diff --git a/guides/databases/vector-embeddings.md b/guides/databases/vector-embeddings.md
index 96344915f..302f2cd25 100644
--- a/guides/databases/vector-embeddings.md
+++ b/guides/databases/vector-embeddings.md
@@ -48,10 +48,9 @@ If the database calculates vector embeddings on write it automatically regenerat
 
 ::: info Local Testing with H2 and SQLite
 On H2 and SQLite the `CQL.vectorEmbedding` function is emulated using a hash-based algorithm to support local testing. For PostgreSQL, customers must define their own `vector_embedding` function for both testing and production use.
-:::
 
-> [!warning] Java only and <Beta/>
-> The `vector_embedding` function is currently in beta and only supported by the CAP Java runtime.
+In CAP Node.js, the [`@cap-js/ai`](https://github.com/cap-js/ai) plugin adds an `ai-sqlite` database kind that generates real embeddings locally on SQLite with an [ONNX](https://onnx.ai) model. It requires a configured embedding model and is experimental, for local development only. <Beta/>
+:::
 
 [Learn more about Vector Embeddings in CAP Java](../../java/cds-data#vector-embeddings) {.learn-more}
 
@@ -100,15 +99,13 @@ Select.from(INCIDENTS)
 ```
 
 ```js [Node.js]
-const response = await new AzureOpenAiEmbeddingClient(
- 'text-embedding-3-small'
-).run({
- input: 'Any incidents with solar inverters this month? How were they resolved?'
-});
-
-const questionEmbedding = response.getEmbedding();
-let similarIncidents = await SELECT.from('Incidents')
-  .where`cosine_similarity(embedding, to_real_vector(${questionEmbedding})) > 0.75`;
+const question =
+  'Any incidents with solar inverters this month? How were they resolved?'
+
+// Compute the question's embedding and find related incidents, all in the database
+const similarIncidents = await SELECT.from('Incidents').where`
+  cosine_similarity(embedding,
+    vector_embedding(${question}, 'QUERY', 'SAP_GXY.20250407')) > 0.75`
 ```
 :::
 
@@ -139,11 +136,31 @@ vector_embedding(text, text_type, model_name, remote_source) → vector
 
 **Database Implementation:**
 - **HANA:** Uses real AI models (SAP built-in models or external remote sources)
-- **SQLite & H2:** Hash-based deterministic implementation for testing. Can be overridden by application developers to use external embedding services.
+- **SQLite & H2:** Hash-based deterministic implementation for testing. Can be overridden by application developers to use external embedding services. In CAP Node.js, the [`@cap-js/ai`](https://github.com/cap-js/ai) plugin with the `ai-sqlite` database kind generates real embeddings locally via an [ONNX](https://onnx.ai) model. <Beta/>
 - **PostgreSQL:** No default implementation. Application developers must define their own `vector_embedding` function.
 
 ## Database-Specific Considerations
 
+### SQLite
+- Hash-based, deterministic `vector_embedding` implementation by default, suitable for local testing.
+- In CAP Node.js, the [`@cap-js/ai`](https://github.com/cap-js/ai) plugin adds an `ai-sqlite` database kind that generates real embeddings locally with an [ONNX](https://onnx.ai) model, without any external service. It is experimental and intended for local development only. <Beta/>
+
+  Install the plugin with its peer dependencies:
+  ```sh
+  npm add -D @cap-js/ai @cap-js/sqlite \
+    @huggingface/hub @huggingface/tokenizers onnxruntime-node@1.20.1
+  ```
+  Then set the database kind and configure an embedding model (there's no default):
+  ```json
+  {
+    "cds": { "requires": { "db": {
+      "kind": "ai-sqlite",
+      "embedding": { "model": "sentence-transformers/all-MiniLM-L6-v2" }
+    } } }
+  }
+  ```
+  On first start, the model is downloaded to `.cds/models` and reused afterwards. See the [`@cap-js/ai` README](https://github.com/cap-js/ai#local-vector-embeddings-with-sqlite-experimental) for the full walkthrough and model selection.
+
 ### PostgreSQL
 - Requires that the [pgvector extension](https://github.com/pgvector/pgvector) is installed on your PostgreSQL instance. Then create the extension in your database:
   ```sql