diff --git a/configs/config.yaml b/configs/config.yaml
index 8405274..cd9a8b0 100644
--- a/configs/config.yaml
+++ b/configs/config.yaml
@@ -1,5 +1,5 @@
 # =================== Lord Of Large Language Multimodal Systems Configuration file =========================== 
-version: 54
+version: 55
 binding_name: null
 model_name: null
 
@@ -121,6 +121,10 @@ data_vectorization_make_persistance: false # If true, the data will be persistan
 
 # Activate internet search
 activate_internet_search: false
+internet_vectorization_chunk_size: 512 # chunk size
+internet_vectorization_overlap_size: 128 # overlap between chunks size
+internet_vectorization_nb_chunks: 2 # number of chunks to use
+internet_nb_search_pages: 3 # number of pages to select
 
 # Helpers
 pdf_latex_path: null
diff --git a/lollms/configs/config.yaml b/lollms/configs/config.yaml
index 8405274..cd9a8b0 100644
--- a/lollms/configs/config.yaml
+++ b/lollms/configs/config.yaml
@@ -1,5 +1,5 @@
 # =================== Lord Of Large Language Multimodal Systems Configuration file =========================== 
-version: 54
+version: 55
 binding_name: null
 model_name: null
 
@@ -121,6 +121,10 @@ data_vectorization_make_persistance: false # If true, the data will be persistan
 
 # Activate internet search
 activate_internet_search: false
+internet_vectorization_chunk_size: 512 # chunk size
+internet_vectorization_overlap_size: 128 # overlap between chunks size
+internet_vectorization_nb_chunks: 2 # number of chunks to use
+internet_nb_search_pages: 3 # number of pages to select
 
 # Helpers
 pdf_latex_path: null
diff --git a/lollms/internet.py b/lollms/internet.py
new file mode 100644
index 0000000..a7fe99d
--- /dev/null
+++ b/lollms/internet.py
@@ -0,0 +1,167 @@
+
+def get_favicon_url(url):
+    import requests
+    from bs4 import BeautifulSoup
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    favicon_link = soup.find('link', rel='icon') or soup.find('link', rel='shortcut icon')
+    
+    if favicon_link:
+        favicon_url = favicon_link['href']
+        if not favicon_url.startswith('http'):
+            favicon_url = url + favicon_url
+        return favicon_url
+    
+    return None
+
+
+def get_root_url(url):
+    from urllib.parse import urlparse
+    parsed_url = urlparse(url)
+    root_url = parsed_url.scheme + "://" + parsed_url.netloc
+    return root_url
+
+
+def format_url_parameter(value:str):
+    encoded_value = value.strip().replace("\"","")
+    return encoded_value
+
+
+def get_relevant_text_block(
+                                url,
+                                driver,
+                                config,
+                                vectorizer
+                            ):
+    from bs4 import BeautifulSoup    
+    # Load the webpage
+    driver.get(url)
+
+    # Wait for JavaScript to execute and get the final page source
+    html_content = driver.page_source
+
+    # Parse the HTML content
+    soup = BeautifulSoup(html_content, "html.parser")
+    # Example: Remove all <script> and <style> tags
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    all_text = soup.get_text()
+    # Example: Remove leading/trailing whitespace and multiple consecutive line breaks
+    vectorizer.add_document(url,all_text, config.internet_vectorization_chunk_size, config.internet_vectorization_overlap_size)
+
+
+def extract_results(url, max_num, driver=None):
+    from bs4 import BeautifulSoup    
+
+    # Load the webpage
+    driver.get(url)
+
+    # Wait for JavaScript to execute and get the final page source
+    html_content = driver.page_source
+
+    # Parse the HTML content
+    soup = BeautifulSoup(html_content, "html.parser")
+
+    # Detect that no outputs are found
+    Not_found = soup.find("No results found")
+
+    if Not_found : 
+        return []    
+
+    # Find the <ol> tag with class="react-results--main"
+    ol_tag = soup.find("ol", class_="react-results--main")
+
+    # Initialize an empty list to store the results
+    results_list = []
+
+    try:
+        # Find all <li> tags within the <ol> tag
+        li_tags = ol_tag.find_all("li")
+
+        # Loop through each <li> tag, limited by max_num
+        for index, li_tag in enumerate(li_tags):
+            if index > max_num*3:
+                break
+
+            try:
+                # Find the three <div> tags within the <article> tag
+                div_tags = li_tag.find_all("div")
+
+                # Extract the link, title, and content from the <div> tags
+                links = div_tags[0].find_all("a")
+                href_value = links[1].get('href')
+                span = links[1].find_all("span")
+                link = span[0].text.strip()
+
+                title = div_tags[2].text.strip()
+                content = div_tags[3].text.strip()
+
+                # Add the extracted information to the list
+                results_list.append({
+                    "link": link,
+                    "href": href_value,
+                    "title": title,
+                    "brief": content
+                })
+            except Exception:
+                pass
+    except:
+        pass
+    return results_list
+    
+def internet_search(query, chromedriver_path, config, model = None):
+    """
+    Perform an internet search using the provided query.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        dict: The search result as a dictionary.
+    """
+
+    from selenium import webdriver
+    from selenium.webdriver.chrome.options import Options
+    from safe_store.text_vectorizer import TextVectorizer, VectorizationMethod
+
+    vectorizer = TextVectorizer(VectorizationMethod.TFIDF_VECTORIZER, model = model)
+
+    formatted_text = ""
+    nb_non_empty = 0
+    # Configure Chrome options
+    chrome_options = Options()
+    chrome_options.add_argument("--no-sandbox")
+    chrome_options.add_argument("--disable-dev-shm-usage")
+    chrome_options.add_argument("--remote-debugging-port=9222")
+    chrome_options.add_argument("--headless")  # Run Chrome in headless mode
+
+    # Set path to chromedriver executable (replace with your own path)
+    if chromedriver_path is None: 
+        chromedriver_path = ""#"/snap/bin/chromium.chromedriver"    
+
+    # Create a new Chrome webdriver instance
+    try:
+        driver = webdriver.Chrome(executable_path=chromedriver_path, options=chrome_options)
+    except:
+        driver = webdriver.Chrome(options=chrome_options)
+
+    results = extract_results(
+                                f"https://duckduckgo.com/?q={format_url_parameter(query)}&t=h_&ia=web",
+                                config.internet_nb_search_pages,
+                                driver
+                            )
+    for i, result in enumerate(results):
+        title = result["title"]
+        brief = result["brief"]
+        href = result["href"]
+        get_relevant_text_block(href, driver, config, vectorizer)
+        nb_non_empty += 1
+        if nb_non_empty>=config.internet_nb_search_pages:
+            break
+    vectorizer.index()
+
+    # Close the browser
+    driver.quit()
+    docs, sorted_similarities = vectorizer.recover_text(query, config.internet_vectorization_nb_chunks)
+    return docs, sorted_similarities
diff --git a/lollms/personality.py b/lollms/personality.py
index 2590c47..4575760 100644
--- a/lollms/personality.py
+++ b/lollms/personality.py
@@ -342,6 +342,14 @@ Date: {{date}}
         f' </div>',
         f' </details>\n'
         ])
+    
+    def internet_search(self, query ):
+        """
+        Do internet search and return the result
+        """
+        from lollms.internet import internet_search
+        return internet_search(query, "", self.config, self.model)
+
 
     def step_start(self, step_text, callback: Callable[[str, MSG_TYPE, dict, list], bool]=None):
         """This triggers a step start
@@ -2084,6 +2092,13 @@ class APScript(StateMachine):
         f' </div>',
         f' </details>\n'
         ])
+    
+    def internet_search(self, query ):
+        """
+        Do internet search and return the result
+        """
+        return self.personality.internet_search(query)
+    
     def step_start(self, step_text, callback: Callable[[str, MSG_TYPE, dict, list], bool]=None):
         """This triggers a step start