Research Assistant Tutorial

Build an interactive research assistant from academic papers.

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What You'll Build

By the end of this tutorial, you'll have a research assistant that can: - Extract structured knowledge from research papers - Answer questions about the paper's content - Find relevant sections based on semantic search - Visualize concept relationships

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Tutorial Overview

Step	Topic	What You'll Learn
1	Extract Knowledge	Parse a research paper and extract concepts
2	Semantic Search	Build a searchable knowledge abstract
3	Q&A System	Create an interactive Q&A interface

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Prerequisites

• Hyper-Extract installed: pip install hyperextract
• OpenAI API key configured
• A research paper (PDF or text format)

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Example Use Cases

Use Case 1: Paper Review

Quickly understand a new paper by asking questions: - "What are the main contributions?" - "How does this compare to prior work?" - "What are the limitations?"

Use Case 2: Literature Review

Build a knowledge abstract from multiple papers: - Extract concepts from 10+ papers - Search across all papers - Find connections between works

Use Case 3: Teaching Assistant

Help students understand complex papers: - Visualize concept maps - Answer student questions - Generate summaries

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Project Structure

research-assistant/
├── paper.md              # Your research paper
├── knowledge_base/       # Extracted knowledge
├── research_assistant.py # Main application
└── requirements.txt      # Dependencies

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Next Steps

Ready to start? Begin with Step 1: Extract Knowledge.