Metadata-Version: 2.3
Name: hyperbee
Version: 0.0.1.5
Summary: The official Python library for the hyperbee API
Project-URL: Homepage, https://github.com/HyperbeeAI/hive-python
Project-URL: Repository, https://github.com/HyperbeeAI/hive-python
Author-email: Hive <support@hyperbee.ai>
License-Expression: Apache-2.0
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: OS Independent
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: >=3.7.1
Requires-Dist: anyio<5,>=3.5.0
Requires-Dist: cached-property; python_version < '3.8'
Requires-Dist: distro<2,>=1.7.0
Requires-Dist: httpx<1,>=0.23.0
Requires-Dist: openai<2,>=1.10.0
Requires-Dist: pydantic<3,>=1.9.0
Requires-Dist: sniffio
Requires-Dist: tqdm>4
Requires-Dist: typing-extensions<5,>=4.7
Provides-Extra: datalib
Requires-Dist: numpy>=1; extra == 'datalib'
Requires-Dist: pandas-stubs>=1.1.0.11; extra == 'datalib'
Requires-Dist: pandas>=1.2.3; extra == 'datalib'
Description-Content-Type: text/markdown

# Hive Python API library


The Hive Python library provides convenient access to the Hive 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).

## Documentation

The REST API documentation can be found [on hyperbee docs](https://api.hyperbee.ai/redoc).

## Installation

```sh
pip install hyperbee
```

## Usage


```python
import os
from hyperbee import Hive

client = Hive(
    # This is the default and can be omitted
    api_key=os.environ.get("HIVE_API_KEY"),
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
    model="hive",
)
```

While you can provide an `api_key` keyword argument,
we recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)
to add `HIVE_API_KEY="My API Key"` to your `.env` file
so that your API Key is not stored in source control.

## Async usage

Simply import `AsyncHive` instead of `Hive` and use `await` with each API call:

```python
import os
import asyncio
from hyperbee import AsyncHive

client = AsyncHive(
    # This is the default and can be omitted
    api_key=os.environ.get("HIVE_API_KEY"),
)


async def main() -> None:
    chat_completion = await client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": "Say this is a test",
            }
        ],
        model="hive",
    )


asyncio.run(main())
```

Functionality between the synchronous and asynchronous clients is otherwise identical.

## Streaming Responses

We provide support for streaming responses using Server Side Events (SSE).

```python
from hyperbee import Hive

client = Hive()

stream = client.chat.completions.create(
    model="hive",
    messages=[{"role": "user", "content": "Say this is a test"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")
```

The async client uses the exact same interface.

```python
from hyperbee import AsyncHive

client = AsyncHive()


async def main():
    stream = await client.chat.completions.create(
        model="hive",
        messages=[{"role": "user", "content": "Say this is a test"}],
        stream=True,
    )
    async for chunk in stream:
        print(chunk.choices[0].delta.content or "", end="")


asyncio.run(main())
```

## Module-level client

> [!IMPORTANT]
> We highly recommend instantiating client instances instead of relying on the global client.

We also expose a global client instance that is accessible in a similar fashion to versions prior to v1.

```py
import hyperbee

# optional; defaults to `os.environ['HIVE_API_KEY']`
hyperbee.api_key = '...'

# all client options can be configured just like the `Hive` instantiation counterpart
hyperbee.base_url = "https://..."
hyperbee.default_headers = {"x-foo": "true"}

completion = hyperbee.chat.completions.create(
    model="hive",
    messages=[
        {
            "role": "user",
            "content": "How do I output all files in a directory using Python?",
        },
    ],
)
print(completion.choices[0].message.content)
```

The API is the exact same as the standard client instance based API.

This is intended to be used within REPLs or notebooks for faster iteration, **not** in application code.

We recommend that you always instantiate a client (e.g., with `client = Hive()`) in application code because:

- It can be difficult to reason about where client options are configured
- It's not possible to change certain client options without potentially causing race conditions
- It's harder to mock for testing purposes
- It's not possible to control cleanup of network connections

## 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 provide helper methods for things like:

- Serializing back into JSON, `model.model_dump_json(indent=2, exclude_unset=True)`
- Converting to a dictionary, `model.model_dump(exclude_unset=True)`

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`.

## Nested params

Nested parameters are dictionaries, typed using `TypedDict`, for example:

```python
from hyperbee import Hive

client = Hive()

completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Can you generate an example json object describing a fruit?",
        }
    ],
    model="hive",
    response_format={"type": "json_object"},
)
```


## 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 `hyperbee.APIConnectionError` is raised.

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

All errors inherit from `hyperbee.APIError`.

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 hyperbee import Hive

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

# Or, configure per-request:
client.with_options(max_retries=5).chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "How can I get the name of the current day in Node.js?",
        }
    ],
    model="hive",
)
```

### Timeouts

By default requests time out after 10 minutes. 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 hyperbee import Hive

# Configure the default for all requests:
client = Hive(
    # 20 seconds (default is 10 minutes)
    timeout=20.0,
)

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

# Override per-request:
client.with_options(timeout=5 * 1000).chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "How can I list all files in a directory using Python?",
        }
    ],
    model="hive",
)
```

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 `HIVE_LOG` to `debug`.

```shell
$ export HIVE_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 hyperbee import Hive

client = Hive()
response = client.chat.completions.with_raw_response.create(
    messages=[{
        "role": "user",
        "content": "Say this is a test",
    }],
    model="hive",
)
print(response.headers.get('X-My-Header'))

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

These methods return an [`LegacyAPIResponse`](https://github.com/openai/openai-python/tree/main/src/openai/_legacy_response.py) object. This is a legacy class as we're changing it slightly in the next major version.

For the sync client this will mostly be the same with the exception
of `content` & `text` will be methods instead of properties. In the
async client, all methods will be async.

A migration script will be provided & the migration in general should
be smooth.

#### `.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.

As such, `.with_streaming_response` methods return a different [`APIResponse`](https://github.com/openai/openai-python/tree/main/src/openai/_response.py) object, and the async client returns an [`AsyncAPIResponse`](https://github.com/openai/openai-python/tree/main/src/openai/_response.py) object.

```python
with client.chat.completions.with_streaming_response.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
    model="hive",
) 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.

### 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
import httpx
from hyperbee import Hive

client = Hive(
    # Or use the `HIVE_BASE_URL` env var
    base_url="http://my.test.server.example.com:8083",
    http_client=httpx.Client(
        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.

## Requirements

Python 3.7 or higher.
