Merge pull request #2966 from meilisearch/update-vector-search-utms

feat: update UTM params on vector & hybrid search content

learn/ai_powered_search/getting_started_with_ai_search.mdx

@@ -5,7 +5,7 @@ description: AI-powered search is an experimental technology that uses LLMs to r
 
 # Getting started with AI-powered search <NoticeTag type="experimental" label="experimental" />
 
-[AI-powered search](https://meilisearch.com/solutions/vector-search?utm_campaign=vector-search&utm_source=aipowered-tutorial), sometimes also called vector search and hybrid search, is an experimental technology that uses [large language models](https://en.wikipedia.org/wiki/Large_language_model) to retrieve search results based on the meaning and context of a query.
+[AI-powered search](https://meilisearch.com/solutions/vector-search?utm_campaign=vector-search&utm_source=docs&utm_content=getting-started-with-ai-search), sometimes also called vector search and hybrid search, is an experimental technology that uses [large language models](https://en.wikipedia.org/wiki/Large_language_model) to retrieve search results based on the meaning and context of a query.
 
 This tutorial will walk you through configuring AI-powered search in your Meilisearch project. You will activate the vector store setting, generate document embeddings with OpenAI, and perform your first search.
 
@@ -35,7 +35,7 @@ To ensure proper scaling of Meilisearch Cloud's latest AI-powered search offerin
 
 ### Self-hosted instances
 
-Use [the `/experimental-features` route](/reference/api/experimental_features) to activate vector search during runtime:
+Use [the `/experimental-features` route](/reference/api/experimental_features?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide) to activate vector search during runtime:
 
 ```sh
 curl \
@@ -50,7 +50,7 @@ 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. Use OpenAI for this tutorial.
 
-Use the `embedders` index setting of the [update `/settings` endpoint](/reference/api/settings) 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 a default [OpenAI](https://platform.openai.com/) embedder:
 
 ```sh
 curl \
@@ -88,7 +88,7 @@ Perform AI-powered searches with `q` and `hybrid` to retrieve search results usi
 curl \
   -X POST 'http://localhost:7700/indexes/kitchenware/search' \
   -H 'content-type: application/json' \
-  --data-binary '{ 
+  --data-binary '{
     "q": "kitchen utensils made of wood",
     "hybrid": {
       "embedder": "default",

learn/experimental/vector_search.mdx

@@ -6,11 +6,11 @@ sidebarDepth: 3
 
 # Vector search
 
-[Vector search](https://meilisearch.com/solutions/vector-search?utm_campaign=vector-search&utm_source=docs) is an experimental technology that uses Large Language Models to retrieve search results based on the meaning and context of a query.
+[Vector search](https://meilisearch.com/solutions/vector-search?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide) is an experimental technology that uses Large Language Models to retrieve search results based on the meaning and context of a query.
 
 This feature can improve search relevancy for queries that do not to match keywords in your dataset, allow your users to search images and other non-textual media, suggest related products in webshops, and create conversational search interfaces.
 
-Vector search is available to all users. [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=docs) is the recommended way of using vector search.
+Vector search is available to all users. [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide) is the recommended way of using vector search.
 
 ## Using vector search
 
@@ -52,7 +52,7 @@ It is also possible to [supply custom embeddings](#generate-custom-embeddings-ma
 
 <Tabs.Container labels={["OpenAI", "Hugging Face", "Ollama", "REST"]}>
 <Tabs.Content label="OpenAI">
-Use the `embedders` index setting of the [update `/settings` endpoint](/reference/api/settings) to configure one or more embedders for an index:
+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 one or more embedders for an index:
 
 ```sh
 curl \
@@ -81,9 +81,9 @@ It is mandatory to pass an OpenAI API key through the `OPENAI_API_KEY` environme
 </Tabs.Content>
 
 <Tabs.Content label="Hugging Face">
-The Hugging Face embedder computes embeddings locally. This is a resource-intensive operation and might affect indexing performance. You may be able to significantly improve performance by [compiling a CUDA-compatible Meilisearch binary](/guides/ai/computing_hugging_face_embeddings_gpu).
+The Hugging Face embedder computes embeddings locally. This is a resource-intensive operation and might affect indexing performance. You may be able to significantly improve performance by [compiling a CUDA-compatible Meilisearch binary](/guides/ai/computing_hugging_face_embeddings_gpu?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide).
 
-Use the `embedders` index setting of the [update `/settings` endpoint](/reference/api/settings) to configure one or more embedders for an index:
+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 one or more embedders for an index:
 
 ```sh
 curl \
@@ -106,7 +106,7 @@ curl \
 </Tabs.Content>
 
 <Tabs.Content label="Ollama">
-Use the `embedders` index setting of the [update `/settings` endpoint](/reference/api/settings) to configure one or more embedders for an index:
+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 one or more embedders for an index:
 
 ```sh
 curl \
@@ -137,7 +137,7 @@ curl \
 <Tabs.Content label="REST">
 The generic REST source is compatible with any embedder with a REST API without a dedicated Meilisearch `source`.
 
-Use the `embedders` index setting of the [update `/settings` endpoint](/reference/api/settings) to configure one or more embedders for an index:
+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 one or more embedders for an index:
 
 ```sh
 curl \
@@ -206,7 +206,7 @@ curl \
   }'
 ```
 
-Then, use [the `/documents` endpoint](/reference/api/documents) to upload vectorized documents. Store vector data in your documents' `_vectors` field:
+Then, use [the `/documents` endpoint](/reference/api/documents?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide) to upload vectorized documents. Store vector data in your documents' `_vectors` field:
 
 ```sh
 curl -X POST -H 'content-type: application/json' \
@@ -270,7 +270,7 @@ curl -X POST -H 'content-type: application/json' \
 - `semanticRatio`: number between `0` and `1`. `0` indicates full keyword search, `1` indicates full semantic search. Defaults to `0.5`
 - `embedder`: string, indicating one of the embedders configured for the queried index. Defaults to `"default"`
 
-`hybrid` can be used together with [other search parameters](/reference/api/search), including [`filter`](/reference/api/search#filter) and [`sort`](/reference/api/search#sort):
+`hybrid` 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?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide#filter) and [`sort`](/reference/api/search?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide#sort):
 
 ```sh
 curl -X POST -H 'content-type: application/json' \
@@ -298,7 +298,7 @@ curl -X POST -H 'content-type: application/json' \
 
 `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), including [`filter`](/reference/api/search#filter) and [`sort`](/reference/api/search#sort):
+`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' \
@@ -311,7 +311,7 @@ curl -X POST -H 'content-type: application/json' \
 ```
 
 <Capsule intent="tip" title="Other resources">
-Check out the Meilisearch blog post for a [guide on implementing semantic search with LangChain](/learn/cookbooks/langchain).
+Check out the Meilisearch blog post for a [guide on implementing semantic search with LangChain](/learn/cookbooks/langchain?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide).
 </Capsule>
 
 ## Deactivate vector search
@@ -330,7 +330,7 @@ If you don't remove all embedders, Meilisearch will continue auto-generating emb
 
 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):
+Alternatively, use [the `/experimental` route](/reference/api/experimental_features?utm_campaign=vector-search&utm_source=docs&utm_medium=vector-search-guide):
 
 ```sh
 curl \