feast-dev · franciscojavierarceo · Apr 2, 2025 · Mar 1, 2025 · Mar 2, 2025 · Mar 2, 2025
@@ -1567,14 +1567,20 @@ def _get_feature_view_and_df_for_online_write(
                     else df.to_dict(orient="list")
                 )
                 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
-                    )
+                    transformed_rows = []
+
+                    for i, row in df.iterrows():
+                        output = feature_view.feature_transformation.udf(row.to_dict())
+                        if i == 0:
+                            transformed_rows = output
+                        else:
+                            for k in output:
+                                if isinstance(output[k], list):
+                                    transformed_rows[k].extend(output[k])
+                                else:
+                                    transformed_rows[k].append(output[k])
+
+                    transformed_data = pd.DataFrame(transformed_rows)
                 else:
                     transformed_data = feature_view.feature_transformation.udf(
                         input_dict

@@ -18,12 +18,21 @@
 from datetime import datetime, timedelta
 from typing import Any
 
-from feast import Entity, FeatureStore, FeatureView, FileSource, RepoConfig
+import pandas as pd
+
+from feast import (
+    Entity,
+    FeatureStore,
+    FeatureView,
+    FileSource,
+    RepoConfig,
+    RequestSource,
+)
 from feast.driver_test_data import create_driver_hourly_stats_df
 from feast.field import Field
 from feast.infra.online_stores.sqlite import SqliteOnlineStoreConfig
 from feast.on_demand_feature_view import on_demand_feature_view
-from feast.types import Float32, Float64, Int64
+from feast.types import Array, Float32, Float64, Int64, PdfBytes, String, ValueType
 
 
 class TestOnlineWrites(unittest.TestCase):
@@ -144,3 +153,84 @@ def test_online_retrieval(self):
                 "conv_rate_plus_acc",
             ]
         )
+
+
+class TestOnlineWritesWithTransform(unittest.TestCase):
+    def test_transform_on_write_pdf(self):
+        with tempfile.TemporaryDirectory() as data_dir:
+            self.store = FeatureStore(
+                config=RepoConfig(
+                    project="test_write_to_online_store_with_transform",
+                    registry=os.path.join(data_dir, "registry.db"),
+                    provider="local",
+                    entity_key_serialization_version=2,
+                    online_store=SqliteOnlineStoreConfig(
+                        path=os.path.join(data_dir, "online.db")
+                    ),
+                )
+            )
+
+            chunk = Entity(
+                name="chunk_id",
+                description="Chunk ID",
+                value_type=ValueType.STRING,
+                join_keys=["chunk_id"],
+            )
+
+            document = Entity(
+                name="document_id",
+                description="Document ID",
+                value_type=ValueType.STRING,
+                join_keys=["document_id"],
+            )
+
+            input_request_pdf = RequestSource(
+                name="pdf_request_source",
+                schema=[
+                    Field(name="document_id", dtype=String),
+                    Field(name="pdf_bytes", dtype=PdfBytes),
+                    Field(name="file_name", dtype=String),
+                ],
+            )
+
+            @on_demand_feature_view(
+                entities=[chunk, document],
+                sources=[input_request_pdf],
+                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="L2",
+                    ),
+                ],
+                mode="python",
+                write_to_online_store=True,
+                singleton=True,
+            )
+            def transform_pdf_on_write_view(inputs: dict[str, Any]) -> dict[str, Any]:
+                k = 10
+                return {
+                    "document_id": ["doc_1", "doc_2"],
+                    "chunk_id": ["chunk-1", "chunk-2"],
+                    "vector": [[0.5] * k, [0.4] * k],
+                    "chunk_text": ["chunk text 1", "chunk text 2"],
+                }
+
+            self.store.apply([chunk, document, transform_pdf_on_write_view])
+
+            sample_pdf = 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. And this is another sentence. Finally, this is the third sentence.)\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"
+            sample_input = {
+                "pdf_bytes": sample_pdf,
+                "file_name": "sample_pdf",
+                "document_id": "doc_1",
+            }
+            input_df = pd.DataFrame([sample_input])
+
+            self.store.write_to_online_store(
+                feature_view_name="transform_pdf_on_write_view",
+                df=input_df,
+            )
@@ -1314,7 +1314,7 @@ def test_docling_transform(self):
 
         EMBED_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"
         VECTOR_LEN = 10
-        MAX_TOKENS = 64  # Small token limit for demonstration
+        MAX_TOKENS = 5  # Small token limit for demonstration
         tokenizer = AutoTokenizer.from_pretrained(EMBED_MODEL_ID)
         chunker = HybridChunker(
             tokenizer=tokenizer, max_tokens=MAX_TOKENS, merge_peers=True
@@ -1421,37 +1421,49 @@ def docling_transform_docs(inputs: dict[str, Any]):
                 sample_input
             )
 
-            self.store.apply([docling_transform_docs])
+            sample_inputs = {}
+            for key in sample_input.keys():
+                sample_inputs[key] = [sample_input[key], sample_input_2[key]]
 
             assert docling_output == {
-                "document_id": ["doc_1"],
-                "chunk_id": ["chunk-0"],
-                "vector": [[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]],
+                "document_id": ["doc_1", "doc_1", "doc_1"],
+                "chunk_id": ["chunk-0", "chunk-1", "chunk-2"],
+                "vector": [
+                    [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
+                    [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
+                    [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
+                ],
                 "chunk_text": [
-                    "Let's have fun with Natural Language Processing on PDFs."
+                    "Let's have fun",
+                    "with Natural Language Processing on",
+                    "PDFs.",
                 ],
             }
 
-            input_df = pd.DataFrame([sample_input])
-            self.store.write_to_online_store("docling_transform_docs", input_df)
+            self.store.apply([docling_transform_docs])
+
+            multiple_inputs_df = pd.DataFrame(sample_inputs)
+            self.store.write_to_online_store(
+                "docling_transform_docs", multiple_inputs_df
+            )
 
             conn = self.store._provider._online_store._conn
             document_table = self.store._provider._online_store._conn.execute(
-                "SELECT name FROM sqlite_master WHERE type='table' and name like '%docling%';"
+                "SELECT name FROM sqlite_master WHERE type='table' and name like '%docling_transform%';"
             ).fetchall()[0][0]
             written_data = pd.read_sql_query(f"select * from {document_table}", conn)
-            assert (
+            assert sorted(
                 written_data[written_data["feature_name"] == "document_id"][
                     "vector_value"
-                ].values[0]
-                == "doc_1"
-            )
-            assert (
-                written_data[written_data["feature_name"] == "chunk_id"][
-                    "vector_value"
-                ].values[0]
-                == "chunk-0"
-            )
+                ]
+                .unique()
+                .tolist()
+            ) == ["doc_1", "doc_2"]
+            assert sorted(
+                written_data[written_data["feature_name"] == "chunk_id"]["vector_value"]
+                .unique()
+                .tolist()
+            ) == ["chunk-0", "chunk-1", "chunk-2"]
 
             online_features = self.store.get_online_features(
                 features=[
@@ -1461,14 +1473,11 @@ def docling_transform_docs(inputs: dict[str, Any]):
                 ],
                 entity_rows=[{"document_id": "doc_1", "chunk_id": "chunk-0"}],
             ).to_dict()
+
             online_features == {
                 "document_id": ["doc_1"],
                 "chunk_id": ["chunk-0"],
                 "chunk_text": [
                     "Let's have fun with Natural Language Processing on PDFs."
                 ],
             }
-
-            multiple_inputs_df = pd.DataFrame([sample_input, sample_input_2])
-            # note this test needs to be updated with writing to the online store to verify this behavior works
-            assert multiple_inputs_df is not None