Is a graph database better than a vector database for RAG?

Neither is better in general. Vector databases suit semantic search over passages and are quicker to set up. Graph databases suit questions about relationships and multi-hop connections. Many RAG systems start with vectors and add a graph where relationship questions matter.

GraphRAG is a family of approaches that build a graph of entities and relationships from text and use it during retrieval, sometimes with summaries of related clusters. It helps answer questions that span many documents or require following connections, at the cost of more complex indexing.

Can one database do both vector and graph search?

Some can. Several graph databases support vector indexes, and some vector or relational databases add graph-like features. Combined systems simplify operations, but check performance and query capabilities for your workload before consolidating. Separate specialised systems can still be the better choice at large scale or for complex traversals.

Graph Database vs Vector Database

A vector database stores embeddings and finds items with similar meaning, which suits semantic search over documents and chunks. A graph database stores entities and the relationships between them, which suits questions that follow connections, such as who reports to whom or which services depend on a library. Many AI systems combine both: vectors to find, graphs to connect.

What a vector database does

A vector database stores high-dimensional embeddings with metadata and finds the nearest vectors to a query using approximate nearest neighbour indexes. In RAG, documents are chunked and embedded; queries are embedded at search time; the closest chunks are returned. It works well when the answer lives in a passage whose meaning resembles the question.

What a graph database does

A graph database stores nodes, such as people, documents, services, or concepts, and edges, such as authored, depends on, or mentions, with properties. Queries traverse relationships: find all services that depend on a library, or all decisions linked to a project. It works well when answers require following connections across many items.

Strengths and weaknesses

Vector search handles fuzzy, natural-language questions with little setup, but it struggles with multi-hop reasoning, exact relationships, and aggregation. Graphs answer structural questions precisely but require modelling entities and relations, and keeping that model current as data changes.

Running both side by side

Hybrid approaches use vectors to find relevant entry points, then graphs to expand along relationships, or graphs to scope a search before vector ranking. GraphRAG approaches build a graph of entities and relationships from text, sometimes with community summaries, and retrieve along the graph alongside text chunks. Some databases support both vectors and graph queries.

Operational considerations

Vector databases need embedding pipelines, re-indexing when content or models change, and tuning of index parameters. Graph databases need extraction pipelines, often using models to identify entities and relations, plus schema design and deduplication of entities. Both need access controls applied at query time.

Choosing between them

List the questions users ask. If most are what does the documentation say about this, start with vector search. If many are how is this connected to that, or what is affected if this changes, invest in a graph. If both appear, start with vectors and add a graph for the relationship-heavy subset.

How RDK stores and retrieves

RDK indexes vault notes and code into chunks that agents search before calling a model, the vector side of the picture. Notes in tools such as Obsidian already contain links between pages, which can feed a lightweight graph layer on top of chunk retrieval.

Costs and scaling

Vector databases scale with the number and dimensionality of embeddings; memory for indexes is often the main cost. Graph databases scale with nodes and edges and the complexity of traversals. Building a graph from unstructured text usually requires model calls for entity and relation extraction, which is a significant upfront cost.

Example questions

Vector-friendly: how to configure retries, what does the policy say about refunds, find notes similar to this idea. Graph-friendly: which teams own services that call this API, which documents cite this decision, what changed between these two releases and who approved it. Mixed: summarise everything related to this customer, which needs both similarity and relationships.

Aspect	Vector database	Graph database
Stores	Embeddings plus metadata	Nodes, edges, properties
Answers	What is similar to this?	How are these connected?
Setup effort	Low: chunk and embed	Higher: model entities and relations
Weakness	Multi-hop and exact relations	Fuzzy language, upkeep of the model
RAG role	Find relevant passages	Expand along relationships, GraphRAG

Frequently asked questions

Is a graph database better than a vector database for RAG?: Neither is better in general. Vector databases suit semantic search over passages and are quicker to set up. Graph databases suit questions about relationships and multi-hop connections. Many RAG systems start with vectors and add a graph where relationship questions matter.
What is GraphRAG?: GraphRAG is a family of approaches that build a graph of entities and relationships from text and use it during retrieval, sometimes with summaries of related clusters. It helps answer questions that span many documents or require following connections, at the cost of more complex indexing.
Can one database do both vector and graph search?: Some can. Several graph databases support vector indexes, and some vector or relational databases add graph-like features. Combined systems simplify operations, but check performance and query capabilities for your workload before consolidating. Separate specialised systems can still be the better choice at large scale or for complex traversals.