RAG Search beta

AI-powered enterprise search with RAG on QNAP NAS

Leverage AI-powered RAG to explore, understand, and generate insights from your stored content using natural language queries—unlocking deeper knowledge and discovery.

RAG Search

AI-powered enterprise search with RAG on QNAP NAS

AI-enhanced enterprise search and knowledge management

Are you facing these challenges?

Overwhelming file volume and information overload? Finding the right document is time-consuming and complex.
Fragmented knowledge? Requires reviewing multiple files to grasp complete information.
Missed insights and inefficiencies: Traditional searches only retrieve information rather than understanding relationships between data.

AI-enhanced enterprise search and knowledge management

Integrating RAG-based AI search technology not only enhances search accuracy and relevance but also plays a key role in optimizing search and analysis efficiency in enterprise search:

Intuitive: Natural language search allows for effortless and intuitive information retrieval.
Exploratory – AI understands context and user intent to provide highly relevant results.
Intelligent summarization: Retrieves relevant documents and generates insights such as data summaries, comparison tables, and more.

QNAP NAS pioneers RAG Search, transforming your NAS into an enterprise search powerhouse.

Qsirch is a powerful search engine designed exclusively for QNAP NAS. It further integrates cloud-based Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology.

What can Qsirch RAG Search provide?

Latest data
Relevant insights
Summarized information

Qsirch RAG search helps you quickly find, organize, and analyze the massive amounts of data stored on your NAS, whether it's daily work files, lengthy reports, or research documents. Get the key information you need in a fraction of the time:

Sales reports - Generates structured tables displaying sales revenue, growth rate, and trends.
Marketing campaigns - Presents conversion rates, click-through rates, and audience reach in structured tables.
Meeting minutes - Creates concise summaries with decisions and action items.
Project management - Offers progress updates, risk assessments, and document links.
Research papers - Extracts research objectives, methodologies, and results.

Flexible and reliable enterprise search that simplifies knowledge management.

Simply enter an API key or download the model to get started. RAG search supports two modes:

Cloud-based LLMs：Supports Google Gemini, OpenAI ChatGPT, Microsoft Azure OpenAI, and OpenAI-compatible models like DeepSeek and xAI Grok.
On-prem LLMs：Supports open-source models such as DeepSeek, Phi, Mistral, and Gemma for local deployment with enhanced data privacy.

Check LLM privacy policies Read the tutorial

Select specific NAS folders for RAG Search. Only the most relevant content from the selected data, based on your search query, is sent to the cloud-based LLM for analysis, improving result accuracy while ensuring stronger data control.

RAG Search supports various document formats , including Word, Excel, PowerPoint, PDF, Email, and TXT, allowing you to search within specific formats. It delivers accurate answers and summaries, helping you quickly grasp key information and extract insights from large datasets.

RAG search not only provides critical information but also references to five relevant documents, empowering you to gain deeper insights from your data. Build a robust knowledge hub for future tracking, analysis, and application.

Simply enter an API key or download the model to get started. RAG search supports two modes:

Cloud-based LLMs：Supports Google Gemini, OpenAI ChatGPT, Microsoft Azure OpenAI, and OpenAI-compatible models like DeepSeek and xAI Grok.
On-prem LLMs：Supports open-source models such as DeepSeek, Phi, Mistral, and Gemma for local deployment with enhanced data privacy.

Check LLM privacy policies Read the tutorial

Real-time and multilingual knowledge retrieval

Multi-turn conversations

Create topic-based chatrooms with auto-saved history, or switch to temporary mode for added privacy.
Instant access to latest data

Ensures real-time search results even if NAS files are frequently updated.
Multilingual AI search

Search and retrieves content in 23 languages, enabling seamless access to information across multilingual datasets.

The ideal choice for on-prem intelligence ──

QNAP Edge AI Storage Server

Run LLMs locally without cloud access. QNAP’s Edge AI servers let you deploy private, secure, and real-time RAG search using your internal data.

QAI-h1290FX (Coming soon)

Run AI securely on-premises—no cloud upload needed
Supports popular LLMs: DeepSeek, Phi, Mistral, Gemma
Recommended GPUs: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation, RTX 6000 Ada, or RTX 4000 Ada for optimal AI performance

Read tutorial

On-prem LLM deployment requires Qsirch 6.0.0+, LLM Core 1.0.0, and NVIDIA GPU drivers.

Boost productivity with AI-powered search on QNAP NAS

Whether you are an enterprise managing vast documents, a professional handling extensive reports and internal data, or an organization seeking a fast AI-driven search solution with limited technical resources, Qsirch RAG Search delivers an efficient and intelligent knowledge management experience.

Sales & Marketing Teams
Engineers & Developers
Writers & Content Creators
Academic & Research Institutions
Legal & Government Agencies
Healthcare Organizations

Sales & Marketing Teams
Engineers & Developers
Writers & Content Creators
Academic & Research Institutions
Legal & Government Agencies
Healthcare Organizations

NAS system requirements for RAG Search

Qsirch 5.6.0 (or later)
64-bit x86 and 64-bit ARM-based NAS
QTS 5.0.1 (or later) or QuTS hero h5.0.1 (or later)
Minimum 2GB RAM (4GB RAM recommended for optimum performance.)
Cloud LLM–based RAG search: Requires configuring Qsirch with an OpenAI-compatible API to generate answers using cloud-hosted large language models.
On-prem LLM–based RAG search: Requires a compatibility-tested GPU to ensure large language models can be loaded and run locally on the NAS.