[Search] Refactor vector & semantic search under AI-powered search

deploy-manage/deploy/self-managed/install-elasticsearch-with-docker.md

@@ -90,7 +90,7 @@ docker pull docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0-beta1
     ::::
 
 
-    {{ml-cap}} features such as [semantic search with ELSER](../../../solutions/search/vector/sparse-vector-elser.md) require a larger container with more than 1GB of memory. If you intend to use the {{ml}} capabilities, then start the container with this command:
+    {{ml-cap}} features such as [semantic search with ELSER](/solutions/search/semantic-search/semantic-search-elser-ingest-pipelines.md) require a larger container with more than 1GB of memory. If you intend to use the {{ml}} capabilities, then start the container with this command:
 
     ```sh
     docker run --name es01 --net elastic -p 9200:9200 -it -m 6GB -e "xpack.ml.use_auto_machine_memory_percent=true" docker.elastic.co/elasticsearch/elasticsearch:9.0.0-beta1

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

@@ -6,7 +6,7 @@ mapped_pages:
 
 # ELSER [ml-nlp-elser]
 
-Elastic Learned Sparse EncodeR - or ELSER - is a retrieval model trained by Elastic that enables you to perform [semantic search](../../../solutions/search/vector/sparse-vector-elser.md) to retrieve more relevant search results. This search type provides you search results based on contextual meaning and user intent, rather than exact keyword matches.
+Elastic Learned Sparse EncodeR - or ELSER - is a retrieval model trained by Elastic that enables you to perform [semantic search](/solutions/search/semantic-search.md) to retrieve more relevant search results. This search type provides you search results based on contextual meaning and user intent, rather than exact keyword matches.
 
 ELSER is an out-of-domain model which means it does not require fine-tuning on your own data, making it adaptable for various use cases out of the box.
 
@@ -42,7 +42,7 @@ If you want to learn more about the ELSER V2 improvements, refer to [this blog p
 
 ### Upgrading to ELSER v2 [upgrade-elser-v2]
 
-ELSER v2 is not backward compatible. If you indexed your data with ELSER v1, you need to reindex it with an ingest pipeline referencing ELSER v2 to be able to use v2 for search. This [tutorial](../../../solutions/search/vector/sparse-vector-elser.md) shows you how to create an ingest pipeline with an {{infer}} processor that uses ELSER v2, and how to reindex your data through the pipeline.
+ELSER v2 is not backward compatible. If you indexed your data with ELSER v1, you need to reindex it with an ingest pipeline referencing ELSER v2 to be able to use v2 for search. This [tutorial](/solutions/search/semantic-search/semantic-search-elser-ingest-pipelines.md) shows you how to create an ingest pipeline with an {{infer}} processor that uses ELSER v2, and how to reindex your data through the pipeline.
 
 Additionally, the `elasticearch-labs` GitHub repository contains an interactive [Python notebook](https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/model-upgrades/upgrading-index-to-use-elser.ipynb) that walks through upgrading an index to ELSER V2.
 
@@ -298,7 +298,7 @@ To gain the biggest value out of ELSER trained models, consider to follow this l
 ## Further reading [further-readings]
 
 * [Perform semantic search with `semantic_text` using the ELSER endpoint](../../../solutions/search/semantic-search/semantic-search-semantic-text.md)
-* [Perform semantic search with ELSER](../../../solutions/search/vector/sparse-vector-elser.md)
+* [Perform semantic search with ELSER](/solutions/search/semantic-search/semantic-search-elser-ingest-pipelines.md)
 
 ## Benchmark information [elser-benchmarks]