Properties

The pushing of the deployment to a runtime instance will create a properties file containing the details of the configuration. This file is automatically generated by the ecosystem.Ai server. It is possible to manually edit the properties file, however this should be avoided if at all possible to avoid configuration errors and versioning issues.

The properties file is a text file that contains key-value pairs, where each key represents a specific configuration parameter and its corresponding value. The location of the properties file can be configured using the environment variables. Bleow is an example of the generated properties file format.


# ======================================================================================================================
# Predictor: dynamic-recommender - dynamic recommender example - 001
# Generated by: admin@ecosystem.ai at 2025-04-01T06:59:15.000768Z
# ======================================================================================================================
### prediction case (Prediction Case Id) ###
predictor.name=dynamic-recommender
# ======================================================================================================================
date.format=yyyy-MM-dd'T'HH:mm:ss.SSSSSSXXX
user.profiles=ecosystem_meta
# ======================================================================================================================
### mongodb setup (MongoDB Setup) ###
mongo.ecosystem.user=mongo-user
mongo.server=ecosystem-server
mongo.port=12344
mongo.authentication.source=admin
 
### (MongoDB Connect String) ###
mongo.connect=mongodb://mongo-user:mongo@ecosystem-server:1234/?authSource=admin
 
### logging setup (Logging) ###
logging.database=logging
logging.collection=ecosystemruntime
logging.collection.response=ecosystemruntime_response
 
### paths (Data Path) and (Models Path) ###
user.data=/data/
user.generated.models=/data/deployed/
 
### case specific parameters (Pre-Score Class) and (Post-Score Class) ###
plugin.prescore=com.ecosystem.plugin.customer.PreScoreDynamic
plugin.postscore=com.ecosystem.plugin.customer.PlatformDynamicEngagement
 
### models (Prediction Model) ###
#mojo.key=GLM_1_AutoML_20210818_132258.zip,XGBoost_1_AutoML_20210818_132258.zip
 
### model selector (Model Selector) ###
#predictor.selector.setup={database:'mongodb',db:'ecosystem', table:'table',lookup:{key:'key',value:123,fields:'selector_key'}}
#predictor.selector.model={'key_value_a':[1],'key_value_b':[2]}
 
### parameter lookup (Parameters from Data Source) ###
predictor.param.lookup={predictor:'dynamic-recommender',mojo:1,database:'mongodb',db:'telecommunications',table:'customer_feature_store',lookup:{"value":123,"key":"customer"},result:{parm1:'field1', parm2:'field2'}}
predictor.param.lookup.features=customer,balance,segment,location
 
### Offer Matrix (Offer Matrix) ###
#predictor.offer.matrix={database:'mongodb',db:'telecommunications', table:'product_offer_matrix'}
 
### dynamic pulse responder params (Additional Corpora) ###
predictor.corpora=[{"database":"mongodb","name":"location_details","type":"static","db":"recommender_demos","table":"location_details","key":"location"},{"database":"mongodb","name":"dynamic_engagement","update":true,"type":"dynamic_engagement","uuid":"ef931e11-b504-4c4d-958e-47d97c8361af","db":"ecosystem_meta","table":"dynamic_engagement"},{"database":"mongodb","name":"dynamic_engagement","update":true,"type":"dynamic_engagement_options","uuid":"ef931e11-b504-4c4d-958e-47d97c8361af","db":"telecommunications","table":"recommender_gsm_options"}]
 
#predictor.whitelist.lookup={name:'recommender',database:'mongodb',db:'ecosystem',table:'recommender_whitelist'}
#predictor.whitelist.logicin=true
 
#predictor.offer.budget.params={name:'case_name',description:'Sample budget',date_from:'2021-02-01',date_to:'2021-03-01',date_from_name:'CalendarFrom',date_to_name:'CalendarTo',x_name:'customer_segment',x_name_source:'customer_segment',y_name:'offer_counter_group',y_name_source:'offer_matrix.offer_counter_group',number:'Number', number_source:'result.offer_value',group_description:'GroupDescription',generate_if_empty:true}
 
### Multi Armed Bandit (Epsilon) and (Cache Duration)###
#predictor.epsilon=0.05
#predictor.offercache=0
predictor.epsilon=0.0
predictor.offercache=0
 
#predictor.pattern=0,1,2,6,8,9,10,11
#predictor.patternduration=10000
 
# Advanced settings: Shapley Values settings for contributions (review performance). Skip process for dynamic update process. Logging details adds feature store to log.
model.explainability=false
skip.count.process=5
logging.detail=true
 
### end of properties ###