Python Client performing entity lookup with peppol
Project description
Peppol Lookup
This is client library for fetching business entities and verifying whether they support electronic invoices
Default api url should be: https://directory.peppol.eu
Table of contents
Installation
To install this package you can use following command
pip install peppol_lookup
Usage
To get started it is important to get auth token. This is provided by Kong Gateway and in our team it is Tahmid that can provide this.
Api usage
This client supports both sync and async clients depending on the use case. These can be also used with python context managers.
Another thing to note is that consent api can be instantiated with and without
oneid_info cookie. Also if consent api is instantiated with oneid_info cookie
site is also required, if site is not provided then ValueError is raised.
Instantiation with oneid_info cookie is required for user related methods like
getting user consents and saving consent event.
Under are some examples of usage
Sync version
# Standard
from peppol_lookup import PeppolApiApi
api = PeppolApiApi(api_url="https://directory.peppol.eu")
response = api.search("My Company")
print(response.data)
# Context manager
from peppol_lookup import PeppolApiApi
with PeppolApiApi(api_url="https://directory.peppol.eu") as client:
response = client.search("My Company")
print(response.data)
Async version
# Standard
from peppol_lookup import AsyncPeppolApi
api = AsyncPeppolApi(api_url="https://directory.peppol.eu")
response = await api.search("My Company")
print(response.data)
# Context manager
from peppol_lookup import AsyncPeppolApi
async with AsyncPeppolApi(api_url="https://directory.peppol.eu") as client:
response = await api.search("My Company")
print(response.data)
Retry strategies
This API library supports 2 retry strategies for both syns and async clients. Simple and progressive retry strategies.
Simple retry strategy
This is basic retry strategy that tries request and if there is an error will wait constant time and try again. This process will repeat number of times that is defined
Example of usage:
# Example of simple retry strategy that will try 3 times with 1 second in-between
# Sync example
from peppol_lookup import PeppolApiApi, SimpleRetryStrategy
api = PeppolApi(api_url="https://directory.peppol.eu", retry_strategy=SimpleRetryStrategy(retries=3, delay=1))
response = api.search("My Company")
# Async example
from peppol_lookup import AsyncPeppolApi, AsyncSimpleRetryStrategy
api = AsyncPeppolApi(api_url="https://directory.peppol.eu", retry_strategy=AsyncSimpleRetryStrategy(retries=3, delay=1))
response = await api.search("My Company")
Progressive retry strategy
This strategy will have number of retries with progressive back-off by fixed factor.
Be careful when using this strategy since in sync mode can block execution for a long time
Example of usage:
# Example of progressive retry strategy that will try 3 times with delay multiplied
# by factor on every retry. In example bellow after first failure it will wait
# 1 second, then 3 seconds and finally 9 seconds
# Sync example
from peppol_lookup import PeppolApiApi, ProgressiveRetryStrategy
api = PeppolApi(api_url="https://directory.peppol.eu", retry_strategy=ProgressiveRetryStrategy(retries=3, delay=1, factor=3))
response = api.search("My Company")
# Async example
from peppol_lookup import AsyncPeppolApi, AsyncProgressiveRetryStrategy
api = AsyncPeppolApi(api_url="https://directory.peppol.eu", retry_strategy=AsyncProgressiveRetryStrategy(retries=3, delay=1, factor=3))
response = await api.search("My Company")
Writing custom retry strategy
If you need special strategy it is possible to create own strategies. You would need to extend one of the base classes depending whether you need Sync or Async version.
For example:
import httpx
from consent_api import BaseRetryStrategy, AsyncBaseRetryStrategy, MethodData
class MyRetryStrategy(BaseRetryStrategy):
def __init__(self, custom_property1, custom_property2):
...
def __call__(
self,
func: httpx.request,
method_data: MethodData,
):
# some custom logic that handles response
# result: httpx.Response = func(**method_data)
class AsyncMyRetryStrategy(AsyncBaseRetryStrategy):
def __init__(self, custom_property1, custom_property2):
...
async def __call__(
self,
func: httpx.request,
method_data: MethodData,
):
# some custom logic that handles response
# result: httpx.Response = await func(**method_data)
API Methods
- search(query: str | None = None, name: str | None = None, country: str | None = None, format: Literal["json", "xml"] = "json", ) -> Response[SearchResponse] - Perform entity lookup based on the query, name and country
Parameters in looup:
query- free text, can be anythingname- free text, can be anythingcountry- ISO country code like EN or NO for example
When performin search at least one parameter is needed
Types
We have following types:
class ParticipantId(TypedDict):
"""
Secheme is identifier and value is combination of {issuing_agency_code}:{identififier}
Scheme seems to always be `iso6523-actorid-upis`
issuing_agency_code - numeric value assigned by ISO 6523 to specific national or
international registration authorities
identififier - depends on issuing agency
Some examples:
- 0088: Global Location Number (GLN)
- 0106: Dutch Chamber of Commerce (KVK)
- 0192: Norwegian Organization Number (Organisasjonsnummer)
"""
scheme: Literal["iso6523-actorid-upis"] | str
value: str
class DocType(TypedDict):
scheme: str
value: str
class EntityName(TypedDict):
"""
Name of the entity and language is ISO language code
"""
name: str
class Entity(TypedDict):
name: list[EntityName]
countryCode: str
regDate: str
class SearchMatch(TypedDict):
participantID: ParticipantId
docTypes: list[DocType]
entities: list[Entity]
SearchResponse = TypedDict(
"SearchResponse",
{
"version": str,
"total-result-count": int,
"used-result-count": int,
"result-page-index": int,
"result-page-count": int,
"first-result-index": int,
"last-result-index": int,
"query-terms": str,
"creation-dt": str,
"matches": list[SearchMatch],
},
)
Development
NOTE This project requires python3.12 or later
For development you need to clone project by running:
git clone {git source}
Once cloning is completed go to new folder and setup virtual environment
python3 -m venv ./venv
source ./venv/bin/activate
pip install -r requirements/dev.txt
This will install all packages that are needed for local development and testing
Once virtual environment is installed you can update scripts, or run tests
Testing
We are using pytest to run tests. All of the tests are located in ./src/tests.
If file names are starting with test_ they will be automatically discovered.
Also all of the functions that start with def test_ will be ran as tests.
To run tests you need to run following command:
pytest
# For verbose output
pytest -v
If you want to run tests in parallel to speed up execution (just remember rate limiting of 2req/s)
# Run with 10 workers, generally number of workers shouldn't exceed number of
# cpu cores
pytest -n 10
To generate coverage report you need to run following command:
pytest --cov --cov-report=html
This will run tests and generate coverage report. This will create folder htmlcov and
put report there. You need to open htmlcov/index.html
In case you want just coverage report without tests you can run couple of commands:
# Generate command line report
coverage report
# Generate html report as in example above
coverage html
Note: You still need to run pytest --cov --cov-report=html before running commands above because with pytest command it will discover tests and generate .coverage file
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