Vectors: Difference between revisions

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[[wp:Vector_database|Vector databases]] like [[wp:Neo4j|Neo4j]] have been important for quite some time now. They are ever more important now that [[Artificial Intelligence]] is mainstream. A vector database is a collection of data stored as mathematical representations. Vector databases make it possible for computer programs to draw comparisons, identify relationships, and understand context. They enable '''Semantic Search''' which is search based on meaning rather than exact text matching. While semantic searching has been around for decades, tagging and ontologies have morphed into LLMs. Vector databases enable the creation of advanced artificial intelligence (AI) programs like large language models (LLMs).

There are many open source vector databases<ref>[[wp:Vector_database#Implementations]]</ref> such as Apache Cassandra, [[Elasticsearch]], Meilisearch and MongoDB. (~~Apparently~~ [[MariaDB]]<ref>https://mariadb.org/amazon-mariadb-vector/</ref> and [[Postgres]]<ref>https://github.com/pgvector/pgvector</ref> offer vector capability.)

There are many open source vector databases<ref>[[wp:Vector_database#Implementations]]</ref> such as Apache Cassandra, [[Elasticsearch]], Meilisearch and MongoDB. (Even traditional Relational Databases [[MariaDB]]<ref>https://mariadb.org/amazon-mariadb-vector/</ref> and [[Postgres]]<ref>https://github.com/pgvector/pgvector</ref> offer vector capability now.)

One interesting open source vector database is '''Memgraph'''. Memgraph is like Neo4j without the cost. Memgraph uses the same Cypher query language as Neo4j. However, it is written in C++ and integrates better with Python than Neo4j, which uses Java to build applications. An interesting case study is how NASA is building a People Knowledge Graph with LLMs and Memgraph<ref>https://www.theregister.com/2025/05/07/nasa_people_memgraph/</ref>. In the NASA case study, they use [[Ollama]] which is a locally deployed AI model runner which can be thought of as like [[Docker Desktop]] for running [[Docker]] images.

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 [[wp:Vector_database|Vector databases]] like [[wp:Neo4j|Neo4j]] have been important for quite some time now. They are ever more important now that [[Artificial Intelligence]] is mainstream. A vector database is a collection of data stored as mathematical representations. Vector databases make it possible for computer programs to draw comparisons, identify relationships, and understand context. They enable '''Semantic Search''' which is search based on meaning rather than exact text matching. While semantic searching has been around for decades, tagging and ontologies have morphed into LLMs. Vector databases enable the creation of advanced artificial intelligence (AI) programs like large language models (LLMs).
-There are many open source vector databases<ref>[[wp:Vector_database#Implementations]]</ref> such as Apache Cassandra, [[Elasticsearch]], Meilisearch and MongoDB. (Apparently [[MariaDB]]<ref>https://mariadb.org/amazon-mariadb-vector/</ref> and [[Postgres]]<ref>https://github.com/pgvector/pgvector</ref> offer vector capability.)
+There are many open source vector databases<ref>[[wp:Vector_database#Implementations]]</ref> such as Apache Cassandra, [[Elasticsearch]], Meilisearch and MongoDB. (Even traditional Relational Databases [[MariaDB]]<ref>https://mariadb.org/amazon-mariadb-vector/</ref> and [[Postgres]]<ref>https://github.com/pgvector/pgvector</ref> offer vector capability now.)
 One interesting open source vector database is '''Memgraph'''. Memgraph is like Neo4j without the cost. Memgraph uses the same Cypher query language as Neo4j. However, it is written in C++ and integrates better with Python than Neo4j, which uses Java to build applications. An interesting case study is how NASA is building a People Knowledge Graph with LLMs and Memgraph<ref>https://www.theregister.com/2025/05/07/nasa_people_memgraph/</ref>. In the NASA case study, they use [[Ollama]] which is a locally deployed AI model runner which can be thought of as like [[Docker Desktop]] for running [[Docker]] images.