diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 6cec7208..c6614195 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -4,21 +4,29 @@ on: release: types: [published] +permissions: + contents: write + jobs: - build: - strategy: - matrix: - python-version: ["3.10", "3.11", "3.12"] - os: [ubuntu-latest, macos-latest, windows-latest] - runs-on: ${{ matrix.os }} + publish: + runs-on: ubuntu-latest + + permissions: + # This permission is used for trusted publishing: + # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/ + # + # Trusted publishing has to also be configured on PyPI for each package: + # https://docs.pypi.org/trusted-publishers/adding-a-publisher/ + id-token: write + steps: - uses: actions/checkout@v4 - - name: Set up Python ${{ matrix.python-version }} + - name: Set up Python id: setup-python uses: actions/setup-python@v4 with: - python-version: ${{ matrix.python-version }} + python-version: "3.12" - name: Install poetry run: pip install -U poetry @@ -40,5 +48,8 @@ jobs: - name: Install dependencies run: poetry install --no-interaction - - name: Run tests - run: poetry publish --build + - name: Build using poetry + run: poetry build + + - name: Publish package distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/examples/Advanced output handling.ipynb b/examples/Advanced output handling.ipynb index ca4bd544..2d02457c 100644 --- a/examples/Advanced output handling.ipynb +++ b/examples/Advanced output handling.ipynb @@ -81,6 +81,8 @@ "source": [ "In case the critic is not satisfied, the `InvalidOutput` exception will be returned to the agent, as if a regular tool was called. If everything is OK, we just return the agent's output.\n", "\n", + "The MotleyOutputHandler constructor has a `max_iterations` argument. It's the maximum number of times the exception will be returned to the agent. If the agent doesn't provide a valid output after `max_iterations`, a `OutputHandlerMaxIterationsExceeded` exception will be raised. It has `last_call_args` and `last_call_kwargs` attributes that store the arguments that the agent used in the last call. Also, it stores the last exception that was raised in the `last_exception` attribute.\n", + "\n", "The output schema is inferred automatically from the `handle_output` method signature. You can also specify it directly using the `_args_schema` property (see below)." ] }, @@ -104,7 +106,7 @@ "coder = ReActToolCallingAgent(\n", " name=\"coder\",\n", " tools=[PythonREPLTool()],\n", - " output_handler=CoderOutputHandler(),\n", + " output_handler=CoderOutputHandler(max_iterations=3),\n", " verbose=True,\n", ")\n", "\n", @@ -544,7 +546,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -558,7 +560,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/examples/Blog with Images.ipynb b/examples/Blog with Images.ipynb index d712208d..8cb1b3e8 100644 --- a/examples/Blog with Images.ipynb +++ b/examples/Blog with Images.ipynb @@ -13,7 +13,8 @@ "id": "16b8ccc4-b34b-47c3-94ee-cbd82ef4c8f2", "metadata": {}, "source": [ - "We use this example of creating a web search-based blog with illustrations to show how to \n\n", + "We use this example of creating a web search-based blog with illustrations to show how to \n", + "\n", "* instantiate one agent each from Langchain, CrewAI, and LlamaIndex\n", "* give agents as tools to other agents\n", "* create tasks and link them using the >> operator" @@ -59,7 +60,7 @@ "from motleycrew.storage import MotleyKuzuGraphStore\n", "from motleycrew import MotleyCrew\n", "from motleycrew.agents.crewai import CrewAIMotleyAgent\n", - "from motleycrew.agents.langchain.react import ReActMotleyAgent\n", + "from motleycrew.agents.langchain.tool_calling_react import ReActToolCallingAgent\n", "from motleycrew.agents.llama_index import ReActLlamaIndexMotleyAgent\n", "from motleycrew.tools.image.dall_e import DallEImageGeneratorTool\n", "from motleycrew.common import configure_logging\n", @@ -113,9 +114,9 @@ "outputs": [], "source": [ "# You can give agents as tools to other agents\n", - "writer = ReActMotleyAgent(\n", + "writer = ReActToolCallingAgent(\n", " name=\"AI writer agent\",\n", - " description=\"\"\"Conduct a comprehensive analysis of the latest advancements in AI in 2024.\n", + " prompt_prefix=\"\"\"Conduct a comprehensive analysis of the latest advancements in AI in 2024.\n", " Identify key trends, breakthrough technologies, and potential industry impacts.\n", " Your final answer MUST be a full analysis report\"\"\",\n", " tools=[researcher],\n", diff --git a/examples/Caching and observability.ipynb b/examples/Caching and observability.ipynb index e36204a6..12265147 100644 --- a/examples/Caching and observability.ipynb +++ b/examples/Caching and observability.ipynb @@ -105,7 +105,7 @@ "\n", "writer = ReActMotleyAgent(\n", " name=\"writer\",\n", - " description=\"Using the results of a web search, write an article on the latest advancements in AI in 2024.\",\n", + " prompt_prefix=\"Using the results of a web search, write an article on the latest advancements in AI in 2024.\",\n", " tools=[duckduckgo_search],\n", ")\n", "\n", diff --git a/examples/Math via python code with a single agent.ipynb b/examples/Math via python code with a single agent.ipynb index 69d7e0d2..d783ab4d 100644 --- a/examples/Math via python code with a single agent.ipynb +++ b/examples/Math via python code with a single agent.ipynb @@ -41,7 +41,7 @@ "WORKING_DIR = Path(os.path.realpath(\".\"))\n", "\n", "\n", - "try: \n", + "try:\n", " from motleycrew import MotleyCrew\n", "except ImportError:\n", " # if we are running this from source\n", @@ -493,7 +493,7 @@ " backstory=\"\"\"You are a high school math teacher with a passion for problem-solving.\n", "To solve a math problem, you first reason about it, step by step, then generate the code to solve it exactly,\n", "using sympy, then use the REPL tool to evaluate that code, and then\n", - "use the output to generate a human-readable solution in LaTeX format. \n", + "use the output to generate a human-readable solution in LaTeX format.\n", "Don't use dashes to indicate bullet points, don't output any backticks, just the LaTeX\"\"\",\n", " verbose=True,\n", " delegation=False,\n", @@ -501,7 +501,7 @@ ")\n", "\n", "problems = [\n", - " \"Problem: If $725x + 727y = 1500$ and $729x+ 731y = 1508$, \"\n", + " \"If $725x + 727y = 1500$ and $729x+ 731y = 1508$, \"\n", " \"what are the values of $x$, $y$, and $x - y$ ?\",\n", "]\n", "\n", @@ -511,6 +511,7 @@ " name=\"solve math problem\",\n", " description=f\"\"\"Create a nice human-readable solution to the following problem:\n", " {problems[0]}\"\"\",\n", + " additional_params={\"expected_output\": \"human-readable solution in LaTeX format\"},\n", " agent=solver,\n", ")\n", "\n", diff --git a/examples/Validating agent output.ipynb b/examples/Validating agent output.ipynb index bcf23f68..54d37e78 100644 --- a/examples/Validating agent output.ipynb +++ b/examples/Validating agent output.ipynb @@ -245,7 +245,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -259,7 +259,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/examples/__init__.py b/examples/__init__.py new file mode 100644 index 00000000..ab5a5d6c --- /dev/null +++ b/examples/__init__.py @@ -0,0 +1 @@ +# Has to be here to make the examples package importable for integration tests diff --git a/examples/aider_code_generation.py b/examples/aider_code_generation.py index 90c6e773..adf67658 100644 --- a/examples/aider_code_generation.py +++ b/examples/aider_code_generation.py @@ -34,11 +34,15 @@ def main(): crew = MotleyCrew() - git_repo_path = r"../../motleycrew-code-generation-example" # cloned repository path + git_repo_path = ( + r"../../motleycrew-code-generation-example" # cloned repository path + ) tests_file = os.path.join(git_repo_path, "test_math_functions.py") target_files = [tests_file] - aider_tool = AiderTool(fnames=target_files, git_dname=git_repo_path, auto_commits=False) + aider_tool = AiderTool( + fnames=target_files, git_dname=git_repo_path, auto_commits=False + ) shell_tool = ShellTool() developer = CrewAIMotleyAgent( @@ -59,6 +63,7 @@ def main(): f"After go to the directory {git_repo_path} and run created unit tests. " f"If the tests were executed successfully, return the result of execution, " f"if not, rewrite the tests and rerun them until they are working.", + additional_params={"expected_output": "result of tests execution"}, agent=developer, ) diff --git a/examples/blog_with_images.nblink b/examples/blog_with_images.nblink deleted file mode 100644 index 2250ae07..00000000 --- a/examples/blog_with_images.nblink +++ /dev/null @@ -1,6 +0,0 @@ -{ - "path": "../../../examples/Blog with Images.ipynb", - "extra-media": [ - "../../../examples/images" - ] -} \ No newline at end of file diff --git a/examples/crewai_output_handler.py b/examples/crewai_output_handler.py new file mode 100644 index 00000000..0106570f --- /dev/null +++ b/examples/crewai_output_handler.py @@ -0,0 +1,78 @@ +from pathlib import Path +import os +import sys + +from dotenv import load_dotenv + +from motleycrew.agents.crewai import CrewAIMotleyAgent +from motleycrew.common import configure_logging +from motleycrew.tasks import SimpleTask +from motleycrew.common.exceptions import InvalidOutput + +from langchain_community.tools import DuckDuckGoSearchRun +from langchain_core.tools import StructuredTool + +WORKING_DIR = Path(os.path.realpath("..")) + +try: + from motleycrew import MotleyCrew +except ImportError: + # if we are running this from source + motleycrew_location = os.path.realpath(WORKING_DIR / "..") + sys.path.append(motleycrew_location) + + +def main(): + crew = MotleyCrew() + + search_tool = DuckDuckGoSearchRun() + + def check_output(output: str): + if "medicine" not in output.lower(): + raise InvalidOutput( + "Add more information about AI applications in medicine." + ) + return {"checked_output": output.lower()} + + output_handler = StructuredTool.from_function( + name="output_handler", + description="Output handler", + func=check_output, + ) + + researcher = CrewAIMotleyAgent( + role="Senior Research Analyst", + goal="Uncover cutting-edge developments in AI and data science, doing web search if necessary", + backstory="""You work at a leading tech think tank. + Your expertise lies in identifying emerging trends. + You have a knack for dissecting complex data and presenting actionable insights.""", + delegation=False, + output_handler=output_handler, + verbose=True, + tools=[search_tool], + ) + + # Create tasks for agent + + analysis_report_task = SimpleTask( + crew=crew, + name="produce comprehensive analysis report on AI advancements", + description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024. + Identify key trends, breakthrough technologies, and potential industry impacts. + Your final answer MUST be a full analysis report""", + additional_params={"expected_output": "full analysis report"}, + agent=researcher, + ) + + # Get your crew to work! + result = crew.run() + + # Get the outputs of the task + print(analysis_report_task.output) + return analysis_report_task.output + + +if __name__ == "__main__": + configure_logging(verbose=True) + load_dotenv() + main() diff --git a/examples/delegation_demo.py b/examples/delegation_demo.py index a6da82bd..dd87e629 100644 --- a/examples/delegation_demo.py +++ b/examples/delegation_demo.py @@ -10,7 +10,7 @@ from motleycrew.storage import MotleyKuzuGraphStore from motleycrew.agents.crewai import CrewAIMotleyAgent -from motleycrew.agents.langchain.react import ReActMotleyAgent +from motleycrew.agents.langchain.tool_calling_react import ReActToolCallingAgent from motleycrew.agents.llama_index import ReActLlamaIndexMotleyAgent from motleycrew.tools.image.dall_e import DallEImageGeneratorTool from motleycrew.common import configure_logging @@ -25,11 +25,15 @@ motleycrew_location = os.path.realpath(WORKING_DIR / "..") sys.path.append(motleycrew_location) -if "Dropbox" in WORKING_DIR.parts and platform.system() == "Windows": - # On Windows, kuzu has file locking issues with Dropbox - DB_PATH = os.path.realpath(os.path.expanduser("~") + "/Documents/research_db") +if __name__ == '__main__': + if "Dropbox" in WORKING_DIR.parts and platform.system() == "Windows": + # On Windows, kuzu has file locking issues with Dropbox + DB_PATH = os.path.realpath(os.path.expanduser("~") + "/Documents/research_db") + else: + DB_PATH = os.path.realpath(WORKING_DIR / "research_db") + else: - DB_PATH = os.path.realpath(WORKING_DIR / "research_db") + DB_PATH = os.path.realpath(WORKING_DIR / "tests/research_db") def main(): @@ -43,17 +47,19 @@ def main(): researcher = CrewAIMotleyAgent( role="Senior Research Analyst", goal="Uncover cutting-edge developments in AI and data science, doing web search if necessary", + backstory="""You work at a leading tech think tank. - Your expertise lies in identifying emerging trends. - You have a knack for dissecting complex data and presenting actionable insights.""", + Your expertise lies in identifying emerging trends. + You have a knack for dissecting complex data and presenting actionable insights.""", verbose=True, tools=[search_tool], ) # You can give agents as tools to other agents - writer = ReActMotleyAgent( + writer = ReActToolCallingAgent( name="AI writer agent", - description="You are an experienced writer with a passion for technology.", + prompt_prefix="You are an experienced writer with a passion for technology.", + description="Experienced writer with a passion for technology.", tools=[researcher], verbose=True, ) @@ -61,7 +67,7 @@ def main(): # Illustrator illustrator = ReActLlamaIndexMotleyAgent( name="Illustrator", - description="Create beautiful and insightful illustrations for a blog post", + prompt_prefix="Create beautiful and insightful illustrations for a blog post", tools=[DallEImageGeneratorTool(os.path.realpath("./images"))], ) diff --git a/examples/llama_index_output_handler.py b/examples/llama_index_output_handler.py new file mode 100644 index 00000000..0f243206 --- /dev/null +++ b/examples/llama_index_output_handler.py @@ -0,0 +1,65 @@ +from dotenv import load_dotenv +from langchain_community.tools import DuckDuckGoSearchRun + + +from motleycrew import MotleyCrew +from motleycrew.agents.llama_index import ReActLlamaIndexMotleyAgent +from motleycrew.common import configure_logging +from motleycrew.tasks import SimpleTask +from motleycrew.common.exceptions import InvalidOutput +from motleycrew.common import AsyncBackend + +from langchain_core.tools import StructuredTool + + +def main(): + """Main function of running the example.""" + search_tool = DuckDuckGoSearchRun() + + def check_output(output: str): + if "medicine" not in output.lower(): + raise InvalidOutput( + "Add more information about AI applications in medicine." + ) + + return {"checked_output": output} + + output_handler = StructuredTool.from_function( + name="output_handler", + description="Output handler", + func=check_output, + ) + + # TODO: add LlamaIndex native tools + researcher = ReActLlamaIndexMotleyAgent( + prompt_prefix="Your goal is to uncover cutting-edge developments in AI and data science", + tools=[search_tool], + output_handler=output_handler, + verbose=True, + max_iterations=16, # default is 10, we add more because the output handler may reject the output + ) + + crew = MotleyCrew(async_backend=AsyncBackend.NONE) + + # Create tasks for your agents + task = SimpleTask( + crew=crew, + name="produce comprehensive analysis report on AI advancements", + description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024. + Identify key trends, breakthrough technologies, and potential industry impacts. + Your final answer MUST be a full analysis report""", + agent=researcher, + ) + + # Get your crew to work! + crew.run() + + print(task.output) + return task.output + + +if __name__ == "__main__": + configure_logging(verbose=True) + + load_dotenv() + main() diff --git a/examples/old/delegation_crewai.py b/examples/old/delegation_crewai.py index 41547c35..8b52ea50 100644 --- a/examples/old/delegation_crewai.py +++ b/examples/old/delegation_crewai.py @@ -31,6 +31,7 @@ def main(): backstory="""You work at a leading tech think tank. Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting actionable insights.""", + description="Tool Research Analysis", delegation=False, verbose=True, tools=[search_tool], @@ -77,7 +78,7 @@ def main(): agent=writer, ) - [analysis_report_task, literature_summary_task] >> blog_post_task + # [analysis_report_task, literature_summary_task] >> blog_post_task # Get your crew to work! result = crew.run() @@ -89,6 +90,5 @@ def main(): if __name__ == "__main__": configure_logging(verbose=True) - load_dotenv() main() diff --git a/examples/old/single_llama_index.py b/examples/old/single_llama_index.py index 6e7d7bad..f7704f89 100644 --- a/examples/old/single_llama_index.py +++ b/examples/old/single_llama_index.py @@ -6,20 +6,25 @@ from motleycrew.agents.llama_index import ReActLlamaIndexMotleyAgent from motleycrew.common import configure_logging from motleycrew.tasks import SimpleTask +from motleycrew.common.exceptions import InvalidOutput +from motleycrew.common import AsyncBackend + +from langchain_core.tools import StructuredTool def main(): """Main function of running the example.""" search_tool = DuckDuckGoSearchRun() + # TODO: add LlamaIndex native tools researcher = ReActLlamaIndexMotleyAgent( - description="Your goal is to uncover cutting-edge developments in AI and data science", + prompt_prefix="Your goal is to uncover cutting-edge developments in AI and data science", tools=[search_tool], verbose=True, ) - crew = MotleyCrew() + crew = MotleyCrew(async_backend=AsyncBackend.NONE) # Create tasks for your agents task = SimpleTask( diff --git a/motleycrew/agents/crewai/agent_with_config.py b/motleycrew/agents/crewai/agent_with_config.py index 19d2a28f..f139db39 100644 --- a/motleycrew/agents/crewai/agent_with_config.py +++ b/motleycrew/agents/crewai/agent_with_config.py @@ -1,4 +1,5 @@ """ Module description """ + from typing import Any, Optional, List from langchain_core.runnables import RunnableConfig @@ -8,12 +9,13 @@ try: from crewai import Agent + from crewai.memory.contextual.contextual_memory import ContextualMemory except ImportError: Agent = object + ContextualMemory = object class CrewAIAgentWithConfig(Agent): - def __init__(self, *args, **kwargs): """Subclass for CrewAI Agent that overrides the execute_task method to include a config parameter. @@ -37,14 +39,17 @@ def execute_task( """Execute a task with the agent. Args: - task (Any): Task to execute. - context (:obj:`str`, optional): Context to execute the task in. - tools (:obj:`List[Any]`, optional): Tools to use for the task. - config (:obj:`RunnableConfig`, optional): Runnable config. + task: Task to execute. + context: Context to execute the task in. + tools: Tools to use for the task. Returns: - Any: Output of the agent + Output of the agent """ + if self.tools_handler: + # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling") + self.tools_handler.last_used_tool = {} + task_prompt = task.prompt() if context: @@ -52,12 +57,30 @@ def execute_task( task=task_prompt, context=context ) - tools = self._parse_tools(tools or self.tools) + if self.crew and self.crew.memory: + contextual_memory = ContextualMemory( + self.crew._short_term_memory, + self.crew._long_term_memory, + self.crew._entity_memory, + ) + memory = contextual_memory.build_context_for_task(task, context) + if memory.strip() != "": + task_prompt += self.i18n.slice("memory").format(memory=memory) + + tools = tools or self.tools + # type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]" + parsed_tools = self._parse_tools(tools or []) self.create_agent_executor(tools=tools) - self.agent_executor.tools = tools + self.agent_executor.tools = parsed_tools self.agent_executor.task = task - self.agent_executor.tools_description = render_text_description(tools) - self.agent_executor.tools_names = self.__tools_names(tools) + + self.agent_executor.tools_description = render_text_description(parsed_tools) + self.agent_executor.tools_names = self.__tools_names(parsed_tools) + + if self.crew and self.crew._train: + task_prompt = self._training_handler(task_prompt=task_prompt) + else: + task_prompt = self._use_trained_data(task_prompt=task_prompt) result = self.agent_executor.invoke( { @@ -67,10 +90,8 @@ def execute_task( }, config=config, )["output"] - if self.max_rpm: self._rpm_controller.stop_rpm_counter() - return result @staticmethod diff --git a/motleycrew/agents/crewai/crewai.py b/motleycrew/agents/crewai/crewai.py index 349c736c..a7a8d759 100644 --- a/motleycrew/agents/crewai/crewai.py +++ b/motleycrew/agents/crewai/crewai.py @@ -3,13 +3,16 @@ from typing import Any, Optional, Sequence from langchain_core.runnables import RunnableConfig +from langchain_core.tools import StructuredTool +from langchain_core.pydantic_v1 import BaseModel, Field -from motleycrew.agents.parent import MotleyAgentParent from motleycrew.agents.crewai import CrewAIAgentWithConfig -from motleycrew.common import MotleySupportedTool +from motleycrew.agents.parent import MotleyAgentParent from motleycrew.common import MotleyAgentFactory -from motleycrew.tracking import add_default_callbacks_to_langchain_config +from motleycrew.common import MotleySupportedTool from motleycrew.common.utils import ensure_module_is_installed +from motleycrew.tools import MotleyTool +from motleycrew.tracking import add_default_callbacks_to_langchain_config try: from crewai import Task as CrewAI__Task @@ -21,26 +24,40 @@ class CrewAIMotleyAgentParent(MotleyAgentParent): def __init__( self, goal: str, + prompt_prefix: str | None = None, + description: str | None = None, name: str | None = None, agent_factory: MotleyAgentFactory[CrewAIAgentWithConfig] | None = None, tools: Sequence[MotleySupportedTool] | None = None, + output_handler: MotleySupportedTool | None = None, verbose: bool = False, ): """Description Args: goal (str): + prompt_prefix (:obj:`str`, optional): + description (:obj:`str`, optional): name (:obj:`str`, optional): agent_factory (:obj:`MotleyAgentFactory`, optional): tools (:obj:`Sequence[MotleySupportedTool]`, optional: verbose (bool): """ + + if output_handler: + raise NotImplementedError( + "Output handler is not supported for CrewAI agents " + "because of the specificity of CrewAI's prompts." + ) + ensure_module_is_installed("crewai") super().__init__( - description=goal, + prompt_prefix=prompt_prefix, + description=description or goal, name=name, agent_factory=agent_factory, tools=tools, + output_handler=output_handler, verbose=verbose, ) @@ -65,13 +82,27 @@ def invoke( langchain_tools = [tool.to_langchain_tool() for tool in self.tools.values()] config = add_default_callbacks_to_langchain_config(config) - crewai_task = CrewAI__Task(description=prompt) + additional_params = input.get("additional_params") or {} + expected_output = additional_params.get("expected_output") + if not expected_output: + raise ValueError("Expected output is required for CrewAI tasks") + + crewai_task = CrewAI__Task( + description=prompt, + expected_output=expected_output, + ) output = self.agent.execute_task( - task=crewai_task, context=input.get("context"), tools=langchain_tools, config=config + task=crewai_task, + context=input.get("context"), + tools=langchain_tools, + config=config, ) return output + def materialize(self): + super().materialize() + # TODO: what do these do? def set_cache_handler(self, cache_handler: Any) -> None: """Description @@ -116,9 +147,41 @@ def from_agent( wrapped_agent = CrewAIMotleyAgentParent( goal=agent.goal, + prompt_prefix=agent.prompt_prefix, + description=agent.description, name=agent.role, tools=tools, verbose=verbose, ) wrapped_agent._agent = agent return wrapped_agent + + def as_tool(self) -> MotleyTool: + if not self.description: + raise ValueError("Agent must have a description to be called as a tool") + + class CrewAIAgentInputSchema(BaseModel): + prompt: str = Field(..., description="Prompt to be passed to the agent") + expected_output: str = Field( + ..., description="Expected output of the agent" + ) + + def call_agent(prompt: str, expected_output: str): + return self.invoke( + { + "prompt": prompt, + "additional_params": {"expected_output": expected_output}, + } + ) + + # To be specialized if we expect structured input + return MotleyTool.from_langchain_tool( + StructuredTool( + name=self.name.replace( + " ", "_" + ).lower(), # OpenAI doesn't accept spaces in function names + description=self.description, + func=call_agent, + args_schema=CrewAIAgentInputSchema, + ) + ) diff --git a/motleycrew/agents/crewai/crewai_agent.py b/motleycrew/agents/crewai/crewai_agent.py index 3f10bc8a..0927c079 100644 --- a/motleycrew/agents/crewai/crewai_agent.py +++ b/motleycrew/agents/crewai/crewai_agent.py @@ -1,32 +1,37 @@ """ Module description """ + from typing import Optional, Any, Sequence from motleycrew.tools import MotleyTool from motleycrew.common import MotleySupportedTool from motleycrew.common import LLMFramework from motleycrew.common.llms import init_llm -from motleycrew.agents.abstract_parent import MotleyAgentAbstractParent from motleycrew.agents.crewai import CrewAIMotleyAgentParent from motleycrew.agents.crewai import CrewAIAgentWithConfig class CrewAIMotleyAgent(CrewAIMotleyAgentParent): def __init__( - self, - role: str, - goal: str, - backstory: str, - delegation: bool = False, - tools: Sequence[MotleySupportedTool] | None = None, - llm: Optional[Any] = None, - verbose: bool = False, + self, + role: str, + goal: str, + backstory: str, + prompt_prefix: str | None = None, + description: str | None = None, + delegation: bool = False, + tools: Sequence[MotleySupportedTool] | None = None, + llm: Optional[Any] = None, + output_handler: MotleySupportedTool | None = None, + verbose: bool = False, ): - """ Description + """Description Args: role (str): goal (str): backstory (str): + prompt_prefix (str): + description (str, optional): delegation (bool): tools (:obj:`Sequence[MotleySupportedTool]`, optional): llm (:obj:'Any', optional): @@ -59,8 +64,11 @@ def agent_factory(tools: dict[str, MotleyTool]): super().__init__( goal=goal, + prompt_prefix=prompt_prefix, + description=description, name=role, agent_factory=agent_factory, tools=tools, + output_handler=output_handler, verbose=verbose, ) diff --git a/motleycrew/agents/langchain/langchain.py b/motleycrew/agents/langchain/langchain.py index c81b468d..8e49da90 100644 --- a/motleycrew/agents/langchain/langchain.py +++ b/motleycrew/agents/langchain/langchain.py @@ -1,32 +1,23 @@ """ Module description """ -from typing import Any, Optional, Sequence, Callable, Union +from typing import Any, Optional, Sequence from langchain.agents import AgentExecutor -from langchain.agents.agent import RunnableAgent -from langchain_core.agents import AgentFinish, AgentAction -from langchain_core.runnables import Runnable, RunnableConfig, RunnableLambda -from langchain_core.runnables.history import RunnableWithMessageHistory, GetSessionHistoryCallable from langchain_core.chat_history import InMemoryChatMessageHistory -from langchain_core.language_models import BaseLanguageModel -from langchain_core.prompts import BasePromptTemplate -from langchain_core.tools import BaseTool -from langchain_core.tools import Tool - +from langchain_core.runnables import RunnableConfig +from langchain_core.runnables.history import RunnableWithMessageHistory, GetSessionHistoryCallable +from motleycrew.agents.mixins import LangchainOutputHandlingAgentMixin from motleycrew.agents.parent import MotleyAgentParent - -from motleycrew.tools import MotleyTool -from motleycrew.tracking import add_default_callbacks_to_langchain_config -from motleycrew.common import MotleySupportedTool, logger from motleycrew.common import MotleyAgentFactory -from motleycrew.common import LLMFramework -from motleycrew.common.llms import init_llm +from motleycrew.common import MotleySupportedTool, logger +from motleycrew.tracking import add_default_callbacks_to_langchain_config -class LangchainMotleyAgent(MotleyAgentParent): +class LangchainMotleyAgent(MotleyAgentParent, LangchainOutputHandlingAgentMixin): def __init__( self, + prompt_prefix: str | None = None, description: str | None = None, name: str | None = None, agent_factory: MotleyAgentFactory[AgentExecutor] | None = None, @@ -38,6 +29,7 @@ def __init__( """Description Args: + prompt_prefix (:obj:`str`, optional): description (:obj:`str`, optional): name (:obj:`str`, optional): agent_factory (:obj:`MotleyAgentFactory`, optional): @@ -50,6 +42,7 @@ def __init__( See Langchain `RunnableWithMessageHistory` get_session_history param for more details. """ super().__init__( + prompt_prefix=prompt_prefix, description=description, name=name, agent_factory=agent_factory, @@ -58,44 +51,14 @@ def __init__( verbose=verbose, ) + self._agent_finish_blocker_tool = self._create_agent_finish_blocker_tool() + if chat_history is True: chat_history = InMemoryChatMessageHistory() self.get_session_history_callable = lambda _: chat_history else: self.get_session_history_callable = chat_history - self._agent_finish_blocker_tool = self._create_agent_finish_blocker_tool() - - def _create_agent_finish_blocker_tool(self) -> BaseTool: - """Create a tool that will force the agent to retry if it attempts to return the output - bypassing the output handler. - """ - - def create_agent_finish_blocking_message(input: Any) -> str: - return f"{input}\n\nYou must use {self.output_handler.name} to return the final output." - - return Tool.from_function( - name="agent_finish_blocker", - description="", - func=create_agent_finish_blocking_message, - ) - - def _block_agent_finish(self, input: Any): - """Intercept AgentFinish for forcing output via output handler. - If the agent attempts to return the output bypassing the output handler, - a tool call to the agent_finish_blocker_tool will be made - so that one more AgentExecutor iteration is forced. - """ - if isinstance(input, AgentFinish) and self.output_handler: - return [ - AgentAction( - tool=self._agent_finish_blocker_tool.name, - tool_input={"input": input.return_values}, - log="\nDetected AgentFinish, blocking it to force output via output handler.\n", - ) - ] - return input - def materialize(self): """Materialize the agent and wrap it in RunnableWithMessageHistory if needed.""" if self.is_materialized: @@ -107,24 +70,32 @@ def materialize(self): if self.output_handler: self._agent.tools += [self._agent_finish_blocker_tool] - if isinstance(self._agent.agent, Runnable): - logger.info("Patching agent to force output via output handler") - agent_with_forced_output_via_handler = self._agent.agent | RunnableLambda( - self._block_agent_finish - ) - self._agent.agent = agent_with_forced_output_via_handler - - elif isinstance(self._agent.agent, RunnableAgent): - logger.info("Patching agent to force output via output handler") - agent_with_forced_output_via_handler = self._agent.agent.runnable | RunnableLambda( - self._block_agent_finish - ) - self._agent.agent.runnable = agent_with_forced_output_via_handler - - else: - logger.warning( - "Agent is not a Runnable, cannot patch it to force output via output handler" - ) + object.__setattr__( + self._agent.agent, "plan", self.agent_plan_decorator(self._agent.agent.plan) + ) + + object.__setattr__( + self._agent, + "_take_next_step", + self.take_next_step_decorator(self._agent._take_next_step), + ) + + prepared_output_handler = None + for tool in self.agent.tools: + if tool.name == self.output_handler.name: + prepared_output_handler = tool + + object.__setattr__( + prepared_output_handler, + "_run", + self._run_tool_direct_decorator(prepared_output_handler._run), + ) + + object.__setattr__( + prepared_output_handler, + "run", + self.run_tool_direct_decorator(prepared_output_handler.run), + ) if self.get_session_history_callable: logger.info("Wrapping agent in RunnableWithMessageHistory") @@ -163,24 +134,15 @@ def invoke( config["configurable"].get("session_id") or "default" ) - caught_direct_output, output = self._run_and_catch_output( - action=self.agent.invoke, - action_args=( - {"input": prompt}, - config, - ), - action_kwargs=kwargs, - ) - - if not caught_direct_output: - output = output.get("output") # unpack native Langchain output - + output = self.agent.invoke({"input": prompt}, config, **kwargs) + output = output.get("output") return output @staticmethod def from_agent( agent: AgentExecutor, goal: str, + description: str | None = None, tools: Sequence[MotleySupportedTool] | None = None, verbose: bool = False, ) -> "LangchainMotleyAgent": @@ -189,6 +151,7 @@ def from_agent( Args: agent (AgentExecutor): goal (str): + description (:obj:`str`, optional) tools(:obj:`Sequence[MotleySupportedTool]`, optional): verbose (bool): @@ -202,6 +165,8 @@ def from_agent( if tools or agent.tools: tools = list(tools or []) + list(agent.tools or []) - wrapped_agent = LangchainMotleyAgent(description=goal, tools=tools, verbose=verbose) + wrapped_agent = LangchainMotleyAgent( + prompt_prefix=goal, description=description, tools=tools, verbose=verbose + ) wrapped_agent._agent = agent return wrapped_agent diff --git a/motleycrew/agents/langchain/react.py b/motleycrew/agents/langchain/react.py index 4403e1f7..1c9a096b 100644 --- a/motleycrew/agents/langchain/react.py +++ b/motleycrew/agents/langchain/react.py @@ -26,6 +26,7 @@ class ReActMotleyAgent(LangchainMotleyAgent): def __init__( self, tools: Sequence[MotleySupportedTool], + prompt_prefix: str | None = None, description: str | None = None, name: str | None = None, prompt: str | None = None, @@ -41,6 +42,7 @@ def __init__( Args: tools (Sequence[MotleySupportedTool]): + prompt_prefix (:obj:`str`, optional): description (:obj:`str`, optional): name (:obj:`str`, optional): prompt (:obj:`str`, optional): Prompt to use. If not provided, uses hwchase17/react. @@ -88,6 +90,7 @@ def agent_factory( return agent_executor super().__init__( + prompt_prefix=prompt_prefix, description=description, name=name, agent_factory=agent_factory, diff --git a/motleycrew/agents/langchain/tool_calling_react.py b/motleycrew/agents/langchain/tool_calling_react.py index f63df381..6e7bc636 100644 --- a/motleycrew/agents/langchain/tool_calling_react.py +++ b/motleycrew/agents/langchain/tool_calling_react.py @@ -1,28 +1,25 @@ from typing import Sequence, List, Optional -from langchain_core.messages import BaseMessage, HumanMessage, ToolMessage +from langchain.agents import AgentExecutor +from langchain.agents.format_scratchpad.tools import format_to_tool_messages +from langchain.agents.output_parsers.tools import ToolsAgentOutputParser +from langchain.tools.render import render_text_description +from langchain_core.agents import AgentFinish, AgentActionMessageLog from langchain_core.language_models import BaseChatModel +from langchain_core.messages import BaseMessage, HumanMessage, ToolMessage +from langchain_core.messages.base import merge_content +from langchain_core.prompts import MessagesPlaceholder from langchain_core.prompts.chat import ChatPromptTemplate from langchain_core.runnables import Runnable, RunnablePassthrough, RunnableLambda from langchain_core.runnables.history import GetSessionHistoryCallable from langchain_core.tools import BaseTool -from langchain_core.agents import AgentFinish, AgentActionMessageLog -from langchain_core.prompts import MessagesPlaceholder -from langchain_core.messages.base import merge_content - -from langchain.agents import AgentExecutor -from langchain.agents.format_scratchpad.tools import format_to_tool_messages -from langchain.agents.output_parsers.tools import ToolsAgentOutputParser - -from langchain.tools.render import render_text_description from motleycrew.agents.langchain import LangchainMotleyAgent -from motleycrew.tools import MotleyTool -from motleycrew.common import MotleySupportedTool from motleycrew.common import LLMFramework +from motleycrew.common import MotleySupportedTool from motleycrew.common.llms import init_llm from motleycrew.common.utils import print_passthrough - +from motleycrew.tools import MotleyTool default_think_prompt = ChatPromptTemplate.from_messages( [ @@ -58,6 +55,7 @@ MessagesPlaceholder(variable_name="chat_history", optional=True), ("user", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), + MessagesPlaceholder(variable_name="additional_notes", optional=True), ] ) @@ -81,6 +79,7 @@ MessagesPlaceholder(variable_name="chat_history", optional=True), ("user", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), + MessagesPlaceholder(variable_name="additional_notes", optional=True), ] ) default_act_prompt_without_output_handler = default_act_prompt.partial( @@ -236,7 +235,8 @@ def create_tool_calling_react_agent( RunnablePassthrough.assign( agent_scratchpad=lambda x: merge_consecutive_messages( format_to_tool_messages(x["intermediate_steps"]) - ) + ), + additional_notes=lambda x: x.get("additional_notes") or [], ) | {"thought": think_chain, "background": RunnablePassthrough()} | RunnableLambda(print_passthrough) @@ -250,6 +250,7 @@ class ReActToolCallingAgent(LangchainMotleyAgent): def __init__( self, tools: Sequence[MotleySupportedTool], + prompt_prefix: str | None = None, description: str | None = None, name: str | None = None, think_prompt: ChatPromptTemplate | None = None, @@ -322,6 +323,7 @@ def agent_factory( return agent_executor super().__init__( + prompt_prefix=prompt_prefix, description=description, name=name, agent_factory=agent_factory, diff --git a/motleycrew/agents/llama_index/llama_index.py b/motleycrew/agents/llama_index/llama_index.py index 6413fc19..5ed18c79 100644 --- a/motleycrew/agents/llama_index/llama_index.py +++ b/motleycrew/agents/llama_index/llama_index.py @@ -1,32 +1,44 @@ """ Module description """ +import uuid from typing import Any, Optional, Sequence try: from llama_index.core.agent import AgentRunner + from llama_index.core.chat_engine.types import ChatResponseMode + from llama_index.core.agent.types import TaskStep, TaskStepOutput + from llama_index.core.chat_engine.types import AgentChatResponse except ImportError: AgentRunner = None + ChatResponseMode = None + TaskStep = None + TaskStepOutput = None + AgentChatResponse = None from langchain_core.runnables import RunnableConfig -from motleycrew.agents.parent import MotleyAgentParent +from motleycrew.agents.parent import MotleyAgentParent, DirectOutput from motleycrew.common import MotleySupportedTool from motleycrew.common import MotleyAgentFactory from motleycrew.common.utils import ensure_module_is_installed class LlamaIndexMotleyAgent(MotleyAgentParent): + def __init__( self, + prompt_prefix: str | None = None, description: str | None = None, name: str | None = None, agent_factory: MotleyAgentFactory[AgentRunner] | None = None, tools: Sequence[MotleySupportedTool] | None = None, + output_handler: MotleySupportedTool | None = None, verbose: bool = False, ): """Description Args: + prompt_prefix (:obj:`str`, optional): description (:obj:`str`, optional): name (:obj:`str`, optional): agent_factory (:obj:`MotleyAgentFactory`, optional): @@ -35,12 +47,67 @@ def __init__( """ super().__init__( description=description, + prompt_prefix=prompt_prefix, name=name, agent_factory=agent_factory, tools=tools, + output_handler=output_handler, verbose=verbose, ) + def run_step_decorator(self): + """Decorator for inclusion in the call chain of the agent, the output handler tool""" + ensure_module_is_installed("llama_index") + + def decorator(func): + def wrapper( + task_id: str, + step: Optional[TaskStep] = None, + input: Optional[str] = None, + mode: ChatResponseMode = ChatResponseMode.WAIT, + **kwargs: Any, + ): + + try: + cur_step_output = func(task_id, step, input, mode, **kwargs) + except DirectOutput as output_exc: + self.direct_output = output_exc + output = AgentChatResponse(str(output_exc.output)) + task_step = TaskStep(task_id=task_id, step_id=str(uuid.uuid4())) + cur_step_output = TaskStepOutput( + output=output, is_last=True, next_steps=[], task_step=task_step + ) + return cur_step_output + + if self.output_handler is None: + return cur_step_output + + if cur_step_output.is_last: + cur_step_output.is_last = False + task_id = cur_step_output.task_step.task_id + output_task_step = TaskStep( + task_id=task_id, + step_id=str(uuid.uuid4()), + input="You must call the `{}` tool to return the output.".format( + self.output_handler.name + ), + ) + + cur_step_output.next_steps.append(output_task_step) + + step_queue = self._agent.state.get_step_queue(task_id) + step_queue.extend(cur_step_output.next_steps) + + return cur_step_output + + return wrapper + + return decorator + + def materialize(self): + super(LlamaIndexMotleyAgent, self).materialize() + self._agent._run_step = self.run_step_decorator()(self._agent._run_step) + def invoke( self, input: dict, @@ -60,12 +127,17 @@ def invoke( prompt = self.prepare_for_invocation(input=input) output = self.agent.chat(prompt) + + if self.output_handler: + return self.direct_output.output + return output.response @staticmethod def from_agent( agent: AgentRunner, description: Optional[str] = None, + prompt_prefix: Optional[str] = None, tools: Sequence[MotleySupportedTool] | None = None, verbose: bool = False, ) -> "LlamaIndexMotleyAgent": @@ -74,6 +146,7 @@ def from_agent( Args: agent (AgentRunner): description (:obj:`str`, optional): + prompt_prefix (:obj:`str`, optional): tools (:obj:`Sequence[MotleySupportedTool]`, optional): verbose (:obj:`bool`, optional): @@ -81,6 +154,8 @@ def from_agent( LlamaIndexMotleyAgent: """ ensure_module_is_installed("llama_index") - wrapped_agent = LlamaIndexMotleyAgent(description=description, tools=tools, verbose=verbose) + wrapped_agent = LlamaIndexMotleyAgent( + description=description, prompt_prefix=prompt_prefix, tools=tools, verbose=verbose + ) wrapped_agent._agent = agent return wrapped_agent diff --git a/motleycrew/agents/llama_index/llama_index_react.py b/motleycrew/agents/llama_index/llama_index_react.py index 8160a5bb..915aabe9 100644 --- a/motleycrew/agents/llama_index/llama_index_react.py +++ b/motleycrew/agents/llama_index/llama_index_react.py @@ -21,15 +21,19 @@ class ReActLlamaIndexMotleyAgent(LlamaIndexMotleyAgent): def __init__( self, + prompt_prefix: str | None = None, description: str | None = None, name: str | None = None, tools: Sequence[MotleySupportedTool] | None = None, llm: LLM | None = None, + output_handler: MotleySupportedTool | None = None, verbose: bool = False, + max_iterations: int = 10, ): """Description Args: + prompt_prefix (:obj:`str`, optional): description (:obj:`str`, optional): name (:obj:`str`, optional): tools (:obj:`Sequence[MotleySupportedTool]`, optional): @@ -47,14 +51,17 @@ def agent_factory(tools: dict[str, MotleyTool]) -> ReActAgent: tools=llama_index_tools, llm=llm, verbose=verbose, + max_iterations=max_iterations, callback_manager=CallbackManager(callbacks), ) return agent super().__init__( + prompt_prefix=prompt_prefix, description=description, name=name, agent_factory=agent_factory, tools=tools, + output_handler=output_handler, verbose=verbose, ) diff --git a/motleycrew/agents/mixins.py b/motleycrew/agents/mixins.py new file mode 100644 index 00000000..5b24f92f --- /dev/null +++ b/motleycrew/agents/mixins.py @@ -0,0 +1,130 @@ +from typing import Any, Optional, Callable, Union, Dict, List, Tuple + +from langchain_core.agents import AgentFinish, AgentAction +from langchain_core.callbacks import CallbackManagerForChainRun +from langchain_core.messages import AIMessage +from langchain_core.tools import BaseTool, Tool + +from motleycrew.agents.parent import DirectOutput +from motleycrew.tools import MotleyTool + + +class LangchainOutputHandlingAgentMixin: + """A mixin for Langchain-based agents that support output handlers.""" + + output_handler: Optional[MotleyTool] = None + _agent_finish_blocker_tool: Optional[MotleyTool] = None + + def _create_agent_finish_blocker_tool(self) -> BaseTool: + """Create a tool that will force the agent to retry if it attempts to return the output + bypassing the output handler. + """ + + def create_agent_finish_blocking_message(input: Any = None) -> str: + return ( + f"You must call the `{self.output_handler.name}` tool to return the final output.\n" + ) + + return Tool.from_function( + name="agent_finish_blocker", + description="", + func=create_agent_finish_blocking_message, + ) + + def _is_blocker_action(self, action: AgentAction) -> bool: + """Checks whether the action of the response blocking tool""" + return bool( + isinstance(action, AgentAction) and action.tool == self._agent_finish_blocker_tool.name + ) + + def agent_plan_decorator(self, func: Callable): + """Decorator for Agent.plan() method that intercepts AgentFinish events""" + + def wrapper( + intermediate_steps: List[Tuple[AgentAction, str]], + callbacks: "Callbacks" = None, + **kwargs: Any, + ) -> Union[AgentAction, AgentFinish]: + additional_notes = [] + + if self.output_handler: + to_remove_steps = [] + for intermediate_step in intermediate_steps: + action, action_output = intermediate_step + if self._is_blocker_action(action): + # Add the interaction telling the LLM that it must use the output handler + additional_notes.append(("ai", action.tool_input)) + additional_notes.append(("user", action_output)) + to_remove_steps.append(intermediate_step) + + for to_remove_step in to_remove_steps: + intermediate_steps.remove(to_remove_step) + + if additional_notes: + kwargs["additional_notes"] = additional_notes + + step = func(intermediate_steps, callbacks, **kwargs) + + if not isinstance(step, AgentFinish): + return step + + if self.output_handler is not None: + return AgentAction( + tool=self._agent_finish_blocker_tool.name, + tool_input=step.log, + log="\nDetected AgentFinish, blocking it to force output via output handler.\n", + ) + return step + + return wrapper + + def take_next_step_decorator(self, func: Callable): + """ + Decorator for AgentExecutor._take_next_step() method that catches DirectOutput exceptions. + """ + + def wrapper( + name_to_tool_map: Dict[str, BaseTool], + color_mapping: Dict[str, str], + inputs: Dict[str, str], + intermediate_steps: List[Tuple[AgentAction, str]], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]: + + try: + step = func( + name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager + ) + except DirectOutput as direct_ex: + message = str(direct_ex.output) + return AgentFinish( + return_values={"output": direct_ex.output}, + messages=[AIMessage(content=message)], + log=message, + ) + return step + + return wrapper + + def _run_tool_direct_decorator(self, func: Callable): + """Decorator of the tool's _run method, for intercepting a DirectOutput exception""" + + def wrapper(*args, **kwargs): + try: + result = func(*args, **kwargs) + except DirectOutput as direct_exc: + return direct_exc + return result + + return wrapper + + def run_tool_direct_decorator(self, func: Callable): + """Decorator of the tool's run method, for intercepting a DirectOutput exception""" + + def wrapper(*args, **kwargs): + result = func(*args, **kwargs) + if isinstance(result, DirectOutput): + raise result + return result + + return wrapper diff --git a/motleycrew/agents/output_handler.py b/motleycrew/agents/output_handler.py index 3a1d5c03..337f0e64 100644 --- a/motleycrew/agents/output_handler.py +++ b/motleycrew/agents/output_handler.py @@ -5,6 +5,7 @@ from motleycrew.agents.abstract_parent import MotleyAgentAbstractParent from motleycrew.common.exceptions import InvalidOutput +from motleycrew.common import Defaults from motleycrew.tools import MotleyTool @@ -22,7 +23,16 @@ class MotleyOutputHandler(MotleyTool, ABC): _exceptions_to_handle: tuple[Exception] = (InvalidOutput,) """Exceptions that should be returned to the agent when raised in the `handle_output` method.""" - def __init__(self): + def __init__(self, max_iterations: int = Defaults.DEFAULT_OUTPUT_HANDLER_MAX_ITERATIONS): + """Initialize the output handler tool. + + Args: + max_iterations (int): Maximum number of iterations to run the output handler. + If an exception is raised in the `handle_output` method, the output handler will return + the exception to the agent unless the number of iterations exceeds `max_iterations`, + in which case the output handler will raise OutputHandlerMaxIterationsExceeded. + """ + self.max_iterations = max_iterations langchain_tool = self._create_langchain_tool() super().__init__(langchain_tool) diff --git a/motleycrew/agents/parent.py b/motleycrew/agents/parent.py index 0d671ba8..3f500ab6 100644 --- a/motleycrew/agents/parent.py +++ b/motleycrew/agents/parent.py @@ -1,25 +1,35 @@ """ Module description """ -from typing import TYPE_CHECKING, Optional, Sequence, Any, Callable, Dict, Union, Tuple -from functools import wraps import inspect +from typing import ( + TYPE_CHECKING, + Optional, + Sequence, + Any, + Callable, + Dict, + Type, + Union, + Tuple, +) -from langchain_core.tools import Tool -from langchain_core.runnables import Runnable from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessage, SystemMessage +from langchain_core.runnables import Runnable from langchain_core.tools import StructuredTool +from langchain_core.tools import Tool +from motleycrew.agents.output_handler import MotleyOutputHandler from pydantic import BaseModel from motleycrew.agents.abstract_parent import MotleyAgentAbstractParent -from motleycrew.agents.output_handler import MotleyOutputHandler -from motleycrew.tools import MotleyTool from motleycrew.common import MotleyAgentFactory, MotleySupportedTool +from motleycrew.common import logger, Defaults from motleycrew.common.exceptions import ( AgentNotMaterialized, CannotModifyMaterializedAgent, InvalidOutput, + OutputHandlerMaxIterationsExceeded, ) -from motleycrew.common import logger +from motleycrew.tools import MotleyTool if TYPE_CHECKING: from motleycrew import MotleyCrew @@ -31,8 +41,10 @@ def __init__(self, output: Any): class MotleyAgentParent(MotleyAgentAbstractParent, Runnable): + def __init__( self, + prompt_prefix: str | None = None, description: str | None = None, name: str | None = None, agent_factory: MotleyAgentFactory | None = None, @@ -43,6 +55,7 @@ def __init__( """Description Args: + prompt_prefix (:obj:`str`, optional): description (:obj:`str`, optional): name (:obj:`str`, optional): agent_factory (:obj:`MotleyAgentFactory`, optional): @@ -52,6 +65,7 @@ def __init__( """ self.name = name or description self.description = description # becomes tool description + self.prompt_prefix = prompt_prefix self.agent_factory = agent_factory self.tools: dict[str, MotleyTool] = {} self.output_handler = output_handler @@ -69,22 +83,26 @@ def __repr__(self): def __str__(self): return self.__repr__() - def compose_prompt(self, input_dict: dict, prompt: ChatPromptTemplate | str) -> str: + def compose_prompt( + self, input_dict: dict, prompt: ChatPromptTemplate | str + ) -> Union[str, ChatPromptTemplate]: # TODO: always cast description and prompt to ChatPromptTemplate first? prompt_messages = [] - if not self.description and not prompt: + if not self.prompt_prefix and not prompt: raise Exception("Cannot compose agent prompt without description or prompt") - if self.description: - if isinstance(self.description, ChatPromptTemplate): - prompt_messages += self.description.invoke(input_dict).to_messages() + if self.prompt_prefix: + if isinstance(self.prompt_prefix, ChatPromptTemplate): + prompt_messages += self.prompt_prefix.invoke(input_dict).to_messages() - elif isinstance(self.description, str): - prompt_messages.append(SystemMessage(content=self.description)) + elif isinstance(self.prompt_prefix, str): + prompt_messages.append(SystemMessage(content=self.prompt_prefix)) else: - raise ValueError("Agent description must be a string or a ChatPromptTemplate") + raise ValueError( + "Agent description must be a string or a ChatPromptTemplate" + ) if prompt: if isinstance(prompt, ChatPromptTemplate): @@ -96,6 +114,7 @@ def compose_prompt(self, input_dict: dict, prompt: ChatPromptTemplate | str) -> else: raise ValueError("Prompt must be a string or a ChatPromptTemplate") + # TODO: pass the unformatted messages list to agents that can handle it prompt = "\n\n".join([m.content for m in prompt_messages]) + "\n" return prompt @@ -117,7 +136,6 @@ def _prepare_output_handler(self) -> Optional[MotleyTool]: """ Wraps the output handler in one more tool layer, adding the necessary stuff for returning direct output through output handler. - Expects agent's later invocation through _run_and_catch_output method below. """ if not self.output_handler: return None @@ -126,18 +144,32 @@ def _prepare_output_handler(self) -> Optional[MotleyTool]: if isinstance(self.output_handler, MotleyOutputHandler): exceptions_to_handle = self.output_handler.exceptions_to_handle description = self.output_handler.description + max_iterations = self.output_handler.max_iterations + else: exceptions_to_handle = (InvalidOutput,) description = self.output_handler.description or f"Output handler" assert isinstance(description, str) description += "\n ONLY RETURN THE FINAL RESULT USING THIS TOOL!" + max_iterations = Defaults.DEFAULT_OUTPUT_HANDLER_MAX_ITERATIONS + + iteration = 0 def handle_agent_output(*args, **kwargs): assert self.output_handler + nonlocal iteration + try: + iteration += 1 output = self.output_handler._run(*args, **kwargs) except exceptions_to_handle as exc: - return f"{exc.__class__.__name__}: {str(exc)}" + if iteration <= max_iterations: + return f"{exc.__class__.__name__}: {str(exc)}" + raise OutputHandlerMaxIterationsExceeded( + last_call_args=args, + last_call_kwargs=kwargs, + last_exception=exc, + ) raise DirectOutput(output) @@ -150,32 +182,6 @@ def handle_agent_output(*args, **kwargs): return MotleyTool.from_langchain_tool(prepared_output_handler) - @staticmethod - def _run_and_catch_output( - action: Callable, action_args: tuple, action_kwargs: Dict[str, Any] - ) -> Tuple[bool, Any]: - """ - Catcher for the direct output from the output handler (see _prepare_output_handler). - - Args: - action (Callable): the action inside which the output handler is supposed to be called. - Usually the invocation method of the underlying agent. - action_args (tuple): the args for the action - action_kwargs (tuple): the kwargs for the action - - Returns: - tuple[bool, Any]: a tuple with a boolean indicating whether the output was caught - via DirectOutput and the output itself - """ - assert callable(action) - - try: - output = action(*action_args, **action_kwargs) - except DirectOutput as output_exc: - return True, output_exc.output - - return False, output - def materialize(self): if self.is_materialized: logger.info("Agent is already materialized, skipping materialization") @@ -186,9 +192,13 @@ def materialize(self): if inspect.signature(self.agent_factory).parameters.get("output_handler"): logger.info("Agent factory accepts output handler, passing it") - self._agent = self.agent_factory(tools=self.tools, output_handler=output_handler) + self._agent = self.agent_factory( + tools=self.tools, output_handler=output_handler + ) elif output_handler: - logger.info("Agent factory does not accept output handler, passing it as a tool") + logger.info( + "Agent factory does not accept output handler, passing it as a tool" + ) tools_with_output_handler = self.tools.copy() tools_with_output_handler[output_handler.name] = output_handler self._agent = self.agent_factory(tools=tools_with_output_handler) @@ -232,11 +242,11 @@ def add_tools(self, tools: Sequence[MotleySupportedTool]): if motley_tool.name not in self.tools: self.tools[motley_tool.name] = motley_tool - def as_tool(self, input_schema: Optional[BaseModel] = None) -> MotleyTool: + def as_tool(self, input_schema: Optional[Type[BaseModel]] = None) -> MotleyTool: """Description Args: - input_schema (:obj:`BaseModel`, optional): + input_schema (:obj:`Type[BaseModel]`, optional): Returns: MotleyTool: @@ -256,7 +266,9 @@ def call_agent(*args, **kwargs): # To be specialized if we expect structured input return MotleyTool.from_langchain_tool( Tool( - name=self.name, + name=self.name.replace( + " ", "_" + ).lower(), # OpenAI doesn't accept spaces in function names description=self.description, func=call_agent, args_schema=input_schema, diff --git a/motleycrew/common/defaults.py b/motleycrew/common/defaults.py index e6795f16..d2e3fdb0 100644 --- a/motleycrew/common/defaults.py +++ b/motleycrew/common/defaults.py @@ -1,10 +1,11 @@ """ Module description """ + from motleycrew.common import LLMFamily from motleycrew.common import GraphStoreType class Defaults: - """ Description + """Description Attributes: DEFAULT_LLM_FAMILY (str): @@ -15,8 +16,10 @@ class Defaults: MODULE_INSTALL_COMMANDS (dict): DEFAULT_NUM_THREADS (int): DEFAULT_EVENT_LOOP_SLEEP (int): + DEFAULT_OUTPUT_HANDLER_MAX_ITERATIONS (int): """ + DEFAULT_LLM_FAMILY = LLMFamily.OPENAI DEFAULT_LLM_NAME = "gpt-4o" DEFAULT_LLM_TEMPERATURE = 0.0 @@ -35,3 +38,4 @@ class Defaults: DEFAULT_NUM_THREADS = 4 DEFAULT_EVENT_LOOP_SLEEP = 1 + DEFAULT_OUTPUT_HANDLER_MAX_ITERATIONS = 5 diff --git a/motleycrew/common/exceptions.py b/motleycrew/common/exceptions.py index e1fe31f2..eb101b42 100644 --- a/motleycrew/common/exceptions.py +++ b/motleycrew/common/exceptions.py @@ -1,16 +1,18 @@ """ Module description""" -from typing import Any, Optional + +from typing import Any, Dict, Optional from motleycrew.common import Defaults class LLMFamilyNotSupported(Exception): - """ Description + """Description Args: llm_framework (str): llm_family (str): """ + def __init__(self, llm_framework: str, llm_family: str): self.llm_framework = llm_framework self.llm_family = llm_family @@ -21,7 +23,7 @@ def __str__(self) -> str: class LLMFrameworkNotSupported(Exception): def __init__(self, llm_framework: str): - """ Description + """Description Args: llm_framework (str): @@ -33,11 +35,12 @@ def __str__(self) -> str: class AgentNotMaterialized(Exception): - """ Description + """Description + + Args: + agent_name (str): + """ - Args: - agent_name (str): - """ def __init__(self, agent_name: str): self.agent_name = agent_name @@ -46,11 +49,12 @@ def __str__(self) -> str: class CannotModifyMaterializedAgent(Exception): - """ Description + """Description Args: agent_name (str): """ + def __init__(self, agent_name: str | None): self.agent_name = agent_name @@ -65,7 +69,7 @@ class TaskDependencyCycleError(Exception): class IntegrationTestException(Exception): - """ Integration tests exception + """Integration tests exception Args: test_names (list[str]): list of names of failed integration tests @@ -79,18 +83,21 @@ def __str__(self): class IpynbIntegrationTestResultNotFound(Exception): - """ Ipynb integration test not found result file exception + """Ipynb integration test not found result file exception Args: ipynb_path (str): path to running ipynb result_path (str): path to execution result file """ + def __init__(self, ipynb_path: str, result_path: str): self.ipynb_path = ipynb_path self.result_path = result_path def __str__(self): - return "File result {} of the ipynb {} execution, not found.".format(self.result_path, self.ipynb_path) + return "File result {} of the ipynb {} execution, not found.".format( + self.result_path, self.ipynb_path + ) class ModuleNotInstalled(Exception): @@ -111,7 +118,7 @@ def __str__(self): msg = "{} is not installed".format(self.module_name) if self.install_command is not None: - msg = "{}, {}".format(msg, self.install_command) + msg = "{}, please install ({})".format(msg, self.install_command) return "{}.".format(msg) @@ -133,4 +140,22 @@ def __str__(self): class InvalidOutput(Exception): """Raised in output handlers when an agent's output is not accepted""" + pass + + +class OutputHandlerMaxIterationsExceeded(BaseException): + """Raised when the output handlers iteration limit is exceeded""" + + def __init__( + self, + last_call_args: tuple, + last_call_kwargs: Dict[str, Any], + last_exception: Exception, + ): + self.last_call_args = last_call_args + self.last_call_kwargs = last_call_kwargs + self.last_exception = last_exception + + def __str__(self): + return "Maximum number of output handler iterations exceeded" diff --git a/motleycrew/crew/crew.py b/motleycrew/crew/crew.py index 44121e07..a5073259 100644 --- a/motleycrew/crew/crew.py +++ b/motleycrew/crew/crew.py @@ -1,7 +1,7 @@ import asyncio +from typing import Collection, Generator, Sequence, Optional, Any, List, Tuple import threading import time -from typing import Collection, Optional, Any, Tuple from motleycrew.agents.parent import MotleyAgentParent from motleycrew.common import logger, AsyncBackend, Defaults @@ -103,9 +103,8 @@ def register_tasks(self, tasks: Collection[Task]): def _prepare_next_unit_for_dispatch( self, running_sync_tasks: set - ) -> Tuple[MotleyAgentParent, Task, TaskUnit]: + ) -> Generator[MotleyAgentParent, Task, TaskUnit]: """Retrieve and prepare the next unit for dispatch. - Args: running_sync_tasks (set): Collection of currently running forced synchronous tasks @@ -262,8 +261,12 @@ async def _run_async(self) -> list[TaskUnit]: done_units=done_units, ) - for agent, next_task, next_unit in self._prepare_next_unit_for_dispatch(running_tasks): - async_task = asyncio.create_task(MotleyCrew._async_invoke_agent(agent, next_unit)) + for agent, next_task, next_unit in self._prepare_next_unit_for_dispatch( + running_tasks + ): + async_task = asyncio.create_task( + MotleyCrew._async_invoke_agent(agent, next_unit) + ) async_units[async_task] = (next_task, next_unit) if not async_units: @@ -289,7 +292,9 @@ def get_available_tasks(self) -> list[Task]: Task.NODE_CLASS.get_label(), Task.TASK_IS_UPSTREAM_LABEL, ) - available_task_nodes = self.graph_store.run_cypher_query(query, container=Task.NODE_CLASS) + available_task_nodes = self.graph_store.run_cypher_query( + query, container=Task.NODE_CLASS + ) return [task for task in self.tasks if task.node in available_task_nodes] def add_task_unit_to_graph(self, task: Task, unit: TaskUnitType): diff --git a/motleycrew/tasks/simple.py b/motleycrew/tasks/simple.py index b9862958..dd045386 100644 --- a/motleycrew/tasks/simple.py +++ b/motleycrew/tasks/simple.py @@ -4,15 +4,16 @@ PROMPT_TEMPLATE_WITH_DEPS (str): """ + from __future__ import annotations -from typing import TYPE_CHECKING, Any, Sequence, List, Optional -from motleycrew.tasks.task import Task -from motleycrew.tasks import TaskUnit +from typing import TYPE_CHECKING, Any, Sequence, List, Optional from motleycrew.agents.abstract_parent import MotleyAgentAbstractParent -from motleycrew.tools import MotleyTool from motleycrew.common import logger +from motleycrew.tasks import TaskUnit +from motleycrew.tasks.task import Task +from motleycrew.tools import MotleyTool if TYPE_CHECKING: from motleycrew.crew import MotleyCrew @@ -28,9 +29,11 @@ def compose_simple_task_prompt_with_dependencies( - description: str, upstream_task_units: List[TaskUnit], default_task_name: str = "Unnamed task" + description: str, + upstream_task_units: List[TaskUnit], + default_task_name: str = "Unnamed task", ) -> str: - """ Description + """Description Args: description (str): @@ -59,17 +62,17 @@ def compose_simple_task_prompt_with_dependencies( class SimpleTaskUnit(TaskUnit): - """ Description + """Description Attributes: name (str): prompt (str): - message_history (:obj:`list` of :obj:`str`): """ + name: str prompt: str - message_history: List[str] = [] + additional_params: Optional[dict[str, Any]] = None class SimpleTask(Task): @@ -81,10 +84,9 @@ def __init__( agent: MotleyAgentAbstractParent | None = None, tools: Sequence[MotleyTool] | None = None, documents: Sequence[Any] | None = None, - creator_name: str | None = None, - return_to_creator: bool = False, + additional_params: dict[str, Any] | None = None, ): - """ Description + """Description Args: crew (MotleyCrew): @@ -93,8 +95,7 @@ def __init__( agent (:obj:`MotleyAgentAbstractParent`, optional): tools (:obj:`Sequence[MotleyTool]`, optional): documents (:obj:`Sequence[Any]`, optional): - creator_name (:obj:`str`, optional): - return_to_creator (:obj:`bool`, optional): + additional_kwargs (:obj:`dict`, optional): """ super().__init__(name=name or description, task_unit_class=SimpleTaskUnit, crew=crew) self.description = description @@ -102,14 +103,11 @@ def __init__( self.tools = tools or [] # should tasks own agents or should agents own tasks? self.documents = documents # to be passed to an auto-init'd retrieval, later on - self.creator_name = creator_name or "Human" - self.return_to_creator = ( - return_to_creator # for orchestrator to know to send back to creator - ) + self.additional_params = additional_params or {} self.output = None # to be filled in by the agent(s) once the task is complete def register_completed_unit(self, unit: SimpleTaskUnit) -> None: - """ Description + """Description Args: unit (SimpleTaskUnit): @@ -124,7 +122,7 @@ def register_completed_unit(self, unit: SimpleTaskUnit) -> None: self.set_done() def get_next_unit(self) -> SimpleTaskUnit | None: - """ Description + """Description Returns: :obj:`SimpleTaskUnit`, None: @@ -142,10 +140,11 @@ def get_next_unit(self) -> SimpleTaskUnit | None: return SimpleTaskUnit( name=self.name, prompt=prompt, + additional_params=self.additional_params, ) def get_worker(self, tools: Optional[List[MotleyTool]]) -> MotleyAgentAbstractParent: - """ Description + """Description Args: tools (:obj:`List[MotleyTool]`, :obj:`None`): @@ -160,7 +159,9 @@ def get_worker(self, tools: Optional[List[MotleyTool]]) -> MotleyAgentAbstractPa if hasattr(self.agent, "is_materialized") and self.agent.is_materialized and tools: logger.warning( - "Agent %s is already materialized, can't add extra tools %s", self.agent, tools + "Agent %s is already materialized, can't add extra tools %s", + self.agent, + tools, ) if hasattr(self.agent, "add_tools") and tools: diff --git a/motleycrew/tools/__init__.py b/motleycrew/tools/__init__.py index 8500ae6c..52f3f6a0 100644 --- a/motleycrew/tools/__init__.py +++ b/motleycrew/tools/__init__.py @@ -1,9 +1,10 @@ -from .tool import MotleyTool +from motleycrew.tools.tool import MotleyTool from .autogen_chat_tool import AutoGenChatTool +from .code.postgresql_linter import PostgreSQLLinterTool +from .code.python_linter import PythonLinterTool +from .html_render_tool import HTMLRenderTool from .image.dall_e import DallEImageGeneratorTool from .llm_tool import LLMTool from .mermaid_evaluator_tool import MermaidEvaluatorTool from .python_repl import PythonREPLTool -from .code.postgresql_linter import PostgreSQLLinterTool -from .code.python_linter import PythonLinterTool diff --git a/motleycrew/tools/code/python_linter.py b/motleycrew/tools/code/python_linter.py index 2c764db4..b70c967c 100644 --- a/motleycrew/tools/code/python_linter.py +++ b/motleycrew/tools/code/python_linter.py @@ -4,11 +4,14 @@ from langchain_core.tools import StructuredTool from langchain_core.pydantic_v1 import BaseModel, Field +try: + from aider.linter import Linter +except ImportError: + Linter = None + from motleycrew.common.utils import ensure_module_is_installed from motleycrew.tools import MotleyTool -Linter = None - class PythonLinterTool(MotleyTool): diff --git a/motleycrew/tools/html_render_tool.py b/motleycrew/tools/html_render_tool.py new file mode 100644 index 00000000..f730b318 --- /dev/null +++ b/motleycrew/tools/html_render_tool.py @@ -0,0 +1,150 @@ +from datetime import datetime +from pathlib import Path +from typing import Tuple, Optional + +from motleycrew.common.utils import ensure_module_is_installed + +try: + from selenium import webdriver + from selenium.webdriver.chrome.service import Service +except ImportError: + webdriver = None + Service = None + +from langchain.tools import Tool +from langchain_core.pydantic_v1 import BaseModel, Field + +from motleycrew.tools import MotleyTool +from motleycrew.common import logger + + +class HTMLRenderer: + def __init__( + self, + work_dir: str, + executable_path: str | None = None, + headless: bool = True, + window_size: Optional[Tuple[int, int]] = None, + ): + """Helper for rendering HTML code as an image""" + ensure_module_is_installed( + "selenium", + "see documentation: https://pypi.org/project/selenium/, ChromeDriver is also required", + ) + + self.work_dir = Path(work_dir).resolve() + self.html_dir = self.work_dir / "html" + self.images_dir = self.work_dir / "images" + + self.options = webdriver.ChromeOptions() + if headless: + self.options.add_argument("--headless") + self.service = Service(executable_path=executable_path) + + self.window_size = window_size + + def render_image(self, html: str, file_name: str | None = None): + """Create image with png extension from html code + + Args: + html (str): html code for rendering image + file_name (str): file name with not extension + Returns: + file path to created image + """ + logger.info("Trying to render image from HTML code") + html_path, image_path = self.build_save_file_paths(file_name) + browser = webdriver.Chrome(options=self.options, service=self.service) + try: + if self.window_size: + logger.info("Setting window size to {}".format(self.window_size)) + browser.set_window_size(*self.window_size) + + url = "data:text/html;charset=utf-8,{}".format(html) + browser.get(url) + + logger.info("Taking screenshot") + is_created_img = browser.get_screenshot_as_file(image_path) + finally: + browser.close() + browser.quit() + + if not is_created_img: + logger.error("Failed to render image from HTML code {}".format(image_path)) + return "Failed to render image from HTML code" + + with open(html_path, "w") as f: + f.write(html) + logger.info("Saved the HTML code to {}".format(html_path)) + logger.info("Saved the rendered HTML screenshot to {}".format(image_path)) + + return image_path + + def build_save_file_paths(self, file_name: str | None = None) -> Tuple[str, str]: + """Builds paths to html and image files + + Args: + file_name (str): file name with not extension + + Returns: + tuple[str, str]: html file path and image file path + """ + + # check exists dirs: + for _dir in (self.work_dir, self.html_dir, self.images_dir): + if not _dir.exists(): + _dir.mkdir(parents=True) + + file_name = file_name or datetime.now().strftime("%Y_%m_%d__%H_%M") + html_path = self.html_dir / "{}.html".format(file_name) + image_path = self.images_dir / "{}.png".format(file_name) + + return str(html_path), str(image_path) + + +class HTMLRenderTool(MotleyTool): + + def __init__( + self, + work_dir: str, + executable_path: str | None = None, + headless: bool = True, + window_size: Optional[Tuple[int, int]] = None, + ): + """Tool for rendering HTML as image + + Args: + work_dir (str): Directory for saving images and html files + """ + renderer = HTMLRenderer( + work_dir=work_dir, + executable_path=executable_path, + headless=headless, + window_size=window_size, + ) + langchain_tool = create_render_tool(renderer) + super(HTMLRenderTool, self).__init__(langchain_tool) + + +class HTMLRenderToolInput(BaseModel): + """Input for the HTMLRenderTool. + + Attributes: + html (str): + """ + + html: str = Field(description="HTML code for rendering") + + +def create_render_tool(renderer: HTMLRenderer): + """Create langchain tool from HTMLRenderer.render_image method + + Returns: + Tool: + """ + return Tool.from_function( + func=renderer.render_image, + name="HTML rendering tool", + description="A tool for rendering HTML code as an image", + args_schema=HTMLRenderToolInput, + ) diff --git a/motleycrew/tracking/callbacks.py b/motleycrew/tracking/callbacks.py index c5c6048e..8e4c7317 100644 --- a/motleycrew/tracking/callbacks.py +++ b/motleycrew/tracking/callbacks.py @@ -231,7 +231,11 @@ def _on_function_call_start( params["parent_run_id"] = self.check_parent_id(parent_id) params["name"] = tool.name - params["input"] = '{{"query":"{}"}}'.format(function_call.get("query")) + + params_inputs = [] + for k, v in function_call.items(): + params_inputs.append('{{"{}":"{}"}}'.format(k, v)) + params["input"] = "\n".join(params_inputs) return params @@ -254,7 +258,7 @@ def _on_function_call_end( params = self._get_initial_track_event_params( LunaryRunType.TOOL, LunaryEventName.END, event_id ) - params["output"] = payload.get(EventPayload.FUNCTION_OUTPUT) + params["output"] = payload.get(EventPayload.FUNCTION_OUTPUT) if payload is not None else "" return params def _on_agent_step_start( @@ -302,11 +306,16 @@ def 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b/tests/itest_cache/validating_agent_output_ipynb/links.duckduckgo.com/d.js/f4ef541991a94de10dd5f1d211dd02d82db92305d09cded2c8592d736736b9f1.pkl new file mode 100644 index 00000000..1b16ee75 Binary files /dev/null and b/tests/itest_cache/validating_agent_output_ipynb/links.duckduckgo.com/d.js/f4ef541991a94de10dd5f1d211dd02d82db92305d09cded2c8592d736736b9f1.pkl differ diff --git a/tests/itest_golden_data/advanced_output_handling_ipynb.json b/tests/itest_golden_data/advanced_output_handling_ipynb.json index 5a9b75f6..923e2cb1 100644 --- a/tests/itest_golden_data/advanced_output_handling_ipynb.json +++ b/tests/itest_golden_data/advanced_output_handling_ipynb.json @@ -1 +1 @@ -"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n swapped = False\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n swapped = True\n if not swapped:\n break\n return arr\n\n# Test the bubble sort function\nsample_array = [64, 34, 25, 12, 22, 11, 90]\nsorted_array = bubble_sort(sample_array)\nprint(sorted_array)\n\nThe `bubble_sort` function sorts an array using the bubble sort algorithm. It works by repeatedly stepping through the list, comparing adjacent elements and swapping them if they are in the wrong order. This process is repeated until the list is sorted. The outer loop runs `n` times, where `n` is the length of the array, and the inner loop runs `n-i-1` times to avoid re-checking the already sorted elements. An optimization is added by using a `swapped` flag to detect if any swaps were made during an iteration. If no swaps were made, the array is already sorted, and the algorithm can terminate early. The test case demonstrates the function by sorting a sample array." \ No newline at end of file +"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n swapped = False\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n swapped = True\n if not swapped:\n break\n return arr\n\n# Test the bubble sort function\nsample_array = [64, 34, 25, 12, 22, 11, 90]\nsorted_array = bubble_sort(sample_array)\nprint(sorted_array)\n\nThis Python program implements the bubble sort algorithm. The `bubble_sort` function takes a list `arr` as input and sorts it in ascending order. It works by repeatedly stepping through the list, comparing adjacent elements, and swapping them if they are in the wrong order. The outer loop runs `n` times, where `n` is the length of the list, and the inner loop runs `n-i-1` times to avoid re-checking the already sorted elements. An optimization is added with a `swapped` flag to detect if any swaps were made during a pass. If no swaps were made, the list is already sorted, and the loop breaks early. The test case demonstrates the function by sorting a sample array `[64, 34, 25, 12, 22, 11, 90]`, and the sorted result `[11, 12, 22, 25, 34, 64, 90]` is printed." \ No newline at end of file diff --git a/tests/itest_golden_data/delegation_demo.json b/tests/itest_golden_data/delegation_demo.json new file mode 100644 index 00000000..c0973b84 --- /dev/null +++ b/tests/itest_golden_data/delegation_demo.json @@ -0,0 +1 @@ +"I cannot generate the illustrations for the blog post with the provided tool due to persistent errors." \ No newline at end of file diff --git a/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json b/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json index 3ad8a85f..80143b36 100644 --- a/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json +++ b/tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json @@ -1 +1 @@ -"Agent stopped due to iteration limit or time limit." \ No newline at end of file +"To solve the system of linear equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nwe first represent the system in matrix form and solve it using sympy. The solutions for \\( x \\) and \\( y \\) are:\n\n\\[ x = -23 \\]\n\\[ y = 25 \\]\n\nNext, we calculate \\( x - y \\):\n\n\\[ x - y = -23 - 25 = -48 \\]\n\nThus, the values of \\( x \\), \\( y \\), and \\( x - y \\) are:\n\n\\[ x = -23 \\]\n\\[ y = 25 \\]\n\\[ x - y = -48 \\]" \ No newline at end of file diff --git a/tests/itest_golden_data/multi_step_research_agent_ipynb.json b/tests/itest_golden_data/multi_step_research_agent_ipynb.json index 0c8ca25f..1fb98e46 100644 --- a/tests/itest_golden_data/multi_step_research_agent_ipynb.json +++ b/tests/itest_golden_data/multi_step_research_agent_ipynb.json @@ -1 +1 @@ -"Why did Arjuna kill Karna, his half-brother?\n\nArjuna killed Karna, his half-brother, primarily due to Krishna's influence and command. During their duel, Karna's chariot got stuck in the mud, and he struggled to free it. Karna called out to Arjuna and Krishna to honorably let him fix his chariot. Krishna responded by questioning the honor in Karna's past actions, specifically mentioning the humiliation of Draupadi and the killing of Abhimanyu. These references highlighted Karna's participation in dishonorable acts, justifying his killing even in a defenseless state. Despite Arjuna's hesitation upon seeing Karna defenseless, Krishna forcefully commanded, \"Kill him!\" Arjuna obeyed Krishna's command, leading to Karna's death by Arjuna's arrow." \ No newline at end of file +"Why did Arjuna kill Karna, his half-brother?\n\nKrishna played a crucial role in Arjuna's decision to kill Karna. During their duel, Karna's chariot got stuck in the mud, leaving him defenseless. As Karna struggled to free the wheel, he called out to Arjuna and Krishna to honorably let him fix his chariot. Krishna responded by questioning the honor in Karna's past actions, specifically mentioning the humiliation of Draupadi and the killing of Abhimanyu. This appeal to Karna's previous misdeeds served to justify the need for retribution. Krishna then forcefully commanded Arjuna to kill Karna, despite Arjuna's hesitation. Obeying Krishna's command, Arjuna shot an arrow that struck and killed Karna." \ No newline at end of file diff --git a/tests/itest_golden_data/validating_agent_output_ipynb.json b/tests/itest_golden_data/validating_agent_output_ipynb.json index 19031866..cb9bcc5e 100644 --- a/tests/itest_golden_data/validating_agent_output_ipynb.json +++ b/tests/itest_golden_data/validating_agent_output_ipynb.json @@ -1 +1 @@ -"## Comprehensive Analysis of AI Advancements in 2024\n\n### Key Trends in AI\n\n1. **Generative AI Adoption**:\n - Generative AI tools like ChatGPT have reached mass adoption, significantly impacting various industries. In 2024, organizations are not only adopting generative AI but also deriving substantial business value from it. This trend is expected to continue, with more people and businesses utilizing generative AI models for diverse applications.\n\n2. **Multimodal AI**:\n - Multimodal AI, which processes multiple input types such as text, images, and sound, is gaining traction. This approach mimics the human ability to process diverse sensory information and is expected to lead to more sophisticated AI applications.\n\n3. **Smaller and Open Source Models**:\n - There is a shift towards smaller language models and open-source advancements. These models are becoming more accessible, allowing for customized local models and data pipelines. This trend is democratizing AI, making it more available to smaller organizations and individual developers.\n\n4. **Model Optimization**:\n - Model optimization is becoming more accessible, addressing issues like GPU shortages and cloud costs. This trend is crucial for making AI more efficient and cost-effective.\n\n5. **Transfer Learning**:\n - Transfer learning, which uses preexisting models as a starting point for new tasks, is reducing the time and data needed to develop accurate models. This trend is expected to create new job opportunities focused on developing and maintaining these models.\n\n### Breakthrough Technologies\n\n1. **Advanced Generative AI Models**:\n - State-of-the-art generative AI models are becoming more useful for non-tech individuals. This democratization of AI technology is expected to lead to widespread experimentation and innovation.\n\n2. **Claude by Anthropic**:\n - Anthropic, founded by former OpenAI executives, has made significant strides with its large language model, Claude. This model is expected to compete with other leading AI technologies and drive further advancements in the field.\n\n3. **AI in Business Operations**:\n - AI is driving significant reductions in operational expenses, with businesses expecting a 37% reduction in costs in 2024. This is due to AI's ability to lower error rates and improve efficiency.\n\n### Potential Industry Impacts\n\n1. **Economic Transformation**:\n - 2024 is expected to be a pivotal year for AI, with generative AI triggering a metamorphosis across the global economic landscape. Businesses are waking up to the broad potential of AI, leading to transformative changes in various sectors.\n\n2. **Job Market Evolution**:\n - The rise of AI technologies is creating new job opportunities, particularly in developing, maintaining, and optimizing AI models. However, it may also lead to job displacement in certain sectors, necessitating a focus on reskilling and upskilling the workforce.\n\n3. **Environmental and Political Impacts**:\n - The adoption of AI is expected to have significant environmental and political impacts. For instance, AI can contribute to more efficient resource management and decision-making processes, but it also raises concerns about data privacy and ethical considerations.\n\n4. **Industry-Specific Applications**:\n - Different industries are adopting AI at varying rates. For example, sectors like healthcare, finance, and manufacturing are seeing substantial AI integration, leading to improved outcomes and efficiencies.\n\n### AI Applications in Medicine\n\n1. **Multimodal AI in Healthcare**:\n - AI is being used to assimilate information from diverse sources such as medical images, textual clinical reports, and voice data. This multimodal approach is driving new understandings about health and healthcare, leading to more accurate diagnoses and personalized treatment plans.\n\n2. **Improving Health Outcomes**:\n - AI technologies are transforming care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI is being used to predict patient outcomes, optimize treatment plans, and monitor patient health in real-time.\n\n3. **Complementary Tool in Healthcare**:\n - AI is increasingly seen as a complementary tool rather than a replacement in healthcare. It assists healthcare professionals in making more informed decisions, thereby enhancing the quality of care provided to patients.\n\n4. **Responsible Data Usage**:\n - The responsible usage of patient data is crucial in AI applications in medicine. Ensuring data privacy and security while leveraging AI for healthcare advancements is a key focus area.\n\n### Conclusion\n\nThe advancements in AI in 2024 are marked by significant trends, breakthrough technologies, and transformative industry impacts. Generative AI, multimodal AI, and model optimization are key trends driving the field forward. Breakthrough technologies like advanced generative models and Claude by Anthropic are setting new benchmarks. The industry impacts are profound, with economic transformation, job market evolution, and sector-specific applications leading the way. In healthcare, AI is revolutionizing care delivery, improving health outcomes, and enhancing patient safety. As AI continues to evolve, it is crucial for stakeholders to stay informed and adapt to these changes to harness the full potential of this revolutionary technology." \ No newline at end of file +"### Comprehensive Analysis of the Latest Advancements in AI in 2024\n\n#### Key Trends in AI 2024\n\n1. **Multimodal AI**:\n - Multimodal AI goes beyond traditional single-mode data processing to integrate and process multiple types of data (e.g., text, images, audio) simultaneously. This trend is expected to enhance the capabilities of AI systems, making them more versatile and effective in various applications.\n\n2. **Open Source AI Models**:\n - Open source pretrained AI models are gaining significant traction. These models empower businesses to accelerate growth by combining them with private or real-time data, enhancing productivity and cost-efficiency. Companies like IBM are actively contributing to this space.\n\n3. **Generative AI**:\n - Generative AI tools, such as ChatGPT, have reached mass adoption and are becoming increasingly useful for the general public. This trend is expected to continue, with more people tinkering with numerous small AI models.\n\n4. **Model Optimization and Accessibility**:\n - Model optimization is becoming more accessible, allowing for customized local models and data pipelines. This trend is driven by the need to reduce GPU shortages and cloud costs.\n\n5. **Ethics and Safety**:\n - There is a deepening focus on ethics, safety, and the evolving regulatory landscape in AI development and deployment strategies. This reflects a more sophisticated and cautious approach to AI.\n\n6. **Realistic Expectations**:\n - The industry is moving towards more realistic expectations of AI capabilities, avoiding the hype and focusing on practical, achievable outcomes.\n\n#### Breakthrough Technologies in AI 2024\n\n1. **AI-Powered Scientific Discovery**:\n - AI is playing a crucial role in scientific discovery, enabling researchers to make breakthroughs faster and more efficiently.\n\n2. **Elastocalorics**:\n - This technology, which involves materials that change temperature when subjected to mechanical stress, is among the top emerging technologies of 2024.\n\n3. **Precision Education**:\n - AI is fostering breakthroughs in precision education, with advanced natural language processing and machine learning systems being launched to create personalized learning experiences.\n\n#### AI Applications in Medicine\n\n1. **Medical Imaging and Diagnostics**:\n - AI is revolutionizing medical imaging by improving the accuracy and speed of diagnostics. AI algorithms can analyze medical images such as X-rays, MRIs, and CT scans to detect abnormalities and assist radiologists in making more accurate diagnoses.\n\n2. **Personalized Medicine**:\n - AI is enabling personalized medicine by analyzing large datasets of patient information to identify patterns and predict individual responses to treatments. This allows for more tailored and effective treatment plans.\n\n3. **Drug Discovery and Development**:\n - AI is accelerating the drug discovery process by predicting how different compounds will interact with targets in the body. This reduces the time and cost associated with bringing new drugs to market.\n\n4. **Virtual Health Assistants**:\n - AI-powered virtual health assistants are providing patients with 24/7 access to medical information and support. These assistants can answer health-related questions, provide medication reminders, and help manage chronic conditions.\n\n5. **Predictive Analytics for Patient Care**:\n - AI is being used to predict patient outcomes and identify those at risk of developing certain conditions. This allows for early intervention and more proactive management of patient health.\n\n#### Potential Industry Impacts\n\n1. **Manufacturing**:\n - The manufacturing industry is expected to see the largest financial impact due to AI. AI-driven automation and optimization are forecasted to significantly enhance productivity and reduce operational costs.\n\n2. **Cost Reduction**:\n - AI is expected to drive a 37% reduction in costs for businesses in 2024. This is due to the dramatic reduction in mistakes and operational expenses facilitated by AI technologies.\n\n3. **Business Value from Generative AI**:\n - Organizations are beginning to derive substantial business value from generative AI. According to a McKinsey Global Survey, 65% of respondents report that their organizations are regularly using generative AI, nearly double the percentage from the previous year.\n\n4. **Economic Transformation**:\n - 2024 is expected to be a watershed moment for generative AI, triggering a metamorphosis across the global economic landscape as businesses recognize its broad potential.\n\n### Conclusion\n\nThe advancements in AI in 2024 are marked by significant trends such as multimodal AI, open source models, and generative AI. Breakthrough technologies like AI-powered scientific discovery and precision education are set to revolutionize various sectors. The manufacturing industry, in particular, is poised to benefit greatly from AI, with substantial cost reductions and productivity gains. In healthcare, AI is transforming medical imaging, personalized medicine, drug discovery, and patient care. As AI continues to evolve, the focus on ethics, safety, and realistic expectations will be crucial in ensuring its responsible and effective deployment." \ No newline at end of file diff --git a/tests/run_integration_tests.py b/tests/run_integration_tests.py index 853ed073..4f960dc6 100644 --- a/tests/run_integration_tests.py +++ b/tests/run_integration_tests.py @@ -1,39 +1,35 @@ -import shutil -from typing import Optional - -import os import argparse -from pathlib import Path -import traceback import difflib import json +import os +import shutil +import sys +import traceback from copy import copy from functools import partial +from pathlib import Path +from typing import Optional -from dotenv import load_dotenv import nbformat +from dotenv import load_dotenv +from motleycache import enable_cache, set_cache_location, set_strong_cache, set_cache_blacklist from nbconvert.preprocessors import ExecutePreprocessor from nbformat.v4.nbbase import new_code_cell +project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, project_root) # ensure the examples are importable + +from examples.delegation_demo import main as delegation_demo_main +from examples.old.single_llama_index import main as single_llama_index_main +from motleycrew.common import logger, configure_logging from motleycrew.common.exceptions import ( IntegrationTestException, IpynbIntegrationTestResultNotFound, ) -from motleycrew.common import logger, configure_logging - -from motleycache import ( - enable_cache, - set_cache_location, - set_strong_cache, -) - -from examples.old.delegation_crewai import main as delegation_crewai_main -from examples.old.single_llama_index import main as single_llama_index_main - INTEGRATION_TESTS = { "single_llama_index": single_llama_index_main, - "delegation_crewai": delegation_crewai_main, + "delegation_demo": delegation_demo_main, } IPYNB_INTEGRATION_TESTS = { @@ -72,7 +68,8 @@ def get_args_parser(): """Argument parser""" parser = argparse.ArgumentParser( - description="Run integration tests", formatter_class=argparse.ArgumentDefaultsHelpFormatter + description="Run integration tests", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( "--test-name", @@ -83,7 +80,10 @@ def get_args_parser(): ) parser.add_argument("--cache-dir", type=str, help="Cache directory", default=DEFAULT_CACHE_DIR) parser.add_argument( - "--golden-dir", type=str, help="Reference data directory", default=DEFAULT_GOLDEN_DIR + "--golden-dir", + type=str, + help="Reference data directory", + default=DEFAULT_GOLDEN_DIR, ) parser.add_argument( "--update-golden", @@ -186,22 +186,6 @@ def run_ipynb(ipynb_path: str, strong_cache: bool = False, cache_sub_dir: str = return result -def set_tiktoken_cache_dir(cache_dir: str) -> str: - """Set tiktoken cache directory to env, return its old value to set it back after the tests""" - old_value = os.environ.get(TIKTOKEN_CACHE_DIR_ENV_VAR) - - tiktoken_cache_dir = os.path.join(cache_dir, "tiktoken_cache") - os.environ[TIKTOKEN_CACHE_DIR_ENV_VAR] = tiktoken_cache_dir - return old_value - - -def unset_tiktoken_cache_dir(old_value: Optional[str] = None): - """Restore the tiktoken cache environment variable, so it is not used outside the tests""" - if old_value is not None: - os.environ[TIKTOKEN_CACHE_DIR_ENV_VAR] = old_value - os.environ.pop(TIKTOKEN_CACHE_DIR_ENV_VAR, None) - - def build_ipynb_integration_tests(is_minimal: bool = False) -> dict: """Build and return dict of ipynb integration tests functions""" test_functions = {} @@ -238,8 +222,6 @@ def run_integration_tests( integration_test_key = "minimal_{}".format(test_key) integration_tests[integration_test_key] = test_value - old_tiktoken_cache_dir = set_tiktoken_cache_dir(cache_dir) - for current_test_name, test_fn in integration_tests.items(): if test_name is not None and test_name != current_test_name: continue @@ -260,7 +242,10 @@ def run_integration_tests( set_cache_location(cache_sub_dir) if current_test_name in IPYNB_INTEGRATION_TESTS: - test_fn_kwargs = {"strong_cache": strong_cache, "cache_sub_dir": cache_sub_dir} + test_fn_kwargs = { + "strong_cache": strong_cache, + "cache_sub_dir": cache_sub_dir, + } else: test_fn_kwargs = {} @@ -272,14 +257,15 @@ def run_integration_tests( ): if update_golden: logger.info( - "Skipping check and updating golden data for test: %s", current_test_name + "Skipping check and updating golden data for test: %s", + current_test_name, ) write_content(golden_dir, current_test_name, test_result) else: excepted_result = read_golden_data(golden_dir, current_test_name) compare_results(test_result, excepted_result) - except Exception as e: + except BaseException as e: logger.error("Test %s failed: %s", current_test_name, str(e)) failed_tests[current_test_name] = traceback.format_exc() @@ -291,7 +277,6 @@ def run_integration_tests( raise IntegrationTestException(test_names=list(failed_tests.keys())) logger.info("All tests passed!") - unset_tiktoken_cache_dir(old_tiktoken_cache_dir) def main(): @@ -302,6 +287,7 @@ def main(): args = parser.parse_args() enable_cache() + set_cache_blacklist(["*openaipublic.blob.core.windows.net/encodings*"]) run_integration_tests( cache_dir=args.cache_dir, golden_dir=args.golden_dir, diff --git a/tests/test_agents/__init__.py b/tests/test_agents/__init__.py index e69de29b..313c43ae 100644 --- a/tests/test_agents/__init__.py +++ b/tests/test_agents/__init__.py @@ -0,0 +1,21 @@ +from typing import Type +from langchain_core.tools import BaseTool +from langchain_core.pydantic_v1 import BaseModel, Field + + +class MockToolInput(BaseModel): + """Input for the MockTool tool.""" + + tool_input: str = Field(description="tool_input") + + +class MockTool(BaseTool): + """Mock tool for run agent tests""" + + name: str = "mock tool" + description: str = "Mock tool for tests" + + args_schema: Type[BaseModel] = MockToolInput + + def _run(self, tool_input: str, *args, **kwargs): + return tool_input diff --git a/tests/test_agents/test_agents.py b/tests/test_agents/test_agents.py index 6c540488..7c00a0e2 100644 --- a/tests/test_agents/test_agents.py +++ b/tests/test_agents/test_agents.py @@ -1,7 +1,6 @@ import os import pytest -from langchain_community.tools import DuckDuckGoSearchRun from langchain_core.prompts.chat import ChatPromptTemplate from motleycrew.agents.crewai.crewai_agent import CrewAIMotleyAgent from motleycrew.agents.langchain.tool_calling_react import ReActToolCallingAgent @@ -9,6 +8,7 @@ from motleycrew.common.exceptions import AgentNotMaterialized, CannotModifyMaterializedAgent from motleycrew.tools.python_repl import create_repl_tool from motleycrew.tools.tool import MotleyTool +from tests.test_agents import MockTool os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY" @@ -22,12 +22,13 @@ def crewai_agent(self): agent = CrewAIMotleyAgent( role="Senior Research Analyst", goal="Uncover cutting-edge developments in AI and data science", - backstory="""You work at a leading tech think tank. + prompt_prefix="""You work at a leading tech think tank. Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting actionable insights.""", + backstory="", verbose=True, delegation=False, - tools=[DuckDuckGoSearchRun()], + tools=[MockTool()], ) return agent @@ -35,8 +36,9 @@ def crewai_agent(self): def langchain_agent(self): agent = ReActToolCallingAgent( name="AI writer agent", - description="Generate AI-generated content", - tools=[DuckDuckGoSearchRun()], + prompt_prefix="Generate AI-generated content", + description="AI-generated content", + tools=[MockTool()], verbose=True, ) return agent @@ -44,8 +46,9 @@ def langchain_agent(self): @pytest.fixture(scope="class") def llama_index_agent(self): agent = ReActLlamaIndexMotleyAgent( - description="Uncover cutting-edge developments in AI and data science", - tools=[DuckDuckGoSearchRun()], + prompt_prefix="Uncover cutting-edge developments in AI and data science", + description="AI researcher", + tools=[MockTool()], verbose=True, ) return agent @@ -62,7 +65,7 @@ def agent(self, request, crewai_agent, langchain_agent, llama_index_agent): @pytest.mark.parametrize("agent", test_agents_names, indirect=True) def test_add_tools(self, agent): assert len(agent.tools) == 1 - tools = [DuckDuckGoSearchRun()] + tools = [MockTool()] agent.add_tools(tools) assert len(agent.tools) == 1 @@ -90,5 +93,5 @@ def test_compose_prompt(self, agent): task_dict = {"topic": "AI"} prompt = agent.compose_prompt(input_dict=task_dict, prompt=task_prompt) - assert str(agent.description) in prompt + assert str(agent.prompt_prefix) in prompt assert "What are the latest AI trends?" in prompt diff --git a/tests/test_agents/test_langchain_output_handler.py b/tests/test_agents/test_langchain_output_handler.py new file mode 100644 index 00000000..84621751 --- /dev/null +++ b/tests/test_agents/test_langchain_output_handler.py @@ -0,0 +1,113 @@ +import pytest +from langchain_core.agents import AgentFinish, AgentAction + +from motleycrew.agents import MotleyOutputHandler +from motleycrew.agents.langchain.tool_calling_react import ReActToolCallingAgent +from motleycrew.agents.parent import DirectOutput +from motleycrew.common.exceptions import InvalidOutput, OutputHandlerMaxIterationsExceeded +from tests.test_agents import MockTool + +invalid_output = "Add more information about AI applications in medicine." + + +class ReportOutputHandler(MotleyOutputHandler): + def handle_output(self, output: str): + if "medical" not in output.lower(): + raise InvalidOutput(invalid_output) + + return {"checked_output": output} + + +def fake_agent_plan(intermediate_steps, step, **kwargs): + return step + + +def fake_agent_take_next_step( + name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager +): + + output_handler = name_to_tool_map.get("output_handler") + result = output_handler._run(inputs) + + if isinstance(result, DirectOutput): + raise result + + return result + + +@pytest.fixture +def agent(): + agent = ReActToolCallingAgent( + tools=[MockTool()], + verbose=True, + chat_history=True, + output_handler=ReportOutputHandler(max_iterations=5), + ) + agent.materialize() + object.__setattr__(agent._agent, "plan", fake_agent_plan) + object.__setattr__(agent.agent, "plan", agent.agent_plan_decorator(agent.agent.plan)) + + object.__setattr__(agent._agent, "_take_next_step", fake_agent_take_next_step) + object.__setattr__( + agent._agent, + "_take_next_step", + agent.take_next_step_decorator(agent.agent._take_next_step), + ) + return agent + + +@pytest.fixture +def run_kwargs(agent): + agent_executor = agent.agent.bound.bound.last.default.bound + + run_kwargs = { + "name_to_tool_map": {tool.name: tool for tool in agent_executor.tools}, + "color_mapping": {}, + "inputs": {}, + "intermediate_steps": [], + } + return run_kwargs + + +def test_agent_plan(agent): + agent_executor = agent.agent + agent_action = AgentAction("tool", "tool_input", "tool_log") + step = agent_executor.plan([], agent_action) + assert agent_action == step + + return_values = {"output": "test_output"} + agent_finish = AgentFinish(return_values=return_values, log="test_output") + + step = agent_executor.plan([], agent_finish) + assert isinstance(step, AgentAction) + assert step.tool == agent._agent_finish_blocker_tool.name + assert step.tool_input == "test_output" + + +def test_agent_take_next_step(agent, run_kwargs): + + # test wrong output + input_data = "Latest advancements in AI in 2024." + run_kwargs["inputs"] = input_data + step_result = agent.agent._take_next_step(**run_kwargs) + assert step_result == f"{InvalidOutput.__name__}: {invalid_output}" + + # test correct output + input_data = "Latest advancements in medical AI in 2024." + run_kwargs["inputs"] = input_data + step_result = agent.agent._take_next_step(**run_kwargs) + assert isinstance(step_result, AgentFinish) + assert isinstance(step_result.return_values, dict) + output_result = step_result.return_values.get("output") + assert output_result == {"checked_output": input_data} + + +def test_output_handler_max_iteration(agent, run_kwargs): + input_data = "Latest advancements in AI in 2024." + run_kwargs["inputs"] = input_data + + with pytest.raises(OutputHandlerMaxIterationsExceeded): + for iteration in range(agent.output_handler.max_iterations + 1): + agent.agent._take_next_step(**run_kwargs) + + assert iteration == agent.output_handler.max_iterations diff --git a/tests/test_agents/test_llama_index_output_handler.py b/tests/test_agents/test_llama_index_output_handler.py new file mode 100644 index 00000000..fd451617 --- /dev/null +++ b/tests/test_agents/test_llama_index_output_handler.py @@ -0,0 +1,176 @@ +import uuid +from collections import deque +import pytest + +from langchain_core.tools import StructuredTool + +try: + from llama_index.core.agent.types import Task, TaskStep, TaskStepOutput + from llama_index.core.chat_engine.types import AgentChatResponse + from llama_index.core.agent.runner.base import TaskState +except ImportError: + Task = None + TaskStep = None + TaskStepOutput = None + AgentChatResponse = None + TaskState = None + +from motleycrew.agents.llama_index import ReActLlamaIndexMotleyAgent +from motleycrew.agents import MotleyOutputHandler +from motleycrew.common.exceptions import ( + InvalidOutput, + OutputHandlerMaxIterationsExceeded, +) +from tests.test_agents import MockTool + + +invalid_output = "Add more information about AI applications in medicine." + + +class ReportOutputHandler(MotleyOutputHandler): + def handle_output(self, output: str): + if "medical" not in output.lower(): + raise InvalidOutput(invalid_output) + + return {"checked_output": output} + + +def fake_run_step(*args, **kwargs): + task_step_output = kwargs.get("task_step_output") + output_handler = kwargs.get("output_handler") + output_handler_input = kwargs.get("output_handler_input") + if output_handler: + output_handler_result = output_handler._run(output_handler_input) + task_step_output.output = AgentChatResponse(response=output_handler_result) + + return task_step_output + + +@pytest.fixture +def agent(): + + agent = ReActLlamaIndexMotleyAgent( + description="Your goal is to uncover cutting-edge developments in AI and data science", + tools=[MockTool()], + output_handler=ReportOutputHandler(max_iterations=5), + verbose=True, + ) + agent.materialize() + agent._agent._run_step = fake_run_step + agent._agent._run_step = agent.run_step_decorator()(agent._agent._run_step) + + return agent + + +@pytest.fixture +def task_data(agent): + if agent is None: + return + + task = Task(input="User input", memory=agent._agent.memory) + task_step = TaskStep(task_id=task.task_id, step_id=str(uuid.uuid4()), input="Test input") + + task_state = TaskState( + task=task, + step_queue=deque([task_step]), + ) + agent._agent.state.task_dict[task.task_id] = task_state + + task_step_output = TaskStepOutput( + is_last=False, + task_step=task_step, + output=AgentChatResponse(response="Test response"), + next_steps=[], + ) + return task, task_step_output + + +def find_output_handler(agent: ReActLlamaIndexMotleyAgent) -> StructuredTool: + agent_worker = agent.agent.agent_worker + output_handler = None + for tool in agent_worker._get_tools(""): + if tool.metadata.name == "output_handler": + output_handler = tool.to_langchain_tool() + break + return output_handler + + +def test_run_step(agent, task_data): + if agent is None: + return + + task, task_step_output = task_data + + # test not last output + cur_step_output = agent._agent._run_step("", task_step_output=task_step_output) + assert task_step_output == cur_step_output + assert not cur_step_output.next_steps + + # test is last output + task_step_output.is_last = True + cur_step_output = agent._agent._run_step("", task_step_output=task_step_output) + + assert task_step_output == cur_step_output + assert not cur_step_output.is_last + assert cur_step_output.next_steps + + step_queue = agent._agent.state.get_step_queue(task.task_id) + _task_step = step_queue.pop() + + assert _task_step.task_id == task.task_id + assert _task_step.input == "You must call the `{}` tool to return the output.".format( + agent.output_handler.name + ) + + # test direct output + output_handler = find_output_handler(agent) + if output_handler is None: + return + + # test wrong output + output_handler_input = "Latest advancements in AI in 2024." + cur_step_output = agent._agent._run_step( + "", + task_step_output=task_step_output, + output_handler=output_handler, + output_handler_input=output_handler_input, + ) + assert cur_step_output.output.response == "InvalidOutput: {}".format(invalid_output) + + # test correct output + output_handler_input = "Latest advancements in medical AI in 2024." + cur_step_output = agent._agent._run_step( + "", + task_step_output=task_step_output, + output_handler=output_handler, + output_handler_input=output_handler_input, + ) + assert cur_step_output.is_last + assert cur_step_output.output.response == "{{'checked_output': '{}'}}".format( + output_handler_input + ) + assert hasattr(agent, "direct_output") + assert agent.direct_output.output == {"checked_output": output_handler_input} + + +def test_output_handler_max_iteration(agent, task_data): + if agent is None: + return + + task, task_step_output = task_data + + output_handler = find_output_handler(agent) + if output_handler is None: + return + + output_handler_input = "Latest advancements in AI in 2024." + with pytest.raises(OutputHandlerMaxIterationsExceeded): + for iteration in range(agent.output_handler.max_iterations + 1): + + agent._agent._run_step( + "", + task_step_output=task_step_output, + output_handler=output_handler, + output_handler_input=output_handler_input, + ) + assert iteration == agent.output_handler.max_iterations diff --git a/tests/test_tools/test_html_render_tool.py b/tests/test_tools/test_html_render_tool.py new file mode 100644 index 00000000..318b9e0b --- /dev/null +++ b/tests/test_tools/test_html_render_tool.py @@ -0,0 +1,25 @@ +import os + +import pytest + +from motleycrew.tools import HTMLRenderTool + + +@pytest.mark.fat +@pytest.mark.parametrize( + "html_code", + [ + "

Test html

", + "

Test html

", + ], +) +def test_render_tool(tmpdir, html_code): + html_render_tool = HTMLRenderTool(work_dir=str(tmpdir), window_size=(800, 600), headless=False) + + image_path = html_render_tool.invoke(html_code) + assert os.path.exists(image_path) + image_dir, image_file_name = os.path.split(image_path) + image_name = ".".join(image_file_name.split(".")[:-1]) + html_file_name = "{}.html".format(image_name) + html_file_path = os.path.join(tmpdir, "html", html_file_name) + assert os.path.exists(html_file_path)