uhsr 0.2.6

Unified Hyperbolic Spectral Retrieval (UHSR) - a novel text retrieval algorithm combining lexical and semantic search.

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.

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🛠️ How It Works

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

Step	Description
1️⃣ Lexical Filtering	Uses BM25 to rank documents by keyword relevance
2️⃣ Semantic Scoring	Computes similarity using FAISS or Pinecone
3️⃣ Fusion Process	Blends scores via logistic normalization & harmonic fusion
4️⃣ Spectral Re-Ranking	Uses graph Laplacian analysis to boost central candidates
5️⃣ (Optional) AI Reranking	Uses OpenAI API or Hugging Face Cross-Encoders

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🌍 Supported Retrieval Methods

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

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📌 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.

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🎯 Intended Use

UHSR is designed for:

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

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📂 Code & Documentation

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

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🔥 Try UHSR today and revolutionize your search engine! 🚀