{"repo":"TaimoorKhan10/Enterprise-RAG-Framework","free":true,"listed":false,"github":"https://github.com/TaimoorKhan10/Enterprise-RAG-Framework","clone":"git clone https://github.com/TaimoorKhan10/Enterprise-RAG-Framework.git","description":"Production-ready Retrieval Augmented Generation (RAG) system with hybrid retrieval, advanced evaluation metrics, and monitoring. Build enterprise LLM applications with reduced hallucinations, better context management, and comprehensive observability.","language":"Python","stars":16,"topics":["ai-monitoring","embeddings","enterprise-ai","evaluation-metrics","faiss","fastapi","hybrid-search","knowledge-base","llm","nlp"],"license":null,"category":"ai-agents","readme_excerpt":"Enterprise-RAG-Framework Production-grade Retrieval-Augmented Generation with enterprise features, comprehensive evaluation, and monitoring Connect your LLMs to your data. Enterprise-grade. Production-ready. 🌟 Overview Enterprise-RAG-Framework is a production-grade Retrieval Augmented Generation system designed for enterprise applications. It combines state-of-the-art retrieval techniques with advanced context augmentation to enable LLMs to access and reason over your organization's knowledge base with unprecedented accuracy and transparency. While consumer-grade RAG systems may work for basic applications, enterprise environments demand more: sophisticated retrieval algorithms, comprehensive evaluation metrics, robust monitoring, and seamless deployment options. Enterprise-RAG-Framework delivers all of these capabilities in a modular, extensible package. 🚀 Key Features Retrieval Engine - 🔄 Advanced Hybrid Retrieval : Combines sparse (BM25) and dense (embedding) retrieval for optimal results - 🧠 Intelligent Re-ranking : Cross-encoder reranking to prioritize the most relevant context - 🧮 Multi-Vector Indexing : Index different semantic representations of documents for specialized queries - 📈 Adaptive Retrieval : Dynamic selection of retrieval strategies based on query characteristics Document Processing - 📄 Multi-Format Support : Process PDFs, DOCX, TXT, HTML, Markdown, and more - 📸 OCR Integration : Extract text from images and scanned documents - 🧩 Smart Chunking : ","default_branch":null,"files":null,"tree":[],"storefront":"/r/TaimoorKhan10","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/TaimoorKhan10/Enterprise-RAG-Framework/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}