Files
LEANN/apps/history_data/wechat_history.py
Andy Lee 8899734952 refactor: Unify examples interface with BaseRAGExample (#12)
* refactor: Unify examples interface with BaseRAGExample

- Create BaseRAGExample base class for all RAG examples
- Refactor 4 examples to use unified interface:
  - document_rag.py (replaces main_cli_example.py)
  - email_rag.py (replaces mail_reader_leann.py)
  - browser_rag.py (replaces google_history_reader_leann.py)
  - wechat_rag.py (replaces wechat_history_reader_leann.py)
- Maintain 100% parameter compatibility with original files
- Add interactive mode support for all examples
- Unify parameter names (--max-items replaces --max-emails/--max-entries)
- Update README.md with new examples usage
- Add PARAMETER_CONSISTENCY.md documenting all parameter mappings
- Keep main_cli_example.py for backward compatibility with migration notice

All default values, LeannBuilder parameters, and chunking settings
remain identical to ensure full compatibility with existing indexes.

* fix: Update CI tests for new unified examples interface

- Rename test_main_cli.py to test_document_rag.py
- Update all references from main_cli_example.py to document_rag.py
- Update tests/README.md documentation

The tests now properly test the new unified interface while maintaining
the same test coverage and functionality.

* fix: Fix pre-commit issues and update tests

- Fix import sorting and unused imports
- Update type annotations to use built-in types (list, dict) instead of typing.List/Dict
- Fix trailing whitespace and end-of-file issues
- Fix Chinese fullwidth comma to regular comma
- Update test_main_cli.py to test_document_rag.py
- Add backward compatibility test for main_cli_example.py
- Pass all pre-commit hooks (ruff, ruff-format, etc.)

* refactor: Remove old example scripts and migration references

- Delete old example scripts (mail_reader_leann.py, google_history_reader_leann.py, etc.)
- Remove migration hints and backward compatibility
- Update tests to use new unified examples directly
- Clean up all references to old script names
- Users now only see the new unified interface

* fix: Restore embedding-mode parameter to all examples

- All examples now have --embedding-mode parameter (unified interface benefit)
- Default is 'sentence-transformers' (consistent with original behavior)
- Users can now use OpenAI or MLX embeddings with any data source
- Maintains functional equivalence with original scripts

* docs: Improve parameter categorization in README

- Clearly separate core (shared) vs specific parameters
- Move LLM and embedding examples to 'Example Commands' section
- Add descriptive comments for all specific parameters
- Keep only truly data-source-specific parameters in specific sections

* docs: Make example commands more representative

- Add default values to parameter descriptions
- Replace generic examples with real-world use cases
- Focus on data-source-specific features in examples
- Remove redundant demonstrations of common parameters

* docs: Reorganize parameter documentation structure

- Move common parameters to a dedicated section before all examples
- Rename sections to 'X-Specific Arguments' for clarity
- Remove duplicate common parameters from individual examples
- Better information architecture for users

* docs: polish applications

* docs: Add CLI installation instructions

- Add two installation options: venv and global uv tool
- Clearly explain when to use each option
- Make CLI more accessible for daily use

* docs: Clarify CLI global installation process

- Explain the transition from venv to global installation
- Add upgrade command for global installation
- Make it clear that global install allows usage without venv activation

* docs: Add collapsible section for CLI installation

- Wrap CLI installation instructions in details/summary tags
- Keep consistent with other collapsible sections in README
- Improve document readability and navigation

* style: format

* docs: Fix collapsible sections

- Make Common Parameters collapsible (as it's lengthy reference material)
- Keep CLI Installation visible (important for users to see immediately)
- Better information hierarchy

* docs: Add introduction for Common Parameters section

- Add 'Flexible Configuration' heading with descriptive sentence
- Create parallel structure with 'Generation Model Setup' section
- Improve document flow and readability

* docs: nit

* fix: Fix issues in unified examples

- Add smart path detection for data directory
- Fix add_texts -> add_text method call
- Handle both running from project root and examples directory

* fix: Fix async/await and add_text issues in unified examples

- Remove incorrect await from chat.ask() calls (not async)
- Fix add_texts -> add_text method calls
- Verify search-complexity correctly maps to efSearch parameter
- All examples now run successfully

* feat: Address review comments

- Add complexity parameter to LeannChat initialization (default: search_complexity)
- Fix chunk-size default in README documentation (256, not 2048)
- Add more index building parameters as CLI arguments:
  - --backend-name (hnsw/diskann)
  - --graph-degree (default: 32)
  - --build-complexity (default: 64)
  - --no-compact (disable compact storage)
  - --no-recompute (disable embedding recomputation)
- Update README to document all new parameters

* feat: Add chunk-size parameters and improve file type filtering

- Add --chunk-size and --chunk-overlap parameters to all RAG examples
- Preserve original default values for each data source:
  - Document: 256/128 (optimized for general documents)
  - Email: 256/25 (smaller overlap for email threads)
  - Browser: 256/128 (standard for web content)
  - WeChat: 192/64 (smaller chunks for chat messages)
- Make --file-types optional filter instead of restriction in document_rag
- Update README to clarify interactive mode and parameter usage
- Fix LLM default model documentation (gpt-4o, not gpt-4o-mini)

* feat: Update documentation based on review feedback

- Add MLX embedding example to README
- Clarify examples/data content description (two papers, Pride and Prejudice, Chinese README)
- Move chunk parameters to common parameters section
- Remove duplicate chunk parameters from document-specific section

* docs: Emphasize diverse data sources in examples/data description

* fix: update default embedding models for better performance

- Change WeChat, Browser, and Email RAG examples to use all-MiniLM-L6-v2
- Previous Qwen/Qwen3-Embedding-0.6B was too slow for these use cases
- all-MiniLM-L6-v2 is a fast 384-dim model, ideal for large-scale personal data

* add response highlight

* change rebuild logic

* fix some example

* feat: check if k is larger than #docs

* fix: WeChat history reader bugs and refactor wechat_rag to use unified architecture

* fix email wrong -1 to process all file

* refactor: reorgnize all examples/ and test/

* refactor: reorganize examples and add link checker

* fix: add init.py

* fix: handle certificate errors in link checker

* fix wechat

* merge

* docs: update README to use proper module imports for apps

- Change from 'python apps/xxx.py' to 'python -m apps.xxx'
- More professional and pythonic module calling
- Ensures proper module resolution and imports
- Better separation between apps/ (production tools) and examples/ (demos)

---------

Co-authored-by: yichuan520030910320 <yichuan_wang@berkeley.edu>
2025-08-03 23:06:24 -07:00

775 lines
30 KiB
Python

import json
import os
import re
import subprocess
import time
from datetime import datetime
from pathlib import Path
from typing import Any
from llama_index.core import Document
from llama_index.core.readers.base import BaseReader
class WeChatHistoryReader(BaseReader):
"""
WeChat chat history reader that extracts chat data from exported JSON files.
Reads WeChat chat history from exported JSON files (from wechat-exporter tool)
and creates documents with embedded metadata similar to the Chrome history reader structure.
Also includes utilities for automatic WeChat chat history export.
"""
def __init__(self) -> None:
"""Initialize."""
self.packages_dir = Path(__file__).parent.parent.parent / "packages"
self.wechat_exporter_dir = self.packages_dir / "wechat-exporter"
self.wechat_decipher_dir = self.packages_dir / "wechat-decipher-macos"
def check_wechat_running(self) -> bool:
"""Check if WeChat is currently running."""
try:
result = subprocess.run(["pgrep", "-f", "WeChat"], capture_output=True, text=True)
return result.returncode == 0
except Exception:
return False
def install_wechattweak(self) -> bool:
"""Install WeChatTweak CLI tool."""
try:
# Create wechat-exporter directory if it doesn't exist
self.wechat_exporter_dir.mkdir(parents=True, exist_ok=True)
wechattweak_path = self.wechat_exporter_dir / "wechattweak-cli"
if not wechattweak_path.exists():
print("Downloading WeChatTweak CLI...")
subprocess.run(
[
"curl",
"-L",
"-o",
str(wechattweak_path),
"https://github.com/JettChenT/WeChatTweak-CLI/releases/latest/download/wechattweak-cli",
],
check=True,
)
# Make executable
wechattweak_path.chmod(0o755)
# Install WeChatTweak
print("Installing WeChatTweak...")
subprocess.run(["sudo", str(wechattweak_path), "install"], check=True)
return True
except Exception as e:
print(f"Error installing WeChatTweak: {e}")
return False
def restart_wechat(self):
"""Restart WeChat to apply WeChatTweak."""
try:
print("Restarting WeChat...")
subprocess.run(["pkill", "-f", "WeChat"], check=False)
time.sleep(2)
subprocess.run(["open", "-a", "WeChat"], check=True)
time.sleep(5) # Wait for WeChat to start
except Exception as e:
print(f"Error restarting WeChat: {e}")
def check_api_available(self) -> bool:
"""Check if WeChatTweak API is available."""
try:
result = subprocess.run(
["curl", "-s", "http://localhost:48065/wechat/allcontacts"],
capture_output=True,
text=True,
timeout=5,
)
return result.returncode == 0 and result.stdout.strip()
except Exception:
return False
def _extract_readable_text(self, content: str) -> str:
"""
Extract readable text from message content, removing XML and system messages.
Args:
content: The raw message content (can be string or dict)
Returns:
Cleaned, readable text
"""
if not content:
return ""
# Handle dictionary content (like quoted messages)
if isinstance(content, dict):
# Extract text from dictionary structure
text_parts = []
if "title" in content:
text_parts.append(str(content["title"]))
if "quoted" in content:
text_parts.append(str(content["quoted"]))
if "content" in content:
text_parts.append(str(content["content"]))
if "text" in content:
text_parts.append(str(content["text"]))
if text_parts:
return " | ".join(text_parts)
else:
# If we can't extract meaningful text from dict, return empty
return ""
# Handle string content
if not isinstance(content, str):
return ""
# Remove common prefixes like "wxid_xxx:\n"
clean_content = re.sub(r"^wxid_[^:]+:\s*", "", content)
clean_content = re.sub(r"^[^:]+:\s*", "", clean_content)
# If it's just XML or system message, return empty
if clean_content.strip().startswith("<") or "recalled a message" in clean_content:
return ""
return clean_content.strip()
def _is_text_message(self, content: str) -> bool:
"""
Check if a message contains readable text content.
Args:
content: The message content (can be string or dict)
Returns:
True if the message contains readable text, False otherwise
"""
if not content:
return False
# Handle dictionary content
if isinstance(content, dict):
# Check if dict has any readable text fields
text_fields = ["title", "quoted", "content", "text"]
for field in text_fields:
if content.get(field):
return True
return False
# Handle string content
if not isinstance(content, str):
return False
# Skip image messages (contain XML with img tags)
if "<img" in content and "cdnurl" in content:
return False
# Skip emoji messages (contain emoji XML tags)
if "<emoji" in content and "productid" in content:
return False
# Skip voice messages
if "<voice" in content:
return False
# Skip video messages
if "<video" in content:
return False
# Skip file messages
if "<appmsg" in content and "appid" in content:
return False
# Skip system messages (like "recalled a message")
if "recalled a message" in content:
return False
# Check if there's actual readable text (not just XML or system messages)
# Remove common prefixes like "wxid_xxx:\n" and check for actual content
clean_content = re.sub(r"^wxid_[^:]+:\s*", "", content)
clean_content = re.sub(r"^[^:]+:\s*", "", clean_content)
# If after cleaning we have meaningful text, consider it readable
if len(clean_content.strip()) > 0 and not clean_content.strip().startswith("<"):
return True
return False
def _concatenate_messages(
self,
messages: list[dict],
max_length: int = 128,
time_window_minutes: int = 30,
overlap_messages: int = 0,
) -> list[dict]:
"""
Concatenate messages based on length and time rules.
Args:
messages: List of message dictionaries
max_length: Maximum length for concatenated message groups. Use -1 to disable length constraint.
time_window_minutes: Time window in minutes to group messages together. Use -1 to disable time constraint.
overlap_messages: Number of messages to overlap between consecutive groups
Returns:
List of concatenated message groups
"""
if not messages:
return []
concatenated_groups = []
current_group = []
current_length = 0
last_timestamp = None
for message in messages:
# Extract message info
content = message.get("content", "")
message_text = message.get("message", "")
create_time = message.get("createTime", 0)
message.get("fromUser", "")
message.get("toUser", "")
message.get("isSentFromSelf", False)
# Extract readable text
readable_text = self._extract_readable_text(content)
if not readable_text:
readable_text = message_text
# Skip empty messages
if not readable_text.strip():
continue
# Check time window constraint (only if time_window_minutes != -1)
if time_window_minutes != -1 and last_timestamp is not None and create_time > 0:
time_diff_minutes = (create_time - last_timestamp) / 60
if time_diff_minutes > time_window_minutes:
# Time gap too large, start new group
if current_group:
concatenated_groups.append(
{
"messages": current_group,
"total_length": current_length,
"start_time": current_group[0].get("createTime", 0),
"end_time": current_group[-1].get("createTime", 0),
}
)
# Keep last few messages for overlap
if overlap_messages > 0 and len(current_group) > overlap_messages:
current_group = current_group[-overlap_messages:]
current_length = sum(
len(
self._extract_readable_text(msg.get("content", ""))
or msg.get("message", "")
)
for msg in current_group
)
else:
current_group = []
current_length = 0
# Check length constraint (only if max_length != -1)
message_length = len(readable_text)
if max_length != -1 and current_length + message_length > max_length and current_group:
# Current group would exceed max length, save it and start new
concatenated_groups.append(
{
"messages": current_group,
"total_length": current_length,
"start_time": current_group[0].get("createTime", 0),
"end_time": current_group[-1].get("createTime", 0),
}
)
# Keep last few messages for overlap
if overlap_messages > 0 and len(current_group) > overlap_messages:
current_group = current_group[-overlap_messages:]
current_length = sum(
len(
self._extract_readable_text(msg.get("content", ""))
or msg.get("message", "")
)
for msg in current_group
)
else:
current_group = []
current_length = 0
# Add message to current group
current_group.append(message)
current_length += message_length
last_timestamp = create_time
# Add the last group if it exists
if current_group:
concatenated_groups.append(
{
"messages": current_group,
"total_length": current_length,
"start_time": current_group[0].get("createTime", 0),
"end_time": current_group[-1].get("createTime", 0),
}
)
return concatenated_groups
def _create_concatenated_content(self, message_group: dict, contact_name: str) -> str:
"""
Create concatenated content from a group of messages.
Args:
message_group: Dictionary containing messages and metadata
contact_name: Name of the contact
Returns:
Formatted concatenated content
"""
messages = message_group["messages"]
start_time = message_group["start_time"]
end_time = message_group["end_time"]
# Format timestamps
if start_time:
try:
start_timestamp = datetime.fromtimestamp(start_time)
start_time_str = start_timestamp.strftime("%Y-%m-%d %H:%M:%S")
except (ValueError, OSError):
start_time_str = str(start_time)
else:
start_time_str = "Unknown"
if end_time:
try:
end_timestamp = datetime.fromtimestamp(end_time)
end_time_str = end_timestamp.strftime("%Y-%m-%d %H:%M:%S")
except (ValueError, OSError):
end_time_str = str(end_time)
else:
end_time_str = "Unknown"
# Build concatenated message content
message_parts = []
for message in messages:
content = message.get("content", "")
message_text = message.get("message", "")
create_time = message.get("createTime", 0)
is_sent_from_self = message.get("isSentFromSelf", False)
# Extract readable text
readable_text = self._extract_readable_text(content)
if not readable_text:
readable_text = message_text
# Format individual message
if create_time:
try:
timestamp = datetime.fromtimestamp(create_time)
# change to YYYY-MM-DD HH:MM:SS
time_str = timestamp.strftime("%Y-%m-%d %H:%M:%S")
except (ValueError, OSError):
time_str = str(create_time)
else:
time_str = "Unknown"
sender = "[Me]" if is_sent_from_self else "[Contact]"
message_parts.append(f"({time_str}) {sender}: {readable_text}")
concatenated_text = "\n".join(message_parts)
# Create final document content
doc_content = f"""
Contact: {contact_name}
Time Range: {start_time_str} - {end_time_str}
Messages ({len(messages)} messages, {message_group["total_length"]} chars):
{concatenated_text}
"""
# TODO @yichuan give better format and rich info here!
doc_content = f"""
{concatenated_text}
"""
return doc_content, contact_name
def load_data(self, input_dir: str | None = None, **load_kwargs: Any) -> list[Document]:
"""
Load WeChat chat history data from exported JSON files.
Args:
input_dir: Directory containing exported WeChat JSON files
**load_kwargs:
max_count (int): Maximum amount of chat entries to read.
wechat_export_dir (str): Custom path to WeChat export directory.
include_non_text (bool): Whether to include non-text messages (images, emojis, etc.)
concatenate_messages (bool): Whether to concatenate messages based on length rules.
max_length (int): Maximum length for concatenated message groups (default: 1000).
time_window_minutes (int): Time window in minutes to group messages together (default: 30).
overlap_messages (int): Number of messages to overlap between consecutive groups (default: 2).
"""
docs: list[Document] = []
max_count = load_kwargs.get("max_count", 1000)
wechat_export_dir = load_kwargs.get("wechat_export_dir", None)
include_non_text = load_kwargs.get("include_non_text", False)
concatenate_messages = load_kwargs.get("concatenate_messages", False)
max_length = load_kwargs.get("max_length", 1000)
time_window_minutes = load_kwargs.get("time_window_minutes", 30)
# Default WeChat export path
if wechat_export_dir is None:
wechat_export_dir = "./wechat_export_test"
if not os.path.exists(wechat_export_dir):
print(f"WeChat export directory not found at: {wechat_export_dir}")
return docs
try:
# Find all JSON files in the export directory
json_files = list(Path(wechat_export_dir).glob("*.json"))
print(f"Found {len(json_files)} WeChat chat history files")
count = 0
for json_file in json_files:
if count >= max_count and max_count > 0:
break
try:
with open(json_file, encoding="utf-8") as f:
chat_data = json.load(f)
# Extract contact name from filename
contact_name = json_file.stem
if concatenate_messages:
# Filter messages to only include readable text messages
readable_messages = []
for message in chat_data:
try:
content = message.get("content", "")
if not include_non_text and not self._is_text_message(content):
continue
readable_text = self._extract_readable_text(content)
if not readable_text and not include_non_text:
continue
readable_messages.append(message)
except Exception as e:
print(f"Error processing message in {json_file}: {e}")
continue
# Concatenate messages based on rules
message_groups = self._concatenate_messages(
readable_messages,
max_length=max_length,
time_window_minutes=time_window_minutes,
overlap_messages=0, # No overlap between groups
)
# Create documents from concatenated groups
for message_group in message_groups:
if count >= max_count and max_count > 0:
break
doc_content, contact_name = self._create_concatenated_content(
message_group, contact_name
)
doc = Document(
text=doc_content,
metadata={"contact_name": contact_name},
)
docs.append(doc)
count += 1
print(
f"Created {len(message_groups)} concatenated message groups for {contact_name}"
)
else:
# Original single-message processing
for message in chat_data:
if count >= max_count and max_count > 0:
break
# Extract message information
message.get("fromUser", "")
message.get("toUser", "")
content = message.get("content", "")
message_text = message.get("message", "")
create_time = message.get("createTime", 0)
is_sent_from_self = message.get("isSentFromSelf", False)
# Handle content that might be dict or string
try:
# Check if this is a readable text message
if not include_non_text and not self._is_text_message(content):
continue
# Extract readable text
readable_text = self._extract_readable_text(content)
if not readable_text and not include_non_text:
continue
except Exception as e:
# Skip messages that cause processing errors
print(f"Error processing message in {json_file}: {e}")
continue
# Convert timestamp to readable format
if create_time:
try:
timestamp = datetime.fromtimestamp(create_time)
time_str = timestamp.strftime("%Y-%m-%d %H:%M:%S")
except (ValueError, OSError):
time_str = str(create_time)
else:
time_str = "Unknown"
# Create document content with metadata header and contact info
doc_content = f"""
Contact: {contact_name}
Is sent from self: {is_sent_from_self}
Time: {time_str}
Message: {readable_text if readable_text else message_text}
"""
# Create document with embedded metadata
doc = Document(
text=doc_content, metadata={"contact_name": contact_name}
)
docs.append(doc)
count += 1
except Exception as e:
print(f"Error reading {json_file}: {e}")
continue
print(f"Loaded {len(docs)} WeChat chat documents")
except Exception as e:
print(f"Error reading WeChat history: {e}")
return docs
return docs
@staticmethod
def find_wechat_export_dirs() -> list[Path]:
"""
Find all WeChat export directories.
Returns:
List of Path objects pointing to WeChat export directories
"""
export_dirs = []
# Look for common export directory names
possible_dirs = [
Path("./wechat_export"),
Path("./wechat_export_direct"),
Path("./wechat_chat_history"),
Path("./chat_export"),
]
for export_dir in possible_dirs:
if export_dir.exists() and export_dir.is_dir():
json_files = list(export_dir.glob("*.json"))
if json_files:
export_dirs.append(export_dir)
print(
f"Found WeChat export directory: {export_dir} with {len(json_files)} files"
)
print(f"Found {len(export_dirs)} WeChat export directories")
return export_dirs
@staticmethod
def export_chat_to_file(
output_file: str = "wechat_chat_export.txt",
max_count: int = 1000,
export_dir: str | None = None,
include_non_text: bool = False,
):
"""
Export WeChat chat history to a text file.
Args:
output_file: Path to the output file
max_count: Maximum number of entries to export
export_dir: Directory containing WeChat JSON files
include_non_text: Whether to include non-text messages
"""
if export_dir is None:
export_dir = "./wechat_export_test"
if not os.path.exists(export_dir):
print(f"WeChat export directory not found at: {export_dir}")
return
try:
json_files = list(Path(export_dir).glob("*.json"))
with open(output_file, "w", encoding="utf-8") as f:
count = 0
for json_file in json_files:
if count >= max_count and max_count > 0:
break
try:
with open(json_file, encoding="utf-8") as json_f:
chat_data = json.load(json_f)
contact_name = json_file.stem
f.write(f"\n=== Chat with {contact_name} ===\n")
for message in chat_data:
if count >= max_count and max_count > 0:
break
from_user = message.get("fromUser", "")
content = message.get("content", "")
message_text = message.get("message", "")
create_time = message.get("createTime", 0)
# Skip non-text messages unless requested
if not include_non_text:
reader = WeChatHistoryReader()
if not reader._is_text_message(content):
continue
readable_text = reader._extract_readable_text(content)
if not readable_text:
continue
message_text = readable_text
if create_time:
try:
timestamp = datetime.fromtimestamp(create_time)
time_str = timestamp.strftime("%Y-%m-%d %H:%M:%S")
except (ValueError, OSError):
time_str = str(create_time)
else:
time_str = "Unknown"
f.write(f"[{time_str}] {from_user}: {message_text}\n")
count += 1
except Exception as e:
print(f"Error processing {json_file}: {e}")
continue
print(f"Exported {count} chat entries to {output_file}")
except Exception as e:
print(f"Error exporting WeChat chat history: {e}")
def export_wechat_chat_history(self, export_dir: str = "./wechat_export_direct") -> Path | None:
"""
Export WeChat chat history using wechat-exporter tool.
Args:
export_dir: Directory to save exported chat history
Returns:
Path to export directory if successful, None otherwise
"""
try:
import subprocess
import sys
# Create export directory
export_path = Path(export_dir)
export_path.mkdir(exist_ok=True)
print(f"Exporting WeChat chat history to {export_path}...")
# Check if wechat-exporter directory exists
if not self.wechat_exporter_dir.exists():
print(f"wechat-exporter directory not found at: {self.wechat_exporter_dir}")
return None
# Install requirements if needed
requirements_file = self.wechat_exporter_dir / "requirements.txt"
if requirements_file.exists():
print("Installing wechat-exporter requirements...")
subprocess.run(["uv", "pip", "install", "-r", str(requirements_file)], check=True)
# Run the export command
print("Running wechat-exporter...")
result = subprocess.run(
[
sys.executable,
str(self.wechat_exporter_dir / "main.py"),
"export-all",
str(export_path),
],
capture_output=True,
text=True,
check=True,
)
print("Export command output:")
print(result.stdout)
if result.stderr:
print("Export errors:")
print(result.stderr)
# Check if export was successful
if export_path.exists() and any(export_path.glob("*.json")):
json_files = list(export_path.glob("*.json"))
print(
f"Successfully exported {len(json_files)} chat history files to {export_path}"
)
return export_path
else:
print("Export completed but no JSON files found")
return None
except subprocess.CalledProcessError as e:
print(f"Export command failed: {e}")
print(f"Command output: {e.stdout}")
print(f"Command errors: {e.stderr}")
return None
except Exception as e:
print(f"Export failed: {e}")
print("Please ensure WeChat is running and WeChatTweak is installed.")
return None
def find_or_export_wechat_data(self, export_dir: str = "./wechat_export_direct") -> list[Path]:
"""
Find existing WeChat exports or create new ones.
Args:
export_dir: Directory to save exported chat history if needed
Returns:
List of Path objects pointing to WeChat export directories
"""
export_dirs = []
# Look for existing exports in common locations
possible_export_dirs = [
Path("./wechat_database_export"),
Path("./wechat_export_test"),
Path("./wechat_export"),
Path("./wechat_export_direct"),
Path("./wechat_chat_history"),
Path("./chat_export"),
]
for export_dir_path in possible_export_dirs:
if export_dir_path.exists() and any(export_dir_path.glob("*.json")):
export_dirs.append(export_dir_path)
print(f"Found existing export: {export_dir_path}")
# If no existing exports, try to export automatically
if not export_dirs:
print("No existing WeChat exports found. Starting direct export...")
# Try to export using wechat-exporter
exported_path = self.export_wechat_chat_history(export_dir)
if exported_path:
export_dirs = [exported_path]
else:
print(
"Failed to export WeChat data. Please ensure WeChat is running and WeChatTweak is installed."
)
return export_dirs