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59 lines
1.9 KiB
59 lines
1.9 KiB
from swarms.structs.agent import Agent
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from swarms.structs.sequential_workflow import SequentialWorkflow
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def streaming_callback(token):
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buffer.append(token)
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if len(buffer) >= 20 or token.endswith("\n"):
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print("".join(buffer), end="", flush=True)
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buffer.clear()
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def run_workflow_with_streaming_callback(task, streaming_callback):
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agent1 = Agent(
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name="Research Agent",
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description="A research agent that can answer questions",
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model_name="gpt-4o",
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system_prompt=(
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"You are a ResearchAgent. Your task is to research and gather "
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"information about the given topic. Provide comprehensive research "
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"findings and key insights."
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),
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max_loops=1,
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interactive=True,
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verbose=True,
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)
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agent2 = Agent(
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name="Analysis Agent",
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description="An analysis agent that draws conclusions from research",
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model_name="gpt-4o-mini",
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system_prompt=(
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"You are an AnalysisAgent. Your task is to analyze the research "
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"provided by the previous agent and draw meaningful conclusions. "
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"Provide detailed analysis and actionable insights."
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),
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max_loops=1,
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interactive=True,
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verbose=True,
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)
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workflow = SequentialWorkflow(
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id="research_analysis_workflow",
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name="Research Analysis Workflow",
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description="A sequential workflow that researches and analyzes topics",
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agents=[agent1, agent2],
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max_loops=1,
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output_type="str",
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streaming_callback=streaming_callback,
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multi_agent_collab_prompt=True,
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)
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return workflow.run(task)
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if __name__ == "__main__":
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buffer = []
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run_workflow_with_streaming_callback(
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task="What are the latest advancements in AI?",
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streaming_callback=streaming_callback,
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) |