diff --git a/docarray/index/abstract.py b/docarray/index/abstract.py
index 03ab7361f62..923a95de6d1 100644
--- a/docarray/index/abstract.py
+++ b/docarray/index/abstract.py
@@ -384,13 +384,14 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs):
         """index Documents into the index.
 
         :param docs: Documents to index.
+
+        !!! note
+            Passing a sequence of Documents that is not a DocList
+            (such as a List of Docs) comes at a performance penalty.
+            This is because the Index needs to check compatibility between itself and
+            the data. With a DocList as input this is a single check; for other inputs
+            compatibility needs to be checked for every Document individually.
         """
-        if not isinstance(docs, (BaseDoc, DocList)):
-            self._logger.warning(
-                'Passing a sequence of Documents that is not a DocList comes at '
-                'a performance penalty, since compatibility with the schema of Index '
-                'needs to be checked for every Document individually.'
-            )
         self._logger.debug(f'Indexing {len(docs)} documents')
         docs_validated = self._validate_docs(docs)
         data_by_columns = self._get_col_value_dict(docs_validated)
@@ -399,7 +400,7 @@ def index(self, docs: Union[BaseDoc, Sequence[BaseDoc]], **kwargs):
     def find(
         self,
         query: Union[AnyTensor, BaseDoc],
-        search_field: str = 'embedding',
+        search_field: str = '',
         limit: int = 10,
         **kwargs,
     ) -> FindResult:
@@ -432,7 +433,7 @@ def find(
     def find_batched(
         self,
         queries: Union[AnyTensor, DocList],
-        search_field: str = 'embedding',
+        search_field: str = '',
         limit: int = 10,
         **kwargs,
     ) -> FindResultBatched:
@@ -511,7 +512,7 @@ def filter_batched(
     def text_search(
         self,
         query: Union[str, BaseDoc],
-        search_field: str = 'text',
+        search_field: str = '',
         limit: int = 10,
         **kwargs,
     ) -> FindResult:
@@ -539,7 +540,7 @@ def text_search(
     def text_search_batched(
         self,
         queries: Union[Sequence[str], Sequence[BaseDoc]],
-        search_field: str = 'text',
+        search_field: str = '',
         limit: int = 10,
         **kwargs,
     ) -> FindResultBatched:
diff --git a/docarray/utils/find.py b/docarray/utils/find.py
index 405f3e75f15..8fc32a1226f 100644
--- a/docarray/utils/find.py
+++ b/docarray/utils/find.py
@@ -26,7 +26,7 @@ class _FindResult(NamedTuple):
 def find(
     index: AnyDocArray,
     query: Union[AnyTensor, BaseDoc],
-    embedding_field: str = 'embedding',
+    search_field: str = '',
     metric: str = 'cosine_sim',
     limit: int = 10,
     device: Optional[str] = None,
@@ -61,7 +61,7 @@ class MyDocument(BaseDoc):
     top_matches, scores = find(
         index=index,
         query=query,
-        embedding_field='embedding',
+        search_field='embedding',
         metric='cosine_sim',
     )
 
@@ -70,7 +70,7 @@ class MyDocument(BaseDoc):
     top_matches, scores = find(
         index=index,
         query=query,
-        embedding_field='embedding',
+        search_field='embedding',
         metric='cosine_sim',
     )
     ```
@@ -79,7 +79,7 @@ class MyDocument(BaseDoc):
 
     :param index: the index of Documents to search in
     :param query: the query to search for
-    :param embedding_field: the tensor-like field in the index to use
+    :param search_field: the tensor-like field in the index to use
         for the similarity computation
     :param metric: the distance metric to use for the similarity computation.
         Can be one of the following strings:
@@ -94,11 +94,11 @@ class MyDocument(BaseDoc):
         where the first element contains the closes matches for the query,
         and the second element contains the corresponding scores.
     """
-    query = _extract_embedding_single(query, embedding_field)
+    query = _extract_embedding_single(query, search_field)
     return find_batched(
         index=index,
         query=query,
-        embedding_field=embedding_field,
+        search_field=search_field,
         metric=metric,
         limit=limit,
         device=device,
@@ -109,7 +109,7 @@ class MyDocument(BaseDoc):
 def find_batched(
     index: AnyDocArray,
     query: Union[AnyTensor, DocList],
-    embedding_field: str = 'embedding',
+    search_field: str = '',
     metric: str = 'cosine_sim',
     limit: int = 10,
     device: Optional[str] = None,
@@ -145,7 +145,7 @@ class MyDocument(BaseDoc):
     results = find_batched(
         index=index,
         query=query,
-        embedding_field='embedding',
+        search_field='embedding',
         metric='cosine_sim',
     )
     top_matches, scores = results[0]
@@ -155,7 +155,7 @@ class MyDocument(BaseDoc):
     results = find_batched(
         index=index,
         query=query,
-        embedding_field='embedding',
+        search_field='embedding',
         metric='cosine_sim',
     )
     top_matches, scores = results[0]
@@ -165,7 +165,7 @@ class MyDocument(BaseDoc):
 
     :param index: the index of Documents to search in
     :param query: the query to search for
-    :param embedding_field: the tensor-like field in the index to use
+    :param search_field: the tensor-like field in the index to use
         for the similarity computation
     :param metric: the distance metric to use for the similarity computation.
         Can be one of the following strings:
@@ -183,12 +183,12 @@ class MyDocument(BaseDoc):
     if descending is None:
         descending = metric.endswith('_sim')  # similarity metrics are descending
 
-    embedding_type = _da_attr_type(index, embedding_field)
+    embedding_type = _da_attr_type(index, search_field)
     comp_backend = embedding_type.get_comp_backend()
 
     # extract embeddings from query and index
-    index_embeddings = _extract_embeddings(index, embedding_field, embedding_type)
-    query_embeddings = _extract_embeddings(query, embedding_field, embedding_type)
+    index_embeddings = _extract_embeddings(index, search_field, embedding_type)
+    query_embeddings = _extract_embeddings(query, search_field, embedding_type)
 
     # compute distances and return top results
     metric_fn = getattr(comp_backend.Metrics, metric)
@@ -209,18 +209,18 @@ class MyDocument(BaseDoc):
 
 def _extract_embedding_single(
     data: Union[DocList, BaseDoc, AnyTensor],
-    embedding_field: str,
+    search_field: str,
 ) -> AnyTensor:
     """Extract the embeddings from a single query,
     and return it in a batched representation.
 
     :param data: the data
-    :param embedding_field: the embedding field
+    :param search_field: the embedding field
     :param embedding_type: type of the embedding: torch.Tensor, numpy.ndarray etc.
     :return: the embeddings
     """
     if isinstance(data, BaseDoc):
-        emb = next(AnyDocArray._traverse(data, embedding_field))
+        emb = next(AnyDocArray._traverse(data, search_field))
     else:  # treat data as tensor
         emb = data
     if len(emb.shape) == 1:
@@ -232,22 +232,22 @@ def _extract_embedding_single(
 
 def _extract_embeddings(
     data: Union[AnyDocArray, BaseDoc, AnyTensor],
-    embedding_field: str,
+    search_field: str,
     embedding_type: Type,
 ) -> AnyTensor:
     """Extract the embeddings from the data.
 
     :param data: the data
-    :param embedding_field: the embedding field
+    :param search_field: the embedding field
     :param embedding_type: type of the embedding: torch.Tensor, numpy.ndarray etc.
     :return: the embeddings
     """
     emb: AnyTensor
     if isinstance(data, DocList):
-        emb_list = list(AnyDocArray._traverse(data, embedding_field))
+        emb_list = list(AnyDocArray._traverse(data, search_field))
         emb = embedding_type._docarray_stack(emb_list)
     elif isinstance(data, (DocVec, BaseDoc)):
-        emb = next(AnyDocArray._traverse(data, embedding_field))
+        emb = next(AnyDocArray._traverse(data, search_field))
     else:  # treat data as tensor
         emb = cast(AnyTensor, data)
 
diff --git a/tests/index/hnswlib/test_configurations.py b/tests/index/hnswlib/test_configurations.py
new file mode 100644
index 00000000000..dff64fdcc19
--- /dev/null
+++ b/tests/index/hnswlib/test_configurations.py
@@ -0,0 +1,27 @@
+import numpy as np
+import pytest
+from pydantic import Field
+
+from docarray import BaseDoc
+from docarray.index import HnswDocumentIndex
+from docarray.typing import NdArray
+
+pytestmark = [pytest.mark.slow, pytest.mark.index]
+
+
+class MyDoc(BaseDoc):
+    tens: NdArray
+
+
+def test_configure_dim(tmp_path):
+    class Schema(BaseDoc):
+        tens: NdArray = Field(dim=10)
+
+    index = HnswDocumentIndex[Schema](work_dir=str(tmp_path))
+
+    assert index._hnsw_indices['tens'].dim == 10
+
+    docs = [Schema(tens=np.random.random((10,))) for _ in range(10)]
+    index.index(docs)
+
+    assert index.num_docs() == 10
diff --git a/tests/units/util/test_find.py b/tests/units/util/test_find.py
index 90b3c7005d8..96837cddc7f 100644
--- a/tests/units/util/test_find.py
+++ b/tests/units/util/test_find.py
@@ -52,7 +52,7 @@ def test_find_torch(random_torch_query, random_torch_index, metric):
     top_k, scores = find(
         random_torch_index,
         random_torch_query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric=metric,
     )
@@ -69,7 +69,7 @@ def test_find_torch_tensor_query(random_torch_query, random_torch_index):
     top_k, scores = find(
         random_torch_index,
         query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric='cosine_sim',
     )
@@ -83,7 +83,7 @@ def test_find_torch_stacked(random_torch_query, random_torch_index):
     top_k, scores = find(
         random_torch_index,
         random_torch_query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric='cosine_sim',
     )
@@ -97,7 +97,7 @@ def test_find_np(random_nd_query, random_nd_index, metric):
     top_k, scores = find(
         random_nd_index,
         random_nd_query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric=metric,
     )
@@ -114,7 +114,7 @@ def test_find_np_tensor_query(random_nd_query, random_nd_index):
     top_k, scores = find(
         random_nd_index,
         query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric='cosine_sim',
     )
@@ -128,7 +128,7 @@ def test_find_np_stacked(random_nd_query, random_nd_index):
     top_k, scores = find(
         random_nd_index,
         random_nd_query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric='cosine_sim',
     )
@@ -142,7 +142,7 @@ def test_find_batched_torch(random_torch_batch_query, random_torch_index, metric
     results = find_batched(
         random_torch_index,
         random_torch_batch_query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric=metric,
     )
@@ -162,7 +162,7 @@ def test_find_batched_torch_tensor_query(random_torch_batch_query, random_torch_
     results = find_batched(
         random_torch_index,
         query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric='cosine_sim',
     )
@@ -186,7 +186,7 @@ def test_find_batched_torch_stacked(
     results = find_batched(
         random_torch_index,
         random_torch_batch_query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric='cosine_sim',
     )
@@ -203,7 +203,7 @@ def test_find_batched_np(random_nd_batch_query, random_nd_index, metric):
     results = find_batched(
         random_nd_index,
         random_nd_batch_query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric=metric,
     )
@@ -223,7 +223,7 @@ def test_find_batched_np_tensor_query(random_nd_batch_query, random_nd_index):
     results = find_batched(
         random_nd_index,
         query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric='cosine_sim',
     )
@@ -244,7 +244,7 @@ def test_find_batched_np_stacked(random_nd_batch_query, random_nd_index, stack_w
     results = find_batched(
         random_nd_index,
         random_nd_batch_query,
-        embedding_field='tensor',
+        search_field='tensor',
         limit=7,
         metric='cosine_sim',
     )
@@ -266,7 +266,7 @@ class MyDoc(BaseDoc):
     top_k, scores = find(
         index,
         query,
-        embedding_field='embedding',
+        search_field='embedding',
         limit=7,
     )
     assert len(top_k) == 7
@@ -284,7 +284,7 @@ class MyDoc(BaseDoc):
     top_k, scores = find(
         index,
         query,
-        embedding_field='embedding',
+        search_field='embedding',
         limit=7,
     )
     assert len(top_k) == 7
@@ -314,7 +314,7 @@ class MyDoc(BaseDoc):
     top_k, scores = find(
         index,
         query,
-        embedding_field='inner__embedding',
+        search_field='inner__embedding',
         limit=7,
     )
     assert len(top_k) == 7
@@ -350,7 +350,7 @@ class MyDoc(BaseDoc):
     top_k, scores = find(
         index,
         query,
-        embedding_field='embedding',
+        search_field='embedding',
         limit=7,
     )
     assert len(top_k) == 7
@@ -360,7 +360,7 @@ class MyDoc(BaseDoc):
     top_k, scores = find(
         index,
         query,
-        embedding_field='embedding2',
+        search_field='embedding2',
         limit=7,
     )
     assert len(top_k) == 7
@@ -370,7 +370,7 @@ class MyDoc(BaseDoc):
     top_k, scores = find(
         index,
         query,
-        embedding_field='embedding3',
+        search_field='embedding3',
         limit=7,
     )
     assert len(top_k) == 7
@@ -380,7 +380,7 @@ class MyDoc(BaseDoc):
     top_k, scores = find(
         index,
         query,
-        embedding_field='embedding4',
+        search_field='embedding4',
         limit=7,
     )
     assert len(top_k) == 7