text: = DocArrayRetriever(    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 56', metadata={'id': '1f33e58b6468ab722f3786b96b20afe6', 'year': 56, 'color': 'red'})]HnswDocumentIndexâ€‹HnswDocumentIndex is a lightweight Document Index implementation that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite.Learn more here: https://docs.docarray.org/user_guide/storing/index_hnswlib/from docarray.index import HnswDocumentIndex# initialize the indexdb = HnswDocumentIndex[MyDoc](work_dir="hnsw_index")# index datadb.index(    [        MyDoc(            title=f"My document {i}",            title_embedding=embeddings.embed_query(f"query {i}"),            year=i,            color=random.choice(["red", "green", "blue"]),        )        for i in range(100)    ])# optionally, you can create a filter queryfilter_query = {"year": {"$lte": 90}}# create a retrieverretriever = DocArrayRetriever(    index=db,    embeddings=embeddings,