update links

Author: charlotte-hoblik

solutions/search/hybrid-semantic-text.md

@@ -105,7 +105,7 @@ POST _tasks/<task_id>/_cancel
 
 After reindexing the data into the `semantic-embeddings` index, you can perform hybrid search to combine semantic and lexical search results. Choose between [retrievers](retrievers-overview.md) or [{{esql}}](elasticsearch://reference/query-languages/esql.md) syntax to execute the query.
 
-For an overview of all query types supported by `semantic_text` fields and guidance on when to use them, see [How to query semantic_text](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text#default-and-custom-endpoints)
+For an overview of all query types supported by `semantic_text` fields and guidance on when to use them, see [Querying `semantic_text` fields](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text.md#querying-semantic-text-fields).
 
 ::::{tab-set}
 :group: query-type

solutions/search/semantic-search.md

@@ -40,7 +40,7 @@ This diagram summarizes the relative complexity of each workflow:
 
 ### Option 1: `semantic_text` [_semantic_text_workflow]
 
-The simplest way to use NLP models in the {{stack}} is through the [`semantic_text` workflow](semantic-search/semantic-search-semantic-text.md). We recommend using this approach because it abstracts away a lot of manual work. All you need to do is create an index mapping to start ingesting, embedding, and querying data. There is no need to define model-related settings and parameters, or to create {{infer}} ingest pipelines. For guidance on the available query types for `semantic_text`, see [How to query semantic_text](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text#default-and-custom-endpoints).
+The simplest way to use NLP models in the {{stack}} is through the [`semantic_text` workflow](semantic-search/semantic-search-semantic-text.md). We recommend using this approach because it abstracts away a lot of manual work. All you need to do is create an index mapping to start ingesting, embedding, and querying data. There is no need to define model-related settings and parameters, or to create {{infer}} ingest pipelines. For guidance on the available query types for `semantic_text`, see [Querying `semantic_text` fields](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text.md#querying-semantic-text-fields).
 
 To learn more about supported services, refer to [](/explore-analyze/elastic-inference/inference-api.md) and the [{{infer}} API](https://www.elastic.co/docs/api/doc/elasticsearch/group/endpoint-inference) documentation. For an end-to-end tutorial, refer to [Semantic search with `semantic_text`](semantic-search/semantic-search-semantic-text.md).
 

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

@@ -156,7 +156,7 @@ POST /_query?format=txt
 
 ## Further examples and reading [semantic-text-further-examples]
 
-* For an overview of all query types supported by `semantic_text` fields and guidance on when to use them, see [How to query semantic_text](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text#default-and-custom-endpoints).
+* For an overview of all query types supported by `semantic_text` fields and guidance on when to use them, see [Querying `semantic_text` fields](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text.md#querying-semantic-text-fields).
 * If you want to use `semantic_text` in hybrid search, refer to [this notebook](https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/search/09-semantic-text.ipynb) for a step-by-step guide.
 * For more information on how to optimize your ELSER endpoints, refer to [the ELSER recommendations](/explore-analyze/machine-learning/nlp/ml-nlp-elser.md#elser-recommendations) section in the model documentation.
 * To learn more about model autoscaling, refer to the [trained model autoscaling](../../../deploy-manage/autoscaling/trained-model-autoscaling.md) page.