Building a Deep Researcher using Python and Streamlit

Overview

Build a DeepResearcher, an AI-powered research tool that generates search queries, gathers web content via DuckDuckGo, filters useful pages, extracts key info, and synthesizes a final report using OpenAI—all through a clean, modular Python architecture and a frontend created by Streamlit.

Syllabus

Course 1: Building Reusable LLM Components in Python
Course 2: Automating Web Content Retrieval and Parsing in Python
Course 3: Creating a Researcher in Python with OpenAI
Course 4: Making a Frontend for our Researcher with Streamlit

Courses

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2 hours
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Learn to design a prompt-driven workflow for LLM apps. Build a Prompt Manager for templates with defaults and a robust LLM Manager that wraps OpenAI API calls. Through hands-on examples, you'll manage prompts cleanly, inject dynamic context, handle errors, and structure interactions for real-world use.
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2 hours
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Learn to build a robust web search and content extraction module in Python. Use duckduckgo_search, httpx, and html-to-markdown to query, fetch HTML, and convert to Markdown. Enhance it with URL deduplication, error logging, and retries via tenacity for safe, reliable scraping.
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3 hours
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Learn to build the control logic for a multi-step AI research assistant. Structure the main python script to manage input, control iteration, and orchestrate modules for query generation, relevance checks, context extraction, and report creation. Implement an LLM-driven workflow for iterative search and comprehensive report generation.
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2 hours
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Learn Streamlit, a Python framework for building interactive web apps, by turning a CLI research assistant into a responsive interface. Master widgets, layout, and state management while connecting an AI research pipeline to a real-time front end. By the end, you'll have a fully interactive Python-powered research assistant.