Setting Up the Basic Structure for DeepResearcher

Introduction: Laying the Foundation for DeepResearcher

Welcome to the first lesson of the " Creating a Researcher in Python with OpenAI " course! In this course, you will learn how to build DeepResearcher, an AI-powered research tool that can search the web, gather information, and generate a final report — all using Python . Before we dive into the details of web searching and AI, it’s important to set up a solid foundation. A clear project structure will help you keep your code organized, make it easier to add new features, and help you debug problems as you go. In this lesson, we’ll walk through the basic structure of the DeepResearcher project and explain how the main program is set up. By the end of this lesson, you’ll understand how the main parts of the project fit together and be ready to start building out each piece in future lessons.

Recall: Project Flowchart

Let’s start with a quick reminder about how the DeepResearcher works: This flowchart illustrates the step-by-step process, showing how each component fits into the overall workflow. The LLM (Language Model) and Web components work together to automate the research process.

Understanding the Main Program

Now, let’s look at the main file of our project: main.py . This file is the entry point for DeepResearcher . It’s where the program starts running. Let’s break down the key parts of this file step by step.

1. Importing Functions

At the top of the file, we import a function from another part of our project: Python from deepresearcher.web.web_searcher import clear_visited_pages This allows us to use the clear_visited_pages function in our main program.

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Python

def generate_initial_search_queries(user_query: str):
    pass

def perform_iterative_research(user_query: str, new_search_queries: list, all_search_queries: list, iteration_limit: int):
    pass

def generate_final_report(user_query: str, contexts: list):
    pass

def generate_initial_search_queries(user_query: str):
    pass

def perform_iterative_research(user_query: str, new_search_queries: list, all_search_queries: list, iteration_limit: int):
    pass

def generate_final_report(user_query: str, contexts: list):
    pass

Python

def research_main():
    user_query = input("Enter your research query/topic: ").strip()
    iteration_limit = input("Max number of iterations (default 10): ").strip()
    iteration_limit = int(iteration_limit) if iteration_limit.isdigit() else 10

    clear_visited_pages()
    new_search_queries = generate_initial_search_queries(user_query)
    if not new_search_queries:
        return

    all_search_queries = new_search_queries.copy()
    aggregated_contexts = perform_iterative_research(user_query, new_search_queries, all_search_queries, iteration_limit)
    generate_final_report(user_query, aggregated_contexts)

def research_main():
    user_query = input("Enter your research query/topic: ").strip()
    iteration_limit = input("Max number of iterations (default 10): ").strip()
    iteration_limit = int(iteration_limit) if iteration_limit.isdigit() else 10

    clear_visited_pages()
    new_search_queries = generate_initial_search_queries(user_query)
    if not new_search_queries:
        return

    all_search_queries = new_search_queries.copy()
    aggregated_contexts = perform_iterative_research(user_query, new_search_queries, all_search_queries, iteration_limit)
    generate_final_report(user_query, aggregated_contexts)

Python

if __name__ == "__main__":
    research_main()

if __name__ == "__main__":
    research_main()

Python

def generate_initial_search_queries(user_query: str):
    pass

def perform_iterative_research(user_query: str, new_search_queries: list, all_search_queries: list, iteration_limit: int):
    pass

def generate_final_report(user_query: str, contexts: list):
    pass

Python

def research_main():
    user_query = input("Enter your research query/topic: ").strip()
    iteration_limit = input("Max number of iterations (default 10): ").strip()
    iteration_limit = int(iteration_limit) if iteration_limit.isdigit() else 10

    clear_visited_pages()
    new_search_queries = generate_initial_search_queries(user_query)
    if not new_search_queries:
        return

    all_search_queries = new_search_queries.copy()
    aggregated_contexts = perform_iterative_research(user_query, new_search_queries, all_search_queries, iteration_limit)
    generate_final_report(user_query, aggregated_contexts)

Python

if __name__ == "__main__":
    research_main()

Introduction: Laying the Foundation for DeepResearcher

Recall: Project Flowchart

Understanding the Main Program

1. Importing Functions

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2. Defining Function Stubs

3. The Main Function

4. Running the Program

Summary and What’s Next