X-Git-Url: https://gerrit.onap.org/r/gitweb?a=blobdiff_plain;f=Readme.md;h=5aa173339676a36acfe134d9093c130a4ded1bdb;hb=126843b5dbc7987fca4f03ea5c85f683474a0a53;hp=d8f1f024142291df33d7fdab6909956600873f95;hpb=c5aea4a8bc398fc1c6220875e55b9520fd7f7524;p=aai%2Fmodel-loader.git

diff --git a/Readme.md b/Readme.md
index d8f1f02..5aa1733 100644
--- a/Readme.md
+++ b/Readme.md
@@ -1,7 +1,7 @@
 # Introduction
 
 The A&AI Model Loader Service is an application that facilitates the distribution and ingestion of 
-new service and resource models from the SDC to the A&AI.
+new service and resource models and VNF catalogs from the SDC to the A&AI.
 
 ## Features
 
@@ -11,7 +11,18 @@ The Model Loader:
 * polls the UEB/DMaap cluster for notification events
 * downloads artifacts from SDC upon receipt of a distribution event
 * pushes distribution components to A&AI
+		    
+### VNF Catalog loading
 
+The Model Loader supports two methods for supplying VNF Catalog data for loading into A&AI:
+
+* Embedded TOSCA image and vendor data<br/>VNF Catalog data can be embedded within the TOSCA yaml files contained in the CSAR.
+
+
+* VNF Catalog XML files<br/>VNF Catalog data in the form of XML files can be supplied in the CSAR under the path `Artifacts/Deployment/VNF_CATALOG`
+
+**Note: Each CSAR should provide VNF Catalog information using only one of the above methods. If a CSAR contains both TOSCA and XML VNF Catalog information, a deploy failure will be logged and published to SDC, and no VNF Catalog data will be loaded into A&AI** 
+		    
 ## Compiling Model Loader
 
 Model Loader can be compiled by running `mvn clean install`
@@ -35,7 +46,7 @@ You will be mounting these as data volumes when you start the Docker container.
 
 The following file must be present in this directory on the host machine:
     
-_model-loader.properties_
+_model-loader.properties_  
 
     # Always false.  TLS Auth currently not supported 
     ml.distribution.ACTIVE_SERVER_TLS_AUTH=false
@@ -43,10 +54,10 @@ _model-loader.properties_
     # Address/port of the SDC
     ml.distribution.ASDC_ADDRESS=<SDC-Hostname>:8443
     
-    # DMaaP consumer group.  
+    # Kafka consumer group.  
     ml.distribution.CONSUMER_GROUP=aai-ml-group
     
-    # DMaaP consumer ID
+    # Kafka consumer ID
     ml.distribution.CONSUMER_ID=aai-ml
     
     # SDC Environment Name.  This must match the environment name configured on the SDC
@@ -62,10 +73,10 @@ _model-loader.properties_
     # obfuscate the cleartext password:  http://www.eclipse.org/jetty/documentation/9.4.x/configuring-security-secure-passwords.html
     ml.distribution.PASSWORD=OBF:<password>
     
-    # How often (in seconds) to poll the DMaaP cluster for new model events
+    # How often (in seconds) to poll the Kafka topic for new model events
     ml.distribution.POLLING_INTERVAL=<integer>
     
-    # Timeout value (in seconds) when polling DMaaP for new model events
+    # Timeout value (in seconds) when polling the Kafka topic for new model events
     ml.distribution.POLLING_TIMEOUT=<integer>
     
     # Username to use when connecting to the SDC
@@ -73,9 +84,6 @@ _model-loader.properties_
     
     # Artifact type we want to download from the SDC (the values below will typically suffice)
     ml.distribution.ARTIFACT_TYPES=MODEL_QUERY_SPEC,TOSCA_CSAR
-    
-    # List of message bus addresses on which to listen for distribution events
-    ml.distribution.MSG_BUS_ADDRESSES=<host1>,<host2>
 
     # URL of the A&AI
     ml.aai.BASE_URL=https://<AAI-Hostname>:8443
@@ -127,6 +135,6 @@ You can now start the Docker container for the _Model Loader Service_, e.g:
 	    {{your docker repo}}/model-loader
     
 where
- 
+
     {{your docker repo}}
 is the Docker repository you have published your image to.