AI connector blueprint for the Aleph Alpha Luminous-Base Embedding Mo…

…del (opensearch-project#1940) * Add files via upload Signed-off-by: Ulan Yisaev <[email protected]> Adding an AI connector blueprint for the Aleph Alpha Luminous-Base Embedding Model. Signed-off-by: Ulan Yisaev <[email protected]> * Update aleph_alpha_connector_luminous_base_embedding_blueprint.md Fixed all comments. Signed-off-by: Ulan Yisaev <[email protected]> --------- Signed-off-by: Ulan Yisaev <[email protected]>
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+# Aleph Alpha connector blueprint example for luminous-base embedding model
+This is an AI connector blueprint for the [Aleph Alpha Luminous-Base Embedding Model](https://docs.aleph-alpha.com/api/semantic-embed/).
+This model is particularly effective for German language applications, providing nuanced and contextually relevant embeddings. 
+
+## 1. Add connector endpoint to trusted URLs:
+
+```json
+PUT /_cluster/settings
+{
+    "persistent": {
+        "plugins.ml_commons.trusted_connector_endpoints_regex": [
+            "^https://api\\.aleph-alpha\\.com/.*$"
+        ]
+    }
+}
+```
+
+## 2. Create connector for Aleph Alpha:
+
+```json
+POST /_plugins/_ml/connectors/_create
+{
+  "name": "Aleph Alpha Connector: luminous-base, representation: document",
+  "description": "The connector to the Aleph Alpha luminous-base embedding model with representation: document",
+  "version": 1,
+  "protocol": "http",
+  "parameters": {
+    "endpoint": "api.aleph-alpha.com",
+    "representation": "document",
+    "normalize": true
+  },
+  "credential": {
+    "AlephAlpha_API_Token": "<PLEASE ADD YOUR ALEPH ALPHA API TOKEN HERE>"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+	  "url": "https://${parameters.endpoint}/semantic_embed",
+      "headers": {
+        "Content-Type": "application/json",
+        "Accept": "application/json",
+        "Authorization": "Bearer ${credential.AlephAlpha_API_Token}"
+      },
+      "request_body": "{ \"model\": \"luminous-base\", \"prompt\": \"${parameters.input}\", \"representation\": \"${parameters.representation}\", \"normalize\": ${parameters.normalize}}",
+      "pre_process_function": "\n    StringBuilder builder = new StringBuilder();\n    builder.append(\"\\\"\");\n    String first = params.text_docs[0];\n    builder.append(first);\n    builder.append(\"\\\"\");\n    def parameters = \"{\" +\"\\\"input\\\":\" + builder + \"}\";\n    return  \"{\" +\"\\\"parameters\\\":\" + parameters + \"}\";",
+      "post_process_function": "\n      def name = \"sentence_embedding\";\n      def dataType = \"FLOAT32\";\n      if (params.embedding == null || params.embedding.length == 0) {\n        return params.message;\n      }\n      def shape = [params.embedding.length];\n      def json = \"{\" +\n                 \"\\\"name\\\":\\\"\" + name + \"\\\",\" +\n                 \"\\\"data_type\\\":\\\"\" + dataType + \"\\\",\" +\n                 \"\\\"shape\\\":\" + shape + \",\" +\n                 \"\\\"data\\\":\" + params.embedding +\n                 \"}\";\n      return json;\n    "
+    }
+  ]
+}
+```
+
+Sample response:
+```json
+{
+  "connector_id": "bRa3QI0BWgGoN0Ye9K2u"
+}
+```
+
+## 3. Create model group:
+
+```json
+POST /_plugins/_ml/model_groups/_register
+{
+    "name": "remote_model_group",
+    "description": "This is an example description"
+}
+```
+
+Sample response:
+```json
+{
+  "model_group_id": "XRbIP40BWgGoN0YeZq1V",
+  "status": "CREATED"
+}
+```
+
+## 4. Register model to model group & deploy model:
+
+```json
+POST /_plugins/_ml/models/_register
+{
+  "name": "Luminous-base embedding model",
+  "function_name": "remote",
+  "model_group_id": "XRbIP40BWgGoN0YeZq1V",
+  "description": "embedding model, representation: document",
+  "connector_id": "bRa3QI0BWgGoN0Ye9K2u"
+}
+```
+
+Sample response:
+```json
+{
+  "task_id": "r6R9PIsBQRofe4CSlUoG",
+  "status": "CREATED"
+}
+```
+Get model id from task
+```json
+GET /_plugins/_ml/tasks/r6R9PIsBQRofe4CSlUoG
+```
+Deploy model, in this demo the model id is `sKR9PIsBQRofe4CSlUov`
+```json
+POST /_plugins/_ml/models/sKR9PIsBQRofe4CSlUov/_deploy
+```
+
+## 5. Test model inference
+
+```json
+POST /_plugins/_ml/models/sKR9PIsBQRofe4CSlUov/_predict
+{
+  "parameters": {
+    "input": "Test string with German characters: Moët Hennessy"
+  }
+}
+```
+
+Sample response:
+```json
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            5120
+          ],
+          "data": [
+            -0.012756348,
+            0.001159668,
+            0.0025634766,
+            ...
+          ]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+