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77 lines
1.4 KiB
77 lines
1.4 KiB
"""
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tool decorated func [search_api] -> agent which parses the docs of the tool func
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-> injected into prompt -> agent will output json containing tool usage -> agent output will be parsed -> tool executed
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-> terminal response can be returned to agent for self-healing
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"""
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import os
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from dotenv import load_dotenv
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# Import the OpenAIChat model and the Agent struct
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from swarms import Agent, OpenAIChat
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# Load the environment variables
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load_dotenv()
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# Define a tool
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def search_api(query: str, description: str):
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"""Search the web for the query
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Args:
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query (str): _description_
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Returns:
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_type_: _description_
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"""
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return f"Search results for {query}"
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def weather_api(
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query: str,
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):
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"""_summary_
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Args:
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query (str): _description_
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"""
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print(f"Getting the weather for {query}")
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def rapid_api(query: str):
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"""_summary_
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Args:
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query (str): _description_
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"""
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print(f"Getting the weather for {query}")
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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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llm=llm,
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max_loops=1,
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dashboard=True,
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tools=[search_api, weather_api, rapid_api],
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)
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# Run the workflow on a task
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out = agent.run("Generate a 10,000 word blog on health and wellness.")
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print(out)
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