kilm-aiplatform 0.3.1
The official Python library for the AI Platform API
Requires Python: >=3.8

# AI Platform Python API library

[![PyPI version](https://img.shields.io/pypi/v/ai-platform.svg)](https://pypi.org/project/ai-platform/)

The AI Platform Python library provides convenient access to the AI Platform REST API from any Python 3.7+
application. The library includes type definitions for all request params and response fields,
and offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx).

It is generated with [Stainless](https://www.stainlessapi.com/).

## Documentation

The REST API documentation can be found [on docs.ai-platform.com](https://docs.ai-platform.com). The full API of this library can be found in [api.md](api.md).

## Installation

```sh
# install from this staging repo
pip install kilm-aiplatform
```

> [!NOTE]
> Once this package is [published to PyPI](https://app.stainlessapi.com/docs/guides/publish), this will become: `pip install --pre ai-platform`

## Usage

The full API of this library can be found in [api.md](api.md).

```python
import os
from aiplatform import AIPlatform
from aiplatform.types import ChatCompletionCreateResponse

client = AIPlatform(
    access_key=config.API_GATEWAY_ACCESS_KEY,
    access_secret_key=config.API_GATEWAY_ACCESS_SECRET_KEY,
    base_url=config.API_GATEWAY_URL,
)

completion_create_response: ChatCompletionCreateResponse = await client.chat_completions.create(
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Hello, how are you?"
        }
    ],
    model="kilm-poem",
    max_tokens=1024,
)
print(completion_create_response)
```

## Async usage

Simply import `AsyncAIPlatform` instead of `AIPlatform` and use `await` with each API call:

```python
import os
import asyncio
from aiplatform import AsyncAIPlatform

client = AsyncAIPlatform(
    # This is the default and can be omitted
    access_key=os.environ.get("AI_PLATFORM_ACCESS_KEY"),
)

async def main() -> None:
  completion_create_response = await client.completions.create(
      model="string",
      prompt={},
  )
  print(completion_create_response.id)

asyncio.run(main())
```

Functionality between the synchronous and asynchronous clients is otherwise identical.

## Using types

Nested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev) which also provide helper methods for things like:

- Serializing back into JSON, `model.to_json()`
- Converting to a dictionary, `model.to_dict()`

Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set `python.analysis.typeCheckingMode` to `basic`.

## Handling errors

When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of `ai-platform.APIConnectionError` is raised.

When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of `ai-platform.APIStatusError` is raised, containing `status_code` and `response` properties.

All errors inherit from `ai-platform.APIError`.

```python
import ai-platform
from aiplatform import AIPlatform

client = AIPlatform()

try:
    client.completions.create(
        model="string",
        prompt={},
    )
except ai-platform.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__) # an underlying Exception, likely raised within httpx.
except ai-platform.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except ai-platform.APIStatusError as e:
    print("Another non-200-range status code was received")
    print(e.status_code)
    print(e.response)
```

Error codes are as followed:

| Status Code | Error Type                   |
| ----------- | ---------------------------- |
| 400         | `BadRequestError`          |
| 401         | `AuthenticationError`      |
| 403         | `PermissionDeniedError`    |
| 404         | `NotFoundError`            |
| 422         | `UnprocessableEntityError` |
| 429         | `RateLimitError`           |
| >=500       | `InternalServerError`      |
| N/A         | `APIConnectionError`       |

### Retries

Certain errors are automatically retried 2 times by default, with a short exponential backoff.
Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,
429 Rate Limit, and >=500 Internal errors are all retried by default.

You can use the `max_retries` option to configure or disable retry settings:

```python
from aiplatform import AIPlatform

# Configure the default for all requests:
client = AIPlatform(
    # default is 2
    max_retries=0,
)

# Or, configure per-request:
client.with_options(max_retries = 5).completions.create(
    model="string",
    prompt={},
)
```

### Timeouts

By default requests time out after 1 minute. You can configure this with a `timeout` option,
which accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/#fine-tuning-the-configuration) object:

```python
from aiplatform import AIPlatform

# Configure the default for all requests:
client = AIPlatform(
    # 20 seconds (default is 1 minute)
    timeout=20.0,
)

# More granular control:
client = AIPlatform(
    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)

# Override per-request:
client.with_options(timeout = 5.0).completions.create(
    model="string",
    prompt={},
)
```

On timeout, an `APITimeoutError` is thrown.

Note that requests that time out are [retried twice by default](#retries).

## Advanced

### Logging

We use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.

You can enable logging by setting the environment variable `AI_PLATFORM_LOG` to `debug`.

```shell
$ export AI_PLATFORM_LOG=debug
```

### How to tell whether `None` means `null` or missing

In an API response, a field may be explicitly `null`, or missing entirely; in either case, its value is `None` in this library. You can differentiate the two cases with `.model_fields_set`:

```py
if response.my_field is None:
  if 'my_field' not in response.model_fields_set:
    print('Got json like {}, without a "my_field" key present at all.')
  else:
    print('Got json like {"my_field": null}.')
```

### Accessing raw response data (e.g. headers)

The "raw" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,

```py
from aiplatform import AIPlatform

client = AIPlatform()
response = client.completions.with_raw_response.create(
    model="string",
    prompt={},
)
print(response.headers.get('X-My-Header'))

completion = response.parse()  # get the object that `completions.create()` would have returned
print(completion.id)
```

These methods return an [`APIResponse`](https://github.com/stainless-sdks/ai-platform-python/tree/main/src/ai-platform/_response.py) object.

The async client returns an [`AsyncAPIResponse`](https://github.com/stainless-sdks/ai-platform-python/tree/main/src/ai-platform/_response.py) with the same structure, the only difference being `await`able methods for reading the response content.

#### `.with_streaming_response`

The above interface eagerly reads the full response body when you make the request, which may not always be what you want.

To stream the response body, use `.with_streaming_response` instead, which requires a context manager and only reads the response body once you call `.read()`, `.text()`, `.json()`, `.iter_bytes()`, `.iter_text()`, `.iter_lines()` or `.parse()`. In the async client, these are async methods.

```python
with client.completions.with_streaming_response.create(
    model="string",
    prompt={},
) as response :
    print(response.headers.get('X-My-Header'))

    for line in response.iter_lines():
      print(line)
```

The context manager is required so that the response will reliably be closed.

### Making custom/undocumented requests

This library is typed for convenient access to the documented API.

If you need to access undocumented endpoints, params, or response properties, the library can still be used.

#### Undocumented endpoints

To make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other
http verbs. Options on the client will be respected (such as retries) will be respected when making this
request.

```py
import httpx

response = client.post(
    "/foo",
    cast_to=httpx.Response,
    body={"my_param": True},
)

print(response.headers.get("x-foo"))
```

#### Undocumented request params

If you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request
options.

#### Undocumented response properties

To access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You
can also get all the extra fields on the Pydantic model as a dict with
[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).

### Configuring the HTTP client

You can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:

- Support for proxies
- Custom transports
- Additional [advanced](https://www.python-httpx.org/advanced/#client-instances) functionality

```python
from aiplatform import AIPlatform, DefaultHttpxClient

client = AIPlatform(
    # Or use the `AI_PLATFORM_BASE_URL` env var
    base_url="http://my.test.server.example.com:8083",
    http_client=DefaultHttpxClient(proxies="http://my.test.proxy.example.com", transport=httpx.HTTPTransport(local_address="0.0.0.0")),
)
```

### Managing HTTP resources

By default the library closes underlying HTTP connections whenever the client is [garbage collected](https://docs.python.org/3/reference/datamodel.html#object.__del__). You can manually close the client using the `.close()` method if desired, or with a context manager that closes when exiting.

## Versioning

This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:

1. Changes that only affect static types, without breaking runtime behavior.
2. Changes to library internals which are technically public but not intended or documented for external use. _(Please open a GitHub issue to let us know if you are relying on such internals)_.
3. Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an [issue](https://www.github.com/stainless-sdks/ai-platform-python/issues) with questions, bugs, or suggestions.

## Requirements

Python 3.7 or higher.