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62 lines
1.5 KiB
62 lines
1.5 KiB
import os
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from swarms.structs.queue_swarm import TaskQueueSwarm
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from swarms import Agent, OpenAIChat
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from swarms.prompts.finance_agent_sys_prompt import (
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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# Example usage:
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api_key = os.getenv("OPENAI_API_KEY")
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# Model
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model = OpenAIChat(
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openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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# Initialize your agents (assuming the Agent class and model are already defined)
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agents = [
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Agent(
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agent_name=f"Financial-Analysis-Agent-Task-Queue-swarm-{i}",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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llm=model,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="finance_agent.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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for i in range(10)
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]
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# Create a Swarm with the list of agents
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swarm = TaskQueueSwarm(
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agents=agents,
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return_metadata_on=True,
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autosave_on=True,
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save_file_path="swarm_run_metadata.json",
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)
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# Add tasks to the swarm
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swarm.add_task(
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"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?"
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)
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swarm.add_task("Analyze the financial risks of investing in tech stocks.")
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# Keep adding tasks as needed...
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# swarm.add_task("...")
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# Run the swarm and get the output
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out = swarm.run()
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# Print the output
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print(out)
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# Export the swarm metadata
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swarm.export_metadata()
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