ajayji 0.1.3

Python SDK for the Ajayji Local Daemon

ajayji-python-sdk

ajayji-python-sdk

Summary

Ajayji Local SDK API: Local daemon API for the Ajayji application, allowing native Python SDK integration for Data Scientists and developers.

Table of Contents

• ajayji-python-sdk
  • SDK Installation
  • IDE Support
  • SDK Example Usage
  • Available Resources and Operations
  • Retries
  • Error Handling
  • Server Selection
  • Custom HTTP Client
  • Resource Management
  • Debugging

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+<UNSET>.git

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+<UNSET>.git

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+<UNSET>.git

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 ajayji 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 = [
#     "ajayji",
# ]
# ///

from ajayji 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.

• PyCharm Pydantic Plugin

SDK Example Usage

Example

# Synchronous Example
from ajayji import SDK


with SDK() as sdk:

    res = sdk.stateless_execution.ask(query="<value>")

    # Handle response
    print(res)

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

# Asynchronous Example
from ajayji import SDK
import asyncio

async def main():

    async with SDK() as sdk:

        res = await sdk.stateless_execution.ask_async(query="<value>")

        # Handle response
        print(res)

asyncio.run(main())

Available Resources and Operations

Available methods

DataIngestionAndTools

• create_database - Provision a Local Database Tool
• create_vector_db - Provision a Vector DB Tool

MemoryManagement

• load_model - Load a model into active memory
• stop_model - Unload the active model from memory

ModelManagement

• convert_model - Convert Model to CoreML
• pull_model - Download a model from Hugging Face

PersonaOrchestration

• invoke_persona - Invoke a Persona Webhook

StatelessExecution

• ask - Ask the active LLM a question statelessly

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 ajayji import SDK
from ajayji.utils import BackoffStrategy, RetryConfig


with SDK() as sdk:

    res = sdk.stateless_execution.ask(query="<value>",
        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 ajayji import SDK
from ajayji.utils import BackoffStrategy, RetryConfig


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

    res = sdk.stateless_execution.ask(query="<value>")

    # Handle response
    print(res)

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

Example

from ajayji import SDK, errors


with SDK() as sdk:
    res = None
    try:

        res = sdk.stateless_execution.ask(query="<value>")

        # 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)

Error Classes

Primary error:

• SDKError: The base class for HTTP error responses.

Less common errors (5)

Network errors:

• httpx.RequestError: Base class for request errors.
  • httpx.ConnectError: HTTP client was unable to make a request to a server.
  • httpx.TimeoutException: HTTP request timed out.

Inherit from SDKError:

• ResponseValidationError: Type mismatch between the response data and the expected Pydantic model. Provides access to the Pydantic validation error via the cause attribute.

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 ajayji import SDK


with SDK(
    server_url="http://localhost:14321",
) as sdk:

    res = sdk.stateless_execution.ask(query="<value>")

    # 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 ajayji 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 ajayji import SDK
from ajayji.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 ajayji import SDK
def main():

    with SDK() as sdk:
        # Rest of application here...


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

    async with SDK() 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 ajayji import SDK
import logging

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