enum import LLMFrameworkEnum +from nat.cli.register_workflow import register_llm_client +from nat.data_models.llm import LLMBaseConfig +from nat.data_models.retry_mixin import RetryMixin +from nat.data_models.thinking_mixin import ThinkingMixin +from nat.llm.azure_openai_llm import AzureOpenAIModelConfig +from nat.llm.nim_llm import NIMModelConfig +from nat.llm.openai_llm import OpenAIModelConfig +from nat.llm.utils.thinking import BaseThinkingInjector +from nat.llm.utils.thinking import FunctionArgumentWrapper +from nat.llm.utils.thinking import patch_with_thinking +from nat.utils.exception_handlers.automatic_retries import patch_with_retry +from nat.utils.type_utils import override + +ModelType = TypeVar("ModelType") + + +def _patch_autogen_client_based_on_config(client: ModelType, llm_config: LLMBaseConfig) -> ModelType: + """Patch AutoGen client with NAT mixins (retry, thinking). + + Args: + client (ModelType): The AutoGen LLM client to patch. + llm_config (LLMBaseConfig): The LLM configuration containing mixin settings. + + Returns: + ModelType: The patched AutoGen LLM client. + """ + + from autogen_core.models import SystemMessage + + class AutoGenThinkingInjector(BaseThinkingInjector): + """Thinking injector for AutoGen message format. + Injects a system message at the start of the message list. + + Args: + system_prompt: The system prompt to inject. + *args: The rest of the arguments to the function. + **kwargs: The rest of the keyword arguments to the function. + + Returns: + FunctionArgumentWrapper: An object that contains the transformed args and kwargs. + + """ + + @override + def inject(self, messages: list, *args: Any, **kwargs: Any) -> FunctionArgumentWrapper: + """Inject thinking system prompt into AutoGen messages. + Args: + messages (list): List of AutoGen messages (UserMessage, AssistantMessage, SystemMessage + *args (Any): Additional positional arguments + **kwargs (Any): Additional keyword arguments + + Returns: + FunctionArgumentWrapper: Wrapper containing modified args and kwargs + """ + system_message = SystemMessage(content=self.system_prompt) + new_messages = [system_message] + messages + return FunctionArgumentWrapper(new_messages, *args, **kwargs) + + # Apply retry mixin if configured + if isinstance(llm_config, RetryMixin): + client = patch_with_retry(client, + retries=llm_config.num_retries, + retry_codes=llm_config.retry_on_status_codes, + retry_on_messages=llm_config.retry_on_errors) + + # Apply thinking mixin if configured + if isinstance(llm_config, ThinkingMixin) and llm_config.thinking_system_prompt is not None: + client = patch_with_thinking( + client, + AutoGenThinkingInjector(system_prompt=llm_config.thinking_system_prompt, + function_names=[ + "create", + "acreate", + "create_stream", + "acreate_stream", + ])) + + return client + + +@register_llm_client(config_type=OpenAIModelConfig, wrapper_type=LLMFrameworkEnum.AUTOGEN) +async def openai_autogen(llm_config: OpenAIModelConfig, _builder: Builder) -> AsyncGenerator[ModelType, None]: + """Create OpenAI client for AutoGen integration. + + Args: + llm_config (OpenAIModelConfig): OpenAI model configuration + _builder (Builder): NAT builder instance + + Yields: + AsyncGenerator[ModelType, None]: Configured AutoGen OpenAI client + """ + from autogen_core.models import ModelFamily + from autogen_core.models import ModelInfo + from autogen_ext.models.openai import OpenAIChatCompletionClient + + # Extract AutoGen-compatible configuration + config_obj = { + **llm_config.model_dump( + exclude={"type", "model_name", "thinking"}, + by_alias=True, + exclude_none=True, + ), + } + + # Define model info for AutoGen 0.7.4 (replaces model_capabilities) + model_info = ModelInfo(vision=False, + function_calling=True, + json_output=True, + family=ModelFamily.UNKNOWN, + structured_output=True, + multiple_system_messages=True) + + # Add required AutoGen 0.7.4 parameters + config_obj.update({"model_info": model_info}) + + # Create AutoGen OpenAI client + client = OpenAIChatCompletionClient(model=llm_config.model_name, **config_obj) + + # Apply NAT mixins and yield patched client + yield _patch_autogen_client_based_on_config(client, llm_config) + + +@register_llm_client(config_type=AzureOpenAIModelConfig, wrapper_type=LLMFrameworkEnum.AUTOGEN) +async def azure_openai_autogen(llm_config: AzureOpenAIModelConfig, + _builder: Builder) -> AsyncGenerator[ModelType, None]: + """Create Azure OpenAI client for AutoGen integration. + + Args: + llm_config (AzureOpenAIModelConfig): Azure OpenAI model configuration + _builder (Builder): NAT builder instance + + Yields: + AsyncGenerator[ModelType, None]: Configured AutoGen Azure OpenAI client + """ + from autogen_core.models import ModelFamily + from autogen_core.models import ModelInfo + from autogen_ext.models.openai import AzureOpenAIChatCompletionClient + + config_obj = { + "api_key": + llm_config.api_key, + "base_url": + f"{llm_config.azure_endpoint}/openai/deployments/{llm_config.azure_deployment}", + "api_version": + llm_config.api_version, + **llm_config.model_dump( + exclude={"type", "azure_deployment", "thinking", "azure_endpoint", "api_version"}, + by_alias=True, + exclude_none=True, + ), + } + + model_info = ModelInfo(vision=False, + function_calling=True, + json_output=True, + family=ModelFamily.UNKNOWN, + structured_output=True, + multiple_system_messages=True) + + config_obj.update({"model_info": model_info}) + + client = AzureOpenAIChatCompletionClient( + model=llm_config.azure_deployment, # Use deployment name for Azure + **config_obj) + + # Apply NAT mixins and yield patched client + yield _patch_autogen_client_based_on_config(client, llm_config) + + +@register_llm_client(config_type=NIMModelConfig, wrapper_type=LLMFrameworkEnum.AUTOGEN) +async def nim_autogen(llm_config: NIMModelConfig, _builder: Builder) -> AsyncGenerator[ModelType, None]: + """Create NVIDIA NIM client for AutoGen integration. + + Args: + llm_config (NIMModelConfig): NIM model configuration + _builder (Builder): NAT builder instance + + Yields: + Configured AutoGen NIM client (via OpenAI compatibility) + """ + from autogen_core.models import ModelFamily + from autogen_core.models import ModelInfo + from autogen_ext.models.openai import OpenAIChatCompletionClient + + # Extract NIM configuration for OpenAI-compatible client + config_obj = { + **llm_config.model_dump( + exclude={"type", "model_name", "thinking"}, + by_alias=True, + exclude_none=True, + ), + } + + # Define model info for AutoGen 0.7.4 (replaces model_capabilities) + model_info = ModelInfo(vision=False, + function_calling=True, + json_output=True, + family=ModelFamily.UNKNOWN, + structured_output=True, + multiple_system_messages=True) + + # Add required AutoGen 0.7.4 parameters + config_obj.update({"model_info": model_info}) + + # NIM uses OpenAI-compatible API + client = OpenAIChatCompletionClient(model=llm_config.model_name, **config_obj) + + # Apply NAT mixins and yield patched client + yield _patch_autogen_client_based_on_config(client, llm_config) diff --git a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/register.py b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/register.py new file mode 100644 index 000000000..658e40287 --- /dev/null +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/register.py @@ -0,0 +1,22 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# flake8: noqa +# isort:skip_file +"""AutoGen plugin registration for NAT components.""" + +from . import llm +from . import tool_wrapper +from . import autogen_callback_handler diff --git a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py new file mode 100644 index 000000000..57bdf8a30 --- /dev/null +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py @@ -0,0 +1,167 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tool wrapper for AutoGen integration with NAT.""" + +import logging +from collections.abc import AsyncIterator +from collections.abc import Callable +from dataclasses import is_dataclass + +# PythonType not available in AutoGen 0.7.4, using Any instead +from typing import Any + +from autogen_core.tools import FunctionTool +from pydantic.dataclasses import dataclass as pydantic_dataclass + +from nat.builder.builder import Builder +from nat.builder.framework_enum import LLMFrameworkEnum +from nat.builder.function import Function +from nat.cli.register_workflow import register_tool_wrapper +from nat.utils.type_utils import DecomposedType + +logger = logging.getLogger(__name__) + + +def resolve_type(t: Any) -> Any: + """Return the non-None member of a Union/PEP 604 union; + otherwise return the type unchanged. + + Args: + t (Any): The type to resolve. + + Returns: + Any: The resolved type. + """ + resolved = DecomposedType(t) + return resolved.type + + +@register_tool_wrapper(wrapper_type=LLMFrameworkEnum.AUTOGEN) +def autogen_tool_wrapper( + name: str, + fn: Function, + _builder: Builder # pylint: disable=W0613 +) -> Any: # Changed from Callable[..., Any] to Any to allow FunctionTool return + """Wrap a NAT `Function` as an AutoGen `FunctionTool`. + + Args: + name (str): The name of the tool. + fn (Function): The NAT function to wrap. + _builder (Builder): The NAT workflow builder to access registered components. + + Returns: + Any: The AutoGen FunctionTool wrapping the NAT function. + """ + + import inspect + + async def callable_ainvoke(*args: Any, **kwargs: Any) -> Any: + """Async function to invoke the NAT function. + + Args: + *args: Positional arguments to pass to the NAT function. + **kwargs: Keyword arguments to pass to the NAT function. + Returns: + Any: The result of invoking the NAT function. + """ + return await fn.acall_invoke(*args, **kwargs) + + async def callable_astream(*args: Any, **kwargs: Any) -> AsyncIterator[Any]: + """Async generator to stream results from the NAT function. + + Args: + *args (Any): Positional arguments to pass to the NAT function. + **kwargs (Any): Keyword arguments to pass to the NAT function. + Yields: + Any: Streamed items from the NAT function. + """ + async for item in fn.acall_stream(*args, **kwargs): + yield item + + def nat_function( + func: Callable[..., Any] | None = None, + *, + name: str = name, + description: str | None = fn.description, + input_schema: Any = fn.input_schema, + ) -> Callable[..., Any]: + """ + Decorator to wrap a function as a NAT function. + + Args: + func (Callable): The function to wrap. + name (str): The name of the function. + description (str): The description of the function. + input_schema (BaseModel): The Pydantic model defining the input schema. + + Returns: + Callable[..., Any]: The wrapped function. + """ + if func is None: + raise ValueError("'func' must be provided.") + + # If input_schema is a dataclass, convert it to a Pydantic model + if input_schema is not None and is_dataclass(input_schema): + input_schema = pydantic_dataclass(input_schema) + + def decorator(func_to_wrap: Callable[..., Any]) -> Callable[..., Any]: + """ + Decorator to set metadata on the function. + """ + # Set the function's metadata + if name is not None: + func_to_wrap.__name__ = name + if description is not None: + func_to_wrap.__doc__ = description + + # Set signature only if input_schema is provided + params: list[inspect.Parameter] = [] + annotations: dict[str, Any] = {} + + if input_schema is not None: + annotations = {} + params = [] + model_fields = getattr(input_schema, "model_fields", {}) + for param_name, model_field in model_fields.items(): + resolved_type = resolve_type(model_field.annotation) + default = model_field.default if model_field.is_required() is False else inspect._empty + params.append( + inspect.Parameter(param_name, + inspect.Parameter.POSITIONAL_OR_KEYWORD, + annotation=resolved_type, + default=default)) + annotations[param_name] = resolved_type + setattr(func_to_wrap, "__signature__", inspect.Signature(parameters=params)) + setattr(func_to_wrap, "__annotations__", annotations) + + return func_to_wrap + + # If func is None, return the decorator itself to be applied later + if func is None: + return decorator + # Otherwise, apply the decorator to the provided function + return decorator(func) + + if fn.has_streaming_output and not fn.has_single_output: + logger.debug("Creating streaming FunctionTool for: %s", name) + callable_tool = nat_function(func=callable_astream) + else: + logger.debug("Creating non-streaming FunctionTool for: %s", name) + callable_tool = nat_function(func=callable_ainvoke) + return FunctionTool( + func=callable_tool, + name=name, + description=fn.description or "No description provided.", + ) diff --git a/packages/nvidia_nat_autogen/tests/__init__.py b/packages/nvidia_nat_autogen/tests/__init__.py new file mode 100644 index 000000000..cf7c586a5 --- /dev/null +++ b/packages/nvidia_nat_autogen/tests/__init__.py @@ -0,0 +1,14 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/packages/nvidia_nat_autogen/tests/test_autogen_callback_handler.py b/packages/nvidia_nat_autogen/tests/test_autogen_callback_handler.py new file mode 100644 index 000000000..e2fde1cd6 --- /dev/null +++ b/packages/nvidia_nat_autogen/tests/test_autogen_callback_handler.py @@ -0,0 +1,614 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Test AutoGen Callback Handler""" + +import threading +import time +from unittest.mock import AsyncMock +from unittest.mock import Mock +from unittest.mock import PropertyMock +from unittest.mock import patch + +import pytest + +from nat.plugins.autogen.autogen_callback_handler import AutoGenProfilerHandler + + +class TestAutoGenProfilerHandler: + """Test cases for AutoGenProfilerHandler.""" + + def test_init(self): + """Test initialization of AutoGenProfilerHandler.""" + handler = AutoGenProfilerHandler() + assert isinstance(handler._lock, type(threading.Lock())) # pylint: disable=protected-access + assert isinstance(handler.last_call_ts, float) + assert handler._original_tool_call is None # pylint: disable=protected-access + assert handler._original_llm_call is None # pylint: disable=protected-access + assert handler._instrumented is False # pylint: disable=protected-access + + def test_instrument_already_instrumented(self): + """Test instrument when already instrumented.""" + handler = AutoGenProfilerHandler() + handler._instrumented = True # pylint: disable=protected-access + + with patch('nat.plugins.autogen.autogen_callback_handler.logger') as mock_logger: + handler.instrument() + mock_logger.debug.assert_called_with("AutoGenProfilerHandler already instrumented; skipping.") + + def test_instrument_import_failure(self): + """Test instrument when AutoGen imports fail.""" + handler = AutoGenProfilerHandler() + # Force import failure by patching the import to raise an exception + with patch('nat.plugins.autogen.autogen_callback_handler.logger') as mock_logger: + with patch('builtins.__import__', side_effect=ImportError("Mock import error")): + handler.instrument() + mock_logger.exception.assert_called_with("AutoGen import failed; skipping instrumentation") + + def test_uninstrument_failure(self): + """Test uninstrument with exception.""" + handler = AutoGenProfilerHandler() + handler._instrumented = True # pylint: disable=protected-access + handler._original_llm_call = Mock() # pylint: disable=protected-access + + with patch('nat.plugins.autogen.autogen_callback_handler.logger') as mock_logger: + # Force an exception by making the import fail + with patch('builtins.__import__', side_effect=ImportError("Mock import error")): + handler.uninstrument() + mock_logger.exception.assert_called_with("Failed to uninstrument AutoGenProfilerHandler") + + def test_lock_mechanism(self): + """Test that lock mechanism works properly.""" + handler = AutoGenProfilerHandler() + # Test that we can acquire the lock + with handler._lock: # pylint: disable=protected-access + # Lock should be held + pass + + # Lock should be released + assert handler._lock.acquire(blocking=False) # pylint: disable=protected-access + handler._lock.release() # pylint: disable=protected-access + + def test_last_call_timestamp_update(self): + """Test that last call timestamp is updated correctly.""" + handler = AutoGenProfilerHandler() + original_ts = handler.last_call_ts + time.sleep(0.01) # Small delay + + # Simulate updating timestamp + with handler._lock: # pylint: disable=protected-access + handler.last_call_ts = time.time() + + assert handler.last_call_ts > original_ts + + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + def test_step_manager_property(self, mock_get: Mock): + """Test step_manager property access. + + Args: + mock_get (Mock): Mock for Context.get method. + """ + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + assert handler.step_manager == mock_step_manager + + def test_monkey_patch_functions_exist(self): + """Test that monkey patch functions can be created.""" + handler = AutoGenProfilerHandler() + llm_patch = handler._llm_call_monkey_patch() # pylint: disable=protected-access + tool_patch = handler._tool_call_monkey_patch() # pylint: disable=protected-access + + assert callable(llm_patch) + assert callable(tool_patch) + + def test_time_tracking(self): + """Test last call timestamp tracking.""" + handler = AutoGenProfilerHandler() + original_ts = handler.last_call_ts + + # Update timestamp + time.sleep(0.001) # Small delay to ensure timestamp changes + handler.last_call_ts = time.time() + + assert handler.last_call_ts > original_ts + + def test_context_integration(self): + """Test context and step manager integration.""" + with patch('nat.plugins.autogen.autogen_callback_handler.Context.get') as mock_get: + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + assert handler.step_manager == mock_step_manager + + @patch('nat.plugins.autogen.autogen_callback_handler.logger') + def test_successful_instrumentation(self, mock_logger): + """Test successful instrumentation path. + + Args: + mock_logger (Mock): Mock for the logger. + """ + _ = mock_logger # Unused in this test + handler = AutoGenProfilerHandler() + + with patch('builtins.__import__') as mock_import: + # Mock successful imports + mock_autogen_core = Mock() + mock_autogen_ext = Mock() + mock_base_tool = Mock() + mock_client = Mock() + + mock_autogen_core.tools.BaseTool = mock_base_tool + mock_autogen_ext.models.openai.OpenAIChatCompletionClient = mock_client + + def side_effect(name, *args, **kwargs): + if 'autogen_core.tools' in name: + return mock_autogen_core.tools + elif 'autogen_ext.models.openai' in name: + return mock_autogen_ext.models.openai + return Mock() + + mock_import.side_effect = side_effect + + # Mock original methods + mock_base_tool.run_json = Mock() + mock_client.create = Mock() + + handler.instrument() + + # Verify instrumentation succeeded + assert handler._instrumented # pylint: disable=protected-access + assert handler._original_tool_call is not None # pylint: disable=protected-access + assert handler._original_llm_call is not None # pylint: disable=protected-access + + @patch('nat.plugins.autogen.autogen_callback_handler.logger') + def test_successful_uninstrumentation(self, mock_logger): + """Test successful uninstrumentation path. + + Args: + mock_logger (Mock): Mock for the logger. + """ + handler = AutoGenProfilerHandler() + handler._instrumented = True # pylint: disable=protected-access + + # Mock original methods + mock_original_llm = Mock() + mock_original_tool = Mock() + handler._original_llm_call = mock_original_llm # pylint: disable=protected-access + handler._original_tool_call = mock_original_tool # pylint: disable=protected-access + + with patch('builtins.__import__') as mock_import: + # Mock successful imports + mock_autogen_core = Mock() + mock_autogen_ext = Mock() + mock_base_tool = Mock() + mock_client = Mock() + + mock_autogen_core.tools.BaseTool = mock_base_tool + mock_autogen_ext.models.openai.OpenAIChatCompletionClient = mock_client + + def side_effect(name, *args, **kwargs): + if 'autogen_core.tools' in name: + return mock_autogen_core.tools + elif 'autogen_ext.models.openai' in name: + return mock_autogen_ext.models.openai + return Mock() + + mock_import.side_effect = side_effect + + handler.uninstrument() + + # Verify restoration + assert mock_client.create == mock_original_llm + assert mock_base_tool.run_json == mock_original_tool + assert not handler._instrumented # pylint: disable=protected-access + mock_logger.debug.assert_called_with("AutoGenProfilerHandler uninstrumented successfully.") + + +@pytest.mark.asyncio +async def test_integration_flow(): + """Test the complete integration flow.""" + handler = AutoGenProfilerHandler() + + # Test that handler starts uninitialized + assert not handler._instrumented # pylint: disable=protected-access + + # Test instrument/uninstrument cycle + handler.instrument() # Should handle missing imports gracefully + handler.uninstrument() # Should handle gracefully + + +class TestLLMCallMonkeyPatch: + """Test LLM call monkey patch functionality.""" + + def test_llm_call_monkey_patch_creation(self): + """Test that LLM call monkey patch can be created.""" + handler = AutoGenProfilerHandler() + patch_func = handler._llm_call_monkey_patch() # pylint: disable=protected-access + assert callable(patch_func) + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + @patch('nat.plugins.autogen.autogen_callback_handler.logger') + async def test_llm_wrapped_call_basic_flow(self, mock_logger: Mock, mock_get: Mock): + """Test basic LLM wrapped call flow. + + Args: + mock_logger (Mock): Mock for the logger. + mock_get (Mock): Mock for Context.get method. + """ + # Setup mocks + _ = mock_logger # Unused in this test + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function + mock_output = Mock() + mock_output.content = ["test output"] + mock_output.choices = [] # Empty choices to avoid processing + mock_output.usage = None # No usage to avoid type error + mock_output.model_extra = {} # Empty model_extra + original_func = AsyncMock(return_value=mock_output) + handler._original_llm_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._llm_call_monkey_patch() # pylint: disable=protected-access + + # Mock args with proper structure + mock_args = [Mock()] + mock_args[0]._raw_config = {"model": "gpt-4"} + mock_args[0].model = "gpt-4" + + kwargs = {"messages": [{"content": "Hello"}, {"content": ["text part", {"text": "nested text"}]}]} + + # Call wrapped function + await wrapped_func(*mock_args, **kwargs) + + # Verify original function was called + original_func.assert_called_once_with(*mock_args, **kwargs) + + # Verify step manager interactions + assert mock_step_manager.push_intermediate_step.call_count == 2 # Start and end events + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + @patch('nat.plugins.autogen.autogen_callback_handler.logger') + async def test_llm_wrapped_call_with_exception(self, mock_logger: Mock, mock_get: Mock): + """Test LLM wrapped call with exception handling. + + Args: + mock_logger (Mock): Mock for the logger. + mock_get (Mock): Mock for Context.get method. + """ + # Setup mocks + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function that raises exception + original_func = AsyncMock(side_effect=Exception("LLM error")) + handler._original_llm_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._llm_call_monkey_patch() # pylint: disable=protected-access + + # Mock args + mock_args = [Mock()] + mock_args[0]._raw_config = {} + mock_args[0].model = "gpt-4" + kwargs = {"messages": [{"content": "test"}]} + + # Call wrapped function + with pytest.raises(Exception): + await wrapped_func(*mock_args, **kwargs) + + # Verify error handling + mock_logger.error.assert_called() + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + async def test_llm_wrapped_call_model_name_fallback(self, mock_get: Mock): + """Test model name fallback when _raw_config fails. + + Args: + mock_logger (Mock): Mock for the logger. + """ + # Setup mocks + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function + mock_output = Mock() + mock_output.content = ["output"] + mock_output.choices = [] # Empty choices + mock_output.usage = None # No usage + mock_output.model_extra = {} # Empty model_extra + original_func = AsyncMock(return_value=mock_output) + handler._original_llm_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._llm_call_monkey_patch() # pylint: disable=protected-access + + # Mock args without _raw_config but ensure model returns a string + mock_args = [Mock()] + mock_args[0]._raw_config = {} # Empty dict so get() returns None + mock_args[0].model = "fallback-model" + kwargs = {"messages": []} + + # Call wrapped function + await wrapped_func(*mock_args, **kwargs) + + # Verify call completed + original_func.assert_called_once() + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + async def test_llm_wrapped_call_complex_content(self, mock_get: Mock): + """Test LLM wrapped call with complex message content. + + Args: + mock_get (Mock): Mock for Context.get method. + """ + # Setup mocks + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function with complex output + mock_output = Mock() + mock_output.content = ["part1", "part2"] + mock_output.choices = [Mock()] + mock_output.choices[0].model_dump = Mock(return_value={"role": "assistant"}) + mock_output.usage = Mock() + mock_output.usage.model_dump = Mock(return_value={"total_tokens": 50}) + + original_func = AsyncMock(return_value=mock_output) + handler._original_llm_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._llm_call_monkey_patch() # pylint: disable=protected-access + + # Mock args + mock_args = [Mock()] + mock_args[0]._raw_config = {"model": "gpt-4"} + kwargs = {"messages": [{"content": ["text_part", {"text": "nested_text"}, {"type": "image"}]}]} + + # Call wrapped function + result = await wrapped_func(*mock_args, **kwargs) + + # Verify result + assert result == mock_output + assert mock_step_manager.push_intermediate_step.call_count == 2 + + +class TestToolCallMonkeyPatch: + """Test tool call monkey patch functionality.""" + + def test_tool_call_monkey_patch_creation(self): + """Test that tool call monkey patch can be created.""" + handler = AutoGenProfilerHandler() + patch_func = handler._tool_call_monkey_patch() # pylint: disable=protected-access + assert callable(patch_func) + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + async def test_tool_wrapped_call_basic_flow(self, mock_get: Mock): + """Test basic tool wrapped call flow. + + Args: + mock_get (Mock): Mock for Context.get method. + """ + # Setup mocks + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function + original_func = AsyncMock(return_value="tool result") + handler._original_tool_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._tool_call_monkey_patch() # pylint: disable=protected-access + + # Mock args + mock_tool = Mock() + mock_tool.name = "test_tool" + mock_call_data = Mock() + mock_call_data.kwargs = {"param": "value"} + + # Call wrapped function + result = await wrapped_func(mock_tool, mock_call_data) + + # Verify result + assert result == "tool result" + original_func.assert_called_once_with(mock_tool, mock_call_data) + assert mock_step_manager.push_intermediate_step.call_count == 2 + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + @patch('nat.plugins.autogen.autogen_callback_handler.logger') + async def test_tool_wrapped_call_with_exception(self, mock_logger: Mock, mock_get: Mock): + """Test tool wrapped call with exception handling. + + Args: + mock_logger (Mock): Mock for the logger. + mock_get (Mock): Mock for Context.get method. + """ + # Setup mocks + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function that raises exception + original_func = AsyncMock(side_effect=Exception("Tool error")) + handler._original_tool_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._tool_call_monkey_patch() # pylint: disable=protected-access + + # Mock args + mock_tool = Mock() + mock_tool.name = "failing_tool" + mock_call_data = {"kwargs": {"param": "value"}} + + # Call wrapped function + with pytest.raises(Exception) as exc_info: + await wrapped_func(mock_tool, mock_call_data) + + # Verify error handling + assert "Tool error" in str(exc_info.value) + mock_logger.error.assert_called() + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + async def test_tool_wrapped_call_various_input_formats(self, mock_get: Mock): + """Test tool wrapped call with various input formats.""" + # Setup mocks + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function + original_func = AsyncMock(return_value="result") + handler._original_tool_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._tool_call_monkey_patch() # pylint: disable=protected-access + + # Test with dict input format + mock_tool = Mock() + mock_tool.name = "test_tool" + mock_call_data = {"kwargs": {"param": "value"}} + + result = await wrapped_func(mock_tool, mock_call_data) + assert result == "result" + + +class TestErrorHandlingPaths: + """Test error handling in various code paths.""" + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + @patch('nat.plugins.autogen.autogen_callback_handler.logger') + async def test_llm_model_name_error_handling(self, mock_logger: Mock, mock_get: Mock): + """Test error handling when getting model name fails.""" + # Setup mocks + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function + mock_output = Mock() + mock_output.content = ["output"] + mock_output.choices = [] # Empty choices to avoid processing + # Create proper usage mock that has model_dump method + mock_usage = Mock() + mock_usage.model_dump.return_value = { + 'completion_tokens': 100, + 'prompt_tokens': 50, + 'total_tokens': 150, + 'completion_tokens_details': None, + 'prompt_tokens_details': None + } + mock_output.usage = mock_usage + original_func = AsyncMock(return_value=mock_output) + handler._original_llm_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._llm_call_monkey_patch() # pylint: disable=protected-access + + # Mock args that will cause error in model name retrieval + mock_args = [Mock()] + # Make _raw_config access raise exception + type(mock_args[0])._raw_config = PropertyMock(side_effect=Exception("Config error")) + mock_args[0].model = "fallback" + + kwargs = {"messages": []} + + # Call wrapped function + await wrapped_func(*mock_args, **kwargs) + + # Verify exception was logged + mock_logger.exception.assert_called() + + @pytest.mark.asyncio + @patch('nat.plugins.autogen.autogen_callback_handler.Context.get') + @patch('nat.plugins.autogen.autogen_callback_handler.logger') + async def test_llm_input_processing_error(self, mock_logger: Mock, mock_get: Mock): + """Test error handling in input processing.""" + # Setup mocks + mock_context = Mock() + mock_step_manager = Mock() + mock_context.intermediate_step_manager = mock_step_manager + mock_get.return_value = mock_context + + handler = AutoGenProfilerHandler() + + # Mock original function + original_func = AsyncMock(return_value=Mock(content=["output"])) + handler._original_llm_call = original_func # pylint: disable=protected-access + + # Get wrapped function + wrapped_func = handler._tool_call_monkey_patch() # pylint: disable=protected-access + + # Mock args that will cause error in input processing + mock_tool = Mock() + mock_tool.name = "test_tool" + # Create problematic input that will cause exception + problematic_input = Mock() + type(problematic_input).kwargs = PropertyMock(side_effect=Exception("Input error")) + + # Call wrapped function and expect exception + with pytest.raises(Exception) as exc_info: + await wrapped_func(mock_tool, problematic_input) + + # Verify error was logged and exception message is correct + mock_logger.error.assert_called() + assert "Input error" in str(exc_info.value) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/packages/nvidia_nat_autogen/tests/test_llm.py b/packages/nvidia_nat_autogen/tests/test_llm.py new file mode 100644 index 000000000..6a0cedee2 --- /dev/null +++ b/packages/nvidia_nat_autogen/tests/test_llm.py @@ -0,0 +1,636 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Test LLM for Autogen""" + +from typing import Any +from unittest.mock import Mock +from unittest.mock import patch + +import pytest + +from nat.builder.builder import Builder +from nat.data_models.llm import LLMBaseConfig +from nat.data_models.retry_mixin import RetryMixin +from nat.data_models.thinking_mixin import ThinkingMixin +from nat.llm.azure_openai_llm import AzureOpenAIModelConfig +from nat.llm.nim_llm import NIMModelConfig +from nat.llm.openai_llm import OpenAIModelConfig +from nat.plugins.autogen.llm import _patch_autogen_client_based_on_config + + +class MockRetryConfig(LLMBaseConfig, RetryMixin): + """Mock config with retry mixin.""" + + num_retries: int = 3 + retry_on_status_codes: list = [500, 502, 503] + retry_on_errors: list[Any] | None = ["timeout"] + + +class MockThinkingConfig(LLMBaseConfig, ThinkingMixin): + """Mock config with thinking mixin.""" + + thinking_system_prompt: str = "Think step by step" + + +class MockCombinedConfig(LLMBaseConfig, RetryMixin, ThinkingMixin): + """Mock config with both mixins.""" + + num_retries: int = 3 + retry_on_status_codes: list = [500, 502, 503] + retry_on_errors: list[Any] | None = ["timeout"] + thinking_system_prompt: str = "Think step by step" + + +class TestPatchAutoGenClient: + """Test cases for _patch_autogen_client_based_on_config function.""" + + def test_patch_with_no_mixins(self): + """Test patching client with no mixins.""" + mock_client = Mock() + base_config = LLMBaseConfig() + + result = _patch_autogen_client_based_on_config(mock_client, base_config) + assert result == mock_client + + @patch('nat.plugins.autogen.llm.patch_with_retry') + def test_patch_with_retry_mixin(self, mock_patch_retry): + """Test patching client with retry mixin.""" + mock_client = Mock() + mock_patched_client = Mock() + mock_patch_retry.return_value = mock_patched_client + + retry_config = MockRetryConfig() + retry_config.num_retries = 5 + retry_config.retry_on_status_codes = [500, 503] + retry_config.retry_on_errors = ["timeout", "connection"] + + result = _patch_autogen_client_based_on_config(mock_client, retry_config) + + mock_patch_retry.assert_called_once_with(mock_client, + retries=5, + retry_codes=[500, 503], + retry_on_messages=["timeout", "connection"]) + assert result == mock_patched_client + + @patch('nat.plugins.autogen.llm.patch_with_thinking') + def test_patch_with_thinking_mixin(self, mock_patch_thinking): + """Test patching client with thinking mixin.""" + mock_client = Mock() + mock_patched_client = Mock() + mock_patch_thinking.return_value = mock_patched_client + + # Create a mock thinking config that has a non-None thinking_system_prompt + thinking_config = Mock(spec=MockThinkingConfig) + thinking_config.thinking_system_prompt = "Think step by step" + + result = _patch_autogen_client_based_on_config(mock_client, thinking_config) + + mock_patch_thinking.assert_called_once() + args, _kwargs = mock_patch_thinking.call_args + assert args[0] == mock_client + assert result == mock_patched_client + + @patch('nat.plugins.autogen.llm.patch_with_retry') + @patch('nat.plugins.autogen.llm.patch_with_thinking') + def test_patch_with_both_mixins(self, mock_patch_thinking, mock_patch_retry): + """Test patching client with both retry and thinking mixins.""" + mock_client = Mock() + mock_retry_client = Mock() + mock_final_client = Mock() + mock_patch_retry.return_value = mock_retry_client + mock_patch_thinking.return_value = mock_final_client + + class CombinedConfig(MockRetryConfig, MockThinkingConfig): + """Combined config for testing.""" + pass + + config = Mock(spec=CombinedConfig) + config.thinking_system_prompt = "Think carefully" + config.num_retries = 3 + config.retry_on_status_codes = [500, 502] + config.retry_on_errors = ["timeout"] + + result = _patch_autogen_client_based_on_config(mock_client, config) + + # Should apply retry first, then thinking + mock_patch_retry.assert_called_once() + mock_patch_thinking.assert_called_once() + assert result == mock_final_client + + +class TestConfigValidation: + """Test configuration validation and model creation.""" + + def test_openai_config_creation(self): + """Test OpenAI model config creation.""" + config = OpenAIModelConfig(model_name="gpt-4", api_key="test-key", base_url="https://api.openai.com/v1") + assert config.model_name == "gpt-4" + assert config.api_key == "test-key" + assert config.base_url == "https://api.openai.com/v1" + + def test_azure_config_creation(self): + """Test Azure OpenAI model config creation.""" + config = AzureOpenAIModelConfig(azure_deployment="test-deployment", + azure_endpoint="https://test.openai.azure.com/", + api_key="test-key", + api_version="2023-12-01-preview") + assert config.azure_deployment == "test-deployment" + assert config.azure_endpoint == "https://test.openai.azure.com/" + assert config.api_key == "test-key" + assert config.api_version == "2023-12-01-preview" + + def test_nim_config_creation(self): + """Test NIM model config creation.""" + config = NIMModelConfig(model_name="llama-3.1-70b", + base_url="https://nim.api.nvidia.com/v1", + api_key="test-key") + assert config.model_name == "llama-3.1-70b" + assert config.base_url == "https://nim.api.nvidia.com/v1" + assert config.api_key == "test-key" + + +class TestAutoGenIntegration: + """Test AutoGen integration patterns.""" + + def test_client_instantiation_pattern(self): + """Test the general pattern of client instantiation.""" + # Test that we can create basic configurations without errors + config = OpenAIModelConfig(api_key="test-key", model_name="gpt-4") + assert config.api_key == "test-key" + assert config.model_name == "gpt-4" + + def test_model_info_requirements(self): + """Test basic model info requirements.""" + # Test configuration validation + config = AzureOpenAIModelConfig(azure_deployment="gpt-4", + api_key="test-key", + azure_endpoint="https://test.openai.azure.com", + api_version="2024-02-01") + assert config.azure_deployment == "gpt-4" + assert config.api_key == "test-key" + + +class TestThinkingInjector: + """Test thinking injector functionality.""" + + def test_thinking_injector_creation(self): + """Test that thinking injector can be created.""" + # Test the integration pattern for thinking injection + mock_client = Mock() + thinking_config = Mock(spec=MockThinkingConfig) + thinking_config.thinking_system_prompt = "Think carefully" + + with patch('nat.plugins.autogen.llm.patch_with_thinking') as mock_patch: + _patch_autogen_client_based_on_config(mock_client, thinking_config) + mock_patch.assert_called_once() + + # Verify the injector is passed correctly + args, _kwargs = mock_patch.call_args + assert args[0] == mock_client + assert args[1] is not None # AutoGenThinkingInjector instance + + +class TestLLMClientFunctions: + """Test LLM client creation functions.""" + + @pytest.mark.asyncio + @patch('builtins.__import__') + async def test_openai_autogen_generator(self, mock_import): + """Test OpenAI client async generator.""" + from nat.plugins.autogen.llm import openai_autogen + + # Mock the AutoGen imports + mock_client = Mock() + mock_model_info = Mock() + + def import_side_effect(name, *_args, **_kwargs) -> Mock: + """Side effect function to mock imports. + + Args: + name (str): The name of the module being imported. + *_args: Additional positional arguments. + **_kwargs: Additional keyword arguments. + + Returns: + Mock: A mock module or object based on the import name. + """ + _, _ = _args, _kwargs # Unused + if 'autogen_ext.models.openai' in name: + mock_module = Mock() + mock_module.OpenAIChatCompletionClient = Mock(return_value=mock_client) + return mock_module + elif 'autogen_core.models' in name: + mock_module = Mock() + mock_module.ModelInfo = Mock(return_value=mock_model_info) + return mock_module + return Mock() + + mock_import.side_effect = import_side_effect + + config = OpenAIModelConfig(api_key="test-key", model_name="gpt-4") + mock_builder = Mock() + + # Test the async context manager + gen = openai_autogen(config, mock_builder) + client = await gen.__anext__() + + assert client is not None + + @pytest.mark.asyncio + @patch('builtins.__import__') + async def test_azure_openai_autogen_generator(self, mock_import): + """Test Azure OpenAI client async generator.""" + from nat.plugins.autogen.llm import azure_openai_autogen + + # Mock the AutoGen imports + mock_client = Mock() + mock_model_info = Mock() + + def import_side_effect(name, *_args, **_kwargs) -> Mock: + """Side effect function to mock imports. + + Args: + name (str): The name of the module being imported. + *_args: Additional positional arguments. + **_kwargs: Additional keyword arguments. + + Returns: + Mock: A mock module or object based on the import name. + """ + if 'autogen_ext.models.openai' in name: + mock_module = Mock() + mock_module.AzureOpenAIChatCompletionClient = Mock(return_value=mock_client) + return mock_module + elif 'autogen_core.models' in name: + mock_module = Mock() + mock_module.ModelInfo = Mock(return_value=mock_model_info) + return mock_module + return Mock() + + mock_import.side_effect = import_side_effect + + config = AzureOpenAIModelConfig(azure_deployment="gpt-4", + api_key="test-key", + azure_endpoint="https://test.openai.azure.com", + api_version="2024-02-01") + mock_builder = Mock() + + # Test the async generator + gen = azure_openai_autogen(config, mock_builder) + client = await gen.__anext__() + + assert client is not None + + @pytest.mark.asyncio + @patch('builtins.__import__') + async def test_nim_autogen_generator(self, mock_import): + """Test NIM client async generator.""" + from nat.plugins.autogen.llm import nim_autogen + + # Mock the AutoGen imports + mock_client = Mock() + mock_model_info = Mock() + + def import_side_effect(name, *args: Any, **kwargs: Any) -> Mock: + """Side effect function to mock imports. + + Args: + name (str): The name of the module being imported. + *args (Any): Additional positional arguments. + **kwargs (Any): Additional keyword arguments. + + Returns: + Mock: A mock module or object based on the import name. + """ + if 'autogen_ext.models.openai' in name: + mock_module = Mock() + mock_module.OpenAIChatCompletionClient = Mock(return_value=mock_client) + return mock_module + elif 'autogen_core.models' in name: + mock_module = Mock() + mock_module.ModelInfo = Mock(return_value=mock_model_info) + return mock_module + return Mock() + + mock_import.side_effect = import_side_effect + + config = NIMModelConfig(base_url="https://nim.api.nvidia.com/v1", api_key="test-key", model_name="test-model") + mock_builder = Mock() + + # Test the async generator + gen = nim_autogen(config, mock_builder) + client = await gen.__anext__() + + assert client is not None + + +class TestAutoGenThinkingInjector: + """Test AutoGenThinkingInjector functionality.""" + + def test_thinking_injector_inject(self): + """Test thinking injector message injection.""" + # Since AutoGenThinkingInjector is defined inside the function, + # we test through the integration pattern + mock_client = Mock() + thinking_config = Mock(spec=MockThinkingConfig) + thinking_config.thinking_system_prompt = "Think carefully" + + with patch('nat.plugins.autogen.llm.patch_with_thinking') as mock_patch: + _patch_autogen_client_based_on_config(mock_client, thinking_config) + + # Verify patch_with_thinking was called with injector + mock_patch.assert_called_once() + args, _kwargs = mock_patch.call_args + assert args[0] == mock_client + + # The second argument should be an injector instance + injector = args[1] + assert injector is not None + assert hasattr(injector, 'inject') + + +class TestLLMClientGeneratorsFull: + """Test complete LLM client generator flows.""" + + @pytest.mark.asyncio + @patch('builtins.__import__') + async def test_openai_autogen_complete_flow(self, mock_import): + """Test complete OpenAI client creation with all configurations.""" + # Mock AutoGen imports + mock_autogen_core = Mock() + mock_autogen_ext = Mock() + mock_model_info_class = Mock() + mock_client_class = Mock() + + mock_autogen_core.models.ModelFamily.UNKNOWN = "UNKNOWN" + mock_autogen_core.models.ModelInfo = mock_model_info_class + mock_autogen_ext.models.openai.OpenAIChatCompletionClient = mock_client_class + + def side_effect(name, *_args, **_kwargs) -> Mock: + """Side effect function to mock imports. + + Args: + name (str): The name of the module being imported. + *_args: Additional positional arguments. + **_kwargs: Additional keyword arguments. + + Returns: + Mock: A mock module or object based on the import name. + """ + if name == 'autogen_core.models': + return mock_autogen_core + elif name == 'autogen_ext.models.openai': + return mock_autogen_ext + return Mock() + + mock_import.side_effect = side_effect + + from nat.plugins.autogen.llm import openai_autogen + + # Create comprehensive config + config = OpenAIModelConfig(api_key="test-api-key", + model_name="gpt-4-turbo", + base_url="https://api.openai.com/v1", + temperature=0.7) + builder = Mock(spec=Builder) + + # Mock client and ModelInfo creation + mock_client = Mock() + mock_model_info = Mock() + mock_client_class.return_value = mock_client + mock_model_info_class.return_value = mock_model_info + + # Test the generator + with patch('nat.plugins.autogen.llm._patch_autogen_client_based_on_config') as mock_patch: + mock_patch.return_value = mock_client + + # Test that we can call the function without errors + generator = openai_autogen(config, builder) + assert generator is not None + + # Verify patching function exists + assert mock_patch is not None + + @pytest.mark.asyncio + @patch('builtins.__import__') + async def test_azure_openai_config_building(self, mock_import): + """Test Azure OpenAI configuration building.""" + # Mock AutoGen imports + mock_autogen_core = Mock() + mock_autogen_ext = Mock() + mock_client_class = Mock() + + mock_autogen_core.models.ModelFamily.UNKNOWN = "UNKNOWN" + mock_autogen_core.models.ModelInfo = Mock() + mock_autogen_ext.models.openai.OpenAIChatCompletionClient = mock_client_class + + def side_effect(name, *_args, **_kwargs) -> Mock: + """Side effect function to mock imports. + + Args: + name (str): The name of the module being imported. + *_args: Additional positional arguments. + **_kwargs: Additional keyword arguments. + + Returns: + Mock: A mock module or object based on the import name. + """ + if name == 'autogen_core.models': + return mock_autogen_core + elif name == 'autogen_ext.models.openai': + return mock_autogen_ext + return Mock() + + mock_import.side_effect = side_effect + + from nat.plugins.autogen.llm import azure_openai_autogen + + # Create Azure config + config = AzureOpenAIModelConfig(api_key="azure-test-key", + azure_deployment="gpt-4-deployment", + azure_endpoint="https://test.openai.azure.com", + api_version="2024-02-01") + builder = Mock(spec=Builder) + + # Mock client creation + mock_client = Mock() + mock_client_class.return_value = mock_client + + # Test the generator + generator = azure_openai_autogen(config, builder) + with patch('nat.plugins.autogen.llm._patch_autogen_client_based_on_config') as mock_patch: + mock_patch.return_value = mock_client + + # Test that we can call the function without errors + assert generator is not None + + @pytest.mark.asyncio + @patch('builtins.__import__') + async def test_nim_autogen_config_handling(self, mock_import): + """Test NIM configuration handling.""" + # Mock AutoGen imports + mock_autogen_core = Mock() + mock_autogen_ext = Mock() + mock_client_class = Mock() + + mock_autogen_core.models.ModelFamily.UNKNOWN = "UNKNOWN" + mock_autogen_core.models.ModelInfo = Mock() + mock_autogen_ext.models.openai.OpenAIChatCompletionClient = mock_client_class + + def side_effect(name, *_args, **_kwargs) -> Mock: + """Side effect function to mock imports. + + Args: + name (str): The name of the module being imported. + *_args: Additional positional arguments. + **_kwargs: Additional keyword arguments. + + Returns: + Mock: A mock module or object based on the import name. + """ + if name == 'autogen_core.models': + return mock_autogen_core + elif name == 'autogen_ext.models.openai': + return mock_autogen_ext + return Mock() + + mock_import.side_effect = side_effect + + from nat.plugins.autogen.llm import nim_autogen + + # Create NIM config + config = NIMModelConfig(api_key="nim-test-key", + model_name="llama-3.1-70b-instruct", + base_url="https://integrate.api.nvidia.com/v1") + builder = Mock(spec=Builder) + + # Mock client creation + mock_client = Mock() + mock_client_class.return_value = mock_client + + # Test the generator + generator = nim_autogen(config, builder) + with patch('nat.plugins.autogen.llm._patch_autogen_client_based_on_config') as mock_patch: + mock_patch.return_value = mock_client + + # Test that we can call the function without errors + assert generator is not None + + +class TestMixinCombinations: + """Test various mixin combinations and edge cases.""" + + def test_retry_mixin_only(self): + """Test patching with only retry mixin.""" + mock_client = Mock() + + class RetryOnlyConfig(LLMBaseConfig, RetryMixin): + """Config with only retry mixin.""" + pass + + config = RetryOnlyConfig() + config.num_retries = 5 + config.retry_on_status_codes = [500, 502, 503, 504] + config.retry_on_errors = ["timeout", "connection_error"] + + with patch('nat.plugins.autogen.llm.patch_with_retry') as mock_patch_retry: + with patch('nat.plugins.autogen.llm.patch_with_thinking') as mock_patch_thinking: + mock_patch_retry.return_value = mock_client + + result = _patch_autogen_client_based_on_config(mock_client, config) + + # Only retry should be applied + mock_patch_retry.assert_called_once_with(mock_client, + retries=5, + retry_codes=[500, 502, 503, 504], + retry_on_messages=["timeout", "connection_error"]) + mock_patch_thinking.assert_not_called() + assert result == mock_client + + def test_thinking_mixin_only(self): + """Test patching with only thinking mixin.""" + mock_client = Mock() + + # Create a mock config that mimics ThinkingMixin behavior + config = Mock(spec=ThinkingMixin) + config.thinking_system_prompt = "Analyze this step by step" + + with patch('nat.plugins.autogen.llm.patch_with_retry') as mock_patch_retry: + with patch('nat.plugins.autogen.llm.patch_with_thinking') as mock_patch_thinking: + mock_patch_thinking.return_value = mock_client + + result = _patch_autogen_client_based_on_config(mock_client, config) + + # Only thinking should be applied + mock_patch_retry.assert_not_called() + mock_patch_thinking.assert_called_once() + assert result == mock_client + + def test_thinking_with_none_prompt_skipped(self): + """Test that thinking mixin with None prompt is skipped.""" + mock_client = Mock() + + # Create a mock config with None prompt + config = Mock(spec=ThinkingMixin) + config.thinking_system_prompt = None + + with patch('nat.plugins.autogen.llm.patch_with_thinking') as mock_patch_thinking: + result = _patch_autogen_client_based_on_config(mock_client, config) + + # Thinking should not be applied when prompt is None + mock_patch_thinking.assert_not_called() + assert result == mock_client + + +class TestAutoGenThinkingInjectorDetails: + """Test AutoGenThinkingInjector internal behavior.""" + + @patch('nat.plugins.autogen.llm.patch_with_thinking') + def test_thinking_injector_creation_and_usage(self, mock_patch_thinking): + """Test thinking injector creation without complex mocking.""" + mock_client = Mock() + + # Create a real config with thinking functionality + # Use OpenAIModelConfig which has all the necessary fields + config = OpenAIModelConfig( + base_url="https://example.com", + api_key="test-key", + model_name="nvidia/nvidia-nemotron-test", # Use a model that matches pattern + thinking=True # Enable thinking + ) + + # Verify our config is indeed an instance of ThinkingMixin + assert isinstance(config, ThinkingMixin), f"Config type: {type(config)}, MRO: {type(config).__mro__}" + assert config.thinking_system_prompt is not None, f"Thinking prompt: {config.thinking_system_prompt}" + + _patch_autogen_client_based_on_config(mock_client, config) + + # Verify patch_with_thinking was called + mock_patch_thinking.assert_called_once() + + # Extract the injector that was passed + call_args = mock_patch_thinking.call_args + injector = call_args[0][1] # Second argument to patch_with_thinking + + # Verify injector has correct system prompt (based on model pattern) + assert injector.system_prompt == "/think" + + # Verify function names are correctly configured + expected_function_names = ["create", "acreate", "create_stream", "acreate_stream"] + assert injector.function_names == expected_function_names + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/packages/nvidia_nat_autogen/tests/test_register.py b/packages/nvidia_nat_autogen/tests/test_register.py new file mode 100644 index 000000000..730e3f4a1 --- /dev/null +++ b/packages/nvidia_nat_autogen/tests/test_register.py @@ -0,0 +1,63 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Test register.py file""" + +import pytest + + +class TestRegisterModule: + """Test cases for register module.""" + + def test_imports_exist(self): + """Test that required imports exist and work.""" + try: + from nat.plugins.autogen import llm # pylint: disable=import-outside-toplevel + from nat.plugins.autogen import tool_wrapper # pylint: disable=import-outside-toplevel + assert llm is not None + assert tool_wrapper is not None + except ImportError as e: + pytest.fail(f"Failed to import required modules: {e}") + + def test_register_module_importable(self): + """Test that the register module can be imported.""" + try: + from nat.plugins.autogen import register # pylint: disable=import-outside-toplevel + assert register is not None + except ImportError as e: + pytest.fail(f"Failed to import register module: {e}") + + def test_llm_module_functions(self): + """Test that LLM module has expected functions.""" + from nat.plugins.autogen import llm # pylint: disable=import-outside-toplevel + + # Check for expected functions + expected_functions = ['openai_autogen', 'azure_openai_autogen', 'nim_autogen'] + + for func_name in expected_functions: + assert hasattr(llm, func_name), f"Function {func_name} not found in llm module" + + def test_tool_wrapper_module_functions(self): + """Test that tool_wrapper module has expected functions.""" + from nat.plugins.autogen import tool_wrapper # pylint: disable=import-outside-toplevel + + # Check for expected functions + expected_functions = ['resolve_type', 'autogen_tool_wrapper'] + + for func_name in expected_functions: + assert hasattr(tool_wrapper, func_name), f"Function {func_name} not found in tool_wrapper module" + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/packages/nvidia_nat_autogen/tests/test_tool_wrapper.py b/packages/nvidia_nat_autogen/tests/test_tool_wrapper.py new file mode 100644 index 000000000..c4c043693 --- /dev/null +++ b/packages/nvidia_nat_autogen/tests/test_tool_wrapper.py @@ -0,0 +1,296 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Test tool_wrapper.py file """ + +import inspect +import types +from dataclasses import dataclass +from unittest.mock import AsyncMock +from unittest.mock import Mock +from unittest.mock import patch + +import pytest +from pydantic import BaseModel + +from nat.builder.builder import Builder +from nat.builder.function import Function +from nat.plugins.autogen.tool_wrapper import autogen_tool_wrapper +from nat.plugins.autogen.tool_wrapper import resolve_type + + +class MockInputSchema(BaseModel): + """Mock input schema for tool wrapper.""" + + param1: str + param2: int + param3: float = 3.14 + + +@dataclass +class MockDataclassSchema: + """Mock dataclass schema for tool wrapper.""" + + param1: str + param2: int + + +class TestResolveType: + """Test cases for resolve_type function.""" + + def test_resolve_union_type(self): + """Test resolving Union types.""" + union_type = str | None + result = resolve_type(union_type) + # For typing.Union, returns the original unchanged since it only handles types.UnionType + assert result == union_type + + def test_resolve_pep604_union(self): + """Test resolving PEP 604 union types (str | None).""" + union_type = str | None + result = resolve_type(union_type) + # Should return str (the non-None type) + assert result == union_type + + def test_resolve_non_union_type(self): + """Test resolving non-union types.""" + result = resolve_type(int) + assert result is int + + def test_resolve_complex_union(self): + """Test resolving union with multiple non-None types.""" + union_type = str | int | None + result = resolve_type(union_type) + # For typing.Union, the function returns the original type unchanged + # since it only handles types.UnionType (PEP 604 style) + assert result == union_type + + def test_resolve_all_none_union(self): + """Test resolving union with only None types.""" + union_type = None | type(None) + result = resolve_type(union_type) + # Should return the original type if no non-None found + assert result == union_type + + +class TestAutoGenToolWrapper: + """Test cases for autogen_tool_wrapper function.""" + + @pytest.fixture + def mock_function(self): + """Create a mock NAT function.""" + mock_fn = Mock(spec=Function) + mock_fn.description = "Test function description" + mock_fn.input_schema = MockInputSchema + mock_fn.has_streaming_output = False + mock_fn.has_single_output = True + mock_fn.acall_invoke = AsyncMock(return_value="test_result") + mock_fn.acall_stream = AsyncMock() + return mock_fn + + @pytest.fixture + def mock_builder(self): + """Create a mock builder.""" + return Mock(spec=Builder) + + def test_autogen_tool_wrapper_basic(self, mock_function, mock_builder): + """Test basic tool wrapper functionality.""" + with patch('nat.plugins.autogen.tool_wrapper.FunctionTool') as mock_function_tool: + mock_tool = Mock() + mock_function_tool.return_value = mock_tool + + result = autogen_tool_wrapper("test_tool", mock_function, mock_builder) + + mock_function_tool.assert_called_once() + call_args = mock_function_tool.call_args + assert call_args[1]['name'] == "test_tool" + assert call_args[1]['description'] == "Test function description" + assert callable(call_args[1]['func']) + assert result == mock_tool + + def test_autogen_tool_wrapper_streaming(self, mock_function, mock_builder): + """Test tool wrapper with streaming output.""" + mock_function.has_streaming_output = True + mock_function.has_single_output = False + + with patch('nat.plugins.autogen.tool_wrapper.FunctionTool') as mock_function_tool: + mock_tool = Mock() + mock_function_tool.return_value = mock_tool + + result = autogen_tool_wrapper("test_tool", mock_function, mock_builder) + + mock_function_tool.assert_called_once() + # Should use streaming callable + assert result == mock_tool + + def test_autogen_tool_wrapper_no_description(self, mock_function, mock_builder): + """Test tool wrapper with no description.""" + _ = mock_builder # Unused in this test + mock_function.description = None + + with patch('nat.plugins.autogen.tool_wrapper.FunctionTool') as mock_function_tool: + mock_tool = Mock() + mock_function_tool.return_value = mock_tool + + autogen_tool_wrapper("test_tool", mock_function, mock_builder) + call_args = mock_function_tool.call_args + assert call_args[1]['description'] == "No description provided." + + @pytest.mark.asyncio + async def test_callable_ainvoke(self, mock_function, mock_builder): + """Test the async invoke callable.""" + with patch('nat.plugins.autogen.tool_wrapper.FunctionTool'): + autogen_tool_wrapper("test_tool", mock_function, mock_builder) + + # Test that acall_invoke would be called + result = await mock_function.acall_invoke("arg1", param="value") + assert result == "test_result" + mock_function.acall_invoke.assert_called_once_with("arg1", param="value") + + @pytest.mark.asyncio + async def test_callable_astream(self, mock_function, mock_builder): + """Test the async stream callable.""" + mock_function.has_streaming_output = True + mock_function.has_single_output = False + + async def mock_stream(): + yield "item1" + yield "item2" + + mock_function.acall_stream = mock_stream + + with patch('nat.plugins.autogen.tool_wrapper.FunctionTool'): + autogen_tool_wrapper("test_tool", mock_function, mock_builder) + + # Test that acall_stream would work + items = [] + async for item in mock_function.acall_stream(): + items.append(item) + + assert items == ["item1", "item2"] + + +class TestNatFunctionDecorator: + """Test the nat_function decorator pattern.""" + + def test_function_metadata_setting(self): + """Test that function metadata is set correctly.""" + + def test_func(): + """Test function.""" + + # Mock the decorator pattern from the source + name = "test_name" + description = "test_description" + input_schema = MockInputSchema + + # Set metadata like the decorator does + test_func.__name__ = name + test_func.__doc__ = description + + # Test signature creation + annotations = getattr(input_schema, "__annotations__", {}) or {} + params = [] + for param_name, param_annotation in annotations.items(): + resolved_type = resolve_type(param_annotation) + params.append( + inspect.Parameter(param_name, inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=resolved_type)) + annotations[param_name] = resolved_type + + # Create signature + signature = inspect.Signature(parameters=params) + # Note: Cannot actually set __signature__ on function objects in tests + # so we test the signature creation separately + + # Verify metadata + assert test_func.__name__ == name + assert test_func.__doc__ == description + assert signature is not None + assert len(signature.parameters) == 3 + + def test_signature_creation_with_schema(self): + """Test signature creation with input schema.""" + input_schema = MockInputSchema + annotations = getattr(input_schema, "__annotations__", {}) or {} + + params = [] + processed_annotations = {} + for param_name, param_annotation in annotations.items(): + resolved_type = resolve_type(param_annotation) + params.append( + inspect.Parameter(param_name, inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=resolved_type)) + processed_annotations[param_name] = resolved_type + + signature = inspect.Signature(parameters=params) + + # Verify signature has correct parameters + assert "param1" in signature.parameters + assert "param2" in signature.parameters + assert "param3" in signature.parameters + assert signature.parameters["param1"].annotation is str + assert signature.parameters["param2"].annotation is int + assert signature.parameters["param3"].annotation is float + + def test_no_input_schema_handling(self): + """Test handling when no input schema is provided.""" + + def test_func(): + pass + + # When input_schema is None, no signature should be set + input_schema = None + + if input_schema is not None: + # This block should not execute + pytest.fail("Should not process None input_schema") + + # Function should remain unchanged when no schema + original_signature = inspect.signature(test_func) + assert original_signature is not None + + +class TestTypeResolution: + """Test type resolution in various scenarios.""" + + def test_resolve_type_with_complex_types(self): + """Test resolve_type with complex type annotations.""" + # Test with list type + list_type = list[str] + result = resolve_type(list_type) + assert result == list_type + + # Test with dict type + dict_type = dict[str, int] + result = resolve_type(dict_type) + assert result == dict_type + + def test_resolve_type_with_optional(self): + """Test resolve_type with Optional types.""" + + optional_str = str | None + result = resolve_type(optional_str) + # For typing.Union (which Optional[str] is), returns the original unchanged + assert result == optional_str + + def test_union_type_detection(self): + """Test union type detection.""" + union_type = str | int | None + origin = types.UnionType if hasattr(types, 'UnionType') else type(union_type) + + # Test that we can detect union types + assert origin is not None + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/packages/nvidia_nat_test/src/nat/test/llm.py b/packages/nvidia_nat_test/src/nat/test/llm.py index 3611b1dfe..30e582551 100644 --- a/packages/nvidia_nat_test/src/nat/test/llm.py +++ b/packages/nvidia_nat_test/src/nat/test/llm.py @@ -33,9 +33,9 @@ from nat.data_models.llm import LLMBaseConfig -class TestLLMConfig(LLMBaseConfig, name="nat_test_llm"): - """Test LLM configuration.""" - __test__ = False +class MockLLMConfig(LLMBaseConfig, name="nat_test_llm"): + """Mock LLM configuration.""" + response_seq: list[str] = Field( default=[], description="Returns the next element in order (wraps)", @@ -66,14 +66,14 @@ async def async_sleep(self) -> None: await asyncio.sleep(self._delay_ms / 1000.0) -@register_llm_provider(config_type=TestLLMConfig) -async def test_llm_provider(config: TestLLMConfig, builder: Builder): +@register_llm_provider(config_type=MockLLMConfig) +async def test_llm_provider(config: MockLLMConfig, builder: Builder): """Register the `nat_test_llm` provider for the NAT registry.""" yield LLMProviderInfo(config=config, description="Test LLM provider") -@register_llm_client(config_type=TestLLMConfig, wrapper_type=LLMFrameworkEnum.LANGCHAIN) -async def test_llm_langchain(config: TestLLMConfig, builder: Builder): +@register_llm_client(config_type=MockLLMConfig, wrapper_type=LLMFrameworkEnum.LANGCHAIN) +async def test_llm_langchain(config: MockLLMConfig, builder: Builder): """LLM client for LangChain/LangGraph.""" chooser = _ResponseChooser(response_seq=config.response_seq, delay_ms=config.delay_ms) @@ -99,8 +99,8 @@ async def astream(self, messages: Any, **_kwargs: Any) -> AsyncGenerator[str]: yield LangChainTestLLM() -@register_llm_client(config_type=TestLLMConfig, wrapper_type=LLMFrameworkEnum.LLAMA_INDEX) -async def test_llm_llama_index(config: TestLLMConfig, builder: Builder): +@register_llm_client(config_type=MockLLMConfig, wrapper_type=LLMFrameworkEnum.LLAMA_INDEX) +async def test_llm_llama_index(config: MockLLMConfig, builder: Builder): try: from llama_index.core.base.llms.types import ChatMessage @@ -134,8 +134,8 @@ async def astream_chat(self, yield LITestLLM() -@register_llm_client(config_type=TestLLMConfig, wrapper_type=LLMFrameworkEnum.CREWAI) -async def test_llm_crewai(config: TestLLMConfig, builder: Builder): +@register_llm_client(config_type=MockLLMConfig, wrapper_type=LLMFrameworkEnum.CREWAI) +async def test_llm_crewai(config: MockLLMConfig, builder: Builder): """LLM client for CrewAI.""" chooser = _ResponseChooser(response_seq=config.response_seq, delay_ms=config.delay_ms) @@ -149,8 +149,8 @@ def call(self, messages: list[dict[str, str]] | None = None, **kwargs: Any) -> s yield CrewAITestLLM() -@register_llm_client(config_type=TestLLMConfig, wrapper_type=LLMFrameworkEnum.SEMANTIC_KERNEL) -async def test_llm_semantic_kernel(config: TestLLMConfig, builder: Builder): +@register_llm_client(config_type=MockLLMConfig, wrapper_type=LLMFrameworkEnum.SEMANTIC_KERNEL) +async def test_llm_semantic_kernel(config: MockLLMConfig, builder: Builder): """LLM client for SemanticKernel.""" try: @@ -178,8 +178,8 @@ async def get_streaming_chat_message_contents(self, chat_history: Any, yield SKTestLLM() -@register_llm_client(config_type=TestLLMConfig, wrapper_type=LLMFrameworkEnum.AGNO) -async def test_llm_agno(config: TestLLMConfig, builder: Builder): +@register_llm_client(config_type=MockLLMConfig, wrapper_type=LLMFrameworkEnum.AGNO) +async def test_llm_agno(config: MockLLMConfig, builder: Builder): """LLM client for agno.""" chooser = _ResponseChooser(response_seq=config.response_seq, delay_ms=config.delay_ms) diff --git a/pyproject.toml b/pyproject.toml index 8316540cf..dcb118d10 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,6 +76,7 @@ source = "https://github.com/NVIDIA/NeMo-Agent-Toolkit" all = ["nvidia-nat-all"] # meta-package adk = ["nvidia-nat-adk"] agno = ["nvidia-nat-agno"] +autogen = ["nvidia-nat-autogen"] crewai = ["nvidia-nat-crewai"] data-flywheel = ["nvidia-nat-data-flywheel"] ingestion = ["nvidia-nat-ingestion"] # meta-package @@ -105,6 +106,7 @@ examples = [ "nat_agno_personal_finance", "nat_haystack_deep_research_agent", "nat_alert_triage_agent", + "nat_autogen_demo", "nat_automated_description_generation", "nat_email_phishing_analyzer", "nat_multi_frameworks", @@ -167,6 +169,7 @@ config-settings = { editable_mode = "compat" } nvidia-nat-all = { workspace = true } nvidia-nat-agno = { workspace = true } nvidia-nat-adk = { workspace = true } +nvidia-nat-autogen = { workspace = true } nvidia-nat-crewai = { workspace = true } nvidia-nat-data-flywheel = { workspace = true } nvidia-nat-ingestion = { workspace = true } @@ -191,6 +194,7 @@ nat_adk_demo = { path = "examples/frameworks/adk_demo", editable = true } nat_agno_personal_finance = { path = "examples/frameworks/agno_personal_finance", editable = true } nat_haystack_deep_research_agent = { path = "examples/frameworks/haystack_deep_research_agent", editable = true } nat_alert_triage_agent = { path = "examples/advanced_agents/alert_triage_agent", editable = true } +nat_autogen_demo = { path = "examples/frameworks/nat_autogen_demo", editable = true } nat_automated_description_generation = { path = "examples/custom_functions/automated_description_generation", editable = true } nat_email_phishing_analyzer = { path = "examples/evaluation_and_profiling/email_phishing_analyzer", editable = true } nat_multi_frameworks = { path = "examples/frameworks/multi_frameworks", editable = true } @@ -345,6 +349,7 @@ force-single-line = true known-first-party = ["aiq", "nat", "nat_*", "_utils"] known-third-party = [ "agno", + "autogen", "crewai", "google", "langchain", diff --git a/src/nat/builder/framework_enum.py b/src/nat/builder/framework_enum.py index e72321ec1..38b83f288 100644 --- a/src/nat/builder/framework_enum.py +++ b/src/nat/builder/framework_enum.py @@ -23,3 +23,4 @@ class LLMFrameworkEnum(str, Enum): SEMANTIC_KERNEL = "semantic_kernel" AGNO = "agno" ADK = "adk" + AUTOGEN = "autogen" diff --git a/src/nat/profiler/decorators/framework_wrapper.py b/src/nat/profiler/decorators/framework_wrapper.py index 7a89ed0a4..ab999e77c 100644 --- a/src/nat/profiler/decorators/framework_wrapper.py +++ b/src/nat/profiler/decorators/framework_wrapper.py @@ -34,6 +34,7 @@ "semantic_kernel": False, "agno": False, "adk": False, + "autogen": False, } callback_handler_var: ContextVar[Any | None] = ContextVar("callback_handler_var", default=None) @@ -131,6 +132,20 @@ async def wrapper(workflow_config, builder): _library_instrumented["adk"] = True logger.debug("ADK callback handler registered") + if LLMFrameworkEnum.AUTOGEN in frameworks and not _library_instrumented["autogen"]: + try: + from nat.plugins.autogen.autogen_callback_handler import AutoGenProfilerHandler + except ImportError as e: + logger.warning( + "AutoGen profiler not available. " + + "Install NAT with AutoGen extras: pip install 'nvidia-nat[autogen]'. Error: %s", + e) + else: + handler = AutoGenProfilerHandler() + handler.instrument() + _library_instrumented["autogen"] = True + logger.debug("AutoGen callback handler registered") + # IMPORTANT: actually call the wrapped function as an async context manager async with func(workflow_config, builder) as result: yield result diff --git a/uv.lock b/uv.lock index b027fef15..86c4da9f7 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 1 requires-python = ">=3.11, <3.14" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'win32'", @@ -19,6 +19,7 @@ members = [ "nvidia-nat-adk", "nvidia-nat-agno", "nvidia-nat-all", + "nvidia-nat-autogen", "nvidia-nat-crewai", "nvidia-nat-data-flywheel", "nvidia-nat-ingestion", @@ -46,18 +47,18 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "typing-extensions" }, ] -sdist = { url = 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b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/llm.py index 46ba8b29d..c8079ceea 100644 --- a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/llm.py +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/llm.py @@ -15,8 +15,7 @@ """AutoGen LLM client registrations for NAT.""" from collections.abc import AsyncGenerator -from typing import Any -from typing import TypeVar +from typing import Any, TypeVar from nat.builder.builder import Builder from nat.builder.framework_enum import LLMFrameworkEnum @@ -27,9 +26,7 @@ from nat.llm.azure_openai_llm import AzureOpenAIModelConfig from nat.llm.nim_llm import NIMModelConfig from nat.llm.openai_llm import OpenAIModelConfig -from nat.llm.utils.thinking import BaseThinkingInjector -from nat.llm.utils.thinking import FunctionArgumentWrapper -from nat.llm.utils.thinking import patch_with_thinking +from nat.llm.utils.thinking import (BaseThinkingInjector, FunctionArgumentWrapper, patch_with_thinking) from nat.utils.exception_handlers.automatic_retries import patch_with_retry from nat.utils.type_utils import override @@ -111,8 +108,7 @@ async def openai_autogen(llm_config: OpenAIModelConfig, _builder: Builder) -> As Yields: AsyncGenerator[ModelType, None]: Configured AutoGen OpenAI client """ - from autogen_core.models import ModelFamily - from autogen_core.models import ModelInfo + from autogen_core.models import ModelFamily, ModelInfo from autogen_ext.models.openai import OpenAIChatCompletionClient # Extract AutoGen-compatible configuration @@ -134,6 +130,7 @@ async def openai_autogen(llm_config: OpenAIModelConfig, _builder: Builder) -> As # Add required AutoGen 0.7.4 parameters config_obj.update({"model_info": model_info}) + config_obj.pop("model", None) # Create AutoGen OpenAI client client = OpenAIChatCompletionClient(model=llm_config.model_name, **config_obj) @@ -154,8 +151,7 @@ async def azure_openai_autogen(llm_config: AzureOpenAIModelConfig, Yields: AsyncGenerator[ModelType, None]: Configured AutoGen Azure OpenAI client """ - from autogen_core.models import ModelFamily - from autogen_core.models import ModelInfo + from autogen_core.models import ModelFamily, ModelInfo from autogen_ext.models.openai import AzureOpenAIChatCompletionClient config_obj = { @@ -200,8 +196,7 @@ async def nim_autogen(llm_config: NIMModelConfig, _builder: Builder) -> AsyncGen Yields: Configured AutoGen NIM client (via OpenAI compatibility) """ - from autogen_core.models import ModelFamily - from autogen_core.models import ModelInfo + from autogen_core.models import ModelFamily, ModelInfo from autogen_ext.models.openai import OpenAIChatCompletionClient # Extract NIM configuration for OpenAI-compatible client diff --git a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py index 57bdf8a30..35bbcab48 100644 --- a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py @@ -116,6 +116,10 @@ def nat_function( if input_schema is not None and is_dataclass(input_schema): input_schema = pydantic_dataclass(input_schema) + from pydantic.dataclasses import create_model_from_dataclass + if input_schema is not None and is_dataclass(input_schema): + input_schema = create_model_from_dataclass(input_schema) + def decorator(func_to_wrap: Callable[..., Any]) -> Callable[..., Any]: """ Decorator to set metadata on the function. @@ -143,8 +147,9 @@ def decorator(func_to_wrap: Callable[..., Any]) -> Callable[..., Any]: annotation=resolved_type, default=default)) annotations[param_name] = resolved_type - setattr(func_to_wrap, "__signature__", inspect.Signature(parameters=params)) - setattr(func_to_wrap, "__annotations__", annotations) + if input_schema is not None: + func_to_wrap.__signature__ = inspect.Signature(parameters=params) ++ func_to_wrap.__annotations__ = annotations return func_to_wrap From 02b30a8b6f18b499896206136e448b3669d8c5dd Mon Sep 17 00:00:00 2001 From: Onkar Kulkarni Date: Thu, 9 Oct 2025 15:11:32 -0700 Subject: [PATCH 3/4] [synopsys][onkkul] Addressed coderabbit comments Signed-off-by: Onkar Kulkarni --- .../src/nat/plugins/autogen/tool_wrapper.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py index 35bbcab48..856a2c048 100644 --- a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py @@ -116,10 +116,6 @@ def nat_function( if input_schema is not None and is_dataclass(input_schema): input_schema = pydantic_dataclass(input_schema) - from pydantic.dataclasses import create_model_from_dataclass - if input_schema is not None and is_dataclass(input_schema): - input_schema = create_model_from_dataclass(input_schema) - def decorator(func_to_wrap: Callable[..., Any]) -> Callable[..., Any]: """ Decorator to set metadata on the function. From 73079e4e3484083a1810fc14c5f7f710ef48d752 Mon Sep 17 00:00:00 2001 From: Onkar Kulkarni Date: Thu, 9 Oct 2025 15:20:04 -0700 Subject: [PATCH 4/4] [synopsys][onkkul] Fixed typo Signed-off-by: Onkar Kulkarni --- .../src/nat/plugins/autogen/tool_wrapper.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py index 856a2c048..79a8a7f91 100644 --- a/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py +++ b/packages/nvidia_nat_autogen/src/nat/plugins/autogen/tool_wrapper.py @@ -15,10 +15,8 @@ """Tool wrapper for AutoGen integration with NAT.""" import logging -from collections.abc import AsyncIterator -from collections.abc import Callable +from collections.abc import AsyncIterator, Callable from dataclasses import is_dataclass - # PythonType not available in AutoGen 0.7.4, using Any instead from typing import Any @@ -145,7 +143,7 @@ def decorator(func_to_wrap: Callable[..., Any]) -> Callable[..., Any]: annotations[param_name] = resolved_type if input_schema is not None: func_to_wrap.__signature__ = inspect.Signature(parameters=params) -+ func_to_wrap.__annotations__ = annotations + func_to_wrap.__annotations__ = annotations return func_to_wrap