SPOILER ALERT 🚨 Microsoft Postgres team is set to release the ai_embedding extension soon, which will complement pgvector, making Postgres an even more attractive option for AI use cases. So, what exactly does this extension aim to address? Imagine you store Airbnb lodging options in Postgres, including embeddings/vectors for each lodging description. When a user searches for a place to stay, presently, you can use OpenAI + Postgres for natural language processing in the manner below. First, generate an embedding for the user's prompt: const embeddingResp = await this.#openai.embeddings.create( { model: "text-embedding-ada-002", input: "Looking for an apartment in NYC" } ); Next, take the generated embedding and do a similarity search in Postgres to provide the most relevant recommendations for the user: 
const res = await this.#client.query( "SELECT name, description, price " + "FROM airbnb_listing WHERE 1 - (description_embedding <=> $1) > 0.8 " + "ORDER BY similarity DESC LIMIT 3", ['[' + userPromptEmbedding + ']']); I've detailed this process in this article: medium.com/@magda7817/better… Now, what changes with the upcoming Microsoft extension? You'll be able to call the OpenAI API directly from the database, straight from SQL, making the code more streamlined and efficient. const res = await this.#client.query( "SELECT name, description, price " + "FROM airbnb_listing WHERE 1 - " + "(description_embedding <=> " + "ai_embedding('Looking for an apartment in NYC') > 0.8 " + "ORDER BY similarity DESC LIMIT 3”); Good luck to your team @clairegiordano . Thanks for making Postgres awesome.