diff --git a/apps/chatgpt_data/__init__.py b/apps/chatgpt_data/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/chatgpt_data/chatgpt_reader.py b/apps/chatgpt_data/chatgpt_reader.py
new file mode 100644
index 0000000..dff964f
--- /dev/null
+++ b/apps/chatgpt_data/chatgpt_reader.py
@@ -0,0 +1,413 @@
+"""
+ChatGPT export data reader.
+
+Reads and processes ChatGPT export data from chat.html files.
+"""
+
+import re
+from pathlib import Path
+from typing import Any
+from zipfile import ZipFile
+
+from bs4 import BeautifulSoup
+from llama_index.core import Document
+from llama_index.core.readers.base import BaseReader
+
+
+class ChatGPTReader(BaseReader):
+    """
+    ChatGPT export data reader.
+
+    Reads ChatGPT conversation data from exported chat.html files or zip archives.
+    Processes conversations into structured documents with metadata.
+    """
+
+    def __init__(self, concatenate_conversations: bool = True) -> None:
+        """
+        Initialize.
+
+        Args:
+            concatenate_conversations: Whether to concatenate messages within conversations for better context
+        """
+        try:
+            from bs4 import BeautifulSoup  # noqa
+        except ImportError:
+            raise ImportError("`beautifulsoup4` package not found: `pip install beautifulsoup4`")
+
+        self.concatenate_conversations = concatenate_conversations
+
+    def _extract_html_from_zip(self, zip_path: Path) -> str | None:
+        """
+        Extract chat.html from ChatGPT export zip file.
+
+        Args:
+            zip_path: Path to the ChatGPT export zip file
+
+        Returns:
+            HTML content as string, or None if not found
+        """
+        try:
+            with ZipFile(zip_path, "r") as zip_file:
+                # Look for chat.html or conversations.html
+                html_files = [
+                    f
+                    for f in zip_file.namelist()
+                    if f.endswith(".html") and ("chat" in f.lower() or "conversation" in f.lower())
+                ]
+
+                if not html_files:
+                    print(f"No HTML chat file found in {zip_path}")
+                    return None
+
+                # Use the first HTML file found
+                html_file = html_files[0]
+                print(f"Found HTML file: {html_file}")
+
+                with zip_file.open(html_file) as f:
+                    return f.read().decode("utf-8", errors="ignore")
+
+        except Exception as e:
+            print(f"Error extracting HTML from zip {zip_path}: {e}")
+            return None
+
+    def _parse_chatgpt_html(self, html_content: str) -> list[dict]:
+        """
+        Parse ChatGPT HTML export to extract conversations.
+
+        Args:
+            html_content: HTML content from ChatGPT export
+
+        Returns:
+            List of conversation dictionaries
+        """
+        soup = BeautifulSoup(html_content, "html.parser")
+        conversations = []
+
+        # Try different possible structures for ChatGPT exports
+        # Structure 1: Look for conversation containers
+        conversation_containers = soup.find_all(
+            ["div", "section"], class_=re.compile(r"conversation|chat", re.I)
+        )
+
+        if not conversation_containers:
+            # Structure 2: Look for message containers directly
+            conversation_containers = [soup]  # Use the entire document as one conversation
+
+        for container in conversation_containers:
+            conversation = self._extract_conversation_from_container(container)
+            if conversation and conversation.get("messages"):
+                conversations.append(conversation)
+
+        # If no structured conversations found, try to extract all text as one conversation
+        if not conversations:
+            all_text = soup.get_text(separator="\n", strip=True)
+            if all_text:
+                conversations.append(
+                    {
+                        "title": "ChatGPT Conversation",
+                        "messages": [{"role": "mixed", "content": all_text, "timestamp": None}],
+                        "timestamp": None,
+                    }
+                )
+
+        return conversations
+
+    def _extract_conversation_from_container(self, container) -> dict | None:
+        """
+        Extract conversation data from a container element.
+
+        Args:
+            container: BeautifulSoup element containing conversation
+
+        Returns:
+            Dictionary with conversation data or None
+        """
+        messages = []
+
+        # Look for message elements with various possible structures
+        message_selectors = ['[class*="message"]', '[class*="chat"]', "[data-message]", "p", "div"]
+
+        for selector in message_selectors:
+            message_elements = container.select(selector)
+            if message_elements:
+                break
+        else:
+            message_elements = []
+
+        # If no structured messages found, treat the entire container as one message
+        if not message_elements:
+            text_content = container.get_text(separator="\n", strip=True)
+            if text_content:
+                messages.append({"role": "mixed", "content": text_content, "timestamp": None})
+        else:
+            for element in message_elements:
+                message = self._extract_message_from_element(element)
+                if message:
+                    messages.append(message)
+
+        if not messages:
+            return None
+
+        # Try to extract conversation title
+        title_element = container.find(["h1", "h2", "h3", "title"])
+        title = title_element.get_text(strip=True) if title_element else "ChatGPT Conversation"
+
+        # Try to extract timestamp from various possible locations
+        timestamp = self._extract_timestamp_from_container(container)
+
+        return {"title": title, "messages": messages, "timestamp": timestamp}
+
+    def _extract_message_from_element(self, element) -> dict | None:
+        """
+        Extract message data from an element.
+
+        Args:
+            element: BeautifulSoup element containing message
+
+        Returns:
+            Dictionary with message data or None
+        """
+        text_content = element.get_text(separator=" ", strip=True)
+
+        # Skip empty or very short messages
+        if not text_content or len(text_content.strip()) < 3:
+            return None
+
+        # Try to determine role (user/assistant) from class names or content
+        role = "mixed"  # Default role
+
+        class_names = " ".join(element.get("class", [])).lower()
+        if "user" in class_names or "human" in class_names:
+            role = "user"
+        elif "assistant" in class_names or "ai" in class_names or "gpt" in class_names:
+            role = "assistant"
+        elif text_content.lower().startswith(("you:", "user:", "me:")):
+            role = "user"
+            text_content = re.sub(r"^(you|user|me):\s*", "", text_content, flags=re.IGNORECASE)
+        elif text_content.lower().startswith(("chatgpt:", "assistant:", "ai:")):
+            role = "assistant"
+            text_content = re.sub(
+                r"^(chatgpt|assistant|ai):\s*", "", text_content, flags=re.IGNORECASE
+            )
+
+        # Try to extract timestamp
+        timestamp = self._extract_timestamp_from_element(element)
+
+        return {"role": role, "content": text_content, "timestamp": timestamp}
+
+    def _extract_timestamp_from_element(self, element) -> str | None:
+        """Extract timestamp from element."""
+        # Look for timestamp in various attributes and child elements
+        timestamp_attrs = ["data-timestamp", "timestamp", "datetime"]
+        for attr in timestamp_attrs:
+            if element.get(attr):
+                return element.get(attr)
+
+        # Look for time elements
+        time_element = element.find("time")
+        if time_element:
+            return time_element.get("datetime") or time_element.get_text(strip=True)
+
+        # Look for date-like text patterns
+        text = element.get_text()
+        date_patterns = [r"\d{4}-\d{2}-\d{2}", r"\d{1,2}/\d{1,2}/\d{4}", r"\w+ \d{1,2}, \d{4}"]
+
+        for pattern in date_patterns:
+            match = re.search(pattern, text)
+            if match:
+                return match.group()
+
+        return None
+
+    def _extract_timestamp_from_container(self, container) -> str | None:
+        """Extract timestamp from conversation container."""
+        return self._extract_timestamp_from_element(container)
+
+    def _create_concatenated_content(self, conversation: dict) -> str:
+        """
+        Create concatenated content from conversation messages.
+
+        Args:
+            conversation: Dictionary containing conversation data
+
+        Returns:
+            Formatted concatenated content
+        """
+        title = conversation.get("title", "ChatGPT Conversation")
+        messages = conversation.get("messages", [])
+        timestamp = conversation.get("timestamp", "Unknown")
+
+        # Build message content
+        message_parts = []
+        for i, message in enumerate(messages):
+            role = message.get("role", "mixed")
+            content = message.get("content", "")
+            msg_timestamp = message.get("timestamp", "")
+
+            if role == "user":
+                prefix = "[You]"
+            elif role == "assistant":
+                prefix = "[ChatGPT]"
+            else:
+                prefix = "[Message]"
+
+            # Add timestamp if available
+            if msg_timestamp:
+                prefix += f" ({msg_timestamp})"
+
+            message_parts.append(f"{prefix}: {content}")
+
+        concatenated_text = "\n\n".join(message_parts)
+
+        # Create final document content
+        doc_content = f"""Conversation: {title}
+Date: {timestamp}
+Messages ({len(messages)} messages):
+
+{concatenated_text}
+"""
+        return doc_content
+
+    def load_data(self, input_dir: str | None = None, **load_kwargs: Any) -> list[Document]:
+        """
+        Load ChatGPT export data.
+
+        Args:
+            input_dir: Directory containing ChatGPT export files or path to specific file
+            **load_kwargs:
+                max_count (int): Maximum number of conversations to process
+                chatgpt_export_path (str): Specific path to ChatGPT export file/directory
+                include_metadata (bool): Whether to include metadata in documents
+        """
+        docs: list[Document] = []
+        max_count = load_kwargs.get("max_count", -1)
+        chatgpt_export_path = load_kwargs.get("chatgpt_export_path", input_dir)
+        include_metadata = load_kwargs.get("include_metadata", True)
+
+        if not chatgpt_export_path:
+            print("No ChatGPT export path provided")
+            return docs
+
+        export_path = Path(chatgpt_export_path)
+
+        if not export_path.exists():
+            print(f"ChatGPT export path not found: {export_path}")
+            return docs
+
+        html_content = None
+
+        # Handle different input types
+        if export_path.is_file():
+            if export_path.suffix.lower() == ".zip":
+                # Extract HTML from zip file
+                html_content = self._extract_html_from_zip(export_path)
+            elif export_path.suffix.lower() == ".html":
+                # Read HTML file directly
+                try:
+                    with open(export_path, encoding="utf-8", errors="ignore") as f:
+                        html_content = f.read()
+                except Exception as e:
+                    print(f"Error reading HTML file {export_path}: {e}")
+                    return docs
+            else:
+                print(f"Unsupported file type: {export_path.suffix}")
+                return docs
+
+        elif export_path.is_dir():
+            # Look for HTML files in directory
+            html_files = list(export_path.glob("*.html"))
+            zip_files = list(export_path.glob("*.zip"))
+
+            if html_files:
+                # Use first HTML file found
+                html_file = html_files[0]
+                print(f"Found HTML file: {html_file}")
+                try:
+                    with open(html_file, encoding="utf-8", errors="ignore") as f:
+                        html_content = f.read()
+                except Exception as e:
+                    print(f"Error reading HTML file {html_file}: {e}")
+                    return docs
+
+            elif zip_files:
+                # Use first zip file found
+                zip_file = zip_files[0]
+                print(f"Found zip file: {zip_file}")
+                html_content = self._extract_html_from_zip(zip_file)
+
+            else:
+                print(f"No HTML or zip files found in {export_path}")
+                return docs
+
+        if not html_content:
+            print("No HTML content found to process")
+            return docs
+
+        # Parse conversations from HTML
+        print("Parsing ChatGPT conversations from HTML...")
+        conversations = self._parse_chatgpt_html(html_content)
+
+        if not conversations:
+            print("No conversations found in HTML content")
+            return docs
+
+        print(f"Found {len(conversations)} conversations")
+
+        # Process conversations into documents
+        count = 0
+        for conversation in conversations:
+            if max_count > 0 and count >= max_count:
+                break
+
+            if self.concatenate_conversations:
+                # Create one document per conversation with concatenated messages
+                doc_content = self._create_concatenated_content(conversation)
+
+                metadata = {}
+                if include_metadata:
+                    metadata = {
+                        "title": conversation.get("title", "ChatGPT Conversation"),
+                        "timestamp": conversation.get("timestamp", "Unknown"),
+                        "message_count": len(conversation.get("messages", [])),
+                        "source": "ChatGPT Export",
+                    }
+
+                doc = Document(text=doc_content, metadata=metadata)
+                docs.append(doc)
+                count += 1
+
+            else:
+                # Create separate documents for each message
+                for message in conversation.get("messages", []):
+                    if max_count > 0 and count >= max_count:
+                        break
+
+                    role = message.get("role", "mixed")
+                    content = message.get("content", "")
+                    msg_timestamp = message.get("timestamp", "")
+
+                    if not content.strip():
+                        continue
+
+                    # Create document content with context
+                    doc_content = f"""Conversation: {conversation.get("title", "ChatGPT Conversation")}
+Role: {role}
+Timestamp: {msg_timestamp or conversation.get("timestamp", "Unknown")}
+Message: {content}
+"""
+
+                    metadata = {}
+                    if include_metadata:
+                        metadata = {
+                            "conversation_title": conversation.get("title", "ChatGPT Conversation"),
+                            "role": role,
+                            "timestamp": msg_timestamp or conversation.get("timestamp", "Unknown"),
+                            "source": "ChatGPT Export",
+                        }
+
+                    doc = Document(text=doc_content, metadata=metadata)
+                    docs.append(doc)
+                    count += 1
+
+        print(f"Created {len(docs)} documents from ChatGPT export")
+        return docs
diff --git a/apps/chatgpt_rag.py b/apps/chatgpt_rag.py
new file mode 100644
index 0000000..3c92d04
--- /dev/null
+++ b/apps/chatgpt_rag.py
@@ -0,0 +1,186 @@
+"""
+ChatGPT RAG example using the unified interface.
+Supports ChatGPT export data from chat.html files.
+"""
+
+import sys
+from pathlib import Path
+
+# Add parent directory to path for imports
+sys.path.insert(0, str(Path(__file__).parent))
+
+from base_rag_example import BaseRAGExample
+from chunking import create_text_chunks
+
+from .chatgpt_data.chatgpt_reader import ChatGPTReader
+
+
+class ChatGPTRAG(BaseRAGExample):
+    """RAG example for ChatGPT conversation data."""
+
+    def __init__(self):
+        # Set default values BEFORE calling super().__init__
+        self.max_items_default = -1  # Process all conversations by default
+        self.embedding_model_default = (
+            "sentence-transformers/all-MiniLM-L6-v2"  # Fast 384-dim model
+        )
+
+        super().__init__(
+            name="ChatGPT",
+            description="Process and query ChatGPT conversation exports with LEANN",
+            default_index_name="chatgpt_conversations_index",
+        )
+
+    def _add_specific_arguments(self, parser):
+        """Add ChatGPT-specific arguments."""
+        chatgpt_group = parser.add_argument_group("ChatGPT Parameters")
+        chatgpt_group.add_argument(
+            "--export-path",
+            type=str,
+            default="./chatgpt_export",
+            help="Path to ChatGPT export file (.zip or .html) or directory containing exports (default: ./chatgpt_export)",
+        )
+        chatgpt_group.add_argument(
+            "--concatenate-conversations",
+            action="store_true",
+            default=True,
+            help="Concatenate messages within conversations for better context (default: True)",
+        )
+        chatgpt_group.add_argument(
+            "--separate-messages",
+            action="store_true",
+            help="Process each message as a separate document (overrides --concatenate-conversations)",
+        )
+        chatgpt_group.add_argument(
+            "--chunk-size", type=int, default=512, help="Text chunk size (default: 512)"
+        )
+        chatgpt_group.add_argument(
+            "--chunk-overlap", type=int, default=128, help="Text chunk overlap (default: 128)"
+        )
+
+    def _find_chatgpt_exports(self, export_path: Path) -> list[Path]:
+        """
+        Find ChatGPT export files in the given path.
+
+        Args:
+            export_path: Path to search for exports
+
+        Returns:
+            List of paths to ChatGPT export files
+        """
+        export_files = []
+
+        if export_path.is_file():
+            if export_path.suffix.lower() in [".zip", ".html"]:
+                export_files.append(export_path)
+        elif export_path.is_dir():
+            # Look for zip and html files
+            export_files.extend(export_path.glob("*.zip"))
+            export_files.extend(export_path.glob("*.html"))
+
+        return export_files
+
+    async def load_data(self, args) -> list[str]:
+        """Load ChatGPT export data and convert to text chunks."""
+        export_path = Path(args.export_path)
+
+        if not export_path.exists():
+            print(f"ChatGPT export path not found: {export_path}")
+            print(
+                "Please ensure you have exported your ChatGPT data and placed it in the correct location."
+            )
+            print("\nTo export your ChatGPT data:")
+            print("1. Sign in to ChatGPT")
+            print("2. Click on your profile icon → Settings → Data Controls")
+            print("3. Click 'Export' under Export Data")
+            print("4. Download the zip file from the email link")
+            print("5. Extract or place the file/directory at the specified path")
+            return []
+
+        # Find export files
+        export_files = self._find_chatgpt_exports(export_path)
+
+        if not export_files:
+            print(f"No ChatGPT export files (.zip or .html) found in: {export_path}")
+            return []
+
+        print(f"Found {len(export_files)} ChatGPT export files")
+
+        # Create reader with appropriate settings
+        concatenate = args.concatenate_conversations and not args.separate_messages
+        reader = ChatGPTReader(concatenate_conversations=concatenate)
+
+        # Process each export file
+        all_documents = []
+        total_processed = 0
+
+        for i, export_file in enumerate(export_files):
+            print(f"\nProcessing export file {i + 1}/{len(export_files)}: {export_file.name}")
+
+            try:
+                # Apply max_items limit per file
+                max_per_file = -1
+                if args.max_items > 0:
+                    remaining = args.max_items - total_processed
+                    if remaining <= 0:
+                        break
+                    max_per_file = remaining
+
+                # Load conversations
+                documents = reader.load_data(
+                    chatgpt_export_path=str(export_file),
+                    max_count=max_per_file,
+                    include_metadata=True,
+                )
+
+                if documents:
+                    all_documents.extend(documents)
+                    total_processed += len(documents)
+                    print(f"Processed {len(documents)} conversations from this file")
+                else:
+                    print(f"No conversations loaded from {export_file}")
+
+            except Exception as e:
+                print(f"Error processing {export_file}: {e}")
+                continue
+
+        if not all_documents:
+            print("No conversations found to process!")
+            print("\nTroubleshooting:")
+            print("- Ensure the export file is a valid ChatGPT export")
+            print("- Check that the HTML file contains conversation data")
+            print("- Try extracting the zip file and pointing to the HTML file directly")
+            return []
+
+        print(f"\nTotal conversations processed: {len(all_documents)}")
+        print("Now starting to split into text chunks... this may take some time")
+
+        # Convert to text chunks
+        all_texts = create_text_chunks(
+            all_documents, chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap
+        )
+
+        print(f"Created {len(all_texts)} text chunks from {len(all_documents)} conversations")
+        return all_texts
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    # Example queries for ChatGPT RAG
+    print("\n🤖 ChatGPT RAG Example")
+    print("=" * 50)
+    print("\nExample queries you can try:")
+    print("- 'What did I ask about Python programming?'")
+    print("- 'Show me conversations about machine learning'")
+    print("- 'Find discussions about travel planning'")
+    print("- 'What advice did ChatGPT give me about career development?'")
+    print("- 'Search for conversations about cooking recipes'")
+    print("\nTo get started:")
+    print("1. Export your ChatGPT data from Settings → Data Controls → Export")
+    print("2. Place the downloaded zip file or extracted HTML in ./chatgpt_export/")
+    print("3. Run this script to build your personal ChatGPT knowledge base!")
+    print("\nOr run without --query for interactive mode\n")
+
+    rag = ChatGPTRAG()
+    asyncio.run(rag.run())