Embeddings, the AI model that returns a vector against the input, is not deterministic. Every run can be different. That means that semantic similarity scores that are close can flip position between trials of the same input with those value variations in the embeddings vector return.
If you are not directly using the semantic search vector store endpoint, you are also having an AI model create its own search query, with even more variation between turns with different inputs driving what the AI generates its tool call based on.
OpenAI could explain the reason, but doesn’t.