> In our [previous article](https://www.nebula-graph.io/posts/When_AI_Meets_Graph_Databases_Innovating_with_Multimodal_Data_Fusion), we explored how graph databases—leveraging their unique topological structure and a four-step framework of content analysis, semantic alignment, domain modeling, and relationship map—establish a robust intelligent data foundation for enterprises. This approach fundamentally addresses the challenge of integrating multi-source, heterogeneous data. In this article, we’ll examine how this powerful data foundation unlocks intelligent applications across the enterprise, transforming the very way organizations operate and make decisions.

# Intelligent Systems: Innovations Powered by the Intelligent Data Foundations

With this robust data foundation in place, innovation accelerates. From intelligent Q&A systems that deliver precise, context-aware responses, to advanced analytics that uncover hidden patterns and insights, to the seamless transfer and utilization of data assets—this intelligent core becomes the engine driving next-generation applications. It’s where the latent value of enterprise data is fully unlocked, transforming real-world business operations.

## Intelligent Q&A: The Leap from Data to Knowledge

Traditional question-answering systems rely heavily on keyword matching, extracting isolated fragments from siloed data sources. They often fall short when faced with complex, context-rich queries. For instance, when a user asks, “What factors might be related to a customer’s loan application being rejected?” a conventional system may return a single, surface-level answer—such as “insufficient credit score”—while overlooking critical but hidden factors like transaction anomalies or complex guarantee relationships. This fragmented output hinders comprehensive decision-making.

In contrast, an intelligent Q&A system, powered by a robust intelligent data foundation, represents a fundamental shift—from data retrieval to knowledge understanding. When a user submits a query, a LLM first interprets the underlying intent. The system then taps into the unified, interconnected data within the intelligent foundation, using the graph database’s powerful relationship traversal capabilities to explore paths between the “customer” node and related entities such as “credit score,” “transaction anomaly,” and “guarantee default.”

The graph database is pivotal: it rapidly identifies all relevant entities and their connections, ensuring the response captures not just direct causes but also indirect, contextually significant relationships. The system then synthesizes these distributed yet interlinked insights into a coherent, multi-dimensional answer—delivering “one question, full insight.” Users gain accurate, holistic responses, dramatically improving both the speed and accuracy of decision-making.

## Intelligent Analysis: Uncovering Hidden Value

The vast data accumulated during business operations often conceals valuable patterns and risks that remain invisible to traditional, single-dimensional analysis. Conventional methods fail to construct the rich, interconnected views needed to understand complex realities.

An intelligent analysis system, built on a robust intelligent data foundation, overcomes these limitations by leveraging the graph database’s “global relationship network.” This enables deep exploration of implicit connections across multimodal data, revealing hidden risks and opportunities that span organizational and data silos.

Graph databases excel not only in rapid data retrieval but also in uncovering deeper insights through multi-hop relationship traversal. By connecting fragmented data points—such as transactions, behaviors, and relationships—across degrees of separation, they allow organizations to construct comprehensive risk profiles and holistic customer views. This transforms analytics from reactive reporting to proactive early warning.

This powerful capability drives breakthroughs in fintech, marketing, healthcare, and beyond, delivering unprecedented, actionable insights across the enterprise.

## [Data MCP Marketplace](https://www.nebula-graph.io/posts/Announcing_the_Open_Source_Release_of_NebulaGraph_MCP_Server): Unlocking Data Assets Value

Traditional data management is plagued by inconsistent formats, misaligned semantics, and opaque relationships across departments. This fragmentation perpetuates “data silos”, hinders efficient sharing and circulation of data assets, and leads to costly duplication and redundancy.

The Data MCP Marketplace emerges as a transformative solution. Built on the intelligent data foundation, it centrally integrates and standardizes data assets scattered across disparate business systems, creating a unified, on-demand “data resource pool”.

For example, within a bank, risk management, marketing, and customer service teams can access and share a single, semantically consistent version of customer relationship data through the marketplace. This eliminates redundant data collection and processing, ensures organizational alignment, and dramatically increases data utilization and trust.

As the foundational engine of the MCP Data Marketplace, graph databases provide two critical guarantees for secure and efficient data asset sharing:

* Consistency Assurance: By leveraging the unified semantic layer of the intelligent data foundation, graph databases ensure that data accessed across departments maintains consistent meaning and context. This eliminates ambiguities and prevents business conflicts caused by “same term, different meanings”.

* Traceability Assurance: Graph databases capture the full lifecycle of data, including its origin, transformations, and dependencies, by modeling data lineage as explicit relationships. When a department uses a data asset, it can trace back through connected nodes to identify its source, processing history, and downstream impacts—ensuring data provenance, compliance, reliability, and full auditability.

The establishment of the Data MCP Marketplace has transformed data assets from siloed, department-specific resources into shared enterprise capital. This shift not only significantly reduces data management costs and eliminates redundant investments but also fuels innovation through cross-departmental data integration. Data becomes truly “liquid”—flowing freely to where it creates the most value—driving growth and maximizing its strategic impact.

These innovations are not isolated advancements; together, they signify a deeper, enterprise-wide transformation: the evolution from a traditional “data-driven” paradigm to a more sophisticated “knowledge-driven” model. In a knowledge-driven organization, decisions are no longer based merely on surface-level correlations in historical data, but on a deep understanding of underlying connections, context, and causal relationships.

The intelligent data foundation, powered by graph databases, provides the essential infrastructure to transform vast, heterogeneous data into structured, interconnected knowledge. It enables enterprises to move beyond reactive analytics to proactive intelligence—transitioning from simply being *data-driven* to truly being *knowledge-driven*.

# Future Tendency: The Infinite Potential of Graph Databases and AI

From integrating siloed data to powering intelligent Q&A, analytics, and Data MCP Marketplaces, the fusion of graph databases and AI has rapidly reshaped enterprise intelligence. As AI evolves, this synergy will unlock even deeper insights, autonomous knowledge discovery, and adaptive systems—driving a new era of cognitive, knowledge-driven enterprises.

![https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%20(part%202).png](https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%20(part%202).png)

In application scenarios, the fusion of graph databases and AI will transform diverse fields.

## Smart City Development
Graph databases will integrate massive traffic, energy, and public service data into a dynamic urban operations network. AI, leveraging this interconnected structure, can analyze real-time relationships between traffic flow, weather, and events to optimize signal timing. It can uncover patterns linking energy use, industrial distribution, and population density for intelligent grid management. By mapping public service supply against community needs, it enables precise planning of schools, hospitals, and infrastructure—empowering cities to truly “think and respond”.

## Healthcare
By integrating a patient’s genetic data, medical history, imaging, and lifestyle into a unified health graph, AI can deliver more accurate diagnoses and personalized treatments. In infectious disease control, AI can analyze a “patient-contact-location-variant” network to rapidly trace transmission chains, predict outbreaks, and inform effective public health interventions.

## Personalized Recommendations 
Graph databases and AI will transcend the limits of behavior-based recommendations. By integrating social connections, interests, contexts, and emotional cues into a rich, multidimensional user profile network, AI can uncover deeper intent and relationships. This shifts recommendations from simple “similar items” to truly anticipating user needs—delivering truly personalized, context-aware experiences.

## Financial Risk Management
The integration of graph databases and AI enables more precise risk detection. By constructing a comprehensive graph of users, transactions, merchants, affiliates, and market conditions, AI can monitor hidden risk pathways in real time. It can uncover money laundering through complex transaction chains and predict defaults by analyzing corporate equity and guarantee networks—strengthening financial security with deeper, proactive insights.

## Scientific Research and Innovation
AI and graph databases will accelerate knowledge discovery. In materials science, for instance, AI can analyze graph-connected data on composition, structure, processing, and performance to identify promising new material combinations—dramatically shortening R&D cycles and driving innovation.

# Conclusion
Ultimately, the immense promise of graph databases and AI lies in their shared focus on the defining feature of the “Internet of Everything”: **relationships**. In a world where natural systems and human activities are deeply interconnected, value resides not just in data points, but in the connections between them.

Graph databases like [NebulaGraph](https://www.nebula-graph.io/) make these relationships explicit and navigable through their node-link structure, while AI leverages this connectedness to understand context and uncover hidden patterns. Together, they enable enterprises to move beyond data volume and see the essential structure beneath.

In this article, we’ll examine how the powerful intelligent data foundation unlocks intelligent applications across the enterprise, transforming the very way organizations operate and make decisions.

When AI Meets Graph Databases: Innovating with Multimodal Data Fusion (Part II)

# Data Challenges in the AI Era

As intelligence transforms industries, data is exploding in volume and variety. Banks generate structured transactions, unstructured customer call recordings, and semi-structured JSON profiles. Hospitals manage free-text medical notes, numerical lab results, and diagnostic images. This flood of multi-source, heterogeneous data is no longer an exception—it’s the norm.

Legacy data systems, built for siloed, single-format processing, can’t keep up. They handle one data type at a time, missing the rich connections between them. But modern AI demands more: it needs holistic, context-rich insights drawn from all available data dimensions.

The challenge has shifted. It’s no longer just about storage—it’s about understanding. In the AI era, systems must mimic human cognition, linking disparate data points across modalities to form meaningful networks.

At present, the integration of multi-source heterogeneous data has become an inevitable trend, and Graph Database is one of the key technologies to solve this problem.

# Why Do We Need Graph Databases?  

## The Limits of Traditional Data Approaches

Traditional data processing methods are struggling to keep up in today’s complex landscape. Early storage models created fragmented, isolated “data silos” with little to no connection between them—making it nearly impossible to see the full picture or unlock the true value hidden in the data.

Taking enterprise customer management as an example, a customer’s profile might live in one table, purchase history in another, and service interactions in yet another. To understand their full journey, you need cross-table joins. But as data grows, these queries become slow and unwieldy with latency jumps from milliseconds to minutes. Worse, mismatched fields during joins can introduce errors, leading to inaccurate insights and flawed business decisions.

The result? Slow, inefficient analytics, overlooked relationships, and a widening disconnect between raw data and actionable insights.

## New Demands in the AI Era: Semantic Understanding and Multimodal Fusion

Traditional databases have inherent shortcomings when dealing with multimodal data. Complex implicit associations exist between multimodal data. However, the two-dimensional table structure of traditional databases cannot intuitively express such associations, making the fusion analysis of multimodal data difficult to achieve. AI’s demand for deep semantic understanding further highlights the shortcomings of traditional databases in handling complex and nonlinear relationships.

## From Multimodal Data to Relationships Analytics in One Step

To solve the data connectivity challenge, graph databases reframe relationships intuitively: disparate data points become “nodes”, and their logical connections are explicitly modeled as “edges”. This structure enables “one-click” data association—no complex joins required.

Graph databases seamlessly integrate structured and unstructured data into a unified model. For instance, when analyzing how a product’s visual features relate to user sentiment, an “image node” can be directly linked to a “comment text node” via an edge. By combining AI-driven image and text analysis, these connections reveal hidden patterns between visuals and emotions—enabling deeper semantic understanding and powerful cross-modal analytics in the AI era.

# How Do Graph Databases Empower Intelligent Data Foundations?

The data intelligence foundation is the core infrastructure for enterprises to achieve intelligent transformation, aiming to integrate multi-source heterogeneous data and provide unified, efficient data support for intelligent applications. Its construction follows a four-step framework: “content analysis, semantic alignment, domain modeling, and relationship map.” In this process, graph databases—natively designed to handle entities and relationships—play a critical role at every stage, serving as the cornerstone for multimodal data fusion and value extraction.

![https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-1.png](https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-1.png)

## Content Quarks: Turning Raw Data into Structured Building Blocks

Content analysis kicks off the data intelligence foundation. It’s all about unpacking massive, messy raw data—text, images, audio, documents—and extracting the essentials: entities, attributes, and relationships. Think of it as breaking data down into tiny, atomic units we call “content quarks”.

![https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-2.png](https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-2.png)

Advanced tools make this possible: OCR reads text in images, speech recognition converts audio to text, and LLM parse meaning from documents. Together, they transform unstructured data into clean, structured fragments.

By defining entity and relationship types upfront, graph databases provide a clear blueprint for extraction. For example, when processing payment records, a pre-built schema can guide the system to spot “User ID”, “Merchant Code”, or actions like “transfers to” with precision. This not only reduces errors but ensures consistency—setting the stage for smarter, more reliable insights down the line.

## Semantic Alignment: Breaking Down “Data Silos” and Building a Unified Semantic Space

The goal of semantic alignment is to map data from different systems and with different naming conventions into a unified semantic space, thereby achieving seamless cross-source data connection and interoperability.

![https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-3.png](https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-3.png)

This process combines the power of large language models (LLMs) for semantic understanding, data lineage analysis, and business-specific rules to identify synonyms across systems. For instance, “buyer ID” in an e-commerce platform and “account holder number” in a bank system can be recognized as the same core concept: a “user unique identifier.”  

Graph databases are a natural fit for this task. Using their native node-edge structure, they can merge different names for the same real-world entity into a single, unified node. Attributes on that node preserve the original labels from each source—like a “User X” node tagged with Customer ID: 123 and User Number: 456.

This approach allows systems to automatically recognize that different names refer to the same entity—effectively breaking down long-standing data silos and paving the way for powerful, cross-scenario analytics.  

## Domain Modeling: Flexible Data Structures for Every Use Case

Different business scenarios demand different data perspectives. Risk control focuses on user networks, suspicious transactions, and blacklisted merchants, while marketing looks at user preferences, behaviors, and campaign engagement. Domain modeling tailors the data structure to these specific needs by defining relevant concepts and business rules.

![https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-4.png](https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-4.png)

Here, graph databases act like a “customizable shelf”—flexible and easy to rearrange. Instead of rigid table schemas, they represent core ideas as nodes and connections as edges. This makes it simple to model complex relationships, like linking “blacklisted merchants” to “abnormal transactions” in fraud detection.

Best of all, the model can evolve with the business. Need to add “logistics information”? Just introduce a new node and connect it—no schema overhaul required. This agility makes graph databases ideal for building scalable, future-proof data models.

## Relationship Map: Connecting the Dots at Scale

The relationship map is the culmination of the four-step data intelligence framework—bringing together all entities and connections uncovered during content analysis, semantic alignment, and domain modeling. It forms a unified, global graph that integrates multimodal data into a single, coherent network, enabling deep data fusion and efficient querying.

![https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-5.png](https://www-cdn.nebula-graph.io/pic-upload/Multimodal%20Data%20Fusion%201-5.png)

This integrated graph consolidates fragmented data into one connected space. Powered by a robust graph computing engine, it uncovers hidden patterns and complex relationships that traditional systems miss.

The graph database becomes the central “hub” for storage and computation. It efficiently handles billions of nodes and edges while enabling fast, multi-hop traversals and complex pattern searches. For example, in fraud detection, querying “User A” can instantly reveal their transactions, linked merchants, triggered risk rules, and even indirect connections to known bad actors—like tracing a detective’s case map in real time.

With everything interconnected, the graph turns scattered data into actionable intelligence, unlocking the full value of enterprise multimodal data and empowering smarter, faster decisions.

# Graph Databases: The Engine of Data Intelligence

Graph databases provide a standardized framework for content extraction, enable a unified semantic layer for data alignment, offer a flexible structure for domain-specific modeling, and serve as a high-performance engine for storing and querying the relationship map.

More than just a database, graph databases represented by [NebulaGraph](https://www.nebula-graph.io/) is the core enabler of multimodal, heterogeneous data fusion—turning fragmented information into connected knowledge. By unlocking deep relationships and hidden patterns, graph databases power advanced applications like intelligent analytics, real-time risk detection, and precision marketing, laying a strong, scalable foundation for the intelligence of enterprise.


>In the first half, this article delved into the core role of graph databases in addressing the challenges of multimodal and heterogeneous data and detailed how they empower the intelligent data foundation. In the next part, we will further explore application innovation based on this and the exciting potential of integrating graph databases with AI. Stay tuned.

This article explores the core role of graph databases in addressing the challenges of multimodal and heterogeneous data and explains how they empower the intelligent data foundation.

When AI Meets Graph Databases: Innovating with Multimodal Data Fusion (Part I)

Interact with biomedical knowledge graphs using graph-based retrieval augmented generation.

BioGraphRAG - Biomedical Knowledge Graph Retrieval Augmented Generation

Over the past few weeks, the Model Context Protocol (MCP) has sparked widespread discussion and attention within the technical community. Inspired by this momentum, I have contributed to several related open-source projects, including [implementing McpToolSpec for the official LlamaIndex repository](https://github.com/run-llama/llama_index/pull/17795) and collaborating with Xuanwo and FrostMing to develop [a Model Context Protocol Server for Apache OpenDAL™](https://github.com/Xuanwo/mcp-server-opendal).

As the only protocol currently offering a significant ecosystem advantage, MCP presents an exciting opportunity. In this context, [we have implemented a simple NebulaGraph MCP Server based on NebulaGraph 3.x](https://github.com/PsiACE/nebulagraph-mcp-server). This marks the first foundational component in the NebulaGraph X MCP ecosystem, opening up new avenues for exploring the potential of combining AI with graph databases. 

# What is Model Context Protocol (MCP)?

MCP, introduced by the Anthropic team, is an open protocol designed to provide AI systems with a unified and efficient way to access diverse data resources. By adopting a client-server architecture, MCP standardizes data access, addressing the fragmentation issues that traditional AI systems face when interacting with various data sources.

Key Features of MCP:

* **Standardized Communication**: MCP enables AI assistants to seamlessly access a wide range of data sources, including local data, remote services, and enterprise tools.
* **Openness and Ecosystem Advantage**: The MCP community already boasts nearly 1,000 MCP Server implementations, spanning search engines, weather services, databases, and more, facilitating rapid tool integration.
* **Flexible Architecture**: By defining clear interaction protocols between clients and servers, MCP empowers developers to build scalable and reusable systems.

While challenges around security and reliability remain, these issues are expected to be addressed as the protocol evolves and matures with improved development practices.


# Features of NebulaGraph MCP Server

Given my recent focus on Graph RAG and Agentic Workflow, leveraging large language models (LLMs) and intelligent agents to uncover insights from graph data has become a critical area of exploration. To facilitate this, I quickly developed the [NebulaGraph MCP Server](https://github.com/PsiACE/nebulagraph-mcp-server), enabling NebulaGraph 3.x to integrate into the MCP ecosystem as a tool for LLMs like Claude and GPT to invoke and utilize.

Built on FastMCP , the NebulaGraph MCP Server adheres to the core specifications of MCP, providing a lightweight and efficient connection service for graph databases. By exposing NebulaGraph's capabilities through standardized tool interfaces, this server allows LLMs to effortlessly interact with NebulaGraph data for basic graph exploration tasks.

### Multiple Transmission Modes:

* Supports both stdio and SSE (Server-Sent Events) transmission modes, catering to diverse development and deployment scenarios.

### Basic Graph Exploration Capabilities:

* Graph Space Listing: Enables models to query available graph spaces.
* Schema Querying: Supports querying schema definitions for specified graph spaces.
* Query Execution: Facilitates executing NebulaGraph queries via the MCP Server.

### Built-in Operator Templates:

* Includes templates for common operations like pathfinding and neighbor discovery, allowing LLMs to quickly retrieve preliminary insights from graph data.

To demonstrate its functionality, I built a simple example using McpToolSpec from LlamaIndex and ReActAgent. Below is a screenshot showcasing its capabilities:

![image](https://www-cdn.nebula-graph.io/blogs/3799122350000_llamaindex_mcptoolspec.png) 

# Conclusion
The NebulaGraph MCP Server represents an initial step in contributing to the MCP ecosystem, aiming to bridge the gap between language models and graph databases. As the protocol matures and its ecosystem expands, we hope it can play a vital role in real-world applications. If you're interested in this direction, we invite you to explore, experiment, and collaborate with us!

Together, let’s unlock the full potential of AI-powered graph databases!

The NebulaGraph MCP Server based on NebulaGraph 3.x is now open source. This marks the first foundational component in the NebulaGraph X MCP ecosystem, opening up new avenues for exploring the potential of combining AI with graph databases. 

Announcing the Open-Source Release of NebulaGraph MCP Server: Exploring the Infinite Possibilities of AI + Graph Databases

Imagine asking ChatGPT, “How did the 2023 global chip shortage impact Tesla’s suppliers in Shenzhen through three degrees of separation?” Despite its eloquence, the LLM would likely falter—not because it lacks data, but because it lacks a way to map hidden, dynamic relationships. This is where graph databases like NebulaGraph become essential for the next generation of AI and LLMs.

As AI shifts from statistical pattern recognition to knowledge-driven reasoning, traditional databases (SQL, NoSQL) hit a wall. Their rigid tables and documents can’t model the interconnectedness of real-world data—supply chains, fraud networks, or molecular interactions. This blog explores how graph technology is rewriting the rules of intelligent systems.

# LLM Hallucinations: When “Confidently Wrong” Becomes Dangerous
LLMs hallucinate because they infer answers from patterns, not facts. Without grounding in verified knowledge, they risk generating plausible but dangerous outputs. Hallucinations aren’t just errors—they’re liabilities because they lack access to verified, structured knowledge. A medical chatbot might invent a drug interaction that doesn’t exist, risking patient safety.

# The Limitations of Traditional Databases
One of the biggest limitations of traditional databases is that they treat data points as isolated entities. Relational databases force data into tables, losing nuanced connections. For example, mapping a 10-layer corporate ownership structure requires 50+ JOINs in SQL, slowing queries to minutes. Vector databases retrieve semantically similar data but miss contextual relationships.

AI often struggles with the isolated data because so much of its power comes from understanding relationships. Graph databases, by contrast, are designed to store and manage data as interconnected entities, allowing AI to make sense of the links between seemingly disparate elements.

# Knowledge Graphs for Intelligent AI Systems
Graph databases are the backbone of such knowledge graphs, providing a powerful means of organizing and traversing complex webs of information. They allow AI systems to dynamically explore and connect various pieces of data, enhancing both the depth and the accuracy of the answers provided. Knowledge graphs are already being used in domains such as finance, healthcare, and education, where the complexity of the data requires more than a surface-level understanding.

Consider a fraud detection system for a major bank. Instead of treating each transaction as an isolated data point, a graph-powered AI can look at an entire network of transactions—cross-referencing user relationships, transaction frequency, locations, and any outliers that indicate something suspicious. It’s this networked understanding that enables real-time decision-making, stopping fraud as it happens rather than analyzing it after the damage is done.

# Graph RAG: Minimize Hallucinations of Traditional RAG
Graph RAG (Retrieval-Augmented Generation) is an advanced evolution of traditional RAG that integrates knowledge graphs into the retrieval process, significantly enhancing contextual understanding and multi-hop reasoning capabilities. Graph RAG addresses hallucinations of traditional RAG by leveraging the structured relationships in knowledge graphs, enabling more precise and contextually rich information retrieval. This structured approach minimizes hallucinations by grounding responses in factual, interconnected data, leading to more accurate and reliable answers.

# Two Major Methods to Implement Graph RAG

- **Build Your Graph RAG with NebulaGraph**: This method requires some coding work and solid AI knowledge. By leveraging NebulaGraph’s powerful graph database capabilities, you can construct a knowledge graph that integrates with your existing data sources. The approach of [Implementing Graph RAG with NebulaGraph](https://www.nebula-graph.io/posts/implement_graph_rag_with_nebulagraph) allows for customization and fine-tuning of the graph structure and retrieval algorithms, providing a robust solution for complex enterprise applications. It involves steps such as data ingestion, graph construction, and integration with large language models (LLMs) to enable sophisticated query handling and response generation.

- **Choose NebulaGraph RAG**: For those without coding experience, [NebulaGraph RAG](https://www.nebula-graph.io/posts/announcing_nebulagraph_RAG) offers an intelligent app-building platform that simplifies the process. This platform abstracts away the technical complexities, allowing users to build and deploy graph-powered AI applications with minimal effort. It provides a user-friendly interface for creating knowledge graphs, configuring retrieval parameters, and generating responses, making it accessible to a wider range of users within the organization.

# Build Tomorrow’s AI with NebulaGraph Today
The next AI breakthrough won’t come from bigger LLMs—it’ll come from smarter data infrastructure. NebulaGraph bridges the gap between LLMs’ linguistic brilliance and the structured intelligence of real-world knowledge. In the race to AGI, graph databases aren’t optional—they’re the bedrock. As OpenAI’s CTO once mused, “The future of AI isn’t just neural networks; it’s neural networks married to knowledge.”

Discover how graph databases like NebulaGraph are powering the future of AI and LLMs by enabling complex reasoning, reducing hallucinations, and unlocking scalable knowledge-driven applications.

Why Graph Databases Like NebulaGraph Are Essential for the Next Generation of AI and LLMs

Since NebulaGraph’s [joint introduction with LlamaIndex](https://www.nebula-graph.io/posts/graph-RAG) in August 2023, Graph RAG has been at the forefront of a technological revolution. It addresses the limitations of conventional search enhancement techniques by providing a more contextually aware and data-trained approach, ensuring that the responses delivered are not only precise but also well-informed.

Today, at the VLDB 2024 LLM+ KG Workshop, we are thrilled to announce the groundbreaking platform designed under the Graph RAG idea to simplify the creation of knowledge applications without the need for programming skills, NebulaGraph RAG.

![image](https://www-cdn.nebula-graph.io/blogs/wey_presenting.JPG)

*Wey Gu, the GenAI lead at NebulaGraph, presenting NebulaGraph RAG at VLDB 2024 LLM+ KG workshop*

# What is NebulaGraph RAG?
NebulaGraph RAG is a minimalistic knowledge application builder that puts the power of Graph RAG technology at your fingertips. It's the go-to solution for users to create search engines, chat bots, and content generation apps through a simple dialogue interface. By activating and circulating internal knowledge, NebulaGraph RAG maximizes its value, turning insights into actions.

# Why NebulaGraph RAG?
For many enterprises, the rich expertise and business insights of team members were often confined to a few, creating knowledge silos and consultation bottlenecks. For domain experts without a computer science background, GenAI technologies like DAG, Workflow, Agent, and RAG can be daunting, posing a significant barrier to the adoption of AI capabilities. As a result, only personnel of the data or AI department typically utilize these advanced tools, akin to how only a few people master complex BI and advanced statistical functions in Excel.

NebulaGraph RAG lowers the barrier to entry for these advanced tools, empowering every employee to create and optimize applications based on their domain knowledge. Guided by an intuitive dialogue interface, users can construct and refine applications as naturally as conversing with a colleague. From next-gen search engines to customer service Q&A systems, report generators, and troubleshooting tools, NebulaGraph RAG integrates cutting-edge technologies with simplicity at its core.

![image](https://www-cdn.nebula-graph.io/blogs/RAG-user-interface.png)
*User Application Interface: GraphRAG Answer Referenced Graph Context*

## Main Benefits for Users
- **Simplified Custom Solution Creation**: Enterprises can effortlessly build customized knowledge applications tailored to their complex needs, optimizing internal workflows without delving into technical details.

- **Inclusive Knowledge Application Building**: With a well-designed user interface and streamlined processes, employees can independently create knowledge bases and build applications. This inclusive approach accelerates the flow of information and resources within the enterprise, enhancing decision-making speed and accuracy.

- **Seamless Integration of Cutting-Edge Technology at Reduced Costs**: By leveraging the latest advanced RAG and Graph RAG technologies without additional development or costly training, businesses can quickly innovate in knowledge management and application creation, accelerating business innovation and value creation.

- **Instant Access to Internal Knowledge Bases**: The search and Q&A capabilities of NebulaGraph RAG enable businesses to rapidly construct search engines for internal knowledge bases, increasing the speed and efficiency with which employees access critical information.

![image](https://www-cdn.nebula-graph.io/blogs/RAG-build-apps.jpg)

*App building interface: Set up, maintain, and optimize apps through natural language*

# Ready to Elevate Your Knowledge Management into RAG Apps?

Compared to other toB RAG products on the market, NebulaGraph RAG excels not only in its exceptional Graph RAG capabilities, but also user-friendliness and accessibility. While other solutions may require extensive programming knowledge and technical expertise, NebulaGraph RAG ensures that even non-technical users can harness the power of advanced AI technologies. Leveraging NebulaGraph RAG will reduce costs of knowledge management, and surely foster a more inclusive and innovative work environment.
Interested in transforming your enterprise's knowledge application capabilities? [Fill out the form](https://www.nebula-graph.io/contact) for a free trial!

We are thrilled to announce NebulaGraph RAG,  the groundbreaking platform designed to simplify the creation of knowledge applications without the need for programming skills

Announcing NebulaGraph RAG: From Knowledge to Apps in Minutes

The advent of AIGC has introduced a new paradigm in content creation, where AI systems generate a wide array of media types, from text to complex visual art. However, the complexity of relationships within data can be challenging for traditional databases. Graph databases offer a solution with their ability to model and navigate the intricate connections between data points, providing a powerful infrastructure for AIGC systems.

# Graph Databases: A Catalyst for AIGC Innovation

## Complex Relationship Management
Traditional relational databases excel in handling structured data but often fall short when dealing with complex relationships. Graph databases, however, are specifically designed to manage complex relationships between entities. They visually represent the connections and dependencies between data points in a graph form. In AIGC scenarios, this capability is particularly vital as it involves understanding and analyzing vast amounts of text and data. Graph databases can efficiently store and query relationships between entities, aiding AI systems in comprehending contextual information deeply and uncovering the underlying complex relationships within data.

## Advanced Querying Capabilities
The advanced querying capabilities of graph databases, such as hybrid vector search and graph pattern matching, offer AIGC an unprecedented ability to retrieve and integrate information. The input for large models is no longer confined to a single document or sentence but can perform a leapfrog search across the entire knowledge graph, achieving cross-domain and cross-document associative queries. This "chain of thought" querying, made possible by the advanced querying features of graph databases, enables AI to mimic human thought processes, navigating flexibly between multiple knowledge points to generate comprehensive and accurate responses.

## Enhancing Efficiency and Quality
The application of graph algorithms through graph databases can significantly speed up AIGC processes and improve the quality of content. Graph databases support the storage of vectorized graphs, allowing graph algorithms to operate on them and solve certain problems within AIGC. For instance, graph algorithms can capture entities and their relationships within a paragraph or summarize and categorize all entities and their relationships in a book. This capability enables graph-based AIGC systems to understand vast amounts of natural language text efficiently without the need for expert intervention. Moreover, graph algorithms can quickly locate relevant information nodes and their relationships, reducing unnecessary information processing steps and thus greatly enhancing the efficiency of content generation and the quality of AIGC.

## Visualization of Reasoning Chains
The visual capabilities of graph databases provide an intuitive way to display the reasoning process of AIGC systems. Through graphical interfaces, users can clearly see every step and logical chain in the reasoning process. This visualization not only enhances the system's transparency and credibility but also helps users better understand the sources and basis of the generated content. In fields such as academic research and business decision-making, this visual reasoning chain is crucial for improving the accuracy and reliability of decision-making.

# Real-world Applications of Graph Databases in AIGC
## Intelligent Q&A Systems
With the support of graph databases, intelligent Q&A systems can not only perform information retrieval and reasoning through traditional knowledge graphs but also support vectorized knowledge graphs. This means the system can manage and retrieve vector data, integrating intelligent Q&A technology based on graph queries and graph calculations with advanced technology based on vector retrieval. This integration not only improves the accuracy and efficiency of Q&A but also significantly reduces additional data storage and management overhead, bringing a smoother and smarter experience to users. At the same time, with the aid of AIGC technology, the system can generate richer and more personalized content, further enhancing user experience and satisfaction.

## News Media and Short Videos
The combination of graph databases and AIGC technology has shown great potential in the fields of internet news media and self-media short videos. By efficiently managing complex relational data through graph databases, AIGC technology can accurately analyze user behavior and automatically generate personalized content. In news recommendations, the combination of the two can quickly match user interests with news hotspots; in the field of short videos, it can achieve intelligent editing and distribution, enhancing user experience and platform efficiency. This integrated application is leading a new trend in the production and dissemination of internet content.

## Content Creation Assistance
For content creators, graph databases provide immense value by integrating vast data resources from the internet into a comprehensive knowledge graph, facilitating tasks such as inspiration, material search, and content integration. Creators can use this graph for inspiration, material search, and content integration. The support of graph databases makes content creation more efficient, accurate, and creative.

# Core Value of Graph Databases to AIGC
## Enhancing Content Quality and Richness
Graph databases provide a comprehensive network of entity relationships as a knowledge base support for AIGC systems. This allows AIGC systems to fully consider the complex relationships and diverse connections between entities when generating content, resulting in content that is closer to reality, rich in depth and breadth. In addition, by combining the reasoning capabilities of large models, AIGC systems can also generate personalized content according to users' specific needs and preferences, further enhancing the richness and appeal of the content.

## Reducing Generation Costs
Although the construction and maintenance costs of graph databases are relatively high, once they are built and integrated with AIGC systems, their efficient data processing and utilization capabilities will significantly reduce the costs associated with content generation. Since graph databases have the advantages of fully utilizing existing data resources, greatly reducing expert intervention, and improving the efficiency of new data collection and processing, the costs at multiple stages of content generation tasks will be significantly reduced. This is of great significance for enterprises and institutions that require a large output of content.

## Improving the Accuracy and Interpretability of Reasoning
The graph model used in graph databases can help make the reasoning process clearer and traceable. When using graph databases as the basis for reasoning in AIGC systems, it can ensure that every step of the reasoning process has a clear logical basis and data support. This interpretability not only enhances users' trust in the generated content but also helps to identify potential problems and areas for improvement. At the same time, further improving the transparency and credibility of the reasoning process by visualizing the reasoning chain is also an important direction for future development.

# Challenges and Future Prospects
## Construction Costs and Technical Thresholds
Currently, the method of relying on experts to manually construct knowledge graphs is relatively costly and technically demanding, which limits the wider application of knowledge graphs. To reduce construction costs and improve usability, the market has begun to see the emergence of some automated construction tools and technical means, such as the automated AIGC approach we currently provide, which supports the low-cost and efficient construction of knowledge graphs. At the same time, strengthening cross-domain cooperation and exchange also helps to promote the popularization and expansion of the application range of graph database technology.

## Data Quality and Updating Issues
Data quality is crucial for AIGC, and data cleaning is often costly, which is a major challenge faced by the industry. Based on graph databases, users can use graph pattern matching, association relationship analysis, and other means to effectively identify and handle erroneous or low-quality data. To ensure the completeness, accuracy, and timeliness of the data in the graph database, we can take various measures, such as introducing missing association relationship prediction technology and generation technology, anomaly detection technology based on graph algorithms and graph analysis, to timely capture and process new data source information; at the same time, strengthen the data verification and cleaning process to ensure the accuracy and integrity of the data; and establish an effective data sharing and collaboration mechanism to promote the integration and utilization of cross-domain data.

# Industry Application Expansion and Standardization
As technology continues to mature and application scenarios expand, the integration of graph databases and AIGC will be applied in more industries and achieve significant results. To promote the healthy development and widespread application of this field, it is particularly important to establish unified standards and specifications to guide the research and development and application practices of technology. These standards and specifications can cover aspects such as data formats, interface protocols, and performance evaluation methods to ensure interoperability and compatibility between different systems and platforms. At the same time, standardization work can also promote technical exchange and cooperation across fields. The vector data management related functions in the database are being considered for inclusion in the future versions of SQL and GQL standards, and this topic is still actively discussed in the industry. This standardization process will undoubtedly help further collaboration and integration of AIGC and databases, thereby promoting the common progress and development of the entire industry.

# Conclusion
As a solid foundation for AIGC, graph databases have demonstrated powerful capabilities in organizing, querying, and reasoning information, significantly enhancing the quality and efficiency of content generation. With ongoing technological advancements and expanding application scenarios, the application prospects of graph databases in the field of AIGC will undoubtedly be broader. 

[The Graph RAG proposed by NebulaGraph](https://www.nebula-graph.io/posts/graph-RAG) last year is undoubtedly a profound insight and positive response to this trend. If you’re interested in developing your AI products empowered by one of the leading graph databases, NebulaGraph, feel free to [reach out](https://www.nebula-graph.io/contact).

Graph databases are the backbone for AIGC because the advanced querying capabilities enhance AI's ability to understand complex data relationships.

Graph Databases: The Ultimate Tool for Enhancing AIGC Quality and Efficiency

*This blog is written by [Diego Sanmartin](https://www.linkedin.com/in/dsanmart/) and originally published on [Medium](https://medium.com/@dsanmart/kg-rag-bridging-the-gap-between-knowledge-and-creativity-of-llm-based-agents-d03754bfb9c1 )*

Large language models (LLMs) have emerged as potential sparks for Artificial General Intelligence (AGI), offering hope for the development of versatile intelligent systems 👀.

# ➕ Bonus: What are intelligent systems?
As a bonus before delving into the topic of the article, it’s nice to understand the historical context and definitions of intelligent systems. The essence of intelligence has been a constant subject of study over time, reflecting centuries of philosophical inquiry and scientific exploration, as can be seen in the following quotes:
> Denis Diderot (1746): “If they find a parrot who could answer to everything, I would claim it to be an intelligent being without hesitation.” 🐦

> Alan Turing (1950): “If a machine can engage in a conversation with a human without being detected as a machine, it has demonstrated human intelligence.”

Now, let’s go into the fun part and explore the concept of current AI Agents.

# What is an AI Agent? 🤖
An AI Agent can be defined as an artificial entity that is capable of sensing its environment, making decisions, and executing actions. This concept brings us back to the fundamental idea of the robot’s sense-plan-act loop, which allows robots to interact with their environment. Following this definition, when we break down an AI Agent into its elemental components, we can fundamentally decompose it into three core elements: perception, brain, and action.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*8w9U4wagJt-2NPahHG7EaQ.png)
*Core components of an AI Agent: Perception, Brain, and Action.*

Recent advancements in multimodality and tool execution have notably enhanced both the perception and action components. Today our focus will be directed towards the brain component.

# LLM-based Agents 📈
In the diagram below, the brain within the agent is responsible for making decisions, planning, and reasoning, all while interacting with the storage of memory and knowledge. Current agents leverage LLMs for this, given their efficiency in handling reasoning and decision-making through emerging prompt engineering techniques like Chain of Thought (CoT) and ReAct (Reason and Act).

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*iS7GHja76EWyqvP950Kxhg.png)
*Brain decomposition of an LLM-Based Agent*

However, despite their capabilities on these tasks, LLMs confront several challenges that make them unreliable for knowledge-intensive tasks. This is due to several factors, including:

1. Their disposition to generate factually incorrect information — often called hallucinations.
2. Limitations related to processing long context lengths effectively, without loosing important details in the middle.
3. The phenomenon of catastrophic forgetting, where the model forgets previously learned information as it learns new information.

As AI Agents start being implemented in real-life applications, there is a growing demand for them to be reliable and accurate agents. Hence, figuring out how to bridge the gap between the creative potential of LLMs and the reliable knowledge required in LLM-based agents remains a formidable challenge.

This post delves into the KG-RAG (Knowledge Graph-Retrieval Augmented Generation) framework, also known as Graph RAG, designed explicitly to confront some of the most persistent challenges encountered in LLMs. Preliminary experiments from this framework, demonstrate notable improvements in the reduction of hallucinated content and suggest a promising path toward developing intelligent systems adept at handling knowledge-intensive tasks.

# What can be done with KG-RAG? 🧠
KG-RAG enhances LLMs by integrating structured Knowledge Graphs (KGs) that store vast amounts of explicit, factual information. This integration reduces the reliance on the latent, often flawed knowledge of LLMs, enabling LLM-based agents with reliable storage of memory and knowledge to mitigate the generation of hallucinated content.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*STBiHaXLPHpRHvB8EuNp6w.png)
*Representation of how the brain integrates LLMs for dynamic reasoning and decision-making, alongside KGs for structured knowledge and memory storage.*

# What is a Knowledge Graph? 🤔

A Knowledge Graph (KG) is a method used to represent and store informational facts. It consists of nodes (or entities) and edges, which represent the relationships between these entities.

For example, in the knowledge graph shown, entities such as “Mona Lisa,” “Da Vinci,” and “Louvre” are connected by relationships like “painted,” “is in,” and “located in.” This visual framework not only helps to depict how different entities are related but also facilitates the retrieval of interconnected information efficiently.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*uttrGDDtlvCbAAo5OLMZBA.png)

*Knowledge graph visualization example*
A good advantage of using KGs is that they enable us to search for “things, not strings”. They store vast amounts of explicit facts in the form of precise, updatable, and interpretable knowledge triples, which can be represented as (entity)-[relationship]->(entity). Additionally, KGs can actively evolve with the continuous addition of knowledge.

Actually, KGs is what Google uses in Search to provide answers to your questions. As illustrated below, when a user queries “Who is the CEO of Entrepreneur First?” Google uses its Knowledge Graph to fetch and display relevant information. It identifies “Entrepreneur First” as an entity and navigates through the “CEO” relationship to arrive at Alice Bentick and shows how this connection was established in 2011 when she began her tenure as CEO. The KG’s ability to associate Alice Bentick with Entrepreneur First allows Google Search to present a concise and accurate answer to the query.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*q8DbVC28e5TkB5uN7BquZQ.png)

*Google search results for “Who is the CEO of Entrepreneur First?”*

Additionally, it provides more information that is connected through relationships to the target node (Alice Bentinck) on the right panel.

# Motivations behind KG-RAG 💪

The current baseline for the development of AI Agents is creating dense vector representations of chunks of text and retrieving them through a similarity search. This method works well for simple cases.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*6JbO2c4ZNuPVeoz0dlGHLA.png)

*Example of successful vector retrieval of documents*

This method allows AI to quickly find relevant documents based on text similarity, efficiently handling straightforward queries. However, as the volume of similar documents increases, challenges emerge in finding those chunks that contain the exact information needed.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*Pwi2yP3tgb2UhfG4nWzqvA.png)
*Representation of the scalability issues in vector embeddings based RAG*

These challenges motivate the implementation of structured KGs, enabling the navigation through massive amounts of information in a more efficient and accurate manner.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*kKwjmTxygk-TSWREunFkHw.png)

# KG-RAG Methodology 🎩

KG-RAG consists of three main stages:

1. **KG Construction**: Converts unstructured text into a structured KG. This step is crucial to maintain the quality of information and can affect later stages if it is not done correctly.
2. **Retrieval**: Carried out through a Chain of Explorations (CoE), a novel retrieval algorithm that employs LLM reasoning to explore nodes and relationships within the KG. It ensures that the retrieval process is both relevant and accurate leveraging the benefits of dense vector representations to locate relevant nodes and relationships within large KGs.
3. **Response Generation**: Generates coherent and contextually appropriate responses.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*hyT3JZcs7JuGf-DllMwH5A.png)
*Diagram illustrating the KG-RAG workflow*

# Preliminary Results 👀
Here is an example comparison of KG-RAG against the baseline vector RAG approach. This illustrates how KG-RAG effectively handles complex queries by exploring and reasoning through specific relationships to generate responses.

![image](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*EWGM-Btmbl0194oVHXpP2w.png)
*KG-RAG vs Dense Vector RAG vs no RAG on the ComplexWebQuestions dataset*

# The Future of Intelligent Agents 🔮
The integration of KGs and LLMs through frameworks like KG-RAG suggests a promising path toward developing intelligent systems capable of tackling knowledge-intensive tasks with high reliability. This approach mitigates some of the current limitations in LLM-based Agents and encourages further research and practical applications of this method. There is a significant opportunity to improve the current capabilities of KG Construction by developing curated high-quality datasets specifically tailored for this field.

KG-RAG holds promise as a potential solution to equip LLMs with external, updatable knowledge that is both reliable and efficient. Furthermore, with the continuous advancements in AI hardware accelerators, the reduction in response times (with KG-RAG and reasoning processes happening behind) will make the interactions with these agents feel more natural to humans.

# Bonus Analogy 💭
From a perspective of human cognition, the analogy of the “extended mind”, becomes particularly relevant. Just as humans extend their cognitive capabilities using tools such as smartphones and notebooks to offload memory and manage complex tasks, LLM-based agents can similarly benefit from external cognitive extensions to enhance their cognitive capacities.

# Closing Note 👋
This medium article is a very short summary of the KG-RAG research paper. If you found this post insightful, delving into the [full paper](https://arxiv.org/abs/2405.12035?source=post_page-----d03754bfb9c1--------------------------------) will provide a more comprehensive understanding of our findings and methodologies.

This post discusses the KG-RAG (Knowledge Graph-Retrieval Augmented Generation) framework or Graph RAG, which addresses common challenges in LLMs. Early experiments show it reduces hallucinated content and could aid in creating systems for knowledge-intensive tasks.

KG-RAG: Bridging the Gap Between Knowledge and Creativity of LLM-based Agents

Graph RAG has been revolutionizing the industry since its [joint introduction](https://www.nebula-graph.io/posts/graph-RAG) with NebulaGraph and LlamaIndex in August 2023, capitalizing on the momentum of LLM and RAG (Retrieval-augmented Generation). In this blog post, we will walk you through what Graph RAG is, why it is revolutionizing, and how to build your Graph RAG that draws on data context to answer complex, multi-part questions with NebulaGraph.

# Understanding Graph RAG and Its Rising Popularity

This cutting-edge technology has garnered widespread interest due to its capability to harness knowledge graphs alongside LLMs, thereby offering a more comprehensive contextual understanding for search engines. This results in more cost-efficient, intelligent, and precise search outcomes. Beyond providing more accurate responses to build user trust, this technology has surmounted the primary obstacles of traditional search augmentation techniques: the absence of context comprehension and data training. Consequently, these advanced retrieval enhancement techniques are poised to expand their reach into various industries, such as medical diagnosis assistance, content creation and recommendation, interactive gaming, and knowledge graph construction.

Microsoft underscored the importance of Graph RAG in the blog post [GraphRAG: Unlocking LLM Discovery on Narrative Private Data](https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/), authored by Jonathan Larson, Senior Principal Data Architect, and Steven Truitt, Principal Program Manager. They illustrated the effectiveness of Graph RAG by contrasting it with a baseline RAG system, concluding that the Graph RAG approach enables the LLM to anchor itself in the graph, resulting in a superior answer that includes provenance through original supporting text. Meanwhile, the baseline RAG struggles with queries that necessitate aggregation of information across the dataset to formulate an answer.

# Building Your Graph RAG with NebulaGraph

NebulaGraph database has integrated with LLM frameworks such as [LlamaIndex](https://www.llamaindex.ai/) and [LangChain](https://www.langchain.com/); you could simply focus on LLM orchestration logic and pipeline design without worrying about complex implementation. You could take a trial to check whether the Graph RAG performs as awesome as it is said with simply 4 steps before exploring further to integrate it into applications or other scenarios. 

***Note:*** 

*As LlamaIndex evolves rapidly, the recent refactor/break change has led to some of the Graph RAG tutorials becoming non-runnable. This blog is here for the updated tutorials of Graph RAG so that you can hands on it without errors.*

*We have been exploring so many in-house approaches before we upstream them later. We believe the basic concept of Graph + RAG in LlamaIndex is worth exploring, so we put the original workshop and notebooks in the reference chapter.*

## Set up your NebulaGraph cluster

You could start with any one of the three methods below to quickly set up a NebulaGraph cluster:

- start the NebulaGraph cluster via [NebulaGraph Lite](https://github.com/nebula-contrib/nebulagraph-lite) in Google Colab
- [deploy the cluster with Docker extension](https://www.nebula-graph.io/posts/nebulagraph-database-on-docker-desktop)
- instantiate a [NebulaGraphStore](https://docs.llamaindex.ai/en/stable/examples/index_structs/knowledge_graph/NebulaGraphKGIndexDemo/)

```
import os
from llama_index.core import KnowledgeGraphIndex, SimpleDirectoryReader
from llama_index.core import StorageContext
from llama_index.graph_stores.nebula import NebulaGraphStore

os.environ["NEBULA_USER"] = "root"
os.environ["NEBULA_PASSWORD"] = "nebula"
os.environ["NEBULA_ADDRESS"] = "127.0.0.1:9669"


space_name = "paul_graham_essay"
edge_types, rel_prop_names = ["relationship"], ["relationship"]  # default, could be omit if create from an empty kg
tags = ["entity"]  # default, could be omit if create from an empty kg

graph_store = NebulaGraphStore(
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags)

storage_context = StorageContext.from_defaults(graph_store=graph_store)
```

Now, with your NebulaGraph cluster set up, you could hands on the knowledge graph building section. **Please make sure to run all the steps below in the Jupyter environment.**

## Dependencies
The implementation of Graph RAG requires the below dependencies.

```
pip install llama-index
pip install llama-index-llms-ollama
pip install llama-index-graph-stores-nebula
pip install ipython-ngql
pip install llama-index-embeddings-huggingface
```

## Create a LlamaIndex Knowledge Graph Index

`KnowledgeGraphIndex` handles automated knowledge graph construction from unstructured text as well as entity-based querying. You need to set up a LlamaIndex Knowledge Graph Index Before creating a Graph RAG query engine. In this example, we 
will demonstrate how to build KG from textual data by creating a document object before moving on to create an index.

```
from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    KnowledgeGraphIndex,
)

graph_store.query("SHOW HOSTS")

Settings.chunk_size = 512

documents = SimpleDirectoryReader(
    "data"
).load_data()
```

```
kg_index = KnowledgeGraphIndex.from_documents(
    documents,
    storage_context=storage_context,
    max_triplets_per_chunk=10,
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags,
    max_knowledge_sequence=15,
)
```

## Create A Naive Graph RAG Retriever
The last step comes to creating a Graph RAG retriever, on top of which the query engine will be built so that you could query with natural language questions.

```
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import KnowledgeGraphRAGRetriever

graph_rag_retriever = KnowledgeGraphRAGRetriever(
    storage_context=storage_context,
    verbose=True,
)

query_engine = RetrieverQueryEngine.from_args(
    graph_rag_retriever,
)
response = query_engine.query("What's the relationship between Bob and Alice")
```

Now, you’ve tried Graph RAG and if you’d like to explore more on how to retrieve via both GraphStore and VectorStore, you could find more demo examples here: [https://www.siwei.io/tutors/GraphRAG/101.html](https://www.siwei.io/tutors/GraphRAG/101.html).

Join our RAG discussion and let us know how your Graph RAG goes in our [Slack channel](https://join.slack.com/t/nebulagraph/shared_invite/zt-2h51ct4t0-IlXFaU01mpb3t2WgQ8qqNA).

Reference:

- [https://bit.ly/graph-rag-workshop](https://bit.ly/graph-rag-workshop)
- [https://siwei.io/graph-enabled-llama-index/kg_and_vector_RAG.html](https://siwei.io/graph-enabled-llama-index/kg_and_vector_RAG.html)
- [https://siwei.io/demo-dumps/graph-rag/GraphRAG.html](https://siwei.io/demo-dumps/graph-rag/GraphRAG.html)






This blog will walk you through what Graph RAG is, why it is revolutionizing, and how to build your Graph RAG that draws on data context to answer complex, multi-part questions with NebulaGraph.

Implementing Graph RAG with NebulaGraph

>*This article was written by Rajesh K, and originally published at [Medium](https://pub.towardsai.net/knowledge-graph-qa-using-gemini-and-nebulagraph-lite-2ca3a889f62c).*

Graph databases and knowledge graphs are among the most widely adopted solutions for managing data represented as graphs, consisting of nodes (entities) and edges (relationships). A graph database stores information using a graph-based data model, enabling querying and traversal of the graph structure through specialized graph query languages.

Knowledge graphs extend the capabilities of graph databases by incorporating mechanisms to infer and derive new knowledge from the existing graph data. This added expressiveness allows for more advanced data analysis and extraction of insights across the interconnected data points within the graph.

This article will delve into brief introduction on knowledge graphs followed by generation of knowledge graph using LlamaIndex and nebulagraph-lite.

# What is a Knowledge Graph?
A Knowledge Graph serves as a graphical portrayal of interconnected ideas, items, and their relationships, depicted as a network. It includes real-world entities like objects, people, places, and situations. At its core, a Knowledge Graph usually relies on a graph database, which is specifically crafted to manage data by storing discrete pieces of information and the connections between them.

The core components of KG are :

Entities are real-world items or concepts. These consist of humans, places, activities, and summary thoughts. In graph shape, that is a manner of visualizing relationships among information units, entities are represented as nodes or factors on a graph.

Examples:

- Person/s: Barack Obama, Serena Williams
- Places/Locations: New York City, The Great Pyramids
- Events/Events: World War II, 2008 economic crisis
- Abstraction/Ideology: Democracy, Gravity

Relationships describe relationships between objects and describe how they interact or interact with each other. Relationships are represented as edges connecting the corresponding nodes within the expertise graph. The route of this flow can be unidirectional or bidirectional, relying on the character of the entity courting.

# Categories of Knowledge Graphs

Knowledge graph has the ability to:

- **Effectively manipulate and visualize heterogeneous information**: This includes dealing with records of numerous systems inside a unified framework, facilitating clean and insightful illustration.
- **Integrate with newer data resources**: Knowledge graphs has the inherent flexibility to deal and incorporate facts from novel sources, fostering non-stop expansion of the know-how base.
- **Comprehend and depict relationships across any information store**: They can discover and represent the interconnections among entities dwelling within various information repositories, allowing a holistic understanding of the underlying relationships.

Following are the graph categories:

- **RDF (Resource Description Framework) Triple Stores**: These class focus on storing and dealing with information dependent according to the RDF framework, which utilizes triples (situation, predicate, object) to represent understanding.
- **Labeled Property Graphs**: This class makes a speciality of graphs in which nodes and edges are enriched with informative labels, presenting a more expressive and nuanced illustration of the facts.

# RDF (Resource Description Framework) Graphs
RDF graphs, standing for Resource Description Framework graphs, are a way to represent statistics of the web similar to network structure. They are essentially a collection of statements constructed around topics, predicates, and objects.

Imagine a sentence like “Paris is the capital of France”. In an RDF graph, “Paris” will be the issue, “is the capital of” would be the predicate, and “France” will be the item. Together, these three elements form a single “triple” that represents a fact. An RDF graph can incorporate many such triples, constructing an internet of interconnected records.

The Resource Description Framework (RDF) Triple Store constitutes a standardized facts version for know-how representation. Within this model, every element is assigned a unique identifier the use of Uniform Resource Identifiers (URIs). This mechanism ensures machine-readable identity of topics, predicates, and gadgets. Furthermore, RDF Triple Stores leverage a standardized question language known as SPARQL. This language allows the retrieval of records from the store. Owing to the standardized nature of both information representation and querying, RDF Triple Stores show off interoperability with another understanding graph that clings to the RDF framework.

![image](https://miro.medium.com/v2/resize:fit:720/format:webp/1*3FLR-xCZpg7yUDSxZvOTVw.png)

The graph above depicts individuals (round nodes) inside a social community. Lines (directed links) constitute friendships among them. Additionally, hasincome is related to every individual (dark-rimmed node). Diamond-fashioned nodes depict the opportunity of extra data current within the community (triples).

Pros of RDF graphs:

- **Interoperability**: RDF is a W3C trendy, which means that specific systems can apprehend and alternate records stored in RDF graphs. This makes it a great choice for sharing data throughout platforms and packages.
- **Standardization**: Due to the standardized format, RDF graphs include a trendy question language known as SPARQL. This makes it less difficult to discover and examine the records saved inside the graph.
- **Reasoning and Inference**: RDF graphs can leverage ontologies (think of them as formal descriptions of ideas) to purpose about the information. This allows the machine to infer new statistics that isn’t explicitly stated inside the graph.
- **Flexibility**: RDF graphs can represent an extensive form of facts types and relationships. This makes them appropriate for modeling complicated domains and integrating facts from exceptional sources.

Cons of RDF graphs:

- **Complexity for Deep Searches**: Traversing large RDF graphs for deep searches can be computationally expensive. This can gradual down queries that need to discover many connections.
- **Strict Structure**: RDF information is saved as &quot;triples&quot; (difficulty, predicate, object). This can be less flexible than different graph fashions that allow for properties on entities or relationships themselves.
- **Steeper Learning Curve**: Understanding and operating with RDF requires a good hold close of the underlying standards and the SPARQL question language. This can pose a challenge for new users.

# Labeled Property Graphs (LPGs)
Labeled property graphs (LPGs) are a special type of graph database model used to represent information with interconnected services and their relationships. Here is a breakdown of their highlights:

- **Nodes**: These represent masculine or feminine elements in realities. Each node has a very unique description and can be assigned one or more labels to indicate its type or size (e.g., “person”, “product”).
- **Properties**: Nodes can have key charge pairs associated with them, which store additional data about the entity. These fields allow for basic definitions of the elements in the graph.
- **Edges**: These represent connections between nodes, and show relationships between entities. Like nodes, edges can be marked with a selection of methods (e.g., “knows”, “buys”) and can also have their own properties associated with them.

Key characteristics of LPGs:

- **Rich data structure**: The ability to have properties on both nodes and edges allows for denser and more informative data representation compared to other models like RDF.
- **Efficient storage and querying**: The LPG structure often leads to efficient storage mechanisms and faster traversal of connections within the graph for queries.
- **Flexibility**: LPGs are flexible due to the lack of a predefined schema. This allows for modeling diverse data relationships.

# RDF vs. Property Graphs
![image](https://miro.medium.com/v2/resize:fit:720/format:webp/1*hFk11H42pSuOTsB8jVfeGQ.png)

# Property Graphs QA with LLM
Property graphs and Large Language Models (LLMs) are powerful tools that can be used together to gain new insights from data. Here’s how they can work together:

## Data Augmentation:

- LLMs can be used to generate text descriptions for nodes and edges in a property graph. This can enrich the data and make it easier for other tools or users to understand the relationships.
- LLMs can also be used to generate new nodes and edges based on existing data in the graph. This can be helpful for tasks like anomaly detection or fraud prediction.

## Querying and Exploration:

- LLMs can be used to create natural language interfaces for querying property graphs. This allows users to ask questions about the data in a more intuitive way than using a traditional graph query language.
- LLMs can also be used to summarize the results of graph queries and generate explanations for the findings.

Reasoning and Inference:

LLMs can be used to perform reasoning tasks over property graphs. This could involve inferring new relationships between nodes based on existing data or identifying inconsistencies in the graph.

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*D3bN_RXwF-hmQ97qRYB9Pw.png)

Below is demonstration for the stepwise implementation of Knowledge Graph using [LlamaIndex](https://docs.llamaindex.ai/en/stable/) KnowledgeGraphIndex and [NebulaGraph Lite Reference](https://github.com/nebula-contrib/nebulagraph-lite) using Google Gemini LLM and Collab.

>Generate API Key for Gemini

Head on to https://aistudio.google.com/app/prompts/new_chat and generate a new API Key.

>Load the PDF document

```
! mkdir ad && cd ad
! curl https://arxiv.org/pdf/2106.07178.pdf --output AD1.pdf
! mv *.pdf ad/
```
```
! pip install -q transformers

%pip install llama_index pyvis Ipython langchain pypdf  llama-index-llms-huggingface  llama-index-embeddings-langchain llama-index-embeddings-huggingface
%pip install --upgrade --quiet llama-index-llms-gemini  google-generativeai
%pip install --upgrade --quiet llama-index-graph-stores-nebula nebulagraph-lite
```

>Import the google API key

```
import os

from google.colab import userdata
GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY')
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
```

>Import the required modules and libraries

```
import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
                         ServiceContext,
                         KnowledgeGraphIndex)
from llama_index.core import SimpleDirectoryReader
from llama_index.core.storage.storage_context import StorageContext
from pyvis.network import Network

from llama_index.llms.huggingface import HuggingFaceLLM
```
Check for supported gemini Models; here we will be using Gemini 1.0 pro model.

```
import google.generativeai as genai


for m in genai.list_models():
    if "generateContent" in m.supported_generation_methods:
        print(m.name)
        print(m)
```
```
from llama_index.llms.gemini import Gemini

llm = Gemini(model="models/gemini-1.0-pro-latest")
```
>Import the BGE embedding
```
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import ServiceContext


embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
```
>Load the contents of the ad directory
```
documents = SimpleDirectoryReader("/content/ad").load_data()
print(len(documents))
```

>Start the Nebula graph store lite version docker instance locally.

```
from nebulagraph_lite import nebulagraph_let
n = nebulagraph_let(debug=False)
n.start()
```

>Set up namepsace named “nebula_ad “ and nodes in nebula store

```
%ngql --address 127.0.0.1 --port 9669 --user root --password nebula
```
```
# If not, create it with the following commands from NebulaGraph's console:
%ngql CREATE SPACE nebula_ad(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1)
```
```
import time

print("Waiting...")

# Delay for 10 seconds
time.sleep(10)

%ngql --address 127.0.0.1 --port 9669 --user root --password nebula
%ngql USE nebula_ad;
%ngql CREATE TAG entity(name string);
%ngql CREATE EDGE relationship(relationship string);
```
>Load the document data to the graph store

```
import os
os.environ["NEBULA_USER"] = "root"
os.environ["NEBULA_PASSWORD"] = "nebula"  # default is "nebula"
os.environ[
    "NEBULA_ADDRESS"
] = "127.0.0.1:9669"  # assumed we have NebulaGraph installed locally

space_name = "nebula_ad"
edge_types, rel_prop_names = ["relationship"], [
    "relationship"
]  # default, could be omit if create from an empty kg
tags = ["entity"]  # default, could be omit if create from an empty kg
```
```
from llama_index.core import StorageContext
from llama_index.graph_stores.nebula import NebulaGraphStore

graph_store = NebulaGraphStore(
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags,
)
storage_context = StorageContext.from_defaults(graph_store=graph_store)
```
```
from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512
```
>update the nodes data in graph store
```
# NOTE: can take a while!
index = KnowledgeGraphIndex.from_documents(
    documents,
    storage_context=storage_context,
    max_triplets_per_chunk=10,
    space_name=space_name,
    edge_types=edge_types,
    rel_prop_names=rel_prop_names,
    tags=tags,
    include_embeddings=True
)
```
>Check the inserted graph data in nebula store
```
# Query some random Relationships with Cypher
%ngql USE nebula_ad;
%ngql MATCH ()-[e]->() RETURN e LIMIT 10
```
>Output

![image](https://miro.medium.com/v2/resize:fit:640/format:webp/1*kyN7zp1yjDDEc5D9fzh32A.png)

>Now query the indexed data

```
query_engine = index.as_query_engine()
```
```
from IPython.display import display, Markdown

response = query_engine.query(
    "Tell me about Anomaly?",
)
display(Markdown(f"<b>{response}</b>"))
```
Anomalies, also known as outliers, exceptions, peculiarities, rarities, novelties, etc., in different application fields, refer to abnormal objects that are significantly different from the standard, normal, or expected.

```
from IPython.display import display, Markdown

response = query_engine.query(
    "What are graph anomolies?",
)
display(Markdown(f"<b>{response}</b>"))
```
Graph anomalies can be defined as structural anomalies.

# Conclusion
These simple Knowledge graphs demonstrably capture intricate relationships between entities. This capability facilitates significantly more precise, diverse, and complex querying and reasoning. These can also be extended to complex RDF-based ontology graphs. In the future, we will refine more on the different aspects of how Knowledge graphs can be utilized for RAG and LLM fine-tuning.

Happy Graphing!



This article delves into brief introduction on knowledge graphs followed by generation of knowledge graph using llamaindex and nebulagraph-lite.

Knowledge Graph QA Using Gemini and NebulaGraph Lite

This comprehensive blog is a written transcription of Louis Guitton's talk at a recent conference, where he shared his insights around knowledge on graph RAG and LLMs.

Graphs and Language

As you most probably know, a graph database is designed to store data as connections rather than tables. These connections are nodes or points on a graph, or lines depicting relationships between data points. 

Graph databases look like a network linking various pieces of data, revealing complex patterns and variations. Graph data can power a typical machine learning model by capturing and leveraging intricate relationships between data points. This creates the foundation for the use of graph databases in AI applications. 

According to research by [MarketsandMarkets](https://www.marketsandmarkets.com/Market-Reports/graph-database-market-126230231.html), the graph database market is expected to grow by 20.2% annually. This high growth is primarily propelled by the increasing demand among businesses to develop applications that are fueled by interconnected data . The most familiar example of such applications is a product recommendation engine, common across e-commerce sites. 

And now as we enter the era where more organizations are leveraging artificial intelligence to build applications, the significance of graph technology becomes even more pronounced. 

So it’s highly likely that you are on a quest to explore the utilization of graph databases in AI applications, and this guide is here to offer you precisely that solution. 

Let’s explore how businesses are using graph databases like NebulaGraph for AI applications.  

# Benefits of using graph databases in AI applications

Graph machine learning offers unprecedented data insights, which can add tremendous value to a business. 

These are the key benefits that organizations are gaining as a result of using graph databases to power AI applications: 

## Improving data analytics

Data scientists can use graph algorithms to reveal complex patterns hidden in datasets. Graph data is rich with connections and relationships, which give a clearer understanding of organizational information. 

This leads to better business operations, e.g., hyper-targeted marketing campaigns and streamlined manufacturing workflows. 

## Improving application speed and performance

Compared to traditional tabular databases, graphs offer training data that is more user-friendly for AI and ML algorithms. 

Graph technology can accelerate deep learning processes for businesses that have vast amounts of data. This means that real-time applications built on graph algorithms become more responsive.

Developers can build AI apps faster using graph technology, and end users can enjoy a seamless experience when they interact with these apps. 

## Improving AI accuracy 

Graph machine learning connects relevant data points, meaning that applications yield more precise responses to prompts and queries. 

Developers can build AI applications with graph analytics to increase transparency, so they can see the inner workings of the neural network. 

In addition, data scientists can study the nodes and relationships in the AI model. They can create a highly accurate AI mode when they are able to identify how the data is accessed and processed.  

# How do graph databases work with AI applications? 

Artificial intelligence (AI) applications use vast amounts of data to generate insights. These insights are the related data points or context that the AI model links together automatically. 

Using graph technology, data scientists give context to the neural networks that power applications. 

Here are the major ways that graph technology enhances AI. Please note that these approaches apply both when you are using a commercial graph database option or an [open source graph database](https://nebula-graph.io/): 

## Graph databases offer connected features which drive accuracy

Accuracy is everything in matters of data, and graph databases do a great job here. Connected features are the metrics that show the link between two data points or nodes in a graph database. 

Graph machine learning models become more accurate with more connections. These connected features are critical in artificial intelligence models that detect criminal behavior, such as online fraud. 

Criminals tend to hide their online activities to avoid detection, but their activities are all connected. Graph analytics enable data scientists to see patterns that they may otherwise miss without the help of a graph database. This enables law enforcement authorities to accurately predict and prevent fraud.  

## Graph databases provide context for efficiency

Most organizational data is stored in tables with rows and columns, e.g., a spreadsheet. Machine learning algorithms can use tabular data for training models, but this requires a lot of computing power and human intervention. This means that the algorithm training process becomes slow and expensive. 

Graph machine learning, however, makes AI training more efficient. Graphs store data as nodes and lines rather than tables and indexes. This allows data scientists to train models on already connected data, and this accelerates machine learning pipelines. 

## Graph databases provide a foundation for credibility

[AI hallucinations](https://zapier.com/blog/ai-hallucinations/) are a common problem, where a tool like ChatGPT may present incorrect information as fact. This usually happens because of insufficient data or low quality training data. 

Unfortunately, AI hallucinations can erode trust, making it more difficult for individuals and companies to rely on AI outputs. 

Graph technology is one way to overcome this problem. Since training data is represented as nodes and lines, data scientists can trace which elements the model uses to generate its responses. 

This is particularly important for predictive applications in industries such as finance or healthcare. AI applications in these industries need to be credible to avoid ethical problems.

## Graph databases provide context for decisions

This is possible specifically through knowledge graphs. But first, what is a knowledge graph? 

We define a knowledge graph as an interconnected dataset that has contextual meaning. To put it in a much simpler way, knowledge graphs are used to link relevant information together, just as a human would use past experience to make a choice. 

A knowledge graph uses underlying data to add meaning (context) to the end user's query. The graph algorithms essentially decide which data is relevant to the keyword. This enables users to find exactly what they're searching for in their data. 

An example is the [Thomson Reuters Knowledge Graph Feed](https://www.thomsonreuters.com/en/press-releases/2017/october/thomson-reuters-launches-first-of-its-kind-knowledge-graph-feed.html). It finds relationships between billions of data points to help customers make investment decisions. 

# Emerging fields where graph databases can power AI applications

Graph technology enables data scientists to see how neural networks function. They can build AI applications that are more accurate and transparent. 

Organizations can then use these applications to make faster and better decisions. The following are the major use cases that demonstrate how graph databases function in real-world applications. 

## Fraud detection

Data science plays a critical role in online fraud detection. [Fraud rings](https://www.securitymagazine.com/articles/97824-fraud-rings-detecting-and-defeating-cybercriminal-networks) cultivate different digital personas and false identities to trap unsuspecting victims. 

They then disappear after stealing identities or money, often without a trace. However, graph technology can effectively identify patterns from how fraudsters behave online. One fraud ring can have thousands of data points, e.g., fake social media profiles, online transactions, fraudulent addresses, etc. 

Since graph algorithms can run at a large scale, fraud detectors can use link prediction techniques to identify suspicious user patterns across numerous online platforms. Therefore, they can spot fraud rings and prevent incidents in real-time. 

## Product recommendations

Graph databases are commonly used in recommendation engines. These can be e-commerce websites, hotel booking sites, product comparison platforms, etc. Graph algorithms find links between customer purchases, product searches, and reviews. They also find similarities between different types of customers on an eCommerce platform. 

This interconnected data is what informs the product recommendations. This data powers features like "frequently bought together", "frequently viewed together", or "buy again". Combined, these features increase opportunities for upselling, cross-selling, and repeat sales. An example is [eBay's ShopBot](https://medium.com/@rjpittman/ebay-shopbot-beta-under-the-hood-bedf69157a70), which is linked to Google Assistant to improve customer search results. 

## Social networks

Graph technology plays a vital role in social networks in two main ways. First, it identifies how users are interconnected on the platform, e.g., who has the highest engagements and influence, suspended or inactive accounts, etc. 

Secondly, graph data can identify fake profiles or bots that skew user data. Bot accounts often inflate the active user numbers by automatically reposting or sharing content. However, graph algorithms can identify fake accounts by examining the complex patterns around their behavior. 

For example, a genuine, highly-engaging account and a bot account have different qualities or relationships with other users. A real account has higher two-way engagement than a bot account. The difference in engagement enables platforms to filter out the fake accounts. 


## Location services 

Applications that link to location services need to provide relevant information to users on demand. Examples of such information can be traffic and weather reports, live streaming content, or pest alerts for farmers. 

Location services are also used in marketing campaigns to target customers with content, depending on where they are. Location information is essentially the context behind these applications. 

Graph analytics can determine location connections through subscriber identity module (SIM) tracking and GPS sensors. 

## Regulatory compliance

From a data science perspective, regulatory compliance is a highly dynamic process. Companies accumulate sensitive user data from numerous endpoints, all of which must be protected by the highest privacy standards. 

For example, the General Data Protection Regulation (GDPR) compliance is difficult for businesses. [Over 1,000 businesses](https://www.consultancy.uk/news/31493/four-years-on-companies-still-struggle-with-gdpr-compliance) were fined for noncompliance in 2022. 

Unfortunately, personal data is usually spread out in many different repositories. It can be housed in data centers, on-premise servers, employee accounts, etc. 

With graph technology, your organization can easily connect all the places where sensitive data resides. These connections make it possible for compliance measures to apply across all the data. They also allow a business to delete all personal data if customers request it. 

Other areas of use include robotics, Internet of Things (IoT),Enterprise Content Management, Mobility, and Digital Asset Management. In robotics, for example, adaptive robotics require artificial intelligence and machine learning to process their environment. These machines use data to keep functioning when their surroundings change or if a component breaks down. An example is the Flexiv Rizon adaptive robot, an AI-powered robotic arm used in manufacturing industries. Graph databases are used to store the data that adaptive machines generate. These include object recognition data from sensors, or positioning data from global positioning system (GPS) integrations. They can also be used to simulate and predict the behavior of the robot.  

# Conclusion 

Graph databases uncover hidden patterns and connections that traditional databases may miss.

This rich structure enables AI applications to make more context-aware, accurate predictions. The result is improved user experiences, enhanced data understanding, and more effective decision-making across various industries.

In essence, graph databases empower AI to harness the power of interconnected data.

If your business is looking for more ways to leverage big data in AI applications, then you need to start incorporating graph databases as an essential pillar across your AI development ecosystem.[ Get started](https://docs.nebula-graph.io/3.6.0/#getting_started). 

Find out why graph databases hold the key to unlocking the full potential of AI applications. Uncover opportunities to make the most of your data and spur growth.

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