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  • License: Apache-2.0
  • Author: Speakeasy
  • Requires: Python >=3.10
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Project description

openapi

Developer-friendly & type-safe Python SDK specifically catered to leverage openapi API.

Built by Speakeasy License: MIT



[!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

MKA1 API: The MKA1 API is a RESTful API that provides access to the MKA1 platform. Learn how to get started with the API and the TypeScript SDK here.

Table of Contents

SDK Installation

[!TIP] To finish publishing your SDK to PyPI you must run your first generation action.

[!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 uv, pip, or poetry package managers.

uv

uv is a fast Python package installer and resolver, designed as a drop-in replacement for pip and pip-tools. It's recommended for its speed and modern Python tooling capabilities.

uv add git+https://github.com/MeetKai/mk1-sdks.git#subdirectory=python

PIP

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

pip install git+https://github.com/MeetKai/mk1-sdks.git#subdirectory=python

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 git+https://github.com/MeetKai/mk1-sdks.git#subdirectory=python

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 meetkai-mka1 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 = ">=3.10"
# dependencies = [
#     "meetkai-mka1",
# ]
# ///

from meetkai_mka1 import SDK

sdk = SDK(
  # 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.

SDK Example Usage

Example

# Synchronous Example
from meetkai_mka1 import SDK


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    sdk.permissions.llm.grant(resource_type="completion", resource_id="my-completion-123", user_id="user-abc456", role="writer")

    # Use the SDK ...

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

# Asynchronous Example
import asyncio
from meetkai_mka1 import SDK

async def main():

    async with SDK(
        bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
    ) as sdk:

        await sdk.permissions.llm.grant_async(resource_type="completion", resource_id="my-completion-123", user_id="user-abc456", role="writer")

        # Use the SDK ...

asyncio.run(main())

Authentication

Per-Client Security Schemes

This SDK supports the following security scheme globally:

Name Type Scheme
bearer_auth http HTTP Bearer

To authenticate with the API the bearer_auth parameter must be set when initializing the SDK client instance. For example:

from meetkai_mka1 import SDK


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    sdk.permissions.llm.grant(resource_type="completion", resource_id="my-completion-123", user_id="user-abc456", role="writer")

    # Use the SDK ...

Available Resources and Operations

Available methods

AgentRuns

AgentSchedules

Agents

Auth.ApiKey

Guardrails

Llm.Batches

Llm.Chat

  • create_chat - [Deprecated] Chat completions for OpenAI SDK/client usage :warning: Deprecated
  • stream - [Deprecated] Streaming chat completions for generated SDK usage :warning: Deprecated

Llm.Classify

  • classify - Classify text into predefined categories

Llm.Conversations

Llm.Embeddings

Llm.Evals

Llm.Extract

Llm.Feedback

Llm.Files

Llm.FineTuning

Llm.Images

  • create - Generate images from text descriptions

Llm.McpVault

Llm.MemoryStores

Llm.Models

Llm.Prompts

Llm.Responses

  • create - Create an agent-powered response with tool support
  • list - List all responses with pagination
  • get - Retrieve response by ID with status and results
  • update - Update a response
  • delete - Permanently delete a response and its data
  • cancel - Cancel an in-progress background response
  • wake - Wake a sleeping background response
  • list_input_items - List paginated input items for a response
  • compact - Compact a conversation

Llm.Skills

Llm.Speech

Llm.Usage

Llm.VectorStores

Permissions.Llm

  • grant - Grant permission to a user or make public
  • revoke - Revoke permission from a user or remove public access
  • check - Check user permission

Sandbox

SandboxUsage

Search.Graphrag

Search.Tables

Search.TextStore

Server-sent event streaming

Server-sent events are used to stream content from certain operations. These operations will expose the stream as Generator that can be consumed using a simple for loop. The loop will terminate when the server no longer has any events to send and closes the underlying connection.

The stream is also a Context Manager and can be used with the with statement and will close the underlying connection when the context is exited.

from meetkai_mka1 import SDK


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    res = sdk.llm.responses.create(model="meetkai:functionary-urdu-mini-pak", input="What is the capital of France?", stream=False, store=True, background=False, parallel_tool_calls=True, max_tool_calls=64, truncation="auto", service_tier="auto")

    with res as event_stream:
        for event in event_stream:
            # handle event
            print(event, flush=True)

File uploads

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 meetkai_mka1 import SDK


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    res = sdk.llm.files.upload(file={
        "file_name": "example.file",
        "content": open("example.file", "rb"),
    }, purpose="assistants")

    # Handle response
    print(res)

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 meetkai_mka1 import SDK
from meetkai_mka1.utils import BackoffStrategy, RetryConfig


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    sdk.permissions.llm.grant(resource_type="completion", resource_id="my-completion-123", user_id="user-abc456", role="writer",
        RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

    # Use the SDK ...

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 meetkai_mka1 import SDK
from meetkai_mka1.utils import BackoffStrategy, RetryConfig


with SDK(
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    sdk.permissions.llm.grant(resource_type="completion", resource_id="my-completion-123", user_id="user-abc456", role="writer")

    # Use the SDK ...

Error Handling

SDKError is the base class for all HTTP error responses. It has the following properties:

Property Type Description
err.message str Error message
err.status_code int HTTP response status code eg 404
err.headers httpx.Headers HTTP response headers
err.body str HTTP body. Can be empty string if no body is returned.
err.raw_response httpx.Response Raw HTTP response
err.data Optional. Some errors may contain structured data. See Error Classes.

Example

from meetkai_mka1 import SDK, errors


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:
    res = None
    try:

        res = sdk.llm.files.content(file_id="file-abc123")

        # Handle response
        print(res)


    except errors.SDKError as e:
        # The base class for HTTP error responses
        print(e.message)
        print(e.status_code)
        print(e.body)
        print(e.headers)
        print(e.raw_response)

        # Depending on the method different errors may be thrown
        if isinstance(e, errors.GetFileContentBadRequestError):
            print(e.data.error)  # Optional[models.BadRequestError]

Error Classes

Primary error:

  • SDKError: The base class for HTTP error responses.
Less common errors (19)

Network errors:

Inherit from SDKError:

* Check the method documentation to see if the error is applicable.

Server Selection

Select Server by Index

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 Description
0 https://apigw.mka1.com MKA1 API Gateway
1 / Relative server URL (configurable via SDK constructor)

Example

from meetkai_mka1 import SDK


with SDK(
    server_idx=0,
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    sdk.permissions.llm.grant(resource_type="completion", resource_id="my-completion-123", user_id="user-abc456", role="writer")

    # Use the SDK ...

Override Server URL Per-Client

The 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 meetkai_mka1 import SDK


with SDK(
    server_url="https://apigw.mka1.com",
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    sdk.permissions.llm.grant(resource_type="completion", resource_id="my-completion-123", user_id="user-abc456", role="writer")

    # Use the SDK ...

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 meetkai_mka1 import SDK
import httpx

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

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

from meetkai_mka1 import SDK
from meetkai_mka1.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 = SDK(async_client=CustomClient(httpx.AsyncClient()))

Resource Management

The SDK 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 meetkai_mka1 import SDK
def main():

    with SDK(
        bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
    ) as sdk:
        # Rest of application here...


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

    async with SDK(
        bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
    ) as sdk:
        # 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 meetkai_mka1 import SDK
import logging

logging.basicConfig(level=logging.DEBUG)
s = SDK(debug_logger=logging.getLogger("meetkai_mka1"))

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

Project details

Verified details

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Maintainers
Avatar for jorgejuniormk from gravatar.com jorgejuniormk

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache-2.0
  • Author: Speakeasy
  • Requires: Python >=3.10

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