QNAP Supercharges Qsirch App with AI-Powered RAG Search, Making NAS Smart Knowledge Hub

QNAP Systems, Inc. introduces RAG Search Beta, a feature in Qsirch 5.6.0, the AI-powered NAS search engine.

By integrating cloud-based public Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) technology, QNAP pioneers the adoption of RAG search capabilities within NAS as a private cloud, delivering highly accurate, contextual, and AI-powered search and knowledge retrieval beyond traditional file and image searches. RAG Search transforms QNAP NAS into an advanced AI-driven knowledge hub, enabling enterprises, professionals, and individuals to streamline information exploration with data sovereignty.

“As businesses generate and store ever-growing volumes of data, the demand for efficient, intelligent, and secure search solutions has never been greater,” said Amol Narkhede, director, SaaS backup and data management BU, QNAP. “With Qsirch RAG Search, we integrate advanced AI-driven search solutions directly into QNAP NAS, allowing users to search and analyze data in a more intuitive and insightful way. RAG Search provides businesses greater flexibility and control over data retrieval, enabling customized searches and the choice of the most suitable LLM service to significantly enhance the efficiency and accuracy of knowledge discovery.“

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Key Features of RAG Search

• AI-enhanced contextual search
  Understands user intent and refines queries for precise, relevant results, enabling professionals to find files, retrieve insights, summarize complex data, and make informed decisions.
• Customizable LLM selection
  Supports OpenAI ChatGPT, Google Gemini, Microsoft Azure OpenAI, and other OpenAI API compatible LLM models (such as DeepSeek and xAi Grok, etc.), enabling flexible AI-powered search integration.
• Tailored data search scope
  Select specific NAS folders for retrieval, and RAG Search only uploads the most relevant content to the cloud LLM for analysis, improving accuracy and strengthening data control.
• On-demand customizable file formats
  Supports a variety of file formats, including Word, Excel, PowerPoint, PDF, TXT, and Email (.eml). Users can select specific formats for retrieval and analysis based on their needs, enabling knowledge discovery across different document formats.
• Traceable insights
  Reference up to five relevant documents in search results, enhancing data validation and deeper analysis.
• Real-time knowledge retrieval
  Search results reflect the latest file versions on NAS for accurate and up-to-date information.
• Multilingual document analysis
  Search and retrieve content in 23 languages, enabling access to information across multilingual datasets.

Resources:
About RAG Search Beta in Qsirch 5.6.0    
How to use large language models for Qsirch RAG search?