think step by step on if this script is complete and if yes how to make this script robust and well refactored and scalable , here is the script "# Import necessary modules and classes
import logging
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, SerpAPIWrapper, LLMChain
from langchain.schema import AgentAction, AgentFinish
from typing import List, Union
from memory.contextual.contextual_memory import ContextualMemory
from memory.short_term.short_term_memory import ShortTermMemory
from memory.long_term.long_term_memory import LongTermMemory
from memory.entity.entity_memory import EntityMemory
from memory.short_term.short_term_memory_item import ShortTermMemoryItem
from memory.long_term.long_term_memory_item import LongTermMemoryItem
from memory.entity.entity_memory_item import EntityMemoryItem
from database import Database
from datetime import datetime

# Configure logging to log errors to a file named 'agent.log'
logging.basicConfig(filename='agent.log', level=logging.ERROR)

# Initialize the database to store conversation history and preferences
db = Database()

# Initialize memory components
# ShortTermMemory for storing recent interactions and context
stm = ShortTermMemory()
# LongTermMemory for storing historical data and insights
ltm = LongTermMemory()
# EntityMemory for storing structured information about entities and their relationships
em = EntityMemory()
# ContextualMemory for building relevant context based on the task and memory components
contextual_memory = ContextualMemory(stm, ltm, em)

# Define the tools available to the agent
search = SerpAPIWrapper()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="useful for when you need to answer questions about current events or find information"
    )
]

# Define the prompt template for the agent
template = """You are an AI assistant with long-term memory. Your goal is to help the user with their tasks while remembering their preferences.

Context:
{context}

User preferences:
{entities}

Current conversation:
{history}
Human: {input}
Assistant:"""

prompt = StringPromptTemplate(
    input_variables=["context", "entities", "history", "input"], 
    template=template
)

# Define the output parser for parsing the agent's output
class CustomOutputParser(AgentOutputParser):
    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
        # Implement custom output parsing logic
        # Parse the LLM output and return either an AgentAction or AgentFinish object
        # based on the parsed output
        if "Action:" in llm_output:
            # Extract the action and action input from the LLM output
            action_output = llm_output.split("Action:")[1].strip()
            action, action_input = action_output.split("[")
            action_input = action_input.strip("]").strip()
            return AgentAction(tool=action.strip(), tool_input=action_input, log=llm_output)
        else:
            # Return the LLM output as an AgentFinish object
            return AgentFinish(return_values={"output": llm_output}, log=llm_output)

output_parser = CustomOutputParser()

# Define the language model (LLM) for the agent
llm = OpenAI(temperature=0)

# Create the agent by combining the LLM, prompt, output parser, and tools
llm_chain = LLMChain(llm=llm, prompt=prompt)
agent = LLMSingleActionAgent(
    llm_chain=llm_chain, 
    output_parser=output_parser,
    stop=["\nHuman:"], 
    allowed_tools=tools
)

agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)

# Function to retrieve relevant preferences from the entity memory based on user input
def retrieve_preferences(user_input):
    try:
        relevant_preferences = em.search(user_input)
        return [pref["context"] for pref in relevant_preferences]
    except Exception as e:
        logging.error(f"Error retrieving preferences: {str(e)}")
        return []

# Function to update preferences in the entity memory based on user feedback
def update_preference(preference, feedback):
    try:
        entity_item = EntityMemoryItem(
            name=preference,
            type="preference",
            description=feedback,
            relationships=""
        )
        em.save(entity_item)
    except Exception as e:
        logging.error(f"Error updating preference: {str(e)}")

# Function to handle user feedback
def handle_feedback(user_input):
    try:
        # Extract the preference and feedback from user input
        preference, feedback = extract_feedback(user_input)
        update_preference(preference, feedback)
    except Exception as e:
        logging.error(f"Error handling feedback: {str(e)}")

# Function to check if the user input is feedback
def is_feedback(user_input):
    # Implement logic to determine if the user input is a feedback
    # Example: Check for specific keywords or patterns
    return "feedback:" in user_input.lower()

# Function to extract preference and feedback from user input
def extract_feedback(user_input):
    # Implement logic to extract the preference and feedback from user input
    # Example: Split the user input based on a specific delimiter
    parts = user_input.split(":")
    preference = parts[1].strip()
    feedback = parts[2].strip()
    return preference, feedback

# Main interaction loop
while True:
    try:
        user_input = input("Human: ")
        if user_input.lower() == "exit":
            break

        # Check if the user is providing feedback
        if is_feedback(user_input):
            handle_feedback(user_input)
            continue
        
        # Save user input to short-term memory
        stm_item = ShortTermMemoryItem(data=user_input, agent="Human")
        stm.save(stm_item)
        
        # Retrieve relevant preferences
        relevant_preferences = retrieve_preferences(user_input)
        
        # Build context using the contextual memory
        context = contextual_memory.build_context_for_task(user_input, "")
        
        # Prepare the prompt with context, preferences, and conversation history
        prompt_with_context = prompt.format(
            context=context,
            entities="\n".join(relevant_preferences),
            history=stm.search(user_input),
            input=user_input
        )
        
        # Run the agent with the prepared prompt
        response = agent_executor.run(prompt_with_context)
        print(f"Assistant: {response}")
        
        # Save the assistant's response to short-term memory
        stm_item = ShortTermMemoryItem(data=response, agent="Assistant")
        stm.save(stm_item)
        
        # Save the conversation history to the database
        db.save_conversation(user_input, response)
        
        # Evaluate the quality of the response (e.g., using user feedback or other metrics)
        # For demonstration purposes, this function needs to be implemented separately
        
        # Save the interaction to long-term memory
        ltm_item = LongTermMemoryItem(
            agent="Assistant",
            task=user_input,
            expected_output=response,
            datetime=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            quality=0  # Placeholder for quality score, needs to be implemented
        )
        ltm.save(ltm_item)
        
    except Exception as e:
        logging.error(f"An error occurred: {str(e)}")
        print("Oops! Something went wrong. Please try again.")

# Close the database connection
db.close()
"    and here is the memory framework used "{
    "contextual": {
        "contextual_memory.py": "from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory\n\n\nclass ContextualMemory:\n    def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):\n        self.stm = stm\n        self.ltm = ltm\n        self.em = em\n\n    def build_context_for_task(self, task, context) -> str:\n        \"\"\"\n        Automatically builds a minimal, highly relevant set of contextual information\n        for a given task.\n        \"\"\"\n        query = f\"{task.description} {context}\".strip()\n\n        if query == \"\":\n            return \"\"\n\n        context = []\n        context.append(self._fetch_ltm_context(task.description))\n        context.append(self._fetch_stm_context(query))\n        context.append(self._fetch_entity_context(query))\n        return \"\\n\".join(filter(None, context))\n\n    def _fetch_stm_context(self, query) -> str:\n        \"\"\"\n        Fetches recent relevant insights from STM related to the task's description and expected_output,\n        formatted as bullet points.\n        \"\"\"\n        stm_results = self.stm.search(query)\n        formatted_results = \"\\n\".join([f\"- {result}\" for result in stm_results])\n        return f\"Recent Insights:\\n{formatted_results}\" if stm_results else \"\"\n\n    def _fetch_ltm_context(self, task) -> str:\n        \"\"\"\n        Fetches historical data or insights from LTM that are relevant to the task's description and expected_output,\n        formatted as bullet points.\n        \"\"\"\n        ltm_results = self.ltm.search(task, latest_n=2)\n        if not ltm_results:\n            return None\n\n        formatted_results = [\n            suggestion\n            for result in ltm_results\n            for suggestion in result[\"metadata\"][\"suggestions\"]\n        ]\n        formatted_results = list(dict.fromkeys(formatted_results))\n        formatted_results = \"\\n\".join([f\"- {result}\" for result in formatted_results])\n\n        return f\"Historical Data:\\n{formatted_results}\" if ltm_results else \"\"\n\n    def _fetch_entity_context(self, query) -> str:\n        \"\"\"\n        Fetches relevant entity information from Entity Memory related to the task's description and expected_output,\n        formatted as bullet points.\n        \"\"\"\n        em_results = self.em.search(query)\n        formatted_results = \"\\n\".join(\n            [f\"- {result['context']}\" for result in em_results]\n        )\n        return f\"Entities:\\n{formatted_results}\" if em_results else \"\"\n",
        "__init__.py": "",
        "__pycache__": {}
    },
    "entity": {
        "entity_memory.py": "from crewai.memory.entity.entity_memory_item import EntityMemoryItem\nfrom crewai.memory.memory import Memory\nfrom crewai.memory.storage.rag_storage import RAGStorage\n\n\nclass EntityMemory(Memory):\n    \"\"\"\n    EntityMemory class for managing structured information about entities\n    and their relationships using SQLite storage.\n    Inherits from the Memory class.\n    \"\"\"\n\n    def __init__(self, embedder_config=None):\n        storage = RAGStorage(\n            type=\"entities\", allow_reset=False, embedder_config=embedder_config\n        )\n        super().__init__(storage)\n\n    def save(self, item: EntityMemoryItem) -> None:\n        \"\"\"Saves an entity item into the SQLite storage.\"\"\"\n        data = f\"{item.name}({item.type}): {item.description}\"\n        super().save(data, item.metadata)\n",
        "entity_memory_item.py": "class EntityMemoryItem:\n    def __init__(\n        self,\n        name: str,\n        type: str,\n        description: str,\n        relationships: str,\n    ):\n        self.name = name\n        self.type = type\n        self.description = description\n        self.metadata = {\"relationships\": relationships}\n",
        "__init__.py": "",
        "__pycache__": {}
    },
  "long_term": {
        "long_term_memory.py": "from typing import Any, Dict\n\nfrom crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem\nfrom crewai.memory.memory import Memory\nfrom crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage\n\n\nclass LongTermMemory(Memory):\n    \"\"\"\n    LongTermMemory class for managing cross runs data related to overall crew's\n    execution and performance.\n    Inherits from the Memory class and utilizes an instance of a class that\n    adheres to the Storage for data storage, specifically working with\n    LongTermMemoryItem instances.\n    \"\"\"\n\n    def __init__(self):\n        storage = LTMSQLiteStorage()\n        super().__init__(storage)\n\n    def save(self, item: LongTermMemoryItem) -> None:\n        metadata = item.metadata\n        metadata.update({\"agent\": item.agent, \"expected_output\": item.expected_output})\n        self.storage.save(\n            task_description=item.task,\n            score=metadata[\"quality\"],\n            metadata=metadata,\n            datetime=item.datetime,\n        )\n\n    def search(self, task: str, latest_n: int) -> Dict[str, Any]:\n        return self.storage.load(task, latest_n)\n",
        "long_term_memory_item.py": "from typing import Any, Dict, Union\n\n\nclass LongTermMemoryItem:\n    def __init__(\n        self,\n        agent: str,\n        task: str,\n        expected_output: str,\n        datetime: str,\n        quality: Union[int, float] = None,\n        metadata: Dict[str, Any] = None,\n    ):\n        self.task = task\n        self.agent = agent\n        self.quality = quality\n        self.datetime = datetime\n        self.expected_output = expected_output\n        self.metadata = metadata if metadata is not None else {}\n",
        "__init__.py": "",
        "__pycache__": {}
    },
    "memory.py": "from typing import Any, Dict\n\nfrom crewai.memory.storage.interface import Storage\n\n\nclass Memory:\n    \"\"\"\n    Base class for memory, now supporting agent tags and generic metadata.\n    \"\"\"\n\n    def __init__(self, storage: Storage):\n        self.storage = storage\n\n    def save(\n        self, value: Any, metadata: Dict[str, Any] = None, agent: str = None\n    ) -> None:\n        metadata = metadata or {}\n        if agent:\n            metadata[\"agent\"] = agent\n        self.storage.save(value, metadata)\n\n    def search(self, query: str) -> Dict[str, Any]:\n        return self.storage.search(query)\n",
    "short_term": {
        "short_term_memory.py": "from crewai.memory.memory import Memory\nfrom crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem\nfrom crewai.memory.storage.rag_storage import RAGStorage\n\n\nclass ShortTermMemory(Memory):\n    \"\"\"\n    ShortTermMemory class for managing transient data related to immediate tasks\n    and interactions.\n    Inherits from the Memory class and utilizes an instance of a class that\n    adheres to the Storage for data storage, specifically working with\n    MemoryItem instances.\n    \"\"\"\n\n    def __init__(self, embedder_config=None):\n        storage = RAGStorage(type=\"short_term\", embedder_config=embedder_config)\n        super().__init__(storage)\n\n    def save(self, item: ShortTermMemoryItem) -> None:\n        super().save(item.data, item.metadata, item.agent)\n\n    def search(self, query: str, score_threshold: float = 0.35):\n        return self.storage.search(query=query, score_threshold=score_threshold)\n",
        "short_term_memory_item.py": "from typing import Any, Dict\n\n\nclass ShortTermMemoryItem:\n    def __init__(self, data: Any, agent: str, metadata: Dict[str, Any] = None):\n        self.data = data\n        self.agent = agent\n        self.metadata = metadata if metadata is not None else {}\n",
        "__init__.py": "",
        "__pycache__": {}
    },
    "storage": {
        "interface.py": "from typing import Any, Dict\n\n\nclass Storage:\n    \"\"\"Abstract base class defining the storage interface\"\"\"\n\n    def save(self, key: str, value: Any, metadata: Dict[str, Any]) -> None:\n        pass\n\n    def search(self, key: str) -> Dict[str, Any]:\n        pass\n",
        "ltm_sqlite_storage.py": "import json\nimport sqlite3\nfrom typing import Any, Dict, Union\n\nfrom crewai.utilities import Printer\nfrom crewai.utilities.paths import db_storage_path\n\n\nclass LTMSQLiteStorage:\n    \"\"\"\n    An updated SQLite storage class for LTM data storage.\n    \"\"\"\n\n    def __init__(self, db_path=f\"{db_storage_path()}/long_term_memory_storage.db\"):\n        self.db_path = db_path\n        self._printer: Printer = Printer()\n        self._initialize_db()\n\n    def _initialize_db(self):\n        \"\"\"\n        Initializes the SQLite database and creates LTM table\n        \"\"\"\n        try:\n            with sqlite3.connect(self.db_path) as conn:\n                cursor = conn.cursor()\n                cursor.execute(\n                    \"\"\"\n                    CREATE TABLE IF NOT EXISTS long_term_memories (\n                        id INTEGER PRIMARY KEY AUTOINCREMENT,\n                        task_description TEXT,\n                        metadata TEXT,\n                        datetime TEXT,\n                        score REAL\n                    )\n                \"\"\"\n                )\n\n                conn.commit()\n        except sqlite3.Error as e:\n            self._printer.print(\n                content=f\"MEMORY ERROR: An error occurred during database initialization: {e}\",\n                color=\"red\",\n            )\n\n    def save(\n        self,\n        task_description: str,\n        metadata: Dict[str, Any],\n        datetime: str,\n        score: Union[int, float],\n    ) -> None:\n        \"\"\"Saves data to the LTM table with error handling.\"\"\"\n        try:\n            with sqlite3.connect(self.db_path) as conn:\n                cursor = conn.cursor()\n                cursor.execute(\n                    \"\"\"\n                INSERT INTO long_term_memories (task_description, metadata, datetime, score)\n                VALUES (?, ?, ?, ?)\n            \"\"\",\n                    (task_description, json.dumps(metadata), datetime, score),\n                )\n                conn.commit()\n        except sqlite3.Error as e:\n            self._printer.print(\n                content=f\"MEMORY ERROR: An error occurred while saving to LTM: {e}\",\n                color=\"red\",\n            )\n\n    def load(self, task_description: str, latest_n: int) -> Dict[str, Any]:\n        \"\"\"Queries the LTM table by task description with error handling.\"\"\"\n        try:\n            with sqlite3.connect(self.db_path) as conn:\n                cursor = conn.cursor()\n                cursor.execute(\n                    f\"\"\"\n                    SELECT metadata, datetime, score\n                    FROM long_term_memories\n                    WHERE task_description = ?\n                    ORDER BY datetime DESC, score ASC\n                    LIMIT {latest_n}\n                \"\"\",\n                    (task_description,),\n                )\n                rows = cursor.fetchall()\n                if rows:\n                    return [\n                        {\n                            \"metadata\": json.loads(row[0]),\n                            \"datetime\": row[1],\n                            \"score\": row[2],\n                        }\n                        for row in rows\n                    ]\n\n        except sqlite3.Error as e:\n            self._printer.print(\n                content=f\"MEMORY ERROR: An error occurred while querying LTM: {e}\",\n                color=\"red\",\n            )\n        return None\n",
        "rag_storage.py": "import contextlib\nimport io\nimport logging\nimport os\nfrom typing import Any, Dict\n\nfrom embedchain import App\nfrom embedchain.llm.base import BaseLlm\nfrom embedchain.vectordb.chroma import InvalidDimensionException\n\nfrom crewai.memory.storage.interface import Storage\nfrom crewai.utilities.paths import db_storage_path\n\n\n@contextlib.contextmanager\ndef suppress_logging(\n    logger_name=\"chromadb.segment.impl.vector.local_persistent_hnsw\",\n    level=logging.ERROR,\n):\n    logger = logging.getLogger(logger_name)\n    original_level = logger.getEffectiveLevel()\n    logger.setLevel(level)\n    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(\n        io.StringIO()\n    ), contextlib.suppress(UserWarning):\n        yield\n    logger.setLevel(original_level)\n\n\nclass FakeLLM(BaseLlm):\n    pass\n\n\nclass RAGStorage(Storage):\n    \"\"\"\n    Extends Storage to handle embeddings for memory entries, improving\n    search efficiency.\n    \"\"\"\n\n    def __init__(self, type, allow_reset=True, embedder_config=None):\n        super().__init__()\n        if (\n            not os.getenv(\"OPENAI_API_KEY\")\n            and not os.getenv(\"OPENAI_BASE_URL\") == \"https://api.openai.com/v1\"\n        ):\n            os.environ[\"OPENAI_API_KEY\"] = \"fake\"\n        config = {\n            \"app\": {\n                \"config\": {\"name\": type, \"collect_metrics\": False, \"log_level\": \"ERROR\"}\n            },\n            \"chunker\": {\n                \"chunk_size\": 5000,\n                \"chunk_overlap\": 100,\n                \"length_function\": \"len\",\n                \"min_chunk_size\": 150,\n            },\n            \"vectordb\": {\n                \"provider\": \"chroma\",\n                \"config\": {\n                    \"collection_name\": type,\n                    \"dir\": f\"{db_storage_path()}/{type}\",\n                    \"allow_reset\": allow_reset,\n                },\n            },\n        }\n\n        if embedder_config:\n            config[\"embedder\"] = embedder_config\n\n        self.app = App.from_config(config=config)\n        self.app.llm = FakeLLM()\n        if allow_reset:\n            self.app.reset()\n\n    def save(self, value: Any, metadata: Dict[str, Any]) -> None:\n        self._generate_embedding(value, metadata)\n\n    def search(\n        self,\n        query: str,\n        limit: int = 3,\n        filter: dict = None,\n        score_threshold: float = 0.35,\n    ) -> Dict[str, Any]:\n        with suppress_logging():\n            try:\n                results = (\n                    self.app.search(query, limit, where=filter)\n                    if filter\n                    else self.app.search(query, limit)\n                )\n            except InvalidDimensionException:\n                self.app.reset()\n                return []\n        return [r for r in results if r[\"metadata\"][\"score\"] >= score_threshold]\n\n    def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> Any:\n        with suppress_logging():\n            self.app.add(text, data_type=\"text\", metadata=metadata)\n",
        "__pycache__": {}
    },
    "__init__.py": "from .entity.entity_memory import EntityMemory\nfrom .long_term.long_term_memory import LongTermMemory\nfrom .short_term.short_term_memory import ShortTermMemory\n",
    "__pycache__": {}
}