Add further guidance regarding document template and custom embedders

learn/ai_powered_search/search_with_user_provided_embeddings.mdx

@@ -28,6 +28,10 @@ curl \
   }'
 ```
 
+<Warning>
+Embedders with `source: userProvided` are incompatible with  `documentTemplate` and `documentTemplateMaxBytes`.
+</Warning>
+
 ## Add documents to Meilisearch
 
 Next, use [the `/documents` endpoint](/reference/api/documents?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide) to upload vectorized documents. Place vector data in your documents' `_vectors` field:

reference/api/settings.mdx

@@ -2668,7 +2668,7 @@ This field is incompatible with `rest` and `userProvided` embedders.
 
 ##### `documentTemplate`
 
-`documentTemplate` is a string containing a [Liquid template](https://shopify.github.io/liquid/basics/introduction). Meillisearch interpolates the template for each document and sends the resulting text to the embedder. The embedder then generates document vectors based on this text.
+`documentTemplate` is a string containing a [Liquid template](https://shopify.github.io/liquid/basics/introduction). When using an embedding generation service such as OpenAI, Meillisearch interpolates the template for each document and sends the resulting text to the embedder. The embedder then generates document vectors based on this text. If used with a custom embedder, Meilisearch will return an error.
 
 You may use the following context values:
 

snippets/samples/code_samples_export_post_1.mdx

@@ -13,4 +13,11 @@ curl \
     }
   }'
 ```
+
+```java Java
+Map<String, ExportIndexFilter> indexes = new HashMap<>();
+indexes.put("*", ExportIndexFilter.builder().overrideSettings(true).build());
+ExportRequest request = ExportRequest.builder().url("TARGET_INSTANCE_URL").indexes(indexes).build();
+client.export(request);
+```
 </CodeGroup>