Fern Python Library

The Fern Python library provides convenient access to the Fern APIs from Python.

Table of Contents

• Installation
• Reference
• Usage
• Async Client
• Exception Handling
• Advanced
  • Access Raw Response Data
  • Retries
  • Timeouts
  • Custom Client
• Contributing

Installation

pip install fern-fai-sdk

Reference

A full reference for this library is available here.

Usage

Instantiate and use the client with the following:

import datetime

from fern_fai_sdk import FernAI

client = FernAI(
    token="YOUR_TOKEN",
)
client.analytics.generate_all_insights(
    start_date=datetime.datetime.fromisoformat(
        "2024-01-15 09:30:00+00:00",
    ),
    end_date=datetime.datetime.fromisoformat(
        "2024-01-15 09:30:00+00:00",
    ),
)

Async Client

The SDK also exports an async client so that you can make non-blocking calls to our API. Note that if you are constructing an Async httpx client class to pass into this client, use httpx.AsyncClient() instead of httpx.Client() (e.g. for the httpx_client parameter of this client).

import asyncio
import datetime

from fern_fai_sdk import AsyncFernAI

client = AsyncFernAI(
    token="YOUR_TOKEN",
)


async def main() -> None:
    await client.analytics.generate_all_insights(
        start_date=datetime.datetime.fromisoformat(
            "2024-01-15 09:30:00+00:00",
        ),
        end_date=datetime.datetime.fromisoformat(
            "2024-01-15 09:30:00+00:00",
        ),
    )


asyncio.run(main())

Exception Handling

When the API returns a non-success status code (4xx or 5xx response), a subclass of the following error will be thrown.

from fern_fai_sdk.core.api_error import ApiError

try:
    client.analytics.generate_all_insights(...)
except ApiError as e:
    print(e.status_code)
    print(e.body)

Advanced

Access Raw Response Data

The SDK provides access to raw response data, including headers, through the .with_raw_response property. The .with_raw_response property returns a "raw" client that can be used to access the .headers and .data attributes.

from fern_fai_sdk import FernAI

client = FernAI(
    ...,
)
response = client.analytics.with_raw_response.generate_all_insights(...)
print(response.headers)  # access the response headers
print(response.data)  # access the underlying object

Retries

The SDK is instrumented with automatic retries with exponential backoff. A request will be retried as long as the request is deemed retryable and the number of retry attempts has not grown larger than the configured retry limit (default: 2).

A request is deemed retryable when any of the following HTTP status codes is returned:

• 408 (Timeout)
• 429 (Too Many Requests)
• 5XX (Internal Server Errors)

Use the max_retries request option to configure this behavior.

client.analytics.generate_all_insights(..., request_options={
    "max_retries": 1
})

Timeouts

The SDK defaults to a 60 second timeout. You can configure this with a timeout option at the client or request level.

from fern_fai_sdk import FernAI

client = FernAI(
    ...,
    timeout=20.0,
)


# Override timeout for a specific method
client.analytics.generate_all_insights(..., request_options={
    "timeout_in_seconds": 1
})

Custom Client

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 fern_fai_sdk import FernAI

client = FernAI(
    ...,
    httpx_client=httpx.Client(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

Contributing

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!