Move content out of learn/experimental section

.code-samples.meilisearch.yaml

@@ -1215,40 +1215,6 @@ index_settings_tutorial_api_put_setting_1: |-
 index_settings_tutorial_api_task_1: |-
   curl \
     -X GET 'http://localhost:7700/tasks/TASK_UID'
-get_embedders_1: |-
-  curl \
-    -X GET 'http://localhost:7700/indexes/INDEX_NAME/settings/embedders'
-update_embedders_1: |-
-  curl \
-    -X PATCH 'http://localhost:7700/indexes/INDEX_NAME/settings' \
-    -H 'Content-Type: application/json' \
-    --data-binary '{
-      "embedders": {
-        "default": {
-          "source":  "openAi",
-          "apiKey": "anOpenAiApiKey",
-          "model": "text-embedding-3-small",
-          "documentTemplate": "A document titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}"
-        }
-      }
-    }'
-reset_embedders_1: |-
-  curl \
-    -X DELETE 'http://localhost:7700/indexes/INDEX_NAME/settings/embedders'
-search_parameter_guide_hybrid_1: |-
-  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
-    -H 'content-type: application/json' \
-    --data-binary '{
-      "q": "kitchen utensils",
-      "hybrid": {
-        "semanticRatio": 0.9,
-        "embedder": "default"
-      }
-    }'
-search_parameter_guide_vector_1: |-
-  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
-    -H 'content-type: application/json' \
-    --data-binary '{ "vector": [0, 1, 2] }'
 get_search_cutoff_1: |-
   curl \
     -X GET 'http://localhost:7700/indexes/movies/settings/search-cutoff-ms'
@@ -1314,16 +1280,6 @@ analytics_event_bind_event_1: |-
       "objectId": "0",
       "position": 0
     }'
-search_parameter_reference_retrieve_vectors_1: |-
-  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
-    -H 'content-type: application/json' \
-    --data-binary '{
-      "q": "kitchen utensils",
-      "retrieveVectors": true,
-      "hybrid": {
-        "embedder": "default"
-      }
-    }'
 search_parameter_reference_distinct_1: |-
   curl \
     -X POST 'http://localhost:7700/indexes/INDEX_NAME/search' \
@@ -1391,3 +1347,68 @@ update_localized_attribute_settings_1: |-
 reset_localized_attribute_settings_1: |-
   curl \
     -X DELETE 'http://localhost:7700/indexes/INDEX_NAME/settings/localized-attributes'
+experimental_get_metrics_1: |-
+  curl \
+    -X GET 'http://localhost:7700/metrics'
+experimental_post_logs_stderr_1: |-
+  curl \
+    -X POST http://localhost:7700/logs/stderr \
+    -H 'Content-Type: application/json' \
+    --data-binary '{
+        "target": "milli=trace,index_scheduler=info,actix_web=off"
+    }'
+experimental_post_logs_stream_1: |-
+  curl \
+    -X POST http://localhost:7700/logs/stream \
+    -H 'Content-Type: application/json' \
+    --data-binary '{
+      "mode": "human",
+      "target": "index_scheduler=trace"
+    }'
+experimental_delete_logs_stream_1: |-
+  curl \
+    -X DELETE http://localhost:7700/logs/stream
+experimental_get_embedders_1: |-
+  curl \
+    -X GET 'http://localhost:7700/indexes/INDEX_NAME/settings/embedders'
+experimental_update_embedders_1: |-
+  curl \
+    -X PATCH 'http://localhost:7700/indexes/INDEX_NAME/settings' \
+    -H 'Content-Type: application/json' \
+    --data-binary '{
+      "embedders": {
+        "default": {
+          "source":  "openAi",
+          "apiKey": "anOpenAiApiKey",
+          "model": "text-embedding-3-small",
+          "documentTemplate": "A document titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}"
+        }
+      }
+    }'
+experimental_reset_embedders_1: |-
+  curl \
+    -X DELETE 'http://localhost:7700/indexes/INDEX_NAME/settings/embedders'
+experimental_search_parameter_guide_hybrid_1: |-
+  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
+    -H 'content-type: application/json' \
+    --data-binary '{
+      "q": "kitchen utensils",
+      "hybrid": {
+        "semanticRatio": 0.9,
+        "embedder": "default"
+      }
+    }'
+experimental_search_parameter_guide_vector_1: |-
+  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
+    -H 'content-type: application/json' \
+    --data-binary '{ "vector": [0, 1, 2] }'
+experimental_search_parameter_reference_retrieve_vectors_1: |-
+  curl -X POST 'localhost:7700/indexes/INDEX_NAME/search' \
+    -H 'content-type: application/json' \
+    --data-binary '{
+      "q": "kitchen utensils",
+      "retrieveVectors": true,
+      "hybrid": {
+        "embedder": "default"
+      }
+    }'

config/redirects.json

@@ -498,5 +498,45 @@
     "source": "/docs/learn/contributing/contributing_docs",
     "destination": "/docs/learn/resources/contributing_docs",
     "permanent": true
+  },
+  {
+    "source": "/docs/learn/experimental/overview",
+    "destination": "/docs/learn/resources/experimental_features_overview",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/experimental/max_number_of_batched_tasks",
+    "destination": "/docs/learn/self_hosted/configure_meilisearch_at_launch",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/experimental/metrics",
+    "destination": "/docs/reference/api/metrics",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/experimental/reduce-indexing-memory-usage",
+    "destination": "/docs/learn/self_hosted/configure_meilisearch_at_launch",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/experimental/log_customization",
+    "destination": "/docs/reference/api/logs",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/experimental/vector_search",
+    "destination": "/docs/learn/ai_powered_search/getting_started_with_ai_search",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/experimental/search_queue_size",
+    "destination": "/docs/learn/self_hosted/configure_meilisearch_at_launch",
+    "permanent": true
+  },
+  {
+    "source": "/docs/learn/experimental/replication_parameter",
+    "destination": "/docs/learn/self_hosted/configure_meilisearch_at_launch",
+    "permanent": true
   }
 ]

config/sidebar-learn.json

@@ -44,6 +44,16 @@
         "label": "Getting started with AI-powered search",
         "slug": "getting_started_with_ai_search"
       },
+      {
+        "source": "learn/ai_powered_search/search_with_user_provided_embeddings.mdx",
+        "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",
@@ -412,6 +422,11 @@
         "label": "Known limitations",
         "slug": "known_limitations"
       },
+      {
+        "source": "learn/resources/experimental_features_overview.mdx",
+        "label": "Experimental features overview",
+        "slug": "experimental_features_overview"
+      },
       {
         "source": "learn/resources/faq.mdx",
         "label": "FAQ",
@@ -453,46 +468,6 @@
     "title": "Experimental",
     "slug": "experimental",
     "routes": [
-      {
-        "source": "learn/experimental/overview.mdx",
-        "label": "Overview",
-        "slug": "overview"
-      },
-      {
-        "source": "learn/experimental/max_number_of_batched_tasks.mdx",
-        "label": "Limit number of batched tasks",
-        "slug": "max_number_of_batched_tasks"
-      },
-      {
-        "source": "learn/experimental/metrics.mdx",
-        "label": "Metrics",
-        "slug": "metrics"
-      },
-      {
-        "source": "learn/experimental/reduce-indexing-memory-usage.mdx",
-        "label": "Reduce indexing memory usage",
-        "slug": "reduce-indexing-memory-usage"
-      },
-      {
-        "source": "learn/experimental/log_customization.mdx",
-        "label": "Log customization",
-        "slug": "log_customization"
-      },
-      {
-        "source": "learn/experimental/vector_search.mdx",
-        "label": "Vector search",
-        "slug": "vector_search"
-      },
-      {
-        "source": "learn/experimental/replication_parameters.mdx",
-        "label": "Replication parameters",
-        "slug": "replication_parameters"
-      },
-      {
-        "source": "learn/experimental/search_queue_size.mdx",
-        "label": "Search queue size",
-        "slug": "search_queue_size"
-      }
     ]
   }
 ]

config/sidebar-reference.json

@@ -82,6 +82,16 @@
         "source": "reference/api/experimental_features.mdx",
         "label": "Experimental features",
         "slug": "experimental_features"
+      },
+      {
+        "source": "reference/api/metrics.mdx",
+        "label": "Metrics",
+        "slug": "metrics"
+      },
+      {
+        "source": "reference/api/logs.mdx",
+        "label": "Logs",
+        "slug": "logs"
       }
     ]
   },

learn/ai_powered_search/deactivate_ai_powered_search.mdx

@@ -0,0 +1,33 @@
+---
+title: Deactivate AI-powered search — Meilisearch documentation
+description: This guide shows how to disable Meilisearch's AI-powered search.
+---
+
+# Deactivate vector search <NoticeTag type="experimental" label="experimental" />
+
+This guide shows how to disable Meilisearch's AI-powered search.
+
+## Remove embedder configuration
+
+Manually remove all embedder configuration from your index:
+
+<CodeSamples id="experimental_reset_embedders_1" />
+
+<Capsule intent="danger">
+If you don't remove all configured embedders, Meilisearch will continue auto-generating embeddings for you documents. This will happen even if `vectorStore` has been set to `false` and may lead to unexpected expenses when using OpenAI's paid tiers.
+</Capsule>
+
+## Disable the vector store
+
+If using Meilisearch Cloud, navigate to your project overview and find "Experimental features", then uncheck the "Vector store" box.
+
+Alternatively, use [the `/experimental` route](/reference/api/experimental_features?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide):
+
+```sh
+curl \
+  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -H 'Content-Type: application/json'  \
+  --data-binary '{
+    "vectorStore": false
+  }'
+```

learn/ai_powered_search/getting_started_with_ai_search.mdx

@@ -48,7 +48,7 @@ curl \
 
 ## Generate vector embeddings with OpenAI
 
-Next, you must generate vector embeddings for all documents in your dataset. Embeddings are mathematical representations of the meanings of words and sentences in your documents. Meilisearch relies on external providers to generate these embeddings. Use OpenAI for this tutorial.
+Next, you must generate vector embeddings for all documents in your dataset. Embeddings are mathematical representations of the meanings of words and sentences in your documents. Meilisearch relies on external providers to generate these embeddings. This tutorial uses an OpenAI embedder, but Meilisearch also supports embedders from HuggingFace, Ollama, and any embedder accessible via a RESTful API.
 
 Use the `embedders` index setting of the [update `/settings` endpoint](/reference/api/settings?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide) to configure a default [OpenAI](https://platform.openai.com/) embedder:
 

learn/ai_powered_search/search_user_provided_embeddings.mdx

@@ -0,0 +1,69 @@
+---
+title: Use AI-powered search with user-provided embeddings — Meilisearch documentation
+description: This guide shows how to perform AI-powered searches with user-generated embeddings instead of relying on a third-party tool.
+---
+
+# Use AI-powered search with user-provided embeddings <NoticeTag type="experimental" label="experimental" />
+
+This guide shows how to perform AI-powered searches with user-generated embeddings instead of relying on a third-party tool.
+
+## Requirements
+
+- A Meilisearch project with AI-powered search activated
+
+## Configure a custom embedder
+
+Configure the `embedder` index setting, settings its source to `userProvided`:
+
+```sh
+curl \
+  -X PATCH 'http://localhost:7700/indexes/movies/settings' \
+  -H 'Content-Type: application/json' \
+  --data-binary '{
+    "embedders": {
+      "image2text": {
+        "source":  "userProvided",
+        "dimensions": 3
+      }
+    }
+  }'
+```
+
+## Add documents to Meilisearch
+
+Next, use [the `/documents` endpoint](/reference/api/documents?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide) to upload vectorized documents. Place vector data in your documents' `_vectors` field:
+
+```sh
+curl -X POST -H 'content-type: application/json' \
+'localhost:7700/indexes/products/documents' \
+--data-binary '[
+    { "id": 0, "_vectors": {"image2text": [0, 0.8, -0.2]}, "text": "frying pan" },
+    { "id": 1, "_vectors": {"image2text": [1, -0.2, 0]}, "text": "baking dish" }
+]'
+```
+
+## Vector search with user-provided embeddings
+
+When using a custom embedder, you must vectorize both your documents and user queries.
+
+Once you have the query's vector, pass it to the `vector` search parameter to perform an AI-powered search:
+
+```sh
+curl -X POST -H 'content-type: application/json' \
+  'localhost:7700/indexes/products/search' \
+  --data-binary '{ "vector": [0, 1, 2] }'
+```
+
+`vector` must be an array of numbers indicating the search vector. You must generate these yourself when using vector search with user-provided embeddings.
+
+`vector` can be used together with [other search parameters](/reference/api/search?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide), including [`filter`](/reference/api/search#filter) and [`sort`](/reference/api/search#sort):
+
+```sh
+curl -X POST -H 'content-type: application/json' \
+  'localhost:7700/indexes/products/search' \
+  --data-binary '{
+    "vector": [0, 1, 2],
+    "filter": "price < 10",
+    "sort": ["price:asc"]
+  }'
+```