v1.13: AI-powered search breaking changes

.code-samples.meilisearch.yaml

@@ -1327,18 +1327,6 @@ distinct_attribute_guide_distinct_parameter_1: |-
       "q": "white shirt",
       "distinct": "sku"
     }'
-get_similar_post_1: |-
-  curl \
-    -X POST 'MEILISEARCH_URL/indexes/INDEX_NAME/similar' \
-    -H 'Content-Type: application/json' \
-    -H 'Authorization: Bearer DEFAULT_SEARCH_API_KEY' \
-    --data-binary '{
-      "id": TARGET_DOCUMENT_ID,
-      "embedder": "EMBEDDER_NAME"
-    }'
-get_similar_get_1: |-
-  curl \
-    -X GET 'MEILISEARCH_URL/indexes/INDEX_NAME/similar?id=TARGET_DOCUMENT_ID&embedder=EMBEDDER_NAME'
 search_parameter_reference_ranking_score_threshold_1: |-
   curl \
   -X POST 'MEILISEARCH_URL/indexes/INDEX_NAME/search' \
@@ -1372,12 +1360,22 @@ reset_localized_attribute_settings_1: |-
     -X DELETE 'MEILISEARCH_URL/indexes/INDEX_NAME/settings/localized-attributes'
 get_all_batches_1: |-
   curl \
-    -X GET 'http://localhost:7700/batches'
+    -X GET 'http://MEILISEARCH_URL/batches'
 get_batch_1: |-
   curl \
-    -X GET 'http://localhost:7700/batches/BATCH_UID'
-
-### Code samples for experimental features
+    -X GET 'http://MEILISEARCH_URL/batches/BATCH_UID'
+get_similar_post_1: |-
+  curl \
+    -X POST 'MEILISEARCH_URL/indexes/INDEX_NAME/similar' \
+    -H 'Content-Type: application/json' \
+    -H 'Authorization: Bearer DEFAULT_SEARCH_API_KEY' \
+    --data-binary '{
+      "id": TARGET_DOCUMENT_ID,
+      "embedder": "EMBEDDER_NAME"
+    }'
+get_similar_get_1: |-
+  curl \
+    -X GET 'MEILISEARCH_URL/indexes/INDEX_NAME/similar?id=TARGET_DOCUMENT_ID&embedder=EMBEDDER_NAME'
 get_embedders_1: |-
   curl \
     -X GET 'MEILISEARCH_URL/indexes/INDEX_NAME/settings/embedders'
@@ -1388,7 +1386,7 @@ update_embedders_1: |-
     --data-binary '{
       "default": {
         "source":  "openAi",
-        "apiKey": "anOpenAiApiKey",
+        "apiKey": "OPEN_AI_API_KEY",
         "model": "text-embedding-3-small",
         "documentTemplate": "A document titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}"
       }
@@ -1397,7 +1395,7 @@ reset_embedders_1: |-
   curl \
     -X DELETE 'MEILISEARCH_URL/indexes/INDEX_NAME/settings/embedders'
 search_parameter_guide_hybrid_1: |-
-  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
+  curl -X POST 'MEILISEARCH_URL/indexes/INDEX_NAME/search' \
     -H 'content-type: application/json' \
     --data-binary '{
       "q": "kitchen utensils",
@@ -1407,14 +1405,14 @@ search_parameter_guide_hybrid_1: |-
       }
     }'
 search_parameter_guide_vector_1: |-
-  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
+  curl -X POST 'MEILISEARCH_URL/indexes/INDEX_NAME/search' \
     -H 'content-type: application/json' \
     --data-binary '{ 
       "vector": [0, 1, 2],
       "embedder": "EMBEDDER_NAME"
     }'
 search_parameter_reference_retrieve_vectors_1: |-
-  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
+  curl -X POST 'MEILISEARCH_URL/indexes/INDEX_NAME/search' \
     -H 'content-type: application/json' \
     --data-binary '{
       "q": "kitchen utensils",
@@ -1423,6 +1421,8 @@ search_parameter_reference_retrieve_vectors_1: |-
         "embedder": "EMBEDDER_NAME"
       }
     }'
+
+### Code samples for experimental features
 experimental_get_metrics_1: |-
   curl \
     -X GET 'MEILISEARCH_URL/metrics'

config/redirects.json

@@ -633,5 +633,10 @@
     "source": "/docs/guides/deployment/gcp",
     "destination": "/guides/deployment/running_production",
     "permanent": true
+  },
+  {
+    "source": "/docs/learn/ai_powered_search/deactivate_ai_powered_search",
+    "destination": "/docs/learn/ai_powered_search/getting_started_with_ai_search",
+    "permanent": true
   }
 ]

config/sidebar-learn.json

@@ -49,11 +49,6 @@
         "label": "Use AI-powered search with user-provided embeddings",
         "slug": "search_with_user_provided_embeddings"
       },
-      {
-        "source": "learn/ai_powered_search/deactivate_ai_powered_search.mdx",
-        "label": "Deactivate AI-powered search",
-        "slug": "deactivate_ai_powered_search"
-      },
       {
         "source": "learn/ai_powered_search/difference_full_text_ai_search.mdx",
         "label": "Differences between full-text and AI-powered search",

guides/computing_hugging_face_embeddings_gpu.mdx

@@ -47,23 +47,14 @@ cargo build --release --features cuda
 
 This might take a few moments. Once the compiler is done, you should have a CUDA-compatible Meilisearch binary.
 
-## Enable vector search
+## Configure the Hugging Face embedder
 
 Run your freshly compiled binary:
 
 ```sh
 ./meilisearch
 ```
 
-Next, enable the vector store experimental feature:
-
-```sh
-curl \
-  -X PATCH 'MEILISEARCH_URL/experimental-features/' \
-  -H 'Content-Type: application/json'  \
-  --data-binary '{ "vectorStore": true }'
-```
-
 Then add the Hugging Face embedder to your index settings:
 
 ```sh

guides/embedders/cloudflare.mdx

@@ -13,14 +13,13 @@ This guide will walk you through the process of setting up Meilisearch with Clou
 
 To follow this guide, you'll need:
 
-- A [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=docs&utm_medium=cloudflare-embeddings-guide) project running version 1.10 or above with the Vector store activated.
-- A Cloudflare account with access to Worker AI and an API key. You can sign up for a Cloudflare account at [Cloudflare](https://www.cloudflare.com/).
-- Your Cloudflare account ID.
-- No backend required.
+- A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version >=1.13
+- A Cloudflare account with access to Worker AI and an API key. You can sign up for a Cloudflare account at [Cloudflare](https://www.cloudflare.com/)
+- Your Cloudflare account ID
 
 ## Setting up Meilisearch
 
-To set up an embedder in Meilisearch, you need to configure it to your settings. You can refer to the [Meilisearch documentation](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=cloudflare-embeddings-guide#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](/reference/api/settings) for more details on updating the embedder settings.
 
 Cloudflare Worker AI offers the following embedding models:
 
@@ -66,7 +65,7 @@ Once you've configured the embedder settings, Meilisearch will automatically gen
 
 Please note that Cloudflare may have rate limiting, which is managed by Meilisearch. If you have a free account, the indexation process may take some time, but Meilisearch will handle it with a retry strategy.
 
-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](/reference/api/tasks?utm_campaign=vector-search&utm_source=docs&utm_medium=cloudflare-embeddings-guide#get-tasks).
+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](/reference/api/tasks).
 
 ## Testing semantic search
 
@@ -96,4 +95,4 @@ You can use the Meilisearch API or client libraries to perform searches and retr
 
 By following this guide, you should now have Meilisearch set up with Cloudflare Worker AI 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](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=cloudflare-embeddings-guide#embedders-experimental).
+To explore further configuration options for embedders, consult the [detailed documentation about the embedder setting possibilities](/reference/api/settings).

guides/embedders/cohere.mdx

@@ -13,13 +13,13 @@ This guide will walk you through the process of setting up Meilisearch with Cohe
 
 To follow this guide, you'll need:
 
-- A [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=docs&utm_medium=cohere-embeddings-guide) project running version 1.10 or above with the Vector store activated.
+- A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version >=1.13
 - A Cohere account with an API key for embedding generation. You can sign up for a Cohere account at [Cohere](https://cohere.com/).
 - No backend required.
 
 ## Setting up Meilisearch
 
-To set up an embedder in Meilisearch, you need to configure it to your settings. You can refer to the [Meilisearch documentation](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=cohere-embeddings-guide#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](/reference/api/settings) for more details on updating the embedder settings.
 
 Cohere offers multiple embedding models:
 - `embed-english-v3.0` and `embed-multilingual-v3.0`: 1024 dimensions
@@ -67,7 +67,7 @@ Once you've configured the embedder settings, Meilisearch will automatically gen
 
 Please note that most third-party tools have rate limiting, which is managed by Meilisearch. If you have a free account, the indexation process may take some time, but Meilisearch will handle it with a retry strategy.
 
-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?utm_campaign=vector-search&utm_source=docs&utm_medium=cohere-embeddings-guide#get-tasks).
+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).
 
 ## Testing semantic search
 
@@ -97,5 +97,4 @@ You can use the Meilisearch API or client libraries to perform searches and retr
 
 By following this guide, you should now have Meilisearch set up with Cohere 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](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=cohere-embeddings-guide#embedders-experimental). 
-
+To explore further configuration options for embedders, consult the [detailed documentation about the embedder setting possibilities](/reference/api/settings).

guides/embedders/huggingface.mdx

@@ -17,7 +17,7 @@ You can use Hugging Face and Meilisearch in two ways: running the model locally
 
 To follow this guide, you'll need:
 
-- A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version 1.10 or above with the Vector store activated
+- A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version >=1.13
 - A [Hugging Face account](https://huggingface.co/) with a deployed inference endpoint
 - The endpoint URL and API key of the deployed model on your Hugging Face account
 
@@ -83,4 +83,4 @@ In this request:
 
 You have set up with an embedder using Hugging Face Inference Endpoints. This allows you to use pure semantic search capabilities in your application.
 
-Consult the [embedder setting documentation](/reference/api/settings#embedders-experimental) for more information on other embedder configuration options.
+Consult the [embedder setting documentation](/reference/api/settings) for more information on other embedder configuration options.

guides/embedders/mistral.mdx

@@ -13,13 +13,13 @@ This guide will walk you through the process of setting up Meilisearch with Mist
 
 To follow this guide, you'll need:
 
-- A [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=docs&utm_medium=mistral-embeddings-guide) project running version 1.10 or above with the Vector store activated.
+- A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version >=1.13
 - 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.
 
 ## Setting up Meilisearch
 
-To set up an embedder in Meilisearch, you need to configure it to your settings. You can refer to the [Meilisearch documentation](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=mistral-embeddings-guide#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](/reference/api/settings) for more details on updating the embedder settings.
 
 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.
 
@@ -63,7 +63,7 @@ Once you've configured the embedder settings, Meilisearch will automatically gen
 
 Please note that most third-party tools have rate limiting, which is managed by Meilisearch. If you have a free account, the indexation process may take some time, but Meilisearch will handle it with a retry strategy.
 
-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](/reference/api/tasks?utm_campaign=vector-search&utm_source=docs&utm_medium=mistral-embeddings-guide#get-tasks)
+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](/reference/api/tasks)
 
 ## Testing semantic search
 
@@ -93,5 +93,4 @@ You can use the Meilisearch API or client libraries to perform searches and retr
 
 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](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=mistral-embeddings-guide#embedders-experimental). 
-
+To explore further configuration options for embedders, consult the [detailed documentation about the embedder setting possibilities](/reference/api/settings).

guides/embedders/openai.mdx

@@ -13,13 +13,13 @@ This guide will walk you through the process of setting up Meilisearch with Open
 
 To follow this guide, you'll need:
 
-- A [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=docs&utm_medium=openai-embeddings-guide) project running version 1.10 or above with the Vector store activated.
+- A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version >=1.13
 - An OpenAI account with an API key for embedding generation. You can sign up for an OpenAI account at [OpenAI](https://openai.com/).
 - No backend required.
 
 ## Setting up Meilisearch
 
-To set up an embedder in Meilisearch, you need to configure it to your settings. You can refer to the [Meilisearch documentation](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=openai-embeddings-guide#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](/reference/api/settings) for more details on updating the embedder settings.
 
 OpenAI offers three main embedding models:
 
@@ -53,7 +53,7 @@ Once you've configured the embedder settings, Meilisearch will automatically gen
 
 Please note that OpenAI has rate limiting, which is managed by Meilisearch. If you have a free account, the indexation process may take some time, but Meilisearch will handle it with a retry strategy.
 
-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](/reference/api/tasks?utm_campaign=vector-search&utm_source=docs&utm_medium=openai-embeddings-guide#get-tasks)
+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](/reference/api/tasks)
 
 ## Testing semantic search
 
@@ -83,4 +83,4 @@ You can use the Meilisearch API or client libraries to perform searches and retr
 
 By following this guide, you should now have Meilisearch set up with OpenAI 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](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=openai-embeddings-guide#embedders-experimental).
+To explore further configuration options for embedders, consult the [detailed documentation about the embedder setting possibilities](/reference/api/settings).

guides/embedders/voyage.mdx

@@ -13,13 +13,13 @@ This guide will walk you through the process of setting up Meilisearch with Voya
 
 To follow this guide, you'll need:
 
-- A [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=docs&utm_medium=voyage-embeddings-guide) project running version 1.10 or above with the Vector store activated.
+- A [Meilisearch Cloud](https://www.meilisearch.com/cloud) project running version >=1.13
 - A Voyage AI account with an API key for embedding generation. You can sign up for a Voyage AI account at [Voyage AI](https://www.voyageai.com/).
 - No backend required.
 
 ## Setting up Meilisearch
 
-To set up an embedder in Meilisearch, you need to configure it to your settings. You can refer to the [Meilisearch documentation](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=voyage-embeddings-guide#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](/reference/api/settings) for more details on updating the embedder settings.
 
 Voyage AI offers the following embedding models:
 
@@ -98,4 +98,4 @@ You can use the Meilisearch API or client libraries to perform searches and retr
 
 By following this guide, you should now have Meilisearch set up with Voyage AI 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](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=voyage-embeddings-guide#embedders-experimental).
+To explore further configuration options for embedders, consult the [detailed documentation about the embedder setting possibilities](/reference/api/settings).