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from smolagents import CodeAgent, GoogleSearchTool, VisitWebpageTool, HfApiModel
from src.party_planner.tools.travel_time import calculate_cargo_travel_time
def create_agent(
agent_type: str,
name: str,
model: HfApiModel,
tools: list,
max_steps: int = 10,
additional_imports: list[str] = None,
description: str = "",
interval: int = 0,
verbosity: int = 0,
**kwargs
):
"""
**kwargs can be: managed_agents, final_answer_checks, ...
"""
if agent_type == "code_agent":
return CodeAgent(
name=name,
model=model,
tools=tools,
additional_authorized_imports=additional_imports,
max_steps=max_steps,
description=description,
planning_interval=interval,
verbosity_level=verbosity,
**kwargs
)
return None
if __name__ == "__main__":
import os
from dotenv import load_dotenv
from src.model import get_model
load_dotenv()
# os.environ["SERPER_API_KEY"] = os.getenv("SERPER_API_KEY")
os.environ["SERPAPI_API_KEY"] = os.getenv("SERPAPI_API_KEY")
AgentType = "code_agent"
Name = "web_agent"
Model = get_model(
model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
provider="hf-inference" # "together"
)
ToolNames = [
GoogleSearchTool(provider="serpapi"),
VisitWebpageTool(),
calculate_cargo_travel_time
]
AdditionalImports = ["pandas"]
MaxSteps = 3
Description = "Browses the web to find information"
Interval = 4
# Simple agent served as a baseline for the multi-agent system
Agent = create_agent(
agent_type=AgentType,
name=Name,
model=Model,
tools=ToolNames,
additional_imports=AdditionalImports,
max_steps=MaxSteps,
description=Description,
interval=Interval
)
Task = """Find all Batman filming locations in the world, calculate the time to transfer via cargo plane to
here (we're in Gotham, 40.7128° N, 74.0060° W), and return them to me as a pandas dataframe. Also give me some
supercar factories with the same cargo plane transfer time."""
Prompt = f"""
You're an expert analyst. You make comprehensive reports after visiting many websites.
Don't hesitate to search for many queries at once in a for loop.
For each data point that you find, visit the source url to confirm numbers.
{Task}
"""
result = Agent.run(Prompt)
print('\n' * 2, result)
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