refactor(ai): address review on the LanceDB adapter

- Fix delete() to use single-quote delimiter consistent with _escape
- Fix _distance comment: L2 not squared-L2
- Fix similarity_top_k zero-guard to use explicit None check
- Replace deprecated table_names() with list_tables().tables (lancedb 0.33)
- Add add() Sequence[BaseNode] signature with collections.abc.Sequence import
- Add test_build_where_or_condition for OR filter branch coverage

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/paperless_ai/tests/test_vector_store.py

@@ -143,3 +143,27 @@ class TestPaperlessLanceVectorStoreCrud:
         assert store.vector_dim() == DIM
         store.drop_table()
         assert store.table_exists() is False
+
+    def test_build_where_or_condition(self) -> None:
+        from llama_index.core.vector_stores.types import FilterCondition
+
+        from paperless_ai.vector_store import _build_where
+
+        where = _build_where(
+            MetadataFilters(
+                filters=[
+                    MetadataFilter(
+                        key="document_id",
+                        operator=FilterOperator.EQ,
+                        value="1",
+                    ),
+                    MetadataFilter(
+                        key="document_id",
+                        operator=FilterOperator.EQ,
+                        value="2",
+                    ),
+                ],
+                condition=FilterCondition.OR,
+            ),
+        )
+        assert where == "document_id = '1' OR document_id = '2'"

src/paperless_ai/vector_store.py

@@ -1,5 +1,6 @@
 import json
 import logging
+from collections.abc import Sequence
 from typing import Any
 
 import lancedb
@@ -65,7 +66,7 @@ class PaperlessLanceVectorStore(BasePydanticVectorStore):
         self._uri = uri
         self._table_name = table_name
         self._conn = lancedb.connect(uri)
-        existing = list(self._conn.table_names())
+        existing = self._conn.list_tables().tables
         self._table = (
             self._conn.open_table(table_name) if table_name in existing else None
         )
@@ -75,7 +76,7 @@ class PaperlessLanceVectorStore(BasePydanticVectorStore):
         return self._conn
 
     def table_exists(self) -> bool:
-        return self._table_name in list(self._conn.table_names())
+        return self._table_name in self._conn.list_tables().tables
 
     def vector_dim(self) -> int | None:
         if self._table is None:
@@ -113,7 +114,7 @@ class PaperlessLanceVectorStore(BasePydanticVectorStore):
             "node_content": json.dumps(meta),
         }
 
-    def add(self, nodes: list[BaseNode], **add_kwargs: Any) -> list[str]:
+    def add(self, nodes: Sequence[BaseNode], **add_kwargs: Any) -> list[str]:
         if not nodes:
             return []
         rows = [self._row(node) for node in nodes]
@@ -130,7 +131,7 @@ class PaperlessLanceVectorStore(BasePydanticVectorStore):
 
     def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
         if self._table is not None:
-            self._table.delete(f'doc_id = "{_escape(ref_doc_id)}"')
+            self._table.delete(f"doc_id = '{_escape(ref_doc_id)}'")
 
     def delete_nodes(
         self,
@@ -180,14 +181,14 @@ class PaperlessLanceVectorStore(BasePydanticVectorStore):
     ) -> VectorStoreQueryResult:
         if self._table is None:
             return VectorStoreQueryResult(nodes=[], similarities=[], ids=[])
-        top_k = query.similarity_top_k or 10
+        top_k = query.similarity_top_k if query.similarity_top_k is not None else 10
         search = self._table.search(query.query_embedding).limit(top_k)
         where = _build_where(query.filters)
         if where:
             search = search.where(where)
         rows = search.to_list()
         nodes = self._rows_to_nodes(rows)
-        # LanceDB returns squared-L2 distance; map to a descending similarity.
+        # LanceDB returns an L2 distance (smaller = closer); map to a descending similarity.
         sims = [1.0 / (1.0 + float(row["_distance"])) for row in rows]
         ids = [row["id"] for row in rows]
         return VectorStoreQueryResult(nodes=nodes, similarities=sims, ids=ids)