Add IVF_RQ dimension note

Author: BubbleCal

docs/indexing/index.mdx

@@ -47,7 +47,7 @@ Vector indexes can use different quantization methods to compress vectors and im
 | :----------- | :------- | :---------- |
 | `PQ` (Product Quantization) | Default choice for most vector search scenarios. Use when you need to balance index size and recall. | Divides vectors into subvectors and quantizes each subvector independently. Provides a good balance between compression ratio and search accuracy. |
 | `SQ` (Scalar Quantization) | Use when you need faster indexing or when vector dimensions have consistent value ranges. | Quantizes each dimension independently. Simpler than PQ but typically provides less compression. |
-| `RQ` (RabitQ Quantization) | Use when you need maximum compression or have specific per-dimension requirements. | Per-dimension quantization using a RabitQ codebook. Provides fine-grained control over compression per dimension. |
+| `RQ` (RabitQ Quantization) | Use when you need maximum compression or have specific per-dimension requirements. | Per-dimension quantization using a RabitQ codebook. Provides fine-grained control over compression per dimension. For `IVF_RQ`, vector dimensions must be divisible by `8`. |
 | `None/Flat` | Use for binary vectors (with `hamming` distance) or when you need maximum recall and have sufficient storage. | No quantization—stores raw vectors. Provides the highest accuracy but requires more storage and memory. |
 
 ## Understanding the IVF-PQ Index
@@ -149,4 +149,3 @@ Then the greedy search routine operates as follows:
 * At the top layer (using an arbitrary vertex as an entry point), use the greedy local search routine on the k-ANN graph to get an approximate nearest neighbor at that layer.
 * Using the approximate nearest neighbor found in the previous layer as an entry point, find an approximate nearest neighbor in the next layer with the same method.
 * Repeat until the bottom-most layer is reached. Then use the entry point to find multiple nearest neighbors (e.g. top 10).
-

docs/indexing/quantization.mdx

@@ -15,7 +15,7 @@ Use quantization when:
 
 LanceDB currently exposes multiple quantized vector index types, including:
 - `IVF_PQ` -- Inverted File index with Product Quantization (default). See the [vector indexing guide](/indexing/vector-index) for `IVF_PQ` examples.
-- `IVF_RQ` -- Inverted File index with **RaBitQ** quantization (binary, 1 bit per dimension). See [below](#rabitq-quantization) for details.
+- `IVF_RQ` -- Inverted File index with **RaBitQ** quantization (binary, 1 bit per dimension). Requires vector dimensions divisible by `8`. See [below](#rabitq-quantization) for details.
 
 `IVF_PQ` is the default indexing option in LanceDB and works well in many cases. However, in cases where more drastic compression is needed, RaBitQ is also a reasonable option.
 
@@ -42,6 +42,11 @@ For a deeper dive into the theory and some benchmark results, see the blog post:
 ### Using RaBitQ
 
 You can create an RaBitQ-backed vector index by setting `index_type="IVF_RQ"` when calling `create_index`. 
+
+<Note title="Dimension requirement">
+When using `IVF_RQ`, vector dimensions must be divisible by `8`.
+</Note>
+
 `num_bits` controls how many bits per dimension are used:
 
 ## API Reference
@@ -62,4 +67,3 @@ The full list of parameters to the algorithm are listed below.
   Number of samples per partition during training. Higher values may improve accuracy but increase training time.
 - `target_partition_size`: Optional[int], defaults to None  
   Target number of vectors per partition. Adjust to control partition granularity and memory usage.
-