WeChat

There is not yet a straightforward way to export personal WeChat messages. However if you just need no more than few hundreds of messages for model fine-tuning or few-shot examples, this notebook shows how to create your own chat loader that works on copy-pasted WeChat messages to a list of LangChain messages.

Highly inspired by https://python.langchain.com/docs/integrations/chat_loaders/discord

The process has five steps:

1. Open your chat in the WeChat desktop app. Select messages you need by mouse-dragging or right-click. Due to restrictions, you can select up to 100 messages once a time. CMD/Ctrl + C to copy.
2. Create the chat .txt file by pasting selected messages in a file on your local computer.
3. Copy the chat loader definition from below to a local file.
4. Initialize the WeChatChatLoader with the file path pointed to the text file.
5. Call loader.load() (or loader.lazy_load()) to perform the conversion.

1. Create message dump

This loader only supports .txt files in the format generated by copying messages in the app to your clipboard and pasting in a file. Below is an example.

%%writefile wechat_chats.txt
女朋友 2023/09/16 2:51 PM
天气有点凉

男朋友 2023/09/16 2:51 PM
珍簟凉风著，瑶琴寄恨生。嵇君懒书札，底物慰秋情。

女朋友 2023/09/16 3:06 PM
忙什么呢

男朋友 2023/09/16 3:06 PM
今天只干成了一件像样的事
那就是想你

女朋友 2023/09/16 3:06 PM
[动画表情]

Overwriting wechat_chats.txt

2. Define chat loader

LangChain currently does not support

import logging
import re
from typing import Iterator, List

from langchain_community.chat_loaders import base as chat_loaders
from langchain_core.messages import BaseMessage, HumanMessage

logger = logging.getLogger()


class WeChatChatLoader(chat_loaders.BaseChatLoader):
    def __init__(self, path: str):
        """
        Initialize the Discord chat loader.

        Args:
            path: Path to the exported Discord chat text file.
        """
        self.path = path
        self._message_line_regex = re.compile(
            r"(?P<sender>.+?) (?P<timestamp>\d{4}/\d{2}/\d{2} \d{1,2}:\d{2} (?:AM|PM))",  # noqa
            # flags=re.DOTALL,
        )

    def _append_message_to_results(
        self,
        results: List,
        current_sender: str,
        current_timestamp: str,
        current_content: List[str],
    ):
        content = "\n".join(current_content).strip()
        # skip non-text messages like stickers, images, etc.
        if not re.match(r"\[.*\]", content):
            results.append(
                HumanMessage(
                    content=content,
                    additional_kwargs={
                        "sender": current_sender,
                        "events": [{"message_time": current_timestamp}],
                    },
                )
            )
        return results

    def _load_single_chat_session_from_txt(
        self, file_path: str
    ) -> chat_loaders.ChatSession:
        """
        Load a single chat session from a text file.

        Args:
            file_path: Path to the text file containing the chat messages.

        Returns:
            A `ChatSession` object containing the loaded chat messages.
        """
        with open(file_path, "r", encoding="utf-8") as file:
            lines = file.readlines()

        results: List[BaseMessage] = []
        current_sender = None
        current_timestamp = None
        current_content = []
        for line in lines:
            if re.match(self._message_line_regex, line):
                if current_sender and current_content:
                    results = self._append_message_to_results(
                        results, current_sender, current_timestamp, current_content
                    )
                current_sender, current_timestamp = re.match(
                    self._message_line_regex, line
                ).groups()
                current_content = []
            else:
                current_content.append(line.strip())

        if current_sender and current_content:
            results = self._append_message_to_results(
                results, current_sender, current_timestamp, current_content
            )

        return chat_loaders.ChatSession(messages=results)

    def lazy_load(self) -> Iterator[chat_loaders.ChatSession]:
        """
        Lazy load the messages from the chat file and yield them in the required format.

        Yields:
            A `ChatSession` object containing the loaded chat messages.
        """
        yield self._load_single_chat_session_from_txt(self.path)

API Reference:

• BaseMessage
• HumanMessage

2. Create loader

We will point to the file we just wrote to disk.

loader = WeChatChatLoader(
    path="./wechat_chats.txt",
)

3. Load Messages

Assuming the format is correct, the loader will convert the chats to langchain messages.

from typing import List

from langchain_community.chat_loaders.utils import (
    map_ai_messages,
    merge_chat_runs,
)
from langchain_core.chat_sessions import ChatSession

raw_messages = loader.lazy_load()
# Merge consecutive messages from the same sender into a single message
merged_messages = merge_chat_runs(raw_messages)
# Convert messages from "男朋友" to AI messages
messages: List[ChatSession] = list(map_ai_messages(merged_messages, sender="男朋友"))

API Reference:

• map_ai_messages
• merge_chat_runs
• ChatSession

messages

[{'messages': [HumanMessage(content='天气有点凉', additional_kwargs={'sender': '女朋友', 'events': [{'message_time': '2023/09/16 2:51 PM'}]}, example=False),
   AIMessage(content='珍簟凉风著，瑶琴寄恨生。嵇君懒书札，底物慰秋情。', additional_kwargs={'sender': '男朋友', 'events': [{'message_time': '2023/09/16 2:51 PM'}]}, example=False),
   HumanMessage(content='忙什么呢', additional_kwargs={'sender': '女朋友', 'events': [{'message_time': '2023/09/16 3:06 PM'}]}, example=False),
   AIMessage(content='今天只干成了一件像样的事\n那就是想你', additional_kwargs={'sender': '男朋友', 'events': [{'message_time': '2023/09/16 3:06 PM'}]}, example=False)]}]

Next Steps

You can then use these messages how you see fit, such as fine-tuning a model, few-shot example selection, or directly make predictions for the next message

from langchain_openai import ChatOpenAI

llm = ChatOpenAI()

for chunk in llm.stream(messages[0]["messages"]):
    print(chunk.content, end="", flush=True)

API Reference:

• ChatOpenAI