broken links

website/docs/components/embeddings/index.md

@@ -148,7 +148,7 @@ datasets:
 
 ### Passthrough Embeddings
 
-Datasets that already have embedding columns can utilize the same functionalities (e.g. vector search) as those augmented with Spice-generated embeddings. They should follow the same schema as Spice-generated embeddings (or be altered with a [view](/docs/reference/spicepod#view).
+Datasets that already have embedding columns can utilize the same functionalities (e.g. vector search) as those augmented with Spice-generated embeddings. They should follow the same schema as Spice-generated embeddings (or be altered with a [view](/docs/reference/spicepod#views).
 
 #### Example
 

website/docs/features/embeddings/index.md

@@ -13,9 +13,9 @@ Learn how to define and augment datasets with embedding columns for advanced sea
 
 Spice provides three distinct methods for handling embedding columns in datasets:
 
-1. **[Just-in-Time (JIT) Embeddings](#jit-embeddings)**: Dynamically computes embeddings, on-demand, during query execution, without precomputing data.
-2. **[Accelerated Embeddings](#accelerated-embeddings)**: Precomputes embeddings by transforming and augmenting the source dataset for faster query and search performance.
-3. **[Passthrough Embeddings](#passthrough-embeddings)**: Utilizes pre-existing embeddings directly from the underlying source datasets, bypassing any additional computation.
+1. **[Just-in-Time (JIT) Embeddings](/docs/components/embeddings#jit-embeddings)**: Dynamically computes embeddings, on-demand, during query execution, without precomputing data.
+2. **[Accelerated Embeddings](/docs/components/embeddings#accelerated-embeddings)**: Precomputes embeddings by transforming and augmenting the source dataset for faster query and search performance.
+3. **[Passthrough Embeddings](/docs/components/embeddings#passthrough-embeddings)**: Utilizes pre-existing embeddings directly from the underlying source datasets, bypassing any additional computation.
 
 ## Configuring Embedding Models
 

website/docs/features/search/vector-search.md

@@ -57,7 +57,7 @@ curl -XPOST http://localhost:8090/v1/search \
 
 For more details, see the [API reference for /v1/search](/docs/api/HTTP/post-search).
 
-Spice also supports vector search on datasets with preexisting embeddings. See [below](/docs/features/embeddings#passthrough-embeddings) for compatibility details.
+Spice also supports vector search on datasets with preexisting embeddings. See [below](/docs/components/embeddings#passthrough-embeddings) for compatibility details.
 
 ### Document Retrieval
 
@@ -121,7 +121,7 @@ Datasets that already include embeddings can utilize the same functionalities (e
 
    - The embeddings column must have the following [Arrow data type](../../reference/datatypes/accelerators.md) when loaded into Spice:
      1. `FixedSizeList[Float32 or Float64, N]`, where `N` is the dimension (size) of the embedding vector. `FixedSizeList` is used for efficient storage and processing of fixed-size vectors.
-     2. If the column is [**chunked**](/docs/features/embeddings#chunking), use `List[FixedSizeList[Float32 or Float64, N]]`.
+     2. If the column is [**chunked**](/docs/components/embeddings#chunking), use `List[FixedSizeList[Float32 or Float64, N]]`.
 
 4. **Offset Column for Chunked Data:**
    - If the underlying column is chunked, there must be an additional offset column named `<column_name>_offsets` with the following Arrow data type:

website/docusaurus.config.ts

@@ -27,6 +27,7 @@ const config: Config = {
   organizationName: 'spiceai', // Usually your GitHub org/user name.
   projectName: 'docs', // Usually your repo name.
 
+  onBrokenAnchors: 'throw',
   onBrokenLinks: 'throw',
   onBrokenMarkdownLinks: 'throw',