Pull out scalar UDTF code into snippets

docs/geneva/udfs/index.mdx

@@ -22,7 +22,7 @@ Geneva provides three types of user-defined functions for transforming data. Eac
 | **Refresh** | Incremental | Incremental | Full |
 | **Parallelism** | Fragment-parallel | Fragment-parallel | Partition-parallel |
 | **Inherited columns** | N/A — adds to existing rows | Automatic from query | Independent output schema |
-| **Registration** | `table.add_columns()` | `db.create_materialized_view(udtf=)` | `db.create_udtf_view()` |
+| **Registration** | [`table.add_columns()`](https://lancedb.github.io/geneva/api/table/#geneva.table.Table.add_columns) | [`db.create_scalar_udtf_view()`](https://lancedb.github.io/geneva/api/connection/#geneva.db.Connection.create_scalar_udtf_view) | [`db.create_udtf_view()`](https://lancedb.github.io/geneva/api/connection/#geneva.db.Connection.create_udtf_view) |
 
 ## UDFs (1:1)
 

docs/geneva/udfs/scalar-udtfs.mdx

@@ -5,6 +5,17 @@ description: Use scalar UDTFs for 1:N row expansion — split videos into clips,
 icon: diagram-subtask
 ---
 
+import {
+  PyScalarUdtfIterator,
+  PyScalarUdtfList,
+  PyScalarUdtfBatch,
+  PyCreateScalarUdtfView,
+  PyAddColumnsScalarUdtf,
+  PyIncrementalRefresh,
+  PyChainingUdtfViews,
+  PyDocumentChunkingFull,
+} from '/snippets/geneva_scalar_udtfs.mdx';
+
 <Badge>Beta — introduced in Geneva 0.11.0</Badge>
 
 Standard UDFs produce exactly **one output value per input row**. Scalar UDTFs enable **1:N row expansion** — each source row can produce multiple output rows. The results are stored as a materialized view with MV-style incremental refresh.
@@ -19,24 +30,11 @@ Standard UDFs produce exactly **one output value per input row**. Scalar UDTFs e
 
 Use the `@scalar_udtf` decorator on a function that **yields** output rows. Geneva infers the output schema from the return type annotation.
 
-```python
-from geneva import scalar_udtf
-from typing import Iterator, NamedTuple
-
-class Clip(NamedTuple):
-    clip_start: float
-    clip_end: float
-    clip_bytes: bytes
-
-@scalar_udtf
-def extract_clips(video_path: str, duration: float) -> Iterator[Clip]:
-    """Yields multiple clips per video."""
-    clip_length = 10.0
-    for start in range(0, int(duration), int(clip_length)):
-        end = min(start + clip_length, duration)
-        clip_data = extract_video_segment(video_path, start, end)
-        yield Clip(clip_start=start, clip_end=end, clip_bytes=clip_data)
-```
+<CodeGroup>
+  <CodeBlock filename="Python" language="python" icon="python">
+  {PyScalarUdtfIterator}
+  </CodeBlock>
+</CodeGroup>
 
 Input parameters are bound to source columns **by name** — the parameter `video_path` binds to source column `video_path`, just like standard UDFs.
 
@@ -48,61 +46,34 @@ A scalar UDTF can yield **zero rows** for a source row. The source row is still
 
 If you prefer to build the full list in memory rather than yielding, you can return a `list` instead of an `Iterator`:
 
-```python
-@scalar_udtf
-def extract_clips(video_path: str, duration: float) -> list[Clip]:
-    clips = []
-    for start in range(0, int(duration), 10):
-        end = min(start + 10, duration)
-        clips.append(Clip(clip_start=start, clip_end=end, clip_bytes=b"..."))
-    return clips
-```
+<CodeGroup>
+  <CodeBlock filename="Python" language="python" icon="python">
+  {PyScalarUdtfList}
+  </CodeBlock>
+</CodeGroup>
 
 ### Batched scalar UDTF
 
-For vectorized processing, use `batch=True`. The function receives Arrow arrays and returns a `RecordBatch` of expanded rows:
+For vectorized processing, use `batch=True`. The function receives Arrow arrays and returns a `RecordBatch` of expanded rows. Because the return type `pa.RecordBatch` cannot be inferred, you must supply `output_schema` explicitly:
 
-```python
-@scalar_udtf(batch=True)
-def extract_clips(batch: pa.RecordBatch) -> pa.RecordBatch:
-    """Process rows in batches. Same 1:N semantic per row."""
-    ...
-```
+<CodeGroup>
+  <CodeBlock filename="Python" language="python" icon="python">
+  {PyScalarUdtfBatch}
+  </CodeBlock>
+</CodeGroup>
 
 ## Creating a Scalar UDTF View
 
-Scalar UDTFs use the existing `create_materialized_view` API with a `udtf=` parameter:
-
-```python
-import geneva
-
-db = geneva.connect("/data/mydb")
-videos = db.open_table("videos")
+Scalar UDTFs use the `create_scalar_udtf_view` API:
 
-# Create the 1:N materialized view
-clips = db.create_materialized_view(
-    "clips",
-    query=videos.search(None).select(["video_path", "metadata"]),
-    udtf=extract_clips,
-)
-
-# Populate — runs the UDTF on every source row
-clips.refresh()
-```
+<CodeGroup>
+  <CodeBlock filename="Python" language="python" icon="python">
+  {PyCreateScalarUdtfView}
+  </CodeBlock>
+</CodeGroup>
 
 The `query` parameter controls which source columns are inherited. Columns listed in `.select()` are carried into every child row automatically.
 
-### Inheriting source columns
-
-```python
-# Only video_path and metadata are inherited into the clips table
-clips = db.create_materialized_view(
-    "clips",
-    query=videos.search(None).select(["video_path", "metadata"]),
-    udtf=extract_clips,
-)
-```
-
 ## Inherited Columns
 
 Child rows automatically include the parent's columns — no manual join required. The columns available in the child table are determined by the query's `.select()`:
@@ -131,17 +102,11 @@ The first three rows come from the `/v/a.mp4` source row, the last two from `/v/
 
 Since scalar UDTF views are materialized views, you can add UDF-computed columns to the child table and backfill them:
 
-```python
-@udf(data_type=pa.list_(pa.float32(), 512))
-def clip_embedding(clip_bytes: bytes) -> list[float]:
-    return embed_model.encode(clip_bytes)
-
-# Add an embedding column to the clips table
-clips.add_columns({"embedding": clip_embedding})
-
-# Backfill computes embeddings for all existing clips
-clips.backfill("embedding")
-```
+<CodeGroup>
+  <CodeBlock filename="Python" language="python" icon="python">
+  {PyAddColumnsScalarUdtf}
+  </CodeBlock>
+</CodeGroup>
 
 This is a powerful pattern: expand source rows with a scalar UDTF, then enrich the expanded rows with standard UDFs.
 
@@ -153,77 +118,34 @@ Scalar UDTFs support **incremental refresh**, just like standard materialized vi
 - **Deleted source rows**: Child rows linked to the deleted parent are cascade-deleted.
 - **Updated source rows**: Old children are deleted, UDTF re-runs, new children inserted.
 
-```python
-# Add new videos to the source table
-videos.add(new_video_data)
-
-# Incremental refresh — only processes the new videos
-clips.refresh()
-```
+<CodeGroup>
+  <CodeBlock filename="Python" language="python" icon="python">
+  {PyIncrementalRefresh}
+  </CodeBlock>
+</CodeGroup>
 
 Only the new source rows are processed. Existing clips from previous refreshes are untouched.
 
 ## Chaining UDTF Views
 
 Scalar UDTF views are standard materialized views, so they can serve as the source for further views:
 
-```python
-# videos → clips (1:N)
-clips = db.create_materialized_view(
-    "clips", query=videos.search(None), udtf=extract_clips
-)
-
-# clips → frames (1:N)
-frames = db.create_materialized_view(
-    "frames", query=clips.search(None), udtf=extract_frames
-)
-```
+<CodeGroup>
+  <CodeBlock filename="Python" language="python" icon="python">
+  {PyChainingUdtfViews}
+  </CodeBlock>
+</CodeGroup>
 
 ## Full Example: Document Chunking
 
-```python
-from geneva import connect, scalar_udtf, udf
-from typing import Iterator, NamedTuple
-import pyarrow as pa
-
-class Chunk(NamedTuple):
-    chunk_index: int
-    chunk_text: str
-
-@scalar_udtf
-def chunk_document(text: str) -> Iterator[Chunk]:
-    """Split a document into overlapping chunks."""
-    words = text.split()
-    chunk_size = 500
-    overlap = 50
-    for i, start in enumerate(range(0, len(words), chunk_size - overlap)):
-        chunk_words = words[start:start + chunk_size]
-        yield Chunk(chunk_index=i, chunk_text=" ".join(chunk_words))
-
-db = connect("/data/mydb")
-docs = db.open_table("documents")
-
-# Create chunked view — inherits doc_id, title, etc. from source
-chunks = db.create_materialized_view(
-    "doc_chunks",
-    query=docs.search(None).select(["doc_id", "title", "text"]),
-    udtf=chunk_document,
-)
-chunks.refresh()
-
-# Add embeddings to chunks for semantic search
-@udf(data_type=pa.list_(pa.float32(), 1536))
-def embed_text(chunk_text: str) -> list[float]:
-    return embedding_model.encode(chunk_text)
-
-chunks.add_columns({"embedding": embed_text})
-chunks.backfill("embedding")  # Backfills embeddings on all existing chunks
-
-# Query — parent columns available alongside chunk columns
-chunks.search(None).select(["doc_id", "title", "chunk_text", "embedding"]).to_pandas()
-```
+<CodeGroup>
+  <CodeBlock filename="Python" language="python" icon="python">
+  {PyDocumentChunkingFull}
+  </CodeBlock>
+</CodeGroup>
 
 For a comparison of all three function types (UDFs, Scalar UDTFs, Batch UDTFs), see [Understanding Transforms](/geneva/udfs).
 
 Reference:
-* [`create_materialized_view` API](https://lancedb.github.io/geneva/api/connection/#geneva.db.Connection.create_materialized_view)
+* [`scalar_udtf` API](https://lancedb.github.io/geneva/api/udtf/#geneva.scalar_udtf)
+* [`create_scalar_udtf_view` API](https://lancedb.github.io/geneva/api/connection/#geneva.db.Connection.create_scalar_udtf_view)

docs/snippets/geneva_scalar_udtfs.mdx

@@ -0,0 +1,20 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyAddColumnsScalarUdtf = "@udf(data_type=pa.list_(pa.float32(), 512))\ndef clip_embedding(clip_bytes: bytes) -> list[float]:\n    return embed_model.encode(clip_bytes)\n\n# Add an embedding column to the clips table\nclips.add_columns({\"embedding\": clip_embedding})\n\n# Backfill computes embeddings for all existing clips\nclips.backfill(\"embedding\")\n";
+
+export const PyChainingUdtfViews = "# videos → clips (1:N)\nclips = db.create_scalar_udtf_view(\n    \"clips\", source=videos.search(None), scalar_udtf=extract_clips\n)\n\n# clips → frames (1:N)\nframes = db.create_scalar_udtf_view(\n    \"frames\", source=clips.search(None), scalar_udtf=extract_frames\n)\n";
+
+export const PyCreateScalarUdtfView = "import geneva\n\ndb = geneva.connect(\"/data/mydb\")\nvideos = db.open_table(\"videos\")\n\n# Create the 1:N materialized view\nclips = db.create_scalar_udtf_view(\n    \"clips\",\n    source=videos.search(None).select([\"video_path\", \"metadata\"]),\n    scalar_udtf=extract_clips,\n)\n\n# Populate — runs the UDTF on every source row\nclips.refresh()\n";
+
+export const PyDocumentChunkingFull = "from geneva import connect, scalar_udtf, udf\nfrom typing import Iterator, NamedTuple\nimport pyarrow as pa\n\nclass Chunk(NamedTuple):\n    chunk_index: int\n    chunk_text: str\n\n@scalar_udtf\ndef chunk_document(text: str) -> Iterator[Chunk]:\n    \"\"\"Split a document into overlapping chunks.\"\"\"\n    words = text.split()\n    chunk_size = 500\n    overlap = 50\n    for i, start in enumerate(range(0, len(words), chunk_size - overlap)):\n        chunk_words = words[start:start + chunk_size]\n        yield Chunk(chunk_index=i, chunk_text=\" \".join(chunk_words))\n\ndb = connect(\"/data/mydb\")\ndocs = db.open_table(\"documents\")\n\n# Create chunked view — inherits doc_id, title, etc. from source\nchunks = db.create_scalar_udtf_view(\n    \"doc_chunks\",\n    source=docs.search(None).select([\"doc_id\", \"title\", \"text\"]),\n    scalar_udtf=chunk_document,\n)\nchunks.refresh()\n\n# Add embeddings to chunks for semantic search\n@udf(data_type=pa.list_(pa.float32(), 1536))\ndef embed_text(chunk_text: str) -> list[float]:\n    return embedding_model.encode(chunk_text)\n\nchunks.add_columns({\"embedding\": embed_text})\nchunks.backfill(\"embedding\")  # Backfills embeddings on all existing chunks\n\n# Query — parent columns available alongside chunk columns\nchunks.search(None).select([\"doc_id\", \"title\", \"chunk_text\", \"embedding\"]).to_pandas()\n";
+
+export const PyDocumentChunkingUdtf = "from geneva import scalar_udtf\nfrom typing import Iterator, NamedTuple\n\nclass Chunk(NamedTuple):\n    chunk_index: int\n    chunk_text: str\n\n@scalar_udtf\ndef chunk_document(text: str) -> Iterator[Chunk]:\n    \"\"\"Split a document into overlapping chunks.\"\"\"\n    words = text.split()\n    chunk_size = 500\n    overlap = 50\n    for i, start in enumerate(range(0, len(words), chunk_size - overlap)):\n        chunk_words = words[start:start + chunk_size]\n        yield Chunk(chunk_index=i, chunk_text=\" \".join(chunk_words))\n";
+
+export const PyIncrementalRefresh = "# Add new videos to the source table\nvideos.add(new_video_data)\n\n# Incremental refresh — only processes the new videos\nclips.refresh()\n";
+
+export const PyScalarUdtfBatch = "@scalar_udtf(batch=True, output_schema=clip_schema)\ndef extract_clips(batch: pa.RecordBatch) -> pa.RecordBatch:\n    \"\"\"Process rows in batches. Same 1:N semantic per row.\"\"\"\n    ...\n";
+
+export const PyScalarUdtfIterator = "from geneva import scalar_udtf\nfrom typing import Iterator, NamedTuple\n\nclass Clip(NamedTuple):\n    clip_start: float\n    clip_end: float\n    clip_bytes: bytes\n\n@scalar_udtf\ndef extract_clips(video_path: str, duration: float) -> Iterator[Clip]:\n    \"\"\"Yields multiple clips per video.\"\"\"\n    clip_length = 10.0\n    for start in range(0, int(duration), int(clip_length)):\n        end = min(start + clip_length, duration)\n        clip_data = extract_video_segment(video_path, start, end)\n        yield Clip(clip_start=start, clip_end=end, clip_bytes=clip_data)\n";
+
+export const PyScalarUdtfList = "@scalar_udtf\ndef extract_clips(video_path: str, duration: float) -> list[Clip]:\n    clips = []\n    for start in range(0, int(duration), 10):\n        end = min(start + 10, duration)\n        clips.append(Clip(clip_start=start, clip_end=end, clip_bytes=b\"...\"))\n    return clips\n";
+

docs/snippets/geneva_udfs_index.mdx

@@ -0,0 +1,8 @@
+{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
+
+export const PyRegistrationScalarUdtf = "db = geneva.connect(\"/data/mydb\")\ndb.create_scalar_udtf_view(\"my_view\", source=my_source, scalar_udtf=my_scalar_udtf)\n";
+
+export const PyRegistrationUdf = "mock_table.add_columns({\"col\": my_udf})\n";
+
+export const PyRegistrationUdtf = "db = geneva.connect(\"/data/mydb\")\ndb.create_udtf_view(\"my_view\", source=my_source, udtf=my_udtf)\n";
+

docs/snippets/tables.mdx

@@ -1,5 +1,7 @@
 {/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
 
+export const PyWriteWithConcurrency = "import pyarrow as pa\nfrom lancedb.scannable import Scannable\nfrom random import random\n\nVECTOR_DIM = 4\n\nschema = pa.schema(\n    [\n        pa.field(\"id\", pa.int64()),\n        pa.field(\"vector\", pa.list_(pa.float32(), VECTOR_DIM)),\n    ]\n)\n\ndef make_batch(batch_idx: int, rows_per_batch: int) -> pa.RecordBatch:\n    start = batch_idx * rows_per_batch\n    stop = start + rows_per_batch\n    row_ids = list(range(start, stop))\n    vectors = pa.array(\n        [[random() for _ in range(VECTOR_DIM)] for _ in row_ids],\n        type=pa.list_(pa.float32(), VECTOR_DIM),\n    )\n    return pa.RecordBatch.from_arrays(\n        [\n            pa.array(row_ids, type=pa.int64()),\n            vectors,\n        ],\n        schema=schema,\n    )\n\ndef make_batch_reader(\n    num_batches: int, rows_per_batch: int\n) -> pa.RecordBatchReader:\n    return pa.RecordBatchReader.from_batches(\n        schema,\n        (make_batch(batch_idx, rows_per_batch) for batch_idx in range(num_batches)),\n    )\n\ndef make_large_scannable(num_batches: int, rows_per_batch: int) -> Scannable:\n    total_rows = num_batches * rows_per_batch\n    return Scannable(\n        schema=schema,\n        num_rows=total_rows,\n        reader=lambda: make_batch_reader(num_batches, rows_per_batch),\n    )\n\ndb = tmp_db\ntable = db.create_table(\n    \"bulk_ingest_concurrent\",\n    make_large_scannable(num_batches=1000, rows_per_batch=10000),\n    mode=\"overwrite\",\n)\n";
+
 export const PyAddColumnsCalculated = "# Add a discounted price column (10% discount)\ntable.add_columns({\"discounted_price\": \"cast((price * 0.9) as float)\"})\n";
 
 export const PyAddColumnsDefaultValues = "# Add a stock status column with default value\ntable.add_columns({\"in_stock\": \"cast(true as boolean)\"})\n";
@@ -116,8 +118,6 @@ export const PyVersioningRollback = "table.restore(version_after_mod)\nversions
 
 export const PyVersioningUpdateData = "table.update(where=\"author='Richard'\", values={\"author\": \"Richard Daniel Sanchez\"})\nrows_after_update = table.count_rows(\"author = 'Richard Daniel Sanchez'\")\nprint(f\"Rows updated to Richard Daniel Sanchez: {rows_after_update}\")\n";
 
-export const PyWriteWithConcurrency = "import pyarrow as pa\nfrom lancedb.scannable import Scannable\nfrom random import random\n\nVECTOR_DIM = 4\n\nschema = pa.schema(\n    [\n        pa.field(\"id\", pa.int64()),\n        pa.field(\"vector\", pa.list_(pa.float32(), VECTOR_DIM)),\n    ]\n)\n\ndef make_batch(batch_idx: int, rows_per_batch: int) -> pa.RecordBatch:\n    start = batch_idx * rows_per_batch\n    stop = start + rows_per_batch\n    row_ids = list(range(start, stop))\n    vectors = pa.array(\n        [[random() for _ in range(VECTOR_DIM)] for _ in row_ids],\n        type=pa.list_(pa.float32(), VECTOR_DIM),\n    )\n    return pa.RecordBatch.from_arrays(\n        [\n            pa.array(row_ids, type=pa.int64()),\n            vectors,\n        ],\n        schema=schema,\n    )\n\ndef make_batch_reader(\n    num_batches: int, rows_per_batch: int\n) -> pa.RecordBatchReader:\n    return pa.RecordBatchReader.from_batches(\n        schema,\n        (make_batch(batch_idx, rows_per_batch) for batch_idx in range(num_batches)),\n    )\n\ndef make_large_scannable(num_batches: int, rows_per_batch: int) -> Scannable:\n    total_rows = num_batches * rows_per_batch\n    return Scannable(\n        schema=schema,\n        num_rows=total_rows,\n        reader=lambda: make_batch_reader(num_batches, rows_per_batch),\n    )\n\ndb = tmp_db\ntable = db.create_table(\n    \"bulk_ingest_concurrent\",\n    make_large_scannable(num_batches=1000, rows_per_batch=10000),\n    mode=\"overwrite\",\n)\n";
-
 export const TsAddColumnsCalculated = "// Add a discounted price column (10% discount)\nawait schemaAddTable.addColumns([\n  {\n    name: \"discounted_price\",\n    valueSql: \"cast((price * 0.9) as float)\",\n  },\n]);\n";
 
 export const TsAddColumnsDefaultValues = "// Add a stock status column with default value\nawait schemaAddTable.addColumns([\n  {\n    name: \"in_stock\",\n    valueSql: \"cast(true as boolean)\",\n  },\n]);\n";

pyproject.toml

@@ -12,10 +12,6 @@ dependencies = [
     "polars>=1.39.2",
     "pytest>=9.0.1",
     "pytest-asyncio>=1.3.0",
-<<<<<<< Updated upstream
-    "Pillow>=11.0.0",
-    "geneva>=0.11.0",
-=======
     "Pillow>=12.1.1",
->>>>>>> Stashed changes
+    "geneva>=0.12.0",
 ]

tests/py/test_geneva_defaults.py

@@ -8,8 +8,6 @@
 import pytest
 
 
-# TODO: remove skip once geneva 0.12.0 is on PyPI (head node defaults changed to 4 CPU / 8Gi)
-@pytest.mark.skip(reason="requires geneva>=0.12.0")
 def test_head_node_defaults():
     # If these change, update the Head Node table in performance.mdx
     from geneva.cluster.builder import KubeRayClusterBuilder

tests/py/test_geneva_dependency_verification.py

@@ -40,8 +40,9 @@ def test_env_vars_via_cluster(monkeypatch):
 
 
 def test_pip_manifest(monkeypatch):
-    from unittest.mock import MagicMock
-    mock_conn = MagicMock()
+    import geneva
+    from unittest.mock import MagicMock, create_autospec
+    mock_conn = create_autospec(geneva.db.Connection, instance=True)
     monkeypatch.setattr("geneva.connect", MagicMock(return_value=mock_conn))
 
     # --8<-- [start:pip_manifest]