Merge pull request elastic#72 from saarikabhasi/saarikabhasi/update-notebooks-doc-to-ELSERv2

[Search experience] Update notebooks and docs with ELSER v2

Author: saarikabhasi

README.md

@@ -50,6 +50,9 @@ The [`notebooks`](notebooks/README.md) folder contains a range of executable Pyt
 - [`openai-semantic-search-RAG.ipynb`](./notebooks/integrations/openai/openai-KNN-RAG.ipynb)
 - [`amazon-bedrock-langchain-qa-example.ipynb`](notebooks/integrations/amazon-bedrock/langchain-qa-example.ipynb)
 
+### Model Upgrades
+- [`upgrading-index-to-use-elser.ipynb`](notebooks/model-upgrades/upgrading-index-to-use-elser.ipynb)
+ 
 # Example apps 💻
 
 The [`example-apps`](example-apps/README.md) folder contains example apps that demonstrate Elasticsearch for a number of use cases, using different programming languages and frameworks.

example-apps/relevance-workbench/README.md

@@ -10,7 +10,7 @@ You can also try with your own data by forking this repo and plugging the applic
 
 ## Pre-requisites
 
-To run this demo successfully, you will need an Elasticsearch deployment (> 8.8) with the ELSER model deployed. The easiest way for this is to use Elastic Cloud as described in the next part but you can also deploy Elasticsearch locally.
+To run this demo successfully, you will need an Elasticsearch deployment (> 8.11) with the ELSER model deployed. The easiest way for this is to use Elastic Cloud as described in the next part but you can also deploy Elasticsearch locally.
 
 ## Deploy Elasticsearch in Elastic Cloud
 
@@ -32,7 +32,7 @@ You can follow this [documentation](https://www.elastic.co/guide/en/machine-lear
 
 The best approach is to use Enterprise Search to create a new index and configure the ingest pipeline to enrich the data.
 
-From the landing page in Kibana, navigate to Enterprise Search.
+From the landing page in Kibana, navigate to Search.
 
 ![ent-search-landing](./images/ent-search-landing.png)