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56 lines
1.7 KiB
56 lines
1.7 KiB
import os
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import asyncio
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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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# Set the OpenAI environment to use vLLM
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api_key = os.getenv("OPENAI_API_KEY") or "EMPTY" # for vllm
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api_base = os.getenv("OPENAI_API_BASE") or "http://localhost:8000/v1" # for vllm
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# Create an instance of the OpenAIChat class
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model = OpenAIChat(
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base_url=api_base, api_key=api_key, model="NousResearch/Meta-Llama-3-8B-Instruct", temperature=0.5, streaming=True, verbose=True
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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=2,
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autosave=True,
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# dynamic_temperature_enabled=True,
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dashboard=False,
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verbose=True,
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streaming_on=True,
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# interactive=True, # Set to False to disable interactive mode
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dynamic_temperature_enabled=False,
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saved_state_path="finance_agent.json",
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# tools=[#Add your functions here# ],
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# stopping_token="Stop!",
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# interactive=True,
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# docs_folder="docs", # Enter your folder name
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# pdf_path="docs/finance_agent.pdf",
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# sop="Calculate the profit for a company.",
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# sop_list=["Calculate the profit for a company."],
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user_name="RAH@EntangleIT.com",
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# # docs=
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# # docs_folder="docs",
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retry_attempts=3,
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# context_length=1000,
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# tool_schema = dict
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context_length=200000,
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# tool_schema=
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# tools
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# agent_ops_on=True,
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)
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async def startup_event():
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agent.stream_reponse(
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"What are the components of a startups stock incentive equity plan"
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)
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if __name__ == "__main__":
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asyncio.run(startup_event()) |