Need Debugging Help with errors of azure ai search

# Problem: Azure AI Search Returns Results for Garbage or Random Words.

I just wanted to know what is the right way of using query rewrite or regular semantic hybrid search with non query rewriting on how I can automatically avoid lot of these results for really bad and no related words such as 'aaaa' or 's*x' or any such un related words.

I though of using the re-ranking score but even for word like 'xxxxxx' reranking score is greater than 2.5. If I use threshold like 2 then these results also pop up, if I use 2.5 as threshold then even for good search query lot of matching results are lost.

## Documents
I have 40 documents in the search index. Each document contains a **product title** and **description**.

## Queries When Results Are Not Expected
### Try 1: Using the Older Version of Azure AI Search Without Recent Query Rewrite
(Refer: [[Azure AI Search Query Rewrite Documentation](https://learn.microsoft.com/en-us/azure/search/semantic-how-to-query-rewrite)](https://learn.microsoft.com/en-us/azure/search/semantic-how-to-query-rewrite))

#### Scenarios Inside This Try
1. **Just Semantic Search**
2. **Semantic Hybrid Search** (Semantic + Vectorization)

---
### Case A: Just Semantic Search
- **Input**: 'aaaaaaaaaaaaaaaaa' or 'S*x' or 'random'
- **Code**:
```python
results = search_client.search(
search_text=input_data,
select=["experienceTitle", "experienceDescription"],
semantic_configuration_name='barsv3',
query_type="semantic",
query_language="en-US",
query_speller='lexicon',
top=3
)
```
- **Output**: As expected, empty results.

---
### Case B: Semantic Hybrid Search
- **Input**: 'aaaaaaaaaaaaaaaaa' or 'S*x' or 'random'
- **Code**:
```python
vector_query = VectorizedQuery(
vector=embedding,
k_nearest_neighbors=50,
exhaustive=True,
fields="experienceDescriptionVector,experienceTitleVector"
)

search_client = SearchClient(
endpoint=endpoint,
index_name='bars-v3',
credential=credential,
api_version='2024-11-01-preview'
)

results = search_client.search(
search_text=input_data,
vector_queries=[vector_query],
select=["experienceTitle", "experienceDescription"],
semantic_configuration_name='barsv3',
query_type="semantic",
query_language="en-US",
query_speller='lexicon',
top=3
)
```
- **Output**: Not as expected. Results are returned even though they shouldn’t.
- **Search Results for 'aaaaaaa':**
```json
[
{"productis": 0, "score": 0.0234118290245533, "reranker_score": 1.6579372882843018},
{"productis": 1, "score": 0.026050420477986336, "reranker_score": 1.6370235681533813},
{"productis": 2, "score": 0.025913622230291367, "reranker_score": 1.626389503479004},
{"productis": 3, "score": 0.03205128386616707, "reranker_score": 1.618236780166626}
]
```

---
**Decision**: Use regular semantic search due to errors caused by Semantic Hybrid Search.

---

### Try 2: Newer Version of Azure AI Search Including Query Rewriting
(Refer: [[Azure AI Search Query Rewrite Documentation](https://learn.microsoft.com/en-us/azure/search/semantic-how-to-query-rewrite)](https://learn.microsoft.com/en-us/azure/search/semantic-how-to-query-rewrite))

#### Scenarios Inside This Try
1. **Just Semantic Search + Query Rewrite**
2. **Semantic Hybrid Search + Query Rewrite**

---
### Case A: Just Semantic Search + Query Rewrite
- **Input**: 'aaaaaaaaaaaaaaaaa' or 'S*x' or 'random'
- **Code**:
```python
results = search_client.search(
search_text=input_data,
select=["experienceTitle", "experienceDescription"],
semantic_configuration_name='barsv3',
query_type="semantic",
query_language="en-US",
query_speller='lexicon',
query_rewrites="generative",
debug="queryRewrites",
top=4
)
```
- **Output**: Not as expected.
- **Search Results for 'aaaaaaa':**
```json
[
"meaning of aaaaaaaa",
"what does aaaaaaaa mean",
"define aaaaaaa",
"aaaaaaa meaning"
]
[
{"productis": 0, "score": 0.7754897, "reranker_score": 1.6579372882843018},
{"productis": 1, "score": 0.27041504, "reranker_score": 1.6370235681533813},
{"productis": 2, "score": 1.0258656, "reranker_score": 1.618236780166626},
{"productis": 3, "score": 0.20604418, "reranker_score": 1.524656891822815}
]
```

---
### Case B: Semantic Hybrid Search + Query Rewrite
- **Input**: 'aaaaaaaaaaaaaaaaa' or 'S*x' or 'random'
- **Code**:
```python
results = search_client.search(
search_text=input_data,
select=["experienceTitle", "experienceDescription"],
semantic_configuration_name='barsv3',
query_type="semantic",
query_language="en-US",
query_speller='lexicon',
query_rewrites="generative",
debug="queryRewrites",
top=4
)
```
- **Output**: Not as expected. Results returned despite nonsensical input.

Start with the SearchClient.search calls shown in the issue, comparing semantic-only, semantic hybrid, and query-rewrite requests. Review the linked Azure AI Search query rewrite documentation and reproduce the reported inputs and scores. Done means identifying whether the returned results are expected behavior or a documented service issue, with a clear explanation of how to handle the cases.

Written by the indexing model from the issue text.