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69 lines
1.9 KiB
69 lines
1.9 KiB
from pydantic import BaseModel, Field
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from swarms import Agent
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from swarms.models.popular_llms import Anthropic
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from swarms.tools.openai_tool_creator_decorator import tool
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# Importing the search API tool
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@tool
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def search_api(query: str) -> str:
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"""
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This tool searches the web for information about COVID-19 symptoms.
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"""
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return f"Search API tool called with query: {query}"
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print(search_api("COVID-19 symptoms"))
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# Initialize the schema for the person's information
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class Schema(BaseModel):
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name: str = Field(..., title="Name of the person")
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agent: int = Field(..., title="Age of the person")
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is_student: bool = Field(..., title="Whether the person is a student")
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courses: list[str] = Field(
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..., title="List of courses the person is taking"
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)
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# Convert the schema to a JSON string
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tool_schema = Schema(
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name="Tool Name",
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agent=1,
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is_student=True,
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courses=["Course1", "Course2"],
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)
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# Define the task to generate a person's information
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task = "Generate a person's information based on the following schema:"
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# Initialize the agent
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agent = Agent(
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agent_name="WeatherMan Agent",
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# Set the tool schema to the JSON string -- this is the key difference
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tool_schema=tool_schema,
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llm=Anthropic(),
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max_loops=3,
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autosave=True,
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dashboard=False,
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streaming_on=True,
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tools=[], # or list of tools
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verbose=True,
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interactive=True,
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# Set the output type to the tool schema which is a BaseModel
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output_type=tool_schema, # or dict, or str
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metadata_output_type="json",
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# List of schemas that the agent can handle
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list_tool_schemas=[tool_schema],
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function_calling_format_type="OpenAI",
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function_calling_type="json", # or soon yaml
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execute_tool=True,
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)
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# Run the agent to generate the person's information
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generated_data = agent.run(task)
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# Print the generated data
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print(f"Generated data: {generated_data}")
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