Prepare response method is not picking the most similar result from the similary response method.

[JH255095]

`prepare_response()` returns incorrect top result from `similarity_search_by_vector`

Bug Description

When running `similarity_search_by_vector` and passing in the relevant data, the response returns all rows with their associated similarity scores.

After reviewing the returned objects:

• The highest-scoring match corresponds in this case to Image 3 with a score of 0.439.

• Sorting manually using:

  ```python
  response.similar_objects.sort('score', False).head(1)
  ```

  correctly shows Image 3 at the top.

However, when calling `prepare_response()`, the method returns Image 4, which has the second-highest score (0.3547).
This suggests that the LLM may not be selecting the top-scoring object.

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Expected Behavior

`prepare_response()` should return the object with the highest similarity score, which in this case is Image 3.

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Steps to Reproduce

The relevant demo for reproduction is at:
https://github.com/Teradata/jupyter-demos/tree/multimodal_evs_unstructured_embeddings/VantageCloud_Lake/UseCases/Multimodal_Agentic_Semantic_Search

This happens in this demo but we believe is a general behavior.

1. Run `similarity_search_by_vector` using the first query image row.

2. Inspect the returned `response.similar_objects` and note that Image 3 has the highest score.

3. Sort manually:

   ```python
   response.similar_objects.sort('score', False)
   ```

   and confirm Image 3 appears first.

4. Call:

   ```python
   response.prepare_response()
   ```

   and observe that Image 4 is returned instead of Image 3.

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Questions / Hypotheses

• Is `prepare_response()` using the similarity score ranking correctly?
• Is the LLM receiving the entire ranked list, or could results be truncated before selection?
• Is the model basing its selection on semantic content instead of the numeric score?

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Additional Context

• Image 4’s similarity score of 0.3547 is lower than Image 3’s score of 0.439.
• Manual sorting confirms the vector search results are correct.
• The discrepancy appears isolated to the `prepare_response()` step.
• This happens in this demo

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