comments fixes from Laurent

config/sidebar-guides.json

@@ -67,7 +67,7 @@
       },
       {
         "source": "guides/embedders/mistral.mdx",
-        "label": "Semantic Search with Mistral Embedding",
+        "label": "Semantic search with Mistral embedding",
         "slug": "mistral"
       }
     ]

guides/embedders/mistral.mdx

@@ -3,7 +3,7 @@ title: Semantic Search with Mistral Embedding - Meilisearch documentation
 description: This guide will walk you through the process of setting up Meilisearch with Mistral embeddings to enable semantic search capabilities.
 ---
 
-# Semantic Search with Mistral Embedding
+# Semantic search with Mistral embedding
 
 ## Introduction
 
@@ -17,17 +17,13 @@ To follow this guide, you'll need:
 - A Mistral account with an API key for embedding generation. You can sign up for a Mistral account at [Mistral](https://mistral.ai/).
 - No backend required.
 
-## Explanation
-
-Meilisearch has the ability to automatically generate embeddings for your documents by configuring an embedder within the engine. You can use a local embedder, which may be slower depending on your cloud account, or leverage a third-party API. The third-party API can be a self-hosted API based on Ollama or a service using the dedicated REST option.
-
-By utilizing this configuration, Meilisearch will automatically generate embeddings for your documents based on the template you've set up (optional but recommended) and store the embeddings in the vector store. Meilisearch optimizes the calls to the third-party API by auto-batching the requests. Additionally, Meilisearch uses this configuration to automatically generate embeddings for each search query, which are then used to query the Approximate Nearest Neighbor (ANN) in the vector storage.
-
 ## Setting up Meilisearch
 
-To set up an embedder in Meilisearch, you simply need to add it to your settings. You can refer to the [Meilisearch documentation](https://www.meilisearch.com/docs/reference/api/settings#update-embedder-settings) for more details on updating the embedder settings.
+To set up an embedder in Meilisearch, you need to configure it to your settings. You can refer to the [Meilisearch documentation](https://www.meilisearch.com/docs/reference/api/settings#update-embedder-settings) for more details on updating the embedder settings.
 
-Here's an example configuration for using Mistral embedding:
+While using Mistral to generate embeddings, you'll need to use the model `mistral-embed`. Unlike some other services, Mistral currently offers only one embedding model.
+
+Here's an example of embedder settings for Mistral:
 
 ```json
 {
@@ -69,7 +65,7 @@ Please note that most third-party tools have rate limiting, which is managed by
 
 It's recommended to monitor the tasks queue to ensure everything is running smoothly. You can access the tasks queue using the Cloud UI or the Meilisearch API: https://www.meilisearch.com/docs/reference/api/tasks#get-tasks
 
-## Testing Semantic Search
+## Testing semantic search
 
 With the embedder set up, you can now perform semantic searches using Meilisearch. When you send a search query, Meilisearch will generate an embedding for the query using the configured embedder and then use it to find the most semantically similar documents in the vector store.
 To perform a semantic search, you simply need to make a normal search request but include the hybrid parameter:
@@ -96,3 +92,6 @@ You can use the Meilisearch API or client libraries to perform searches and retr
 ## Conclusion
 
 By following this guide, you should now have Meilisearch set up with Mistral embedding, enabling you to leverage semantic search capabilities in your application. Meilisearch's auto-batching and efficient handling of embeddings make it a powerful choice for integrating semantic search into your project.
+
+To explore further configuration options for embedders, consult the [detailed documentation about the embedder setting possibilities](https://www.meilisearch.com/docs/reference/api/settings#embedders-experimental). 
+