Correct step 2 of custom local models (#7600)

* Update custom-local-models.md Signed-off-by: kolchfa-aws <[email protected]> * Correct response function Signed-off-by: Fanit Kolchina <[email protected]> * More changes Signed-off-by: Fanit Kolchina <[email protected]> * Fix rag cluster setting Signed-off-by: Fanit Kolchina <[email protected]> --------- Signed-off-by: kolchfa-aws <[email protected]> Signed-off-by: Fanit Kolchina <[email protected]>
opensearch-project · Jul 2, 2024 · 0f889fe · 0f889fe
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diff --git a/_ml-commons-plugin/api/model-apis/register-model.md b/_ml-commons-plugin/api/model-apis/register-model.md
@@ -84,7 +84,7 @@ Field | Data type | Required/Optional | Description
 `name`| String | Required | The model name. |
 `version` | String | Required | The model version. |
 `model_format` | String | Required | The portable format of the model file. Valid values are `TORCH_SCRIPT` and `ONNX`. |
-`function_name` | String | Required | For text embedding models, set this parameter to `TEXT_EMBEDDING`. For sparse encoding models, set this parameter to `SPARSE_ENCODING` or `SPARSE_TOKENIZE`. For cross-encoder models, set this parameter to `TEXT_SIMILARITY`.
+`function_name` | String | Required | For text embedding models, set this parameter to `TEXT_EMBEDDING`. For sparse encoding models, set this parameter to `SPARSE_ENCODING` or `SPARSE_TOKENIZE`. For cross-encoder models, set this parameter to `TEXT_SIMILARITY`. For question answering models, set this parameter to `QUESTION_ANSWERING`.
 `model_content_hash_value` | String | Required | The model content hash generated using the SHA-256 hashing algorithm.
 `url` | String | Required | The URL that contains the model. |
 `description` | String | Optional| The model description. |

diff --git a/_ml-commons-plugin/cluster-settings.md b/_ml-commons-plugin/cluster-settings.md
@@ -468,7 +468,7 @@ When set to `true`, this setting enables the search processors for retrieval-aug
 ### Setting
 
 ```
-plugins.ml_commons.agent_framework_enabled: true
+plugins.ml_commons.rag_pipeline_feature_enabled: true
 ```
 
 ### Values

diff --git a/_ml-commons-plugin/custom-local-models.md b/_ml-commons-plugin/custom-local-models.md
@@ -109,7 +109,11 @@ To learn more about model groups, see [Model access control]({{site.url}}{{site.
 
 ## Step 2: Register a local model
 
-To register a remote model to the model group created in step 1, provide the model group ID from step 1 in the following request:
+To register a local model to the model group created in step 1, send a Register Model API request. For descriptions of Register Model API parameters, see [Register a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/model-apis/register-model/). 
+
+The `function_name` corresponds to the model type. For text embedding models, set this parameter to `TEXT_EMBEDDING`. For sparse encoding models, set this parameter to `SPARSE_ENCODING` or `SPARSE_TOKENIZE`. For cross-encoder models, set this parameter to `TEXT_SIMILARITY`. For question answering models, set this parameter to `QUESTION_ANSWERING`. In this example, set `function_name` to `TEXT_EMBEDDING` because you're registering a text embedding model. 
+
+Provide the model group ID from step 1 and send the following request:
 
 ```json
 POST /_plugins/_ml/models/_register
@@ -118,7 +122,7 @@ POST /_plugins/_ml/models/_register
   "version": "1.0.1",
   "model_group_id": "wlcnb4kBJ1eYAeTMHlV6",
   "description": "This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search.",
-  "model_task_type": "TEXT_EMBEDDING",
+  "function_name": "TEXT_EMBEDDING",
   "model_format": "TORCH_SCRIPT",
   "model_content_size_in_bytes": 266352827,
   "model_content_hash_value": "acdc81b652b83121f914c5912ae27c0fca8fabf270e6f191ace6979a19830413",
@@ -143,7 +147,7 @@ POST /_plugins/_ml/models/_register
   "version": "1.0.1",
   "model_group_id": "wlcnb4kBJ1eYAeTMHlV6",
   "description": "This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search.",
-  "model_task_type": "TEXT_EMBEDDING",
+  "function_name": "TEXT_EMBEDDING",
   "model_format": "TORCH_SCRIPT",
   "model_content_size_in_bytes": 266352827,
   "model_content_hash_value": "acdc81b652b83121f914c5912ae27c0fca8fabf270e6f191ace6979a19830413",
@@ -159,8 +163,6 @@ POST /_plugins/_ml/models/_register
 ```
 {% include copy.html %}
 
-For descriptions of Register API parameters, see [Register a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/model-apis/register-model/). The `model_task_type` corresponds to the model type. For text embedding models, set this parameter to `TEXT_EMBEDDING`. For sparse encoding models, set this parameter to `SPARSE_ENCODING` or `SPARSE_TOKENIZE`. For cross-encoder models, set this parameter to `TEXT_SIMILARITY`. For question answering models, set this parameter to `QUESTION_ANSWERING`.
-
 OpenSearch returns the task ID of the register operation:
 
 ```json
@@ -183,7 +185,7 @@ When the operation is complete, the state changes to `COMPLETED`:
 {
   "model_id": "cleMb4kBJ1eYAeTMFFg4",
   "task_type": "REGISTER_MODEL",
-  "function_name": "REMOTE",
+  "function_name": "TEXT_EMBEDDING",
   "state": "COMPLETED",
   "worker_node": [
     "XPcXLV7RQoi5m8NI_jEOVQ"
@@ -229,7 +231,7 @@ When the operation is complete, the state changes to `COMPLETED`:
 {
   "model_id": "cleMb4kBJ1eYAeTMFFg4",
   "task_type": "DEPLOY_MODEL",
-  "function_name": "REMOTE",
+  "function_name": "TEXT_EMBEDDING",
   "state": "COMPLETED",
   "worker_node": [
     "n-72khvBTBi3bnIIR8FTTw"
@@ -379,4 +381,4 @@ The response provides the answer based on the context:
         }
     }
 }
-```
+```
diff --git a/_ml-commons-plugin/pretrained-models.md b/_ml-commons-plugin/pretrained-models.md
@@ -126,7 +126,7 @@ To learn more about model groups, see [Model access control]({{site.url}}{{site.
 
 ## Step 2: Register a local OpenSearch-provided model
 
-To register a remote model to the model group created in step 1, provide the model group ID from step 1 in the following request.
+To register an OpenSearch-provided model to the model group created in step 1, provide the model group ID from step 1 in the following request.
 
 Because pretrained models originate from the ML Commons model repository, you only need to provide the `name`, `version`, `model_group_id`, and `model_format` in the register API request:  
 
@@ -163,7 +163,7 @@ When the operation is complete, the state changes to `COMPLETED`:
 {
   "model_id": "cleMb4kBJ1eYAeTMFFg4",
   "task_type": "REGISTER_MODEL",
-  "function_name": "REMOTE",
+  "function_name": "TEXT_EMBEDDING",
   "state": "COMPLETED",
   "worker_node": [
     "XPcXLV7RQoi5m8NI_jEOVQ"
@@ -209,7 +209,7 @@ When the operation is complete, the state changes to `COMPLETED`:
 {
   "model_id": "cleMb4kBJ1eYAeTMFFg4",
   "task_type": "DEPLOY_MODEL",
-  "function_name": "REMOTE",
+  "function_name": "TEXT_EMBEDDING",
   "state": "COMPLETED",
   "worker_node": [
     "n-72khvBTBi3bnIIR8FTTw"