diff --git a/pageindex/page_index_md.py b/pageindex/page_index_md.py
new file mode 100644
index 0000000..7492465
--- /dev/null
+++ b/pageindex/page_index_md.py
@@ -0,0 +1,204 @@
+import asyncio
+import json
+import re
+import tiktoken
+
+def count_tokens(text, model):
+    enc = tiktoken.encoding_for_model(model)
+    tokens = enc.encode(text)
+    return len(tokens)
+
+
+def extract_nodes_from_markdown(markdown_content):
+    header_pattern = r'^(#{1,6})\s+(.+)$'
+    node_list = []
+    
+    lines = markdown_content.split('\n')
+    for line_num, line in enumerate(lines, 1):
+        line = line.strip()
+        if not line:
+            continue
+            
+        match = re.match(header_pattern, line)
+        if match:
+            title = match.group(2).strip()
+            node_list.append({'node_title': title, 'line_num': line_num})
+
+    return node_list,lines
+
+
+def extract_node_text_content(node_list, markdown_lines, model="gpt-4o"):    
+    all_nodes = []
+    for node in node_list:
+        processed_node = {
+            'title': node['node_title'],
+            'line_num': node['line_num'],
+            'level': len(re.match(r'^(#{1,6})', markdown_lines[node['line_num'] - 1]).group(1))
+        }
+        all_nodes.append(processed_node)
+    
+    for i, node in enumerate(all_nodes):
+        start_line = node['line_num'] - 1 
+        if i + 1 < len(all_nodes):
+            end_line = all_nodes[i + 1]['line_num'] - 1 
+        else:
+            end_line = len(markdown_lines)
+        
+        node['text'] = '\n'.join(markdown_lines[start_line:end_line]).strip()
+        node['text_token_count'] = count_tokens(node['text'], model)
+    
+    return all_nodes
+
+
+def tree_thinning_for_index(node_list, min_node_token=None):
+    def find_all_children(parent_index, parent_level, node_list):
+        children_indices = []
+        
+        for i in range(parent_index + 1, len(node_list)):
+            current_level = node_list[i]['level']
+            
+            if current_level <= parent_level:
+                break
+                
+            children_indices.append(i)
+        
+        return children_indices
+    
+    result_list = node_list.copy()
+    nodes_to_remove = set()
+    
+    for i in range(len(result_list) - 1, -1, -1):
+        if i in nodes_to_remove:
+            continue
+            
+        current_node = result_list[i]
+        current_level = current_node['level']
+        
+        total_tokens = current_node.get('text_token_count', 0)
+        
+        if total_tokens < min_node_token:
+            children_indices = find_all_children(i, current_level, result_list)
+            
+            children_texts = []
+            for child_index in sorted(children_indices):
+                if child_index not in nodes_to_remove:
+                    child_text = result_list[child_index].get('text', '')
+                    if child_text.strip():
+                        children_texts.append(child_text)
+                    nodes_to_remove.add(child_index)
+            
+            if children_texts:
+                parent_text = current_node.get('text', '')
+                merged_text = parent_text
+                for child_text in children_texts:
+                    if merged_text and not merged_text.endswith('\n'):
+                        merged_text += '\n\n'
+                    merged_text += child_text
+                
+                result_list[i]['text'] = merged_text
+                
+                result_list[i]['text_token_count'] = count_tokens(merged_text, "gpt-4o")
+    
+    for index in sorted(nodes_to_remove, reverse=True):
+        result_list.pop(index)
+    
+    return result_list
+
+
+def build_tree_from_nodes(node_list):
+    if not node_list:
+        return []
+    
+    stack = []
+    root_nodes = []
+    node_counter = 1
+    
+    for node in node_list:
+        current_level = node['level']
+        
+        tree_node = {
+            'title': node['title'],
+            'node_id': str(node_counter).zfill(4),
+            'text': node['text'],
+            'line_num': node['line_num'],
+            'nodes': []
+        }
+        node_counter += 1
+        
+        while stack and stack[-1][1] >= current_level:
+            stack.pop()
+        
+        if not stack:
+            root_nodes.append(tree_node)
+        else:
+            parent_node, parent_level = stack[-1]
+            parent_node['nodes'].append(tree_node)
+        
+        stack.append((tree_node, current_level))
+    
+    return root_nodes
+
+
+def clean_tree_for_output(tree_nodes):
+    cleaned_nodes = []
+    
+    for node in tree_nodes:
+        cleaned_node = {
+            'title': node['title'],
+            'node_id': node['node_id'],
+            'text': node['text'],
+            'line_num': node['line_num']
+        }
+        
+        if node['nodes']:
+            cleaned_node['nodes'] = clean_tree_for_output(node['nodes'])
+        
+        cleaned_nodes.append(cleaned_node)
+    
+    return cleaned_nodes
+
+def md_to_tree(md_path, if_thinning=True, min_token_threshold=None):
+    with open(md_path, 'r', encoding='utf-8') as f:
+        markdown_content = f.read()
+    
+    node_list, markdown_lines = extract_nodes_from_markdown(markdown_content)
+    nodes_with_content = extract_node_text_content(node_list, markdown_lines)
+    
+    if if_thinning:
+        thinned_nodes = tree_thinning_for_index(nodes_with_content, min_token_threshold)
+    else:
+        thinned_nodes = nodes_with_content
+    
+    tree_structure = build_tree_from_nodes(thinned_nodes)
+    return tree_structure
+
+
+if __name__ == "__main__":
+    import os
+    import json
+    
+    # Path to the Welcome.md file
+    md_path = os.path.join(os.path.dirname(__file__), '..', 'docs', 'Welcome.md')
+    
+
+    tree_structure = md_to_tree(md_path, if_thinning=True, min_token_threshold=100)
+
+    def print_tree(nodes, indent=0):
+        for node in nodes:
+            prefix = "  " * indent
+            has_children = 'nodes' in node and node['nodes']
+            children_info = f" ({len(node['nodes'])} children)" if has_children else ""
+            print(f"{prefix}- {node['title']} [ID: {node['node_id']}]{children_info}")
+            if has_children:
+                print_tree(node['nodes'], indent + 1)
+    
+    print("\n🌳 Tree Structure:")
+    print_tree(tree_structure)
+    
+    output_path = os.path.join(os.path.dirname(__file__), '..', 'results', 'Welcome_structure.json')
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    
+    with open(output_path, 'w', encoding='utf-8') as f:
+        json.dump(tree_structure, f, indent=2, ensure_ascii=False)
+    
+    print(f"\n💾 Tree structure saved to: {output_path}")
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