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import os | |
from smolagents import CodeAgent, ToolCallingAgent | |
from smolagents import OpenAIServerModel | |
from tools.fetch import fetch_webpage | |
from tools.yttranscript import get_youtube_transcript, get_youtube_title_description | |
import myprompts | |
# --- Basic Agent Definition --- | |
class BasicAgent: | |
def __init__(self): | |
print("BasicAgent initialized.") | |
def __call__(self, question: str) -> str: | |
print(f"Agent received question (first 50 chars): {question[:50]}...") | |
try: | |
# Use the reviewer agent to determine if the question can be answered by a model or requires code | |
print("Calling reviewer agent...") | |
reviewer_answer = reviewer_agent.run(myprompts.review_prompt + "\nThe question is:\n" + question) | |
print(f"Reviewer agent answer: {reviewer_answer}") | |
question = question + '\n' + myprompts.output_format | |
fixed_answer = "" | |
if reviewer_answer == "code": | |
fixed_answer = gaia_agent.run(question) | |
print(f"Code agent answer: {fixed_answer}") | |
elif reviewer_answer == "model": | |
# If the reviewer agent suggests using the model, we can proceed with the model agent | |
print("Using model agent to answer the question.") | |
fixed_answer = model_agent.run(myprompts.model_prompt + "\nThe question is:\n" + question) | |
print(f"Model agent answer: {fixed_answer}") | |
return fixed_answer | |
except Exception as e: | |
error = f"An error occurred while processing the question: {e}" | |
print(error) | |
return error | |
model = OpenAIServerModel( | |
model_id="gpt-4.1-nano", | |
api_base="https://api.openai.com/v1", | |
api_key=os.environ["OPENAI_API_KEY"], | |
) | |
reviewer_agent= ToolCallingAgent(model=model, tools=[]) | |
model_agent = ToolCallingAgent(model=model,tools=[fetch_webpage]) | |
gaia_agent = CodeAgent(tools=[fetch_webpage,get_youtube_title_description,get_youtube_transcript ], model=model) | |
if __name__ == "__main__": | |
# Example usage | |
question = "What was the actual enrollment of the Malko competition in 2023?" | |
agent = BasicAgent() | |
answer = agent(question) | |
print(f"Answer: {answer}") |