Research Assistant Pro - Enhanced CLI

A beautiful, powerful command-line interface for academic research with intelligent citation finding, quality scoring, and batch processing capabilities.

Features

🎨 Beautiful Terminal UI

Rich formatting with colors, panels, and progress bars
Interactive paper browsing with detailed views
Session statistics tracking
Clear, organized output

🔍 Multi-Database Search

Simultaneous search across ArXiv, Semantic Scholar, and PubMed
Real-time progress tracking
Automatic deduplication
Consensus scoring across databases

🎯 Intelligent Citation Finding

Automatic claim extraction from text
NLP-powered relevance matching
Citation quality scoring
Detailed explanations for recommendations

📊 Advanced Quality Scoring

Field-aware scoring (CS, Biology, Medicine, etc.)
Venue reputation analysis
Citation impact metrics
Self-citation detection
Suspicious pattern identification

🚀 Batch Processing

Process multiple queries or documents
CSV and JSON input/output
Comprehensive reports
Efficient parallel processing

📝 Export Capabilities

JSON format for programmatic use
BibTeX format for LaTeX integration
Formatted reports

Installation

# Clone the repository
git clone https://github.com/yourname/ResearchAssistantAgent.git
cd ResearchAssistantAgent

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .

# Download spaCy model (required for claim extraction)
python -m spacy download en_core_web_sm

Quick Start

Basic Search

# Search across all databases
research-assistant-pro search -q "transformer neural networks"

# Search specific databases
research-assistant-pro search -q "CRISPR gene editing" -d pubmed -d semantic_scholar

# Interactive mode with paper details
research-assistant-pro search -q "quantum computing" -i

# Export results
research-assistant-pro search -q "climate change" -e json

Citation Finding

# Find citations for text
research-assistant-pro cite -t "Transformers have revolutionized NLP by enabling parallel processing of sequences."

# Find citations for a file
research-assistant-pro cite -f manuscript.txt

# Interactive mode (type text, press Ctrl+D when done)
research-assistant-pro cite

Batch Processing

# Process multiple queries from JSON
research-assistant-pro batch -i examples/batch_search.json

# Process from CSV with CSV output
research-assistant-pro batch -i queries.csv -o csv

# View example batch files
cat examples/batch_search.json
cat examples/batch_search.csv

Citation Scoring Demo

# Run interactive scoring demo
research-assistant-pro score-demo -f cs  # Computer Science
research-assistant-pro score-demo -f med  # Medicine
research-assistant-pro score-demo -f math  # Mathematics

Configuration

# Set up API keys securely
research-assistant-pro configure

Advanced Usage

Python API

from research_assistant.cli_enhanced import EnhancedResearchAssistant
import asyncio

async def main():
    assistant = EnhancedResearchAssistant()
    
    # Search for papers
    results = await assistant.search_with_progress(
        "deep learning attention mechanisms",
        ["arxiv", "semantic_scholar"],
        max_results=10
    )
    
    # Display results
    assistant.display_search_results(results["papers"])
    
    # Find citations for text
    await assistant.find_citations_interactive(
        "Recent advances in transformers have improved NLP tasks."
    )

asyncio.run(main())

Batch File Format

JSON Format:

[
  {
    "type": "search",
    "data": {
      "query": "machine learning fairness",
      "databases": ["arxiv", "semantic_scholar"],
      "limit": 5
    }
  },
  {
    "type": "cite",
    "data": {
      "text": "Neural networks can exhibit biased behavior when trained on imbalanced datasets."
    }
  }
]

CSV Format:

type,query,databases,limit,text
search,"quantum error correction","arxiv",10,
cite,,,,"Quantum computers require error correction to achieve fault tolerance."

Features in Detail

Citation Explanations

When finding citations, the system provides detailed explanations including:

Why the citation is relevant
Quality assessment breakdown
Confidence scores
Potential warnings or issues
Recommendations (strong/moderate/weak)

Field-Aware Scoring

The scoring system adapts to different academic fields:

Computer Science: Values recent papers, conference venues
Mathematics: Values timeless results, theoretical rigor
Medicine: Values clinical trials, high-impact journals
Physics: Accommodates large collaborations

Smart Deduplication

Papers found across multiple databases are:

Automatically deduplicated by DOI/title
Given higher consensus scores
Merged with combined metadata

Tips and Tricks

Use interactive mode (-i) to explore papers in detail
Export to BibTeX for direct use in LaTeX documents
Batch process literature reviews to save time
Check explanations to understand citation recommendations
Configure field in scoring demo to see field-specific differences

Troubleshooting

No results found

Try broader search terms
Check your internet connection
Ensure API keys are configured (if needed)

Slow performance

Reduce the number of results (-l 5)
Search fewer databases at once
Check rate limits in configuration

Installation issues

Ensure Python 3.11+ is installed
Try upgrading pip: pip install --upgrade pip
Install in a clean virtual environment

Examples

See the examples/ directory for:

cli_demo.py - Interactive demo of all features
batch_search.json - Sample batch processing file
batch_search.csv - CSV format example
scoring_demo.py - Citation quality scoring demonstration

Contributing

We welcome contributions! Please:

Fork the repository
Create a feature branch
Add tests for new functionality
Submit a pull request

License

MIT License - see LICENSE file for details

Support

For issues or questions:

Open an issue on GitHub
Check the documentation
Run research-assistant-pro --help for command options

Made with ❤️ for researchers who deserve better tools

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