AI Research Assistant Usage Guide

Overview

The AI Research Assistant is a powerful tool that combines knowledge graphs, semantic search, and AI agents to help researchers analyze academic papers, identify research gaps, and generate hypotheses. The system supports both local and cloud-based AI models, with a focus on privacy and flexibility.

Prerequisites

• Python 3.8+
• Neo4j Database
• Ollama (for local models)
• OpenAI API key (optional, for cloud models)
• 8GB+ RAM for local models

Installation

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

# Required
export NEO4J_URI="bolt://localhost:7687"
export NEO4J_USER="neo4j"
export NEO4J_PASSWORD="your_password"

# Optional
export MODEL_PROVIDER="local"  # or "openai"
export OPENAI_API_KEY="your_key"  # if using OpenAI

Starting the Application

Run the application:

python main.py

The web interface will be available at http://localhost:8000

Using the Interface

The application provides four main modes of operation:

1. Research Analysis

The Research Analysis tab provides a complete research workflow:

1. Enter a research topic (e.g., "transformer attention mechanisms")
2. Select your preferred model provider (Local or OpenAI)
3. Click "Research"
4. The system will:
   • Fetch relevant papers from arXiv
   • Build a knowledge graph
   • Analyze research trends
   • Identify gaps
   • Generate hypotheses

2. Semantic Search

The Semantic Search tab provides high-level semantic search capabilities:

1. Enter your search query
2. Adjust the similarity threshold (0.0 to 1.0)
   • Higher values (e.g., 0.8) return more precise matches
   • Lower values (e.g., 0.5) return more results but may be less relevant
3. Click "Search"
4. View results with similarity scores

3. Embedding Search

The Embedding Search tab provides direct access to embedding vectors:

1. Enter your search query
2. Adjust parameters:
   • Similarity threshold (0.0 to 1.0)
   • Result limit (1 to 100)
3. Click "Search Embeddings"
4. View results including:
   • Paper details
   • Similarity scores
   • Raw embedding vectors (expandable)

4. Knowledge Graph Visualization

The Knowledge Graph tab provides an interactive visualization of the research network:

1. Search Functionality:

   • Enter search terms in the search box
   • Filter by node type (Papers, Concepts, Authors, Methods)
   • Use the Reset button to clear search and restore view

2. Navigation:

   • Zoom: Use mouse wheel or pinch gesture
   • Pan: Click and drag
   • Focus: Click on a node to center it
   • Reset View: Click the Reset button

3. Node Types and Colors:

   • Papers: Blue
   • Concepts: Red
   • Authors: Green
   • Methods: Orange

4. Search Results:

   • Matching nodes are highlighted in gold
   • Results count is displayed
   • First match is automatically focused

Understanding the Results

Research Analysis Results

• Research Overview: Summary of papers found and analysis time
• Key Concepts: Important terms and concepts identified
• Research Trend Analysis: Insights into dominant areas and emerging topics
• Research Gap Analysis: Identified gaps in current research
• Generated Hypotheses: Suggested research directions

Semantic Search Results

• Similar Papers: Papers ranked by semantic similarity
• Similarity Scores: Percentage indicating how closely each paper matches your query
• Abstracts: Paper summaries for quick review

Embedding Search Results

• Query Embedding: The vector representation of your search query
• Paper Embeddings: Vector representations of matching papers
• Similarity Scores: Cosine similarity between query and paper embeddings
• Raw Vectors: Expandable sections showing the actual embedding vectors

Knowledge Graph Results

• Node Information: Hover over nodes to see details
• Relationship Types: Different types of connections between nodes
• Search Matches: Highlighted nodes showing search results
• Node Types: Color-coded nodes for different entity types

Advanced Usage

Local vs. Cloud Models

The system supports two model providers:

1. Local (Ollama):

   • Models: llama3.1:8b, codellama:7b, nomic-embed-text
   • Advantages: Privacy, no API costs
   • Requirements: 8GB+ RAM

2. Cloud (OpenAI):

   • Models: gpt-4, text-embedding-3-small
   • Advantages: Better performance, no local resources
   • Requirements: OpenAI API key

Knowledge Graph Structure

The Neo4j database stores:

• Paper Nodes: Research papers with metadata and embeddings
• Concept Nodes: Key concepts and terms
• Author Nodes: Paper authors
• Method Nodes: Research methods and techniques
• Relationships: Connections between papers, concepts, authors, and methods

API Endpoints

The system provides several REST API endpoints:

• POST /research: Full research analysis
• POST /semantic-search: Semantic search
• POST /embedding-search: Direct embedding search
• GET /graph-data: Knowledge graph data
• GET /health: Health check

Troubleshooting

Common Issues

1. Neo4j Connection Errors:

   • Verify Neo4j is running
   • Check connection credentials
   • Ensure Neo4j has enough memory

2. Model Loading Issues:

   • For local models: Ensure Ollama is running
   • For OpenAI: Verify API key
   • Check system memory

3. Search Performance:

   • Adjust similarity threshold
   • Reduce result limit
   • Use more specific queries

4. Graph Visualization Issues:

   • Clear browser cache
   • Check browser console for errors
   • Ensure sufficient memory for large graphs

Performance Tips

1. Local Models:

   • Close other memory-intensive applications
   • Use smaller models if memory is limited
   • Consider using cloud models for large datasets

2. Search Optimization:

   • Start with higher similarity thresholds
   • Use specific, focused queries
   • Limit results when possible

3. Graph Visualization:

   • Use type filters to reduce visible nodes
   • Focus on specific subgraphs
   • Use search to find relevant nodes quickly

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for:

• Bug fixes
• Feature requests
• Documentation improvements
• Performance optimizations

License

[Your License Here]

Contact

[Your Contact Information]