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65 lines
1.7 KiB
65 lines
1.7 KiB
from swarms.models import OpenAIChat, BioGPT, Anthropic
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<<<<<<< HEAD
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from swarms.structs import Agent
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=======
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from swarms.structs import Flow
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>>>>>>> 3d3dddaf0c7894ec2df14c51f7dd843c41c878c4
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from swarms.structs.sequential_workflow import SequentialWorkflow
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# Example usage
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api_key = "" # Your actual API key here
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# Initialize the language flow
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llm = OpenAIChat(
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openai_api_key=api_key,
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temperature=0.5,
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max_tokens=3000,
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)
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biochat = BioGPT()
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# Use Anthropic
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anthropic = Anthropic()
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# Initialize the agent with the language flow
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agent1 = Agent(llm=llm, max_loops=1, dashboard=False)
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# Create another agent for a different task
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agent2 = Agent(llm=llm, max_loops=1, dashboard=False)
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# Create another agent for a different task
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agent3 = Agent(llm=biochat, max_loops=1, dashboard=False)
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# agent4 = Agent(llm=anthropic, max_loops="auto")
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=======
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agent1 = Flow(llm=llm, max_loops=1, dashboard=False)
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# Create another agent for a different task
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agent2 = Flow(llm=llm, max_loops=1, dashboard=False)
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# Create another agent for a different task
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agent3 = Flow(llm=biochat, max_loops=1, dashboard=False)
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# agent4 = Flow(llm=anthropic, max_loops="auto")
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>>>>>>> 3d3dddaf0c7894ec2df14c51f7dd843c41c878c4
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# Create the workflow
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workflow = SequentialWorkflow(max_loops=1)
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# Add tasks to the workflow
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workflow.add("Generate a 10,000 word blog on health and wellness.", agent1)
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# Suppose the next task takes the output of the first task as input
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workflow.add("Summarize the generated blog", agent2)
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workflow.add("Create a references sheet of materials for the curriculm", agent3)
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# Run the workflow
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workflow.run()
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# Output the results
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for task in workflow.tasks:
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print(f"Task: {task.description}, Result: {task.result}")
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