fix(python): sanitize bad vectors before Arrow cast (#3158)

## Problem

`on_bad_vectors="drop"` is supposed to remove invalid vector rows before
write, but for some schema-defined vector columns it can still fail
later during Arrow cast instead of dropping the bad row.

Repro:
```python
class MySchema(LanceModel):
    text: str
    embedding: Vector(16)

table = db.create_table("test", schema=MySchema)
table.add(
    [
        {"text": "hello", "embedding": []},
        {"text": "bar", "embedding": [0.1] * 16},
    ],
    on_bad_vectors="drop",
)
```
Before:
```
RuntimeError
Arrow error: C Data interface error: Invalid: ListType can only be casted to FixedSizeListType if the lists are all the expected size.
```
After:
```
rows 1
texts ['bar']
```
## Solution

Make bad-vector sanitization use schema dimensions before cast, while
keeping the handling scoped to vector columns identified by schema
metadata or existing vector-name heuristics.

This also preserves existing integer vector inputs and avoids applying
on_bad_vectors to unrelated fixed-size float columns.


Fixes #1670

Signed-off-by: yaommen <myanstu@163.com>

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.28.0-beta.0"
+current_version = "0.28.0-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

Cargo.lock

@@ -4630,7 +4630,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb"
-version = "0.28.0-beta.0"
+version = "0.28.0-beta.1"
 dependencies = [
  "ahash",
  "anyhow",
@@ -4712,7 +4712,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-nodejs"
-version = "0.28.0-beta.0"
+version = "0.28.0-beta.1"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -4734,7 +4734,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-python"
-version = "0.31.0-beta.0"
+version = "0.31.0-beta.1"
 dependencies = [
  "arrow",
  "async-trait",

docs/src/java/java.md

@@ -14,7 +14,7 @@ Add the following dependency to your `pom.xml`:
 <dependency>
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-core</artifactId>
-    <version>0.28.0-beta.0</version>
+    <version>0.28.0-beta.1</version>
 </dependency>
 ```
 

docs/src/js/interfaces/ClientConfig.md

@@ -53,3 +53,18 @@ optional tlsConfig: TlsConfig;
 ```ts
 optional userAgent: string;
 ```
+
+***
+
+### userId?
+
+```ts
+optional userId: string;
+```
+
+User identifier for tracking purposes.
+
+This is sent as the `x-lancedb-user-id` header in requests to LanceDB Cloud/Enterprise.
+It can be set directly, or via the `LANCEDB_USER_ID` environment variable.
+Alternatively, set `LANCEDB_USER_ID_ENV_KEY` to specify another environment
+variable that contains the user ID value.

java/lancedb-core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
       <groupId>com.lancedb</groupId>
       <artifactId>lancedb-parent</artifactId>
-      <version>0.28.0-beta.0</version>
+      <version>0.28.0-beta.1</version>
       <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.28.0-beta.0</version>
+    <version>0.28.0-beta.1</version>
     <packaging>pom</packaging>
     <name>${project.artifactId}</name>
     <description>LanceDB Java SDK Parent POM</description>

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.28.0-beta.0"
+version = "0.28.0-beta.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.28.0-beta.0",
+	"version": "0.28.0-beta.1",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.28.0-beta.0",
+	"version": "0.28.0-beta.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-arm64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.28.0-beta.0",
+	"version": "0.28.0-beta.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.28.0-beta.0",
+	"version": "0.28.0-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/linux-x64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.28.0-beta.0",
+	"version": "0.28.0-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",

nodejs/npm/win32-arm64-msvc/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.28.0-beta.0",
+  "version": "0.28.0-beta.1",
   "os": [
     "win32"
   ],

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.28.0-beta.0",
+	"version": "0.28.0-beta.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.28.0-beta.0",
+  "version": "0.28.0-beta.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.28.0-beta.0",
+      "version": "0.28.0-beta.1",
       "cpu": [
         "x64",
         "arm64"

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.28.0-beta.0",
+  "version": "0.28.0-beta.1",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

nodejs/src/remote.rs

@@ -92,6 +92,13 @@ pub struct ClientConfig {
     pub extra_headers: Option<HashMap<String, String>>,
     pub id_delimiter: Option<String>,
     pub tls_config: Option<TlsConfig>,
+    /// User identifier for tracking purposes.
+    ///
+    /// This is sent as the `x-lancedb-user-id` header in requests to LanceDB Cloud/Enterprise.
+    /// It can be set directly, or via the `LANCEDB_USER_ID` environment variable.
+    /// Alternatively, set `LANCEDB_USER_ID_ENV_KEY` to specify another environment
+    /// variable that contains the user ID value.
+    pub user_id: Option<String>,
 }
 
 impl From<TimeoutConfig> for lancedb::remote::TimeoutConfig {
@@ -145,6 +152,7 @@ impl From<ClientConfig> for lancedb::remote::ClientConfig {
             id_delimiter: config.id_delimiter,
             tls_config: config.tls_config.map(Into::into),
             header_provider: None, // the header provider is set separately later
+            user_id: config.user_id,
         }
     }
 }

python/python/lancedb/remote/__init__.py

@@ -145,6 +145,33 @@ class TlsConfig:
 
 @dataclass
 class ClientConfig:
+    """Configuration for the LanceDB Cloud HTTP client.
+
+    Attributes
+    ----------
+    user_agent: str
+        User agent string sent with requests.
+    retry_config: RetryConfig
+        Configuration for retrying failed requests.
+    timeout_config: Optional[TimeoutConfig]
+        Configuration for request timeouts.
+    extra_headers: Optional[dict]
+        Additional headers to include in requests.
+    id_delimiter: Optional[str]
+        The delimiter to use when constructing object identifiers.
+    tls_config: Optional[TlsConfig]
+        TLS/mTLS configuration for secure connections.
+    header_provider: Optional[HeaderProvider]
+        Provider for dynamic headers to be added to each request.
+    user_id: Optional[str]
+        User identifier for tracking purposes. This is sent as the
+        `x-lancedb-user-id` header in requests to LanceDB Cloud/Enterprise.
+
+        This can also be set via the `LANCEDB_USER_ID` environment variable.
+        Alternatively, set `LANCEDB_USER_ID_ENV_KEY` to specify another
+        environment variable that contains the user ID value.
+    """
+
     user_agent: str = f"LanceDB-Python-Client/{__version__}"
     retry_config: RetryConfig = field(default_factory=RetryConfig)
     timeout_config: Optional[TimeoutConfig] = field(default_factory=TimeoutConfig)
@@ -152,6 +179,7 @@ class ClientConfig:
     id_delimiter: Optional[str] = None
     tls_config: Optional[TlsConfig] = None
     header_provider: Optional["HeaderProvider"] = None
+    user_id: Optional[str] = None
 
     def __post_init__(self):
         if isinstance(self.retry_config, dict):

python/python/lancedb/table.py

@@ -270,15 +270,17 @@ def _sanitize_data(
         reader,
         on_bad_vectors=on_bad_vectors,
         fill_value=fill_value,
+        target_schema=target_schema,
+        metadata=metadata,
     )
 
     if target_schema is None:
         target_schema, reader = _infer_target_schema(reader)
 
     if metadata:
-        new_metadata = target_schema.metadata or {}
-        new_metadata.update(metadata)
-        target_schema = target_schema.with_metadata(new_metadata)
+        target_schema = target_schema.with_metadata(
+            _merge_metadata(target_schema.metadata, metadata)
+        )
 
     _validate_schema(target_schema)
     reader = _cast_to_target_schema(reader, target_schema, allow_subschema)
@@ -294,7 +296,7 @@ def _cast_to_target_schema(
     # pa.Table.cast expects field order not to be changed.
     # Lance doesn't care about field order, so we don't need to rearrange fields
     # to match the target schema. We just need to correctly cast the fields.
-    if reader.schema == target_schema:
+    if reader.schema.equals(target_schema, check_metadata=True):
         # Fast path when the schemas are already the same
         return reader
 
@@ -314,7 +316,13 @@ def _cast_to_target_schema(
     def gen():
         for batch in reader:
             # Table but not RecordBatch has cast.
-            yield pa.Table.from_batches([batch]).cast(reordered_schema).to_batches()[0]
+            cast_batches = (
+                pa.Table.from_batches([batch]).cast(reordered_schema).to_batches()
+            )
+            if cast_batches:
+                yield pa.RecordBatch.from_arrays(
+                    cast_batches[0].columns, schema=reordered_schema
+                )
 
     return pa.RecordBatchReader.from_batches(reordered_schema, gen())
 
@@ -332,37 +340,51 @@ def _align_field_types(
         if target_field is None:
             raise ValueError(f"Field '{field.name}' not found in target schema")
         if pa.types.is_struct(target_field.type):
-            new_type = pa.struct(
-                _align_field_types(
-                    field.type.fields,
-                    target_field.type.fields,
+            if pa.types.is_struct(field.type):
+                new_type = pa.struct(
+                    _align_field_types(
+                        field.type.fields,
+                        target_field.type.fields,
+                    )
                 )
-            )
+            else:
+                new_type = target_field.type
         elif pa.types.is_list(target_field.type):
-            new_type = pa.list_(
-                _align_field_types(
-                    [field.type.value_field],
-                    [target_field.type.value_field],
-                )[0]
-            )
+            if _is_list_like(field.type):
+                new_type = pa.list_(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0]
+                )
+            else:
+                new_type = target_field.type
         elif pa.types.is_large_list(target_field.type):
-            new_type = pa.large_list(
-                _align_field_types(
-                    [field.type.value_field],
-                    [target_field.type.value_field],
-                )[0]
-            )
+            if _is_list_like(field.type):
+                new_type = pa.large_list(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0]
+                )
+            else:
+                new_type = target_field.type
         elif pa.types.is_fixed_size_list(target_field.type):
-            new_type = pa.list_(
-                _align_field_types(
-                    [field.type.value_field],
-                    [target_field.type.value_field],
-                )[0],
-                target_field.type.list_size,
-            )
+            if _is_list_like(field.type):
+                new_type = pa.list_(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0],
+                    target_field.type.list_size,
+                )
+            else:
+                new_type = target_field.type
         else:
             new_type = target_field.type
-        new_fields.append(pa.field(field.name, new_type, field.nullable))
+        new_fields.append(
+            pa.field(field.name, new_type, field.nullable, target_field.metadata)
+        )
     return new_fields
 
 
@@ -440,6 +462,7 @@ def sanitize_create_table(
             schema = data.schema
 
     if metadata:
+        metadata = _merge_metadata(schema.metadata, metadata)
         schema = schema.with_metadata(metadata)
         # Need to apply metadata to the data as well
         if isinstance(data, pa.Table):
@@ -492,9 +515,9 @@ def _append_vector_columns(
     vector columns to the table.
     """
     if schema is None:
-        metadata = metadata or {}
+        metadata = _merge_metadata(metadata)
     else:
-        metadata = schema.metadata or metadata or {}
+        metadata = _merge_metadata(schema.metadata, metadata)
     functions = EmbeddingFunctionRegistry.get_instance().parse_functions(metadata)
 
     if not functions:
@@ -3211,43 +3234,157 @@ def _handle_bad_vectors(
     reader: pa.RecordBatchReader,
     on_bad_vectors: Literal["error", "drop", "fill", "null"] = "error",
     fill_value: float = 0.0,
+    target_schema: Optional[pa.Schema] = None,
+    metadata: Optional[dict] = None,
 ) -> pa.RecordBatchReader:
-    vector_columns = []
+    vector_columns = _find_vector_columns(reader.schema, target_schema, metadata)
+    if not vector_columns:
+        return reader
 
-    for field in reader.schema:
-        # They can provide a 'vector' column that isn't yet a FSL
-        named_vector_col = (
-            (
-                pa.types.is_list(field.type)
-                or pa.types.is_large_list(field.type)
-                or pa.types.is_fixed_size_list(field.type)
-            )
-            and pa.types.is_floating(field.type.value_type)
-            and field.name == VECTOR_COLUMN_NAME
-        )
-        # TODO: we're making an assumption that fixed size list of 10 or more
-        # is a vector column. This is definitely a bit hacky.
-        likely_vector_col = (
-            pa.types.is_fixed_size_list(field.type)
-            and pa.types.is_floating(field.type.value_type)
-            and (field.type.list_size >= 10)
-        )
-
-        if named_vector_col or likely_vector_col:
-            vector_columns.append(field.name)
+    output_schema = _vector_output_schema(reader.schema, vector_columns)
 
     def gen():
         for batch in reader:
-            for name in vector_columns:
+            pending_dims = []
+            for vector_column in vector_columns:
+                dim = vector_column["expected_dim"]
+                if target_schema is not None and dim is None:
+                    dim = _infer_vector_dim(batch[vector_column["name"]])
+                    pending_dims.append(vector_column)
                 batch = _handle_bad_vector_column(
                     batch,
-                    vector_column_name=name,
+                    vector_column_name=vector_column["name"],
                     on_bad_vectors=on_bad_vectors,
                     fill_value=fill_value,
+                    expected_dim=dim,
+                    expected_value_type=vector_column["expected_value_type"],
                 )
-            yield batch
+            for vector_column in pending_dims:
+                if vector_column["expected_dim"] is None:
+                    vector_column["expected_dim"] = _infer_vector_dim(
+                        batch[vector_column["name"]]
+                    )
+            if batch.schema.equals(output_schema, check_metadata=True):
+                yield batch
+                continue
 
-    return pa.RecordBatchReader.from_batches(reader.schema, gen())
+            cast_batches = (
+                pa.Table.from_batches([batch]).cast(output_schema).to_batches()
+            )
+            if cast_batches:
+                yield pa.RecordBatch.from_arrays(
+                    cast_batches[0].columns,
+                    schema=output_schema,
+                )
+
+    return pa.RecordBatchReader.from_batches(output_schema, gen())
+
+
+def _find_vector_columns(
+    reader_schema: pa.Schema,
+    target_schema: Optional[pa.Schema],
+    metadata: Optional[dict],
+) -> List[dict]:
+    if target_schema is None:
+        vector_columns = []
+        for field in reader_schema:
+            named_vector_col = (
+                _is_list_like(field.type)
+                and pa.types.is_floating(field.type.value_type)
+                and field.name == VECTOR_COLUMN_NAME
+            )
+            likely_vector_col = (
+                pa.types.is_fixed_size_list(field.type)
+                and pa.types.is_floating(field.type.value_type)
+                and (field.type.list_size >= 10)
+            )
+            if named_vector_col or likely_vector_col:
+                vector_columns.append(
+                    {
+                        "name": field.name,
+                        "expected_dim": None,
+                        "expected_value_type": None,
+                    }
+                )
+        return vector_columns
+
+    reader_column_names = set(reader_schema.names)
+    active_metadata = _merge_metadata(target_schema.metadata, metadata)
+    embedding_function_columns = set(
+        EmbeddingFunctionRegistry.get_instance().parse_functions(active_metadata).keys()
+    )
+    vector_columns = []
+    for field in target_schema:
+        if field.name not in reader_column_names:
+            continue
+        if not _is_list_like(field.type) or not pa.types.is_floating(
+            field.type.value_type
+        ):
+            continue
+
+        reader_field = reader_schema.field(field.name)
+        named_vector_col = (
+            field.name in embedding_function_columns
+            or field.name == VECTOR_COLUMN_NAME
+            or (field.name == "embedding" and pa.types.is_fixed_size_list(field.type))
+        )
+        typed_fixed_vector_col = (
+            pa.types.is_fixed_size_list(reader_field.type)
+            and pa.types.is_floating(reader_field.type.value_type)
+            and reader_field.type.list_size >= 10
+        )
+
+        if named_vector_col or typed_fixed_vector_col:
+            vector_columns.append(
+                {
+                    "name": field.name,
+                    "expected_dim": (
+                        field.type.list_size
+                        if pa.types.is_fixed_size_list(field.type)
+                        else None
+                    ),
+                    "expected_value_type": field.type.value_type,
+                }
+            )
+
+    return vector_columns
+
+
+def _vector_output_schema(
+    reader_schema: pa.Schema,
+    vector_columns: List[dict],
+) -> pa.Schema:
+    columns_by_name = {column["name"]: column for column in vector_columns}
+    fields = []
+    for field in reader_schema:
+        column = columns_by_name.get(field.name)
+        if column is None:
+            output_type = field.type
+        else:
+            output_type = _vector_output_type(field, column)
+        fields.append(pa.field(field.name, output_type, field.nullable, field.metadata))
+    return pa.schema(fields, metadata=reader_schema.metadata)
+
+
+def _vector_output_type(field: pa.Field, vector_column: dict) -> pa.DataType:
+    if not _is_list_like(field.type):
+        return field.type
+
+    if vector_column["expected_value_type"] is not None and (
+        pa.types.is_null(field.type.value_type)
+        or pa.types.is_integer(field.type.value_type)
+        or pa.types.is_unsigned_integer(field.type.value_type)
+    ):
+        return pa.list_(vector_column["expected_value_type"])
+
+    if (
+        vector_column["expected_dim"] is not None
+        and pa.types.is_fixed_size_list(field.type)
+        and field.type.list_size != vector_column["expected_dim"]
+    ):
+        return pa.list_(field.type.value_type)
+
+    return field.type
 
 
 def _handle_bad_vector_column(
@@ -3255,6 +3392,8 @@ def _handle_bad_vector_column(
     vector_column_name: str,
     on_bad_vectors: str = "error",
     fill_value: float = 0.0,
+    expected_dim: Optional[int] = None,
+    expected_value_type: Optional[pa.DataType] = None,
 ) -> pa.RecordBatch:
     """
     Ensure that the vector column exists and has type fixed_size_list(float)
@@ -3271,14 +3410,39 @@ def _handle_bad_vector_column(
     fill_value: float, default 0.0
         The value to use when filling vectors. Only used if on_bad_vectors="fill".
     """
+    position = data.column_names.index(vector_column_name)
     vec_arr = data[vector_column_name]
+    if not _is_list_like(vec_arr.type):
+        return data
 
-    has_nan = has_nan_values(vec_arr)
+    if (
+        expected_dim is not None
+        and pa.types.is_fixed_size_list(vec_arr.type)
+        and vec_arr.type.list_size != expected_dim
+    ):
+        vec_arr = pa.array(vec_arr.to_pylist(), type=pa.list_(vec_arr.type.value_type))
+        data = data.set_column(position, vector_column_name, vec_arr)
 
-    if pa.types.is_fixed_size_list(vec_arr.type):
+    if expected_value_type is not None and (
+        pa.types.is_integer(vec_arr.type.value_type)
+        or pa.types.is_unsigned_integer(vec_arr.type.value_type)
+    ):
+        vec_arr = pa.array(vec_arr.to_pylist(), type=pa.list_(expected_value_type))
+        data = data.set_column(position, vector_column_name, vec_arr)
+
+    if pa.types.is_floating(vec_arr.type.value_type):
+        has_nan = has_nan_values(vec_arr)
+    else:
+        has_nan = pa.array([False] * len(vec_arr))
+
+    if expected_dim is not None:
+        dim = expected_dim
+    elif pa.types.is_fixed_size_list(vec_arr.type):
         dim = vec_arr.type.list_size
     else:
-        dim = _modal_list_size(vec_arr)
+        dim = _infer_vector_dim(vec_arr)
+        if dim is None:
+            return data
     has_wrong_dim = pc.not_equal(pc.list_value_length(vec_arr), dim)
 
     has_bad_vectors = pc.any(has_nan).as_py() or pc.any(has_wrong_dim).as_py()
@@ -3316,13 +3480,12 @@ def _handle_bad_vector_column(
                 )
             vec_arr = pc.if_else(
                 is_bad,
-                pa.scalar([fill_value] * dim),
+                pa.scalar([fill_value] * dim, type=vec_arr.type),
                 vec_arr,
             )
         else:
             raise ValueError(f"Invalid value for on_bad_vectors: {on_bad_vectors}")
 
-    position = data.column_names.index(vector_column_name)
     return data.set_column(position, vector_column_name, vec_arr)
 
 
@@ -3343,6 +3506,28 @@ def has_nan_values(arr: Union[pa.ListArray, pa.ChunkedArray]) -> pa.BooleanArray
     return pc.is_in(indices, has_nan_indices)
 
 
+def _is_list_like(data_type: pa.DataType) -> bool:
+    return (
+        pa.types.is_list(data_type)
+        or pa.types.is_large_list(data_type)
+        or pa.types.is_fixed_size_list(data_type)
+    )
+
+
+def _merge_metadata(*metadata_dicts: Optional[dict]) -> dict:
+    merged = {}
+    for metadata in metadata_dicts:
+        if metadata is None:
+            continue
+        for key, value in metadata.items():
+            if isinstance(key, str):
+                key = key.encode("utf-8")
+            if isinstance(value, str):
+                value = value.encode("utf-8")
+            merged[key] = value
+    return merged
+
+
 def _name_suggests_vector_column(field_name: str) -> bool:
     """Check if a field name indicates a vector column."""
     name_lower = field_name.lower()
@@ -3410,6 +3595,16 @@ def _modal_list_size(arr: Union[pa.ListArray, pa.ChunkedArray]) -> int:
     return pc.mode(pc.list_value_length(arr))[0].as_py()["mode"]
 
 
+def _infer_vector_dim(arr: Union[pa.Array, pa.ChunkedArray]) -> Optional[int]:
+    if not _is_list_like(arr.type):
+        return None
+    lengths = pc.list_value_length(arr)
+    lengths = pc.filter(lengths, pc.greater(lengths, 0))
+    if len(lengths) == 0:
+        return None
+    return pc.mode(lengths)[0].as_py()["mode"]
+
+
 def _validate_schema(schema: pa.Schema):
     """
     Make sure the metadata is valid utf8

python/python/tests/test_table.py

@@ -1049,6 +1049,231 @@ def test_add_with_nans(mem_db: DBConnection):
     assert np.allclose(v, np.array([0.0, 0.0]))
 
 
+def test_add_with_empty_fixed_size_list_drops_bad_rows(mem_db: DBConnection):
+    class Schema(LanceModel):
+        text: str
+        embedding: Vector(16)
+
+    table = mem_db.create_table("test_empty_embeddings", schema=Schema)
+    table.add(
+        [
+            {"text": "hello", "embedding": []},
+            {"text": "bar", "embedding": [0.1] * 16},
+        ],
+        on_bad_vectors="drop",
+    )
+
+    data = table.to_arrow()
+    assert data["text"].to_pylist() == ["bar"]
+    assert np.allclose(data["embedding"].to_pylist()[0], np.array([0.1] * 16))
+
+
+def test_add_with_integer_embeddings_preserves_casting(mem_db: DBConnection):
+    class Schema(LanceModel):
+        text: str
+        embedding: Vector(4)
+
+    table = mem_db.create_table("test_integer_embeddings", schema=Schema)
+    table.add(
+        [{"text": "foo", "embedding": [1, 2, 3, 4]}],
+        on_bad_vectors="drop",
+    )
+
+    assert table.to_arrow()["embedding"].to_pylist() == [[1.0, 2.0, 3.0, 4.0]]
+
+
+def test_on_bad_vectors_does_not_handle_non_vector_fixed_size_lists(
+    mem_db: DBConnection,
+):
+    schema = pa.schema(
+        [
+            pa.field("vector", pa.list_(pa.float32(), 4)),
+            pa.field("bbox", pa.list_(pa.float32(), 4)),
+        ]
+    )
+    table = mem_db.create_table("test_bbox_schema", schema=schema)
+
+    with pytest.raises(RuntimeError, match="FixedSizeListType"):
+        table.add(
+            [{"vector": [1.0, 2.0, 3.0, 4.0], "bbox": [0.0, 1.0]}],
+            on_bad_vectors="drop",
+        )
+
+
+def test_on_bad_vectors_does_not_handle_custom_named_fixed_size_lists(
+    mem_db: DBConnection,
+):
+    schema = pa.schema([pa.field("features", pa.list_(pa.float32(), 16))])
+    table = mem_db.create_table("test_custom_named_fixed_size_vector", schema=schema)
+
+    with pytest.raises(RuntimeError, match="FixedSizeListType"):
+        table.add(
+            [
+                {"features": []},
+                {"features": [0.1] * 16},
+            ],
+            on_bad_vectors="drop",
+        )
+
+
+def test_on_bad_vectors_with_schema_list_vector_still_sanitizes(mem_db: DBConnection):
+    schema = pa.schema([pa.field("vector", pa.list_(pa.float32()))])
+    table = mem_db.create_table("test_schema_list_vector", schema=schema)
+    table.add(
+        [
+            {"vector": [1.0, 2.0]},
+            {"vector": [3.0]},
+            {"vector": [4.0, 5.0]},
+        ],
+        on_bad_vectors="drop",
+    )
+
+    assert table.to_arrow()["vector"].to_pylist() == [[1.0, 2.0], [4.0, 5.0]]
+
+
+def test_on_bad_vectors_handles_typed_custom_fixed_vectors_for_list_schema(
+    mem_db: DBConnection,
+):
+    schema = pa.schema([pa.field("vec", pa.list_(pa.float32()))])
+    table = mem_db.create_table("test_typed_custom_fixed_vector", schema=schema)
+    data = pa.table(
+        {
+            "vec": pa.array(
+                [[float("nan")] * 16, [1.0] * 16],
+                type=pa.list_(pa.float32(), 16),
+            )
+        }
+    )
+
+    table.add(data, on_bad_vectors="drop")
+
+    assert table.to_arrow()["vec"].to_pylist() == [[1.0] * 16]
+
+
+def test_on_bad_vectors_fill_preserves_arrow_nested_vector_type(mem_db: DBConnection):
+    schema = pa.schema([pa.field("vector", pa.list_(pa.float32()))])
+    table = mem_db.create_table("test_fill_arrow_nested_type", schema=schema)
+    data = pa.table(
+        {
+            "vector": pa.array(
+                [[1.0, 2.0], [float("nan"), 3.0]],
+                type=pa.list_(pa.float32(), 2),
+            )
+        }
+    )
+
+    table.add(
+        data,
+        on_bad_vectors="fill",
+        fill_value=0.0,
+    )
+
+    assert table.to_arrow()["vector"].to_pylist() == [[1.0, 2.0], [0.0, 0.0]]
+
+
+@pytest.mark.parametrize(
+    ("table_name", "batch1", "expected"),
+    [
+        (
+            "test_schema_list_vector_empty_prefix",
+            pa.record_batch({"vector": [[], []]}),
+            [[], [], [1.0, 2.0], [3.0, 4.0]],
+        ),
+        (
+            "test_schema_list_vector_all_bad_prefix",
+            pa.record_batch({"vector": [[float("nan")] * 3, [float("nan")] * 3]}),
+            [[1.0, 2.0], [3.0, 4.0]],
+        ),
+    ],
+)
+def test_on_bad_vectors_with_schema_list_vector_ignores_invalid_prefix_batches(
+    mem_db: DBConnection,
+    table_name: str,
+    batch1: pa.RecordBatch,
+    expected: list,
+):
+    schema = pa.schema([pa.field("vector", pa.list_(pa.float32()))])
+    table = mem_db.create_table(table_name, schema=schema)
+    batch2 = pa.record_batch({"vector": [[1.0, 2.0], [3.0, 4.0]]})
+    reader = pa.RecordBatchReader.from_batches(batch1.schema, [batch1, batch2])
+
+    table.add(reader, on_bad_vectors="drop")
+
+    assert table.to_arrow()["vector"].to_pylist() == expected
+
+
+def test_on_bad_vectors_with_multiple_vectors_locks_dim_after_final_drop(
+    mem_db: DBConnection,
+):
+    registry = EmbeddingFunctionRegistry.get_instance()
+    func = MockTextEmbeddingFunction.create()
+    metadata = registry.get_table_metadata(
+        [
+            EmbeddingFunctionConfig(
+                source_column="text1", vector_column="vec1", function=func
+            ),
+            EmbeddingFunctionConfig(
+                source_column="text2", vector_column="vec2", function=func
+            ),
+        ]
+    )
+    schema = pa.schema(
+        [
+            pa.field("vec1", pa.list_(pa.float32())),
+            pa.field("vec2", pa.list_(pa.float32())),
+        ],
+        metadata=metadata,
+    )
+    table = mem_db.create_table("test_multi_vector_dim_lock", schema=schema)
+    batch1 = pa.record_batch(
+        {
+            "vec1": [[1.0, 2.0, 3.0], [10.0, 11.0]],
+            "vec2": [[float("nan"), 0.0], [5.0, 6.0]],
+        }
+    )
+    batch2 = pa.record_batch(
+        {
+            "vec1": [[20.0, 21.0], [30.0, 31.0]],
+            "vec2": [[7.0, 8.0], [9.0, 10.0]],
+        }
+    )
+    reader = pa.RecordBatchReader.from_batches(batch1.schema, [batch1, batch2])
+
+    table.add(reader, on_bad_vectors="drop")
+
+    data = table.to_arrow()
+    assert data["vec1"].to_pylist() == [[10.0, 11.0], [20.0, 21.0], [30.0, 31.0]]
+    assert data["vec2"].to_pylist() == [[5.0, 6.0], [7.0, 8.0], [9.0, 10.0]]
+
+
+def test_on_bad_vectors_does_not_handle_non_vector_list_columns(mem_db: DBConnection):
+    schema = pa.schema([pa.field("embedding_history", pa.list_(pa.float32()))])
+    table = mem_db.create_table("test_non_vector_list_schema", schema=schema)
+    table.add(
+        [
+            {"embedding_history": [1.0, 2.0]},
+            {"embedding_history": [3.0]},
+        ],
+        on_bad_vectors="drop",
+    )
+
+    assert table.to_arrow()["embedding_history"].to_pylist() == [
+        [1.0, 2.0],
+        [3.0],
+    ]
+
+
+def test_on_bad_vectors_all_null_schema_vector_batches_do_not_crash(
+    mem_db: DBConnection,
+):
+    schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2), nullable=True)])
+    table = mem_db.create_table("test_all_null_vector_batch", schema=schema)
+
+    table.add([{"vector": None}], on_bad_vectors="drop")
+
+    assert table.to_arrow()["vector"].to_pylist() == [None]
+
+
 def test_restore(mem_db: DBConnection):
     table = mem_db.create_table(
         "my_table",

python/python/tests/test_util.py

@@ -15,8 +15,10 @@ from lancedb.table import (
     _cast_to_target_schema,
     _handle_bad_vectors,
     _into_pyarrow_reader,
-    _sanitize_data,
     _infer_target_schema,
+    _merge_metadata,
+    _sanitize_data,
+    sanitize_create_table,
 )
 import pyarrow as pa
 import pandas as pd
@@ -304,6 +306,117 @@ def test_handle_bad_vectors_noop():
     assert output["vector"] == vector
 
 
+def test_handle_bad_vectors_updates_reader_schema_for_target_schema():
+    data = pa.table({"vector": [[1, 2, 3, 4]]})
+    target_schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 4))])
+
+    output = _handle_bad_vectors(
+        data.to_reader(),
+        on_bad_vectors="drop",
+        target_schema=target_schema,
+    )
+
+    assert output.schema == pa.schema([pa.field("vector", pa.list_(pa.float32()))])
+    assert output.read_all()["vector"].to_pylist() == [[1.0, 2.0, 3.0, 4.0]]
+
+
+def test_sanitize_data_keeps_target_field_metadata():
+    source_field = pa.field(
+        "vector",
+        pa.list_(pa.float32(), 2),
+        metadata={b"source": b"drop-me"},
+    )
+    target_field = pa.field(
+        "vector",
+        pa.list_(pa.float32(), 2),
+        metadata={b"target": b"keep-me"},
+    )
+    data = pa.table(
+        {"vector": pa.array([[1.0, 2.0]], type=pa.list_(pa.float32(), 2))},
+        schema=pa.schema([source_field]),
+    )
+
+    output = _sanitize_data(
+        data,
+        target_schema=pa.schema([target_field]),
+        on_bad_vectors="drop",
+    ).read_all()
+
+    assert output.schema.field("vector").metadata == {b"target": b"keep-me"}
+
+
+def test_sanitize_data_uses_separate_embedding_metadata_for_bad_vectors():
+    registry = EmbeddingFunctionRegistry.get_instance()
+    conf = EmbeddingFunctionConfig(
+        source_column="text",
+        vector_column="custom_vector",
+        function=MockTextEmbeddingFunction.create(),
+    )
+    metadata = registry.get_table_metadata([conf])
+    schema = pa.schema(
+        {
+            "text": pa.string(),
+            "custom_vector": pa.list_(pa.float32(), 10),
+        },
+        metadata={b"note": b"keep-me"},
+    )
+    data = pa.table(
+        {
+            "text": ["bad", "good"],
+            "custom_vector": [[1.0] * 9, [2.0] * 10],
+        }
+    )
+
+    output = _sanitize_data(
+        data,
+        target_schema=schema,
+        metadata=metadata,
+        on_bad_vectors="drop",
+    ).read_all()
+
+    assert output["text"].to_pylist() == ["good"]
+    assert output.schema.metadata[b"note"] == b"keep-me"
+    assert b"embedding_functions" in output.schema.metadata
+
+
+def test_sanitize_create_table_merges_and_overrides_embedding_metadata():
+    registry = EmbeddingFunctionRegistry.get_instance()
+    old_conf = EmbeddingFunctionConfig(
+        source_column="text",
+        vector_column="old_vector",
+        function=MockTextEmbeddingFunction.create(),
+    )
+    new_conf = EmbeddingFunctionConfig(
+        source_column="text",
+        vector_column="custom_vector",
+        function=MockTextEmbeddingFunction.create(),
+    )
+    metadata = registry.get_table_metadata([new_conf])
+    schema = pa.schema(
+        {
+            "text": pa.string(),
+            "custom_vector": pa.list_(pa.float32(), 10),
+        },
+        metadata=_merge_metadata(
+            {b"note": b"keep-me"},
+            registry.get_table_metadata([old_conf]),
+        ),
+    )
+
+    data, schema = sanitize_create_table(
+        pa.table({"text": ["good"]}),
+        schema,
+        metadata=metadata,
+        on_bad_vectors="drop",
+    )
+
+    assert schema.metadata[b"note"] == b"keep-me"
+    assert b"embedding_functions" in schema.metadata
+    assert data.schema.metadata[b"note"] == b"keep-me"
+    funcs = EmbeddingFunctionRegistry.get_instance().parse_functions(schema.metadata)
+    assert set(funcs.keys()) == {"custom_vector"}
+
+
 class TestModel(lancedb.pydantic.LanceModel):
     a: Optional[int]
     b: Optional[int]

python/src/connection.rs

@@ -547,6 +547,7 @@ pub struct PyClientConfig {
     id_delimiter: Option<String>,
     tls_config: Option<PyClientTlsConfig>,
     header_provider: Option<Py<PyAny>>,
+    user_id: Option<String>,
 }
 
 #[derive(FromPyObject)]
@@ -631,6 +632,7 @@ impl From<PyClientConfig> for lancedb::remote::ClientConfig {
             id_delimiter: value.id_delimiter,
             tls_config: value.tls_config.map(Into::into),
             header_provider,
+            user_id: value.user_id,
         }
     }
 }

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.28.0-beta.0"
+version = "0.28.0-beta.1"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true

rust/lancedb/src/remote/client.rs

@@ -52,6 +52,13 @@ pub struct ClientConfig {
     pub tls_config: Option<TlsConfig>,
     /// Provider for custom headers to be added to each request
     pub header_provider: Option<Arc<dyn HeaderProvider>>,
+    /// User identifier for tracking purposes.
+    ///
+    /// This is sent as the `x-lancedb-user-id` header in requests to LanceDB Cloud/Enterprise.
+    /// It can be set directly, or via the `LANCEDB_USER_ID` environment variable.
+    /// Alternatively, set `LANCEDB_USER_ID_ENV_KEY` to specify another environment
+    /// variable that contains the user ID value.
+    pub user_id: Option<String>,
 }
 
 impl std::fmt::Debug for ClientConfig {
@@ -67,6 +74,7 @@ impl std::fmt::Debug for ClientConfig {
                 "header_provider",
                 &self.header_provider.as_ref().map(|_| "Some(...)"),
             )
+            .field("user_id", &self.user_id)
             .finish()
     }
 }
@@ -81,10 +89,41 @@ impl Default for ClientConfig {
             id_delimiter: None,
             tls_config: None,
             header_provider: None,
+            user_id: None,
         }
     }
 }
 
+impl ClientConfig {
+    /// Resolve the user ID from the config or environment variables.
+    ///
+    /// Resolution order:
+    /// 1. If `user_id` is set in the config, use that value
+    /// 2. If `LANCEDB_USER_ID` environment variable is set, use that value
+    /// 3. If `LANCEDB_USER_ID_ENV_KEY` is set, read the env var it points to
+    /// 4. Otherwise, return None
+    pub fn resolve_user_id(&self) -> Option<String> {
+        if self.user_id.is_some() {
+            return self.user_id.clone();
+        }
+
+        if let Ok(user_id) = std::env::var("LANCEDB_USER_ID")
+            && !user_id.is_empty()
+        {
+            return Some(user_id);
+        }
+
+        if let Ok(env_key) = std::env::var("LANCEDB_USER_ID_ENV_KEY")
+            && let Ok(user_id) = std::env::var(&env_key)
+            && !user_id.is_empty()
+        {
+            return Some(user_id);
+        }
+
+        None
+    }
+}
+
 /// How to handle timeouts for HTTP requests.
 #[derive(Clone, Default, Debug)]
 pub struct TimeoutConfig {
@@ -464,6 +503,15 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
             );
         }
 
+        if let Some(user_id) = config.resolve_user_id() {
+            headers.insert(
+                HeaderName::from_static("x-lancedb-user-id"),
+                HeaderValue::from_str(&user_id).map_err(|_| Error::InvalidInput {
+                    message: format!("non-ascii user_id '{}' provided", user_id),
+                })?,
+            );
+        }
+
         Ok(headers)
     }
 
@@ -1072,4 +1120,91 @@ mod tests {
             _ => panic!("Expected Runtime error"),
         }
     }
+
+    #[test]
+    fn test_resolve_user_id_direct_value() {
+        let config = ClientConfig {
+            user_id: Some("direct-user-id".to_string()),
+            ..Default::default()
+        };
+        assert_eq!(config.resolve_user_id(), Some("direct-user-id".to_string()));
+    }
+
+    #[test]
+    fn test_resolve_user_id_none() {
+        let config = ClientConfig::default();
+        // Clear env vars that might be set from other tests
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::remove_var("LANCEDB_USER_ID");
+            std::env::remove_var("LANCEDB_USER_ID_ENV_KEY");
+        }
+        assert_eq!(config.resolve_user_id(), None);
+    }
+
+    #[test]
+    fn test_resolve_user_id_from_env() {
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::set_var("LANCEDB_USER_ID", "env-user-id");
+        }
+        let config = ClientConfig::default();
+        assert_eq!(config.resolve_user_id(), Some("env-user-id".to_string()));
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::remove_var("LANCEDB_USER_ID");
+        }
+    }
+
+    #[test]
+    fn test_resolve_user_id_from_env_key() {
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::remove_var("LANCEDB_USER_ID");
+            std::env::set_var("LANCEDB_USER_ID_ENV_KEY", "MY_CUSTOM_USER_ID");
+            std::env::set_var("MY_CUSTOM_USER_ID", "custom-env-user-id");
+        }
+        let config = ClientConfig::default();
+        assert_eq!(
+            config.resolve_user_id(),
+            Some("custom-env-user-id".to_string())
+        );
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::remove_var("LANCEDB_USER_ID_ENV_KEY");
+            std::env::remove_var("MY_CUSTOM_USER_ID");
+        }
+    }
+
+    #[test]
+    fn test_resolve_user_id_direct_takes_precedence() {
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::set_var("LANCEDB_USER_ID", "env-user-id");
+        }
+        let config = ClientConfig {
+            user_id: Some("direct-user-id".to_string()),
+            ..Default::default()
+        };
+        assert_eq!(config.resolve_user_id(), Some("direct-user-id".to_string()));
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::remove_var("LANCEDB_USER_ID");
+        }
+    }
+
+    #[test]
+    fn test_resolve_user_id_empty_env_ignored() {
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::set_var("LANCEDB_USER_ID", "");
+            std::env::remove_var("LANCEDB_USER_ID_ENV_KEY");
+        }
+        let config = ClientConfig::default();
+        assert_eq!(config.resolve_user_id(), None);
+        // SAFETY: This is only called in tests
+        unsafe {
+            std::env::remove_var("LANCEDB_USER_ID");
+        }
+    }
 }