Fix AstraDB logical operator filtering (#15699)

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This change fixes the AstraDB logical operator filtering (`$and,`
`$or`).
The `metadata` prefix must not be added if the key is `$and` or `$or`.

libs/community/langchain_community/vectorstores/astradb.py

@@ -121,11 +121,21 @@ class AstraDB(VectorStore):
     """
 
     @staticmethod
-    def _filter_to_metadata(filter_dict: Optional[Dict[str, str]]) -> Dict[str, Any]:
+    def _filter_to_metadata(filter_dict: Optional[Dict[str, Any]]) -> Dict[str, Any]:
         if filter_dict is None:
             return {}
         else:
-            return {f"metadata.{mdk}": mdv for mdk, mdv in filter_dict.items()}
+            metadata_filter = {}
+            for k, v in filter_dict.items():
+                if k and k[0] == "$":
+                    if isinstance(v, list):
+                        metadata_filter[k] = [AstraDB._filter_to_metadata(f) for f in v]
+                    else:
+                        metadata_filter[k] = AstraDB._filter_to_metadata(v)
+                else:
+                    metadata_filter[f"metadata.{k}"] = v
+
+            return metadata_filter
 
     def __init__(
         self,
@@ -471,7 +481,7 @@ class AstraDB(VectorStore):
         self,
         embedding: List[float],
         k: int = 4,
-        filter: Optional[Dict[str, str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
     ) -> List[Tuple[Document, float, str]]:
         """Return docs most similar to embedding vector.
 
@@ -512,7 +522,7 @@ class AstraDB(VectorStore):
         self,
         query: str,
         k: int = 4,
-        filter: Optional[Dict[str, str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
     ) -> List[Tuple[Document, float, str]]:
         embedding_vector = self.embedding.embed_query(query)
         return self.similarity_search_with_score_id_by_vector(
@@ -525,7 +535,7 @@ class AstraDB(VectorStore):
         self,
         embedding: List[float],
         k: int = 4,
-        filter: Optional[Dict[str, str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
     ) -> List[Tuple[Document, float]]:
         """Return docs most similar to embedding vector.
 
@@ -548,7 +558,7 @@ class AstraDB(VectorStore):
         self,
         query: str,
         k: int = 4,
-        filter: Optional[Dict[str, str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> List[Document]:
         embedding_vector = self.embedding.embed_query(query)
@@ -562,7 +572,7 @@ class AstraDB(VectorStore):
         self,
         embedding: List[float],
         k: int = 4,
-        filter: Optional[Dict[str, str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> List[Document]:
         return [
@@ -578,7 +588,7 @@ class AstraDB(VectorStore):
         self,
         query: str,
         k: int = 4,
-        filter: Optional[Dict[str, str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
     ) -> List[Tuple[Document, float]]:
         embedding_vector = self.embedding.embed_query(query)
         return self.similarity_search_with_score_by_vector(
@@ -593,7 +603,7 @@ class AstraDB(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        filter: Optional[Dict[str, str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance.
@@ -650,7 +660,7 @@ class AstraDB(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        filter: Optional[Dict[str, str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance.

libs/community/tests/integration_tests/vectorstores/test_astradb.py

@@ -354,6 +354,13 @@ class TestAstraDB:
             filter={"group": "consonant", "ord": ord("q"), "case": "upper"},
         )
         assert res3 == []
+        # filter with logical operator
+        res4 = store_someemb.similarity_search(
+            "x",
+            k=10,
+            filter={"$or": [{"ord": ord("q")}, {"ord": ord("r")}]},
+        )
+        assert {doc.page_content for doc in res4} == {"q", "r"}
 
     def test_astradb_vectorstore_similarity_scale(
         self, store_parseremb: AstraDB