hyperbee 0.0.2.6

The official Python library for the hyperbee API

HyperBee Python API library

The HyperBee Python library provides convenient access to the HyperBee 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.

Documentation

The REST API documentation can be found on hyperbee docs.

Installation

pip install hyperbee

Usage

import os
from hyperbee import HyperBee

client = HyperBee(
    # This is the default and can be omitted
    api_key=os.environ.get("HYPERBEE_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 to add HYPERBEE_API_KEY="My API Key" to your .env file so that your API Key is not stored in source control.

Async usage

Simply import AsyncHyperBee instead of HyperBee and use await with each API call:

import os
import asyncio
from hyperbee import AsyncHyperBee

client = AsyncHyperBee(
    # This is the default and can be omitted
    api_key=os.environ.get("HYPERBEE_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).

from hyperbee import HyperBee

client = HyperBee()

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.

from hyperbee import AsyncHyperBee

client = AsyncHyperBee()


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.

import hyperbee

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

# all client options can be configured just like the `HyperBee` 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 = HyperBee()) 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. Responses are Pydantic models, 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:

from hyperbee import HyperBee

client = HyperBee()

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:

from hyperbee import HyperBee

# Configure the default for all requests:
client = HyperBee(
    # 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 object:

from hyperbee import HyperBee

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

# More granular control:
client = HyperBee(
    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.

Advanced

Logging

We use the standard library logging module.

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

$ export HYPERBEE_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:

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}.')

Configuring the HTTP client

You can directly override the httpx client to customize it for your use case, including:

• Support for proxies
• Custom transports
• Additional advanced functionality

import httpx
from hyperbee import HyperBee

client = HyperBee(
    # Or use the `HYPERBEE_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. 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.