agent.py

import functools
import json
import os
import logging
from groq import Groq
import functions
from tools import tools

# Set up logging
logging.basicConfig(level=logging.DEBUG)

client = Groq(api_key=os.environ["GROQ_API_KEY"])

MODEL = "llama3-groq-70b-8192-tool-use-preview"
all_functions = [func for func in dir(functions) if callable(
    getattr(functions, func)) and not func.startswith("__")]
names_to_functions = {func: functools.partial(
    getattr(functions, func)) for func in all_functions}


def create_message(prompt, message_type):
    logging.debug(
        f"Creating message with prompt: {prompt} and message type: {message_type}")
    system_message = ""

    if message_type == "reasoning_chain":
        system_message = (
            "You are a movie search assistant bot who uses TMDB to help users "
            "find movies. Think step by step and identify the sequence of "
            "reasoning steps that will help to answer the user's query."
        )
    elif message_type == "function_call":
        system_message = (
            "You are a movie search assistant bot who uses TMDB to help users "
            "find movies. Think step by step and identify the sequence of "
            "function calls that will help to answer the user's query. Use the "
            "available functions to gather the necessary data. "
            "Do not call multiple functions when they need to be executed in sequence. "
            "Only call multiple functions when they can be executed in parallel. "
            "Stop with a discover_movie function call that returns a list of movie ids. "
            "Ensure the discover_movie function call includes all the necessary parameters to filter the movies accurately."
        )
    else:
        raise ValueError(
            "Invalid message type. Expected 'reasoning_chain' or 'function_call'")

    return [
        {
            "role": "system",
            "content": system_message,
        },
        {
            "role": "user",
            "content": prompt,
        },
    ]


def get_response(client, model, messages, tool_choice="auto"):
    logging.debug(
        f"Getting response with model: {model}, messages: {messages}, tool_choice: {tool_choice}")
    response = client.chat.completions.create(
        model=model,
        messages=messages,
        tools=tools,
        tool_choice=tool_choice,
        temperature=0,
        max_tokens=4096,
    )
    logging.debug(f"Response: {response}")
    return response


def generate_reasoning_chain(user_prompt):
    messages = create_message(user_prompt, "reasoning_chain")
    logging.debug(f"Generating reasoning chain with messages: {messages}")
    cot_response = get_response(client, MODEL, messages, tool_choice="none")
    logging.info(f"COT response: {cot_response.choices[0].message.content}")
    if cot_response.choices[0].finish_reason == "stop":
        return cot_response.choices[0]
    else:
        logging.error(
            "Failed to generate reasoning chain. Got response: ", cot_response)
        raise Exception("Failed to generate reasoning chain")


def gather_movie_data(messages, iteration=0, max_iterations=2):
    logging.debug(
        f"Gathering movie data with messages: {messages}, iteration: {iteration}")
    response = get_response(client, MODEL, messages, tool_choice="required")
    logging.info(
        f"Gathering movie data response: {response}")
    if response.choices[0].finish_reason == "tool_calls":
        tool_calls = response.choices[0].message.tool_calls
        updated_messages = messages.copy()
        for tool_call in tool_calls:
            logging.info(
                f"Tool call: {tool_call.function.name}, Tool call parameters: {tool_call.function.arguments}")
            tool_output = execute_tool(tool_call)
            logging.info(
                f"Tool call output: {json.dumps(tool_output, indent=2)}")
            if tool_call.function.name == "discover_movie":
                return tool_output["results"]  # A list of movies
            else:
                updated_messages.append(
                    {
                        "tool_call_id": tool_call.id,
                        "role": "tool",
                        "name": tool_call.function.name,
                        "content": str(tool_output),
                    }
                )
        if iteration < max_iterations:
            return gather_movie_data(updated_messages, iteration + 1)
    else:
        raise Exception(
            "Failed to gather movie data. Got response: ", response)


def execute_tool(tool_call):
    logging.debug(f"Executing tool: {tool_call.function.name}")
    function_to_call = names_to_functions[tool_call.function.name]
    function_args = json.loads(tool_call.function.arguments)
    return function_to_call(**function_args)


def chatbot(user_prompt):
    cot_response_choice = generate_reasoning_chain(user_prompt)
    cot = create_message(user_prompt, "function_call")
    cot.append({
        'role': cot_response_choice.message.role,
        'content': cot_response_choice.message.content})
    movie_list = gather_movie_data(cot)
    return movie_list


if __name__ == "__main__":
    print(json.dumps(chatbot("List comedy movies with tom cruise in it"), indent=2))