Embeddings for the same content vary. How can this be fixed?

[vb]

Without the intention to take away from @wclayf 's potential answer, here is a good take on the subject by @curt.kennedy

Splitting text into chunks versus reducing the text

You could keep your full 13,778 token chunk in tact, but embed only pieces of it, and then return the full 13,778 token chunk that correlates to the smaller embedded piece. This way your chunk is coherent and you can feed it into a large context model. As stated, the AI may not fully digest the larger chunk, because the attention is diluted over this larger span of tokens, and some details may get glossed over. But at least you have a coherent chunk in your retrieval, and hopefully, over tim…

You can further read up on potential workarounds that will also improve the workflow.

Are vectors generated by text-embedding-3-small always the same for the same text input?

The only way I see would be more expensive and slower. Which is to not add what you paid embeddings for if there is an embeddings result >.999 from an exhaustive search and the text returned from the database matches.

Hope this helps!