diff --git a/solutions/search/semantic-search/semantic-search-inference.md b/solutions/search/semantic-search/semantic-search-inference.md
index d2482f1804..243bf7547f 100644
--- a/solutions/search/semantic-search/semantic-search-inference.md
+++ b/solutions/search/semantic-search/semantic-search-inference.md
@@ -39,36 +39,44 @@ Click the name of the service you want to use on any of the widgets below to rev
 ## Requirements [infer-service-requirements]
 
 :::::::{tab-set}
+:group: service
 
 ::::::{tab-item} Cohere
+:sync: cohere
 A [Cohere account](https://cohere.com/) is required to use the {{infer}} API with the Cohere service.
 ::::::
 
 ::::::{tab-item} ELSER
+:sync: elser
 ELSER is a model trained by Elastic. If you have an {{es}} deployment, there is no further requirement for using the {{infer}} API with the `elasticsearch` service.
 ::::::
 
 ::::::{tab-item} HuggingFace
+:sync: huggingface
 A [HuggingFace account](https://huggingface.co/) is required to use the {{infer}} API with the HuggingFace service.
 ::::::
 
 ::::::{tab-item} OpenAI
+:sync: openai
 An [OpenAI account](https://openai.com/) is required to use the {{infer}} API with the OpenAI service.
 ::::::
 
 ::::::{tab-item} Azure OpenAI
+:sync: azure-openai
 * An [Azure subscription](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 * Access granted to Azure OpenAI in the desired Azure subscription. You can apply for access to Azure OpenAI by completing the form at [https://aka.ms/oai/access](https://aka.ms/oai/access).
 * An embedding model deployed in [Azure OpenAI Studio](https://oai.azure.com/).
 ::::::
 
 ::::::{tab-item} Azure AI Studio
+:sync: azure-ai-studio
 * An [Azure subscription](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 * Access to [Azure AI Studio](https://ai.azure.com/)
 * A deployed [embeddings](https://ai.azure.com/explore/models?selectedTask=embeddings) or [chat completion](https://ai.azure.com/explore/models?selectedTask=chat-completion) model.
 ::::::
 
 ::::::{tab-item} Google Vertex AI
+:sync: google-vertex-ai
 * A [Google Cloud account](https://console.cloud.google.com/)
 * A project in Google Cloud
 * The Vertex AI API enabled in your project
@@ -77,16 +85,19 @@ An [OpenAI account](https://openai.com/) is required to use the {{infer}} API wi
 ::::::
 
 ::::::{tab-item} Mistral
+:sync: mistral
 * A Mistral Account on [La Plateforme](https://console.mistral.ai/)
 * An API key generated for your account
 ::::::
 
 ::::::{tab-item} Amazon Bedrock
+:sync: amazon-bedrock
 * An AWS Account with [Amazon Bedrock](https://aws.amazon.com/bedrock/) access
 * A pair of access and secret keys used to access Amazon Bedrock
 ::::::
 
 ::::::{tab-item} AlibabaCloud AI Search
+:sync: alibabacloud-ai-search
 * An AlibabaCloud Account with [AlibabaCloud](https://console.aliyun.com) access
 * An API key generated for your account from the [API keys section](https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key)
 ::::::
@@ -98,8 +109,10 @@ An [OpenAI account](https://openai.com/) is required to use the {{infer}} API wi
 Create an {{infer}} endpoint by using the [Create {{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put):
 
 :::::::{tab-set}
+:group: service
 
 ::::::{tab-item} Cohere
+:sync: cohere
 ```console
 PUT _inference/text_embedding/cohere_embeddings <1>
 {
@@ -123,6 +136,7 @@ When using this model the recommended similarity measure to use in the `dense_ve
 ::::::
 
 ::::::{tab-item} ELSER
+:sync: elser
 ```console
 PUT _inference/sparse_embedding/elser_embeddings <1>
 {
@@ -146,6 +160,7 @@ You might see a 502 bad gateway error in the response when using the {{kib}} Con
 ::::::
 
 ::::::{tab-item} HuggingFace
+:sync: huggingface
 First, you need to create a new {{infer}} endpoint on [the Hugging Face endpoint page](https://ui.endpoints.huggingface.co/) to get an endpoint URL. Select the model `all-mpnet-base-v2` on the new endpoint creation page, then select the `Sentence Embeddings` task under the Advanced configuration section. Create the endpoint. Copy the URL after the endpoint initialization has been finished, you need the URL in the following {{infer}} API call.
 
 ```console
@@ -165,6 +180,7 @@ PUT _inference/text_embedding/hugging_face_embeddings <1>
 ::::::
 
 ::::::{tab-item} OpenAI
+:sync: openai
 ```console
 PUT _inference/text_embedding/openai_embeddings <1>
 {
@@ -187,6 +203,7 @@ When using this model the recommended similarity measure to use in the `dense_ve
 ::::::
 
 ::::::{tab-item} Azure OpenAI
+:sync: azure-openai
 ```console
 PUT _inference/text_embedding/azure_openai_embeddings <1>
 {
@@ -212,6 +229,7 @@ It may take a few minutes for your model’s deployment to become available afte
 ::::::
 
 ::::::{tab-item} Azure AI Studio
+:sync: azure-ai-studio
 ```console
 PUT _inference/text_embedding/azure_ai_studio_embeddings <1>
 {
@@ -238,6 +256,7 @@ It may take a few minutes for your model’s deployment to become available afte
 ::::::
 
 ::::::{tab-item} Google Vertex AI
+:sync: google-vertex-ai
 ```console
 PUT _inference/text_embedding/google_vertex_ai_embeddings <1>
 {
@@ -259,6 +278,7 @@ PUT _inference/text_embedding/google_vertex_ai_embeddings <1>
 ::::::
 
 ::::::{tab-item} Mistral
+:sync: mistral
 ```console
 PUT _inference/text_embedding/mistral_embeddings <1>
 {
@@ -276,6 +296,7 @@ PUT _inference/text_embedding/mistral_embeddings <1>
 ::::::
 
 ::::::{tab-item} Amazon Bedrock
+:sync: amazon-bedrock
 ```console
 PUT _inference/text_embedding/amazon_bedrock_embeddings <1>
 {
@@ -299,6 +320,7 @@ PUT _inference/text_embedding/amazon_bedrock_embeddings <1>
 ::::::
 
 ::::::{tab-item} AlibabaCloud AI Search
+:sync: alibabacloud-ai-search
 ```console
 PUT _inference/text_embedding/alibabacloud_ai_search_embeddings <1>
 {
@@ -326,8 +348,10 @@ PUT _inference/text_embedding/alibabacloud_ai_search_embeddings <1>
 The mapping of the destination index - the index that contains the embeddings that the model will create based on your input text - must be created. The destination index must have a field with the [`dense_vector`](elasticsearch://reference/elasticsearch/mapping-reference/dense-vector.md) field type for most models and the [`sparse_vector`](elasticsearch://reference/elasticsearch/mapping-reference/sparse-vector.md) field type for the sparse vector models like in the case of the `elasticsearch` service to index the output of the used model.
 
 :::::::{tab-set}
+:group: service
 
 ::::::{tab-item} Cohere
+:sync: cohere
 ```console
 PUT cohere-embeddings
 {
@@ -354,6 +378,7 @@ PUT cohere-embeddings
 ::::::
 
 ::::::{tab-item} ELSER
+:sync: elser
 ```console
 PUT elser-embeddings
 {
@@ -377,6 +402,7 @@ PUT elser-embeddings
 ::::::
 
 ::::::{tab-item} HuggingFace
+:sync: huggingface
 ```console
 PUT hugging-face-embeddings
 {
@@ -403,6 +429,7 @@ PUT hugging-face-embeddings
 ::::::
 
 ::::::{tab-item} OpenAI
+:sync: openai
 ```console
 PUT openai-embeddings
 {
@@ -431,6 +458,7 @@ PUT openai-embeddings
 ::::::
 
 ::::::{tab-item} Azure OpenAI
+:sync: azure-openai
 ```console
 PUT azure-openai-embeddings
 {
@@ -459,6 +487,7 @@ PUT azure-openai-embeddings
 ::::::
 
 ::::::{tab-item} Azure AI Studio
+:sync: azure-ai-studio
 ```console
 PUT azure-ai-studio-embeddings
 {
@@ -487,6 +516,7 @@ PUT azure-ai-studio-embeddings
 ::::::
 
 ::::::{tab-item} Google Vertex AI
+:sync: google-vertex-ai
 ```console
 PUT google-vertex-ai-embeddings
 {
@@ -515,6 +545,7 @@ PUT google-vertex-ai-embeddings
 ::::::
 
 ::::::{tab-item} Mistral
+:sync: mistral
 ```console
 PUT mistral-embeddings
 {
@@ -543,6 +574,7 @@ PUT mistral-embeddings
 ::::::
 
 ::::::{tab-item} Amazon Bedrock
+:sync: amazon-bedrock
 ```console
 PUT amazon-bedrock-embeddings
 {
@@ -571,6 +603,7 @@ PUT amazon-bedrock-embeddings
 ::::::
 
 ::::::{tab-item} AlibabaCloud AI Search
+:sync: alibabacloud-ai-search
 ```console
 PUT alibabacloud-ai-search-embeddings
 {
@@ -603,8 +636,10 @@ PUT alibabacloud-ai-search-embeddings
 Create an [ingest pipeline](../../../manage-data/ingest/transform-enrich/ingest-pipelines.md) with an [{{infer}} processor](elasticsearch://reference/enrich-processor/inference-processor.md) and use the model you created above to infer against the data that is being ingested in the pipeline.
 
 :::::::{tab-set}
+:group: service
 
 ::::::{tab-item} Cohere
+:sync: cohere
 ```console
 PUT _ingest/pipeline/cohere_embeddings_pipeline
 {
@@ -627,6 +662,7 @@ PUT _ingest/pipeline/cohere_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} ELSER
+:sync: elser
 ```console
 PUT _ingest/pipeline/elser_embeddings_pipeline
 {
@@ -649,6 +685,7 @@ PUT _ingest/pipeline/elser_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} HuggingFace
+:sync: huggingface
 ```console
 PUT _ingest/pipeline/hugging_face_embeddings_pipeline
 {
@@ -671,6 +708,7 @@ PUT _ingest/pipeline/hugging_face_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} OpenAI
+:sync: openai
 ```console
 PUT _ingest/pipeline/openai_embeddings_pipeline
 {
@@ -693,6 +731,7 @@ PUT _ingest/pipeline/openai_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} Azure OpenAI
+:sync: azure-openai
 ```console
 PUT _ingest/pipeline/azure_openai_embeddings_pipeline
 {
@@ -715,6 +754,7 @@ PUT _ingest/pipeline/azure_openai_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} Azure AI Studio
+:sync: azure-ai-studio
 ```console
 PUT _ingest/pipeline/azure_ai_studio_embeddings_pipeline
 {
@@ -737,6 +777,7 @@ PUT _ingest/pipeline/azure_ai_studio_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} Google Vertex AI
+:sync: google-vertex-ai
 ```console
 PUT _ingest/pipeline/google_vertex_ai_embeddings_pipeline
 {
@@ -759,6 +800,7 @@ PUT _ingest/pipeline/google_vertex_ai_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} Mistral
+:sync: mistral
 ```console
 PUT _ingest/pipeline/mistral_embeddings_pipeline
 {
@@ -781,6 +823,7 @@ PUT _ingest/pipeline/mistral_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} Amazon Bedrock
+:sync: amazon-bedrock
 ```console
 PUT _ingest/pipeline/amazon_bedrock_embeddings_pipeline
 {
@@ -803,6 +846,7 @@ PUT _ingest/pipeline/amazon_bedrock_embeddings_pipeline
 ::::::
 
 ::::::{tab-item} AlibabaCloud AI Search
+:sync: alibabacloud-ai-search
 ```console
 PUT _ingest/pipeline/alibabacloud_ai_search_embeddings_pipeline
 {
@@ -840,8 +884,10 @@ Download the file and upload it to your cluster using the [Data Visualizer](../.
 Create embeddings from the text by reindexing the data through the {{infer}} pipeline that uses your chosen model. This step uses the [reindex API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-reindex) to simulate data ingestion through a pipeline.
 
 :::::::{tab-set}
+:group: service
 
 ::::::{tab-item} Cohere
+:sync: cohere
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -865,6 +911,7 @@ The [rate limit of your Cohere account](https://dashboard.cohere.com/billing) ma
 ::::::
 
 ::::::{tab-item} ELSER
+:sync: elser
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -883,6 +930,7 @@ POST _reindex?wait_for_completion=false
 ::::::
 
 ::::::{tab-item} HuggingFace
+:sync: huggingface
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -901,6 +949,7 @@ POST _reindex?wait_for_completion=false
 ::::::
 
 ::::::{tab-item} OpenAI
+:sync: openai
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -924,6 +973,7 @@ The [rate limit of your OpenAI account](https://platform.openai.com/account/limi
 ::::::
 
 ::::::{tab-item} Azure OpenAI
+:sync: azure-openai
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -947,6 +997,7 @@ The [rate limit of your Azure OpenAI account](https://learn.microsoft.com/en-us/
 ::::::
 
 ::::::{tab-item} Azure AI Studio
+:sync: azure-ai-studio
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -970,6 +1021,7 @@ Your Azure AI Studio model deployment may have rate limits in place that might a
 ::::::
 
 ::::::{tab-item} Google Vertex AI
+:sync: google-vertex-ai
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -988,6 +1040,7 @@ POST _reindex?wait_for_completion=false
 ::::::
 
 ::::::{tab-item} Mistral
+:sync: mistral
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -1006,6 +1059,7 @@ POST _reindex?wait_for_completion=false
 ::::::
 
 ::::::{tab-item} Amazon Bedrock
+:sync: amazon-bedrock
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -1024,6 +1078,7 @@ POST _reindex?wait_for_completion=false
 ::::::
 
 ::::::{tab-item} AlibabaCloud AI Search
+:sync: alibabacloud-ai-search
 ```console
 POST _reindex?wait_for_completion=false
 {
@@ -1065,8 +1120,10 @@ If you cancelled the reindexing process, you run the query only a part of the da
 
 
 :::::::{tab-set}
+:group: service
 
 ::::::{tab-item} Cohere
+:sync: cohere
 ```console
 GET cohere-embeddings/_search
 {
@@ -1134,6 +1191,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} ELSER
+:sync: elser
 ```console
 GET elser-embeddings/_search
 {
@@ -1188,6 +1246,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} HuggingFace
+:sync: huggingface
 ```console
 GET hugging-face-embeddings/_search
 {
@@ -1255,6 +1314,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} OpenAI
+:sync: openai
 ```console
 GET openai-embeddings/_search
 {
@@ -1313,6 +1373,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} Azure OpenAI
+:sync: azure-openai
 ```console
 GET azure-openai-embeddings/_search
 {
@@ -1371,6 +1432,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} Azure AI Studio
+:sync: azure-ai-studio
 ```console
 GET azure-ai-studio-embeddings/_search
 {
@@ -1429,6 +1491,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} Google Vertex AI
+:sync: google-vertex-ai
 ```console
 GET google-vertex-ai-embeddings/_search
 {
@@ -1487,6 +1550,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} Mistral
+:sync: mistral
 ```console
 GET mistral-embeddings/_search
 {
@@ -1545,6 +1609,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} Amazon Bedrock
+:sync: amazon-bedrock
 ```console
 GET amazon-bedrock-embeddings/_search
 {
@@ -1603,6 +1668,7 @@ As a result, you receive the top 10 documents that are closest in meaning to the
 ::::::
 
 ::::::{tab-item} AlibabaCloud AI Search
+:sync: alibabacloud-ai-search
 ```console
 GET alibabacloud-ai-search-embeddings/_search
 {