Run format and lint

langchain/vectorstores/qdrant.py

@@ -6,7 +6,6 @@
 from hashlib import md5
 from operator import itemgetter
 from typing import (
-    TYPE_CHECKING,
     Any,
     Callable,
     Dict,
@@ -33,12 +32,11 @@
         "Please install it with `pip install qdrant-client`."
     )
 
-from qdrant_client.http import models as rest
 from qdrant_client.conversions import common_types
+from qdrant_client.http import models as rest
 
-MetadataFilter = Union[
-    Dict[str, Union[str, int, bool, dict, list]], common_types.Filter
-]
+DictFilter = Dict[str, Union[str, int, bool, dict, list]]
+MetadataFilter = Union[DictFilter, common_types.Filter]
 
 
 class Qdrant(VectorStore):
@@ -228,7 +226,8 @@ def similarity_search_with_score(
         if filter is not None and isinstance(filter, dict):
             warnings.warn(
                 "Using dict as a `filter` is deprecated. Please use qdrant-client "
-                "filters directly: https://qdrant.tech/documentation/concepts/filtering/",
+                "filters directly: "
+                "https://qdrant.tech/documentation/concepts/filtering/",
                 DeprecationWarning,
             )
             qdrant_filter = self._qdrant_filter_from_dict(filter)
@@ -499,7 +498,7 @@ def _build_condition(self, key: str, value: Any) -> List[rest.FieldCondition]:
         return out
 
     def _qdrant_filter_from_dict(
-        self, filter: Optional[MetadataFilter]
+        self, filter: Optional[DictFilter]
     ) -> Optional[rest.Filter]:
         if not filter:
             return None

tests/integration_tests/vectorstores/fake_embeddings.py

@@ -26,8 +26,8 @@ class ConsistentFakeEmbeddings(FakeEmbeddings):
     """Fake embeddings which remember all the texts seen so far to return consistent
     vectors for the same texts."""
 
-    def __init__(self):
-        self.known_texts = []
+    def __init__(self) -> None:
+        self.known_texts: List[str] = []
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Return consistent embeddings for each text seen so far."""

tests/integration_tests/vectorstores/test_qdrant.py

@@ -2,14 +2,13 @@
 from typing import Callable, Optional
 
 import pytest
+from qdrant_client.http import models as rest
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
 from langchain.vectorstores import Qdrant
 from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
 
-from qdrant_client.http import models as rest
-
 
 @pytest.mark.parametrize(
     ["content_payload_key", "metadata_payload_key"],