llmFunctionWrapper 1.0.0

A simplified function wrapper for OpenAI and LiteLLM API calls.

llmFunctionWrapper

A Python module designed to simplify the process of creating and managing function calls to OpenAI's API, as well as models using LiteLLM's API framework.

Installation

llmFunctionWrapper is available on PyPi, so installation is easy.

pip install llmFunctionWrapper

Upon installation, make sure to import the package into your project.

from llmFunctionWrapper import ToolWrapper, FunctionRegistry

Quickstart

1. Defining Functions

First, define your functions following standard Python syntax, in the same way OpenAI and LiteLLM expect. For example:

def an_awesome_function(variable1, variable2="A default value for variable2"):
    # Function body ...
    return desiredVariable

2. Wrapping your Functions

Use the ToolWrapper class to create your function descriptions which will be passed to the LLM. Add relevant details such as purpose, parameters, and descriptions.

The description will look something like this:

awesomeFunction = ToolWrapper(
    function_ref=an_awesome_function,
    purpose="An awesome function that does something amazing.",
    variable1=int,
    variable1_description="The first variable that will be used to do some awesome thing.",
    variable2=["option1", "option2"],
    variable2_description="The second variable that will be used to do another awesome thing.",
    required=["variable1", "variable2"]
)

3. Submitting Your Functions to the API

Before you make your API request, you must serialize your function descriptions in OpenAI and LiteLLM's tool format.

unserializedTools = [awesomeFunction] # If you have multiple functions, their descriptions must all be listed here (If you want to use them). Ex. unserializedTools = [awesomeFunction, otherFunction]
tools = [tool.to_dict() for tool in unserializedTools]

Alternatively, you can serialize each function individually in-line:

tools = [awesomeFunction.to_dict(), otherFunction.to_dict()]

After serializing, submit your tools list in your request, in the same way as before.

response = litellm.completion(
            model="gpt-3.5-turbo-1106",
            messages=messages,
            tools=tools,
            tool_choice="auto",
)

Detailed Look at Creating Your ToolWrapper() Function Descriptions

Required and Optional Parameters:

• function_ref (Required, callable): The actual Python function that this wrapper is meant to represent, and which will be called by the LLM. This parameter must be a callable object (e.g., a function or a method).

  function_ref=get_current_weather
  

• purpose (Required, str): A brief description of what the wrapped function does. This should be a human-readable string that clearly communicates the function's purpose.

  purpose="Get the current weather in a given location"
  

• required (Optional, list of str): A list of parameter names that are required for the function to operate. This is useful for specifying which parameters cannot be omitted when calling the function.

  required=["location", "unit"]
  

Parameter Keyword Arguments (Dynamic):

• **kwargs: In addition to the parameters mentioned above, you can specify any number of additional keyword arguments. These are used to define the parameters (variables) that the function assigned to function_ref takes. The keys should be the names of the parameters, and the values should define their types or allowable values (for enums).

  For each parameter of the function assigned to function_ref, you can provide:

  • The parameter type by simply specifying a Python type (e.g., str, int, etc.) as the value that the parameter expects as input.

    location=str
    

  • For enum parameters, instead of a single type, you provide a list of allowable values (e.g., ["celsius", "fahrenheit"] for a temperature unit parameter).

    unit=["celsius", "fahrenheit"]
    

  • You can also append _description to any parameter name (e.g., location_description) as an additional key to provide a human-readable description of what that parameter is for. Every parameter should have an accompanying description key.

    location_description="The city and state, e.g. San Francisco, CA"
    

    Putting this all together, we get our function description.

weatherFunction = ToolWrapper(
    function_ref=get_current_weather,                                           # function reference
    purpose="Get the current weather in a given location.",                     # description of the function
    location=str,                                                               # type of the location argument
    location_description="The city and state, e.g. San Francisco, CA",          # description of the location argument
    unit=["celsius", "fahrenheit"],                                             # possible values for the unit argument
    unit_description="The unit of temperature, e.g. celsius or fahrenheit",     # description of the unit argument
    required=["location"],                                                      # required arguments
)

Permissible Data Types

The following is a list of data types that can be assigned to a parameter (variable).

• variable1=int
• variable1=float
• variable1=str
• variable1=bool
• variable1=list
• variable1=tuple
• variable1=dict
• variable1=None

In addition, enums can be assigned int, float, str, or bool. For example,

• variable1=[12, 19, 17]
• variable1=[18.6, 78.2, 97.0]
• variable1=["first", "second", "last"]
• variable1=[True, False]

Enums can also have various data types. For example,

• variable1=[15, 17.2, "hello", True]

FunctionRegistry

The FunctionRegistry class serves as a central repository for all functions that can be called by the LLM. It ensures that functions are uniquely identified by their names and can be invoked dynamically with arguments specified at runtime.

Note: This class may not be needed - I might be missing a simpler implementation.

Key Methods

• register_function(name, function): Registers a function under a given name. This will never* need to be called directly as it is automatically handled by the ToolWrapper class.
• get_registry(): Returns the current registry of functions.
• call_function(name, **kwargs): Calls a registered function by name, passing keyword arguments.

An example of the FuctionRegistry class is provided in the Parallel Function Call example.

Examples

Simple Single Function Call

A simple function calling example from LiteLLM docs:

import litellm
import json
import os

# set openai api key
os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})

messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]

response = litellm.completion(
    model="gpt-3.5-turbo-1106",
    messages=messages,
    tools=tools,
    tool_choice="auto",  # auto is default, but we'll be explicit
)
print("\nLLM Response1:\n", response)
response_message = response.choices[0].message
tool_calls = response.choices[0].message.tool_calls

Modified for use with llmFunctionWrapper:

import litellm
import json
import os
from llmFunctionWrapper import ToolWrapper

# set openai api key
os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})

weatherFunction = ToolWrapper(
    function_ref=get_current_weather,
    purpose="Get the current weather in a given location.",
    location=str,
    location_description="The city and state, e.g. San Francisco, CA",
    unit=["celsius", "fahrenheit"],
    unit_description="The unit of temperature, e.g. celsius or fahrenheit",
    required=["location"],
)

messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]

unserializedTools = [weatherFunction]
tools = [tool.to_dict() for tool in unserializedTools]

response = litellm.completion(
    model="gpt-3.5-turbo-1106",
    messages=messages,
    tools=tools,
    tool_choice="auto",  # auto is default, but we'll be explicit
)
print("\nLLM Response1:\n", response)
response_message = response.choices[0].message
tool_calls = response.choices[0].message.tool_calls

Note that

weatherFunction = ToolWrapper(
    function_ref=get_current_weather,
    purpose="Get the current weather in a given location.",
    location=str,
    location_description="The city and state, e.g. San Francisco, CA",
    unit=["celsius", "fahrenheit"],
    unit_description="The unit of temperature, e.g. celsius or fahrenheit",
    required=["location"],
)

unserializedTools = [weatherFunction]
tools = [tool.to_dict() for tool in unserializedTools]

Is a direct replacement for, and is identical to

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]

---

Using Multiple Functions

Let's say we want two functions - one to get the weather (from the example above), and one to get the time.

def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})

weatherFunction = ToolWrapper(
    function_ref=get_current_weather,
    purpose="Get the current weather in a given location.",
    location=str,
    location_description="The city and state, e.g. San Francisco, CA",
    unit=["celsius", "fahrenheit"],
    unit_description="The unit of temperature, e.g. celsius or fahrenheit",
    required=["location"],
)

def get_current_time(location):
    """Get the current time in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": location, "time": "3:00 PM"})
    elif "san francisco" in location.lower():
        return json.dumps({"location": location, "time": "12:00 PM"})
    elif "paris" in location.lower():
        return json.dumps({"location": location, "time": "9:00 PM"})
    else:
        return "I don't know the time in " + location

timeFunction = ToolWrapper(
    function_ref= get_current_time,
    purpose="Get the current time in a given location.",
    location=str,
    location_description="The city and state, e.g. San Francisco, CA",
    required=["location"],
)

unserializedTools = [weatherFunction, timeFunction]
tools = [tool.to_dict() for tool in unserializedTools]

Notice that the only difference compared to calling a single function is adding timeFunction to the unserializedTools array.

unserializedTools = [weatherFunction, timeFunction]
tools = [tool.to_dict() for tool in unserializedTools]

---

Parallel Function Call

Expanding upon the example given above, we can utilize parallel function calling with various optimizations as well. The following example was pulled from LiteLLM docs:

import litellm
import json
import os

# set openai api key
os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request

# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})


def test_parallel_function_call():
    try:
        # Step 1: send the conversation and available functions to the model
        messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
        tools = [
            {
                "type": "function",
                "function": {
                    "name": "get_current_weather",
                    "description": "Get the current weather in a given location",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "location": {
                                "type": "string",
                                "description": "The city and state, e.g. San Francisco, CA",
                            },
                            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                        },
                        "required": ["location"],
                    },
                },
            }
        ]
        response = litellm.completion(
            model="gpt-3.5-turbo-1106",
            messages=messages,
            tools=tools,
            tool_choice="auto",  # auto is default, but we'll be explicit
        )
        print("\nFirst LLM Response:\n", response)
        response_message = response.choices[0].message
        tool_calls = response_message.tool_calls

        print("\nLength of tool calls", len(tool_calls))

        # Step 2: check if the model wanted to call a function
        if tool_calls:
            # Step 3: call the function
            # Note: the JSON response may not always be valid; be sure to handle errors
            available_functions = {
                "get_current_weather": get_current_weather,
            }  # only one function in this example, but you can have multiple
            messages.append(response_message)  # extend conversation with assistant's reply

            # Step 4: send the info for each function call and function response to the model
            for tool_call in tool_calls:
                function_name = tool_call.function.name
                function_to_call = available_functions[function_name]
                function_args = json.loads(tool_call.function.arguments)
                function_response = function_to_call(
                    location=function_args.get("location"),
                    unit=function_args.get("unit"),
                )
                messages.append(
                    {
                        "tool_call_id": tool_call.id,
                        "role": "tool",
                        "name": function_name,
                        "content": function_response,
                    }
                )  # extend conversation with function response
            second_response = litellm.completion(
                model="gpt-3.5-turbo-1106",
                messages=messages,
            )  # get a new response from the model where it can see the function response
            print("\nSecond LLM response:\n", second_response)
            return second_response
    except Exception as e:
      print(f"Error occurred: {e}")

test_parallel_function_call()

Modified for use with llmFunctionWrapper:

import litellm
import json
import os
from llmFunctionWrapper import ToolWrapper, FunctionRegistry

# set openai api key
os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request

# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})

weatherFunction = ToolWrapper(
    function_ref=get_current_weather,
    purpose="Get the current weather in a given location.",
    location=str,
    location_description="The city and state, e.g. San Francisco, CA",
    unit=["celsius", "fahrenheit"],
    unit_description="The unit of temperature, e.g. celsius or fahrenheit",
    required=["location"],
)

def test_parallel_function_call():
    try:
        # Step 1: send the conversation and available functions to the model
        messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]

        unserializedTools = [weatherFunction]
        tools = [tool.to_dict() for tool in unserializedTools]

        response = litellm.completion(
            model="gpt-3.5-turbo-1106",
            messages=messages,
            tools=tools,
            tool_choice="auto",  # auto is default, but we'll be explicit
        )
        print("\nFirst LLM Response:\n", response)
        response_message = response.choices[0].message
        tool_calls = response_message.tool_calls

        print("\nLength of tool calls", len(tool_calls))

        # Step 2: check if the model wanted to call a function
        if tool_calls:
            # Step 3: call the function
            # Note: the JSON response may not always be valid; be sure to handle errors
            available_functions = FunctionRegistry.get_registry()
            messages.append(response_message)  # extend conversation with assistant's reply

            # Step 4: send the info for each function call and function response to the model
            for tool_call in tool_calls:
                function_name = tool_call.function.name
                function_args = json.loads(tool_call.function.arguments)
                function_response = FunctionRegistry.call_function(
                    function_name,
                    **function_args
                )
                messages.append(
                    {
                        "tool_call_id": tool_call.id,
                        "role": "tool",
                        "name": function_name,
                        "content": function_response,
                    }
                )  # extend conversation with function response
            second_response = litellm.completion(
                model="gpt-3.5-turbo-1106",
                messages=messages,
            )  # get a new response from the model where it can see the function response
            print("\nSecond LLM response:\n", second_response)
            return second_response
    except Exception as e:
      print(f"Error occurred: {e}")

test_parallel_function_call()

There are a few changes that have occured.

1. The function was defined using ToolWrapper(), exactly as was explained in the Simple Single Function Call example provided.
2. The method for passing functions to call has changed as shown below:

The original implementation

# Step 2: check if the model wanted to call a function
        if tool_calls:
            # Step 3: call the function
            # Note: the JSON response may not always be valid; be sure to handle errors
            available_functions = {
                "get_current_weather": get_current_weather,
            }  # only one function in this example, but you can have multiple
            messages.append(response_message)  # extend conversation with assistant's reply

            # Step 4: send the info for each function call and function response to the model
            for tool_call in tool_calls:
                function_name = tool_call.function.name
                function_to_call = available_functions[function_name]
                function_args = json.loads(tool_call.function.arguments)
                function_response = function_to_call(
                    location=function_args.get("location"),
                    unit=function_args.get("unit"),
                )

Has became

# Step 2: check if the model wanted to call a function
        if tool_calls:
            # Step 3: call the function
            # Note: the JSON response may not always be valid; be sure to handle errors
            messages.append(response_message)  # extend conversation with assistant's reply

            # Step 4: send the info for each function call and function response to the model
            for tool_call in tool_calls:
                function_name = tool_call.function.name
                function_args = json.loads(tool_call.function.arguments)
                function_response = FunctionRegistry.call_function(
                    function_name,
                    **function_args
                )

With this setup, there is no longer a need to specify individual functions/arguments to call, the entire process is automated.