.png&w=640&q=75)
🤓 Latest Submissions
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
17 May 2026
Cloud Pharmacy - Inventory and LLM Assistance
This project introduces a cutting-edge, cloud-ready prototype designed to revolutionize pharmaceutical management and clinical workflows. At its core, the platform provides a robust medical inventory management system, ensuring real-time tracking, scalability, and data integrity through a cloud-native architecture.Beyond standard logistics, the solution integrates an advanced image selection interface, serving as the foundation for an upcoming Computer Vision module. This future-proof component is engineered to automate pill identification and packaging verification, significantly reducing human error. Furthermore, the ecosystem is designed for seamless integration with a specialized Large Language Model (LLM) assistant. This AI-driven companion will empower pharmacists by providing instant access to drug interactions, dosage guidelines, and personalized pharmaceutical advice, bridging the gap between traditional inventory software and intelligent clinical decision support.
10 May 2026
Order Block detector with LLM advice
This implementation demonstrates the successful integration of a 120 billion parameter Large Language Model (LLM) hosted on AMD Cloud with an algorithmic trading system based on the Order Block strategy. The LLM acts as an expert consultant that validates and optimizes trading decisions in real-time. The use of **AMD Cloud GPT OSS 120B** brings an advanced artificial intelligence layer to the trading bot, enabling more nuanced and contextual decision-making than traditional algorithmic rules. ### Key Features - Real-time signal validation by 120B parameter LLM - Expert reasoning for each trading decision - Automatic fallback if LLM is unavailable - Complete logging of LLM consultations - Configurable confidence thresholds
12 Apr 2026
