Vector database: Difference between revisions

Line 8:

https://mariadb.org/amazon-mariadb-vector/</ref> and [[Postgres]]<ref>https://github.com/pgvector/pgvector</ref> offer vector capability now.)

== Commercial ==

'''Pinecone''' is a commercial vector database product. They have a page [https://www.pinecone.io/learn/vector-database/ What is a Vector Database & How Does it Work? Use Cases + Examples] that goes quite a bit into describing what a vector database is, compared with simple vector indexes or traditional scalar databases, and how a vector database works. The even get into algorithms and discuss things like [[wp:random_projection|random projection]] (the Wikipedia article needs more work but it's the tip of an iceberg of statistics, data science and artificial intelligence).

Random Projection is a topic all unto itself. Professor '''Michael Pyrcz''' at the University of Texas, Austin posts his coursework and code online for all to learn. Here's [https://www.youtube.com/watch?v=bfS7JAjiOMI PGE 383 - Feature Projection] which he starts off with Random Projection. (Note too that he puts all materials onto his GitHub account; e.g. https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_Multidimensional_Scaling.ipynb) It's one of many algorithms in "[[wp:Dimensionality_reduction|dimensionality reduction]]" that have applications in neuroscience, artificial intelligence and [[wp:Recommender_system|recommender systems]].

== Open Source ==

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.

@@ Line 8: / Line 8: @@
 https://mariadb.org/amazon-mariadb-vector/</ref> and [[Postgres]]<ref>https://github.com/pgvector/pgvector</ref> offer vector capability now.)
+== Commercial ==
+'''Pinecone''' is a commercial vector database product. They have a page [https://www.pinecone.io/learn/vector-database/ What is a Vector Database & How Does it Work? Use Cases + Examples] that goes quite a bit into describing what a vector database is, compared with simple vector indexes or traditional scalar databases, and how a vector database works. The even get into algorithms and discuss things like [[wp:random_projection|random projection]] (the Wikipedia article needs more work but it's the tip of an iceberg of statistics, data science and artificial intelligence).
+Random Projection is a topic all unto itself. Professor '''Michael Pyrcz''' at the University of Texas, Austin posts his coursework and code online for all to learn. Here's [https://www.youtube.com/watch?v=bfS7JAjiOMI PGE 383 - Feature Projection] which he starts off with Random Projection. (Note too that he puts all materials onto his GitHub account; e.g. https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_Multidimensional_Scaling.ipynb)   It's one of many algorithms in "[[wp:Dimensionality_reduction|dimensionality reduction]]" that have applications in neuroscience, artificial intelligence and [[wp:Recommender_system|recommender systems]].
+== Open Source ==
 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.