text: index=db,    embeddings=embeddings,    search_field="title_embedding",    content_field="title",    filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc)    [Document(page_content='My document 28', metadata={'id': 'ca9f3f4268eec7c97a7d6e77f541cb82', 'year': 28, 'color': 'red'})]WeaviateDocumentIndexâ€‹WeaviateDocumentIndex is a document index that is built upon Weaviate vector database.Learn more here: https://docs.docarray.org/user_guide/storing/index_weaviate/# There's a small difference with the Weaviate backend compared to the others.# Here, you need to 'mark' the field used for vector search with 'is_embedding=True'.# So, let's create a new schema for Weaviate that takes care of this requirement.from pydantic import Fieldclass WeaviateDoc(BaseDoc):    title: str    title_embedding: NdArray[32] = Field(is_embedding=True)    year: int    color: strfrom docarray.index import WeaviateDocumentIndex# initialize the indexdbconfig = WeaviateDocumentIndex.DBConfig(host="http://localhost:8080")db = WeaviateDocumentIndex[WeaviateDoc](db_config=dbconfig)# index datadb.index(    [        MyDoc(            title=f"My document {i}",            title_embedding=embeddings.embed_query(f"query {i}"),            year=i,