Fix API URLs

Author: lcawl

deploy-manage/autoscaling/trained-model-autoscaling.md

@@ -46,8 +46,7 @@ If you set the minimum number of allocations to 1, you will be charged even if t
 
 You can enable adaptive allocations by using:
 
-* the create inference endpoint API for ELSER, E5 and models uploaded through Eland that are used as inference services.
-  %TBD URL for APIs
+* the create inference endpoint API for [ELSER](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elser), [E5 and models uploaded through Eland](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch) that are used as inference services.
 * the [start trained model deployment](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-start-trained-model-deployment) or [update trained model deployment](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-update-trained-model-deployment) APIs for trained models that are deployed on {{ml}} nodes.
 
 If the new allocations fit on the current {{ml}} nodes, they are immediately started. If more resource capacity is needed for creating new model allocations, then your {{ml}} node will be scaled up if {{ml}} autoscaling is enabled to provide enough resources for the new allocation. The number of model allocations can be scaled down to 0. They cannot be scaled up to more than 32 allocations, unless you explicitly set the maximum number of allocations to more. Adaptive allocations must be set up independently for each deployment and [{{infer}} endpoint](https://www.elastic.co/docs/api/doc/elasticsearch/group/endpoint-inference).

explore-analyze/machine-learning/nlp/ml-nlp-e5.md

@@ -13,8 +13,7 @@ EmbEddings from bidirEctional Encoder rEpresentations - or E5 -  is a {{nlp}} mo
 
 [Semantic search](../../../solutions/search/semantic-search.md) provides you search results based on contextual meaning and user intent, rather than exact keyword matches.
 
-E5 has two versions: one cross-platform version which runs on any hardware and one version which is optimized for Intel® silicon. The **Model Management** > **Trained Models** page shows you which version of E5 is recommended to deploy based on your cluster’s hardware. However, the recommended way to use E5 is through the {{infer}} API as a service which makes it easier to download and deploy the model and you don’t need to select from different versions.
-% TBD URL for API
+E5 has two versions: one cross-platform version which runs on any hardware and one version which is optimized for Intel® silicon. The **Model Management** > **Trained Models** page shows you which version of E5 is recommended to deploy based on your cluster’s hardware. However, the recommended way to use E5 is through the [{{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch) as a service which makes it easier to download and deploy the model and you don’t need to select from different versions.
 
 Refer to the model cards of the [multilingual-e5-small](https://huggingface.co/elastic/multilingual-e5-small) and the [multilingual-e5-small-optimized](https://huggingface.co/elastic/multilingual-e5-small-optimized) models on HuggingFace for further information including licensing.
 
@@ -45,8 +44,7 @@ PUT _inference/text_embedding/my-e5-model
 
 The API request automatically initiates the model download and then deploy the model.
 
-Refer to the `elasticsearch` {{infer}} service documentation to learn more about the available settings.
-% TBD URL for API
+Refer to the `elasticsearch` [{{infer}} service documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch) to learn more about the available settings.
 
 After you created the E5 {{infer}} endpoint, it’s ready to be used for semantic search. The easiest way to perform semantic search in the {{stack}} is to [follow the `semantic_text` workflow](../../../solutions/search/semantic-search/semantic-search-semantic-text.md).
 

explore-analyze/machine-learning/nlp/ml-nlp-elser.md

@@ -39,8 +39,7 @@ Enabling trained model autoscaling for your ELSER deployment is recommended. Ref
 
 Compared to the initial version of the model, ELSER v2 offers improved retrieval accuracy and more efficient indexing. This enhancement is attributed to the extension of the training data set, which includes high-quality question and answer pairs and the improved FLOPS regularizer which reduces the cost of computing the similarity between a query and a document.
 
-ELSER v2 has two versions: one cross-platform version which runs on any hardware and one version which is optimized for Intel® silicon. The **Model Management** > **Trained Models** page shows you which version of ELSER v2 is recommended to deploy based on your cluster’s hardware. However, the recommended way to use ELSER is through the {{infer}} API as a service which makes it easier to download and deploy the model and you don't need to select from different versions.
-% TBD URL for API
+ELSER v2 has two versions: one cross-platform version which runs on any hardware and one version which is optimized for Intel® silicon. The **Model Management** > **Trained Models** page shows you which version of ELSER v2 is recommended to deploy based on your cluster’s hardware. However, the recommended way to use ELSER is through the [{{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch) as a service which makes it easier to download and deploy the model and you don't need to select from different versions.
 
 If you want to learn more about the ELSER V2 improvements, refer to [this blog post](https://www.elastic.co/search-labs/blog/introducing-elser-v2-part-1).
 
@@ -75,8 +74,7 @@ PUT _inference/sparse_embedding/my-elser-endpoint
 
 The API request automatically initiates the model download and then deploy the model. This example uses [autoscaling](../../../deploy-manage/autoscaling/trained-model-autoscaling.md) through adaptive allocation.
 
-Refer to the ELSER {{infer}} integration documentation to learn more about the available settings.
-% TBD URL for API
+Refer to the [ELSER {{infer}} integration documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elser) to learn more about the available settings.
 
 After you created the ELSER {{infer}} endpoint, it’s ready to be used for semantic search. The easiest way to perform semantic search in the {{stack}} is to [follow the `semantic_text` workflow](../../../solutions/search/semantic-search/semantic-search-semantic-text.md).
 
@@ -309,7 +307,6 @@ To gain the biggest value out of ELSER trained models, consider to follow this l
 
 ::::{important}
 The recommended way to use ELSER is through the {{infer}} API as a service.
-% TBD URL for API
 ::::
 
 The following sections provide information about how ELSER performs on different hardwares and compares the model performance to {{es}} BM25 and other strong baselines.

explore-analyze/machine-learning/nlp/ml-nlp-rerank.md

@@ -44,8 +44,7 @@ Elastic Rerank is available in Elastic Stack version 8.17+:
 
 ## Download and deploy [ml-nlp-rerank-deploy]
 
-To download and deploy Elastic Rerank, use the create inference API to create an {{es}} service `rerank` endpoint.
-% TBD URL for API
+To download and deploy Elastic Rerank, use the [create inference API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch) to create an {{es}} service `rerank` endpoint.
 
 ::::{tip}
 Refer to this [Python notebook](https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/12-semantic-reranking-elastic-rerank.ipynb) for an end-to-end example using Elastic Rerank.
@@ -281,7 +280,7 @@ For detailed benchmark information, including complete dataset results and metho
 **Documentation**:
 
 * [Semantic re-ranking in {{es}} overview](../../../solutions/search/ranking/semantic-reranking.md#semantic-reranking-in-es)
-% TBD URL for API * [Inference API example](../../elastic-inference/inference-api/elasticsearch-inference-integration.md#inference-example-elastic-reranker)
+* [Inference API example](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch)
 
 **Blogs**:
 

solutions/search/hybrid-semantic-text.md

@@ -14,9 +14,7 @@ This tutorial demonstrates how to perform hybrid search, combining semantic sear
 
 In hybrid search, semantic search retrieves results based on the meaning of the text, while full-text search focuses on exact word matches. By combining both methods, hybrid search delivers more relevant results, particularly in cases where relying on a single approach may not be sufficient.
 
-The recommended way to use hybrid search in the {{stack}} is following the `semantic_text` workflow. This tutorial uses the `elasticsearch` service for demonstration, but you can use any service and their supported models offered by the {{infer-cap}} API.
-% TBD URL for API
-
+The recommended way to use hybrid search in the {{stack}} is following the `semantic_text` workflow. This tutorial uses the [`elasticsearch` service](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch) for demonstration, but you can use any service and their supported models offered by the {{infer-cap}} API.
 
 ## Create an index mapping [hybrid-search-create-index-mapping]
 

solutions/search/ranking/semantic-reranking.md

@@ -92,14 +92,10 @@ To use semantic re-ranking in {{es}}, you need to:
 
 1. **Select and configure a re-ranking model**. You have the following options:
 
-    1. Use the Elastic Rerank cross-encoder model via the inference API's {{es}} service.
-       % TBD URL for API
-    2. Use the Cohere Rerank inference endpoint to create a `rerank` endpoint.
-       % TBD URL for API
-    3. Use the Google Vertex AI inference endpoint to create a `rerank` endpoint.
-       % TBD URL for API
-    4. Upload a model to {{es}} from Hugging Face with [Eland](eland://reference/machine-learning.md#ml-nlp-pytorch). You’ll need to use the `text_similarity` NLP task type when loading the model using Eland. Then set up an {{es}} service inference endpoint with the `rerank` endpoint type.
-       % TBD URL for API
+    1. Use the Elastic Rerank cross-encoder model via the [inference API's {{es}} service](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch).
+    2. Use the [Cohere Rerank inference endpoint](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-cohere) to create a `rerank` endpoint.
+    3. Use the [Google Vertex AI inference endpoint](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-googlevertexai) to create a `rerank` endpoint.
+    4. Upload a model to {{es}} from Hugging Face with [Eland](eland://reference/machine-learning.md#ml-nlp-pytorch). You’ll need to use the `text_similarity` NLP task type when loading the model using Eland. Then set up an [{{es}} service inference endpoint](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch) with the `rerank` endpoint type.
 
         Refer to [the Elastic NLP model reference](../../../explore-analyze/machine-learning/nlp/ml-nlp-model-ref.md#ml-nlp-model-ref-text-similarity) for a list of third party text similarity models supported by {{es}} for semantic re-ranking.
 

solutions/search/semantic-search/semantic-search-inference.md

@@ -29,7 +29,7 @@ The following examples use the:
 * `ops-text-embedding-zh-001` model for [AlibabaCloud AI](https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-embedding-api-details)
 
 You can use any Cohere and OpenAI models, they are all supported by the {{infer}} API.
-% TBD URL: For a list of recommended models available on HuggingFace, refer to [the supported model list](../../../explore-analyze/elastic-inference/inference-api/huggingface-inference-integration.md#inference-example-hugging-face-supported-models).
+For a list of recommended models available on HuggingFace, refer to the supported model list in the [API documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-hugging-face).
 
 Click the name of the service you want to use on any of the widgets below to review the corresponding instructions.
 

solutions/search/semantic-search/semantic-search-semantic-text.md

@@ -15,15 +15,11 @@ Semantic text simplifies the {{infer}} workflow by providing {{infer}} at ingest
 
 The recommended way to use [semantic search](../semantic-search.md) in the {{stack}} is following the `semantic_text` workflow. When you need more control over indexing and query settings, you can still use the complete {{infer}} workflow (refer to  [this tutorial](../../../explore-analyze/elastic-inference/inference-api.md) to review the process).
 
-This tutorial uses the `elasticsearch` service for demonstration, but you can use any service and their supported models offered by the {{infer-cap}} API.
-% TBD URL for API
-
+This tutorial uses the [`elasticsearch` service](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-elasticsearch) for demonstration, but you can use any service and their supported models offered by the {{infer-cap}} API.
 
 ## Requirements [semantic-text-requirements]
 
 This tutorial uses the `elasticsearch` service for demonstration, which is created automatically as needed. To use the `semantic_text` field type with an {{infer}} service other than `elasticsearch` service, you must create an inference endpoint using the [Create {{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put).
-% TBD URL for API
-
 
 ## Create the index mapping [semantic-text-index-mapping]
 
@@ -44,7 +40,6 @@ PUT semantic-embeddings
 
 1. The name of the field to contain the generated embeddings.
 2. The field to contain the embeddings is a `semantic_text` field. Since no `inference_id` is provided, the default endpoint `.elser-2-elasticsearch` for the `elasticsearch` service is used. To use a different {{infer}} service, you must create an {{infer}} endpoint first using the [Create {{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put) and then specify it in the `semantic_text` field mapping using the `inference_id` parameter.
-% TBD URL for API
 
 ::::{note}
 If you’re using web crawlers or connectors to generate indices, you have to [update the index mappings](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-put-mapping) for these indices to include the `semantic_text` field. Once the mapping is updated, you’ll need to run a full web crawl or a full connector sync. This ensures that all existing documents are reprocessed and updated with the new semantic embeddings, enabling semantic search on the updated data.