Clarify Vector Database project guidance

explore-analyze/cross-project-search/cross-project-search-tags.md

@@ -34,7 +34,7 @@ The following tags are predefined:
 * `_id`: the project identifier
 * `_organization`: the organization identifier
 * `_region`: the Cloud region where the project is located
-* `_type`: the project type (Observability, Search, Security)
+* `_type`: the project type (`elasticsearch`, `vectordb`, `observability`, or `security`)
 
 Predefined tags always start with an underscore `_`.
 

solutions/search/get-started.md

@@ -32,7 +32,10 @@ For simplicity and speed, try out {{serverless-full}}:
 :::{include} /deploy-manage/deploy/_snippets/create-serverless-project-intro.md
 :::
 
-If you're not sure which project type to choose, select the **{{es}}** project type. This project type provides core {{es}} search capabilities along with additional UI tools to help you build search-powered applications faster.
+Select the project type that matches your workload:
+
+* Select [{{es}} Vector Database](/solutions/vector-database.md) when embeddings and similarity search are central to your application, including semantic search, hybrid search, RAG, and recommendations.
+* Select the [{{es}} project type](/solutions/elasticsearch-solution-project.md) for general-purpose data storage and search, including mixed lexical, time series, and analytics workloads.
 
 When your project is created, you're ready to move on to the next step and to start creating indices, adding data, and performing searches.
 ::::

solutions/search/vector.md

@@ -15,6 +15,8 @@ products:
 # Vector search in {{es}}
 
 :::{tip}
+If you're using {{serverless-full}}, the [{{es}} Vector Database project type](/solutions/vector-database.md) is recommended when embeddings and similarity search are central to your application.
+
 New to semantic search? Start with the [semantic search quickstart](get-started/semantic-search.md), which uses the managed `semantic_text` workflow.
 
 For common vector search use cases and how to apply them, refer to [Vector search use cases](vector/vector-search-use-cases.md).

solutions/search/vector/dense-vector.md

@@ -21,6 +21,10 @@ Dense vectors use neural embeddings to represent semantic meaning. They translat
 - Performing image and multimedia similarity search
 - Delivering content-based recommendations
 
+:::{tip}
+If you're using {{serverless-full}}, the [{{es}} Vector Database project type](/solutions/vector-database.md) is recommended when embeddings and similarity search are central to your application.
+:::
+
 ## Working with dense vectors in {{es}}
 
 :::{tip}
@@ -39,16 +43,27 @@ To implement dense vector search in {{es}}, you need both an index configuration
 2. **Query the index with k-NN search**
    - Use the [`knn` query](knn.md) to retrieve results based on vector similarity
 
-### General purpose and vector optimized projects [vector-profiles]
+### Choose a {{serverless-short}} project configuration [vector-profiles]
 ```{applies_to}
 serverless:
 ```
 
-When you use the [API]({{cloud-serverless-apis}}operation/operation-createelasticsearchproject) to create an {{es-serverless}} project, you can choose between two profiles: `general_purpose` and `vector`.
-The general purpose profile is recommended for most search use cases, including full text search, sparse vectors, and dense vectors that use compression such as BBQ.
-The vector profile is recommended only when your use case is based on uncompressed dense vectors with high dimensionality.
+The available configuration depends on your project type.
+
+:::::{tab-set}
+::::{tab-item} {{es}} project
+When you use the [API]({{cloud-serverless-apis}}operation/operation-createelasticsearchproject) to create an {{es-serverless}} project, select one of two `optimized_for` profiles:
 
-For more information about how the profile affects virtual compute unit (VCU) allocation and costs, refer to [](/deploy-manage/cloud-organization/billing/elasticsearch-billing-dimensions.md).
+* `general_purpose`: Recommended for most search use cases, including full-text search, sparse vectors, and dense vectors that use compression such as BBQ.
+* `vector`: Recommended only for uncompressed, high-dimensional dense vectors.
+
+For information about how the profile affects virtual compute unit (VCU) allocation and costs, refer to [](/deploy-manage/cloud-organization/billing/elasticsearch-billing-dimensions.md).
+::::
+
+::::{tab-item} {{es}} Vector Database project
+Vector Database projects don't use an `optimized_for` profile. Indices use [`index.mode: vectordb_document`](elasticsearch://reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-vectordb-document-mode) automatically.
+::::
+:::::
 
 ## Better Binary Quantization (BBQ) [bbq]