Unified Hyperbolic Spectral Retrieval (UHSR)

Unified Hyperbolic Spectral Retrieval (UHSR) is an advanced hybrid text retrieval model that seamlessly integrates lexical search (BM25) with semantic search (FAISS/Pinecone) while employing spectral re-ranking for interpretable and normalized relevance scores in the [0,1] range.

🚀 Key Features

• 🔍 Hybrid Retrieval: Combines BM25 for lexical scoring and dense vector semantic similarity for contextual understanding.
• 🎯 Multi-Metric Similarity: Supports cosine, euclidean, mahalanobis, manhattan, chebyshev, jaccard, and hamming similarity.
• 🔬 Spectral Re-Ranking: Uses graph Laplacian & Fiedler vector to boost highly relevant candidates.
• ⚡ AI-powered Reranking: Supports Hugging Face Cross-Encoders & OpenAI API-based Reranking.
• 📈 Interpretable Scores: Final relevance scores are logistic-normalized in [0,1] for easy ranking.
• 🚀 Scalable & Efficient: Works with FAISS (local) for fast retrieval and Pinecone (cloud-based) for large-scale vector search.

🛠️ How It Works

UHSR enhances traditional retrieval by blending BM25-based keyword matching with semantic vector representations using the following pipeline:

🌍 Supported Retrieval Methods

• ✅ BM25 (Lexical Matching)
• ✅ FAISS (Local Vector Search)
• ✅ Pinecone (Cloud Vector Search)
• ✅ Hugging Face Rerankers
• ✅ OpenAI API-based Reranking

📌 Why UHSR?

• Better Search Results: Combines exact keyword matching (BM25) with contextual embeddings (Semantic Search).
• Faster & Scalable: Uses FAISS for local retrieval or Pinecone for cloud-based vector search.
• Interpretable Ranking: Outputs normalized scores in [0,1], making it easy to interpret.
• Multi-Metric Similarity: Supports cosine, euclidean, mahalanobis, manhattan, chebyshev, jaccard, and hamming.

🎯 Intended Use

UHSR is designed for:

• Information Retrieval Research
• Search Engines & Recommendation Systems
• NLP Applications in AI & Machine Learning
• Academic & Industry-scale Document Ranking

📂 Code & Documentation

For complete documentation, usage examples, and implementation details, visit the GitHub repository.

Learn More about this package on Medium.

🔥 Try UHSR today and revolutionize your search engine! 🚀