import weaviate
from llama_index.schema import TextNode
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.vector_stores.types import MetadataFilters, MetadataFilter, VectorStoreQuery

client = weaviate.Client(url="http://localhost:8080")
vector_store = WeaviateVectorStore(weaviate_client=client, index_name="TestIndex", text_key="text")

vector_store.add(
    [
        TextNode(
            text="test 1",
            metadata={"article_id": "aaff"},
            embedding=[0, 0, 1],
        ),
        TextNode(
            text="test 2",
            metadata={"article_id": "1234"},
            embedding=[0, 1, 0],
        ),
        TextNode(
            text="test 3",
            metadata={"article_id": "3ff3"},
            embedding=[1, 0, 0],
        )
    ]
)

# -- working query
query = VectorStoreQuery(
    query_embedding=[0, 0, 0],
    similarity_top_k=2,
    filters=MetadataFilters(
        filters=[
            MetadataFilter(key="article_id", value="3ff3") # NOTE: works fine because is not a number
        ]
    )
)
results = vector_store.query(query)
print(results)

# -- below query breaks
query = VectorStoreQuery(
    query_embedding=[0, 0, 0],
    similarity_top_k=2,
    filters=MetadataFilters(
        filters=[
            MetadataFilter(key="article_id", value="1234") # NOTE: breaks because it's a number but the metadata field is a text field
        ]
    )
)
results = vector_store.query(query)
print(results)