fix(rust): infer vector dimensions from list data

rust/lancedb/src/connection.rs

@@ -420,6 +420,11 @@ impl Connection {
     ///
     /// * `name` - The name of the table
     /// * `initial_data` - The initial data to write to the table
+    ///
+    /// Floating-point `List` columns named `vec`, or with `vector` or `embedding`
+    /// in their name, are inferred as vector columns when the first batch has a
+    /// uniform, non-zero list length. The inferred dimension is validated for all
+    /// subsequent batches and stored as a `FixedSizeList`.
     pub fn create_table<T: Scannable + 'static>(
         &self,
         name: impl Into<String>,

rust/lancedb/src/connection/create_table.rs

@@ -8,7 +8,7 @@ use lance_io::object_store::StorageOptionsProvider;
 use crate::{
     Error, Result, Table,
     connection::{merge_storage_options, set_storage_options_provider},
-    data::scannable::{Scannable, WithEmbeddingsScannable},
+    data::scannable::{Scannable, WithEmbeddingsScannable, maybe_infer_vector_schema},
     database::{CreateTableMode, CreateTableRequest, Database},
     embeddings::{EmbeddingDefinition, EmbeddingFunction, EmbeddingRegistry},
     table::WriteOptions,
@@ -147,6 +147,8 @@ impl CreateTableBuilder {
         let embedding_registry = self.embedding_registry.clone();
         let parent = self.parent.clone();
 
+        self.request.data = maybe_infer_vector_schema(self.request.data).await?;
+
         // If embeddings were configured via add_embedding(), wrap the data
         if !self.embeddings.is_empty() {
             let wrapped_data: Box<dyn Scannable> = Box::new(WithEmbeddingsScannable::try_new(

rust/lancedb/src/data/scannable.rs

@@ -18,13 +18,16 @@ use crate::embeddings::{
 };
 use crate::table::{ColumnDefinition, ColumnKind, TableDefinition};
 use crate::{Error, Result};
-use arrow_array::{ArrayRef, RecordBatch, RecordBatchIterator, RecordBatchReader};
+use arrow_array::{ArrayRef, RecordBatch, RecordBatchIterator, RecordBatchReader, cast::AsArray};
-use arrow_schema::{ArrowError, SchemaRef};
+use arrow_cast::{CastOptions, cast_with_options};
+use arrow_schema::{ArrowError, DataType, Schema, SchemaRef};
 use async_trait::async_trait;
 use futures::StreamExt;
 use futures::stream::once;
 use lance_datafusion::utils::StreamingWriteSource;
 
+use super::inspect::infer_dimension;
+
 pub trait Scannable: Send {
     /// Returns the schema of the data.
     fn schema(&self) -> SchemaRef;
@@ -497,6 +500,146 @@ impl Scannable for PeekedScannable {
     }
 }
 
+fn name_suggests_vector_column(name: &str) -> bool {
+    let name = name.to_ascii_lowercase();
+    name == "vec" || name.contains("vector") || name.contains("embedding")
+}
+
+/// Infer fixed dimensions for vector-like floating-point list columns.
+///
+/// Lance vector search requires `FixedSizeList` columns, but Arrow data assembled
+/// from runtime embedding models is often represented as `List`. For vector-like
+/// column names, inspect the first batch and convert uniform, non-empty lists to a
+/// fixed-size schema. Every subsequent batch is cast with strict length checking.
+pub(crate) async fn maybe_infer_vector_schema(
+    data: Box<dyn Scannable>,
+) -> Result<Box<dyn Scannable>> {
+    let input_schema = data.schema();
+    let candidates = input_schema
+        .fields()
+        .iter()
+        .enumerate()
+        .filter(|(_, field)| {
+            name_suggests_vector_column(field.name())
+                && matches!(
+                    field.data_type(),
+                    DataType::List(item) | DataType::LargeList(item)
+                        if item.data_type().is_floating()
+                )
+        })
+        .map(|(index, _)| index)
+        .collect::<Vec<_>>();
+    if candidates.is_empty() {
+        return Ok(data);
+    }
+
+    let mut peeked = PeekedScannable::new(data);
+    let Some(first_batch) = peeked.peek().await else {
+        return Ok(Box::new(peeked));
+    };
+
+    let mut fields = input_schema.fields().iter().cloned().collect::<Vec<_>>();
+    let mut changed = false;
+    for index in candidates {
+        let array = first_batch.column(index);
+        let dimension = match array.data_type() {
+            DataType::List(_) => {
+                infer_dimension::<arrow_array::types::Int32Type>(array.as_list::<i32>())?
+                    .map(i64::from)
+            }
+            DataType::LargeList(_) => {
+                infer_dimension::<arrow_array::types::Int64Type>(array.as_list::<i64>())?
+            }
+            _ => unreachable!(),
+        };
+        let Some(dimension) = dimension.filter(|dimension| *dimension > 0) else {
+            continue;
+        };
+        let dimension = i32::try_from(dimension).map_err(|_| Error::InvalidInput {
+            message: format!(
+                "Vector column '{}' has a dimension larger than i32::MAX",
+                fields[index].name()
+            ),
+        })?;
+        let item = match fields[index].data_type() {
+            DataType::List(item) | DataType::LargeList(item) => item.clone(),
+            _ => unreachable!(),
+        };
+        fields[index] = Arc::new(
+            fields[index]
+                .as_ref()
+                .clone()
+                .with_data_type(DataType::FixedSizeList(item, dimension)),
+        );
+        changed = true;
+    }
+
+    if !changed {
+        return Ok(Box::new(peeked));
+    }
+
+    let output_schema = Arc::new(Schema::new_with_metadata(
+        fields,
+        input_schema.metadata().clone(),
+    ));
+    Ok(Box::new(InferredVectorScannable {
+        inner: peeked,
+        output_schema,
+    }))
+}
+
+struct InferredVectorScannable {
+    inner: PeekedScannable,
+    output_schema: SchemaRef,
+}
+
+impl Scannable for InferredVectorScannable {
+    fn schema(&self) -> SchemaRef {
+        self.output_schema.clone()
+    }
+
+    fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
+        let output_schema = self.output_schema.clone();
+        let stream_schema = output_schema.clone();
+        let stream = self.inner.scan_as_stream().map(move |batch| {
+            let batch = batch?;
+            let columns = batch
+                .columns()
+                .iter()
+                .zip(output_schema.fields())
+                .map(|(array, field)| {
+                    if array.data_type() == field.data_type() {
+                        Ok(array.clone())
+                    } else {
+                        cast_with_options(
+                            array,
+                            field.data_type(),
+                            &CastOptions {
+                                safe: false,
+                                ..Default::default()
+                            },
+                        )
+                        .map_err(Error::from)
+                    }
+                })
+                .collect::<Result<Vec<_>>>()?;
+            Ok(RecordBatch::try_new(output_schema.clone(), columns)?)
+        });
+        Box::pin(SimpleRecordBatchStream {
+            schema: stream_schema,
+            stream,
+        })
+    }
+
+    fn num_rows(&self) -> Option<usize> {
+        self.inner.num_rows()
+    }
+
+    fn rescannable(&self) -> bool {
+        self.inner.rescannable()
+    }
+}
+
 /// Compute the number of write partitions based on data size estimates.
 ///
 /// `sample_bytes` and `sample_rows` come from a representative batch and are

rust/lancedb/src/lib.rs

@@ -72,7 +72,9 @@
 //!
 //! LanceDB uses [arrow-rs](https://github.com/apache/arrow-rs) to define schema, data types and array itself.
 //! It treats [`FixedSizeList<Float16/Float32>`](https://docs.rs/arrow/latest/arrow/array/struct.FixedSizeListArray.html)
-//! columns as vector columns.
+//! columns as vector columns. When creating a table with a floating-point `List`
+//! column named `vec`, or with `vector` or `embedding` in its name, LanceDB infers
+//! a uniform dimension from the first batch and stores it as a `FixedSizeList`.
 //!
 //! For more details, please refer to the [LanceDB documentation](https://docs.lancedb.com).
 //!
@@ -82,6 +84,8 @@
 //! schema of the `RecordBatch` determines the schema of the table.
 //!
 //! Vector columns should be represented as `FixedSizeList<Float16/Float32>` data type.
+//! A vector-like `List<Float16/Float32>` input is also accepted when every vector
+//! has the same runtime dimension.
 //!
 //! ```rust
 //! # use std::sync::Arc;

rust/lancedb/src/query.rs

@@ -1648,8 +1648,8 @@ mod tests {
     use super::*;
     use arrow::{array::downcast_array, compute::concat_batches, datatypes::Int32Type};
     use arrow_array::{
-        FixedSizeListArray, Float32Array, Int32Array, RecordBatch, StringArray, cast::AsArray,
+        FixedSizeListArray, Float32Array, Int32Array, ListArray, RecordBatch, StringArray,
-        types::Float32Type,
+        cast::AsArray, types::Float32Type,
     };
     use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
     use futures::{StreamExt, TryStreamExt};
@@ -2282,6 +2282,91 @@ mod tests {
         );
     }
 
+    #[tokio::test]
+    async fn vector_search_infers_dimension_from_list_array() {
+        let tmp_dir = tempdir().unwrap();
+        let schema = Arc::new(ArrowSchema::new(vec![
+            ArrowField::new("id", DataType::Int32, false),
+            ArrowField::new(
+                "vec",
+                DataType::List(Arc::new(ArrowField::new("item", DataType::Float32, true))),
+                true,
+            ),
+        ]));
+        let vectors = ListArray::from_iter_primitive::<Float32Type, _, _>([
+            Some([Some(0.0), Some(0.0)]),
+            Some([Some(1.0), Some(1.0)]),
+        ]);
+        let batch = RecordBatch::try_new(
+            schema,
+            vec![Arc::new(Int32Array::from(vec![0, 1])), Arc::new(vectors)],
+        )
+        .unwrap();
+        let table = connect(tmp_dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap()
+            .create_table("vectors", batch)
+            .execute()
+            .await
+            .unwrap();
+        assert!(matches!(
+            table.schema().await.unwrap().field(1).data_type(),
+            DataType::FixedSizeList(_, 2)
+        ));
+
+        let results = table
+            .vector_search(&[0.0, 0.0])
+            .unwrap()
+            .limit(1)
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+
+        assert_eq!(results[0]["id"].as_primitive::<Int32Type>().value(0), 0);
+    }
+
+    #[tokio::test]
+    async fn inferred_vector_dimension_is_validated_across_batches() {
+        let tmp_dir = tempdir().unwrap();
+        let schema = Arc::new(ArrowSchema::new(vec![ArrowField::new(
+            "vec",
+            DataType::List(Arc::new(ArrowField::new("item", DataType::Float32, true))),
+            true,
+        )]));
+        let first = RecordBatch::try_new(
+            schema.clone(),
+            vec![Arc::new(
+                ListArray::from_iter_primitive::<Float32Type, _, _>([Some([Some(0.0), Some(0.0)])]),
+            )],
+        )
+        .unwrap();
+        let wrong_dimension = RecordBatch::try_new(
+            schema,
+            vec![Arc::new(
+                ListArray::from_iter_primitive::<Float32Type, _, _>([Some([
+                    Some(1.0),
+                    Some(1.0),
+                    Some(1.0),
+                ])]),
+            )],
+        )
+        .unwrap();
+
+        let result = connect(tmp_dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap()
+            .create_table("vectors", vec![first, wrong_dimension])
+            .execute()
+            .await;
+
+        assert!(result.is_err());
+    }
+
     #[tokio::test]
     async fn test_fast_search_plan() {
         let tmp_dir = tempdir().unwrap();