To summarise the article if you're skipping to the comments, the pgvector allows you to create a "vector" type in your database

    create table documents (
      id bigserial primary key,
      content text,
      embedding vector (1536)
    );
 

Then you can use OpenAI's Embedding API[0] to convert large text blocks into a 1535-dimension vector, which you will store in the database. From there you can used pgvector's cosine distance operator for searching for related documents

You can combine the search results into a prompt, and send that to GPT for a "ChatGPT-like" interface, where it will generate an answer from the documents provided

[0] https://platform.openai.com/docs/guides/embeddings