AzureSearch Bug -- langchain.vectorstores.azuresearch 
Labels: name: area: vector store, name: auto:bug

### System Info

The example found  [here](https://python.langchain.com/docs/integrations/vectorstores/azuresearch) and in particular this code fragment
```
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "langchain-vector-demo"
vector_store: AzureSearch = AzureSearch(
    azure_search_endpoint=vector_store_address,
    azure_search_key=vector_store_password,
    index_name=index_name,
    embedding_function=embeddings.embed_query,
)
```
fails with a message that at the top is:
```
vector_search_configuration is not a known attribute of class <class 'azure.search.documents.indexes.models._index.SearchField'> and will be ignored
semantic_settings is not a known attribute of class <class 'azure.search.documents.indexes.models._index.SearchIndex'> and will be ignored
```
and culminates with:
```
HttpResponseError: (InvalidRequestParameter) The request is invalid. Details: definition : The vector field 'content_vector' must have the property 'vectorSearchConfiguration' set.
Code: InvalidRequestParameter
Message: The request is invalid. Details: definition : The vector field 'content_vector' must have the property 'vectorSearchConfiguration' set.
Exception Details:  (InvalidField) The vector field 'content_vector' must have the property 'vectorSearchConfiguration' set. Parameters: definition
    Code: InvalidField
    Message: The vector field 'content_vector' must have the property 'vectorSearchConfiguration' set. Parameters: definition
```
I am running with `python 3.10`, `openai 1.5.0`, `langchain 0.0.351` and `azure-search-documents 11.4.0`.  If I revert to `azure-search-documents 11.4.0b8` the code works.

This appears to be related to the November 2023 Microsoft API change which introduced the concept of a "profile" which aggregates various vector search settings under one name. As a result of this API change, the old "vector_search_configuration" was deprecated and a new "vector_search_profile" was added, along with a new "profiles" object.  It seems that the Langchain extension has not been updated for this change and expects the old "vector_search_configuration" property which doesn't exist on newer SDK releases.

See the discussion [here](https://stackoverflow.com/questions/77682544/vector-search-configuration-is-not-a-known-attribute-of-class-class-azure-sear/77694628#77694628).

### Who can help?

@hwchase17 
@bas

### Information

- [X] The official example notebooks/scripts
- [ ] 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

The following sample code fragment from [here:(https://python.langchain.com/docs/integrations/vectorstores/azuresearch) fails.

```
import os

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.azuresearch import AzureSearch

os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "YOUR_OPENAI_ENDPOINT"
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
model: str = "text-embedding-ada-002"

vector_store_address: str = "YOUR_AZURE_SEARCH_ENDPOINT"
vector_store_password: str = "YOUR_AZURE_SEARCH_ADMIN_KEY"

embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "langchain-vector-demo"
vector_store: AzureSearch = AzureSearch(
    azure_search_endpoint=vector_store_address,
    azure_search_key=vector_store_password,
    index_name=index_name,
    embedding_function=embeddings.embed_query,
)
```


### Expected behavior

The code should run without failure and return a vector store.