The Schematic Python Library provides convenient access to the Schematic API from applications written in Python.
The library includes type definitions for all request and response fields, and offers both synchronous and asynchronous clients powered by httpx.
- Add this dependency to your project's build file:
pip install schematichq
# or
poetry add schematichq-
Issue an API key for the appropriate environment using the Schematic app.
-
Using this secret key, initialize a client in your application:
from schematic.client import Schematic
client = Schematic("YOUR_API_KEY")The SDK also exports an async client so that you can make non-blocking calls to our API.
from schematic.client import AsyncSchematic
client = AsyncSchematic("YOUR_API_KEY")
async def main() -> None:
await client.companies.get_company(
company_id="company_id",
)
asyncio.run(main())All errors thrown by the SDK will be subclasses of ApiError.
try:
client.companies.get_company(
company_id="company_id",
)
except schematic.core.ApiError as e: # Handle all errors
print(e.status_code)
print(e.body)Create or update users and companies using identify events.
from schematic import EventBodyIdentifyCompany
from schematic.client import Schematic
client = Schematic("YOUR_API_KEY")
client.identify(
keys={
"email": "[email protected]",
"user_id": "your-user-id",
},
company=EventBodyIdentifyCompany(
keys={"id": "your-company-id"},
name="Acme Widgets, Inc.",
traits={
"city": "Atlanta",
},
),
name="Wile E. Coyote",
traits={
"login_count": 24,
"is_staff": false,
},
)This call is non-blocking and there is no response to check.
Track activity in your application using track events; these events can later be used to produce metrics for targeting.
from schematic.client import Schematic
client = Schematic("YOUR_API_KEY")
client.track(
event="some-action",
user={"user_id": "your-user-id"},
company={"id": "your-company-id"},
)This call is non-blocking and there is no response to check.
Although it is faster to create companies and users via identify events, if you need to handle a response, you can use the companies API to upsert companies. Because you use your own identifiers to identify companies, rather than a Schematic company ID, creating and updating companies are both done via the same upsert operation:
from schematic.client import Schematic
client = Schematic("YOUR_API_KEY")
client.companies.upsert_company(
keys={"id": "your-company-id"},
name="Acme Widgets, Inc.",
traits={
"city": "Atlanta",
"high_score": 25,
"is_active": true,
},
)You can define any number of company keys; these are used to address the company in the future, for example by updating the company's traits or checking a flag for the company.
You can also define any number of company traits; these can then be used as targeting parameters.
Similarly, you can upsert users using the Schematic API, as an alternative to using identify events. Because you use your own identifiers to identify users, rather than a Schematic user ID, creating and updating users are both done via the same upsert operation:
from schematic.client import Schematic
client = Schematic("YOUR_API_KEY")
client.companies.upsert_user(
keys={
"email": "[email protected]",
"user_id": "your-user-id",
},
name="Wile E. Coyote",
traits={
"city": "Atlanta",
"high_score": 25,
"is_active": true,
},
company={"id": "your-company-id"},
)You can define any number of user keys; these are used to address the user in the future, for example by updating the user's traits or checking a flag for the user.
You can also define any number of user traits; these can then be used as targeting parameters.
When checking a flag, you'll provide keys for a company and/or keys for a user. You can also provide no keys at all, in which case you'll get the default value for the flag.
from schematic.client import Schematic
client = Schematic("YOUR_API_KEY")
client.check_flag(
"some-flag-key",
company={"id": "your-company-id"},
user={"user_id": "your-user-id"},
)By default, the client will do some local caching for flag checks. If you would like to change this behavior, you can do so using an initialization option to specify the max size of the cache (in terms of number of entries) and the max age of the cache (in milliseconds):
from schematic.client import LocalCache, Schematic
cache_size = 100
cache_ttl = 1000 # in milliseconds
config = SchematicConfig(
cache_providers=[LocalCache[bool](cache_size, cache_ttl)],
)
client = Schematic("YOUR_API_KEY", config)You can also disable local caching entirely; bear in mind that, in this case, every flag check will result in a network request:
from schematic.client import Schematic
config = SchematicConfig(cache_providers=[])
client = Schematic("YOUR_API_KEY", config)You may want to specify default flag values for your application, which will be used if there is a service interruption or if the client is running in offline mode (see below):
from schematic.client import Schematic
config = SchematicConfig(flag_defaults={"some-flag-key": True})
client = Schematic("YOUR_API_KEY", config)In development or testing environments, you may want to avoid making network requests to the Schematic API. You can run Schematic in offline mode by specifying the offline option; in this case, it does not matter what API key you specify:
from schematic.client import Schematic
config = SchematicConfig(offline=True)
client = Schematic("", config)Offline mode works well with flag defaults:
from schematic.client import Schematic
config = SchematicConfig(
flag_defaults={"some-flag-key": True},
offline=True,
)
client = Schematic("", config)
client.check_flag("some-flag-key") # Returns TrueBy default, requests time out after 60 seconds. You can configure this with a timeout option at the client or request level.
from schematic.client import Schematic
client = Schematic(
# All timeouts are 20 seconds
timeout=20.0,
)
# Override timeout for a specific method
client.companies.get_company(..., {
timeout_in_seconds=20.0
})The SDK is instrumented with automatic retries with exponential backoff. A request will be retried as long as the request is deemed retriable and the number of retry attempts has not grown larger than the configured retry limit (default: 2).
A request is deemed retriable when any of the following HTTP status codes is returned:
Use the max_retries request option to configure this behavior.
# Override timeout for a specific method
client.companies.get_company(..., {
max_retries=1 # Only retry once on failure
})You can override the httpx client to customize it for your use-case. Some common use-cases include support for proxies and transports.
import httpx
from schematic.client import Schematic
client = Schematic(
http_client=httpx.Client(
proxies="http://my.test.proxy.example.com",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
)This SDK is in Preview, and there may be breaking changes between versions without a major version update.
To ensure a reproducible environment (and minimize risk of breaking changes), we recommend pinning a specific package version.
While we value open-source contributions to this SDK, this library is generated programmatically. Additions made directly to this library would have to be moved over to our generation code, otherwise they would be overwritten upon the next generated release. Feel free to open a PR as a proof of concept, but know that we will not be able to merge it as-is. We suggest opening an issue first to discuss with us!
On the other hand, contributions to the README are always very welcome!