Error running vector search in Azure Cognitive Search - The 'value' property of the vector query can't be null or an empty array.
Labels: name: area: vector store, name: auto:bug

### System Info

When I run a vector search in Azure Cognitive Search using AzureSearch it fails saying, "The 'value' property of the vector query can't be null or an empty array." (full error at the bottom) My code hasn't changed from last week when it used to work. I've got version 11.4.0b6 of azure-search-documents installed. I suspect that Cognitive Search has changed its signature or implementation but the Langchain connection stuff hasn't been updated.

Someone else has reported the same error message but the workaround doesn't work for me (https://github.com/langchain-ai/langchain/issues/7841).

The following is a simplified version of the code which I got from the samples at https://python.langchain.com/docs/integrations/vectorstores/azuresearch. This also fails with the same error.

`import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.azuresearch import AzureSearch

os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"] = '<service_name>'
os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"] = '<index_name>'
os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"] = '<key>'
os.environ["OPENAI_API_KEY"] = '<key2>'
model = 'text-embedding-ada-002'

embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "website"
vector_store: AzureSearch = AzureSearch(
    azure_search_endpoint='https://<service_name>.search.windows.net',
    azure_search_key='<key>',
    index_name=index_name,
    embedding_function=embeddings.embed_query,
)

docs = vector_store.hybrid_search(
    query="Test query", 
    k=3
)
print(docs[0].page_content)
`

The full error is:
Exception has occurred: HttpResponseError
(InvalidRequestParameter) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Parameter name: vector
Code: InvalidRequestParameter
Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Parameter name: vector
Exception Details:	(InvalidVectorQuery) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
	Code: InvalidVectorQuery
	Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
  File "C:\Work\AI Search\SearchCognitiveSearchScratch.py", line 22, in <module>
    docs = vector_store.hybrid_search(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
azure.core.exceptions.HttpResponseError: (InvalidRequestParameter) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Parameter name: vector
Code: InvalidRequestParameter
Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
Parameter name: vector
Exception Details:	(InvalidVectorQuery) The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'
	Code: InvalidVectorQuery
	Message: The 'value' property of the vector query can't be null or an empty array. Make sure to enclose the vector within a "value" property: '{"vector": { "value": [ <vector contents> ] } }'

### Who can help?

_No response_

### Information

- [X] The official example notebooks/scripts
- [X] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

Run:
pip install azure-search-documents==11.4.0b6
pip install azure-identity

Run (replacing with the keys, service names, index names and service endpoint):
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.azuresearch import AzureSearch

os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"] = '<service_name>'
os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"] = '<index_name>'
os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"] = '<key>'
os.environ["OPENAI_API_KEY"] = '<key2>'
model = 'text-embedding-ada-002'

embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "website"
vector_store: AzureSearch = AzureSearch(
    azure_search_endpoint='https://<service_name>.search.windows.net',
    azure_search_key='<key>',
    index_name=index_name,
    embedding_function=embeddings.embed_query,
)

docs = vector_store.hybrid_search(
    query="Test query", 
    k=3
)
print(docs[0].page_content)


### Expected behavior

Return documents from the index.