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88 lines
1.9 KiB
88 lines
1.9 KiB
import os
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from dotenv import load_dotenv
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from swarms import Agent, OpenAIChat
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from swarms.agents.multion_agent import MultiOnAgent
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from swarms.memory.chroma_db import ChromaDB
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from swarms.tools.tool import tool
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from swarms.utils.code_interpreter import SubprocessCodeInterpreter
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# Load the environment variables
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load_dotenv()
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# Memory
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chroma_db = ChromaDB()
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# MultiOntool
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@tool
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def multion_tool(
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task: str,
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api_key: str = os.environ.get("MULTION_API_KEY"),
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):
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"""
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Executes a task using the MultiOnAgent.
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Args:
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task (str): The task to be executed.
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api_key (str, optional): The API key for the MultiOnAgent. Defaults to the value of the MULTION_API_KEY environment variable.
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Returns:
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The result of the task execution.
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"""
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multion = MultiOnAgent(multion_api_key=api_key)
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return multion(task)
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# Execute the interpreter tool
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@tool
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def execute_interpreter_tool(
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code: str,
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):
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"""
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Executes a single command using the interpreter.
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Args:
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task (str): The command to be executed.
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Returns:
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None
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"""
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out = SubprocessCodeInterpreter(debug_mode=True)
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out = out.run(code)
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return code
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# Get the API key from the environment
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api_key = os.environ.get("OPENAI_API_KEY")
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# Initialize the language model
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llm = OpenAIChat(
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temperature=0.5,
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openai_api_key=api_key,
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)
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# Initialize the workflow
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agent = Agent(
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agent_name="Research Agent",
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agent_description="An agent that performs research tasks.",
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system_prompt="Perform a research task.",
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llm=llm,
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max_loops=1,
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dashboard=True,
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# tools=[multion_tool, execute_interpreter_tool],
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verbose=True,
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long_term_memory=chroma_db,
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stopping_token="done",
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)
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# Run the workflow on a task
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out = agent.run(
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"Generate a 10,000 word blog on health and wellness, and say done"
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" when you are done"
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)
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print(out)
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