agent_configurator.py

import json

import pymongo

from chatbot import Chatbot
from database import Database
from knowledge_base import KnowledgeBase
from search_engine import SearchEngine
from voice_assistant import VoiceAssistant
from web_interface import WebInterface


class AgentConfigurator:
    def __init__(self):
        self.llm_models = {}
        self.human_input_sources = {}
        self.tools = {}
        self.input_sources = ["text", "voice", "image"]

    # Configure agent's parameters based on mode
    def configure_agent(self, agent_id, mode, source):
        # Select the appropriate LLM model based on the task requirements and the agent's mode
        llm_model = self._select_llm_model(mode, source)
        if not llm_model:
            print("Error: No compatible LLM model found.")
            return False

        # Define the human input source based on the task requirements and the agent's mode
        human_input_source = self._define_human_input_source(mode)
        if not human_input_source:
            print("Error: No compatible human input source found.")
            return False

        # Choose the relevant tools based on the task requirements and the agent's mode
        tools = self._choose_tools(mode)
        if not tools:
            print("Error: No compatible tools found.")
            return False

        # Configure agent's parameters based on mode and user's specifications
        if not self._configure_llm_model(agent_id, llm_model):
            return False
        if not self._configure_human_input_source(agent_id, human_input_source):
            return False
        if not self._configure_tools(agent_id, tools):
            return False

        return True

    # Select the appropriate LLM model based on the task requirements and the agent's mode
    def _select_llm_model(self, mode, source):
        # Check if LLM models have been loaded
        if not self.llm_models:
            self._load_llm_models(source)

        # Select the appropriate LLM model based on the task requirements and the agent's mode
        if mode in self.llm_models:
            return self.llm_models[mode]

    # Define the human input source based on the task requirements and the agent's mode
    def _define_human_input_source(self, mode):
        # Check if human input sources have been loaded
        if not self.human_input_sources:
            self._load_human_input_sources()

        # Define the human input source based on the task requirements and the agent's mode
        if mode in self.human_input_sources:
            return self.human_input_sources[mode]

    # Choose the relevant tools based on the task requirements and the agent's mode
    def _choose_tools(self, mode):
        # Check if tools have been loaded
        if not self.tools:
            self._load_tools(mode)

        # Choose the relevant tools based on the task requirements and the agent's mode
        if mode in self.tools:
            return self.tools[mode]
        else:
            return None

    def _select_input_source(self, mode):
        if mode == "text":
            return "text"
        elif mode == "voice":
            return "voice"
        elif mode == "image":
            return "image"
        else:
            return None

    def _select_tools(self, mode):
        if mode == "text":
            return ["NLTK", "spaCy"]
        elif mode == "voice":
            return ["NLTK", "gensim"]
        elif mode == "image":
            return ["spaCy", "gensim"]
        else:
            return None

    # Load LLM models from database or file system
    def _load_llm_models(self, source):
        if source == "database":
            # Establish a connection to the database
            client = pymongo.MongoClient("mongodb://localhost:27017/")
            db = client["llm_models"]
            collection = db["models"]

            # Retrieve the LLM models from the database
            models = {}
            for model in collection.find():
                models[model["name"]] = model["model"]

        elif source == "file_system":
            # Read the LLM models from the file system
            models = {}
            with open("llm_models.json", "r") as f:
                data = json.load(f)
                for model in data["models"]:
                    models[model["name"]] = model["model"]

        else:
            raise ValueError("Invalid source specified")

        # Store the LLM models in a dictionary
        self.llm_models = {
            "text": models["GPT-2"],
            "voice": models["Bert"],
            "image": models["XLNet"]
        }

        return self.llm_models

    # Configure agent's LLM model based on user's specifications
    def _configure_llm_model(self, agent_id, llm_model):
        # Configure agent's LLM model based on user's specifications
        # ...

        return True

    # Load human input sources from database or file system
    def _load_human_input_sources(self):
        # Load human input sources from database or file system
        # ...

        # Store human input sources in dictionary
        self.human_input_sources = {
            "mode1": Chatbot(),
            "mode2": VoiceAssistant(),
            "mode3": WebInterface(),
            # ...
        }

    # Configure agent's human input source based on user's specifications
    def _configure_human_input_source(self, agent_id, human_input_source):
        # Configure agent's human input source based on user's specifications
        # ...

        return True

        # Load tools from database or file system

    # Load tools from database or file system
    def _load_tools(self, mode):
        # Load tools from database or file system
        # ...

        # Choose relevant tools based on mode and task requirements
        if mode == "mode1":
            self.tools["knowledge_base"] = KnowledgeBase()
        elif mode == "mode2":
            self.tools["search_engine"] = SearchEngine()
        elif mode == "mode3":
            self.tools["database"] = Database()

        # Store tools in dictionary
        self.tools = {
            "mode1": self.tools["knowledge_base"],
            "mode2": self.tools["search_engine"],
            "mode3": self.tools["database"],
            # ...
        }

    # Configure agent's tools based on user's specifications
    def _configure_tools(self, agent_id, tools):
        # Configure agent's tools based on user's specifications
        # ...

        return True