Unified Biomedical Knowledge Graph

ubkg-api

The components of the Unified Biomedical Knowledge Graph (UBKG) include:

• The source framework that extracts ontology information from the UMLS to create a set of CSV files (UMLS CSVs)
• The generation framework that appends to the UMLS CSVs assertion data from other ontologies to create a set of ontology CSVs.
• A neo4j ontology knowledge graph populated from the ontology CSVS.
• An API server that provides RESTful endpoints to query the ontology knowledge graph.

This repository contains the source for the API.

The ubkg-api is a Flask web application with Blueprint extensions that provides a REST API for parameterized queries against an instance of a UBKG neo4j instance.

The ubkg-api contains code that is common to all UBKG contexts, including code to handle connecting to a UBKG instance.

Other API implementations, such as the hs-ontology-api, extend the ubkg-api. Endpoints common to all UBKG contexts are deployed in the ubkg-api; other API implementations can both "pass through" calls to ubkg-api endpoints or execute endpoints specific to the implementation.

An API implementation extends the ubkg-api by calling a version of the ubkg-api that has been published as a PyPI.

SmartAPI documentation

The specification for the UBKG API can be found here.

Developer Information

Development/testing environment for ubkg-api

To enhance or fix the ubkg-api, you will need to establish an application development environment on the development machine. Components of the application development environment include:

1. An instance of a UBKG context--i.e., an instance of neo4j populated with UBKG content. Options include:
   • a local bare-metal instance of neo4j
   • a local Docker install of a UBKG distribution built from ubkg-neo4j
   • a cloud-based instance (development or production)
2. A local branch of ubkg-api
3. URLs that execute endpoints against the local instance of ubkg-api

Connecting to a UBKG instance

To connect the local branch of ubkg-api to a neo4j instance that hosts a UBKG context:

1. Copy the file named app.cfg.example in the src/ubkg-api/instance directory to a file named app.cfg.
2. Add to app.cfg the connection information for the neo4j instance. The .gitignore file at the root of this repo will force git to exclude the app.cfg file from commits.

Example UBKG neo4j connect string values for app.cfg

If you are working with a local Docker distribution based on from ubkg-neo4j, then

• SERVER='bolt://localhost:u', where u is the browser port associated with ui_port in the container.cfg file used by the build scripts.
• USERNAME=neo4j
• PASSWORD= the value of neo4j_password from the common container.cfg file.

Starting your neo4j instance

If you are using a local instance of the UBKG, the instance should be running. In particular, if you installed a local Docker instance of UBKG, be sure that Docker Desktop is running. If the neo4j instance is not available, calls to API endpoints will result in a 500 error.

Connecting to the local instance of ubkg-api

For URLs that execute endpoints in your local instance, use the values indicated in the main.py script, in the section prefaced with the comment For local development/testing:

For example, if main.py indicates app.run(host='0.0.0.0', port="5002")

then your test endpoint URLs should start with http://127.0.0.1:5002/.

For example, if you test using PostMan, you can set a global variable corresponding to the first part of your test URLs:

Testing changes

To test changes to ubkg-api, you will need to start a local instance of the API. The following assumes that you have created a local branch of ubkg-api.

From the command line

1. Move to the root of your local branch.

2. Create a Python virtual environment. The following command creates a virtual environment named venv.

   python -m venv venv

3. Activate the virtual environment.

   source venv/bin/activate

4. Move to the /src directory and install dependencies, inclduing the ubkg-api package.

   pip install -r requirements.txt

5. Run app.py to start the local instance of the API.

   python app.py

In PyCharm

1. Create a new project based on a local clone of ubkg-api. PyCharm should establish a virtual environment. (Refer to instructions here.)
2. Use the Python Packages tab to install the packages listed in requirements.txt.
3. In the Terminal window, run app.py.
4. Note that you may need to enable execute permissions on the app.py script before you can run it locally--e.g., with a command like chmod 777 app.py

URL testing

Once you have connected your instance of hs-ontology-api to instances of both neo4j and ubkg-api, run the following tests:

1. Paste the root endpoint URL into a browser window--e.g.,http://127.0.0.1:5002/. You should see a window with the status message Hello! This is UBKG-API service :). The status message verifies that your local instance of hs-ontology-api is connected to an instance of ubkg-api.
2. Add to the root endpoint URL to execute a known endpoint--e.g., http://127.0.0.1:5002/concepts/C0678222/codes. You should see a response from the ubkg-api, depending on the endpoint.
3. If you are only testing endpoints in an extension api (e.g., hs-ontology-api) and are using the PyPi install of ubkg-api, calls to endpoints managed by the ubkg-api will fail with a 500 error. To test endpoints from both hs-ontology-api and ubkg-api, you will need a local instance of ubkg-api that connects to the same instance of neo4j to which the instance of hs-ontology-api connects.

Various methods of testing endpoint URLs are possible, including:

1. curl, from either the command line or a shell script
2. Requests in Postman
3. A Python script using Requests or pytest
4. Executing directly in the browser. This method is suitable for GET endpoints.

Adding new endpoints

Each endpoint in ubkg-api involves:

• One or more functions in the functional script (neo4j_logic.py). The usual use case is a parameterized function that prepares a Cypher query against the target neo4j instance.
• a controller script in the routes path that registers a BluePrint route in Flask and links a route to a function in the functional script.
• a model script in the models path that describes the class that corresponds to the response of the endpoint.

Tasks:

Create a model script

The model script is a class that defines the response for the endpoint.

File path

Create the script in the models path.

Class method

1. __init__: For every key that is returned,
   1. List as a parameter.
   2. Declare the type in the self.openapi_types dictionary.
   3. Declare the mapping in the self.attribute_map dictionary.
   4. Declare an internal property of the class to match the key.

For example, for a string value with key approved_symbol,

       self.openapi_types = {
            'approved_symbol': str
        }
        self.attribute_map = {
            'approved_symbol': 'approved_symbol',
        }
        self._approved_symbol = approved_symbol

2. Add serialize and from_dict methods that refer to the returned key/value pairs. Override the return type of the from_dict to point to the model class.

The following code is from the GeneDetail model class in genedetail.

    def serialize(self):
        return {
            "approved_symbol": self._approved_symbol
        }

    @classmethod
    def from_dict(cls, dikt) -> 'GeneDetail':
        """Returns the dict as a model

        :param dikt: A dict.
        :type: dict
        :return: The GeneDetail of this GeneDetail
        :rtype: GeneDetail
        """
        return util.deserialize_model(dikt, cls)

3. For each key in the response, define getter and setter functions.

   @property
   def approved_symbol(self):
        return self.approved_symbol

    @approved_symbol.setter
    def approved_symbol(self, approved_symbol):
        self._approved_symbol = approved_symbol

Add functional script code to neo4j_logic.py

The neo4j_logic.py script contains endpoint-related functions. The usual use case is a parameterized Cypher query.

Naming convention

1. For functions called directly from controllers, name the function with format model_method_logic. For example, the function that satisfies the POST method for the genedetail controller would be called genedetail_post_logic.
2. Subfunctions called by main functions should be prefixed with an underscore.

Loading large Cypher queries

If the Cypher query used by an endpoint function is complex, store an annotated copy of the query in the cypher directory.

Examples

The methods for returning to GET requests and POST requests are slightly different. You should be able to find examples of either type of function.

Loading Cypher query strings

Large or complex Cypher query strings can be stored in files in the cypher directory and loaded using the loadquerystring function in the common_neo4j_logic.py script.

Following is the excerpt from common_neo4j_logic.py that loads the large Cypher query string used for the genes endpoint.

    # Load Cypher query from file.
    query: str = loadquerystring('codes_code_id_codes.cypher')
    

Nested objects

If your response body is to contain nested objects, you will need to create models for each type of sub-object. The containing model script will need to import the sub-object models.

For an example, review genedetail.py in hs-ontology-api.

Build a controller script

File path

Create a Python package in the routes path.

Define Blueprint

Define a Blueprint object and route for your endpoint. Follow examples in the existing controllers.

Register your Blueprint

In app.py,

1. Import your Blueprint.
2. Register your Blueprint with Flask.

The following snippet registers the Blueprint:

from common_routes.codes.codes_controller import codes_blueprint
self.app.register_blueprint(codes_blueprint)

Updating SmartAPI documentation

To add the specification for a new endpoint to the SmartAPI documentation for hs-ontology-api, update the file hs-ontology-api-spec.yaml.

hs-ontology-api-spec.yaml conforms to Swagger OpenAPI format.

You will need to specify:

1. Paths that correspond to your endpoint routes.
2. Schemas that correspond to the responses from endpoints.

Building and publishing to PyPI

To build and publish the ubkg-api as a PyPI package,

python -m build
python3 -m twine upload dist/*

Requirements

Python 3.9 or newer