Here is the script write it in details ```python
# Import necessary libraries
import os
import json
import faiss
from langchain.llms import OpenAI
from langchain.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain.chains import LLMChain

# Set OpenAI API key (replace with your own)
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"

# Define paths and configurations
persist_directory = "db"
embeddings = HuggingFaceInstructEmbeddings()
llm = OpenAI(temperature=0.7)

# --- Knowledge Agent Class ---
class KnowledgeAgent:
    def __init__(self, vectordb):
        self.vectordb = vectordb
        self.llm = llm

    def process_conversation(self, conversation):
        """
        Extracts knowledge from a conversation and stores it in the vector database.

        Args:
            conversation (list): A list of strings representing the conversation history. 
        """
        knowledge = self._extract_knowledge(conversation)
        self.vectordb.add_texts(knowledge, metadatas=[{"source": "conversation"}])

    def _extract_knowledge(self, conversation):
        """
        Extracts different types of knowledge (preferences, facts, episodes, procedures) from the conversation.

        Args:
            conversation (list): A list of strings representing the conversation history.

        Returns:
            list: A list of extracted knowledge items.
        """
        conversation_history = "\n".join(conversation)

        # --- Define prompts for different knowledge types ---
        preference_prompt = f"""
            ## User Preferences Extraction

            **Conversation:**
            {conversation_history}

            **Extract any user preferences mentioned in the conversation.
            Focus on things like likes, dislikes, allergies, and interests.
            Output a JSON object with the following structure:
            {{"preferences": ["preference 1", "preference 2", ...]}}
            """

        fact_prompt = f"""
            ## Factual Knowledge Extraction

            **Conversation:**
            {conversation_history}

            **Extract any factual statements mentioned in the conversation.
            Focus on objective information like names, dates, locations, and descriptions.
            Output a JSON object with the following structure:
            {{"facts": ["fact 1", "fact 2", ...]}}
            """

        episodic_prompt = f"""
            ## Episodic Memory Extraction

            **Conversation:**
            {conversation_history}

            **Extract any specific events or personal experiences shared in the conversation.
            Focus on extracting episodic information with details like event descriptions, timestamps, and associated emotions.
            Output a JSON object with the following structure:
            {{"episodes": ["episode 1", "episode 2", ...]}}
            """

        procedural_prompt = f"""
            ## Procedural Memory Extraction

            **Conversation:**
            {conversation_history}

            **Extract any step-by-step instructions or procedures mentioned in the conversation.
            Focus on identifying and extracting procedural information that describes how to perform a task or achieve a goal.
            Output a JSON object with the following structure:
            {{"procedures": ["procedure 1", "procedure 2", ...]}}
            """

        # --- Create LLM chains for each knowledge type ---
        preference_chain = LLMChain(llm=self.llm, prompt=preference_prompt)
        fact_chain = LLMChain(llm=self.llm, prompt=fact_prompt)
        episodic_chain = LLMChain(llm=self.llm, prompt=episodic_prompt)
        procedural_chain = LLMChain(llm=self.llm, prompt=procedural_prompt)

        # --- Run chains and extract knowledge --- 
        preferences = self._parse_json_output(preference_chain.run(), "preferences")
        facts = self._parse_json_output(fact_chain.run(), "facts")
        episodes = self._parse_json_output(episodic_chain.run(), "episodes")
        procedures = self._parse_json_output(procedural_chain.run(), "procedures")

        extracted_knowledge = preferences + facts + episodes + procedures
        return extracted_knowledge

    def _parse_json_output(self, json_string, key):
        """
        Parses JSON output from the LLM and extracts the specified key.

        Args:
            json_string (str): The JSON string to parse.
            key (str): The key to extract from the JSON object.

        Returns:
            list: A list of values associated with the specified key. 
        """
        try:
            data = json.loads(json_string)
            return data.get(key, [])
        except json.JSONDecodeError:
            return []

# --- Memory Agent Class --- 
class MemoryAgent:
    def __init__(self, llm, vectordb):
        self.llm = llm
        self.vectordb = vectordb
        self.feedback_store = FAISS(embeddings.embed_query, faiss.IndexFlatL2(embeddings.embed_query("test").shape[-1]), persist_directory=persist_directory + "_feedback")

    def generate_response(self, query, user_id): 
        """
        Generates a response to a user query based on stored knowledge and conversation history.

        Args:
            query (str): The user's query.
            user_id (str): The ID of the user.

        Returns:
            str: The agent's response.
        """
        docs = self.vectordb.similarity_search(query)

        if docs:
            prompt = f"Context: {docs[0].page_content}\n\nUser: {query}\nAgent:"
        else:
            prompt = f"User: {query}\nAgent:"

        llm_chain = LLMChain(llm=self.llm, prompt=prompt, cache=True)
        response = llm_chain.run()

        self._store_feedback(user_id, query, response)

        return response

    def _store_feedback(self, user_id, query, response):
        """
        Stores user feedback in a separate vector store. 

        Args: 
            user_id (str): The ID of the user.
            query (str): The user's query. 
            response (str): The agent's response. 
        """ 
        feedback_entry = f"User ID: {user_id}\nQuery: {query}\nResponse: {response}"
        self.feedback_store.add_texts([feedback_entry], metadatas=[{"user_id": user_id}])

    def update_knowledge(self, user_id, feedback): 
        """
        Updates knowledge based on user feedback. 

        Args:
            user_id (str): The ID of the user.
            feedback (str): The user's feedback on the agent's response. 
        """ 
        feedback_docs = self.feedback_store.similarity_search(f"User ID: {user_id}")

        if feedback_docs:
            for doc in feedback_docs:
                query = doc.metadata["query"]
                response = doc.metadata["response"]

                updated_entry = f"Query: {query}\nResponse: {response}\nFeedback: {feedback}"
                self.feedback_store.add_texts([updated_entry], metadatas=[{"user_id": user_id, "feedback": feedback}])

                self._trigger_knowledge_update(query, response, feedback)

    def _trigger_knowledge_update(self, query, response, feedback):
        """
        (To be implemented) Triggers knowledge update based on patterns in user feedback. 
        """
        pass 

    def forget_knowledge(self):
        """
        (To be implemented) Implements forgetting mechanisms to remove outdated or rarely used knowledge. 
        """
        pass

# --- Initialization ---
# Initialize FAISS index and vectorstore 
index = faiss.IndexFlatL2(embeddings.embed_query("test").shape[-1])
vectordb = FAISS(embeddings.embed_query, index, persist_directory=persist_directory)

# Create knowledge and memory agents
knowledge_agent = KnowledgeAgent(vectordb)
memory_agent = MemoryAgent(llm, vectordb)

# --- Example Usage --- 
# Process a sample conversation
conversation = [
    "User: I love hiking and I'm allergic to peanuts.",
    "Agent: That's great that you enjoy hiking! It's a wonderful way to explore nature and stay active. I'll make sure to keep in mind your peanut allergy when suggesting any food-related activities or recommendations.",
    # ... (rest of the conversation)
]
knowledge_agent.process_conversation(conversation)

# Generate response based on stored knowledge 
user_id = "user123"
user_query = "What are some good hiking trails for beginners?"
response = memory_agent.generate_response(user_query, user_id)
print(response)

# Process user feedback 
user_feedback = "The response was helpful, but I would also like some information on essential hiking gear for beginners."
memory_agent.update_knowledge(user_id, user_feedback)
```