# Source: https://www.gigaspaces.com/data-terms/graph-rag

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Conventional RAG Falls Short with Enterprise Databases

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

5 Top Use Cases For Driving Business With Data Hub Architecture 

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      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
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      * [Logistics](https://www.gigaspaces.com/case-studies/optimizing-supply-chain-with-ai-powered-insights)
      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Resources-menu-thumb.jpg)](https://www.gigaspaces.com/blog/from-rag-to-tag)

Taking the AI leap from RAG to TAG

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Product-menu-thumb.jpg)](https://go.gigaspaces.com/webinar/why-conventional-rag-falls-short)

Conventional RAG Falls Short with Enterprise Databases

[Watch the Webinar![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/webinar/why-conventional-rag-falls-short)

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      * [Integration Data Hub](https://www.gigaspaces.com/technology/integration-data-hub)
      * [API Scaling](https://www.gigaspaces.com/use-cases/application-acceleration)
      * [Hybrid / Multi-cloud Integration](https://www.gigaspaces.com/use-cases/hybrid-or-multi-cloud-integration)
      * [Customer 360](https://www.gigaspaces.com/use-cases/customer-360)
      * Industry Solutions
      * [Retail](https://www.gigaspaces.com/retail-and-ecommerce-2)
      * [Financial Services](https://www.gigaspaces.com/financial-services)
      * [Insurance Companies](https://www.gigaspaces.com/industry-solutions/insurance)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

5 Top Use Cases For Driving Business With Data Hub Architecture 

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      * [Asked & Answered in Natural Language](https://www.gigaspaces.com/how-it-works/asked-answered-natural-language)
      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [Proactive AI Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/How-it-works-menu.jpg)](https://go.gigaspaces.com/effective-ai-governance-wp)

Ensure GenAI compliance and governance 

[Read the Whitepaper![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/effective-ai-governance-wp)

  * [Success Stories![](https://www.gigaspaces.com/wp-content/uploads/2022/11/Path-16484.svg)](https://www.gigaspaces.com/case-studies)

    * By Use Case

      * [Procurement](https://www.gigaspaces.com/use-cases/procurement-supply-chain)
      * [Operations](https://www.gigaspaces.com/use-cases/enhancing-pharmaceutical-operations)
      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
    * By Industry

      * [Logistics](https://www.gigaspaces.com/case-studies/optimizing-supply-chain-with-ai-powered-insights)
      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Company-menu-thumb.jpg)](https://go.gigaspaces.com/webinarod/ecurely-unlocking-structured-data-for-enterprise-ai-0)

Monkey See, AI Do - All about CUA

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Resources-menu-thumb.jpg)](https://www.gigaspaces.com/blog/from-rag-to-tag)

Taking the AI leap from RAG to TAG

[Read the Blog![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://www.gigaspaces.com/blog/from-rag-to-tag)

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GigaSpaces, IBM & AWS make AI safer

[Read How![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://www.gigaspaces.com/news/gigaspaces-partners-with-ibm-and-aws-to-deliver-accurate-safe-genai-with-dbs)

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    * Our Products
      * [eRAG](https://www.gigaspaces.com/products/erag)
        * [GenAI Catalyst](https://www.gigaspaces.com/products/genai-catalyst)
        * [Instant Data](https://www.gigaspaces.com/products/instant-data)
        * [Respond Proactively](https://www.gigaspaces.com/products/proactive-ai)
        * [Act Autonomously](https://www.gigaspaces.com/products/data-autonomy)
      * [Smart DIH](https://www.gigaspaces.com/products/smart-digital-integration-hub)
      * [XAP](https://www.gigaspaces.com/products/xap-imdg)
    * Solutions for
      * [Pharma](https://www.gigaspaces.com/products/erag/pharma)
      * [Procurement](https://www.gigaspaces.com/products/erag/procurement)
  * Solutions
    * [Digital Innovation Over Legacy Systems](https://www.gigaspaces.com/use-cases/digital-innovation-acceleration)
    * [Integration Data Hub](https://www.gigaspaces.com/technology/integration-data-hub)
    * [API Scaling](https://www.gigaspaces.com/use-cases/application-acceleration)
    * [Hybrid/Multi-cloud Integration](https://www.gigaspaces.com/use-cases/hybrid-or-multi-cloud-integration)
    * [Customer 360](https://www.gigaspaces.com/use-cases/customer-360)
    * [Retail](https://www.gigaspaces.com/retail-and-ecommerce-2)
    * [Financial Services](https://www.gigaspaces.com/financial-services)
    * [Insurance Companies](https://www.gigaspaces.com/industry-solutions/insurance)
  * How it Works
    * [eRAG Technology Overview](https://www.gigaspaces.com/how-it-works/erag-how-it-works)
      * [AI-Ready, IT-Friendly](https://www.gigaspaces.com/products/ai-ready)
      * [Semantic Reasoning](https://www.gigaspaces.com/how-it-works/semantic-reasoning)
      * [Questions to SQL Queries](https://www.gigaspaces.com/how-it-works/questions-into-sql-queries)
      * [Asked & Answered in Natural Language](https://www.gigaspaces.com/how-it-works/asked-answered-natural-language)
      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
  * [Success Stories](https://www.gigaspaces.com/case-studies)
    * By Use Case
      * [Procurement](https://www.gigaspaces.com/use-cases/procurement-supply-chain)
      * [Operations](https://www.gigaspaces.com/use-cases/enhancing-pharmaceutical-operations)
      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
    * By Industry
      * [Logistics](https://www.gigaspaces.com/case-studies/optimizing-supply-chain-with-ai-powered-insights)
      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
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    * [Glossary](https://www.gigaspaces.com/data-terms)
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    * [Blog](https://www.gigaspaces.com/blog)
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  * Company
    * [About](https://www.gigaspaces.com/about)
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      * [System Integrators](https://www.gigaspaces.com/partners-lobby/system-integrators)
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# Graph RAG

[More Glossary Terms](https://www.gigaspaces.com/data-terms)

## **What is Graph RAG?**

## 

Graph RAG, or Graph [Retrieval-Augmented Generation](https://www.gigaspaces.com/data-terms/retrieval-augmented-generation), is an advanced method for improving the accuracy and relevance of responses generated by large language models (LLMs). At its core, Graph RAG integrates knowledge graphs—a structured, graph-based representation of interconnected data—with the retrieval-augmented generation (RAG) framework.

## 

Traditional RAG models rely on vector-based search to retrieve relevant documents or information from unstructured datasets before using it to enhance LLM outputs. In contrast, Graph RAG uses the rich semantic relationships in knowledge graphs to retrieve contextually accurate data. This integration not only improves information retrieval but also enables a more nuanced and accurate generation of responses, especially for complex or domain-specific queries.

## 

Graph RAG is a shift in how RAG systems leverage structured data for context-aware response generation. By integrating the power of knowledge graphs, its architecture offers better precision, scalability, and transparency when compared to traditional RAG models.

## 

## **The Key Features of Graph RAG**

## 

**Knowledge Graph Integration:** Graph RAG architecture incorporates knowledge graphs as a core component. These graphs represent data as nodes and relationships so that the system can retrieve contextually interconnected information instead of isolated data points.

## 

**Context-Aware Retrieval:** By leveraging the semantic relationships in knowledge graphs, Graph RAG ensures that retrieved data is both relevant as well as contextually aligned with the query, reducing noise in the response generation process.

## 

**Dynamic Query Adaptation:** Graph RAG dynamically adapts its retrieval process by using the query context to negotiate knowledge graphs efficiently. This adaptability is particularly beneficial for queries requiring multi-hop reasoning or domain-specific expertise.

## 

**Enhanced Explainability:** Unlike traditional RAG models, Graph RAG offers greater transparency in its reasoning process. The structured nature of knowledge graphs allows for easy tracing of the data sources and relationships influencing the generated response.

## **How Graph RAG Differs from Traditional RAG Models**

Graph RAG vs. vector RAG highlights a radical shift in data retrieval and reasoning capabilities. Conventional RAG models primarily rely on vector embeddings to search through unstructured text, making them effective for general-purpose information retrieval but limited when it comes to [handling complex relationships](https://www.gigaspaces.com/blog/rag-technology-increasing-genai-accuracy) or context.

In contrast, Graph RAG architecture uses the semantic structure of knowledge graphs to navigate through interconnected data, offering several advantages:

**Relational Understanding** : While vector-based systems capture word similarity, Graph RAG focuses on the relationships between entities, for more accurate context retrieval.

**Data Structure** : Graph RAG relies on structured knowledge graphs, whereas traditional RAG depends on unstructured or semi-structured datasets.

**Reasoning Capabilities** : The graph-based approach excels in multi-hop reasoning, enabling it to answer queries requiring layered insights, such as “What are the effects of X on Y via Z?”

By combining the structured richness of knowledge graphs with the generative capabilities of LLMs, Graph RAG offers a significant leap in handling complex, relationship-driven queries.

## **The Benefits of Using Graph RAG**

Using Graph RAG offers several compelling benefits:

**Improved Accuracy and Relevance:** By using the semantic depth of knowledge graphs, Graph [RAG ](https://www.gigaspaces.com/data-terms/retrieval-augmented-generation)sees that responses are more accurate and relevant to the context. This is particularly critical for industries like healthcare, finance, and law, where precision is non-negotiable.

**Scalability for Complex Queries:** The ability of Graph RAG [LLM models](https://www.gigaspaces.com/data-terms/llm-evaluation) to negotiate interconnected data nodes allows it to handle complex queries requiring multi-step reasoning, making it suitable for advanced use cases such as scientific research or regulatory compliance.

**Enhanced User Experience:** Graph RAG delivers concise and well-contextualized answers, lessening the cognitive load on users who would otherwise have to wade through lengthy documents retrieved by conventional systems.

**Explainable AI:** The graph-based approach inherently provides a clear map of the [reasoning process](https://www.gigaspaces.com/data-terms/explainable-ai-xai), so that users can understand how and why a particular response was generated, which is key for compliance and auditability in regulated industries.

**Integration with Existing Systems**  
Entities can incorporate Graph RAG architecture into existing workflows for seamless enhancements to search and knowledge management systems. This adaptability cuts implementation costs while maximizing operational efficiency.

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