Add Cohere RAG tutorial link (#6442)

* Add link to Cohere RAG tutorial

Signed-off-by: Tianjing Li <[email protected]>

* upgrade OS version to 2.12, english fixes

Signed-off-by: Tianjing Li <[email protected]>

* Update _search-plugins/conversational-search.md

Signed-off-by: kolchfa-aws <[email protected]>

* Update _search-plugins/conversational-search.md

Co-authored-by: Nathan Bower <[email protected]>
Signed-off-by: kolchfa-aws <[email protected]>

---------

Signed-off-by: Tianjing Li <[email protected]>
Signed-off-by: kolchfa-aws <[email protected]>
Co-authored-by: kolchfa-aws <[email protected]>
Co-authored-by: Nathan Bower <[email protected]>

_search-plugins/conversational-search.md

@@ -226,6 +226,17 @@ POST _bulk
 ```
 {% include copy-curl.html %}
 
+## RAG pipeline
+
+RAG is a technique that retrieves documents from an index, passes them through a seq2seq model, such as an LLM, and then supplements the static LLM information with the dynamically retrieved data in context.
+
+As of OpenSearch 2.12, the RAG technique has only been tested with OpenAI models, the Anthropic Claude model on Amazon Bedrock, and Cohere Command models. 
+{: .warning}
+
+Configuring the Cohere Command model to enable RAG requires using a post-processing function to transform the model output. For more information, see the [Cohere RAG Tutorial](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/tutorials/conversational_search/conversational_search_with_Cohere_Command.md).
+
+### Enabling RAG
+
 ### Step 5: Create a conversation memory
 
 You'll need to create a conversation memory that will store all messages from a conversation. To make the memory easily identifiable, provide a name for the memory in the optional `name` field, as shown in the following example. Because the `name` parameter is not updatable, this is your only opportunity to name your conversation.