02_researcher_with_clarifying_questions.py

import dspy
from tavily import TavilyClient
from typing import Literal

# Connect to and set an LLM
lm = dspy.LM('openai/gpt-5-mini', api_key='YOUR_OPENAI_API_KEY')
dspy.configure(lm=lm)

# Set up our search tools
tavily_client = TavilyClient(api_key=TAVILY_API)

def internet_search(query: str, max_results: int = 5, include_raw_content: bool = False): 
	"""Run a web search""" 
	return tavily_client.search(
		query, max_results=max_results, 
		include_raw_content=include_raw_content, 
		topic="general",
	)

def read_webpage(url: str) -> dict:
	"""Read the content of a URL."""
	response = tavily_client.extract(url)
	return response

# Define our programs
clarifying_signature = "research_request: str, number_of_questions: int -> clarifying_questions: list[str]"
clarifier = dspy.Predict(clarifying_signature)

researcher_signature = "research_request: str, clarifying_questions_and_answers -> report: str"
researcher = dspy.ReAct(researcher_signature, tools=[internet_search, read_webpage])

# Main
#

# Generate our questions
results = clarifier(research_request=research_request, number_of_questions=3)

# Get our answers
q_and_a = []
for question in results.clarifying_questions:
    answer = input(f"{question}\n")
    q_and_a.append({"clarifying_question": question, "user_guidance": answer})

# Run the researcher
report = deep_researcher_with_clarification(
    research_request=research_request, 
    clarifying_questions_and_answers=q_and_a
)
print(result.report)