docs: match the blob guidance to the released Python SDK

lancedb 0.40.0, the latest release, does not include #4529: its query builders' to_arrow() and to_list() take no blob_mode, and a vector, FTS, hybrid or ordered query raises when its projection includes a blob column. Restore the earlier guidance and example, with the snippet regenerated from the restored test. On 0.40.0 "descriptions" works on in-memory tables and "bytes" does not, so the restored note names only "bytes".

docs/web-tests/py/test_multimodal.py

@@ -215,28 +215,18 @@ async def test_query_to_pandas_kwargs(db_path_factory):
         .to_pandas(blob_mode="bytes")
     )
 
-    # Vector / FTS / hybrid queries return "bytes" or "descriptions".
-    # Lazy BlobFile handles need a plain scan.
+    # Vector / FTS / hybrid queries can't materialize blob columns,
+    # so omit them from the projection
     df_vec = (
         tbl.search(query_vector)
         .limit(10)
-        .select(["id", "video"])
-        .to_pandas(blob_mode="bytes", split_blocks=True, self_destruct=True)
+        .select(["id", "vector"])
+        .to_pandas(split_blocks=True, self_destruct=True)
     )
-
-    # to_arrow and to_list take the same blob_mode
-    hits = tbl.search(query_vector).limit(2).select(["id", "video"])
-    arrow_bytes = hits.to_arrow(blob_mode="bytes")
-    rows = hits.to_list(blob_mode="bytes")
     # --8<-- [end:query_to_pandas_kwargs]
 
     assert len(df_lazy) == 1
     assert len(df_bytes) == 1
     assert df_bytes["video"].iloc[0] == b"fake_video_bytes_1"
     assert len(df_vec) == 10
-    assert all(
-        video == f"fake_video_bytes_{i}".encode()
-        for i, video in zip(df_vec["id"], df_vec["video"])
-    )
-    assert arrow_bytes["video"].to_pylist() == [row["video"] for row in rows]
-    assert all(isinstance(row["video"], bytes) for row in rows)
+    assert "video" not in df_vec.columns

docs/web/management/table.mdx

@@ -480,6 +480,8 @@ When you call `to_pandas()` on a local LanceDB table that contains Blob API colu
 - `"bytes"`: eagerly materializes each blob as `bytes`. Use this when you need the raw payload in the DataFrame, for example to decode an image or audio clip in-process.
 - `"descriptions"`: returns blob descriptors (offsets, sizes, and positions) instead of the data itself. Use this when you want to plan I/O without paying the cost of loading every blob.
 
+`"bytes"` is not supported on in-memory tables.
+
 Extra keyword arguments are forwarded to the underlying PyArrow / Lance pandas conversion, so you can also pass options like `split_blocks` or `self_destruct`:
 
 <CodeGroup>
@@ -488,13 +490,10 @@ Extra keyword arguments are forwarded to the underlying PyArrow / Lance pandas c
     </CodeBlock>
 </CodeGroup>
 
-Query builders also accept `blob_mode` on `to_pandas()`, `to_arrow()`, and `to_list()`:
+Query builders also accept `blob_mode` on their `to_pandas()` method:
 
 - Plain scans support `"lazy"`, `"bytes"`, and `"descriptions"` with filters, projections, aliases, `limit`, and `offset`.
-- Vector, FTS, hybrid, and take queries support `"bytes"` and `"descriptions"`. Lazy `BlobFile` handles need a plain scan.
-- In `to_arrow()` and `to_list()`, `"lazy"` returns blob descriptors that carry the row id `fetch_blobs` reads, and `"descriptions"` returns them without it.
-- A plain scan's `to_pandas()` without `timeout` reads the local dataset through Lance's scanner, so it also returns `"bytes"` for legacy blob columns and under namespace query pushdown. In-memory tables have no local dataset, so they take the `to_arrow()` route.
-- Every other `"bytes"` route raises `ValueError` for legacy blob columns and blobs inside lists, and `NotImplementedError` on remote tables and namespace query pushdown, where the blob fetch could read a different table version than the query.
+- Vector, FTS, hybrid, and ordered queries can't materialize blob columns through `to_pandas()`; omit blob columns from the projection for those query shapes.
 - This works on both sync and async query builders. Extra PyArrow kwargs like `split_blocks` and `self_destruct` are still forwarded.
 
 <CodeGroup>

docs/web/snippets/multimodal.mdx

@@ -16,7 +16,7 @@ export const PyMultimodalImports = "import lancedb\nimport pyarrow as pa\nimport
 
 export const PyProcessResults = "# Convert back to PIL Image\nfor _, row in results.iterrows():\n    image_bytes = row['image_blob']\n    image = Image.open(io.BytesIO(image_bytes))\n    print(f\"Retrieved image: {row['filename']}, Size: {image.size}\")\n    # You can now use 'image' with other libraries or display it\n";
 
-export const PyQueryToPandasKwargs = "# Plain scan query: blob_mode is supported end to end\ndf_lazy = (\n    tbl.search()\n    .where(\"id = 1\")\n    .select([\"id\", \"video\"])\n    .to_pandas(blob_mode=\"lazy\")\n)\n\n# Same call shape works on async query builders\ndf_bytes = await (\n    tbl_async.query()\n    .where(\"id = 1\")\n    .select([\"id\", \"video\"])\n    .to_pandas(blob_mode=\"bytes\")\n)\n\n# Vector / FTS / hybrid queries return \"bytes\" or \"descriptions\".\n# Lazy BlobFile handles need a plain scan.\ndf_vec = (\n    tbl.search(query_vector)\n    .limit(10)\n    .select([\"id\", \"video\"])\n    .to_pandas(blob_mode=\"bytes\", split_blocks=True, self_destruct=True)\n)\n\n# to_arrow and to_list take the same blob_mode\nhits = tbl.search(query_vector).limit(2).select([\"id\", \"video\"])\narrow_bytes = hits.to_arrow(blob_mode=\"bytes\")\nrows = hits.to_list(blob_mode=\"bytes\")\n";
+export const PyQueryToPandasKwargs = "# Plain scan query: blob_mode is supported end to end\ndf_lazy = (\n    tbl.search()\n    .where(\"id = 1\")\n    .select([\"id\", \"video\"])\n    .to_pandas(blob_mode=\"lazy\")\n)\n\n# Same call shape works on async query builders\ndf_bytes = await (\n    tbl_async.query()\n    .where(\"id = 1\")\n    .select([\"id\", \"video\"])\n    .to_pandas(blob_mode=\"bytes\")\n)\n\n# Vector / FTS / hybrid queries can't materialize blob columns,\n# so omit them from the projection\ndf_vec = (\n    tbl.search(query_vector)\n    .limit(10)\n    .select([\"id\", \"vector\"])\n    .to_pandas(split_blocks=True, self_destruct=True)\n)\n";
 
 export const PySearchData = "# Search for similar images\nquery_vector = np.random.rand(128).astype(np.float32)\nresults = tbl.search(query_vector).limit(1).to_pandas()\n";