def create_langchain_vectorstore()

in demo-python/code/community-integration/ragas/lib/utils.py [0:0]

• 17 lines of code
• 1 McCabe index (conditional complexity)

def create_langchain_vectorstore(
        azure_search_endpoint: str,
        azure_search_key: str,
        index_name: str,
        embedding_function: Callable,
        search_type: str = "semantic_hybrid",
        vector_field_name: Optional[str] = None):
    os.environ["AZURESEARCH_FIELDS_CONTENT_VECTOR"] = vector_field_name
    os.environ["AZURESEARCH_FIELDS_CONTENT"] = "chunk"
    reload(langchain_community.vectorstores.azuresearch)

    return langchain_community.vectorstores.azuresearch.AzureSearch(
        azure_search_endpoint=azure_search_endpoint,
        azure_search_key=azure_search_key,
        index_name=index_name,
        embedding_function=embedding_function,
        search_type=search_type
    )