PostgreSQL - PGVector

Related Links:
https://www.yugabyte.com/blog/postgresql-pgvector-getting-started/

https://www.timescale.com/blog/postgresql-as-a-vector-database-create-store-and-query-openai-embeddings-with-pgvector/
https://timescale.github.io/python-vector/tsv_python_getting_started_tutorial.html

https://www.enterprisedb.com/blog/what-is-pgvector

https://github.com/timescale/vector-cookbook/tree/main/openai_pgvector_helloworld?ref=timescale.com

Part 2: Store Embeddings in a PostgreSQL Vector Database Using pgvector
Now that we have created embedding vectors for our blog content, the next step is to store the embedding vectors in a vector database to help us perform a fast search over many vectors.

What is a vector database?
A vector database is a database that can handle vector data. Vector databases are useful for:

Semantic search: Vector databases facilitate semantic search, which considers the context or meaning of search terms rather than just exact matches. They are useful for recommendation systems, content discovery, and question-answering systems.
Efficient similarity search: Vector databases are designed for efficient high-dimensional nearest neighbor search, a task where traditional relational databases struggle.
Machine learning: Vector databases store and search embeddings created by machine-learning models. This feature aids in finding items semantically similar to a given item.
Multimedia data handling: Vector databases also excel in working with multimedia data (images, audio, video) by converting them into high-dimensional vectors for efficient similarity search.
NLP and data combination: In Natural Language Processing (NLP), vector databases store high-dimensional vectors representing words, sentences, or documents. They also allow a combination of traditional SQL queries with similarity searches, accommodating both structured and unstructured data.

We’ll use PostgreSQL with the pgvector extension installed as our vector database. Pgvector extends PostgreSQL to handle vector data types and vector similarity search, like nearest neighbor search, which we’ll use to find the k most related embeddings in our database for a given user prompt.

Why use pgvector as a vector database?
Here are five reasons why PostgreSQL is a good choice for storing and handling vector data:

Integrated solution:

By using PostgreSQL as a vector database, you keep your data in one place. This can simplify your architecture by reducing the need for multiple databases or additional services.

Enterprise-level robustness and operations: With a 30-year pedigree, PostgreSQL provides world-class data integrity, operations, and robustness. This includes backups, streaming replication, role-based and row-level security, and ACID compliance.

Full-featured SQL:

PostgreSQL supports a rich set of SQL features, including joins, subqueries, window functions, and more. This allows for powerful and complex queries that can include both traditional relational data and vector data. It also integrates with a plethora of existing data science and data analysis tools.

Scalability and performance: PostgreSQL is known for its robustness and ability to handle large datasets. Using it as a vector database allows you to leverage these characteristics for vector data as well.

Open source: PostgreSQL is open source, which means it's free to download and use, and you can modify it to suit your needs. It also means that it benefits from the collective input of developers all over the world, which often results in high-quality, secure, and up-to-date software. PostgreSQL has a large and active community, so help is readily available. There are many resources, such as documentation, tutorials, forums, and more, to help you troubleshoot and optimize your PostgreSQL database.