<div align="center">
<img src="./assets/main.gif" alt="hypertion">
</div>
---
<p align="center">Making LLM Function-Calling Simpler.</p>
## 🚀 Installation
```sh
pip install hypertion
```
---
## Usage 🤗
#### Create a `HyperFunction` instance
```py
from hypertion import HyperFunction
hyperfunction = HyperFunction()
```
#### Use the `takeover` method to register the function
> Check [notebooks](./notebooks) directory for complex function usage.
```py
from typing import Literal
from typing_extensions import TypedDict
class Settings(TypedDict):
"""
Settings
@param unit: The unit scale to represent temperature.
@param forecast: If set to True, returns the forecasting.
"""
unit: Literal['celsius', 'fahrenheit']
forecast: bool = False
@hyperfunction.takeover
def get_current_weather(location: str, *, settings: Settings):
"""
Get the current weather.
@param location: Location to search for.
@param settings: Settings to use for getting current weather.
"""
info = {
"location": location,
"temperature": "72",
"unit": settings['unit'],
}
if settings['forecast'] is True:
return info | {"forecast": ["sunny", "windy"]}
return info
```
Supported Types: `str` | `int` | `float` | `bool` | `list` | `dict` | `pathlib.Path` | `typing.List` | `typing.Dict` | `typing.NamedTuple` | `typing_extensions.TypedDict` | `pydantic.BaseModel` | `typing.Literal` | `enum.Enum`
#### List registered functions
```python
hyperfunction.registry()
```
```
============================================================
get_current_weather(
location: str,
*,
settings: Settings
):
"""Get the current weather."""
============================================================
```
#### Register a predefined function
```python
from some_module import some_function
hyperfunction.takeover(
some_function,
docstring="<Override docstring for the function>"
)
```
### Use the `format` method to get function schema
> LLM specific formats are available: `functionary`, `gorilla`, `mistral`, `gpt`, `claude`
```python
hyperfunction.format(as_json=True)
# hyperfunction.format('<format>', as_json=True)
```
```json
[
{
"name": "get_current_weather",
"description": "Get the current weather.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "Location to search for."
},
"settings": {
"type": "object",
"properties": {
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
],
"description": "The unit scale to represent temperature."
},
"forecast": {
"type": "boolean",
"description": "If set to True, returns the forecasting."
}
},
"required": [
"unit"
],
"description": "Settings to use for getting current weather."
}
},
"required": [
"location",
"settings"
]
}
}
]
```
---
### Compose function `signature` as function-call object
> Try [Gorilla+Hypertion Colab](https://colab.research.google.com/drive/1DKkXHdebEgj7AfXqw6Ro17KQ1RUBMgco?usp=sharing) for live example.
```python
signature = """
get_current_weather(
location='Kolkata', settings={'unit': 'fahrenheit'}
)
"""
function_call = hyperfunction.compose(signature)
function_call
```
```
get_current_weather(
location='Kolkata',
settings={'unit': 'fahrenheit', 'forecast': False}
)
```
Invoke the `function-call` object
```python
function_call()
```
```
{'location': 'Kolkata', 'temperature': '72', 'unit': 'fahrenheit'}
```
### Compose function `metadata` as function-call object
> Try [Functionary+Hypertion Colab](https://colab.research.google.com/drive/1azzJiAcYRFItlzwEfRPk6UzDUPVAZkUl?usp=sharing) for live example.
```python
name = 'get_current_weather'
arguments = '{"location": "Kolkata", "settings": {"unit": "fahrenheit", "forecast": true}}'
function_call = hyperfunction.compose(name=name, arguments=arguments) # Accepts both JsON and dictionary object
function_call
```
```
get_current_weather(
location='Kolkata',
settings={'unit': 'fahrenheit', 'forecast': True}
)
```
Invoke the `function-call` object
```python
function_call()
```
```
{'location': 'Kolkata', 'temperature': '72', 'unit': 'fahrenheit', 'forecast': ['sunny', 'windy']}
```
> Important: The `hypertion` library lacks the capability to interact directly with LLM-specific APIs, meaning it cannot directly make request to any LLM. Its functionality is to generate schema and invoke signature/metadata generated from LLM(s). This design choice was made to provide more flexibility to developers, allowing them to integrate or adapt different tools and libraries as per their project needs.
#### Combining two `HyperFunction` instance
> Note: A single `HyperFunction` instance can hold multiple functions. Creating a new `HyperFunction` instance is beneficial only if you need a distinct set of functions. This approach is especially effective when deploying Agent(s) to utilize functions designed for particular tasks.
```py
new_hyperfunction = HyperFunction()
@new_hyperfunction.takeover
def new_function(
param1: str,
param2: int = 100
):
"""Description for the new function"""
...
combined_hyperfunction = hyperfunction + new_hyperfunction
combined_hyperfunction.registry()
```
```
============================================================
get_current_weather(
location: str,
*,
settings: Settings
):
"""Get the current weather."""
============================================================
new_function(
param1: str,
param2: int = 100
):
"""Description for the new function"""
============================================================
```
### Conclusion
The key strength of this approach lies in its ability to automate schema creation, sparing developers the time and complexity of manual setup. By utilizing the `takeover` method, the system efficiently manages multiple functions within a `HyperFunction` instance, a boon for deploying Agents in LLM applications. This automation not only streamlines the development process but also ensures precision and adaptability in handling task-specific functions, making it a highly effective solution for agent-driven scenarios.
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"description": "<div align=\"center\">\n <img src=\"./assets/main.gif\" alt=\"hypertion\">\n</div>\n\n---\n\n<p align=\"center\">Making LLM Function-Calling Simpler.</p>\n\n\n## \ud83d\ude80 Installation\n\n```sh\npip install hypertion\n```\n\n---\n\n## Usage \ud83e\udd17\n\n#### Create a `HyperFunction` instance\n\n```py\nfrom hypertion import HyperFunction\n\nhyperfunction = HyperFunction()\n```\n\n#### Use the `takeover` method to register the function\n\n> Check [notebooks](./notebooks) directory for complex function usage.\n\n```py\nfrom typing import Literal\nfrom typing_extensions import TypedDict\n\nclass Settings(TypedDict):\n \"\"\"\n Settings\n @param unit: The unit scale to represent temperature.\n @param forecast: If set to True, returns the forecasting.\n \"\"\"\n unit: Literal['celsius', 'fahrenheit']\n forecast: bool = False\n\n@hyperfunction.takeover\ndef get_current_weather(location: str, *, settings: Settings):\n \"\"\"\n Get the current weather.\n @param location: Location to search for.\n @param settings: Settings to use for getting current weather.\n \"\"\"\n info = {\n \"location\": location,\n \"temperature\": \"72\",\n \"unit\": settings['unit'],\n }\n if settings['forecast'] is True:\n return info | {\"forecast\": [\"sunny\", \"windy\"]}\n \n return info\n```\n\nSupported Types: `str` | `int` | `float` | `bool` | `list` | `dict` | `pathlib.Path` | `typing.List` | `typing.Dict` | `typing.NamedTuple` | `typing_extensions.TypedDict` | `pydantic.BaseModel` | `typing.Literal` | `enum.Enum`\n\n#### List registered functions\n\n```python\nhyperfunction.registry()\n```\n```\n============================================================\nget_current_weather(\n location: str,\n *,\n settings: Settings\n):\n\"\"\"Get the current weather.\"\"\"\n============================================================\n```\n\n#### Register a predefined function\n\n```python\nfrom some_module import some_function\n\nhyperfunction.takeover(\n some_function,\n docstring=\"<Override docstring for the function>\"\n)\n```\n\n### Use the `format` method to get function schema\n\n> LLM specific formats are available: `functionary`, `gorilla`, `mistral`, `gpt`, `claude`\n\n```python\nhyperfunction.format(as_json=True)\n# hyperfunction.format('<format>', as_json=True)\n```\n```json\n[\n {\n \"name\": \"get_current_weather\",\n \"description\": \"Get the current weather.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"Location to search for.\"\n },\n \"settings\": {\n \"type\": \"object\",\n \"properties\": {\n \"unit\": {\n \"type\": \"string\",\n \"enum\": [\n \"celsius\",\n \"fahrenheit\"\n ],\n \"description\": \"The unit scale to represent temperature.\"\n },\n \"forecast\": {\n \"type\": \"boolean\",\n \"description\": \"If set to True, returns the forecasting.\"\n }\n },\n \"required\": [\n \"unit\"\n ],\n \"description\": \"Settings to use for getting current weather.\"\n }\n },\n \"required\": [\n \"location\",\n \"settings\"\n ]\n }\n }\n]\n```\n---\n\n### Compose function `signature` as function-call object\n\n> Try [Gorilla+Hypertion Colab](https://colab.research.google.com/drive/1DKkXHdebEgj7AfXqw6Ro17KQ1RUBMgco?usp=sharing) for live example.\n\n```python\nsignature = \"\"\"\nget_current_weather(\n location='Kolkata', settings={'unit': 'fahrenheit'}\n)\n\"\"\"\nfunction_call = hyperfunction.compose(signature)\nfunction_call\n```\n```\nget_current_weather(\n location='Kolkata',\n settings={'unit': 'fahrenheit', 'forecast': False}\n)\n```\n\nInvoke the `function-call` object\n\n```python\nfunction_call()\n```\n```\n{'location': 'Kolkata', 'temperature': '72', 'unit': 'fahrenheit'}\n```\n\n### Compose function `metadata` as function-call object\n\n> Try [Functionary+Hypertion Colab](https://colab.research.google.com/drive/1azzJiAcYRFItlzwEfRPk6UzDUPVAZkUl?usp=sharing) for live example.\n\n```python\nname = 'get_current_weather'\narguments = '{\"location\": \"Kolkata\", \"settings\": {\"unit\": \"fahrenheit\", \"forecast\": true}}'\nfunction_call = hyperfunction.compose(name=name, arguments=arguments) # Accepts both JsON and dictionary object\nfunction_call\n```\n```\nget_current_weather(\n location='Kolkata',\n settings={'unit': 'fahrenheit', 'forecast': True}\n)\n```\n\nInvoke the `function-call` object\n\n```python\nfunction_call()\n```\n```\n{'location': 'Kolkata', 'temperature': '72', 'unit': 'fahrenheit', 'forecast': ['sunny', 'windy']}\n```\n\n> Important: The `hypertion` library lacks the capability to interact directly with LLM-specific APIs, meaning it cannot directly make request to any LLM. Its functionality is to generate schema and invoke signature/metadata generated from LLM(s). This design choice was made to provide more flexibility to developers, allowing them to integrate or adapt different tools and libraries as per their project needs.\n\n\n#### Combining two `HyperFunction` instance\n\n> Note: A single `HyperFunction` instance can hold multiple functions. Creating a new `HyperFunction` instance is beneficial only if you need a distinct set of functions. This approach is especially effective when deploying Agent(s) to utilize functions designed for particular tasks.\n\n```py\nnew_hyperfunction = HyperFunction()\n\n@new_hyperfunction.takeover\ndef new_function(\n param1: str,\n param2: int = 100\n):\n \"\"\"Description for the new function\"\"\"\n ...\n\ncombined_hyperfunction = hyperfunction + new_hyperfunction\ncombined_hyperfunction.registry()\n```\n```\n============================================================\nget_current_weather(\n location: str,\n *,\n settings: Settings\n):\n\"\"\"Get the current weather.\"\"\"\n============================================================\nnew_function(\n param1: str,\n param2: int = 100\n):\n\"\"\"Description for the new function\"\"\"\n============================================================\n```\n\n### Conclusion\n\nThe key strength of this approach lies in its ability to automate schema creation, sparing developers the time and complexity of manual setup. By utilizing the `takeover` method, the system efficiently manages multiple functions within a `HyperFunction` instance, a boon for deploying Agents in LLM applications. This automation not only streamlines the development process but also ensures precision and adaptability in handling task-specific functions, making it a highly effective solution for agent-driven scenarios.",
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