title	Enhanced Research Assistant
emoji	📚
colorFrom	blue
colorTo	purple
sdk	gradio
sdk_version	4.0.0
python_version	3.10
app_file	app.py
pinned	false
license	mit
repo	https://github.com/nellaivijay/research-assistant

📚 Enhanced Research Assistant - AI Model Selection

🚀 Live Application: https://huggingface.co/spaces/nellaivijay/research-assistant

📦 GitHub Repository: https://github.com/nellaivijay/research-assistant

AI-powered research companion with custom AI model selection, multi-source recommendations, analysis, and workflow management. An enhanced version of paper recommendation systems with comprehensive research workflow features and optional AI-powered analysis.

🏷️ Repository Topics

Primary Topics:

• research-assistant
• paper-recommendation
• ai-models
• machine-learning
• natural-language-processing
• academic-research
• literature-review
• citation-analysis

Technology Topics:

• gradio
• python
• hugging-face
• openai
• anthropic
• google-gemini
• llm
• generative-ai

Development Topics:

• ci-cd
• github-actions
• web-application
• api-integration
• workflow-automation

Use Case Topics:

• education
• research-tools
• productivity
• knowledge-management
• document-analysis
• kaggle
• data-science
• machine-learning-competitions

🔗 Project Links

This project is deployed on multiple platforms for different purposes:

Platform	URL	Purpose
🚀 Hugging Face Space	https://huggingface.co/spaces/nellaivijay/research-assistant	Live application deployment
📦 GitHub Repository	https://github.com/nellaivijay/research-assistant	Source code and development
📚 GitHub Pages	https://nellaivijay.github.io/research-assistant	Documentation website
📖 GitHub Wiki	https://github.com/nellaivijay/research-assistant/wiki	Comprehensive documentation

🧠 Related Projects

• AGI/ASI Papers Analysis: Local Development - Analyze AI papers for AGI/ASI relevance from AI-Papers-of-the-Week

🎓 Educational Purpose

This project is created for educational purposes only to demonstrate:

• AI model integration and comparison
• Research workflow automation
• Modern web application development
• CI/CD pipeline implementation
• Documentation and SEO best practices

The system is designed to help researchers and students learn about AI-powered research tools and modern software development practices.

🎯 Key Features

🤖 AI Model Selection (NEW!)

• 15+ AI Models: Rule-based, OpenAI, Anthropic, Google, Ollama (local), Hugging Face (free)
• Cost Control: Select free (rule-based, local, HF) or paid AI models as needed
• API Key Management: Secure local storage of API keys
• Model Comparison: Compare analysis results across different models
• Flexible Analysis: Switch between basic and advanced analysis
• Local Models: Use Ollama for free local inference (Llama 3, Mistral, Mixtral)
• Custom Endpoints: Bring your own model endpoints

🔍 Multi-Source Recommendations

• Semantic Scholar: Citation-based recommendations using academic graph
• arXiv: Category-based recommendations from recent preprints
• Citation Analysis: Papers that cite similar works
• Smart Ranking: Relevance scoring and filtering

📖 Reading List Management

• Status Tracking: to-read, reading, completed categories
• Personal Organization: Custom tags and priorities
• Progress Tracking: Monitor research progress over time
• Quick Add: Easy paper addition with metadata

📝 Notes & Annotations

• Personal Notes: Add insights and questions for each paper
• Persistent Storage: Notes saved locally for privacy
• Quick Access: Load notes by paper ID
• Research Journal: Build personal knowledge base

📊 Citation Analysis

• Impact Scoring: Calculate paper influence metrics
• Citation Velocity: Track how quickly papers gain citations
• Readability Assessment: Estimate paper complexity
• Topic Identification: Auto-detect research topics
• Key Contributions: Extract main contributions from abstracts

📤 Export Options

• BibTeX: Direct export for LaTeX/Overleaf
• JSON: Structured data for further processing
• Markdown: Human-readable format for sharing
• Citation Styles: Multiple format options

⚖️ Advanced Features (NEW!)

• Model Comparison: Side-by-side analysis comparison across different AI models
• Batch Processing: Analyze multiple papers with a single model selection
• Custom Prompts: Define and save custom analysis prompts for specific needs
• Auto Model Selection: Automatic model selection based on paper complexity
• Cost Optimization: Budget-aware model selection (free/balanced/quality)
• Quality Scoring: Rate analysis quality per model (1-10 scale)
• A/B Testing: Compare model performance on your papers and build preferences

🚀 How It Works

1. Paper Discovery

Enter a paper ID or arXiv URL to get recommendations from multiple academic sources.

2. Analysis & Insights

Get instant analysis of paper impact, readability, and key contributions.

3. Personal Organization

Add papers to your reading list, categorize by status, and track progress.

4. Knowledge Building

Add personal notes and annotations to build your research knowledge base.

5. Export & Share

Export your reading lists in multiple formats for use in other tools.

🎨 Dashboard Tabs

1. 🤖 Model Selection (NEW!)

• Choose from multiple AI analysis models
• Configure API keys for different services
• View model capabilities and costs
• Switch between free and paid analysis

2. 🔍 Paper Recommendations

• Multi-source paper discovery
• Smart ranking and filtering
• Abstract previews
• One-click access to full papers

3. 📖 Reading List

• Personal paper organization
• Status-based categorization
• Progress tracking
• Bulk operations

4. 📝 Notes & Annotations

• Per-paper note-taking
• Persistent storage
• Quick search and retrieval
• Research journal building

5. 📊 Citation Analysis

• Impact metrics
• Trend analysis
• Topic identification
• Contribution extraction

6. 📤 Export

• Multiple format support
• Bibliography generation
• Reading list export
• Citation formatting

7. ⚖️ Model Comparison (NEW!)

• Side-by-side model comparison
• Quality score comparison
• Cost vs quality analysis
• Performance metrics

8. 📦 Batch Processing (NEW!)

• Analyze multiple papers at once
• Single model selection for batch
• Progress tracking
• Results aggregation

9. ✏️ Custom Prompts (NEW!)

• Define custom analysis prompts
• Save and reuse prompts
• Pre-built prompt templates
• Specialized analysis workflows

10. 🎯 Auto Model Selection (NEW!)

• Automatic model selection based on complexity
• Budget preference settings (free/balanced/quality)
• Smart cost optimization
• Quality-aware recommendations

11. 🧪 A/B Testing (NEW!)

• Run A/B tests between any two models
• Record your preferences and reasons
• Build statistics on model performance
• Get personalized model recommendations
• Track win rates over time

🤖 Available AI Models

Free Models

• Rule-Based Analysis: Pattern matching and keyword extraction (no API needed)
• Hugging Face Inference: Mistral 7B, Llama 3 8B, Gemma 7B (free HF API)
• Ollama Local Models: Llama 3, Llama 3 70B, Mistral, Mixtral (requires Ollama)

Paid Models (Optional)

• OpenAI: GPT-4o Mini (low cost), GPT-4o (medium cost)
• Anthropic: Claude 3 Haiku (low cost), Claude 3 Sonnet (medium cost)
• Google: Gemini Pro (low cost), Gemini 1.5 Pro (medium cost)

Custom Models

• Custom Endpoints: Bring your own model API endpoints

Model Capabilities

• Basic Analysis: Topic identification, citation analysis, readability assessment
• Advanced Analysis: Deep insights, summarization, novelty assessment
• Topic Modeling: Research theme identification and clustering
• Comparison Analysis: Cross-paper comparison and synthesis
• Synthesis: Multi-paper synthesis and literature review generation

🧠 Research Workflow Integration

This assistant is designed to integrate into your research workflow:

• Literature Review: Discover relevant papers quickly
• Research Planning: Organize papers by project or topic
• Knowledge Management: Build personal research notes
• Writing Support: Export citations for papers
• Progress Tracking: Monitor reading progress over time

🛠️ Technology Stack

• Frontend: Gradio 4.0+
• Recommendations: Semantic Scholar API, arXiv API
• AI Models: Optional OpenAI, Anthropic integration
• Data Storage: Local JSON files (privacy-focused)
• Analysis: Custom citation and impact algorithms + optional AI analysis
• Deployment: Hugging Face Spaces

🔒 Privacy & Data

• All personal data stored locally
• No external data sharing
• User-controlled reading lists
• Private notes and annotations
• Optional export for backup

📈 Comparison with Basic Recommenders

Feature	Basic Recommenders	Enhanced Research Assistant
Recommendation Sources	1-2 sources	Multiple sources with ranking
Personal Organization	None	Full reading list management
Notes System	None	Per-paper notes system
Citation Analysis	Basic count	Impact scoring, velocity, trends
Export Options	Limited	BibTeX, JSON, Markdown
Research Workflow	Single-purpose	End-to-end workflow support

🎓 Use Cases

• Academic Researchers: Literature review and paper discovery
• Graduate Students: Thesis research and reading organization
• Industry Researchers: Staying current with developments
• Data Scientists: ML/AI paper tracking and analysis
• Research Groups: Shared reading lists and collaboration
• Kaggle Competitors: Research state-of-the-art methods for competitions, find relevant papers for specific problem domains, understand cutting-edge techniques, organize research for competition solutions

🏆 How This Helps Kaggle Competitors

Research State-of-the-Art Methods

• Competition Research: Find cutting-edge papers relevant to specific competition domains (computer vision, NLP, tabular data, etc.)
• Method Discovery: Discover latest techniques and architectures used in top-performing solutions
• Benchmark Papers: Find papers with state-of-the-art results on datasets similar to competition data

Domain-Specific Paper Discovery

• Computer Vision: Find latest CV papers for image classification, object detection, segmentation
• NLP: Discover transformer models, BERT variants, GPT applications for text competitions
• Tabular Data: Research gradient boosting, neural networks for structured data
• Time Series: Find papers on forecasting, anomaly detection, sequence modeling
• Recommendation Systems: Discover collaborative filtering, content-based methods

Solution Development Workflow

• Literature Review: Build comprehensive reading lists for each competition
• Method Selection: Compare different approaches using AI-powered analysis
• Implementation Planning: Organize papers by implementation priority and complexity
• Citation Tracking: Follow citation chains to find foundational and related work

Competitive Advantage

• Stay Current: Get recommendations for recent papers in your competition domain
• Deep Understanding: Use AI analysis to extract key contributions and methodologies
• Efficient Research: Save time with automated paper discovery and analysis
• Knowledge Management: Build personal research library across multiple competitions

Kaggle-Specific Use Cases

1. Before Competition: Research the problem domain and state-of-the-art methods
2. During Competition: quickly find relevant papers for specific approaches
3. Post-Competition: Organize learnings and papers for future reference
4. Cross-Competition: Build knowledge base across different competition types
5. Team Collaboration: Share reading lists and notes with team members

Integration with Kaggle Workflow

• Dataset Analysis: Find papers related to specific types of datasets
• Metric Understanding: Research papers that define competition metrics
• Ensemble Methods: Discover papers on model ensemble techniques
• Feature Engineering: Find papers on feature extraction and engineering
• Hyperparameter Optimization: Research optimization techniques and papers

🛠️ Development & CI/CD

This project uses GitHub Actions for automated CI/CD pipeline:

• Automated Testing: Syntax checks, dependency validation, basic tests
• Continuous Integration: Tests run on every push and pull request
• Automated Deployment: Auto-deploys to Hugging Face Spaces on successful tests
• Security Scanning: Checks for hardcoded credentials and security issues
• GitHub Integration: Hugging Face Space is linked to GitHub repository for seamless deployment

Deployment Flow

1. Code changes pushed to GitHub repository
2. GitHub Actions CI/CD pipeline triggers
3. Automated testing and security scanning
4. If tests pass, automatic deployment to Hugging Face Spaces
5. Live application updated at https://huggingface.co/spaces/nellaivijay/research-assistant

For detailed GitHub setup instructions, see GITHUB_SETUP.md

🚀 Getting Started

Quick Start

1. 🚀 Try the Live App: Visit https://huggingface.co/spaces/nellaivijay/research-assistant
2. 📝 Find Papers: Enter paper IDs or URLs to get recommendations
3. 🔍 Analyze Impact: Review citation analysis and impact scores
4. 📖 Build Reading List: Add relevant papers to your personal list
5. ✏️ Take Notes: Add insights and annotations as you read
6. 📊 Track Progress: Monitor your reading progress over time
7. 📤 Export: Export citations and reading lists as needed

Local Development

For local development setup, see the Installation Guide in the GitHub Wiki.

📝 License

MIT License

🤝 Contributing

This is an enhanced version inspired by librarian-bots/recommend_similar_papers, with additional research workflow features and analysis capabilities.