diff --git a/docs/components/vectordbs/dbs/oracledb.mdx b/docs/components/vectordbs/dbs/oracledb.mdx
index 178414555..ad063866c 100644
--- a/docs/components/vectordbs/dbs/oracledb.mdx
+++ b/docs/components/vectordbs/dbs/oracledb.mdx
@@ -3,6 +3,8 @@ title: "Oracle AI Vector Search"
 description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for semantic and relational queries."
 ---
 
+<!-- Copyright (c) 2026, Oracle and/or its affiliates. -->
+
 [Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) stores embeddings in an Oracle table using the native `VECTOR` data type, so you can combine semantic search over unstructured data with relational queries over business data in a single database.
 
 ### Requirements
diff --git a/docs/components/vectordbs/overview.mdx b/docs/components/vectordbs/overview.mdx
index 1367f2cf9..8fb0bb2dc 100644
--- a/docs/components/vectordbs/overview.mdx
+++ b/docs/components/vectordbs/overview.mdx
@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.
 
 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Oracle AI Vector Search, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
 </Note>
 
 <CardGroup cols={3}>
diff --git a/mem0/configs/vector_stores/oracledb.py b/mem0/configs/vector_stores/oracledb.py
index b9ad6be99..3f0dd5893 100644
--- a/mem0/configs/vector_stores/oracledb.py
+++ b/mem0/configs/vector_stores/oracledb.py
@@ -1,3 +1,4 @@
+# Copyright (c) 2026, Oracle and/or its affiliates.
 """Pydantic configuration for the Oracle AI Vector Search integration."""
 
 import re
@@ -91,8 +92,9 @@ class OracleAIVectorSearchConfig(BaseModel):
                 exclude_none=True,
             )
 
-        if self.index_accuracy and not (0 < self.index_accuracy <= 100):
-            raise ValueError("`index_accuracy` must be between 1 and 100")
+        if self.index_accuracy is not None:
+            if not (0 < self.index_accuracy <= 100):
+                raise ValueError("`index_accuracy` must be between 1 and 100")
 
         if not (0 < self.embedding_model_dims):
             raise ValueError("`embedding_model_dims` must be bigger than 0")
diff --git a/mem0/vector_stores/oracledb.py b/mem0/vector_stores/oracledb.py
index bf568bec7..9cf7dd83c 100644
--- a/mem0/vector_stores/oracledb.py
+++ b/mem0/vector_stores/oracledb.py
@@ -1,3 +1,4 @@
+# Copyright (c) 2026, Oracle and/or its affiliates.
 """Oracle AI Vector Search vector store integration for mem0."""
 
 import array
diff --git a/tests/vector_stores/test_oracledb.py b/tests/vector_stores/test_oracledb.py
index c2b4d87ed..cce8001dd 100644
--- a/tests/vector_stores/test_oracledb.py
+++ b/tests/vector_stores/test_oracledb.py
@@ -1,3 +1,4 @@
+# Copyright (c) 2026, Oracle and/or its affiliates.
 import os
 import uuid
 from contextlib import nullcontext
@@ -267,6 +268,16 @@ def test_index_parameters_reject_non_string_keys():
         )
 
 
+def test_index_accuracy_rejects_zero():
+    with pytest.raises(ValueError, match="index_accuracy.*between 1 and 100"):
+        OracleAIVectorSearchConfig(
+            collection_name=_unique_collection_name(),
+            embedding_model_dims=DIM,
+            client=object(),
+            index_accuracy=0,
+        )
+
+
 def test_index_parameters_canonicalize_int_subclasses():
     class FormattedInt(int):
         def __format__(self, format_spec):