Building a Deep Researcher using Python and Streamlit | CodeSignal Learn

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Building a Deep Researcher using Python and Streamlit

Software Engineering

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4 courses

71 practices

10 hours

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.

4.7

163 learners

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Verified skills you'll gain

INTERMEDIATE

Back-End and API Development

DEVELOPING

Front-End Development

DEVELOPING

Software Design and Architecture

DEVELOPING

System Design and Architecture

Tools you'll use

OpenAI

Python

Streamlit

Course 1

Building Reusable LLM Components in Python

5 lessons

18 practices

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.

Course 2

Automating Web Content Retrieval and Parsing in Python

4 lessons

18 practices

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.

Course 3

Creating a Researcher in Python with OpenAI

5 lessons

21 practices

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.

Course 4

Making a Frontend for our Researcher with Streamlit

4 lessons

14 practices

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.

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