Welcome to the Livepeer AI Python! This library offers a seamless integration with the Livepeer AI API, enabling you to easily incorporate powerful AI capabilities into your Python applications, whether they run in the browser or on the server side.
The SDK can be installed with either pip or poetry package managers.
PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.
pip install livepeer-aiPoetry 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 livepeer-aiGenerally, 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.
# Synchronous Example
from livepeer_ai import Livepeer
s = Livepeer(
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = s.generate.text_to_image(request={
"prompt": "<value>",
})
if res.image_response is not None:
# handle response
passThe same SDK client can also be used to make asychronous requests by importing asyncio.
# Asynchronous Example
import asyncio
from livepeer_ai import Livepeer
async def main():
s = Livepeer(
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = await s.generate.text_to_image_async(request={
"prompt": "<value>",
})
if res.image_response is not None:
# handle response
pass
asyncio.run(main())Available methods
- text_to_image - Text To Image
- image_to_image - Image To Image
- image_to_video - Image To Video
- upscale - Upscale
- audio_to_text - Audio To Text
- segment_anything2 - Segment Anything 2
- llm - LLM
Certain SDK methods accept file objects as part of a request body or multi-part request. It is possible and typically recommended to upload files as a stream rather than reading the entire contents into memory. This avoids excessive memory consumption and potentially crashing with out-of-memory errors when working with very large files. The following example demonstrates how to attach a file stream to a request.
Tip
For endpoints that handle file uploads bytes arrays can also be used. However, using streams is recommended for large files.
from livepeer_ai import Livepeer
s = Livepeer(
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = s.generate.image_to_image(request={
"prompt": "<value>",
"image": {
"file_name": "example.file",
"content": open("example.file", "rb"),
},
})
if res.image_response is not None:
# handle response
passSome 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 livepeer.utils import BackoffStrategy, RetryConfig
from livepeer_ai import Livepeer
s = Livepeer(
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = s.generate.text_to_image(request={
"prompt": "<value>",
},
RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))
if res.image_response is not None:
# handle response
passIf 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 livepeer.utils import BackoffStrategy, RetryConfig
from livepeer_ai import Livepeer
s = Livepeer(
retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = s.generate.text_to_image(request={
"prompt": "<value>",
})
if res.image_response is not None:
# handle response
passHandling 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.SDKError 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 text_to_image_async method may raise the following exceptions:
| Error Type | Status Code | Content Type |
|---|---|---|
| errors.HTTPError | 400, 401, 500 | application/json |
| errors.HTTPValidationError | 422 | application/json |
| errors.SDKError | 4XX, 5XX | */* |
from livepeer_ai import Livepeer
from livepeer_ai.models import errors
s = Livepeer(
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = None
try:
res = s.generate.text_to_image(request={
"prompt": "<value>",
})
if res.image_response is not None:
# handle response
pass
except errors.HTTPError as e:
# handle e.data: errors.HTTPErrorData
raise(e)
except errors.HTTPValidationError as e:
# handle e.data: errors.HTTPValidationErrorData
raise(e)
except errors.SDKError as e:
# handle exception
raise(e)You can override the default server globally by passing a server index to the server_idx: int optional parameter when initializing the SDK client instance. The selected server will then be used as the default on the operations that use it. This table lists the indexes associated with the available servers:
| # | Server | Variables |
|---|---|---|
| 0 | https://dream-gateway.livepeer.cloud |
None |
| 1 | https://livepeer.studio/api/beta/generate |
None |
from livepeer_ai import Livepeer
s = Livepeer(
server_idx=1,
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = s.generate.text_to_image(request={
"prompt": "<value>",
})
if res.image_response is not None:
# handle response
passThe default server can also be overridden globally by passing a URL to the server_url: str optional parameter when initializing the SDK client instance. For example:
from livepeer_ai import Livepeer
s = Livepeer(
server_url="https://dream-gateway.livepeer.cloud",
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = s.generate.text_to_image(request={
"prompt": "<value>",
})
if res.image_response is not None:
# handle response
passThe 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 livepeer_ai import Livepeer
import httpx
http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = Livepeer(client=http_client)or you could wrap the client with your own custom logic:
from livepeer_ai import Livepeer
from livepeer_ai.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 = Livepeer(async_client=CustomClient(httpx.AsyncClient()))This SDK supports the following security scheme globally:
| Name | Type | Scheme |
|---|---|---|
http_bearer |
http | HTTP Bearer |
To authenticate with the API the http_bearer parameter must be set when initializing the SDK client instance. For example:
from livepeer_ai import Livepeer
s = Livepeer(
http_bearer="<YOUR_BEARER_TOKEN_HERE>",
)
res = s.generate.text_to_image(request={
"prompt": "<value>",
})
if res.image_response is not None:
# handle response
passYou 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 livepeer_ai import Livepeer
import logging
logging.basicConfig(level=logging.DEBUG)
s = Livepeer(debug_logger=logging.getLogger("livepeer_ai"))Livepeer AI Runner: An application to run AI pipelines
- SDK Installation
- IDE Support
- SDK Example Usage
- Available Resources and Operations
- File uploads
- Retries
- Error Handling
- Server Selection
- Custom HTTP Client
- Authentication
- Debugging
This SDK is in alpha, 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.
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.