Graph Database in AI and RAG implementations

Meaningful data isn’t just a collection of individual pieces of information—it’s the relationships between those pieces that are crucial for extracting insights. Graph databases—a type of NoSQL database—are designed to capture these relationships directly, making them a perfect fit for AI systems that aim to leverage context, recognize patterns, and connect related information.

In this article, we’ll explore how graph databases work in AI and RAG applications, their advantages, and the ways they can transform complex, interconnected data into actionable insights.

What is a graph database?

A graph database—not to be mistaken with GraphQL (a query language for APIs)—is a type of database built for data with complex relationships. It’s especially useful when understanding the connections between entities is just as important as the data itself, such as in social networks, recommendation engines, or fraud detection.

Traditional relational databases (for example, MySQL or PostgreSQL) are not ideally suited for handling highly connected data. These systems are built around rows and columns, which works well for simple flat records. However, as your data becomes more interconnected, even simple queries can turn into complex operations that require joining multiple tables, slowing performance as the system scales. Over time, this makes your code harder to read, modify, and maintain.

For example, if you want to find out how two users in a social app are connected using a relational database, you may need to join multiple tables to trace the path between them. As the number of users and connections increases, these queries become slower and more difficult to maintain. In contrast, a graph database is specifically designed to handle complex relationships, so tracing connections between users is much more efficient. The same query is simpler to execute and faster to process, even as the network expands.

Components of graph databases

Understanding the core components of graph databases reveals why they are so effective at handling connected data. At a high level, there are three key elements: nodes, edges, and properties.

1. Nodes (or vertices)

Nodes (or vertices) are the fundamental units of a graph and represent the entities or objects in your system. For example, in a social network, you could have nodes for users, posts, groups, or events. Each node stores information specific to that entity—the user node would contain details like the person’s name and age.

Nodes are vital because of how they connect to other elements in the graph—edges and properties. Each node can have multiple properties, like metadata that describes it, and these nodes are linked to each other through edges. These connections form the network, letting the database explore relationships and patterns.

Nodes also have labels that categorize them, such as User or Post, which makes it easier to filter and index the nodes in a database. Internally, nodes are assigned a unique identifier, which allows the database to locate them during queries or traversal operations, where the database moves from one node to another along their connections to explore relationships and find patterns in the data. This identifier ensures that each node is distinct, even if multiple nodes share similar properties, like two users with the same name.

Nodes can participate in multiple relationships simultaneously, meaning that a single user node, for instance, can be connected to other users, posts, groups, and events all at once. Adding new connections or updating existing ones doesn’t slow down the database. This allows graph databases to scale to millions or even billions of entities without requiring structural changes.

2. Edges (or relationships)

Edges, also called relationships, define how nodes are connected in the graph. Each edge has

• a start node, which marks where the edge begins. For example, if the graph database starts at the user node for Alice, and you want to find posts she created, Alice is the start node of the CREATED edge;
• an end node to which the relationship connects. Following the same example, the end node would be the post that Alice authored. So when traversing the CREATED edge from Alice, the database moves directly to the post node without any extra lookup.
• a type that describes the nature of the connection, like FRIENDS_WITH, LIKES, or CREATED. It tells the database—and anyone reading the graph—what kind of relationship exists between the nodes.
• a direction, which indicates how the edge should be traversed. Direction matters when following connections in queries. For example, a CREATED edge naturally flows from the user who authored the post to the post itself, while a LIKES edge might go from the user to the post they liked.

It is also possible for an edge to connect a node to itself, creating what is called a self-edge. A good example is a user linking their own post, creating a SELF_LIKE edge.

3. Properties

Properties are key-value pairs attached to both nodes and edges, providing additional context and detail. On nodes, properties might describe attributes such as a user’s email, age, or location, while on edges, they might record information like a post’s date or a comment’s timestamp.

For example, a CREATED edge might store the date the post was made, or a FRIENDS_WITH edge could include the date the friendship started. These properties let you store extra context directly on the connection.

Properties can store various data structures, including strings, numbers, booleans, dates, and arrays, giving the database flexibility to represent complex information.

Why graph databases are ideal for RAG

Retrieval-augmented generation (RAG) involves connecting a language model to external data so the model’s responses are grounded in accurate, context-rich information. How useful those responses are depends on what the retrieval layer sends into the prompt.

Graph databases are particularly well-suited for RAG because they are designed to handle relationships between data points. In a RAG pipeline, when context is assembled across multiple steps, the graph database can efficiently traverse from one node to another along stored edges, avoiding the need to rebuild connections during query time.

This structure becomes even more crucial when RAG moves beyond simply retrieving plain text and starts working with entities, attributes, and their relationships. Basic RAG setups often rely on vector databases to find text fragments (chunks) based on semantic similarity. That works well for finding content that is “about the same thing,” but it strips away structure. Once documents are chunked and embedded, the links between entities, actions, and sources are no longer explicit. The model receives fragments of text and has to infer how they connect.

With a graph database in the retrieval layer, the system can access not just text but connected context: entities, their properties, and the relationships. Instead of sending isolated data chunks into the prompt, the retrieval step assembles a coherent subgraph that reflects how the data is actually linked in your system. This gives the language model with a clearer context and reduces the risk of mixing unrelated facts. In practice, many teams combine vector search for semantic discovery with graph queries for structured grounding, so the model gets both meaning and structure in the same prompt.

Graph databases also shine in environments where data is constantly changing. In RAG workflows, the context provided to the LLM must be current. New transactions, referrals, or interactions can be added to the graph in real time, ensuring that queries always reflect the latest connections. This is more efficient than relational or document stores, where frequent updates can be costly, and maintaining accurate relationships requires significant engineering effort.

Another advantage is efficient navigation. Unlike relational databases, which scan entire tables, graph queries focus only on the relevant "neighborhood" for the question at hand, which is crucial for RAG when extracting context from massive datasets.

Lastly, graph databases simplify relationship-heavy queries. Query languages like Cypher or Gremlin are optimized for traversing nodes and edges, allowing you to describe patterns rather than manually specifying every join or filter.

Types of graph databases

Not all graph databases are the same. How they store, query, and interpret relationships can vary across models.

Property graphs are the most common type of graph database and are what we’ve covered so far. As we’ve seen, they model data using nodes, edges, and properties, giving you the flexibility to represent entities and their relationships with detail.

RDF (Resource Description Framework) graphs take a different approach to modeling relationships. Instead of working with nodes, edges, and properties, RDF represents data as simple statements called triples, each consisting of three parts.

• A subject, which is the resource you are describing or starting from. This is similar to a node in property graphs.
• A predicate, which describes the type of relationship or action that connects the subject to something else. This is akin to edges in property graphs.
• An object, which is the entity the subject points to, is much like the end node of a property graph.

For example, what would be modeled in a property graph as (User {id: 1})-[:CREATED]->(Post {id: 42}) would be represented in an RDF graph as a triple: User1 CREATED Post42.

RDF is often used for data that must be shared and linked across systems in a standardized way, while property graphs offer more flexibility in how data is represented and queried.

AI use cases of graph databases

As we said, graph databases are ideal for AI and ML systems due to their ability to store and traverse complex, interconnected data in real time. Below, we'll explore the most popular use cases. 

Recommendation engines

In recommendation systems, insights depend on understanding relationships between entities and interactions across a network. Graph databases are well-suited for this task, as they naturally model connections, enabling algorithms to detect patterns efficiently.

With graph databases, it's easier to uncover patterns, identify associations, and generate personalized suggestions. The flexibility of graph models also supports the evolution of recommendation criteria as the underlying data grows or changes. Additionally, graphs offer adaptability: as data evolves or new types of relationships emerge, the system can adjust without requiring a complete restructuring, ensuring that recommendations remain relevant and up to date.

Fraud detection

Fraud detection benefits from the graph model because suspicious activity often emerges from hidden patterns in relationships. Graph databases capture these connections directly, allowing algorithms to recognize anomalies, such as unusual linkages between accounts or transactions. For instance, a single credit card being used across multiple distant locations within a short time can be flagged when all transactions are connected in a graph.

By mapping the network of interactions and comparing it to normal patterns, AI systems can identify irregularities and detect coordinated schemes. Since the relationships are stored directly in the graph, there’s no need to reconstruct links from separate tables, which makes detecting complex fraud much faster.

Semantic search and natural language processing (NLP)

Graph databases enhance AI systems in semantic search and NLP by organizing data around meaning and context, rather than isolated entries. Algorithms can interpret relationships between entities, leading to more accurate and relevant results. This enables AI models to better understand intent, recognize synonyms, and connect related entities, improving performance in applications such as conversational agents and content classification. The graph structure allows AI to reason about context, going beyond simple keyword matching.

Graph database examples: Neo4j, Amazon Neptune, JanusGraph, NebulaGraph, and Ontotext

Let’s review some of the top dedicated graph databases available in the market. Note that there are also multi-model solutions like ArangoDB and ArcadeDB that support graphs alongside other database types, such as vector, document, and key-value.

Neo4j

Neo4j is built for working with connected data at scale, transforming raw data into a usable knowledge graph that reveals how entities are linked.

Neo4j offers a suite of tools designed to make connected data easy to explore and visualize. Data is stored as nodes and relationships and queried using Cypher, a query language that is simple to read and write. The platform also provides capabilities for graph analytics and running graph algorithms on large datasets.

Neo4j can run in the cloud via its managed service, Neo4j AuraDB, on-premises, or in hybrid setups, and it integrates with popular data and cloud platforms.

Neo4j can run in the cloud (via its managed service Neo4j AuraDB), on-premises, or in hybrid setups, and it integrates with popular data and cloud platforms.

Amazon Neptune

Amazon Neptune is a fully managed graph database from AWS. Its graph engine can store and query billions of relationships with minimal latency, making it great for exploring complex networks quickly.

Neptune supports both property and RDF graphs, giving you maximum flexibility.

JanusGraph

JanusGraph is an open-source graph database designed to scale across clusters of machines, making it capable of handling graphs with billions of nodes and relationships while still supporting real-time queries.

JanusGraph does not store data directly; instead, it focuses on the graph model and query layer, relying on other storage backends. Unlike some other graph databases, JanusGraph does not come with built-in visualization tools; Instead, it integrates with external solutions.

JanusGraph supports both transactional workloads and large-scale analytics, and it can integrate with big data tools like Apache Hadoop and Apache Spark for batch processing and graph analysis.

NebulaGraph

NebulaGraph is an open-source, distributed graph database that can handle graphs with billions to trillions of nodes and edges while maintaining low query times.

The system uses a native graph engine and a shared-nothing architecture, so you can scale out by adding more machines without reworking your setup. It runs on-prem, in the cloud, or in hybrid environments, and is often used for things like knowledge graphs, recommendations, and fraud detection, where relationships matter at scale.

On the query side, NebulaGraph uses nGQL, a graph query language that is similar to SQL or Cypher, making it easier for users familiar with these languages to transition. It also supports compatibility with openCypher and GQL, which simplifies migration and lowers the learning curve. NebulaGraph's graph explorer allows users to interact with the database visually.

GraphDB by

Ontotext’s GraphDB is a dedicated RDF graph database built for managing large volumes of structured and unstructured data while preserving semantic relationships between entities. It is designed to support high-performance workloads and provides capabilities such as semantic indexing, SPARQL querying, reasoning (inference), and graph-based data exploration.

GraphDB supports multiple deployment options, including on-premises and cloud environments. It integrates with other systems and data sources through connectors and plugins for technologies such as MongoDB, Kafka, Lucene, Solr, and Elasticsearch.

Graph databases vs vector databases vs relational databases

How does a graph database stack up against other options like vector or relational databases? Let’s find out.

Vector database

Vector databases, like Pinecone, Milvus, Weaviate, Qdrant, and Chroma, store high-dimensional embeddings—numerical representations of unstructured data such as text, images, audio, or video. Each vector captures the features of the original data, and similarity between vectors indicates closeness in meaning or context. They use Approximate Nearest Neighbor (ANN) search and indexing techniques like hierarchical navigable small world (HNSW), locality-sensitive hashing (LSH), or inverted file indexes (IVF) to quickly find similar vectors.

These databases are great for semantic search, recommendation systems based on content similarity, RAG workflows, and computer vision tasks. The key difference from graph databases is in how queries work: graph databases let you traverse explicit relationships between entities, following paths and patterns across nodes and edges. Vector databases, by contrast, operate in a multidimensional space, where queries measure similarity between vectors rather than walking through a network of relationships. This makes the querying experience more about finding “nearest neighbors” in meaning than exploring actual connections.

Relational database

Relational databases store structured data in tables, with rows holding individual records and columns representing attributes. They enable data to be joined across multiple tables using primary and foreign keys.

Their ACID properties—Atomicity, Consistency, Isolation, and Durability—make them ideal for transactional applications, such as eCommerce, banking, or enterprise software, where reliability is essential. Popular examples include MySQL, PostgreSQL, Oracle Database, and Microsoft SQL Server.

The main difference from graph databases lies in how relationships are handled. In relational systems, connections between data are indirect and must be reconstructed on the fly using joins. Relational databases can face performance issues when relationships are highly interconnected or frequently changing, while graph databases are designed to handle such scenarios.