Dr. Chen's Research Assistant: AI That Learns & Synthesizes

Dr. Chen's Complete Workflow

# Initialize the research assistant
assistant = ResearchAssistant(
    researcher_id="dr_chen_phd",
    research_topic="Efficient Transformer Architectures"
)

# Ingest papers
papers = [
    {
        "text": "... full paper text ...",
        "metadata": {
            "title": "Attention Is All You Need",
            "authors": "Vaswani et al.",
            "year": 2017,
            "venue": "NeurIPS"
        }
    },
    # ... 236 more papers
]

print("📚 Starting paper ingestion...")
for i, paper in enumerate(papers):
    result = assistant.ingest_paper(
        paper_text=paper["text"],
        paper_metadata=paper["metadata"]
    )
    
    print(f"\nPaper {i+1}/{len(papers)}")
    print(f"  Findings: {len(result['findings'].get('findings', []))}")
    print(f"  Contradictions: {len(result['contradictions'])}")
    print(f"  Connections: {len(result['connections'])}")
    
    # Alert on contradictions
    if result['contradictions']:
        print(f"  ⚠️  {len(result['contradictions'])} contradictions detected!")
        for c in result['contradictions']:
            print(f"     - {c['explanation']}")

# Get research summary
summary = assistant.get_research_summary()
print(f"\n📊 Research Summary:")
print(f"  Papers Read: {summary['papers_read']}")
print(f"  Findings Extracted: {summary['findings_extracted']}")
print(f"  Contradictions Found: {summary['contradictions_found']}")
print(f"  Connections Made: {summary['connections_made']}")

# Answer research questions
questions = [
    "What are the main approaches to improving transformer efficiency?",
    "How does batch size affect training of large language models?",
    "What are the trade-offs between sparse and dense attention?"
]

for question in questions:
    answer = assistant.answer_research_question(question)
    print(f"\nQ: {question}")
    print(f"A: {answer}")

# Check contradictions
contradictions = assistant.get_contradictions_report()
print(f"\n⚠️  Unresolved Contradictions: {len(contradictions)}")
for c in contradictions:
    print(f"  [{c['severity']}] {c['contradiction']}")

# Generate literature review sections
sections = [
    "Attention Mechanisms",
    "Efficiency Improvements",
    "Training Dynamics"
]

for section in sections:
    review = assistant.generate_literature_review(section)
    print(f"\n📝 Literature Review - {section}")
    print(review)
    print("\n" + "="*60)