lint

pyproject.toml

@@ -44,7 +44,7 @@ test = [
     "mypy==1.11.2",
     "pytest-asyncio==0.24.0",
     "pytest==8.3.2",
-    "pytest-cov==5.0.0"
+    "pytest-cov==5.0.0",
     "Pillow==10.4.0"
 ]
 

src/langchain_google_alloydb_pg/vectorstore.py

@@ -714,7 +714,7 @@ def _embedding_helper(
             )
         return embedding
 
-    def similarity_search(
+    def similarity_search(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -727,7 +727,7 @@ def similarity_search(
             self.asimilarity_search(query, image_uri, k=k, filter=filter, **kwargs)
         )
 
-    async def asimilarity_search(
+    async def asimilarity_search(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -752,7 +752,7 @@ def _select_relevance_score_fn(self) -> Callable[[float], float]:
         elif self.distance_strategy == DistanceStrategy.EUCLIDEAN:
             return self._euclidean_relevance_score_fn
 
-    async def asimilarity_search_with_score(
+    async def asimilarity_search_with_score(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -814,7 +814,7 @@ async def asimilarity_search_with_score_by_vector(
 
         return documents_with_scores
 
-    async def _asimilarity_search_with_relevance_scores(
+    async def _asimilarity_search_with_relevance_scores(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -831,18 +831,19 @@ async def _asimilarity_search_with_relevance_scores(
             k: Number of Documents to return. Defaults to 4.
             **kwargs: kwargs to be passed to similarity search. Should include:
                 score_threshold: Optional, a floating point value between 0 to 1 to
-                    filter the resulting set of retrieved docs
+                    filter the resulting set of retrieved docs.
 
         Returns:
             List of Tuples of (doc, similarity_score)
+
         """
         relevance_score_fn = self._select_relevance_score_fn()
         docs_and_scores = await self.asimilarity_search_with_score(
             query=query, image_uri=image_uri, k=k, **kwargs
         )
         return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
 
-    def _similarity_search_with_relevance_scores(
+    def _similarity_search_with_relevance_scores(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -870,7 +871,7 @@ def _similarity_search_with_relevance_scores(
         )
         return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
 
-    def similarity_search_with_relevance_scores(
+    def similarity_search_with_relevance_scores(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -918,7 +919,7 @@ def similarity_search_with_relevance_scores(
                 )
         return docs_and_similarities
 
-    async def asimilarity_search_with_relevance_scores(
+    async def asimilarity_search_with_relevance_scores(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -966,7 +967,7 @@ async def asimilarity_search_with_relevance_scores(
                 )
         return docs_and_similarities
 
-    async def amax_marginal_relevance_search(
+    async def amax_marginal_relevance_search(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -1057,7 +1058,7 @@ async def amax_marginal_relevance_search_with_score_by_vector(
 
         return [r for i, r in enumerate(documents_with_scores) if i in mmr_selected]
 
-    def similarity_search_with_score(
+    def similarity_search_with_score(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,
@@ -1095,7 +1096,7 @@ def similarity_search_with_score_by_vector(
         )
         return self.engine._run_as_sync(coro)
 
-    def max_marginal_relevance_search(
+    def max_marginal_relevance_search(  # type: ignore[override]
         self,
         query: Optional[str] = None,
         image_uri: Optional[str] = None,

tests/test_alloydb_vectorstore.py

@@ -53,7 +53,7 @@ def get_env_var(key: str, desc: str) -> str:
 
 class FakeImageEmbedding(DeterministicFakeEmbedding):
 
-    def embed_image(self, image_paths: List[str]):
+    def embed_image(self, image_paths: List[str]) -> List[List[float]]:
         return [self.embed_query(path) for path in image_paths]
 
 

tests/test_alloydb_vectorstore_search.py

@@ -53,7 +53,7 @@ def get_env_var(key: str, desc: str) -> str:
 
 class FakeImageEmbedding(DeterministicFakeEmbedding):
 
-    def embed_image(self, image_paths: List[str]):
+    def embed_image(self, image_paths: List[str]) -> List[List[float]]:
         return [self.embed_query(path) for path in image_paths]