# Akvo-ResponseGrouper
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Fast-API Response catalog for pre-computed query
## Install
```
$ pip install AkvoResponseGrouper
```
## Schema Requirements
Please follow [the required schema](https://github.com/akvo/Akvo-ResponseGrouper/blob/main/docs/database.org) before using AkvoResponseGrouper.
## Usage
1. Database Migration
Database migration is the first required step for AkvoResponseGrouper to work. Use the akvo-responsegrouper CLI to migrate all data sources with JSON configuration files to generate Materialized Views that AkvoResponseGrouper can then use.
```bash
$ akvo-responsegrouper
usage: akvo-responsegrouper [-h] [-c CONFIG] [-d]
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
akvo-responsegrouper -c <json_file_config>
-d, --drop Drop the ar_category view table
```
2. Router
To get the new endpoint provided by AkvoResponseGrouper, import the collection route to the App by:
```python
from fastapi import FastAPI
from AkvoResponseGrouper.routes import collection_route
app = FastAPI(
root_path="/",
title="Akvo Response Grouper Demo",
)
app.include_router(collection_route)
@app.get("/", tags=["Dev"])
def read_main():
return "OK"
```
3. Query
## Development
### Run Dev Containers
The dev environment contains two containers: FastAPI backend and PostGres db, to run:
```bash
docker compose up -d
```
Before go to the next step, wait until the service started at [http://localhost:5000](http://localhost:5000).
### Seed Necessary Data
In order to debug the data itself. We need to seed the example form and fake datapoints
###
```bash
docker compose exec backend python -m script.seeder_form
docker compose exec backend python -m script.seeder_datapoint <number_of_datapoint>
```
### Migration
Dev environment uses contents that is available in `Akvo-ResponseGrouper/src/AkvoResponseGrouper`. To create the Category Materialized View via CLI in dev environment:
Upgrade:
```bash
python -m AkvoResponseGrouper.cli.migrate -c './sources/category.json'
```
After upgrade, you can see "AkvoResponseGrouper - Collection" is available in API docs, ussualy [http://locahhost:5000/docs](http://localhost:5000/docs) (Depends on the root path api).
Downgrade:
```
python -m AkvoResponseGrouper.cli.migrate -c './sources/category.json'
```
### Teardown
```
docker compose down -v
```
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"description": "# Akvo-ResponseGrouper\n\n\n[](https://github.com/akvo/Akvo-ResponseGrouper/actions) [](https://img.shields.io/github/repo-size/akvo/Akvo-ResponseGrouper) [](https://coveralls.io/github/akvo/Akvo-ResponseGrouper?branch=main) [](https://img.shields.io/github/languages/count/akvo/Akvo-ResponseGrouper\n) [](https://img.shields.io/github/issues/akvo/Akvo-ResponseGrouper\n) [](https://img.shields.io/github/last-commit/akvo/Akvo-ResponseGrouper/main) [](https://Akvo-ResponseGrouper.readthedocs.io/en/latest/?badge=latest) [](https://github.com/akvo/Akvo-ResponseGrouper/blob/main/LICENSE)\n\nFast-API Response catalog for pre-computed query\n\n## Install\n```\n$ pip install AkvoResponseGrouper\n```\n\n## Schema Requirements\n\nPlease follow [the required schema](https://github.com/akvo/Akvo-ResponseGrouper/blob/main/docs/database.org) before using AkvoResponseGrouper.\n\n## Usage\n\n1. Database Migration\n\nDatabase migration is the first required step for AkvoResponseGrouper to work. Use the akvo-responsegrouper CLI to migrate all data sources with JSON configuration files to generate Materialized Views that AkvoResponseGrouper can then use.\n\n```bash\n$ akvo-responsegrouper\nusage: akvo-responsegrouper [-h] [-c CONFIG] [-d]\n\noptional arguments:\n -h, --help show this help message and exit\n -c CONFIG, --config CONFIG\n akvo-responsegrouper -c <json_file_config>\n -d, --drop Drop the ar_category view table\n```\n\n2. Router\n\nTo get the new endpoint provided by AkvoResponseGrouper, import the collection route to the App by:\n\n```python\nfrom fastapi import FastAPI\nfrom AkvoResponseGrouper.routes import collection_route\n\napp = FastAPI(\n root_path=\"/\",\n title=\"Akvo Response Grouper Demo\",\n)\n\napp.include_router(collection_route)\n\n@app.get(\"/\", tags=[\"Dev\"])\ndef read_main():\n return \"OK\"\n```\n\n3. Query\n\n\n\n## Development\n\n### Run Dev Containers\n\nThe dev environment contains two containers: FastAPI backend and PostGres db, to run:\n\n```bash\ndocker compose up -d\n```\nBefore go to the next step, wait until the service started at [http://localhost:5000](http://localhost:5000).\n\n### Seed Necessary Data\n\nIn order to debug the data itself. We need to seed the example form and fake datapoints\n\n###\n```bash\ndocker compose exec backend python -m script.seeder_form\ndocker compose exec backend python -m script.seeder_datapoint <number_of_datapoint>\n```\n\n### Migration\n\nDev environment uses contents that is available in `Akvo-ResponseGrouper/src/AkvoResponseGrouper`. To create the Category Materialized View via CLI in dev environment:\n\nUpgrade:\n\n```bash\npython -m AkvoResponseGrouper.cli.migrate -c './sources/category.json'\n```\n\nAfter upgrade, you can see \"AkvoResponseGrouper - Collection\" is available in API docs, ussualy [http://locahhost:5000/docs](http://localhost:5000/docs) (Depends on the root path api).\n\nDowngrade:\n```\npython -m AkvoResponseGrouper.cli.migrate -c './sources/category.json'\n```\n\n### Teardown\n```\ndocker compose down -v\n```\n",
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