跳转至

Nvidia Multimodal RAG Knowledge Systems

📊 Level ⭐⭐⭐ | 22.1KB

Build AI-Ready Knowledge Systems Using 5 Essential Multimodal RAG Capabilities | NVIDIA Technical Blog

Build AI-Ready Knowledge Systems Using 5 Essential Multimodal RAG Capabilities | NVIDIA Technical Blog DEVELOPER Home Blog Forums Docs Downloads Training Join Technical Blog Subscribe Related Resources Agentic AI / Generative AI English Build AI-Ready Knowledge Systems Using 5 Essential Multimodal RAG Capabilities Feb 17, 2026 By Shruthii Sathyanarayanan , Sumit Bhattacharya , Punit Kumar , Pranjal Doshi and Nikhil Kulkarni Like Discuss (1) L T F R E Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms, and embedded metadata. Financial reports carry critical insights in tables, engineering manuals rely on diagrams, and legal documents often include annotated or scanned content.  Retrieval-augmented generation (RAG) was created to ground LLMs in trusted enterprise knowledge retrieving relevant source data at query time to reduce hallucinations and improve accuracy. But if a RAG system processes only surrounding text, it misses key signals embedded in tables, charts, and diagrams resulting in incomplete or incorrect answers. An intelligent agent is only as good as the data foundation it s built on. Modern RAG must therefore be inherently multimodal able to understand both visual and textual context to achieve enterprise-grade accuracy. The NVIDIA Enterprise RAG Blueprint is built for this, providing a modular reference architecture that connects unstructured enterprise data to the intelligent systems built on top of it.  The blueprint also serves as a foundational layer for the NVIDIA AI Data Platform , helping to bridge the traditional gap between compute and data. By enabling retrieval and reasoning closer to the data layer, it preserves governance, reduces operational friction, and makes enterprise knowledge immediately usable by intelligent systems. The result is a modern AI data stack storage that can retrieve, enrich, and reason alongside your models. While the Enterprise RAG Blueprint provides many configurable options, this post highlights the following five key configurations that most directly improve accuracy and contextual relevance across enterprise use cases:  Baseline multimodal RAG pipeline Reasoning Query decomposition Filtering metadata for faster and precise retrieval Visual reasoning for multimodal data The post also explains how the blueprint can be embedded into AI data platforms to transform traditional repositories into AI-ready knowledge systems.  Accuracy metrics in this blog are measured using the RAGAS framework , using well-known public datasets. Learn more about evaluating your NVIDIA RAG Blueprint system . 1. Document ingestion and understanding Before an agent can deliver insights, it must be perfectly grounded in your data. This foundational configuration focuses on intelligent document ingestion and core RAG functionality.  The Enterprise RAG Blueprint uses NVIDIA NeMo Retriever to extract multimodal enterprise content text, tables, charts and graphs, and infographics then embeds that content into text for indexing in a vector database. At query time, the blueprint runs semantic retrieval, reranking, and Nemotron LLM to generate a grounded answer. To maximize performance, this baseline intentionally avoids image captioning and heavy reasoning, making it the ideal starting point for production deployments. Deploy this baseline on Docker . Benefits of document ingestion and understanding  This foundational configuration is the blueprint s highest-efficiency pipeline, optimized for accuracy and throughput while keeping GPU cost and time to first token (TTFT) low. This configuration establishes your baseline performance for retrieval quality and LLM grounding. Figure 1. RAG pipeline Table 1 summarizes the overall impact across a few datasets. Accuracy (v2.3 Default) MM = Multimodal, TO = Text-Only Dataset Type Accuracy RAG Battle MM 0.809 KG RAG MM 0.565 FinanceBench MM 0.633 BO767 MM 0.910 HotpotQA TO 0.671 Google Frames MM 0.509 Table 1. Accuracy impact of baseline configuration (higher is better) 2. Reasoning When you turn on reasoning in the RAG blueprint, you enable the LLM to interpret the retrieved evidence, and synthesize logically grounded answers. This is the easiest change to get an accuracy boost for many applications. Enable reasoning for the NVIDIA Enterprise RAG Blueprint . Table 2 summarizes the overall impact across several sample datasets. Accuracy (v2.3 Default) plus Reasoning MM = Multimodal, TO = Text-Only Dataset Type Reasoning on Default RAG Battle MM 0.85 0.809 KG RAG MM 0.58 0.565 FinanceBench MM 0.69 0.633 BO767 MM 0.88 0.91 Table 2. Accuracy impact of enabling reasoning versus baseline configuration (higher is better) Benefits of reasoning  For any use case involving mathematical operations or complex data comparison, a typical simple similarity or hybrid search will not suffice. Reasoning is required to correct errors and ensure precise contextual understanding. Accuracy improvements across datasets averaged ~5%, with several cases demonstrating dramatic reasoning-driven corrections.  Examples In the FinanceBench dataset, the baseline configuration incorrectly computed the Adobe FY2017 operating cash flow ratio as 2.91. After enabling reasoning, the model produced the correct answer, 0.83. In addition, the Ragbattle dataset demonstrates the accuracy improvement from enabling VLM. 3. Query decomposition  Answering complex user questions often requires pulling facts from multiple places in the data foundation. Query decomposition breaks a single question into smaller subqueries, retrieves evidence for each, and recombines the results into a complete, grounded response. Turn on query decomposition for the NVIDIA Enterprise RAG Blueprint . Figure 2. Response accuracy before and after query decomposition Benefits of query decomposition Query decomposition significantly improves accuracy for multihop and context-rich questions that span multiple paragraphs or documents. It does add extra LLM calls (increasing latency and cost), but the accuracy gains are often worth it for mission-critical enterprise use cases. Query decomposition can also be paired with reasoning for an additional boost when needed. Example As NVIDIA AI Data platform partners evolve to offer more relevant and accurate retrieval, this feature can either include some level of query processing as part of the data platform or can be left to the agent. Learn more about how query decomposition can be an approach in some use cases .  Table 3 shows the overall impact across a few datasets. Accuracy (v2.3 Default) plus Query Decomposition MM = Multimodal, TO = Text-Only Dataset Type Query decomposition Default RAG Battle MM 0.854 0.809 FinanceBench MM 0.631 0.633 BO767 MM 0.885 0.91 HotpotQA TO 0.725 0.671 Google Frames MM 0.6 0.5094 Table 3. Accuracy impact of query decomposition versus baseline configuration (higher is better) 4. Filtering metadata for faster and precise retrieval Metadata, such as author, date, category, and security tags, has always been integral to enterprise data. In RAG pipelines, metadata filters can be leveraged to narrow the search space and align retrieved content with the right context, significantly improving retrieval precision and speed.  The RAG blueprint supports custom metadata ingestion and automatic query generation based on that data. To leverage your custom metadata, see Advanced Metadata Filtering with Natural Language Generation . To learn more about what s possible with this feature set, check out the example notebook on the NVIDIA-AI-Blueprints/rag GitHub repo.  Benefits of metadata filtering Metadata filtering narrows the search space for faster retrieval and improves precision by aligning retrieved content with context. This allows developers to leverage metadata without manual filter logic to achieve higher throughput and contextual relevance. When metadata filtering capabilities are embedded directly into AI data platforms, it can make your storage smarter, leading to faster retrieval and lower latency. Example To provide an example, consider two documents that are ingested with the following metadata: custom_metadata = [ { "filename": "ai_guide.pdf", "metadata": { "category": "AI", "priority": 8, "rating": 4.5, "tags": ["machine-learning", "neural-networks"], "created_date": "2024-01-15T10:30:00" } }, { "filename": "engineering_manual.pdf", "metadata": { "category": "engineering", "priority": 5, "rating": 3.8, "tags": ["hardware", "design"], "created_date": "2023-12-20T14:00:00" } } When using metadata with dynamic filter expression, a query such as, “Show me high-rated AI documents with machine learning tags created after January 2024” will translate to one that automatically generates a filtering expression such as: filter_expression = content_metadata["category"] == "AI" and content_metadata["rating"] >= 4.0 and array_contains(content_metadata["tags"], "machine-learning") and content_metadata["created_date"] >= "2024-01-01 With metadata filtering enabled, the system retrieved 10 focused citations from one document, ai_guide.pdf , achieving 100% precision on the target domain while reducing search space by 50%. 5. Visual reasoning for multimodal data  Enterprise data is visually rich. Where traditional text-only embeddings fall short, vision language models (VLMs) such as NVIDIA Nemotron Nano 2 VL (12B) introduce visual reasoning into the pipeline. Learn more about how to leverage a VLM for generation in the RAG Blueprint.  Figure 3. Before and after leveraging a VLM for generation Benefits of visual reasoning  Visual reasoning is crucial for handling real-world enterprise documents. Integrating a VLM in the generation pathway enables the RAG system to interpret images, charts, and infographics, making it possible to accurately answer queries where the information lies in a structured visual element rather than just the surrounding text.  Example  A significant accuracy improvement was observed when a VLM was enabled for the Ragbattle dataset in the RAG Blueprint, especially when the answer was in a visual element. Note that enabling VLM inference can increase response latency from additional image processing. Consider this tradeoff between accuracy and speed based on your requirements. Learn more about the accuracy improvements with VLM for the Ragbattle dataset. Transforming enterprise storage into an active knowledge system The Enterprise RAG Blueprint demonstrates how the progressive adoption of these five capabilities from reasoning and metadata-driven retrieval to multimodal understanding directly enhances the accuracy and groundedness of your intelligent agents. Each capability offers a unique balance between latency, token cost, and contextual precision, providing a flexible, tunable framework that can be adopted to various enterprise use cases. This accelerates the evolution of the data foundation itself. The NVIDIA AI Data Platform transforms enterprise data into AI-searchable knowledge. As NVIDIA partners evolve their storage offerings, this blueprint serves as a reference for delivering embedded RAG capabilities that leverage metadata to enforce permissions, track changes, and provide highly accurate retrieval directly at the storage layer. NVIDIA storage partners are building AI data platforms based on the NVIDIA reference design that are transforming enterprise storage from a passive repository to become an active intelligent system in the AI workflow. The result is a next-generation enterprise data infrastructure: faster, smarter, and purpose-built for the age of generative AI. What s new with the NVIDIA Enterprise RAG Blueprint The latest release of the NVIDIA EnterpriseRAG Blueprint deepens its focus on serving agentic workflows. It introduces first-class document-level summarization with both shallow and deep strategies, enabling agents to quickly assess relevance, narrow search space, and balance accuracy with latency. A new data catalog improves discoverability and governance across large corpora, while upgrades to the best-in-class Nemotron RAG models further enhance retrieval quality, reasoning, and generation performance making RAG a more efficient, agent-ready foundation for enterprise-scale knowledge systems. Get started with enterprise-grade RAG Ready to integrate these five capabilities into your RAG use cases? Access the modular code, documentation, and evaluation notebooks for free within the NVIDIA Enterprise RAG Blueprint . Make your enterprise data AI-ready and transform your production data into an intelligent knowledge system with embedded RAG capabilities with NVIDIA AI Data Platform. Contact an NVIDIA AI storage partner to get started with your own NVIDIA-powered AI data platform.  Discuss (1) Like Tags Agentic AI / Generative AI | Data Center / Cloud | General | Blueprint | Nemotron | Intermediate Technical | Best practice | AI Agent | AI Data Platform | AI-Ready Data | featured | LLMs | Retrieval Augmented Generation (RAG) About the Authors About Shruthii Sathyanarayanan Shruthii Sathyanarayanan is a product marketing manager in the NVIDIA Enterprise Computing group with a focus on enterprise AI and virtualization. Shruthii holds a bachelor s degree in Computer Engineering and Business from the University of Illinois at Urbana-Champaign and has previously held roles in software development and product management. View all posts by Shruthii Sathyanarayanan About Sumit Bhattacharya Sumit Bhattacharya is a senior engineering manager at NVIDIA, working on AI blueprints and conversational AI. His primary area of focus is building scalable, low-latency solutions for Enterprise RAG, data flywheels, and voice agents. He also has extensive experience of working on NLP, dialog systems, and voice assistants. He holds a master s degree in Electrical Engineering from the Indian Institute of Technology, Kharagpur, and has over 18 years of industry experience. View all posts by Sumit Bhattacharya About Punit Kumar Punit Kumar is a senior system software engineer at NVIDIA with a focus on the RAG Blueprint, production RAG systems, and features that improve accuracy and performance. Punit holds a master s degree in Data Science and Engineering from BITS Pilani and a BTech in Computer Science from SKIT Jaipur and has previously held roles in R&D in AI engineering and in data engineering. View all posts by Punit Kumar About Pranjal Doshi Pranjal Doshi is a software engineer at NVIDIA, specializing in retrieval-augmented generation (RAG) and the productionization of large language models. Pranjal holds a master s degree in Computer Science and Engineering from the Indian Institute of Technology (IIT) Kharagpur and focuses on bridging the gap between AI research and scalable, real-world applications. View all posts by Pranjal Doshi About Nikhil Kulkarni Nikhil Kulkarni is a software engineer at NVIDIA specializing in the productization of the RAG Blueprint, with an emphasis on accuracy improvements, performance optimizations, and deployment. Nikhil holds a bachelor s degree in Computer Science and focuses on translating AI models into robust, enterprise-grade architectures. He has previously worked on building speech-based AI agents at NVIDIA. View all posts by Nikhil Kulkarni Comments Related posts Chat With Your Enterprise Data Through Open-Source AI-Q NVIDIA Blueprint Chat With Your Enterprise Data Through Open-Source AI-Q NVIDIA Blueprint NVIDIA NeMo Retriever Delivers Accurate Multimodal PDF Data Extraction 15x Faster NVIDIA NeMo Retriever Delivers Accurate Multimodal PDF Data Extraction 15x Faster Insights, Techniques, and Evaluation for LLM-Driven Knowledge Graphs Insights, Techniques, and Evaluation for LLM-Driven Knowledge Graphs Translate Your Enterprise Data into Actionable Insights with NVIDIA NeMo Retriever Translate Your Enterprise Data into Actionable Insights with NVIDIA NeMo Retriever Scaling Enterprise RAG with Accelerated Ethernet Networking and Networked Storage Scaling Enterprise RAG with Accelerated Ethernet Networking and Networked Storage Related posts Building NVIDIA Nemotron 3 Agents for Reasoning, Multimodal RAG, Voice, and Safety Building NVIDIA Nemotron 3 Agents for Reasoning, Multimodal RAG, Voice, and Safety How to Build Deep Agents for Enterprise Search with NVIDIA AI-Q and LangChain How to Build Deep Agents for Enterprise Search with NVIDIA AI-Q and LangChain Build Next-Gen Physical AI with Edge First LLMs for Autonomous Vehicles and Robotics Build Next-Gen Physical AI with Edge First LLMs for Autonomous Vehicles and Robotics Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo How to Build a Document Processing Pipeline for RAG with Nemotron How to Build a Document Processing Pipeline for RAG with Nemotron L T F R E

深度分析

1. 企业数据天然是多模态的，单语文本 RAG 存在结构性缺陷

企业文档涵盖文本、表格、图表、图片、图表、扫描页和表单，关键洞察往往嵌入在视觉元素而非周围文本中。传统仅处理文本的 RAG 系统会遗漏表格中的关键数据、图表中的趋势和表单中的结构化信息，导致不完整或错误的答案。多模态 RAG 是实现企业级准确率的必要条件而非可选项。

2. NVIDIA Enterprise RAG Blueprint 的模块化设计降低了多模态落地门槛

NVIDIA Enterprise RAG Blueprint 采用模块化参考架构，将文档摄取与理解、推理、查询分解、元数据过滤和视觉推理作为可独立配置的能力。这种设计使企业能够渐进式采用——从基础管道开始，逐步叠加推理（+5% 平均准确率提升）、查询分解（元数据+50% 搜索空间缩减）、VLM 视觉推理（视觉元素问答能力）等能力。

3. 推理能力对 RAG 准确率提升最显著且实现成本最低

启用推理后，RAG 准确率平均提升约 5%。在 FinanceBench 数据集上，基础配置错误计算 Adobe FY2017 经营现金流比率（2.91 vs 正确值 0.83），启用推理后自动修正。推理能力对于涉及数学运算或复杂数据对比的场景尤为关键，是大多数应用最容易获得的准确率提升。

4. 查询分解和元数据过滤是精准检索的重要支柱

查询分解通过将复杂多跳问题分解为子查询，从多个文档检索证据并重组答案，显著提升跨段落/多文档问题的准确率。元数据过滤通过安全标签、日期、类别等属性缩小搜索空间（减少 50% 搜索范围），同时聚焦检索内容与正确上下文，是企业知识管理场景的核心能力。

5. 视觉推理能力解锁企业文档中图像和图表的问答能力

NVIDIA Nemotron Nano 2 VL (12B) VLM 将视觉推理引入 RAG 管道，使系统能够解释图像、图表和信息图，在视觉元素中包含答案的数据集（如 Ragbattle）实现显著准确率提升。存储层嵌入 RAG 能力（NVIDIA AI Data Platform）使数据本身成为可推理的智能知识系统，实现治理保留、运营摩擦降低和权限直接执行。

实践启示

  1. 多模态 RAG 是企业知识管理的必选项：在选型时，文本+表格+图表的联合摄取和检索能力应作为企业 RAG 的基础要求，而非增强功能。

  2. 采用分阶段路径部署多模态 RAG：从基础管道开始 → 启用推理处理数学/复杂对比 → 添加查询分解支持多跳问题 → 叠加元数据过滤和视觉推理 VLM，每阶段交付可衡量的准确率提升。

  3. 金融/法律等高精度场景优先启用推理能力：推理对涉及数值计算和多步逻辑的查询准确率提升最为显著，应在第一阶段就启用而非留到后期。

  4. 元数据驱动检索应在数据摄取阶段同步设计：在设计企业 RAG 时，从一开始就规划好作者、日期、类别、安全标签等元数据的完整摄取，以支撑后续精准过滤。

  5. 推进数据基础设施现代化为 AI 原生：存储层嵌入 RAG 能力（向量检索+语义理解）是企业数据基础设施的未来方向，应纳入 AI 转型路线图。

相关实体

→ 原文存档