Feature/imessage rag support (#131)
This commit is contained in:
Aakash Suresh
0
apps/chatgpt_data/__init__.py
Normal file
0
apps/chatgpt_data/__init__.py
Normal file
413
apps/chatgpt_data/chatgpt_reader.py
Normal file
413
apps/chatgpt_data/chatgpt_reader.py
Normal file
@@ -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 message in 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
|
||||
Reference in New Issue
Block a user