mirror of https://github.com/dapr/dapr-agents.git
69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
from dapr_agents import OpenAIChatClient, NVIDIAChatClient
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from dapr.ext.workflow import DaprWorkflowContext
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from dapr_agents.workflow import WorkflowApp, task, workflow
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from dotenv import load_dotenv
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import os
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import logging
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load_dotenv()
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nvidia_llm = NVIDIAChatClient(
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model="meta/llama-3.1-8b-instruct", api_key=os.getenv("NVIDIA_API_KEY")
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)
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oai_llm = OpenAIChatClient(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o",
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base_url=os.getenv("OPENAI_API_BASE_URL"),
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)
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azoai_llm = OpenAIChatClient(
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api_key=os.getenv("AZURE_OPENAI_API_KEY"),
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azure_deployment="gpt-4o-mini",
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azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
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azure_api_version="2024-12-01-preview",
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)
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@workflow
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def test_workflow(ctx: DaprWorkflowContext):
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"""
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A simple workflow that uses a multi-modal task chain.
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"""
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oai_results = yield ctx.call_activity(invoke_oai, input="Peru")
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azoai_results = yield ctx.call_activity(invoke_azoai, input=oai_results)
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nvidia_results = yield ctx.call_activity(invoke_nvidia, input=azoai_results)
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return nvidia_results
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@task(
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description="What is the name of the capital of {country}?. Reply with just the name.",
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llm=oai_llm,
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)
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def invoke_oai(country: str) -> str:
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pass
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@task(description="What is a famous thing about {capital}?", llm=azoai_llm)
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def invoke_azoai(capital: str) -> str:
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pass
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@task(
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description="Context: {context}. From the previous context. Pick one thing to do.",
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llm=nvidia_llm,
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)
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def invoke_nvidia(context: str) -> str:
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pass
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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wfapp = WorkflowApp()
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results = wfapp.run_and_monitor_workflow_sync(workflow=test_workflow)
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logging.info("Workflow results: %s", results)
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logging.info("Workflow completed successfully.")
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