When to use Knowledge Graphs and Vector Databases

A Knowledge Graph is a potent data structure representing relationships between entities. It comprises nodes (entities or concepts) connected by edges denoting facts or categories. For instance, it can be established that "Delhi" is the capital of "India".

The Knowledge Graph Index is pivotal in scenarios where understanding entity relationships is crucial. It aids in Information Retrieval through Graph RAG, in which, the KnowledgeGraphRAGRetriever processes queries by searching related entities, building a SubGraph, and generating context-based responses.

When confronted with questions spanning multiple chunks of information, vector databases may yield an incomplete list. In contrast, a knowledge graph can provide a complete list.

Also, knowledge graphs offer a distinct advantage of being able to visualize complex relationships. Unlike vector databases, Knowledge Graphs offer precise, specific information, detailing the type and direction of relationships. They support complex queries with logical operators, broadening LLM capabilities. Moreover, Knowledge Graphs enable advanced reasoning and inference, providing indirect derived information.

In situations where questions don't involve specific, well-defined chunks of knowledge, the additional Knowledge Graph retriever may not offer as much assistance when compared to vector databases.

For LLM hallucination, Knowledge Graph could surpass vector databases, offering precise and reasoned information.

The choice between a Knowledge Graph and a Vector Database should be based on the specific requirements of the task at hand.

Author Prarthana Shah