| Name | cspark JSON |
| Version |
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| Summary | A Python SDK for interacting with Coherent Spark APIs |
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| requires_python | >=3.7 |
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restful
sdk
spark
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# Coherent Spark SDK
[![PyPI version][version-img]][version-url]
[![CI build][ci-img]][ci-url]
[![License][license-img]][license-url]
The Coherent Spark Python SDK is designed to elevate the developer experience and
provide convenient access to Coherent Spark APIs.
👋 **Just a heads-up:**
This SDK is supported by the community. If you encounter any bumps while using it,
please report them [by creating a new issue](https://github.com/Coherent-Partners/spark-python-sdk/issues).
## Installation
```bash
pip install cspark # or 'cspark[cli]' for CLI support.
```
> This Python library requires [Python 3.7+](https://www.python.org/downloads/).
## Usage
To use the SDK, you need a Coherent Spark account that lets you access the following:
- Base URL (including the environment and tenant name)
- User authentication ([API key][api-key-docs], [bearer token][bearer-token-docs],
or [OAuth2.0 client credentials][oauth2-docs] details)
- Spark service URI (to locate a specific resource):
- `folder` - the folder name (where the service is located)
- `service` - the service name
- `version` - the semantic version a.k.a revision number (e.g., 0.4.2)
In Spark, a `folder` acts as a container that holds one or more `service`s.
Think of folders as a way to organize and group related services together.
Each `service` represents an Excel model that has been converted into a Spark
service. Services can exist in multiple `version`s, representing different
iterations or updates of that service over time.
When interacting with a Spark service, you are always working with a specific
version - the latest one by default. You can explicitly specify an older
version if you need to work with a previous iteration of the service.
Hence, there are various ways to indicate a Spark service URI in the SDK:
- `{folder}/{service}[{version}]` - _version_ is optional.
- `service/{service_id}`
- `version/{version_id}`
> It is **important** to avoid using URL-encoded characters in the service URI as
> the SDK will take care of URL encoding for you.
Here's an example of how to execute a Spark service:
```py
import cspark.sdk as Spark
spark = Spark.Client(env='my-env', tenant='my-tenant', api_key='my-api-key')
with spark.services as services:
response = services.execute('my-folder/my-service', inputs={'value': 42})
print(response.data)
```
Explore the [examples] and [docs] folders to find out more about the SDK's capabilities.
> **PRO TIP:**
> A service URI locator can be combined with other parameters to locate a specific
> service (or version of it) when it's not a string. For example, you may execute
> a public service using a `UriParams` object by specifying the `folder`, `service`,
> and `public` properties.
```py
import cspark.sdk as Spark
spark = Spark.Client(env='my-env', tenant='my-tenant', api_key='open')
with spark.services as services:
uri = Spark.UriParams(folder='my-folder', service='my-service', public=True)
response = services.execute(uri, inputs={'value': 42})
print(response.data)
# The final URI in this case is:
# 'my-tenant/api/v3/public/folders/my-folder/services/my-service/execute'
```
See the [Uri and UriParams][uri-url] classes for more details.
## Client Options
As shown in the examples above, the `Spark.Client` is your entry point to the SDK.
It is quite flexible and can be configured with the following options:
### Base URL
`base_url` (default: `os.getenv['CSPARK_BASE_URL']`) indicates the base URL of
Coherent Spark APIs. It should include the tenant and environment information.
```py
spark = Spark.Client(base_url='https://excel.my-env.coherent.global/my-tenant')
```
Alternatively, a combination of `env` and `tenant` options can be used to construct
the base URL.
```py
spark = Spark.Client(env='my-env', tenant='my-tenant')
```
> For more advanced customizations, you can extend the `BaseUrl` class and make
> the appropriate changes to support custom `base_url`s.
### Authentication
The SDK supports three types of authentication schemes:
- `api_key` (default: `os.getenv['CSPARK_API_KEY']`) indicates the API key
(also known as synthetic key), which is sensitive and should be kept secure.
```py
spark = Spark.Client(api_key='my-api-key')
```
> **PRO TIP:**
> The Spark platform supports public APIs that can be accessed without any form
> of authentication. In that case, you need to set `api_key` to `open` in order to
> create a `Spark.Client`.
- `token` (default: `os.getenv['CSPARK_BEARER_TOKEN']`) indicates the bearer token.
It can be prefixed with 'Bearer' or not. A bearer token is usually valid for a
limited time and should be refreshed periodically.
```py
spark = Spark.Client(token='Bearer my-access-token') # with prefix
# or
spark = Spark.Client(token='my-access-token') # without prefix
```
- `oauth` (default: `os.getenv['CSPARK_CLIENT_ID']` and `os.getenv['CSPARK_CLIENT_SECRET']` or
`os.getenv['CSPARK_OAUTH_PATH']`) indicates the OAuth2.0 client credentials.
You can either provide the client ID and secret directly or the file path to
the JSON file containing the credentials.
```py
spark = Spark.Client(oauth={'client_id': 'my-client-id', 'client_secret': 'my-client-secret'})
# or
spark = Spark.Client(oauth='path/to/oauth/credentials.json')
```
### Additional Settings
- `timeout` (default: `60_000` ms) indicates the maximum amount of time that the
client should wait for a response from Spark servers before timing out a request.
- `max_retries` (default: `2`) indicates the maximum number of times that the client
will retry a request in case of a temporary failure, such as an unauthorized
response or a status code greater than 400.
- `retry_interval` (default: `1` second) indicates the delay between each retry.
- `logger` (default: `True`) enables or disables the logger for the SDK.
- If `bool`, determines whether or not the SDK should print logs.
- If `dict`, the SDK will print logs in accordance with the specified keyword arguments.
- If `LoggerOptions`, the SDK will print messages based on the specified options:
- `context` (default: `CSPARK v{version}`): defines the context of the logs (e.g., `CSPARK v0.1.6`);
- `disabled` (default: `False`) determines whether the logger should be disabled.
- `colorful` (default: `True`) determines whether the logs should be colorful;
- `timestamp` (default: `True`) determines whether the logs should include timestamps;
- `datefmt` (default: `'%m/%d/%Y, %I:%M:%S %p'`) defines the date format for the logs;
- `level` (default: `DEBUG`) defines the [logging level][logging-level] for the logs.
```py
spark = Spark.Client(logger=False)
# or
spark = Spark.Client(logger={'colorful': False})
```
- `http_client` (default: `None`) indicates the custom HTTP client to use to
perform HTTP requests. It is an instance of [httpx.Client][httpx-client] and
can be used to configure proxy, cookies, timeout, SSL verification, etc.
```py
import httpx
import cspark.sdk as Spark
spark = Spark.Client(
base_url='https://spark.my-env.coherent.global/my-tenant',
token='Bearer my-access-token',
http_client=httpx.Client(proxy='https://my-proxy-url')
)
```
## Client Errors
`SparkError` is the base class for all custom errors thrown by the SDK. There are
two types of it:
- `SparkSdkError`: usually thrown when an argument (user entry) fails to comply
with the expected format. Because it's a client-side error, it will include the invalid
entry as the `cause` in most cases.
- `SparkApiError`: when attempting to communicate with the API, the SDK will wrap
any sort of failure (any error during the roundtrip) into `SparkApiError`, which
includes the HTTP `status` code of the response and the `request_id`, a unique
identifier of the request.
Some of the derived `SparkApiError` are:
| Type | Status | When |
| ------------------------- | ------ | ------------------------------ |
| `BadRequestError` | 400 | invalid request |
| `UnauthorizedError` | 401 | missing or invalid credentials |
| `ForbiddenError` | 403 | insufficient permissions |
| `NotFoundError` | 404 | resource not found |
| `ConflictError` | 409 | resource already exists |
| `RateLimitError` | 429 | too many requests |
| `InternalServerError` | 500 | server-side error |
| `ServiceUnavailableError` | 503 | server is down |
| `UnknownApiError` | `None` | unknown error |
## API Parity
The SDK aims to provide full parity with the Spark APIs over time. Below is a list
of the currently supported APIs.
[Authentication API](./docs/authentication.md) - manages access tokens using
OAuth2.0 Client Credentials flow:
- `Authorization.oauth.retrieve_token(config)` generates new access tokens.
[Folders API](./docs/folders.md) - manages folders:
- `Spark.folders.categories.list()` gets the list of folder categories.
- `Spark.folders.create(data)` creates a new folder with additional info.
- `Spark.folders.find(name)` finds folders by name, status, category, or favorite.
- `Spark.folders.update(id, data)` updates a folder's information by id.
- `Spark.folders.delete(id)` deletes a folder by id, including all its services.
[Services API](./docs/services.md) - manages Spark services:
- `Spark.services.create(data)` creates a new Spark service.
- `Spark.services.execute(uri, inputs)` executes a Spark service.
- `Spark.services.transform(uri, inputs)` executes a Spark service using `Transforms`.
- `Spark.services.get_versions(uri)` lists all the versions of a service.
- `Spark.services.get_swagger(uri)` gets the Swagger documentation of a service.
- `Spark.services.get_schema(uri)` gets the schema of a service.
- `Spark.services.get_metadata(uri)` gets the metadata of a service.
- `Spark.services.search([params])` searches for services with pagination and filtering options.
- `Spark.services.download(uri)` downloads the excel file of a service.
- `Spark.services.recompile(uri)` recompiles a service using specific compiler versions.
- `Spark.services.validate(uri, data)` validates input data using static or dynamic validations.
- `Spark.services.delete(uri)` deletes an existing service, including all its versions.
[Batches API](./docs/batches.md) - manages asynchronous batch processing:
- `Spark.batches.describe()` describes the batch pipelines across a tenant.
- `Spark.batches.create(params, [options])` creates a new batch pipeline.
- `Spark.batches.of(id)` defines a client-side batch pipeline by ID.
- `Spark.batches.of(id).get_info()` gets the details of a batch pipeline.
- `Spark.batches.of(id).get_status()` gets the status of a batch pipeline.
- `Spark.batches.of(id).push(data, [options])` adds input data to a batch pipeline.
- `Spark.batches.of(id).pull([options])` retrieves the output data from a batch pipeline.
- `Spark.batches.of(id).dispose()` closes a batch pipeline.
- `Spark.batches.of(id).cancel()` cancels a batch pipeline.
[Log History API](./docs/history.md) - manages service execution logs:
- `Spark.logs.rehydrate(uri, call_id)` rehydrates the executed model into the original Excel file.
- `Spark.logs.download(data)` downloads service execution logs as `csv` or `json` file.
[ImpEx API](./docs/impex.md) - imports and exports Spark services:
- `Spark.impex.exp(data)` exports Spark entities (versions, services, or folders).
- `Spark.impex.imp(data)` imports previously exported Spark entities into the platform.
- `Spark.impex.exports.cancel(job_id)` cancels an in-progress export job.
[Other APIs](./docs/misc.md) - for other functionality:
- `Spark.health.check()` checks the health status of a Spark environment.
- `Spark.wasm.download(uri)` downloads a service's WebAssembly module.
- `Spark.files.download(url)` downloads temporary files issued by the Spark platform.
## Contributing
Feeling motivated enough to contribute? Great! Your help is always appreciated.
Please read [CONTRIBUTING.md][contributing-url] for details on the code of
conduct, and the process for submitting pull requests.
## Copyright and License
[Apache-2.0][license-url]
<!-- References -->
[version-img]: https://img.shields.io/pypi/v/cspark
[version-url]: https://pypi.python.org/pypi/cspark
[license-img]: https://img.shields.io/pypi/l/cspark
[license-url]: https://github.com/Coherent-Partners/spark-python-sdk/blob/main/LICENSE
[ci-img]: https://github.com/Coherent-Partners/spark-python-sdk/workflows/Build/badge.svg
[ci-url]: https://github.com/Coherent-Partners/spark-python-sdk/actions/workflows/build.yml
[api-key-docs]: https://docs.coherent.global/spark-apis/authorization-api-keys
[bearer-token-docs]: https://docs.coherent.global/spark-apis/authorization-bearer-token
[oauth2-docs]: https://docs.coherent.global/spark-apis/authorization-client-credentials
[contributing-url]: https://github.com/Coherent-Partners/spark-python-sdk/blob/main/CONTRIBUTING.md
[examples]: https://github.com/Coherent-Partners/spark-python-sdk/tree/main/examples
[docs]: https://github.com/Coherent-Partners/spark-python-sdk/tree/main/docs
[uri-url]: https://github.com/Coherent-Partners/spark-python-sdk/blob/main/src/cspark/sdk/resources/_base.py
[logging-level]: https://docs.python.org/3/library/logging.html#logging-levels
[httpx-client]: https://www.python-httpx.org/api/#client
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"description": "# Coherent Spark SDK\n\n[![PyPI version][version-img]][version-url]\n[![CI build][ci-img]][ci-url]\n[![License][license-img]][license-url]\n\nThe Coherent Spark Python SDK is designed to elevate the developer experience and\nprovide convenient access to Coherent Spark APIs.\n\n\ud83d\udc4b **Just a heads-up:**\nThis SDK is supported by the community. If you encounter any bumps while using it,\nplease report them [by creating a new issue](https://github.com/Coherent-Partners/spark-python-sdk/issues).\n\n## Installation\n\n```bash\npip install cspark # or 'cspark[cli]' for CLI support.\n```\n\n> This Python library requires [Python 3.7+](https://www.python.org/downloads/).\n\n## Usage\n\nTo use the SDK, you need a Coherent Spark account that lets you access the following:\n\n- Base URL (including the environment and tenant name)\n- User authentication ([API key][api-key-docs], [bearer token][bearer-token-docs],\n or [OAuth2.0 client credentials][oauth2-docs] details)\n- Spark service URI (to locate a specific resource):\n - `folder` - the folder name (where the service is located)\n - `service` - the service name\n - `version` - the semantic version a.k.a revision number (e.g., 0.4.2)\n\nIn Spark, a `folder` acts as a container that holds one or more `service`s.\nThink of folders as a way to organize and group related services together.\nEach `service` represents an Excel model that has been converted into a Spark\nservice. Services can exist in multiple `version`s, representing different\niterations or updates of that service over time.\n\nWhen interacting with a Spark service, you are always working with a specific\nversion - the latest one by default. You can explicitly specify an older\nversion if you need to work with a previous iteration of the service.\n\nHence, there are various ways to indicate a Spark service URI in the SDK:\n\n- `{folder}/{service}[{version}]` - _version_ is optional.\n- `service/{service_id}`\n- `version/{version_id}`\n\n> It is **important** to avoid using URL-encoded characters in the service URI as\n> the SDK will take care of URL encoding for you.\n\nHere's an example of how to execute a Spark service:\n\n```py\nimport cspark.sdk as Spark\n\nspark = Spark.Client(env='my-env', tenant='my-tenant', api_key='my-api-key')\nwith spark.services as services:\n response = services.execute('my-folder/my-service', inputs={'value': 42})\n print(response.data)\n```\n\nExplore the [examples] and [docs] folders to find out more about the SDK's capabilities.\n\n> **PRO TIP:**\n> A service URI locator can be combined with other parameters to locate a specific\n> service (or version of it) when it's not a string. For example, you may execute\n> a public service using a `UriParams` object by specifying the `folder`, `service`,\n> and `public` properties.\n\n```py\nimport cspark.sdk as Spark\n\nspark = Spark.Client(env='my-env', tenant='my-tenant', api_key='open')\n\nwith spark.services as services:\n uri = Spark.UriParams(folder='my-folder', service='my-service', public=True)\n response = services.execute(uri, inputs={'value': 42})\n print(response.data)\n\n# The final URI in this case is:\n# 'my-tenant/api/v3/public/folders/my-folder/services/my-service/execute'\n```\n\nSee the [Uri and UriParams][uri-url] classes for more details.\n\n## Client Options\n\nAs shown in the examples above, the `Spark.Client` is your entry point to the SDK.\nIt is quite flexible and can be configured with the following options:\n\n### Base URL\n\n`base_url` (default: `os.getenv['CSPARK_BASE_URL']`) indicates the base URL of\nCoherent Spark APIs. It should include the tenant and environment information.\n\n```py\nspark = Spark.Client(base_url='https://excel.my-env.coherent.global/my-tenant')\n```\n\nAlternatively, a combination of `env` and `tenant` options can be used to construct\nthe base URL.\n\n```py\nspark = Spark.Client(env='my-env', tenant='my-tenant')\n```\n\n> For more advanced customizations, you can extend the `BaseUrl` class and make\n> the appropriate changes to support custom `base_url`s.\n\n### Authentication\n\nThe SDK supports three types of authentication schemes:\n\n- `api_key` (default: `os.getenv['CSPARK_API_KEY']`) indicates the API key\n (also known as synthetic key), which is sensitive and should be kept secure.\n\n```py\nspark = Spark.Client(api_key='my-api-key')\n```\n\n> **PRO TIP:**\n> The Spark platform supports public APIs that can be accessed without any form\n> of authentication. In that case, you need to set `api_key` to `open` in order to\n> create a `Spark.Client`.\n\n- `token` (default: `os.getenv['CSPARK_BEARER_TOKEN']`) indicates the bearer token.\n It can be prefixed with 'Bearer' or not. A bearer token is usually valid for a\n limited time and should be refreshed periodically.\n\n```py\nspark = Spark.Client(token='Bearer my-access-token') # with prefix\n# or\nspark = Spark.Client(token='my-access-token') # without prefix\n```\n\n- `oauth` (default: `os.getenv['CSPARK_CLIENT_ID']` and `os.getenv['CSPARK_CLIENT_SECRET']` or\n `os.getenv['CSPARK_OAUTH_PATH']`) indicates the OAuth2.0 client credentials.\n You can either provide the client ID and secret directly or the file path to\n the JSON file containing the credentials.\n\n```py\nspark = Spark.Client(oauth={'client_id': 'my-client-id', 'client_secret': 'my-client-secret'})\n# or\nspark = Spark.Client(oauth='path/to/oauth/credentials.json')\n```\n\n### Additional Settings\n\n- `timeout` (default: `60_000` ms) indicates the maximum amount of time that the\n client should wait for a response from Spark servers before timing out a request.\n\n- `max_retries` (default: `2`) indicates the maximum number of times that the client\n will retry a request in case of a temporary failure, such as an unauthorized\n response or a status code greater than 400.\n\n- `retry_interval` (default: `1` second) indicates the delay between each retry.\n\n- `logger` (default: `True`) enables or disables the logger for the SDK.\n - If `bool`, determines whether or not the SDK should print logs.\n - If `dict`, the SDK will print logs in accordance with the specified keyword arguments.\n - If `LoggerOptions`, the SDK will print messages based on the specified options:\n - `context` (default: `CSPARK v{version}`): defines the context of the logs (e.g., `CSPARK v0.1.6`);\n - `disabled` (default: `False`) determines whether the logger should be disabled.\n - `colorful` (default: `True`) determines whether the logs should be colorful;\n - `timestamp` (default: `True`) determines whether the logs should include timestamps;\n - `datefmt` (default: `'%m/%d/%Y, %I:%M:%S %p'`) defines the date format for the logs;\n - `level` (default: `DEBUG`) defines the [logging level][logging-level] for the logs.\n\n```py\nspark = Spark.Client(logger=False)\n# or\nspark = Spark.Client(logger={'colorful': False})\n```\n\n- `http_client` (default: `None`) indicates the custom HTTP client to use to\n perform HTTP requests. It is an instance of [httpx.Client][httpx-client] and\n can be used to configure proxy, cookies, timeout, SSL verification, etc.\n\n```py\nimport httpx\nimport cspark.sdk as Spark\n\nspark = Spark.Client(\n base_url='https://spark.my-env.coherent.global/my-tenant',\n token='Bearer my-access-token',\n http_client=httpx.Client(proxy='https://my-proxy-url')\n)\n```\n\n## Client Errors\n\n`SparkError` is the base class for all custom errors thrown by the SDK. There are\ntwo types of it:\n\n- `SparkSdkError`: usually thrown when an argument (user entry) fails to comply\n with the expected format. Because it's a client-side error, it will include the invalid\n entry as the `cause` in most cases.\n- `SparkApiError`: when attempting to communicate with the API, the SDK will wrap\n any sort of failure (any error during the roundtrip) into `SparkApiError`, which\n includes the HTTP `status` code of the response and the `request_id`, a unique\n identifier of the request.\n\nSome of the derived `SparkApiError` are:\n\n| Type | Status | When |\n| ------------------------- | ------ | ------------------------------ |\n| `BadRequestError` | 400 | invalid request |\n| `UnauthorizedError` | 401 | missing or invalid credentials |\n| `ForbiddenError` | 403 | insufficient permissions |\n| `NotFoundError` | 404 | resource not found |\n| `ConflictError` | 409 | resource already exists |\n| `RateLimitError` | 429 | too many requests |\n| `InternalServerError` | 500 | server-side error |\n| `ServiceUnavailableError` | 503 | server is down |\n| `UnknownApiError` | `None` | unknown error |\n\n## API Parity\n\nThe SDK aims to provide full parity with the Spark APIs over time. Below is a list\nof the currently supported APIs.\n\n[Authentication API](./docs/authentication.md) - manages access tokens using\nOAuth2.0 Client Credentials flow:\n\n- `Authorization.oauth.retrieve_token(config)` generates new access tokens.\n\n[Folders API](./docs/folders.md) - manages folders:\n\n- `Spark.folders.categories.list()` gets the list of folder categories.\n- `Spark.folders.create(data)` creates a new folder with additional info.\n- `Spark.folders.find(name)` finds folders by name, status, category, or favorite.\n- `Spark.folders.update(id, data)` updates a folder's information by id.\n- `Spark.folders.delete(id)` deletes a folder by id, including all its services.\n\n[Services API](./docs/services.md) - manages Spark services:\n\n- `Spark.services.create(data)` creates a new Spark service.\n- `Spark.services.execute(uri, inputs)` executes a Spark service.\n- `Spark.services.transform(uri, inputs)` executes a Spark service using `Transforms`.\n- `Spark.services.get_versions(uri)` lists all the versions of a service.\n- `Spark.services.get_swagger(uri)` gets the Swagger documentation of a service.\n- `Spark.services.get_schema(uri)` gets the schema of a service.\n- `Spark.services.get_metadata(uri)` gets the metadata of a service.\n- `Spark.services.search([params])` searches for services with pagination and filtering options.\n- `Spark.services.download(uri)` downloads the excel file of a service.\n- `Spark.services.recompile(uri)` recompiles a service using specific compiler versions.\n- `Spark.services.validate(uri, data)` validates input data using static or dynamic validations.\n- `Spark.services.delete(uri)` deletes an existing service, including all its versions.\n\n[Batches API](./docs/batches.md) - manages asynchronous batch processing:\n\n- `Spark.batches.describe()` describes the batch pipelines across a tenant.\n- `Spark.batches.create(params, [options])` creates a new batch pipeline.\n- `Spark.batches.of(id)` defines a client-side batch pipeline by ID.\n- `Spark.batches.of(id).get_info()` gets the details of a batch pipeline.\n- `Spark.batches.of(id).get_status()` gets the status of a batch pipeline.\n- `Spark.batches.of(id).push(data, [options])` adds input data to a batch pipeline.\n- `Spark.batches.of(id).pull([options])` retrieves the output data from a batch pipeline.\n- `Spark.batches.of(id).dispose()` closes a batch pipeline.\n- `Spark.batches.of(id).cancel()` cancels a batch pipeline.\n\n[Log History API](./docs/history.md) - manages service execution logs:\n\n- `Spark.logs.rehydrate(uri, call_id)` rehydrates the executed model into the original Excel file.\n- `Spark.logs.download(data)` downloads service execution logs as `csv` or `json` file.\n\n[ImpEx API](./docs/impex.md) - imports and exports Spark services:\n\n- `Spark.impex.exp(data)` exports Spark entities (versions, services, or folders).\n- `Spark.impex.imp(data)` imports previously exported Spark entities into the platform.\n- `Spark.impex.exports.cancel(job_id)` cancels an in-progress export job.\n\n[Other APIs](./docs/misc.md) - for other functionality:\n\n- `Spark.health.check()` checks the health status of a Spark environment.\n- `Spark.wasm.download(uri)` downloads a service's WebAssembly module.\n- `Spark.files.download(url)` downloads temporary files issued by the Spark platform.\n\n## Contributing\n\nFeeling motivated enough to contribute? Great! Your help is always appreciated.\n\nPlease read [CONTRIBUTING.md][contributing-url] for details on the code of\nconduct, and the process for submitting pull requests.\n\n## Copyright and License\n\n[Apache-2.0][license-url]\n\n<!-- References -->\n[version-img]: https://img.shields.io/pypi/v/cspark\n[version-url]: https://pypi.python.org/pypi/cspark\n[license-img]: https://img.shields.io/pypi/l/cspark\n[license-url]: https://github.com/Coherent-Partners/spark-python-sdk/blob/main/LICENSE\n[ci-img]: https://github.com/Coherent-Partners/spark-python-sdk/workflows/Build/badge.svg\n[ci-url]: https://github.com/Coherent-Partners/spark-python-sdk/actions/workflows/build.yml\n\n[api-key-docs]: https://docs.coherent.global/spark-apis/authorization-api-keys\n[bearer-token-docs]: https://docs.coherent.global/spark-apis/authorization-bearer-token\n[oauth2-docs]: https://docs.coherent.global/spark-apis/authorization-client-credentials\n[contributing-url]: https://github.com/Coherent-Partners/spark-python-sdk/blob/main/CONTRIBUTING.md\n[examples]: https://github.com/Coherent-Partners/spark-python-sdk/tree/main/examples\n[docs]: https://github.com/Coherent-Partners/spark-python-sdk/tree/main/docs\n[uri-url]: https://github.com/Coherent-Partners/spark-python-sdk/blob/main/src/cspark/sdk/resources/_base.py\n[logging-level]: https://docs.python.org/3/library/logging.html#logging-levels\n[httpx-client]: https://www.python-httpx.org/api/#client\n",
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