X-Git-Url: https://gerrit.onap.org/r/gitweb?a=blobdiff_plain;f=Readme.md;h=5aa173339676a36acfe134d9093c130a4ded1bdb;hb=117237ac7b65395e0f29b6cead9f6ffec2e7dc0f;hp=fc66fa837db53dc783e8beb29919cd4eaa094147;hpb=fe17f093d5c61207949eebef95b83baf5139be50;p=aai%2Fmodel-loader.git

diff --git a/Readme.md b/Readme.md
index fc66fa8..5aa1733 100644
--- a/Readme.md
+++ b/Readme.md
@@ -1,8 +1,7 @@
 # Introduction
 
-The A&AI Model Loader Service is an application that facilitates
-distribution, ingestion of new service and resource models, and vnf
-catalogs from the SDC to the A&AI.
+The A&AI Model Loader Service is an application that facilitates the distribution and ingestion of 
+new service and resource models and VNF catalogs from the SDC to the A&AI.
 
 ## Features
 
@@ -13,16 +12,25 @@ The Model Loader:
 * 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`
-A Model Loader docker image can be created by running `docker build -t openecomp/model-loader target`
+A Model Loader docker image can be created by running `docker build -t onap/model-loader target`
 
 ## Running Model Loader 
 
-### Deploying The Micro Service 
-
-Push the Docker image that you have built to your Docker repository and pull it down to the location that you will be running the search service from.
+Push the Docker image to your Docker repository. Pull this down to the host machine.
 
 **Create the following directories on the host machine:**
 
@@ -34,21 +42,22 @@ You will be mounting these as data volumes when you start the Docker container.
 
 **Populate these directories as follows:**
 
-##### Contents of /opt/app/model-loader/appconfig
+#### Contents of /opt/app/model-loader/appconfig
 
 The following file must be present in this directory on the host machine:
     
 _model-loader.properties_  
+
     # Always false.  TLS Auth currently not supported 
     ml.distribution.ACTIVE_SERVER_TLS_AUTH=false
     
     # 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
@@ -64,17 +73,17 @@ _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
     ml.distribution.USER=<username>
     
     # Artifact type we want to download from the SDC (the values below will typically suffice)
-    ml.distribution.ARTIFACT_TYPES=MODEL_INVENTORY_PROFILE,MODEL_QUERY_SPEC,VNF_CATALOG
+    ml.distribution.ARTIFACT_TYPES=MODEL_QUERY_SPEC,TOSCA_CSAR
 
     # URL of the A&AI
     ml.aai.BASE_URL=https://<AAI-Hostname>:8443
@@ -86,7 +95,7 @@ _model-loader.properties_
     ml.aai.NAMED_QUERY_URL=/aai/v*/service-design-and-creation/named-queries/named-query/
     
     # A&AI endpoint to post vnf images
-    ml.aai.VNF_IMAGE_URL=/aai/v8/service-design-and-creation/vnf-images
+    ml.aai.VNF_IMAGE_URL=/aai/v*/service-design-and-creation/vnf-images
     
     # Name of certificate to use in connecting to the A&AI
     ml.aai.KEYSTORE_FILE=aai-os-cert.p12
@@ -116,7 +125,7 @@ The certificate used to connected to the A&AI
 
 **Start the service:**
 
-You can now start the Docker container for the _Search Data Service_, in the following manner:
+You can now start the Docker container for the _Model Loader Service_, e.g:
 
 	docker run -d \
 		-e CONFIG_HOME=/opt/app/model-loader/config/ \
@@ -125,6 +134,7 @@ You can now start the Docker container for the _Search Data Service_, in the fol
 	    --name model-loader \
 	    {{your docker repo}}/model-loader
     
-Where,
+where
 
-    {{your docker repo}} = The Docker repository you have published your image to.
+    {{your docker repo}}
+is the Docker repository you have published your image to.