The article compares and contrasts knowledge graphs and vector databases, both of which are used to store and retrieve data but differ in their methods and strengths. Knowledge graphs are structured representations of information, organized into nodes and edges, and excel at handling complex queries involving relationships between entities. They are beneficial for applications requiring precise and reliable answers, but require data modeling.

Vector databases, on the other hand, store data as numerical vectors, which allows for efficient similarity searches but may struggle with complex relationships and result in less accurate responses. The article argues that combining these two approaches, as FalkorDB does, can offer a more comprehensive understanding of data and enhance the capabilities of AI and NLP applications.

To read more see: https://www.falkordb.com/blog/knowledge-graph-vs-vector-database/