From 2b3eb2e30839f69990e21ef4f496cd504200cd9b Mon Sep 17 00:00:00 2001
From: zaineb-damak <77733791+zaineb-damak@users.noreply.github.com>
Date: Mon, 17 Apr 2023 10:03:38 -0700
Subject: [PATCH] correct grammatical mistake

---
 apps/www/_blog/2023-02-03-openai-embeddings-postgres-vector.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/apps/www/_blog/2023-02-03-openai-embeddings-postgres-vector.mdx b/apps/www/_blog/2023-02-03-openai-embeddings-postgres-vector.mdx
index e6c7aa2fe4f..46001ec1f17 100644
--- a/apps/www/_blog/2023-02-03-openai-embeddings-postgres-vector.mdx
+++ b/apps/www/_blog/2023-02-03-openai-embeddings-postgres-vector.mdx
@@ -69,7 +69,7 @@ Why is this useful? Once we have generated embeddings on multiple texts, it is t
 
 ## Embeddings in practice
 
-At a small scale, you could store your embeddings in a CSV file, load them into Python, and use a library like `numPy` to calculated similarity between them using something like cosine distance or dot product. OpenAI has a cookbook [example](https://github.com/openai/openai-cookbook/blob/main/examples/Semantic_text_search_using_embeddings.ipynb) that does just that. Unfortunately this likely won't scale well:
+At a small scale, you could store your embeddings in a CSV file, load them into Python, and use a library like `numPy` to calculate similarity between them using something like cosine distance or dot product. OpenAI has a cookbook [example](https://github.com/openai/openai-cookbook/blob/main/examples/Semantic_text_search_using_embeddings.ipynb) that does just that. Unfortunately this likely won't scale well:
 
 - What if I need to store and search over a large number of documents and embeddings (more than can fit in memory)?
 - What if I want to create/update/delete embeddings dynamically?