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76 lines
1.8 KiB
76 lines
1.8 KiB
import os
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import asyncio
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from swarms import Agent
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from swarm_models import OpenAIChat
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import time
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import psutil
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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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from dotenv import load_dotenv
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load_dotenv()
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# Get the OpenAI API key from the environment variable
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api_key = os.getenv("OPENAI_API_KEY")
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# Create an instance of the OpenAIChat class
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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 the agent
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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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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output_type="string",
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streaming_on=False,
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)
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# Function to measure time and memory usage
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def measure_time_and_memory(func):
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def wrapper(*args, **kwargs):
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start_time = time.time()
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result = func(*args, **kwargs)
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end_time = time.time()
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memory_usage = psutil.Process().memory_info().rss / 1024**2
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print(f"Time taken: {end_time - start_time} seconds")
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print(f"Memory used: {memory_usage} MB")
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return result
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return wrapper
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# Function to run the agent asynchronously
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@measure_time_and_memory
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async def run_agent_async():
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await asyncio.gather(
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agent.run(
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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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)
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# Function to run the agent on another thread
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@measure_time_and_memory
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def run_agent_thread():
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asyncio.run(run_agent_async())
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# Run the agent asynchronously and on another thread to test the speed
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asyncio.run(run_agent_async())
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run_agent_thread()
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