Fuzzy RAG

Fuzzy RAG is an innovative Retrieval-Augmented Generation (RAG) system that addresses a critical limitation in traditional RAG implementations: the lack of explainability and context-awareness in document retrieval. While conventional RAG systems rank documents purely by semantic similarity, Fuzzy RAG incorporates fuzzy logic to consider multiple contextual factors, providing more relevant and trustworthy results for enterprise knowledge retrieval. ### The Problem Traditional RAG systems face several challenges: 1. **Black-box ranking**: Users don't understand why certain documents are retrieved 2. **Context-blind**: Semantic similarity alone ignores document authority, freshness, and complexity 3. **One-size-fits-all**: No adaptation to query intent (e.g., "show me official docs" vs. "explain simply") 4. **Trust issues**: In enterprise settings, source credibility and recency are crucial but often ignored ### Our Solution Fuzzy RAG introduces a transparent, multi-attribute ranking system: **1. Fuzzy Attribute Extraction** - **Authority**: Automatically scores documents based on status (official: 1.0, approved: 0.8, draft: 0.3) - **Freshness**: Time-based decay function (recent documents score higher) - **Complexity**: Analyzes sentence length and technical density - **Similarity**: Traditional vector similarity (baseline) **2. Fuzzy Rule Engine** Five interpretable rules combine attributes: - Rule 1: High similarity + High authority → Very high relevance - Rule 2: High similarity + High freshness → Very high relevance - Rule 3: Medium similarity + High authority + High freshness → High relevance - Rule 4: Simple query + Low complexity → Relevance bonus - Rule 5: Low similarity cap (unless exceptional authority + freshness) **3. Transparent Comparison** The Streamlit interface displays: - **Baseline ranking** (vector-only) vs. **Fuzzy ranking** side-by-side - Triggered rules with explanations for each result - Attribute scores authority, freshness,complexity