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SDK for interacting with the Benchling Platform.

Project description

Benchling SDK

A Python 3.7+ SDK for the Benchling platform designed to provide typed, fluent interactions with Benchling APIs.

Important! This is a pre-release alpha. Changes to this project may not be backwards compatible.

Getting Started

Installation

Install the dependency via Poetry (if applicable):

poetry add benchling-sdk

Or Pip:

pip install benchling-sdk

Using the SDK

Obtain a valid API key from your Benchling account and provide it to the SDK, along with the URL for the server. Example:

from benchling_sdk.benchling import Benchling
benchling = Benchling(url="https://my.benchling.com", api_key="api_key")

With Benchling now instantiated, make a sample call to get a custom entity with the ID "custom_id".

entity = benchling.custom_entities.get_by_id(entity_id="custom_id")

API calls made by the SDK are synchronous and blocking.

Generators and nextToken

Paginated API endpoints listing objects and supporting the nextToken parameter will produce a PageIterator, which is a Python generator. Example:

requests_generator = benchling.requests.list(schema_id="assaych_test")
next_request = next(requests_generator)

In this example, requests_generator is a generator. Each iteration will return a List of Requests, not an individual Request.

The PageIterator object has an estimated_count() which will return the value of the Result-Count header from the API, if applicable for the endpoint. If the endpoint in question does not support the header, will raise a NotImplementedError instead.

PageIterator also supports first() which will return an optional first element in the list. Using DNA Sequences as an example, if at least one DNA sequence was returned by the results list, first() will return the first DNA sequence.

first() operates independent of iteration, meaning calls to first() after starting iteration will still return the first item from the first page and not the current page.

If no results were available, first() will return None.

from typing import Optional
from benchling_sdk.models import DnaSequence

dna_sequences_generator = benchling.dna_sequences.list()

# Typing is optional to illustrate the expected return
first_sequence: Optional[DnaSequence] = dna_sequences_generator.first()
if first_sequence:
   print(f"The first sequence was {first_sequence.id}")
else:
   print("No sequence found")

Working with Benchling Fields

Many objects in Benchling have the concept of fields. They are represented in the SDK via the benchling_sdk.models.Fields class.

To conveniently construct Fields from a dictionary, we have provided a fields method in the serialization_helper module:

from benchling_sdk.helpers.serialization_helpers import fields
from benchling_sdk.models import CustomEntity

entity = CustomEntity(
    name="My Entity",
    fields=fields({
    "a_field": {"value": "First value"},
    "second_field": {"value": "Second value"},
    })
)

Async Tasks

Many Benchling endpoints that perform expensive operations launch Tasks. These are modeled in the SDK as benchling_sdk.models.AsyncTask.

To simply retrieve the status of a task given its id:

async_task = benchling.tasks.get_by_id("task_id")

This will return the AsyncTask object, which may still be in progress. More commonly, it may be desirable to delay further execution until a task is completed.

In this case, you may block further processing until the task is no longer RUNNING:

completed_task = benchling.tasks.wait_for_task("task_id")

The wait_for_task method will return the task once its status is no longer RUNNING. This does not guarantee the task executed successfully (see benchling_sdk.models.AsyncTaskStatus), only that Benchling considers it complete.

wait_for_task can be configured by optionally specifying interval_wait_seconds for the time to wait between calls and max_wait_seconds which is the maximum number of seconds before wait_for_task will give up and raise benchling_sdk.errors.WaitForTaskExpiredError.

# Check the task status once every 2 seconds for up to 60 seconds
completed_task = benchling.tasks.wait_for_task(task_id="task_id", interval_wait_seconds=2, max_wait_seconds=60)

Unset

The Benchling SDK uses the type benchling_api_client.types.Unset and the constant value benchling_api_client.types.UNSET to represent values that were not present in an interaction with the API. This is to distinguish from values that were explicitly set to None from those that were simply unspecified.

A common example might be updating only specific properties of an object:

from benchling_sdk.models import CustomEntityUpdate

update = CustomEntityUpdate(name="New name")

updated_entity = benchling.custom_entities.update(
    entity_id="entity_id", entity=update
)

All other properties of CustomEntityUpdate besides name will default to UNSET and not be sent with the update. Setting any optional property to None will send a null JSON value. In general, you should not need to set UNSET directly.

When receiving objects from the API, some of their fields may be Unset. To treat UNSET values equivalent to Optional[T], you can use the convenience function unset_as_none():

from benchling_sdk.helpers.serialization_helpers import unset_as_none

sample_value: Union[Unset, None, int] = UNSET

optional_value = unset_as_none(sample_value)
# optional_value will be None

Error Handling

Failed API interactions will generally return a BenchlingError, which will contain some underlying information on the HTTP response such as the status. Example:

from benchling_sdk.errors import BenchlingError

try:
    requests = benchling.requests.get_by_id("request_id")
except BenchlingError as error:
    print(error.status_code)

If an HTTP error code is not returned to the SDK or deserialization fails, an unbounded Exception could be raised instead.

Advanced Use Cases

By default, the Benchling SDK is designed to be opinionated towards most common usage. There is some more advanced configuration available for use cases which require it.

Retries

The SDK will automatically retry certain HTTP calls when the calls fail and certain conditions are met.

The default strategy is to retry calls failing with HTTP status codes 429, 502, 503, and 504. The rationale for these status codes being retried is that many times they are indicative of a temporary network failure or exceeding the rate limit and may be successful upon retry.

Retries will be attempted up to 5 times, with an exponential time delay backoff between calls.

To disable retries, specify None for retry_strategy when constructing Benchling:

benchling = Benchling(url="https://my.benchling.com", api_key="api_key", retry_strategy=None)

Alternatively, instantiate your own benchling_sdk.retry_helpers.RetryStrategy to further customize retry behavior.

BenchlingApiClient Customization (e.g., HTTP Timeout Settings)

While the SDK abstracts most of the HTTP transport layer, access can still be granted via the BenchlingApiClient. A common use case might be extending HTTP timeouts for all calls.

This can be achieved by specifying a function which accepts a default configured instance of BenchlingApiClient and returns a mutated instance with the desired changes.

For example, to set the HTTP timeout to 180 seconds:

from benchling_api_client.benchling_client import BenchlingApiClient

def higher_timeout_client(client: BenchlingApiClient) -> BenchlingApiClient:
    return client.with_timeout(180)


benchling = Benchling(
    url="https://my.benchling.com",
    api_key="api_key",
    client_decorator=higher_timeout_client,
)

To fully customize the BenchlingApiClient and ignore default settings, construct your own instance in lieu of modifying the client argument.

Changing the Base URL

When instantiating Benchling, the path /api/v2 will automatically be appended to the server information provided.

For example, if creating Benchling like this:

benchling = Benchling(url="https://my.benchling.com", api_key="api_key")

API calls will be made to https://my.benchling.com/api/v2.

To specify, an alternative path, set the base_path when creating Benchling:

benchling = Benchling(url="https://my.benchling.com", api_key="api_key", base_path="/api/custom")

In this case, API calls will be made to https://my.benchling.com/api/custom.

Custom API Calls

For making customized API calls to Benchling, the SDK supports an open-ended Benchling.api namespace that exposes varying levels of interaction for HTTP GET, POST, PATCH, and DELETE methods. This is useful for API endpoints which the SDK may not support yet or more granular control at the HTTP layer.

For each verb, there are two related methods. Using GET as an example:

  1. get_response() - Returns a benchling_api_client.types.Response which has been parsed to a JSON dict and is slightly more structured.
  2. get_modeled() - Returns any custom model which extends benchling_sdk.helpers.serialization_helpers.DeserializableModel and must be a Python @dataclass.

Both will automatically retry failures according to RetryStrategy and will marshall errors to BenchlingError.

When calling any of the methods in Benchling.api, specify the full path to the URL except for the scheme and server. This differs from other API services, which will prepend the URL with a base_path.

For example, if wishing to call an endpoint https://my.benchling.com/api/v2/custom-entities?some=param, pass api/v2/custom-entities?some=param for the url.

Here's an example of making a custom call with post_modeled():

from dataclasses import dataclass, field
from typing import Any, Dict
from dataclasses_json import config
from benchling_sdk.helpers.serialization_helpers import DeserializableModel, SerializableModel

@dataclass
class ModeledCustomEntityPost(SerializableModel):
    name: str
    fields: Dict[str, Any]
    # If the property name in the API JSON payload does not match the Python attribute, use
    # field and config to specify the appropriate name for serializing/deserializing
    folder_id: str = field(metadata=config(field_name="folderId"))
    schema_id: str = field(metadata=config(field_name="schemaId"))


@dataclass
class ModeledCustomEntityGet(DeserializableModel):
    id: str
    name: str
    fields: Dict[str, Any]
    folder_id: str = field(metadata=config(field_name="folderId"))

# Assumes `benchling` is already configured and instantiated as `Benchling`
body = ModeledCustomEntityPost(
    name="My Custom Entity Model",
    folder_id="folder_id",
    schema_id="schema_id",
    fields={"My Field": {"value": "Modeled Entity"}},
)

created_entity = benchling.api.post_modeled(
    url="api/v2/custom-entities", body=body, target_type=ModeledCustomEntityGet
)

The returned created_entity will be of type ModeledCustomEntityGet. Classes extending SerializableModel and DeserializableModel will inherit serialize() and deserialize() methods respectively which will act on Python class attributes by default. These can be overridden for more customized serialization needs.

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