diff --git a/docs/src/ann_indexes.md b/docs/src/ann_indexes.md
index 75de5c43..41f26985 100644
--- a/docs/src/ann_indexes.md
+++ b/docs/src/ann_indexes.md
@@ -41,7 +41,7 @@ Querying vector indexes is done via the [search](https://lancedb.github.io/lance
 There are a couple of parameters that can be used to fine-tune the search:
 
 - **limit** (default: 10): The amount of results that will be returned
-- **metric** (default: "l2"): The distance metric to use. By default we use euclidean distance. We also support cosine distance.
+- **metric** (default: "L2"): The distance metric to use. By default we use euclidean distance. We also support cosine distance.
 - **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.
 - **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory. A higher number makes
 search more accurate but also slower.
diff --git a/python/lancedb/query.py b/python/lancedb/query.py
index 949e5ef7..1adb8ccb 100644
--- a/python/lancedb/query.py
+++ b/python/lancedb/query.py
@@ -24,7 +24,7 @@ class LanceQueryBuilder:
     """
 
     def __init__(self, table: "lancedb.table.LanceTable", query: np.ndarray):
-        self._metric = "l2"
+        self._metric = "L2"
         self._nprobes = 20
         self._refine_factor = None
         self._table = table
diff --git a/python/lancedb/table.py b/python/lancedb/table.py
index f798fb37..f633cce5 100644
--- a/python/lancedb/table.py
+++ b/python/lancedb/table.py
@@ -106,11 +106,14 @@ class LanceTable:
     def _dataset_uri(self) -> str:
         return os.path.join(self._conn.uri, f"{self.name}.lance")
 
-    def create_index(self, num_partitions=256, num_sub_vectors=96):
+    def create_index(self, metric="L2", num_partitions=256, num_sub_vectors=96):
         """Create an index on the table.
 
         Parameters
         ----------
+        metric: str, default "L2"
+            The distance metric to use when creating the index. Valid values are "L2" or "cosine".
+            L2 is euclidean distance.
         num_partitions: int
             The number of IVF partitions to use when creating the index.
             Default is 256.
@@ -121,6 +124,7 @@ class LanceTable:
         self._dataset.create_index(
             column=VECTOR_COLUMN_NAME,
             index_type="IVF_PQ",
+            metric=metric,
             num_partitions=num_partitions,
             num_sub_vectors=num_sub_vectors,
         )