diff --git a/5. URL-Scraper Agent.ipynb b/5. URL-Scraper Agent.ipynb
deleted file mode 100644
index aab7b56..0000000
--- a/5. URL-Scraper Agent.ipynb	
+++ /dev/null
@@ -1,99 +0,0 @@
-from langchain.vectorstores import Chroma
-from langchain.embeddings import SentenceTransformerEmbeddings
-from langchain.schema import Document
-from ollama import chat
-import os
-import re
-import requests
-from bs4 import BeautifulSoup
-
-EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'
-LLM_MODEL = 'gemma2:9b'
-CHUNK_SIZE = 1000
-OVERLAP = 200
-CHROMA_PERSIST_DIR = r'\home\Masih\chroma_db\chroma_db'
-
-class ChromaRAGSystem:
-    def __init__(self):
-        # Init embedding model
-        self.embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL)
-        # Vector store instance
-        self.vector_db = None
-        
-    def build_vector_store(self, documents):
-        """Create Chroma vector store from documents"""
-        self.vector_db = Chroma.from_documents(
-            documents=documents,
-            embedding=self.embeddings,
-            persist_directory=CHROMA_PERSIST_DIR
-        )
-        self.vector_db.persist()
-        
-    def load_vector_store(self):
-        """Load existing Chroma vector store"""
-        self.vector_db = Chroma(
-            persist_directory=CHROMA_PERSIST_DIR,
-            embedding_function=self.embeddings
-        )
-        
-    def document_query(self, query, top_k=5):
-        """Retrieve context from documents based on query"""
-        # Perform similarity search across all documents
-        results = self.vector_db.similarity_search(query=query, k=top_k)
-        return [doc.page_content for doc in results]
-
-class AnswerGenerator:
-    def __init__(self, rag_system):
-        self.rag = rag_system
-        
-    def generate_response(self, question):
-        """Generate context-aware answer using LLM"""
-        # Retrieve relevant context from the best matching documents
-        context_chunks = self.rag.document_query(question)
-        context = "\n".join(context_chunks)
-        
-        prompt = f"""با استفاده از متن زیر به سوال پاسخ دهید:
-{context}
-
-اگر پاسخ در متن وجود ندارد عبارت 'پاسخی یافت نشد' را برگردانید
-
-سوال: {question}
-پاسخ:"""
-        
-        response = chat(model=LLM_MODEL, messages=[{'role': 'user', 'content': prompt}])
-        return response['message']['content']
-
-def scrape_url(url):
-    """Scrape the content from a given URL"""
-    response = requests.get(url)
-    soup = BeautifulSoup(response.content, 'html.parser')
-
-    # Extract the article text (adjust this as per the specific page's structure)
-    paragraphs = soup.find_all('p')
-    article_text = "\n".join([para.get_text() for para in paragraphs])
-
-    return article_text
-
-if __name__ == "__main__":
-    url = "https://tosinso.com/articles/40596"
-    article_content = scrape_url(url)
-
-    # Process the scraped content and create a vector store
-    rag_system = ChromaRAGSystem()
-
-    # Chunk the article content
-    chunks = [article_content[i:i+CHUNK_SIZE] for i in range(0, len(article_content), CHUNK_SIZE - OVERLAP)]
-    documents = [Document(page_content=chunk) for chunk in chunks]
-
-    # Build vector store
-    rag_system.build_vector_store(documents)
-
-    # Init answer generator
-    answer_engine = AnswerGenerator(rag_system)
-
-    # The query to be answered
-    query = "تفاوت زیروکلاینت و تین کلاینت با PC در چیست؟"
-
-    # Generate and print the response
-    answer = answer_engine.generate_response(query)
-    print(answer)