Unable to create Vector Store: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}

> Please provide us with the following information:
> ---------------------------------------------------------------

### This issue is for a: (mark with an `x`)
```
- [x] bug report -> please search issues before submitting
- [ ] feature request
- [ ] documentation issue or request
- [ ] regression (a behavior that used to work and stopped in a new release)
```

### Minimal steps to reproduce
>
1) clone the repository
2) cd to Retrieval_Augmented_Generation_(RAG)_samples
3) Update .env file with required values
4) Create an index in azure search service and provide the index name to index_name variable.
5) Deploy text-embedding-ada-002 at Azure Open AI service and use the deployment name and version number for parameters of AzureOpenAIEmbeddings as below:
aoai_embeddings = AzureOpenAIEmbeddings(
azure_deployment="text-embedding-ada-002",
openai_api_version="2", # e.g., "2023-12-01-preview"
)
6) Run sample_rag_langchain..ipynb notebook.

### Any log messages given by the failure
>

### Expected/desired behavior
>

### OS and Version?
> Windows 7, 8 or 10. Linux (which distribution). macOS (Yosemite? El Capitan? Sierra?)

### Versions
>

### Mention any other details that might be useful

aoai_embeddings = AzureOpenAIEmbeddings(
azure_deployment="text-embedding-ada-002", ==> the name of the model deployment in Azure Open AI service.
openai_api_version="2", # e.g., "2023-12-01-preview"
)

vector_store_address: str = os.getenv("AZURE_SEARCH_ENDPOINT") ==> which is https://************.search.windows.net
vector_store_password: str = os.getenv("AZURE_SEARCH_ADMIN_KEY")
> ---------------------------------------------------------------
> Thanks! We'll be in touch soon.

Comece com Retrieval_Augmented_Generation_(RAG)_samples/sample_rag_langchain..ipynb e verifique os valores de .env, o índice do Azure Search, o endpoint do Azure Search e a chave de administrador, além do deployment e da versão da API do AzureOpenAIEmbeddings. Reproduza a criação do vector store e compare os nomes dos recursos e os endpoints configurados com os recursos do Azure; considera-se concluído quando o notebook cria o vector store sem o erro 404.

Escrita pelo modelo de indexação a partir do texto da issue.