AzureSearch.py is using constant field names instead of ours
Labels: name: area: vector store, name: auto:bug

### System Info

langchain 0.0.273

### Who can help?

@hwchase17 


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

### Reproduction

I am trying to use Azure Cognitive Search retriever, however it fails because our fields are different:

Our index looks like this:
![image](https://github.com/langchain-ai/langchain/assets/6962857/df6fe367-ccf1-4644-9759-77a4ddc06ff8)


Our code:

```
        llm = AzureChatOpenAI(
            openai_api_base=config['AZURE_OPENAI_ENDPOINT'],
            openai_api_version=config['AZURE_OPENAI_API_VERSION'],
            deployment_name=config['OPENAI_DEPLOYMENT_NAME'],
            openai_api_key=config['AZURE_OPENAI_API_KEY'],
            openai_api_type=config['OPENAI_API_TYPE'],
            model_name=config['OPENAI_MODEL_NAME'],
            temperature=0)

        embeddings = OpenAIEmbeddings(
                            openai_api_base=config['AZURE_OPENAI_ENDPOINT'],
                            openai_api_type="azure",
                            deployment=config['AZURE_OPENAI_EMBEDDING_DEPLOYED_MODEL_NAME'],
                            openai_api_key=config['AZURE_OPENAI_API_KEY'],
                            model=config['AZURE_OPENAI_EMBEDDING_DEPLOYED_MODEL_NAME'],
                            chunk_size=1)

        fields = [
            SimpleField(
                name="id",
                type=SearchFieldDataType.String,
                key=True,
                filterable=True,
            ),
            SearchableField(
                name="text",
                type=SearchFieldDataType.String,
                searchable=True,
            ),
            SearchField(
                name="embedding",
                type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
                searchable=True,
                vector_search_dimensions=1536,
                vector_search_configuration="default",
            )
        ]
        
        vector_store: AzureSearch = AzureSearch(
            azure_search_endpoint=config['AZURE_SEARCH_SERVICE_ENDPOINT'],
            azure_search_key=config['AZURE_SEARCH_ADMIN_KEY'],
            index_name=config['AZURE_SEARCH_VECTOR_INDEX_NAME'],
            embedding_function=embeddings.embed_query,
            fields=fields,
        )

        retriever = vector_store.as_retriever(search_type="similarity", kwargs={"k": 3})


        # Creating instance of RetrievalQA
        chain = RetrievalQA.from_chain_type(llm=llm,
                                            chain_type="stuff",
                                            retriever=retriever,
                                            return_source_documents=True)

        # Generating response to user's query
        response = chain({"query": config['question']})
```

I traced it all down to the function:  vector_search_with_score in azuresearch.py

```
        results = self.client.search(
            search_text="",
            vectors=[
                Vector(
                    value=np.array(
                        self.embedding_function(query), dtype=np.float32
                    ).tolist(),
                    k=k,
                    fields=FIELDS_CONTENT_VECTOR,
                )
            ],
            select=[FIELDS_ID, FIELDS_CONTENT, FIELDS_METADATA],
            filter=filters,
        )
```

The code is trying to use FIELDS_CONTENT_VECTOR which is a constant and its not our field name.

I guess some other issues may arise with other parts of the code where constants are used.


Why  do we have different field names? 
We are using Microsoft examples to setup all azure indexing, indexers, skillsets and datasources:
https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python/code/azure-search-vector-ingestion-python-sample.ipynb
and their open ai embedding generator deployed as an azure function:
https://github.com/Azure-Samples/azure-search-power-skills/blob/main/Vector/EmbeddingGenerator/README.md

I wrote a blog post series about this
https://medium.com/python-in-plain-english/elevate-chat-ai-applications-mastering-azure-cognitive-search-with-vector-storage-for-llm-a2082f24f798


### Expected behavior

I should be able to define the fields we want to use, but the code uses constants