diff --git a/5. URL-Scraper Agent.ipynb b/5. URL-Scraper Agent.ipynb
index 8b13789..aab7b56 100644
--- a/5. URL-Scraper Agent.ipynb	
+++ b/5. URL-Scraper Agent.ipynb	
@@ -1 +1,99 @@
+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)