replace asciidocalypse eland links

Author: colleenmcginnis

docset.yml

@@ -24,6 +24,7 @@ cross_links:
   - ecs-logging-php
   - ecs-logging-python
   - ecs-logging-ruby
+  - eland
   - elasticsearch
   - elasticsearch-java
   - elasticsearch-js

explore-analyze/machine-learning/data-frame-analytics/ml-trained-models.md

@@ -115,4 +115,4 @@ If you also want to copy the {{dfanalytics-job}} to the new cluster, you can exp
 
 ## Importing an external model to the {{stack}} [import-external-model-to-es]
 
-It is possible to import a model to your {{es}} cluster even if the model is not trained by Elastic {{dfanalytics}}. Eland supports [importing models](asciidocalypse://docs/eland/docs/reference/machine-learning.md) directly through its APIs. Please refer to the latest [Eland documentation](https://eland.readthedocs.io/en/latest/index.md) for more information on supported model types and other details of using Eland to import models with.
+It is possible to import a model to your {{es}} cluster even if the model is not trained by Elastic {{dfanalytics}}. Eland supports [importing models](eland://reference/machine-learning.md) directly through its APIs. Please refer to the latest [Eland documentation](https://eland.readthedocs.io/en/latest/index.md) for more information on supported model types and other details of using Eland to import models with.

explore-analyze/machine-learning/nlp/ml-nlp-import-model.md

@@ -9,7 +9,7 @@ mapped_pages:
 # Import the trained model and vocabulary [ml-nlp-import-model]
 
 ::::{important}
-If you want to install a trained model in a restricted or closed network, refer to [these instructions](asciidocalypse://docs/eland/docs/reference/machine-learning.md#ml-nlp-pytorch-air-gapped).
+If you want to install a trained model in a restricted or closed network, refer to [these instructions](eland://reference/machine-learning.md#ml-nlp-pytorch-air-gapped).
 ::::
 
 After you choose a model, you must import it and its tokenizer vocabulary to your cluster. When you import the model, it must be chunked and imported one chunk at a time for storage in parts due to its size.
@@ -22,7 +22,7 @@ Trained models must be in a TorchScript representation for use with {{stack-ml-f
 
 ## Import with the Eland client installed [ml-nlp-import-script]
 
-1. Install the [Eland Python client](asciidocalypse://docs/eland/docs/reference/installation.md) with PyTorch extra dependencies.
+1. Install the [Eland Python client](eland://reference/installation.md) with PyTorch extra dependencies.
 
     ```shell
     python -m pip install 'eland[pytorch]'
@@ -43,7 +43,7 @@ Trained models must be in a TorchScript representation for use with {{stack-ml-f
     3. Specify the identifier for the model in the Hugging Face model hub.
     4. Specify the type of NLP task. Supported values are `fill_mask`, `ner`, `question_answering`, `text_classification`, `text_embedding`, `text_expansion`, `text_similarity`, and `zero_shot_classification`.
 
-For more details, refer to [asciidocalypse://docs/eland/docs/reference/elasticsearch/elasticsearch-client-eland/machine-learning.md#ml-nlp-pytorch](asciidocalypse://docs/eland/docs/reference/machine-learning.md#ml-nlp-pytorch).
+For more details, refer to [asciidocalypse://docs/eland/docs/reference/elasticsearch/elasticsearch-client-eland/machine-learning.md#ml-nlp-pytorch](eland://reference/machine-learning.md#ml-nlp-pytorch).
 
 ## Import with Docker [ml-nlp-import-docker]
 
@@ -70,7 +70,7 @@ Replace the `$ELASTICSEARCH_URL` with the URL for your {{es}} cluster. Refer to
 The following authentication options are available when using the import script:
 
 * username/password authentication (specified with the `-u` and `-p` options):
-  
+
 ```bash
 eland_import_hub_model --url https://<hostname>:<port> -u <username> -p <password> ...
 ```

raw-migrated-files/stack-docs/elastic-stack/air-gapped-install.md

@@ -151,7 +151,7 @@ Air-gapped setup of this component is, essentially, identical to the setup of th
 Some {{ml}} features, like natural language processing (NLP), require you to deploy trained models. To learn about deploying {{ml}} models in an air-gapped environment, refer to:
 
 * [Deploy ELSER in an air-gapped environment](../../../explore-analyze/machine-learning/nlp/ml-nlp-elser.md#air-gapped-install).
-* [Install trained models in an air-gapped environment with Eland](asciidocalypse://docs/eland/docs/reference/machine-learning.md#ml-nlp-pytorch-air-gapped).
+* [Install trained models in an air-gapped environment with Eland](eland://reference/machine-learning.md#ml-nlp-pytorch-air-gapped).
 
 
 #### 1.13 {{kib}} Product documentation for AI Assistants [air-gapped-kibana-product-documentation]

solutions/search/ranking/semantic-reranking.md

@@ -8,12 +8,12 @@ applies_to:
 
 # Semantic reranking [semantic-reranking]
 
-::::{warning} 
+::::{warning}
 This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
 ::::
 
 
-::::{tip} 
+::::{tip}
 This overview focuses more on the high-level concepts and use cases for semantic re-ranking. For full implementation details on how to set up and use semantic re-ranking in {{es}}, see the [reference documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search#operation-search-body-application-json-retriever) in the Search API docs.
 
 ::::
@@ -26,7 +26,7 @@ Semantic re-ranking requires relatively large and complex machine learning model
 The next sections provide more details on the benefits, use cases, and model types used for semantic re-ranking. The final sections include a practical, high-level overview of how to implement [semantic re-ranking in {{es}}](#semantic-reranking-in-es) and links to the full reference documentation.
 
 
-## Use cases [semantic-reranking-use-cases] 
+## Use cases [semantic-reranking-use-cases]
 
 Semantic re-ranking enables a variety of use cases:
 
@@ -50,11 +50,11 @@ Semantic re-ranking enables a variety of use cases:
 Now that we’ve outlined the value of semantic re-ranking, we’ll explore the specific models that power this process and how they differ.
 
 
-## Cross-encoder and bi-encoder models [semantic-reranking-models] 
+## Cross-encoder and bi-encoder models [semantic-reranking-models]
 
 At a high level, two model types are used for semantic re-ranking: cross-encoders and bi-encoders.
 
-::::{note} 
+::::{note}
 In this version, {{es}} **only supports cross-encoders** for semantic re-ranking.
 ::::
 
@@ -84,7 +84,7 @@ The following is a non-exhaustive list of considerations when choosing between c
 
 
 
-## Semantic re-ranking in {{es}} [semantic-reranking-in-es] 
+## Semantic re-ranking in {{es}} [semantic-reranking-in-es]
 
 In {{es}}, semantic re-rankers are implemented using the {{es}} [Inference API](https://www.elastic.co/docs/api/doc/elasticsearch/group/endpoint-inference) and a [retriever](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search#operation-search-body-application-json-retriever).
 
@@ -95,7 +95,7 @@ To use semantic re-ranking in {{es}}, you need to:
     1. Use the [Elastic Rerank](../inference-api/elasticsearch-inference-integration.md#inference-example-elastic-reranker) cross-encoder model via the inference API’s {{es}} service.
     2. Use the [Cohere Rerank inference endpoint](../inference-api/cohere-inference-integration.md) to create a `rerank` endpoint.
     3. Use the [Google Vertex AI inference endpoint](../inference-api/google-vertex-ai-inference-integration.md) to create a `rerank` endpoint.
-    4. Upload a model to {{es}} from Hugging Face with [Eland](asciidocalypse://docs/eland/docs/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](../inference-api/elasticsearch-inference-integration.md#inference-example-eland) with the `rerank` endpoint type.
+    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](../inference-api/elasticsearch-inference-integration.md#inference-example-eland) 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.
 
@@ -133,7 +133,7 @@ POST _search
 
 
 
-## Learn more [semantic-reranking-learn-more] 
+## Learn more [semantic-reranking-learn-more]
 
 * Read the [retriever reference documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search#operation-search-body-application-json-retriever) for syntax and implementation details
 * Learn more about the [retrievers](../querying-for-search.md) abstraction

solutions/search/site-or-app/clients.md

@@ -19,7 +19,7 @@ applies_to:
 - [.NET](asciidocalypse://docs/elasticsearch-net/docs/reference/index.md)
 - [PHP](asciidocalypse://docs/elasticsearch-php/docs/reference/index.md)
 - [Python](asciidocalypse://docs/elasticsearch-py/docs/reference/index.md)
-  - [Eland](asciidocalypse://docs/eland/docs/reference/index.md) (Python client and toolkit for DataFrames and machine learning)
+  - [Eland](eland://reference/index.md) (Python client and toolkit for DataFrames and machine learning)
 - [Ruby](asciidocalypse://docs/elasticsearch-ruby/docs/reference/index.md)
 - [Rust](asciidocalypse://docs/elasticsearch-rs/docs/reference/index.md)
 
@@ -29,6 +29,6 @@ In addition to official clients, the Elastic community has contributed libraries
 
 - [Community-contributed clients](asciidocalypse://docs/elasticsearch/docs/reference/community-contributed.md)
 
-::::{tip} 
+::::{tip}
 Learn how to [connect to your {{es}} endpoint](/solutions/search/search-connection-details.md).
 ::::