replace localhost:7700 in code samples

guides/computing_hugging_face_embeddings_gpu.mdx

@@ -59,7 +59,7 @@ Next, enable the vector store experimental feature:
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
   -H 'Content-Type: application/json'  \
   --data-binary '{ "vectorStore": true }'
 ```
@@ -68,7 +68,7 @@ Then add the Hugging Face embedder to your index settings:
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/indexes/INDEX_NAME/settings/embedders' \
+  -X PATCH 'MEILISEARCH_URL/indexes/INDEX_NAME/settings/embedders' \
   -H 'Content-Type: application/json' \
   --data-binary '{ "default": { "source": "huggingFace" } }'
 ```

guides/front_end/search_bar_for_docs.mdx

@@ -13,29 +13,15 @@ This tutorial will guide you through the steps of building a relevant and powerf
 
 ## Run a Meilisearch instance
 
-First of all, you need your documentation content to be scraped and pushed into a Meilisearch instance.
-
-You can install and run Meilisearch on your machine using `curl`.
-
-```bash
-curl -L https://install.meilisearch.com | sh
-./meilisearch --master-key=MASTER_KEY
-```
-
-We provide a few [other installation methods](/learn/self_hosted/getting_started_with_self_hosted_meilisearch#setup-and-installation).
-
-Meilisearch is open-source and can run either on your server or on any cloud provider.
+First, create a new Meilisearch project on Meilisearch Cloud. You can also [install and run Meilisearch locally or in another cloud service](/learn/self_hosted/getting_started_with_self_hosted_meilisearch#setup-and-installation).
 
 <Capsule intent="note">
-
 The host URL and the API key you will provide in the next steps correspond to the credentials of this Meilisearch instance.
-In the example above, the host URL is `http://localhost:7700` and the API key is `MASTER_KEY`.
-
 </Capsule>
 
 ## Scrape your content
 
-The Meili team provides and maintains a [scraper tool](https://github.com/meilisearch/docs-scraper) to automatically read the content of your website and store it into an index in Meilisearch.
+The Meilisearch team provides and maintains a [scraper tool](https://github.com/meilisearch/docs-scraper) to automatically read the content of your website and store it into an index in Meilisearch.
 
 ### Configuration file
 
@@ -98,7 +84,7 @@ You can run the scraper with Docker. With our local Meilisearch instance set up
 ```bash
 docker run -t --rm \
   --network=host \
-  -e MEILISEARCH_HOST_URL='http://localhost:7700' \
+  -e MEILISEARCH_HOST_URL='MEILISEARCH_URL' \
   -e MEILISEARCH_API_KEY='MASTER_KEY' \
   -v <absolute-path-to-your-config-file>:/docs-scraper/config.json \
   getmeili/docs-scraper:latest pipenv run ./docs_scraper config.json
@@ -166,7 +152,8 @@ module.exports = {
 }
 ```
 
-The `hostUrl` and the `apiKey` fields are the credentials of the Meilisearch instance. Following on from this tutorial, they are respectively `http://localhost:7700` and `MASTER_KEY`.
+The `hostUrl` and the `apiKey` fields are the credentials of the Meilisearch instance. Following on from this tutorial, they are respectively `MEILISEARCH_URL` and `MASTER_KEY`.
+
 `indexUid` is the index identifier in your Meilisearch instance in which your website content is stored. It has been defined in the [config file](#configuration-file).
 
 These three fields are mandatory, but more [optional fields are available](https://github.com/meilisearch/vuepress-plugin-meilisearch#customization) to customize your search bar.
@@ -208,7 +195,8 @@ _[Docxtemplater](https://docxtemplater.com/) search bar demo_
 </html>
 ```
 
-The `hostUrl` and the `apiKey` fields are the credentials of the Meilisearch instance. Following on from this tutorial, they are respectively `http://localhost:7700` and `MASTER_KEY`.
+The `hostUrl` and the `apiKey` fields are the credentials of the Meilisearch instance. Following on from this tutorial, they are respectively `MEILISEARCH_URL` and `MASTER_KEY`.
+
 `indexUid` is the index identifier in your Meilisearch instance in which your website content is stored. It has been defined in the [config file](#configuration-file).
 `inputSelector` is the `id` attribute of the HTML search input tag.
 

guides/improve_relevancy_large_documents.mdx

@@ -123,7 +123,7 @@ To prevent that from happening, configure `story_id` as the index's distinct att
 
 ```sh
 curl \
-  -X PUT 'http://localhost:7700/indexes/INDEX_NAME/settings/distinct-attribute' \
+  -X PUT 'MEILISEARCH_URL/indexes/INDEX_NAME/settings/distinct-attribute' \
   -H 'Content-Type: application/json' \
   --data-binary '"story_id"'
 ```

learn/ai_powered_search/deactivate_ai_powered_search.mdx

@@ -25,7 +25,7 @@ Alternatively, use [the `/experimental` route](/reference/api/experimental_featu
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
   -H 'Content-Type: application/json'  \
   --data-binary '{
     "vectorStore": false

learn/ai_powered_search/getting_started_with_ai_search.mdx

@@ -39,7 +39,7 @@ Use [the `/experimental-features` route](/reference/api/experimental_features?ut
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
   -H 'Content-Type: application/json'  \
   --data-binary '{
     "vectorStore": true
@@ -54,7 +54,7 @@ Use the `embedders` index setting of the [update `/settings` endpoint](/referenc
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/indexes/kitchenware/settings' \
+  -X PATCH 'MEILISEARCH_URL/indexes/kitchenware/settings' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "embedders": {
@@ -86,7 +86,7 @@ Perform AI-powered searches with `q` and `hybrid` to retrieve search results usi
 
 ```sh
 curl \
-  -X POST 'http://localhost:7700/indexes/kitchenware/search' \
+  -X POST 'MEILISEARCH_URL/indexes/kitchenware/search' \
   -H 'content-type: application/json' \
   --data-binary '{
     "q": "kitchen utensils made of wood",

learn/ai_powered_search/search_with_user_provided_embeddings.mdx

@@ -17,7 +17,7 @@ Configure the `embedder` index setting, settings its source to `userProvided`:
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/indexes/movies/settings' \
+  -X PATCH 'MEILISEARCH_URL/indexes/movies/settings' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "embedders": {

learn/async/task_webhook.mdx

@@ -38,7 +38,7 @@ A common asynchronous operation is adding or updating documents to an index. The
 
 ```sh
 curl \
-  -X POST 'http://localhost:7700/indexes/books/documents' \
+  -X POST 'MEILISEARCH_URL/indexes/books/documents' \
   -H 'Content-Type: application/json' \
   --data-binary '[
     {

learn/engine/datatypes.mdx

@@ -257,7 +257,7 @@ The following query returns patients `0` and `1`:
 
 ```sh
 curl \
-  -X POST 'http://localhost:7700/indexes/clinic_patients/search' \
+  -X POST 'MEILISEARCH_URL/indexes/clinic_patients/search' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "q": "",

learn/filtering_and_sorting/filter_expression_reference.mdx

@@ -179,7 +179,7 @@ This is an experimental feature. Use the experimental features endpoint to activ
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "containsFilter": true
@@ -211,7 +211,7 @@ This is an experimental feature. Use the experimental features endpoint to activ
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "containsFilter": true

learn/multi_search/performing_federated_search.mdx

@@ -21,9 +21,9 @@ Download the following datasets: <a href="/assets/datasets/crm-chats.json">`crm-
 Add the datasets to Meilisearch and create three separate indexes, `profiles`, `chats`, and `tickets`:
 
 ```sh
-curl  -X POST 'http://localhost:7700/indexes/profiles'  -H 'Content-Type: application/json'  --data-binary @crm-profiles.json &&
-curl  -X POST 'http://localhost:7700/indexes/chats'  -H 'Content-Type: application/json'  --data-binary @crm-chats.json &&
-curl  -X POST 'http://localhost:7700/indexes/tickets'  -H 'Content-Type: application/json'  --data-binary @crm-tickets.json
+curl  -X POST 'MEILISEARCH_URL/indexes/profiles'  -H 'Content-Type: application/json'  --data-binary @crm-profiles.json &&
+curl  -X POST 'MEILISEARCH_URL/indexes/chats'  -H 'Content-Type: application/json'  --data-binary @crm-chats.json &&
+curl  -X POST 'MEILISEARCH_URL/indexes/tickets'  -H 'Content-Type: application/json'  --data-binary @crm-tickets.json
 ```
 
 [Use the tasks endpoint](/learn/async/working_with_tasks) to check the indexing status. Once Meilisearch successfully indexed all three datasets, you are ready to perform a federated search.
@@ -36,7 +36,7 @@ Use the `/multi-search` endpoint with the `federation` parameter to query the th
 
 ```sh
 curl \
-  -X POST 'http://localhost:7700/multi-search' \
+  -X POST 'MEILISEARCH_URL/multi-search' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "federation": {},
@@ -90,7 +90,7 @@ Use the `weight` property of the `federation` parameter to boost results coming
 
 ```sh
 curl \
-  -X POST 'http://localhost:7700/multi-search' \
+  -X POST 'MEILISEARCH_URL/multi-search' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "federation": {},

learn/security/generate_tenant_token_scratch.mdx

@@ -36,7 +36,7 @@ Next, find your default search API key. Query the [get an API key endpoint](/ref
 
 ```sh
 curl \
-  -X GET 'http://localhost:7700/keys/API_KEY' \
+  -X GET 'MEILISEARCH_URL/keys/API_KEY' \
   -H 'Authorization: Bearer MASTER_KEY'
 ```
 

learn/security/generate_tenant_token_sdk.mdx

@@ -31,7 +31,7 @@ Next, find your default search API key. Query the [get an API key endpoint](/ref
 
 ```sh
 curl \
-  -X GET 'http://localhost:7700/keys/API_KEY' \
+  -X GET 'MEILISEARCH_URL/keys/API_KEY' \
   -H 'Authorization: Bearer MASTER_KEY'
 ```
 

learn/security/generate_tenant_token_third_party.mdx

@@ -31,7 +31,7 @@ Next, find your default search API key. Query the [get an API key endpoint](/ref
 
 ```sh
 curl \
-  -X GET 'http://localhost:7700/keys/API_KEY' \
+  -X GET 'MEILISEARCH_URL/keys/API_KEY' \
   -H 'Authorization: Bearer MASTER_KEY'
 ```
 

reference/api/documents.mdx

@@ -401,7 +401,7 @@ This is an experimental feature. Use the experimental features endpoint to activ
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "editDocumentsByFunction": true
@@ -439,7 +439,7 @@ Use `context` to pass data to the `function` scope. By default a function only h
 
 ```sh
 curl \
--X POST 'http://localhost:7700/indexes/INDEX_NAME/documents/edit' \
+-X POST 'MEILISEARCH_URL/indexes/INDEX_NAME/documents/edit' \
 -H 'Content-Type: application/json' \
 --data-binary '{
   "function": "doc.title = `${doc.title.to_upper()}`"

reference/api/logs.mdx

@@ -10,7 +10,7 @@ This is an experimental feature. Use the experimental features endpoint to activ
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
   -H 'Content-Type: application/json'  \
   --data-binary '{
     "logsRoute": true

reference/api/metrics.mdx

@@ -12,7 +12,7 @@ This is an experimental feature. Use the experimental features endpoint to activ
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "enableMetrics": true

reference/api/overview.mdx

@@ -102,15 +102,15 @@ Meilisearch supports the following compression methods:
 The code sample below uses the `Content-Encoding: gzip` header, indicating that the request body is compressed using the `gzip` algorithm:
 
 ```
- cat ~/movies.json | gzip | curl -X POST 'http://localhost:7700/indexes/movies/documents' --data-binary @- -H 'Content-Type: application/json' -H 'Content-Encoding: gzip'
+ cat ~/movies.json | gzip | curl -X POST 'MEILISEARCH_URL/indexes/movies/documents' --data-binary @- -H 'Content-Type: application/json' -H 'Content-Encoding: gzip'
 ```
 
 #### Response compression
 
 Meilisearch compresses a response if the request contains the `Accept-Encoding` header. The code sample below uses the `gzip` algorithm:
 
 ```
-curl -sH 'Accept-encoding: gzip' 'http://localhost:7700/indexes/movies/search' | gzip -
+curl -sH 'Accept-encoding: gzip' 'MEILISEARCH_URL/indexes/movies/search' | gzip -
 ```
 
 ## Request body

reference/api/settings.mdx

@@ -2439,7 +2439,7 @@ Use `distribution` when configuring an embedder to correct the returned `_rankin
 
 ```sh
 curl \
-  -X PATCH 'http://localhost:7700/indexes/INDEX_NAME/settings' \
+  -X PATCH 'MEILISEARCH_URL/indexes/INDEX_NAME/settings' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "embedders": {