Semantic vs search embedding

[Innovatix]

Semantic embeddings are better for measuring the similarity between two texts, while search embeddings are better for finding long texts that are relevant to a short query.

Example:

• Semantic embedding: What is the similarity between the sentences: ‘The cat sat on the mat’ and ‘The feline sat on the rug’?
• Search embedding: Find all documents in the database that are relevant to the query: ‘What is the capital of France?’

OpenAI Embedding & Semantic Search using Vector data

Semantic search with Embeddings

I have a list of animals e.g. bird, dog, fish etc. uploaded as embeddings. I then query this list with the query, “which animal has scales?” Using ADA, the results often show “bird” as having a greater cosine similarity than “fish” for this query. I don’t understand why that would be or whether my understanding of embeddings is incorrect. Would appreciate any guidance.