🔍 Semantic Similarity Explainer with AI

This Streamlit app calculates the semantic similarity between two sentences using transformer-based embeddings (all-MiniLM-L6-v2) and uses AI to explain why that specific score makes sense.

Features

• Semantic Similarity Calculation: Uses the all-MiniLM-L6-v2 transformer model to generate 384-dimensional embeddings
• AI-Powered Explanations: Leverages OpenRouter API to explain similarity scores in detail
• Technical Details: Shows embedding dimensions, cosine similarity calculations, and vector statistics
• Interactive Interface: Easy-to-use Streamlit interface with real-time calculations
• History Tracking: Keeps track of previous calculations during the session

How It Works

1. Input: Enter two sentences you want to compare
2. Embedding Generation: The app uses the all-MiniLM-L6-v2 model to convert sentences into dense vector representations
3. Similarity Calculation: Computes cosine similarity between the embeddings
4. AI Explanation: Uses GPT-3.5-turbo via OpenRouter to explain the similarity score
5. Technical Details: Shows detailed information about the embeddings and calculations

Usage

Without API Key

• You can use the similarity calculation feature without an API key
• The app will show similarity scores and technical details
• AI explanations will be skipped

With OpenRouter API Key

• Get your API key from OpenRouter
• Enter it in the sidebar to enable AI explanations
• The app will provide detailed explanations of why sentences have specific similarity scores

Examples

• High Similarity: "The car is fast" vs "The automobile is quick" → ~0.90
• Moderate Similarity: "I love dogs" vs "I hate dogs" → ~0.60 (same topic, opposite sentiment)
• Low Similarity: "The cat sat on the mat" vs "Python is a programming language" → ~0.10

Technical Details

• Model: sentence-transformers/all-MiniLM-L6-v2
• Embedding Dimensions: 384
• Similarity Metric: Cosine similarity
• AI Model: GPT-3.5-turbo (via OpenRouter)

Installation & Local Development

# Clone the repository
git clone https://github.com/Umer-K/semantic-similarity-explainer.git
cd semantic-similarity-explainer


# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py

Environment Variables

• OPENROUTER_API_KEY: Your OpenRouter API key (optional, can also be entered in the UI)

License

MIT License - see LICENSE file for details.