Repository: langchain
Issue: Using AzureSearch with custom vector field names
Labels: Ɑ: vector store,🤖:bug

### System Info

azure-search-documents==11.4.0b9
langchain                 0.0.342
langchain-core            0.0.7

### Who can help?

@hwc

### Information

- [ ] 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

My local.settings.json has the custom field names for Azure Cognitive Search:

```
{
  "IsEncrypted": false,
  "Values": {
    "FUNCTIONS_WORKER_RUNTIME": "python",
    "AZURESEARCH_FIELDS_ID" :"chunk_id",
    "AZURESEARCH_FIELDS_CONTENT" :"chunk",
    "AZURESEARCH_FIELDS_CONTENT_VECTOR " :"vector",
    "AZURESEARCH_FIELDS_TAG" :"metadata",
    "FIELDS_ID" : "chunk_id",
    "FIELDS_CONTENT" : "chunk",
    "FIELDS_CONTENT_VECTOR" : "vector",
    "FIELDS_METADATA" : "metadata",
    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
    "AzureWebJobsFeatureFlags": "EnableWorkerIndexing"
  }
}
```

I also tried to create a Fields array and pass it into the AzureSearch constructor like this:

 ```
 os.environ["AZURE_OPENAI_API_KEY"] = "xx"
            os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xx.openai.azure.com/"
            embeddings = AzureOpenAIEmbeddings(
                azure_deployment="text-embedding-ada-002",
                openai_api_version="2023-05-15",
            )
           
            fields = [
                SimpleField(
                    name="chunk_id",
                    type=SearchFieldDataType.String,
                    key=True,
                    filterable=True,
                ),
                SearchableField(
                    name="chunk",
                    type=SearchFieldDataType.String,
                    searchable=True,
                ),
                SearchField(
                    name="vector",
                    type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
                    searchable=True,
                    vector_search_dimensions=1536,
                    vector_search_configuration="default",
                )
            ]
            
            FIELDS_ID = get_from_env(
                key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id"
            )
            FIELDS_CONTENT = get_from_env(
                key="AZURESEARCH_FIELDS_CONTENT",
                env_key="AZURESEARCH_FIELDS_CONTENT",
                default="content",
            )
            FIELDS_CONTENT_VECTOR = get_from_env(
                key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
                env_key="AZURESEARCH_FIELDS_CONTENT_VECTOR",
                default="content_vector",
            )
            FIELDS_METADATA = get_from_env(
                key="AZURESEARCH_FIELDS_TAG", env_key="AZURESEARCH_FIELDS_TAG", default="metadata"
            )
                        
            vector_store_address: str = "https://xx.search.windows.net"
            vector_store_password: str = "xx"
            vector_store: AzureSearch = AzureSearch(
                azure_search_endpoint=vector_store_address,
                azure_search_key=vector_store_password,
                index_name="vector-1701341754619",
                fiekds=fields,
                embedding_function=embeddings.embed_query
            )
            llm = AzureChatOpenAI(
                azure_deployment="chat",
                openai_api_version="2023-05-15",
            )  
            chain = RetrievalQA.from_chain_type(llm=llm,
                                                chain_type="stuff",
                                                retriever=Element61Retriever(vectorstore=vector_store),
                                                return_source_documents=True)
            result = chain({"query": 'Whats out of scope?'})
            return result
```

However I am always getting:
```
Executed 'Functions.TestCustomRetriever' (Failed, Id=2f243ed8-24bd-414b-af51-6cf1419633a5, Duration=6900ms)
[2023-12-05T15:08:53.252Z] System.Private.CoreLib: Exception while executing function: Functions.TestCustomRetriever. System.Private.CoreLib: Result: Failure
Exception: HttpResponseError: (InvalidRequestParameter) Unknown field 'content_vector' in vector field list.
Code: InvalidRequestParameter
Message: Unknown field 'content_vector' in vector field list.
Exception Details:      (UnknownField) Unknown field 'content_vector' in vector field list.
        Code: UnknownField
```


Please note this is being executed in an Azure Function locally


### Expected behavior

The custom field names should be taken into account