This is a FastAPI project which uses strawberry to query an elasticsearch database.

Live API

This API is live at - https://api-v2.annoq.org/docs.

Installation

Before you begin, make sure you have the following installed:

1. Python: Install Python 3.11 or later. You can download it from python.org.

pyenv install 3.11.9
pyenv local 3.11.9

2. Docker Desktop: Install Docker Desktop. You can download it from docker-desktop.

Project Setup

1. Clone this repository.

git clone https://github.com/USCbiostats/annoq-api-v2.git
cd annoq-api-v2

1. Create a python virtual environment and activate it.

python3 -m venv venv
source venv/bin/activate

1. Install the dependencies

pip install -r requirements.txt

1. Make sure that the Docker Desktop is running. Build the Docker image and start the container.

docker-compose up --build

1. Once the image and containers are made, the containers can be started from Docker Desktop or using the following command

docker-compose up

The fastAPI application would be running on http://0.0.0.0:8000 and the elasticsearch instance would be on http://0.0.0.0:9200

Sample Elasticsearch Data Setup

Follow the https://github.com/USCbiostats/annoq-database repository and use the sample_data folder to setup the sample data for elasticsearch. If necessary, modify file .env to reflect URL of database.

Dynamic Snps class generation

Annoq has 500+ attributes, so the strawberry type for it had to be generated dynamically as it would not make sense to manually write 500 fields. This class has to be executed whenever where are any changes in the schema:

First json schemas were generated which takes the mapping for the elasticsearch database and creates schemas for pydantic Baseclasses. After scripts/class_generators/generated_schemas/snp_schema.json and scripts/class_generators/generated_schemas/snp_aggs_schema.json were generated. The python files of the pydantic Baseclasses - src/graphql/models/generated/snp.py and src/graphql/models/generated/snp_aggs.py were generated using datamodel-codegen.

If there are changes to the number of columns or labels, which would be reflected in data/anno_tree.json or data/api_mapping_anno_tree.json, the following script has to be executed to re-generate the model json file. This will generate scripts/class_generators/generated_schemas/snp_schema.json and scripts/class_generators/generated_schemas/snp_aggs_schema.json

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python3 -m scripts.class_generators.generator

All of this can be done using the bash script and running the following command -

scripts/class_generators/generate_model.sh

Make sure that the above scripts has permissions, if not run

chmod +x scripts/class_generators/generate_model.sh

To run the project

python -m src.main

Testing

To run the tests on the code use the following command

python -m pytest test