diff --git a/5. URL-Scraper Agent.ipynb b/5. URL-Scraper Agent.ipynb
index 62d6964..8b13789 100644
--- a/5. URL-Scraper Agent.ipynb	
+++ b/5. URL-Scraper Agent.ipynb	
@@ -1,116 +1 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9327343-3e11-4a88-b798-95ff4644e2a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.vectorstores import Chroma\n",
-    "from langchain.embeddings import SentenceTransformerEmbeddings\n",
-    "from langchain.schema import Document\n",
-    "from ollama import chat\n",
-    "import os\n",
-    "import re\n",
-    "import requests\n",
-    "from bs4 import BeautifulSoup\n",
-    "\n",
-    "EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'\n",
-    "LLM_MODEL = 'gemma2:9b'\n",
-    "CHUNK_SIZE = 1000\n",
-    "OVERLAP = 200\n",
-    "CHROMA_PERSIST_DIR = r'\\home\\Masih\\chroma_db\\chroma_db'\n",
-    "\n",
-    "class ChromaRAGSystem:\n",
-    "    def __init__(self):\n",
-    "        # Init embedding model\n",
-    "        self.embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL)\n",
-    "        # Vector store instance\n",
-    "        self.vector_db = None\n",
-    "        \n",
-    "    def build_vector_store(self, documents):\n",
-    "        \"\"\"Create Chroma vector store from documents\"\"\"\n",
-    "        self.vector_db = Chroma.from_documents(\n",
-    "            documents=documents,\n",
-    "            embedding=self.embeddings,\n",
-    "            persist_directory=CHROMA_PERSIST_DIR\n",
-    "        )\n",
-    "        self.vector_db.persist()\n",
-    "        \n",
-    "    def load_vector_store(self):\n",
-    "        \"\"\"Load existing Chroma vector store\"\"\"\n",
-    "        self.vector_db = Chroma(\n",
-    "            persist_directory=CHROMA_PERSIST_DIR,\n",
-    "            embedding_function=self.embeddings\n",
-    "        )\n",
-    "        \n",
-    "    def document_query(self, query, top_k=5):\n",
-    "        \"\"\"Retrieve context from documents based on query\"\"\"\n",
-    "        # Perform similarity search across all documents\n",
-    "        results = self.vector_db.similarity_search(query=query, k=top_k)\n",
-    "        return [doc.page_content for doc in results]\n",
-    "\n",
-    "class AnswerGenerator:\n",
-    "    def __init__(self, rag_system):\n",
-    "        self.rag = rag_system\n",
-    "        \n",
-    "    def generate_response(self, question):\n",
-    "        \"\"\"Generate context-aware answer using LLM\"\"\"\n",
-    "        # Retrieve relevant context from the best matching documents\n",
-    "        context_chunks = self.rag.document_query(question)\n",
-    "        context = \"\\n\".join(context_chunks)\n",
-    "        \n",
-    "        prompt = f\"\"\"با استفاده از متن زیر به سوال پاسخ دهید:\n",
-    "{context}\n",
-    "\n",
-    "اگر پاسخ در متن وجود ندارد عبارت 'پاسخی یافت نشد' را برگردانید\n",
-    "\n",
-    "سوال: {question}\n",
-    "پاسخ:\"\"\"\n",
-    "        \n",
-    "        response = chat(model=LLM_MODEL, messages=[{'role': 'user', 'content': prompt}])\n",
-    "        return response['message']['content']\n",
-    "\n",
-    "def scrape_url(url):\n",
-    "    \"\"\"Scrape the content from a given URL\"\"\"\n",
-    "    response = requests.get(url)\n",
-    "    soup = BeautifulSoup(response.content, 'html.parser')\n",
-    "\n",
-    "    # Extract the article text (adjust this as per the specific page's structure)\n",
-    "    paragraphs = soup.find_all('p')\n",
-    "    article_text = \"\\n\".join([para.get_text() for para in paragraphs])\n",
-    "\n",
-    "    return article_text\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    url = \"https://tosinso.com/articles/40596\"\n",
-    "    article_content = scrape_url(url)\n",
-    "\n",
-    "    # Process the scraped content and create a vector store\n",
-    "    rag_system = ChromaRAGSystem()\n",
-    "\n",
-    "    # Chunk the article content\n",
-    "    chunks = [article_content[i:i+CHUNK_SIZE] for i in range(0, len(article_content), CHUNK_SIZE - OVERLAP)]\n",
-    "    documents = [Document(page_content=chunk) for chunk in chunks]\n",
-    "\n",
-    "    # Build vector store\n",
-    "    rag_system.build_vector_store(documents)\n",
-    "\n",
-    "    # Init answer generator\n",
-    "    answer_engine = AnswerGenerator(rag_system)\n",
-    "\n",
-    "    # The query to be answered\n",
-    "    query = \"تفاوت زیروکلاینت و تین کلاینت با PC در چیست؟\"\n",
-    "\n",
-    "    # Generate and print the response\n",
-    "    answer = answer_engine.generate_response(query)\n",
-    "    print(answer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cc9f2290-d16f-4722-857a-7996d4722857",
-   "metadata": {},
-   "outputs": []
+