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Add Cohere RAG tutorial link (#6442) 

* Add link to Cohere RAG tutorial

Signed-off-by: Tianjing Li <tianjinglimail@gmail.com>

* upgrade OS version to 2.12, english fixes

Signed-off-by: Tianjing Li <tianjinglimail@gmail.com>

* Update _search-plugins/conversational-search.md

Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>

* Update _search-plugins/conversational-search.md

Co-authored-by: Nathan Bower <nbower@amazon.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>

---------

Signed-off-by: Tianjing Li <tianjinglimail@gmail.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>
Co-authored-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>
Co-authored-by: Nathan Bower <nbower@amazon.com>
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@ -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.