This project enables the usage of custom features within the Retrieve & Rank service on Bluemix. This project was built in the Python programming language and uses the Flask micro-framework. To use this application, there is a Python script called server.py, which exposes two endpoints to be consumed by the application that uses it.
The Flask server that is created within the script server.py is intended to run as a "sidecar" within the rest of the application; that is, the parent application will make REST API calls to this "sidecar" service rather than making direct calls to the deployed Retrieve & Rank service. The principal difference is that the Flask server will handle the integration/injection of custom features that have been registered.
There are 4 steps to set up the server to integrate custom features:
- Configure your environment. There is a script with path 'bin/install.sh', which will install the dependencies necessary to start the server; if this is your first time using this project, you should run the install.sh script
- Identify the custom scorers for your application. A custom scorer is a Python class that extracts a signal that is to be used for the ranker. There are 3 types of scorers supported, 1.) "QueryDocument" Scorer - Extracts a signal/score based on both the contents of a query and the contents of a Solr Document 2.) "Document" Scorer - Extracts a signal based on the contents of the Solr Document alone 3.) "Query" Scorer - Extracts a signal based on the contents of the query alone These custom scorers are based on the project 'rr_custom_scorers' as part of the cognitive catalyst project (url here); please see this project for additional details. To make sure that the proxy service has the most up to date scorers, go to the 'rr_custom_scorers' project, build a wheel file (using the command pip wheel), and then copy that wheel file to the lib directory here. Re-run the bin/install.sh script to make sure your Python environment has the most up to date dependencies.
- Create a configuration file (see configs/sample_features.json as an example) to configure your application to consume these scorers. The configuration file must be a json file and
must contain a "scorers" field, which is a list of individual scorer configurations. For each of the scorers that you identified in the previous step, the scorer configuration is a JSON object
that must define the following:
- an 'init_args' json object, whose fields are the arguments to the constructor for the scorer
- a 'type' field, which should be either 'query', 'document' or 'query_document', depending on the type of scorer. The type is used to identify the package within the 'rr_scorers' project that contains the appropriate scorer
- a 'module' field, which is the name of the python module which contains the scorer
- a 'class' field, which is the name of the scorer class For comparison, the config/sample_features.json contains a single QueryDocument scorer, in the module nlc_intent_scorer, with the class QuestionDocumentIntentAlignmentScorer
- Start the Flask server by running the command 'python server.py ... ' with the proper parameters. Alternatively, there is a shell script called 'run.sh', which takes a properties/configuration file (for the underlying retrieve and rank service)
- Make calls to the endpoint that is configured when starting the server.py. By default, the host will be localhost and the port will be 9216