AI agent class

agent.py

class Agent():
    def __init__(self) -> None:
        self.llm = OpenaiLanguageModel(anonymize=False)
        self.memory = {}

        # Define standard tools
        calc_tool = Tool("Calculator", "Performs basic mathematical operations", Agent.calculator) # noqa

        self.tools = Tool.all()

    def __call__(self, user_input: str) -> str:

        tool_use_prompt = " "
        if len(self.tools) > 0:
            tool_list = ', '.join([repr(tool) for tool in self.tools])
            tool_use_prompt = f"You can use the following tools: {tool_list}"

        step_prompt = '''You are a useful AI Agent.
        Given the following user input: {}
        What is the first step you should take to answer the user request
        correctly ?
        {}'''.format(user_input, tool_use_prompt)

        # Initialize memory for this user input
        input_key = hash(user_input)

        self.memory[input_key] = []

        print(user_input, "\n")

        print("Thinking...", "\n")

        step_dict = self.llm.generate(step_prompt, output_format=StepBase)

        while True:

            step = step_dict["step"]

            if 'break' in step.lower():
                final_response_prompt = '''
                Given all this: {}
                Give the final answer to this user request: {}
                '''.format(" ,".join(f"{item['step']} -> {item['response']}" for item in self.memory[input_key])
, user_input)

                final_response = self.llm.generate(final_response_prompt)

                print(final_response, "\n")

                break

            print(step, "\n")

            print("Thinking...", "\n")

            response = self.execute_step(step)

            self.memory[input_key].append(
                {
                    "step": step,
                    "response": response
                }
            )

            print(response, "\n")

            print("Thinking...", "\n")

            step_prompt = '''Given the following user input: {}
            You already took the following step or steps
            {}. Is is enough to respond to the user?
            If not what is the next step ? Return a step named 'break' if the problem is solved'''.format(user_input, " ,".join(f"{item['step']} -> {item['response']}" for item in self.memory[input_key])
)

            step_dict = self.llm.generate(step_prompt, output_format=StepBase)

    def execute_step(self, step):

        if len(self.tools) > 0:
            execute_prompt = ''' Given the following task: {}
            And the following tools: {}
            Do you need to use a tool to do this?
            If yes return the id of the tool.
            If not return 10000 as id
            '''.format(step, self.tools)

            tool_id_to_use = self.llm.generate(execute_prompt,
                                               output_format=ToolId)["id"]

            if tool_id_to_use != 10000:
                tool = self.tools[tool_id_to_use]

                # Get the function's signature
                sig = inspect.signature(tool._call_function)
                source_code = inspect.getsource(tool._call_function)
                params = {k: (v.annotation, ...) for k, v in sig.parameters.items()}

                # Create a dynamic BaseModel for the function's parameters
                DynamicModel = create_model("DynamicModel", **params)

                # Now, you can use this model as the output format for `self.llm.generate`
                function_params = self.llm.generate(f"Provide parameters for tool {repr(tool)} to solve this task: {step}. Knowing that this is the source code of the function the tool uses: {source_code}. Your response should be a JSON respecting the following structure {DynamicModel.__annotations__} with the correct values for the problem at hand.")
                
                parsed_data = json.loads(function_params)
                
                # Call the tool with the provided parameters
                response = tool(**parsed_data)

                return response

        return self.llm.generate(f"Given your current knowledge. Execute this step:{step}")

    @staticmethod
    def calculator(operation: str, value1: Union[int, float], value2: Optional[Union[int, float]] = None) -> float:
        if operation == "add":
            return value1 + value2
        elif operation == "subtract":
            return value1 - value2
        elif operation == "multiply":
            return value1 * value2
        elif operation == "divide":
            return value1 / value2
        elif operation == "square_root":
            return value1 ** 0.5
        elif operation == "square":
            return value1 ** 2
        elif operation == "power":
            if value2 is None:
                raise ValueError("For the power operation, value2 (exponent) is required.")
            return value1 ** value2
        else:
            raise ValueError(f"Unsupported operation: {operation}")