Merge branch 'main' into create-experimental-api-routes

guimachiavelli · web-flow · commit 0df6b0831d7f · 2024-11-05T18:38:31.000+01:00
diff --git a/.code-samples.meilisearch.yaml b/.code-samples.meilisearch.yaml
@@ -1311,11 +1311,12 @@ get_similar_post_1: |-
     -H 'Content-Type: application/json' \
     -H 'Authorization: Bearer DEFAULT_SEARCH_API_KEY' \
     --data-binary '{
-      "id": TARGET_DOCUMENT_ID
+      "id": TARGET_DOCUMENT_ID,
+      "embedder": "EMBEDDER_NAME"
     }'
 get_similar_get_1: |-
   curl \
-    -X GET 'http://localhost:7700/indexes/INDEX_NAME/similar?id=TARGET_DOCUMENT_ID'
+    -X GET 'http://localhost:7700/indexes/INDEX_NAME/similar?id=TARGET_DOCUMENT_ID&embedder=EMBEDDER_NAME'
 search_parameter_reference_ranking_score_threshold_1: |-
   curl \
   -X POST 'http://localhost:7700/indexes/INDEX_NAME/search' \
@@ -1329,7 +1330,7 @@ search_parameter_reference_locales_1: |-
   -X POST 'http://localhost:7700/indexes/INDEX_NAME/search' \
   -H 'Content-Type: application/json' \
   --data-binary '{
-    "q": "進撃の巨人",
+    "q": "QUERY TEXT IN JAPANESE",
     "locales": ["jpn"]
   }'
 get_localized_attribute_settings_1: |-
@@ -1376,21 +1377,24 @@ search_parameter_guide_hybrid_1: |-
       "q": "kitchen utensils",
       "hybrid": {
         "semanticRatio": 0.9,
-        "embedder": "default"
+        "embedder": "EMBEDDER_NAME"
       }
     }'
 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] }'
+    --data-binary '{ 
+      "vector": [0, 1, 2],
+      "embedder": "EMBEDDER_NAME"
+    }'
 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"
+        "embedder": "EMBEDDER_NAME"
       }
     }'
 experimental_get_metrics_1: |-
diff --git a/.github/workflows/code-sample-automation.yml b/.github/workflows/code-sample-automation.yml
@@ -0,0 +1,16 @@
+name: Add PR to Code Samples Changes project
+
+on:
+  pull_request:
+    types: [opened, edited, synchronize]
+    paths:
+      - .code-samples.meilisearch.yaml
+
+jobs:
+  add-pr-to-project:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/add-to-project@f5473ace9aeee8b97717b281e26980aa5097023f
+        with:
+          project-url: https://github.com/orgs/meilisearch/projects/44
+          github-token: ${{ secrets.GH_TOKEN }}
diff --git a/assets/misc/meilisearch-collection-postman.json b/assets/misc/meilisearch-collection-postman.json
@@ -1,9 +1,9 @@
 {
 	"info": {
-		"_postman_id": "719caa45-6643-4393-9b84-e8bc6a70d074",
-		"name": "Meilisearch v1.10",
+		"_postman_id": "cc6bb097-033d-4f65-8704-f10e4e4b10d0",
+		"name": "Meilisearch v1.11",
 		"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json",
-		"_exporter_id": "8898306"
+		"_exporter_id": "25294324"
 	},
 	"item": [
 		{
diff --git a/guides/docker.mdx b/guides/docker.mdx
@@ -14,7 +14,7 @@ Docker is a tool that bundles applications into containers. Docker containers en
 Docker containers are distributed in images. To use Meilisearch, use the `docker pull` command to download a Meilisearch image:
 
 ```sh
-docker pull getmeili/meilisearch:v1.10
+docker pull getmeili/meilisearch:v1.11
 ```
 
 Meilisearch deploys a new Docker image with every release of the engine. Each image is tagged with the corresponding Meilisearch version, indicated in the above example by the text following the `:` symbol. You can see [the full list of available Meilisearch Docker images](https://hub.docker.com/r/getmeili/meilisearch/tags#!) on Docker Hub.
@@ -31,7 +31,7 @@ After completing the previous step, use `docker run` to launch the Meilisearch i
 docker run -it --rm \
   -p 7700:7700 \
   -v $(pwd)/meili_data:/meili_data \
-  getmeili/meilisearch:v1.10
+  getmeili/meilisearch:v1.11
 ```
 
 ### Configure Meilisearch
@@ -47,7 +47,7 @@ docker run -it --rm \
   -p 7700:7700 \
   -e MEILI_MASTER_KEY='MASTER_KEY'\
   -v $(pwd)/meili_data:/meili_data \
-  getmeili/meilisearch:v1.10
+  getmeili/meilisearch:v1.11
 ```
 
 #### Passing instance options with CLI arguments
@@ -58,7 +58,7 @@ If you want to pass command-line arguments to Meilisearch with Docker, you must
 docker run -it --rm \
   -p 7700:7700 \
   -v $(pwd)/meili_data:/meili_data \
-  getmeili/meilisearch:v1.10 \
+  getmeili/meilisearch:v1.11 \
   meilisearch --master-key="MASTER_KEY"
 ```
 
@@ -76,7 +76,7 @@ To keep your data intact between reboots, specify a dedicated volume by running
 docker run -it --rm \
   -p 7700:7700 \
   -v $(pwd)/meili_data:/meili_data \
-  getmeili/meilisearch:v1.10
+  getmeili/meilisearch:v1.11
 ```
 
 The example above uses `$(pwd)/meili_data`, which is a directory in the host machine. Depending on your OS, mounting volumes from the host to the container might result in performance loss and is only recommended when developing your application.
@@ -91,7 +91,7 @@ To import a dump, use Meilisearch's `--import-dump` command-line option and spec
 docker run -it --rm \
   -p 7700:7700 \
   -v $(pwd)/meili_data:/meili_data \
-  getmeili/meilisearch:v1.10 \
+  getmeili/meilisearch:v1.11 \
   meilisearch --import-dump /meili_data/dumps/20200813-042312213.dump
 ```
 
@@ -111,7 +111,7 @@ To generate a Meilisearch snapshot with Docker, launch Meilisearch with `--sched
 docker run -it --rm \
   -p 7700:7700 \
   -v $(pwd)/meili_data:/meili_data \
-  getmeili/meilisearch:v1.10 \
+  getmeili/meilisearch:v1.11 \
   meilisearch --schedule-snapshot --snapshot-dir /meili_data/snapshots
 ```
 
@@ -123,7 +123,7 @@ To import a snapshot, launch Meilisearch with the `--import-snapshot` option:
 docker run -it --rm \
   -p 7700:7700 \
   -v $(pwd)/meili_data:/meili_data \
-  getmeili/meilisearch:v1.10 \
+  getmeili/meilisearch:v1.11 \
   meilisearch --import-snapshot /meili_data/snapshots/data.ms.snapshot
 ```
 
diff --git a/guides/running_production.mdx b/guides/running_production.mdx
@@ -46,7 +46,7 @@ Next, you need to make the binary accessible from anywhere in your system. Move
 mv ./meilisearch /usr/local/bin/
 ```
 
-Meilisearch is now running, but it is not publicly accessible.
+Meilisearch is now installed in your system, but it is not publicly accessible.
 
 ## Step 2: Create system user
 
@@ -56,6 +56,12 @@ Running applications as root exposes you to unnecessary security risks. To preve
 useradd -d /var/lib/meilisearch -s /bin/false -m -r meilisearch
 ```
 
+Then give the new user ownership of the Meilisearch binary:
+
+```sh
+chown meilisearch:meilisearch /usr/local/bin/meilisearch
+```
+
 ## Step 3: Create a configuration file
 
 After installing Meilisearch and taking the first step towards keeping your data safe, you need to set up a basic configuration file.
diff --git a/learn/ai_powered_search/getting_started_with_ai_search.mdx b/learn/ai_powered_search/getting_started_with_ai_search.mdx
@@ -50,15 +50,15 @@ curl \
 
 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:
+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 an [OpenAI](https://platform.openai.com/) embedder:
 
 ```sh
 curl \
   -X PATCH 'http://localhost:7700/indexes/kitchenware/settings' \
   -H 'Content-Type: application/json' \
   --data-binary '{
     "embedders": {
-      "default": {
+      "openai": {
         "source":  "openAi",
         "apiKey": "OPEN_AI_API_KEY",
         "model": "text-embedding-3-small",
@@ -91,7 +91,7 @@ curl \
   --data-binary '{
     "q": "kitchen utensils made of wood",
     "hybrid": {
-      "embedder": "default",
+      "embedder": "openai",
       "semanticRatio": 0.7
     }
   }'
diff --git a/learn/filtering_and_sorting/filter_expression_reference.mdx b/learn/filtering_and_sorting/filter_expression_reference.mdx
@@ -161,7 +161,7 @@ NOT genres IN [horror, comedy]
 
 `CONTAINS` filters results containing partial matches to the specified string pattern, similar to a [SQL `LIKE`](https://dev.mysql.com/doc/refman/8.4/en/string-comparison-functions.html#operator_like).
 
-The following expression returns all dairy products whose name start with `"kef"`, such as kefir:
+The following expression returns all dairy products whose names contain `"kef"`:
 
 ```
 dairy_products.name CONTAINS kef
@@ -185,6 +185,40 @@ curl \
     "containsFilter": true
   }'
 ```
+
+This will also enable the [`STARTS WITH`](#starts-with) operator.
+</Capsule>
+
+### `STARTS WITH` <NoticeTag type="experimental" label="experimental" />
+
+`STARTS WITH` filters results whose values start with the specified string pattern.
+
+The following expression returns all dairy products whose name start with `"kef"`:
+
+```
+dairy_products.name STARTS WITH kef
+```
+
+The negated form of the above expression can be written as:
+
+```
+dairy_products.name NOT STARTS WITH kef
+NOT dairy_product.name STARTS WITH kef
+```
+
+<Capsule intent="note" title="Activating `STARTS WITH`">
+This is an experimental feature. Use the experimental features endpoint to activate it:
+
+```sh
+curl \
+  -X PATCH 'http://localhost:7700/experimental-features/' \
+  -H 'Content-Type: application/json' \
+  --data-binary '{
+    "containsFilter": true
+  }'
+```
+
+This will also enable the [`CONTAINS`](#contains) operator.
 </Capsule>
 
 ### `NOT`
diff --git a/learn/indexing/indexing_best_practices.mdx b/learn/indexing/indexing_best_practices.mdx
@@ -60,3 +60,13 @@ If you have followed the previous tips in this guide and are still experiencing
 Indexing is a memory-intensive and multi-threaded operation. The more memory and processor cores available, the faster Meilisearch will index new documents. When trying to improve indexing speed, using a machine with more processor cores is more effective than increasing RAM.
 
 Due to how Meilisearch works, it is best to avoid HDDs (Hard Disk Drives) as they can easily become performance bottlenecks.
+
+## Enable binary quantization when using AI-powered search
+
+If you are experiencing performance issues when indexing documents for AI-powered search, consider enabling [binary quantization](/reference/api/settings#binaryquantized) for your embedders. Binary quantization compresses vectors by representing each dimension with 1-bit values. This reduces the relevancy of semantic search results, but greatly improves performance.
+
+Binary quantization works best with large datasets containing more than 1M documents and using models with more than 1400 dimensions.
+
+<Capsule intent="danger" title="Binary quantization is an irreversible process">
+**Activating binary quantization is irreversible.** Once enabled, Meilisearch converts all vectors and discards all vector data that does fit within 1-bit. The only way to recover the vectors' original values is to re-vectorize the whole index in a new embedder.
+</Capsule>
diff --git a/learn/resources/telemetry.mdx b/learn/resources/telemetry.mdx
@@ -240,13 +240,14 @@ This list is liable to change with every new version of Meilisearch. It's not be
 | `vector_store`                                     | `true` if the vector store feature is enabled, otherwise `false`                            | true
 | `attributes_to_search_on.total_number_of_uses`     | `true` if the vector store feature is enabled, otherwise `false`                            | true
 | `vector.max_vector_size`                           | Highest number of dimensions given for the `vector` parameter in this batch                 | 1536
-| `vector.retrieve_vectors` | true if the retrieve_vectors parameter has been used in this batch. | false |
+| `vector.retrieve_vectors`                          | true if the retrieve_vectors parameter has been used in this batch.                         | false
 | `hybrid.enabled`                                   | `true` if hybrid search been used in the aggregated event, otherwise `false`                | true
 | `hybrid.semantic_ratio`                            | `true` if semanticRatio was used in this batch, otherwise false                             | false
-| `hybrid.embedder`                                  | `true` if a specific embedder was used in this batch, otherwise false                       | true 
 | `embedders.total`                                  | Numbers of defined embedders                                                                | 2
 | `embedders.sources`                                | An array representing the different provided sources                                        | [”huggingFace”, “userProvided”]
 | `embedders.document_template_used`                 | A boolean indicating if one of the provided embedders has a custom template defined         | true
+| `embedders.document_template_max_bytes`            | a value indicating the largest value for document TemplateMaxBytes across all embedder      | 400
+| `embedders.binary_quantization_used`               | `true` if the user updated the binary quantized field of the embedded settings              | `false`
 | `infos.task_queue_webhook`                         | `true` if the instance is launched with a task queue webhook, otherwise `false`             | `false`
 | `infos.experimental_search_queue_size`             | Size of the search queue                                                                    | 750
 | `locales`                                          | List of locales used with `/search` and `/settings` routes                                  | [”fra”, “eng”]
diff --git a/learn/self_hosted/install_meilisearch_locally.mdx b/learn/self_hosted/install_meilisearch_locally.mdx
@@ -54,14 +54,14 @@ These commands launch the **latest stable release** of Meilisearch.
 
 ```bash
 # Fetch the latest version of Meilisearch image from DockerHub
-docker pull getmeili/meilisearch:v1.10
+docker pull getmeili/meilisearch:v1.11
 
 # Launch Meilisearch in development mode with a master key
 docker run -it --rm \
     -p 7700:7700 \
     -e MEILI_ENV='development' \
     -v $(pwd)/meili_data:/meili_data \
-    getmeili/meilisearch:v1.10
+    getmeili/meilisearch:v1.11
 # Use ${pwd} instead of $(pwd) in PowerShell
 ```
 
diff --git a/package.json b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "documentation",
-  "version": "1.10.0",
+  "version": "1.11.0",
   "description": "",
   "main": "index.js",
   "dependencies": {},
diff --git a/reference/api/keys.mdx b/reference/api/keys.mdx
@@ -57,14 +57,12 @@ Accessing the `/keys` route without setting a master key will throw a [`missing_
 
 This value is also used as the `{key}` path variable to [update](#update-a-key), [delete](#delete-a-key), or [get](#get-one-key) a specific key.
 
-<Capsule intent="note">
-Since `key` is a hash of the `uid` and master key, `key` values are deterministic between instances sharing the same configuration. You can determine the value of an API key with the following command, replacing `HYPHENATED_UUID` and `MASTER_KEY` with the correct values for your key and instance:
+If the master key changes, all `key` values are automatically changed.
 
-```sh
-echo -n $HYPHENATED_UUID | openssl dgst -sha256 -hmac $MASTER_KEY
-```
+<Capsule intent="note" title="Reusing API keys between instances">
+Custom API keys are deterministic: `key` is a SHA256 hash of the `uid` and master key. To reuse custom API keys, launch the new instance with the same master key and recreate your API keys with the same `uid`.
 
-This also means that, if the master key changes, all `key` values are automatically changed.
+**You cannot reuse default API keys between instances.** Meilisearch automatically generates their `uid`s the first time you launch an instance.
 </Capsule>
 
 ### `actions`
diff --git a/reference/api/multi_search.mdx b/reference/api/multi_search.mdx
diff --git a/reference/api/search.mdx b/reference/api/search.mdx
diff --git a/reference/api/settings.mdx b/reference/api/settings.mdx
diff --git a/reference/api/similar.mdx b/reference/api/similar.mdx
diff --git a/reference/errors/error_codes.mdx b/reference/errors/error_codes.mdx

Original file line number	Diff line number	Diff line change
`@@ -1,9 +1,9 @@`
`1`	`1`	`{`
`2`	`2`	`"info": {`
`3`		`- "_postman_id": "719caa45-6643-4393-9b84-e8bc6a70d074",`
`4`		`- "name": "Meilisearch v1.10",`
	`3`	`+ "_postman_id": "cc6bb097-033d-4f65-8704-f10e4e4b10d0",`
	`4`	`+ "name": "Meilisearch v1.11",`
`5`	`5`	`"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json",`
`6`		`- "_exporter_id": "8898306"`
	`6`	`+ "_exporter_id": "25294324"`
`7`	`7`	`},`
`8`	`8`	`"item": [`
`9`	`9`	`{`