feast-dev
@@ -1566,16 +1566,34 @@ def _get_feature_view_and_df_for_online_write(
                     if feature_view.singleton
                     else df.to_dict(orient="list")
                 )
-                transformed_data = feature_view.feature_transformation.udf(input_dict)
+                if feature_view.singleton:
+                    transformed_data = df.apply(
+                        feature_view.feature_transformation.udf, axis=1
+                    )
+                    transformed_data = pd.DataFrame(
+                        transformed_data.to_list()
+                    ).applymap(
+                        lambda x: x[0] if isinstance(x, list) and len(x) == 1 else x
+                    )
+                else:
+                    transformed_data = feature_view.feature_transformation.udf(
+                        input_dict
+                    )
                 if feature_view.write_to_online_store:
                     entities = [
                         self.get_entity(entity)
                         for entity in (feature_view.entities or [])
                     ]
                     join_keys = [entity.join_key for entity in entities if entity]
                     join_keys = [k for k in join_keys if k in input_dict.keys()]
-                    transformed_df = pd.DataFrame(transformed_data)
-                    input_df = pd.DataFrame(input_dict)
+                    transformed_df = (
+                        pd.DataFrame(transformed_data)
+                        if not isinstance(transformed_data, pd.DataFrame)
+                        else transformed_data
+                    )
+                    input_df = pd.DataFrame(
+                        [input_dict] if feature_view.singleton else input_dict
+                    )
                     if input_df.shape[0] == transformed_df.shape[0]:
                         for k in input_dict:
                             if k not in transformed_data:

@@ -354,6 +354,10 @@ def online_read(
         feature_name_feast_primitive_type_map = {
             f.name: f.dtype for f in table.features
         }
+        if getattr(table, "write_to_online_store", False):
+            feature_name_feast_primitive_type_map.update(
+                {f.name: f.dtype for f in table.schema}
+            )
         # Build a dictionary mapping composite key -> (res_ts, res)
         results_dict: Dict[
             str, Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]
@@ -394,6 +398,7 @@ def online_read(
                                 "int64_val",
                                 "float_val",
                                 "double_val",
+                                "string_val",
                             ]:
                                 setattr(
                                     val,
@@ -420,7 +425,7 @@ def online_read(
                                 setattr(val, proto_attr, field_value)
                         else:
                             raise ValueError(
-                                f"Unsupported ValueType: {feature_feast_primitive_type} with feature view value {field_value} for feature {field} with value {field_value}"
+                                f"Unsupported ValueType: {feature_feast_primitive_type} with feature view value {field_value} for feature {field} with value type {proto_attr}"
                             )
                         # res[field] = val
                         key_to_use = field.split(":", 1)[-1] if ":" in field else field

@@ -678,6 +678,9 @@ def _construct_random_input(
     ) -> dict[str, Union[list[Any], Any]]:
         rand_dict_value: dict[ValueType, Union[list[Any], Any]] = {
             ValueType.BYTES: [str.encode("hello world")],
+            ValueType.PDF_BYTES: [
+                b"%PDF-1.3\n3 0 obj\n<</Type /Page\n/Parent 1 0 R\n/Resources 2 0 R\n/Contents 4 0 R>>\nendobj\n4 0 obj\n<</Filter /FlateDecode /Length 115>>\nstream\nx\x9c\x15\xcc1\x0e\x820\x18@\xe1\x9dS\xbcM]jk$\xd5\xd5(\x83!\x86\xa1\x17\xf8\xa3\xa5`LIh+\xd7W\xc6\xf7\r\xef\xc0\xbd\xd2\xaa\xb6,\xd5\xc5\xb1o\x0c\xa6VZ\xe3znn%\xf3o\xab\xb1\xe7\xa3:Y\xdc\x8bm\xeb\xf3&1\xc8\xd7\xd3\x97\xc82\xe6\x81\x87\xe42\xcb\x87Vb(\x12<\xdd<=}Jc\x0cL\x91\xee\xda$\xb5\xc3\xbd\xd7\xe9\x0f\x8d\x97 $\nendstream\nendobj\n1 0 obj\n<</Type /Pages\n/Kids [3 0 R ]\n/Count 1\n/MediaBox [0 0 595.28 841.89]\n>>\nendobj\n5 0 obj\n<</Type /Font\n/BaseFont /Helvetica\n/Subtype /Type1\n/Encoding /WinAnsiEncoding\n>>\nendobj\n2 0 obj\n<<\n/ProcSet [/PDF /Text /ImageB /ImageC /ImageI]\n/Font <<\n/F1 5 0 R\n>>\n/XObject <<\n>>\n>>\nendobj\n6 0 obj\n<<\n/Producer (PyFPDF 1.7.2 http://pyfpdf.googlecode.com/)\n/Title (This is a sample title.)\n/Author (Francisco Javier Arceo)\n/CreationDate (D:20250312165548)\n>>\nendobj\n7 0 obj\n<<\n/Type /Catalog\n/Pages 1 0 R\n/OpenAction [3 0 R /FitH null]\n/PageLayout /OneColumn\n>>\nendobj\nxref\n0 8\n0000000000 65535 f \n0000000272 00000 n \n0000000455 00000 n \n0000000009 00000 n \n0000000087 00000 n \n0000000359 00000 n \n0000000559 00000 n \n0000000734 00000 n \ntrailer\n<<\n/Size 8\n/Root 7 0 R\n/Info 6 0 R\n>>\nstartxref\n837\n%%EOF\n"
+            ],
             ValueType.STRING: ["hello world"],
             ValueType.INT32: [1],
             ValueType.INT64: [1],

@@ -58,11 +58,19 @@ def infer_features(
                         f"Failed to infer type for feature '{feature_name}' with value "
                         + f"'{feature_value}' since no items were returned by the UDF."
                     )
-                inferred_type = type(feature_value[0])
                 inferred_value = feature_value[0]
-                if singleton:
-                    inferred_value = feature_value
-                    inferred_type = None  # type: ignore
+                if singleton and isinstance(inferred_value, list):
+                    # If we have a nested list like [[0.5, 0.5, ...]]
+                    if len(inferred_value) > 0:
+                        # Get the actual element type from the inner list
+                        inferred_type = type(inferred_value[0])
+                    else:
+                        raise TypeError(
+                            f"Failed to infer type for nested feature '{feature_name}' - inner list is empty"
+                        )
+                else:
+                    # For non-nested lists or when singleton is False
+                    inferred_type = type(inferred_value)
 
             else:
                 inferred_type = type(feature_value)

@@ -23,6 +23,7 @@
 PRIMITIVE_FEAST_TYPES_TO_VALUE_TYPES = {
     "INVALID": "UNKNOWN",
     "BYTES": "BYTES",
+    "PDF_BYTES": "PDF_BYTES",
     "STRING": "STRING",
     "INT32": "INT32",
     "INT64": "INT64",
@@ -79,6 +80,7 @@ class PrimitiveFeastType(Enum):
     FLOAT32 = 6
     BOOL = 7
     UNIX_TIMESTAMP = 8
+    PDF_BYTES = 9
 
     def to_value_type(self) -> ValueType:
         """
@@ -102,6 +104,7 @@ def __hash__(self):
 
 Invalid = PrimitiveFeastType.INVALID
 Bytes = PrimitiveFeastType.BYTES
+PdfBytes = PrimitiveFeastType.PDF_BYTES
 String = PrimitiveFeastType.STRING
 Bool = PrimitiveFeastType.BOOL
 Int32 = PrimitiveFeastType.INT32
@@ -114,6 +117,7 @@ def __hash__(self):
     Invalid,
     String,
     Bytes,
+    PdfBytes,
     Bool,
     Int32,
     Int64,
@@ -126,6 +130,7 @@ def __hash__(self):
     "INVALID": "Invalid",
     "STRING": "String",
     "BYTES": "Bytes",
+    "PDF_BYTES": "PdfBytes",
     "BOOL": "Bool",
     "INT32": "Int32",
     "INT64": "Int64",
@@ -168,6 +173,7 @@ def __str__(self):
 VALUE_TYPES_TO_FEAST_TYPES: Dict["ValueType", FeastType] = {
     ValueType.UNKNOWN: Invalid,
     ValueType.BYTES: Bytes,
+    ValueType.PDF_BYTES: PdfBytes,
     ValueType.STRING: String,
     ValueType.INT32: Int32,
     ValueType.INT64: Int64,

@@ -48,6 +48,7 @@ class ValueType(enum.Enum):
     BOOL_LIST = 17
     UNIX_TIMESTAMP_LIST = 18
     NULL = 19
+    PDF_BYTES = 20
 
 
 ListType = Union[

@@ -4,6 +4,7 @@
 import sqlite3
 import sys
 import time
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -1056,7 +1057,7 @@ def test_local_milvus() -> None:
     client.drop_collection(collection_name=COLLECTION_NAME)
 
 
-def test_milvus_lite_get_online_documents_v2() -> None:
+def test_milvus_lite_retrieve_online_documents_v2() -> None:
     """
     Test retrieving documents from the online store in local mode.
     """
@@ -1226,6 +1227,199 @@ def test_milvus_lite_get_online_documents_v2() -> None:
         assert len(result["distance"]) == len(results[0])
 
 
+def test_milvus_stored_writes_with_explode() -> None:
+    """
+    Test storing and retrieving exploded document embeddings with Milvus online store.
+    """
+    from feast import (
+        Entity,
+        RequestSource,
+    )
+    from feast.field import Field
+    from feast.on_demand_feature_view import on_demand_feature_view
+    from feast.types import (
+        Array,
+        Bytes,
+        Float32,
+        String,
+        ValueType,
+    )
+
+    random.seed(42)
+    vector_length = 10
+    runner = CliRunner()
+    with runner.local_repo(
+        example_repo_py=get_example_repo("example_rag_feature_repo.py"),
+        offline_store="file",
+        online_store="milvus",
+        apply=False,
+        teardown=False,
+    ) as store:
+        # Define entities and sources
+        chunk = Entity(
+            name="chunk", join_keys=["chunk_id"], value_type=ValueType.STRING
+        )
+        document = Entity(
+            name="document", join_keys=["document_id"], value_type=ValueType.STRING
+        )
+
+        input_explode_request_source = RequestSource(
+            name="document_source",
+            schema=[
+                Field(name="document_id", dtype=String),
+                Field(name="document_text", dtype=String),
+                Field(name="document_bytes", dtype=Bytes),
+            ],
+        )
+
+        @on_demand_feature_view(
+            entities=[chunk, document],
+            sources=[input_explode_request_source],
+            schema=[
+                Field(name="document_id", dtype=String),
+                Field(name="chunk_id", dtype=String),
+                Field(name="chunk_text", dtype=String),
+                Field(
+                    name="vector",
+                    dtype=Array(Float32),
+                    vector_index=True,
+                    vector_search_metric="COSINE",  # Use COSINE like in Milvus test
+                ),
+            ],
+            mode="python",
+            write_to_online_store=True,
+        )
+        def milvus_explode_feature_view(inputs: dict[str, Any]):
+            output: dict[str, Any] = {
+                "document_id": ["doc_1", "doc_1", "doc_2", "doc_2"],
+                "chunk_id": ["chunk-1", "chunk-2", "chunk-1", "chunk-2"],
+                "chunk_text": [
+                    "hello friends",
+                    "how are you?",
+                    "This is a test.",
+                    "Document chunking example.",
+                ],
+                "vector": [
+                    [0.1] * vector_length,
+                    [0.2] * vector_length,
+                    [0.3] * vector_length,
+                    [0.4] * vector_length,
+                ],
+            }
+            return output
+
+        # Apply the feature store configuration
+        store.apply(
+            [
+                chunk,
+                document,
+                input_explode_request_source,
+                milvus_explode_feature_view,
+            ]
+        )
+
+        # Verify feature view registration
+        odfv_applied = store.get_on_demand_feature_view("milvus_explode_feature_view")
+        assert odfv_applied.features[1].vector_index
+        assert odfv_applied.entities == [chunk.name, document.name]
+        assert odfv_applied.entity_columns[0].name == document.join_key
+        assert odfv_applied.entity_columns[1].name == chunk.join_key
+
+        # Write to online store
+        odfv_entity_rows_to_write = [
+            {
+                "document_id": "document_1",
+                "document_text": "Hello world. How are you?",
+            },
+            {
+                "document_id": "document_2",
+                "document_text": "This is a test. Document chunking example.",
+            },
+        ]
+        store.write_to_online_store(
+            feature_view_name="milvus_explode_feature_view",
+            df=odfv_entity_rows_to_write,
+        )
+
+        # Verify feature retrieval
+        fv_entity_rows_to_read = [
+            {
+                "document_id": "doc_1",
+                "chunk_id": "chunk-2",
+            },
+            {
+                "document_id": "doc_2",
+                "chunk_id": "chunk-1",
+            },
+        ]
+
+        online_response = store.get_online_features(
+            entity_rows=fv_entity_rows_to_read,
+            features=[
+                "milvus_explode_feature_view:document_id",
+                "milvus_explode_feature_view:chunk_id",
+                "milvus_explode_feature_view:chunk_text",
+            ],
+        ).to_dict()
+
+        assert sorted(list(online_response.keys())) == sorted(
+            [
+                "chunk_id",
+                "chunk_text",
+                "document_id",
+            ]
+        )
+
+        # Test vector search using Milvus
+        query_embedding = [0.1] * vector_length
+
+        # First get Milvus client and search directly
+        client = store._provider._online_store.client
+        collection_name = client.list_collections()[0]
+        search_params = {
+            "metric_type": "COSINE",
+            "params": {"nprobe": 10},
+        }
+
+        direct_results = client.search(
+            collection_name=collection_name,
+            data=[query_embedding],
+            anns_field="vector",
+            search_params=search_params,
+            limit=2,
+            output_fields=["document_id", "chunk_id", "chunk_text"],
+        )
+
+        # Then use the Feast API
+        feast_results = store.retrieve_online_documents_v2(
+            features=[
+                "milvus_explode_feature_view:document_id",
+                "milvus_explode_feature_view:chunk_id",
+                "milvus_explode_feature_view:chunk_text",
+            ],
+            query=query_embedding,
+            top_k=2,
+        ).to_dict()
+
+        # Validate vector search results
+        assert "document_id" in feast_results
+        assert "chunk_id" in feast_results
+        assert "chunk_text" in feast_results
+        assert "distance" in feast_results
+        assert len(feast_results["distance"]) == 2
+        assert len(feast_results["document_id"]) == 2
+        assert (
+            len(direct_results[0]) == 2
+        )  # Verify both approaches return same number of results
+        del feast_results["distance"]
+
+        assert feast_results == {
+            "document_id": ["doc_2", "doc_1"],
+            "chunk_id": ["chunk-1", "chunk-2"],
+            "chunk_text": ["This is a test.", "how are you?"],
+        }
+
+
 def test_milvus_native_from_feast_data() -> None:
     import random
     from datetime import datetime