flow-ai-sdk

Developer-friendly & type-safe Python SDK specifically catered to leverage flow-ai-sdk API.

[!IMPORTANT] This SDK is not yet ready for production use. To complete setup please follow the steps outlined in your workspace. Delete this section before > publishing to a package manager.

Summary

server: FastAPI backend for Flow Expert

Table of Contents

• flow-ai-sdk
  • SDK Installation
  • IDE Support
  • SDK Example Usage
  • Available Resources and Operations
  • Retries
  • Error Handling
  • Server Selection
  • Custom HTTP Client
  • Resource Management
  • Debugging
• Development
  • Maturity
  • Contributions

SDK Installation

[!NOTE] Python version upgrade policy

Once a Python version reaches its official end of life date, a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.

The SDK can be installed with either pip or poetry package managers.

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install flowai-sdk

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add flowai-sdk

Shell and script usage with uv

You can use this SDK in a Python shell with uv and the uvx command that comes with it like so:

uvx --from flowai-sdk python

It's also possible to write a standalone Python script without needing to set up a whole project like so:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "flowai-sdk",
# ]
# ///

from flowai_sdk import FlowAI

sdk = FlowAI(
  # SDK arguments
)

# Rest of script here...

Once that is saved to a file, you can run it with uv run script.py where script.py can be replaced with the actual file name.

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

• PyCharm Pydantic Plugin

SDK Example Usage

Example

# Synchronous Example
from flowai_sdk import FlowAI


with FlowAI() as flow_ai:

    res = flow_ai.health.get()

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from flowai_sdk import FlowAI

async def main():

    async with FlowAI() as flow_ai:

        res = await flow_ai.health.get_async()

        # Handle response
        print(res)

asyncio.run(main())

Available Resources and Operations

Available methods

api_keys

• list - List API Keys for the authenticated user
• create - Generate a new API Key for the authenticated user
• update - Update an API Key
• revoke - Revoke an API Key

authentication

• sdk_login - Sdk Login
• complete_validator_signup - Complete Validator Signup
• verify_validator_access - Verify Validator Access

batches

• list_mine - List My Batches
• get - Get Batch By Id
• delete - Delete Batch By Id
• get_test_cases - Get Batch Test Cases
• get_validations - Get Validations For Batch
• create_validation_task - Create Validation Task For Batch Endpoint
• get_validation_task - Get Validation Task For Batch Endpoint
• get_status - Get Batch Status Endpoint

clerk_webhooks

• handle - Handle Clerk Webhook Events

datasets

• get_items - Get Dataset Items
• delete - Delete Dataset

health

• get - Get Service Health Status
• head - Check Service Health Status (HEAD)

integrations

• handle_prefect_webhook - Prefect Webhook

jobs

• get_details - Get Job Details
• cancel - Cancel Job
• generate_batch - Generate Batch for Job
• list_batches - List Batches for a Job
• generate_dataset - Generate Dataset for Job
• trigger_pipeline - Trigger Pipeline From Stages

projects

• list - List Projects
• create - Create Project
• get - Get Project
• delete - Delete Project
• update - Update Project
• archive - Archive Project
• get_dataset - Get Project Dataset
• list_validators - List Project Validators
• add_validator - Add Project Validator
• remove_validator - Remove Project Validator
• list_jobs - List Jobs for Project
• create_job - Create Job for Project

root

• get - Root

stages

• ping - Ping Stages
• list - List Stages
• create - Create Stages For Project
• get_details - Get Stage Details
• update - Update Stage
• delete - Delete Stage

test_cases

• list - List Test Cases
• create - Create Test Case
• get - Get Test Case
• update - Update Test Case
• delete - Delete Test Case
• get_validation - Get Single Validation From Batch

users

• get_me - Read Users Me
• update_me - Update User Me
• update_role - Update User Role
• get_basic_info - Get Current User Basic Info

validation_tasks

• get_status - Check Validation Task Status

validations

• list - List Validations
• create - Create Test Case Validation
• get - Get Validation
• update - Update Test Case Validation
• delete - Delete Validation

validator_tasks

• submit_validation - Submit Test Case Validation
• load_test_cases - Load Test Cases For Validator
• update_validation - Edit Test Case Validation
• submit_validator_task - Submit Validator Task

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from flowai_sdk import FlowAI
from flowai_sdk.utils import BackoffStrategy, RetryConfig


with FlowAI() as flow_ai:

    res = flow_ai.health.get(,
        RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

    # Handle response
    print(res)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from flowai_sdk import FlowAI
from flowai_sdk.utils import BackoffStrategy, RetryConfig


with FlowAI(
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
) as flow_ai:

    res = flow_ai.health.get()

    # Handle response
    print(res)

Error Handling

Handling errors in this SDK should largely match your expectations. All operations return a response object or raise an exception.

By default, an API error will raise a errors.APIError exception, which has the following properties:

Property	Type	Description
.status_code	int	The HTTP status code
.message	str	The error message
.raw_response	httpx.Response	The raw HTTP response
.body	str	The response content

When custom error responses are specified for an operation, the SDK may also raise their associated exceptions. You can refer to respective Errors tables in SDK docs for more details on possible exception types for each operation. For example, the handle_async method may raise the following exceptions:

Error Type	Status Code	Content Type
errors.HTTPValidationError	422	application/json
errors.APIError	4XX, 5XX	*/*

Example

from flowai_sdk import FlowAI, errors


with FlowAI() as flow_ai:
    res = None
    try:

        res = flow_ai.clerk_webhooks.handle()

        # Handle response
        print(res)

    except errors.HTTPValidationError as e:
        # handle e.data: errors.HTTPValidationErrorData
        raise(e)
    except errors.APIError as e:
        # handle exception
        raise(e)

Server Selection

Override Server URL Per-Client

The default server can be overridden globally by passing a URL to the server_url: str optional parameter when initializing the SDK client instance. For example:

from flowai_sdk import FlowAI


with FlowAI(
    server_url="http://localhost:8000",
) as flow_ai:

    res = flow_ai.health.get()

    # Handle response
    print(res)

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from flowai_sdk import FlowAI
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = FlowAI(client=http_client)

or you could wrap the client with your own custom logic:

from flowai_sdk import FlowAI
from flowai_sdk.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

s = FlowAI(async_client=CustomClient(httpx.AsyncClient()))

Resource Management

The FlowAI class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.

from flowai_sdk import FlowAI
def main():

    with FlowAI() as flow_ai:
        # Rest of application here...


# Or when using async:
async def amain():

    async with FlowAI() as flow_ai:
        # Rest of application here...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from flowai_sdk import FlowAI
import logging

logging.basicConfig(level=logging.DEBUG)
s = FlowAI(debug_logger=logging.getLogger("flowai_sdk"))

You can also enable a default debug logger by setting an environment variable FLOWAI_DEBUG to true.

Development

Maturity

This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.

Contributions

While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.

SDK Created by Speakeasy