To configure the store explicitly, you can use Config REST API:

PUT /api/kb/config/<storeName>

{
  "chunkOverlap":        150,
  "chunkSize":           1500,
  "embeddingModel":      "sentence-transformers/all-mpnet-base-v2",
  "index":               {
    "type": "hnsw"
  },
  "normalizeEmbeddings": true,
  "vectorStoreType":     "OracleDb"
}

In the example above, the store will be configured to split documents into chunks of no more than 1,500 characters (with a 150 character overlap), embed them using `sentence-transformers/all-mpnet-base-v2` model, normalize the embeddings, create HNSW index for them, and write all embedded document chunks to an external Oracle DB 23ai instance.

To see the current store configuration you can make a `GET` request to the same endpoint:

GET /api/kb/config/<storeName>