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42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import os
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from aworld.agents.llm_agent import Agent
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from aworld.config.conf import AgentConfig
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from aworld.core.task import Task
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from aworld.runner import Runners
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from aworld.config import RunConfig, EngineName
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# Setup, need modify
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os.environ["LLM_PROVIDER"] = "openai"
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os.environ["LLM_MODEL_NAME"] = "gpt-4o"
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os.environ["LLM_API_KEY"] = "your-api-key"
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# os.environ["LLM_BASE_URL"] = "https://api.openai.com/v1"
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# Create agent
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agent_config = AgentConfig(
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llm_provider=os.getenv("LLM_PROVIDER", "openai"),
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llm_model_name=os.getenv("LLM_MODEL_NAME"),
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llm_api_key=os.getenv("LLM_API_KEY"),
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# llm_base_url=os.getenv("LLM_BASE_URL")
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)
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my_agent = Agent(name="my_agent", conf=agent_config)
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# Create tasks
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tasks = [
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Task(input="What is machine learning?", agent=my_agent, id="task1"),
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Task(input="Explain neural networks", agent=my_agent, id="task2"),
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Task(input="What is deep learning?", agent=my_agent, id="task3")
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]
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# Run in parallel (default local run).
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# If you want to run in a distributed environment, you need to submit a job to the Ray cluster.
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results = Runners.sync_run_task(
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task=tasks,
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run_conf=RunConfig(
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engine_name=EngineName.RAY,
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worker_num=len(tasks)
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)
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)
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# Process results
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for task_id, result in results.items():
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print(f"Task {task_id}: {result.answer}") |