| from typing import List | |
| from pydantic import BaseModel | |
| from agents import Agent | |
| class ClarificationData(BaseModel): | |
| questions: List[str] | |
| CLARIFY_INSTRUCTIONS = """ | |
| You are a Research Clarifier. Given a user's research query, generate exactly 3 clarifying questions | |
| that will help focus and refine the research. These questions should help understand: | |
| 1. The specific aspect or angle they want to focus on | |
| 2. The depth or scope of research needed | |
| 3. The intended use or audience for the research | |
| Return your response as JSON matching the ClarificationData model with exactly 3 questions. | |
| """ | |
| def create_clarifier_agent(model: str = "gpt-4o-mini"): | |
| """Create a clarifier agent with configurable model""" | |
| return Agent( | |
| name="ClarifierAgent", | |
| instructions=CLARIFY_INSTRUCTIONS, | |
| model=model, | |
| output_type=ClarificationData, | |
| ) | |
| # Default clarifier agent for backward compatibility | |
| clarifier_agent = create_clarifier_agent() |