Vector Database vs Knowledge Graph: Choosing Your LLM Store
A field guide to the two storage paradigms behind every serious LLM application — scored on retrieval, reasoning, cost, and the questions each one can and cannot answer. A marketplace client in Mumbai came to me with a genuinely good question. Their LLM support agent kept giving almost right answers about their catalog — thousands of products, suppliers, and delivery zones — because the knowledge…
Choosing an LLM storage solution for your application depends on the type of questions you need to answer. Vector databases and knowledge graphs serve different purposes and are not interchangeable. A vector database, such as Qdrant or pgvector, is best suited for similarity-based queries, where the objective is to find the most similar pieces of text to a given question.
This is accomplished by storing high-dimensional embeddings of text chunks and retrieving the nearest neighbors based on their proximity in the embedding space. However, vector databases struggle with multi-condition or relational queries, where the answer requires combining multiple predicates or traversing relationships between entities.
A knowledge graph, like Neo4j, on the other hand, is designed to handle relational queries and store entities and their relationships as nodes and edges. This allows for precise, deterministic queries that can traverse complex relationships and return exact results. Vector databases shine when it comes to retrieval-augmented generation over documents, as they can efficiently retrieve the most relevant chunks of text for an LLM to generate an answer.
They are also relatively easy to set up and scale to handle millions of items. Knowledge graphs, while more complex to implement and operate, are ideal for applications that require precise, structured data and can benefit from the ability to show the exact path of their reasoning process. They excel at answering relational questions and provide transparency into how their answers are derived, which is crucial for trust and accountability in LLM applications.
Ultimately, the decision between a vector database and a knowledge graph should be based on the specific query requirements of your use case, rather than following hype or convenience.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
