Question: I want to create an Azure AI Search index with a vector field using the currently latest version of azure-search-documents v11.4.0. Here is my code: from azure.core.credentials import AzureKeyCredential from azure.search.documents import SearchClient from azure.search.documents.indexes import SearchIndexClient from langchain.embeddings import AzureOpenAIEmbeddings from langchain.text_splitter import TokenTextSplitter from azure.search.documents.indexes.models import ( SearchIndex, SearchField, SearchFieldDataType, SimpleField, SearchableField, SearchIndex, SemanticConfiguration, SemanticField, SearchField, SemanticSearch, VectorSearch, VectorSearchAlgorithmConfiguration, HnswAlgorithmConfiguration ) index_name = AZURE_COGNITIVE_SEARCH_INDEX_NAME key = AZURE_COGNITIVE_SEARCH_KEY credential = AzureKeyCredential(key) def create_index(): # Define the index fields client = SearchIndexClient(service_endpoint, credential) fields = [ SimpleField(name="chunk_id", type=SearchFieldDataType.String, key=True, sortable=True, filterable=True, facetable=True), SimpleField(name="file_name", type=SearchFieldDataType.String), SimpleField(name="url_name", type=SearchFieldDataType.String), SimpleField(name="origin", type=SearchFieldDataType.String, sortable=True, filterable=True, facetable=True), SearchableField(name="content", type=SearchFieldDataType.String), SearchField(name="content_vector", type=SearchFieldDataType.Collection(SearchFieldDataType.Single), searchable=True, vector_search_dimensions=1536, vector_search_configuration="my-vector-config"), ] vector_search=VectorSearch( algorithms=[ HnswAlgorithmConfiguration( name="my-vector-config", kind="hnsw", parameters={ "m": 4, "efConstruction":400, "efSearch":500, "metric":"cosine" } ) ] ) # Create the search index with the semantic settings index = SearchIndex(name=index_name, fields=fields, vector_search=vector_search) return client, index search_client, search_index = create_index() result = search_client.create_or_update_index(search_index) print(f"{result.name} created")  This gives me the following error: 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 tried to copy exact solution provided here: https://learn.microsoft.com/en-us/answers/questions/1395031/how-to-configure-vectorsearchconfiguration-for-a-s which gives me same error as above. I also tried this sample which is part of the official documentation (linked on the pypi page): https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_vector_search.py But here I get this error: Code: InvalidRequestParameter Message: The request is invalid. Details: definition : The field 'contentVector' uses a vector search algorithm configuration 'my-algorithms-config' which is not defined. Exception Details: (UnknownVectorAlgorithmConfiguration) The field 'contentVector' uses a vector search algorithm configuration 'my-algorithms-config' which is not defined. Parameters: definition Code: UnknownVectorAlgorithmConfiguration Message: The field 'contentVector' uses a vector search algorithm configuration 'my-algorithms-config' which is not defined. Parameters: definition  And I also found this other example notebooks from Microsoft about AI-Search: https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-custom-vectorization-sample.ipynb This code also gave me the exact same error as my initial code. I'm trying to get this working for 2 days now and I'm about to give up. There are several different documentations/examples in various different places and every code looks different. Apparently Microsoft changes the function names constantly with almost every package update so most of the examples are probably outdated by now. I have no idea where to find the "latest" documentation that actually provides working code as all examples I tested did not work for me. This has to be the worst python documentation I have ever seen in my life. Even Langchain documenation is great compared to this... EDIT: I just checked the source code of the "SearchField". It takes the following arguments: def __init__(self, **kwargs): super(SearchField, self).__init__(**kwargs) self.name = kwargs["name"] self.type = kwargs["type"] self.key = kwargs.get("key", None) self.hidden = kwargs.get("hidden", None) self.searchable = kwargs.get("searchable", None) self.filterable = kwargs.get("filterable", None) self.sortable = kwargs.get("sortable", None) self.facetable = kwargs.get("facetable", None) self.analyzer_name = kwargs.get("analyzer_name", None) self.search_analyzer_name = kwargs.get("search_analyzer_name", None) self.index_analyzer_name = kwargs.get("index_analyzer_name", None) self.synonym_map_names = kwargs.get("synonym_map_names", None) self.fields = kwargs.get("fields", None) self.vector_search_dimensions = kwargs.get("vector_search_dimensions", None) self.vector_search_profile_name = kwargs.get("vector_search_profile_name", None)  You can see that there is no "vector_search_configuration" nor "vectorSearchConfiguration" argument. I think they renamed it to "vector_search_profile_name" for some reason. Therefore I assume that the sample in the official documentation is the correct one and the other 2 are indeed outdated. But even so I'm still getting an error due to the "my-algorithms-config" not being defined.

Answer: I finally found the answer. Turns out at this moment there is not a single correct sample from Microsoft to properly create an index with a vector field. They renamed a few function names and argument names which makes most other answers (e.g. on Microsoft support pages) outdated. The sample in the official GitHub repo generally uses the correct function and argument names but it is still wrong as they pass the wrong value. A GitHub issue was opened by someone else for this exact problem. The issue got closed after someone claimed he fixed it, even though nothing was fixed. The issue was then reopened 3 weeks ago and as of today 07/12/2023 the issue is still open and the documentation is still incorrect. Long story short this is how to properly define an index with a vector field in azure-search-documents v.11.4.0: from azure.core.credentials import AzureKeyCredential from azure.search.documents import SearchClient from azure.search.documents.indexes import SearchIndexClient from azure.search.documents.indexes.models import ( SearchIndex, SearchField, SearchFieldDataType, SimpleField, SearchableField, SearchIndex, SearchField, VectorSearch, VectorSearchProfile, HnswAlgorithmConfiguration ) service_endpoint = AZURE_COGNITIVE_SEARCH_ENDPOINT index_name = AZURE_COGNITIVE_SEARCH_INDEX_NAME key = AZURE_COGNITIVE_SEARCH_KEY credential = AzureKeyCredential(key) def create_index(): # Define the index fields client = SearchIndexClient(service_endpoint, credential) fields = [ SimpleField(name="chunk_id", type=SearchFieldDataType.String, key=True, sortable=True, filterable=True, facetable=True), SimpleField(name="file_name", type=SearchFieldDataType.String), SimpleField(name="url_name", type=SearchFieldDataType.String), SimpleField(name="origin", type=SearchFieldDataType.String, sortable=True, filterable=True, facetable=True), SearchableField(name="content", type=SearchFieldDataType.String), SearchField(name="content_vector", type=SearchFieldDataType.Collection(SearchFieldDataType.Single), searchable=True, vector_search_dimensions=1536, vector_search_profile_name="my-vector-config"), ] vector_search = VectorSearch( profiles=[VectorSearchProfile(name="my-vector-config", algorithm_configuration_name="my-algorithms-config")], algorithms=[HnswAlgorithmConfiguration(name="my-algorithms-config")], ) index = SearchIndex(name=index_name, fields=fields, vector_search=vector_search) return client, index