wehub-resource-sync
141 lines
7.1 KiB
Python
141 lines
7.1 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import logging
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from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
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from semantic_kernel import Kernel
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from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
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from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
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from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
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from semantic_kernel.contents.chat_history import ChatHistory
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from semantic_kernel.core_plugins.crew_ai import CrewAIEnterprise
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from semantic_kernel.functions.kernel_parameter_metadata import KernelParameterMetadata
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logging.basicConfig(level=logging.INFO)
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async def using_crew_ai_enterprise():
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# Create an instance of the CrewAI Enterprise Crew
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async with CrewAIEnterprise() as crew:
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#####################################################################
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# Using the CrewAI Enterprise Crew directly #
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#####################################################################
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# The required inputs for the Crew must be known in advance. This example is modeled after the
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# Enterprise Content Marketing Crew Template and requires the following inputs:
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inputs = {"company": "CrewAI", "topic": "Agentic products for consumers"}
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# Invoke directly with our inputs
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kickoff_id = await crew.kickoff(inputs)
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print(f"CrewAI Enterprise Crew kicked off with ID: {kickoff_id}")
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# Wait for completion
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result = await crew.wait_for_crew_completion(kickoff_id)
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print("CrewAI Enterprise Crew completed with the following result:")
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print(result)
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#####################################################################
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# Using the CrewAI Enterprise as a Plugin #
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#####################################################################
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# Define the description of the Crew. This will used as the semantic description of the plugin.
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crew_description = (
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"Conducts thorough research on the specified company and topic to identify emerging trends,"
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"analyze competitor strategies, and gather data-driven insights."
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)
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# The required inputs for the Crew must be known in advance. This example is modeled after the
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# Enterprise Content Marketing Crew Template and requires string inputs for the company and topic.
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# We need to describe the type and purpose of each input to allow the LLM to invoke the crew as expected.
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crew_input_parameters = [
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KernelParameterMetadata(
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name="company",
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type="string",
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type_object=str,
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description="The name of the company that should be researched",
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is_required=True,
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),
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KernelParameterMetadata(
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name="topic",
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type="string",
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type_object=str,
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description="The topic that should be researched",
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is_required=True,
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),
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]
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# Create the CrewAI Plugin. This builds a plugin that can be added to the Kernel and invoked like any other
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# plugin. The plugin will contain the following functions:
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# - kickoff: Starts the Crew with the specified inputs and returns the Id of the scheduled kickoff.
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# - kickoff_and_wait: Starts the Crew with the specified inputs and waits for the Crew to complete before
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# returning the result.
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# - wait_for_completion: Waits for the specified Crew kickoff to complete and returns the result.
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# - get_status: Gets the status of the specified Crew kickoff.
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crew_plugin = crew.create_kernel_plugin(
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name="EnterpriseContentMarketingCrew",
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description=crew_description,
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parameters=crew_input_parameters,
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)
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# Configure the kernel for chat completion and add the CrewAI plugin.
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kernel, chat_completion, settings = configure_kernel_for_chat()
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kernel.add_plugin(crew_plugin)
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# Create a chat history to store the system message, initial messages, and the conversation.
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history = ChatHistory()
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history.add_system_message("You are an AI assistant that can help me with research.")
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history.add_user_message(
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"I'm looking for emerging marketplace trends about Crew AI and their concumer AI products."
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)
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# Invoke the chat completion service with enough information for the CrewAI plugin to be invoked.
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response = await chat_completion.get_chat_message_content(history, settings, kernel=kernel)
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print(response)
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# expected output:
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# INFO:semantic_kernel.connectors.ai.open_ai.services.open_ai_handler:OpenAI usage: ...
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# INFO:semantic_kernel.connectors.ai.chat_completion_client_base:processing 1 tool calls in parallel.
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# INFO:semantic_kernel.kernel:Calling EnterpriseContentMarketingCrew-kickoff_and_wait function with args:
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# {"company":"Crew AI","topic":"emerging marketplace trends in consumer AI products"}
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# INFO:semantic_kernel.functions.kernel_function:Function EnterpriseContentMarketingCrew-kickoff_and_wait
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# invoking.
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# INFO:semantic_kernel.core_plugins.crew_ai.crew_ai_enterprise:CrewAI Crew kicked off with Id: *****
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# INFO:semantic_kernel.core_plugins.crew_ai.crew_ai_enterprise:CrewAI Crew with kickoff Id: ***** completed with
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# status: SUCCESS
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# INFO:semantic_kernel.functions.kernel_function:Function EnterpriseContentMarketingCrew-kickoff_and_wait
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# succeeded.
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# Here are some emerging marketplace trends related to Crew AI and their consumer AI products, along with
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# suggested content pieces to explore these trends: ...
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def configure_kernel_for_chat() -> tuple[Kernel, ChatCompletionClientBase, PromptExecutionSettings]:
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kernel = Kernel()
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# You can select from the following chat completion services that support function calling:
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# - Services.OPENAI
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# - Services.AZURE_OPENAI
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# - Services.AZURE_AI_INFERENCE
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# - Services.ANTHROPIC
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# - Services.BEDROCK
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# - Services.GOOGLE_AI
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# - Services.MISTRAL_AI
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# - Services.OLLAMA
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# - Services.ONNX
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# - Services.VERTEX_AI
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# - Services.DEEPSEEK
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# Please make sure you have configured your environment correctly for the selected chat completion service.
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chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.OPENAI)
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# Configure the function choice behavior. Here, we set it to Auto, where auto_invoke=True by default.
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# With `auto_invoke=True`, the model will automatically choose and call functions as needed.
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request_settings.function_choice_behavior = FunctionChoiceBehavior.Auto()
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# Pass the request settings to the kernel arguments.
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kernel.add_service(chat_completion_service)
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return kernel, chat_completion_service, request_settings
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if __name__ == "__main__":
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asyncio.run(using_crew_ai_enterprise())
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