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OpenAI Chat Interface

The openai_chat_interface.py file contains the implementation of the OpenAI_LLM class, which provides an interface to interact with OpenAI's language models and manage chat interactions.

Usage

To use the OpenAI_LLM class, import it as follows:

from openai_chat_interface import OpenAI_LLM, create_message, calculate_cost

Initialize OpenAI_LLM

Create an instance of OpenAI_LLM:

llm = OpenAI_LLM(api_key=None, model="gpt-3.5-turbo", temperature=1.0, system_message='You are a helpful assistant. Answer the user query', user_message='{query}', functions=None, function_call=None)

The constructor takes the following parameters:

  • api_key (optional): Your OpenAI API key. If not provided, it will attempt to load from the environment variable OPENAI_API_KEY.
  • model (optional): The OpenAI language model to use. Default is "gpt-3.5-turbo".
  • temperature (optional): The temperature value for generating responses. Default is 1.0.
  • system_message (optional): The system message to be used in chat interactions. Default is 'You are a helpful assistant. Answer the user query'.
  • user_message (optional): The user message template to be used in chat interactions. Default is '{query}'.
  • functions (optional): The list of functions to be used in chat interactions. Default is None.
  • function_call (optional): The function call type to be used in chat interactions. Default is "auto".

Add Messages

Before running the model, you can add messages to the chat history. Use the add_messages method to add a list of messages:

messages = [
    create_message("user", "What is the capital of France?"),
    create_message("assistant", "The capital of France is Paris.")
]
llm.add_messages(messages)

Run the Model

To run the model and generate a response, use the run method:

llm.run()

By default, this will use the user message template provided during initialization and generate a response based on the chat history.

You can also customize the user message used in this specific run call:

llm.run(messages=[create_message("user", "What is the capital of Spain?")])

In this case, the provided messages will be used for this specific run call instead of the chat history.

Access Response Content

After running the model, you can access the response content using the response_content property:

response_content = llm.response_content
print(response_content)

Access Response Message

You can access the response message object using the response_message property:

response_message = llm.response_message
print(response_message)

Access Response Function

If the response contains a function call, you can access the function call object using the response_function property:

response_function = llm.response_function
print(response_function)

Access Response Function Name

If the response contains a function call, you can access the function name using the response_function_name property:

response_function_name = llm.response_function_name
print(response_function_name)

Access Response Function Arguments

If the response contains a function call, you can access the function arguments using the response_function_arguments property:

response_function_arguments = llm.response_function_arguments
print(response_function_arguments)

Retrieve Finish Reason

After running the model, you can retrieve the finish reason using the finish_reason property:

finish_reason = llm.finish_reason
print(finish_reason)

Clear Memory

To clear the chat history, use the clear_memory method:

llm.clear_memory()

Example Files

The package provides example files that demonstrate the usage of the OpenAI_LLM class. You can refer to these examples to see how to utilize the chat interface effectively.

  • decorator_example.py: Demonstrates the usage of the @openaifunc decorator and the OpenAI_functions class.
  • chat_and_func_example_single_tool_use.py: Demonstrates the usage of the OpenAI_LLM class with a single tool use.
  • chat_and_func_example_multi_tool_use.py: Demonstrates the usage of the OpenAI_LLM class with multiple tool uses.

Feel free to explore and modify these example files to understand how to use the OpenAI_LLM class effectively.

OpenAI Function and Function Collection Classes

This Python package provides classes OpenAI_functions, OpenAI_function_collection, and OpenAI_LLM to dynamically load and manage Python functions marked with the @openaifunc decorator, and interact with OpenAI's language models. This utility can be used to organize and call functions from different modules easily, and to create chat interfaces with OpenAI's models.

How to Use

OpenAI_functions

First, import the package at the top of your Python code:

from openai_decorator import OpenAI_functions, openaifunc

Then, add a @openaifunc decorator to the functions you want to manage:

@openaifunc
def add_numbers(a: int, b: int) -> int:
    """
    This function adds two numbers.
    """
    return a + b

Next, create an instance of OpenAI_functions by loading a Python file containing the decorated functions:

math_functions = OpenAI_functions.from_file("path/to/math_funcs.py")

You can now access the list of functions, mappings, and call the functions:

print(math_functions.func_list)
print(math_functions.func_mapping)
result = math_functions.call_func({"name": "add_numbers", "arguments": "{ \"a\": 3, \"b\": 4 }"})
print(result)  # Output: 7

OpenAI_function_collection

Import the OpenAI_function_collection class:

from openai_decorator import OpenAI_function_collection

Create an instance by loading a folder containing Python files with decorated functions:

all_functions = OpenAI_function_collection.from_folder("path/to/tools")

You can now access the combined function lists, mappings, descriptions, and call the functions across all loaded files:

print(all_functions.func_list)
print(all_functions.func_description)
print(all_functions.func_mapping)
result = all_functions.call_func({"name": "add_numbers", "arguments": "{ \"a\": 5, \"b\": 5 }"})
print(result)  # Output: 10

OpenAI_LLM

The OpenAI_LLM class provides an interface to interact with OpenAI's language models and manage chat interactions.

Example Usage

First, import the required classes and functions:

from openai_chat_interface import OpenAI_LLM, create_message, calculate_cost

Create an instance of OpenAI_LLM:

llm = OpenAI_LLM(api_key="your-api-key", system_message='You are a helpful assistant. Answer the user query')

You can run the model with user input:

user_input = "What's the weather like today?"
llm.run(query=user_input)
print(llm.response_content)  # Outputs the model's response

You can add messages, clear messages, and perform various operations with the chat interface. See the chat_example.py file for a complete example.

Function Descriptions

Function descriptions are extracted from the docstrings within the Python files. You can write standard Python docstrings to describe your functions:

@openaifunc
def multiply_numbers(a: int, b: int) -> int:
    """
    This function multiplies two numbers.
    :param a: The first number to multiply
    :param b: The second number to multiply
    """
    return a * b

The OpenAI_functions class will automatically parse the docstrings and include them in the func_description property.

Function Collection

The OpenAI_function_collection class allows you to manage multiple OpenAI_functions instances in one place. You can load functions from multiple files or an entire folder and access them all through the collection instance.

Example Files

The repository includes example files demonstrating the usage of these classes, including math_funcs.py, weather_funcs.py, main.py, and chat_example.py. Feel free to explore and modify them to understand how to use the package effectively.