Azure Hybrid Search results are not consistent

Hi team,

I am running Azure Hybrid Search on my data which has 12 pdfs[423 chunks], that I am embedding in my vector store, and getting top 12 chunks for a query.

Code snippet:
vector_query = VectorizedQuery(
vector=query_embeddings, k_nearest_neighbors=60,
fields="contentVector")

results = await self.search_client.search(
search_text=query,
vector_queries=[vector_query],
top=12,
filter=filter_expression
)

but the problem I am facing is, the top 12 results are not consistent, and they are changing with different iterations. To solve this I used exhaustiveKNN as well, but it didn't help. Upon reading Azure blogs, I found that some stochasticity may come from BM25, so I set the parameter scoring_statistics='global' & also added session_id.

Code snippet:
vector_query = VectorizedQuery(
vector=query_embeddings, k_nearest_neighbors=60,
fields="contentVector", exhaustive=True)

results = await self.search_client.search(
search_text=query,
vector_queries=[vector_query],
top=12,
filter=filter_expression, scoring_statistics='global'
) #session_id = 'abcd1234xyz',
results = await self._format_metadata(results)

I request the team to please guide me how to get the same consistent output from Hybrid Search, given that I want to optimize based on Search Accuracy[how relevant chunks are] and time to embed and retrieve?

Start from the shown VectorizedQuery and search_client.search calls; reproduce repeated top-12 hybrid queries with exhaustiveKNN, scoring_statistics='global', and the commented session_id. Compare ranking stability and embedding and retrieval times, then document a confirmed cause or minimal reproducible behavior for Azure AI Search.

Written by the indexing model from the issue text.