Merge branch 'EXP-248-multimodal-search' of github.com:meilisearch/documentation into EXP-248-multimodal-search

Author: guimachiavelli

learn/ai_powered_search/image_search_with_multimodal_embeddings.mdx

@@ -32,11 +32,11 @@ Much like other embedders, multimodal embedders must set their `source` to `rest
 
 All multimodal embedders must contain an `indexingFragments` field and a `searchFragments` field. Fragments are sets of embeddings built out of specific parts of document data.
 
-Fragments must follow the structure defined by your chosen model and provider.
+Fragments must follow the structure defined by the REST API of your chosen provider.
 
 ### `indexingFragments`
 
-Use `indexingFragments` to tell Meilisearch what data to use when generating document embeddings.
+Use `indexingFragments` to tell Meilisearch how to send document data to the provider's API when generating document embeddings.
 
 For example, when using VoyageAI's multimodal model, an indexing fragment might look like this:
 
@@ -67,11 +67,11 @@ For example, when using VoyageAI's multimodal model, an indexing fragment might
 
 The example above requests Meilisearch to create two sets of embeddings during indexing: one for the textual description of an image, and another for the actual image.
 
-Each fragment must have one field with a Liquid template where you interpolate document data present in `doc`. In `IMAGE_FRAGMENT_NAME`, that's  `image_url` which outputs the plain URL string in the document field `poster_url`. In `TEXT_FRAGMENT_NAME`, `text` contains a longer string contextualizing two document fields, `title` and `description`.
+Any JSON string value appearing in a fragment is handled as a Liquid template, where you interpolate document data present in `doc`. In `IMAGE_FRAGMENT_NAME`, that's  `image_url` which outputs the plain URL string in the document field `poster_url`. In `TEXT_FRAGMENT_NAME`, `text` contains a longer string contextualizing two document fields, `title` and `description`.
 
 ### `searchFragments`
 
-Use `searchFragments` to tell Meilisearch what data to use when converting user queries into embeddings:
+Use `searchFragments` to tell Meilisearch how to send search query data to the chosen provider's REST API when converting them into embeddings:
 
 ```json
 "searchFragments": {
@@ -98,10 +98,15 @@ Use `searchFragments` to tell Meilisearch what data to use when converting user
 }
 ```
 
-This configuration tells Meilisearch how to submit query data for vectorization.
+In this example, two modes of search are configured:
+
+1. A textual search based on the `q` parameter, which will be embedded as text
+2. An image search based on [data url](https://developer.mozilla.org/en-US/docs/Web/URI/Reference/Schemes/data) rebuilt from the `image.mime` and `image.data` field in the `media` field of the query
 
 Search fragments have access to data present in the query parameters `media` and `q`.
 
+Each semantic search query for this embedder should match exactly one search fragment of this embedder, so the fragments should each have at least one disambiguating field
+
 ### Complete embedder configuration
 
 Your embedder should look similar to this example with all fragments and embedding provider data:
@@ -218,7 +223,7 @@ In most cases you will need a GUI interface that allows users to submit their im
 
 With multimodal embedders you can:
 
-1. Configure Meilisearch to vectorize both images and queries  
+1. Configure Meilisearch to embed both images and queries  
 2. Add image documents — Meilisearch automatically generates embeddings  
 3. Accept text or image input from users  
 4. Run hybrid searches using a mix of textual and input from other types of media, or run pure semantic semantic searches using only non-textual input

snippets/samples/code_samples_facet_search_3.mdx

@@ -49,7 +49,8 @@ client.Index("books").FacetSearch(&meilisearch.FacetSearchRequest{
 ```csharp C#
 var query = new SearchFacetsQuery()
 {
-  FacetQuery = "c"
+  FacetQuery = "c",
+  ExhaustiveFacetCount: true
 };
 await client.Index("books").FacetSearchAsync("genres", query);
 ```

snippets/samples/code_samples_geosearch_guide_filter_usage_4.mdx

@@ -0,0 +1,9 @@
+<CodeGroup>
+
+```bash cURL
+curl \
+  -X POST 'MEILISEARCH_URL/indexes/restaurants/search' \
+  -H 'Content-type:application/json' \
+  --data-binary '{ "filter": "_geoPolygon([45.494181, 9.214024], [45.449484, 9.179175], [45.449486, 9.179177])" }'
+```
+</CodeGroup>

snippets/samples/code_samples_getting_started_add_documents.mdx

@@ -78,14 +78,14 @@ $client->index('movies')->addDocuments($movies);
 // <dependency>
 //   <groupId>com.meilisearch.sdk</groupId>
 //   <artifactId>meilisearch-java</artifactId>
-//   <version>0.15.0</version>
+//   <version>0.16.1</version>
 //   <type>pom</type>
 // </dependency>
 
 // For Gradle
 // Add the following line to the `dependencies` section of your `build.gradle`:
 //
-// implementation 'com.meilisearch.sdk:meilisearch-java:0.15.0'
+// implementation 'com.meilisearch.sdk:meilisearch-java:0.16.1'
 
 // In your .java file:
 import com.meilisearch.sdk;
@@ -192,7 +192,7 @@ namespace Meilisearch_demo
 ```text Rust
 // In your .toml file:
   [dependencies]
-  meilisearch-sdk = "0.29.1"
+  meilisearch-sdk = "0.30.0"
   # futures: because we want to block on futures
   futures = "0.3"
   # serde: required if you are going to use documents

snippets/samples/code_samples_ranking_score_threshold.mdx

@@ -0,0 +1,8 @@
+<CodeGroup>
+
+```dart Dart
+await client
+    .index('INDEX_NAME')
+    .search('badman', SearchQuery(rankingScoreThreshold: 0.2));
+```
+</CodeGroup>

snippets/samples/code_samples_rename_an_index_1.mdx

@@ -6,4 +6,11 @@ curl \
   -H 'Content-Type: application/json' \
   --data-binary '{ "uid": "INDEX_B" }'
 ```
+
+```rust Rust
+curl \
+  -X PATCH 'MEILISEARCH_URL/indexes/INDEX_A' \
+  -H 'Content-Type: application/json' \
+  --data-binary '{ "uid": "INDEX_B" }'
+```
 </CodeGroup>