Telegram

This notebook shows how to use the Telegram chat loader. This class helps map exported Telegram conversations to LangChain chat messages.

The process has three steps:

1. Export the chat .txt file by copying chats from the Telegram app and pasting them in a file on your local computer
2. Create the TelegramChatLoader with the file path pointed to the json file or directory of JSON files
3. Call loader.load() (or loader.lazy_load()) to perform the conversion. Optionally use merge_chat_runs to combine message from the same sender in sequence, and/or map_ai_messages to convert messages from the specified sender to the "AIMessage" class.

1. Create message dump

Currently (2023/08/23) this loader best supports json files in the format generated by exporting your chat history from the Telegram Desktop App.

Important: There are 'lite' versions of telegram such as "Telegram for MacOS" that lack the export functionality. Please make sure you use the correct app to export the file.

To make the export:

1. Download and open telegram desktop
2. Select a conversation
3. Navigate to the conversation settings (currently the three dots in the top right corner)
4. Click "Export Chat History"
5. Unselect photos and other media. Select "Machine-readable JSON" format to export.

An example is below:

%%writefile telegram_conversation.json
{
 "name": "Jiminy",
 "type": "personal_chat",
 "id": 5965280513,
 "messages": [
  {
   "id": 1,
   "type": "message",
   "date": "2023-08-23T13:11:23",
   "date_unixtime": "1692821483",
   "from": "Jiminy Cricket",
   "from_id": "user123450513",
   "text": "You better trust your conscience",
   "text_entities": [
    {
     "type": "plain",
     "text": "You better trust your conscience"
    }
   ]
  },
  {
   "id": 2,
   "type": "message",
   "date": "2023-08-23T13:13:20",
   "date_unixtime": "1692821600",
   "from": "Batman & Robin",
   "from_id": "user6565661032",
   "text": "What did you just say?",
   "text_entities": [
    {
     "type": "plain",
     "text": "What did you just say?"
    }
   ]
  }
 ]
}

Overwriting telegram_conversation.json

2. Create the Chat Loader

All that's required is the file path. You can optionally specify the user name that maps to an ai message as well an configure whether to merge message runs.

from langchain_community.chat_loaders.telegram import TelegramChatLoader

API Reference:TelegramChatLoader

loader = TelegramChatLoader(
    path="./telegram_conversation.json",
)

3. Load messages

The load() (or lazy_load) methods return a list of "ChatSessions" that currently just contain a list of messages per loaded conversation.

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 "Jiminy Cricket" to AI messages
messages: List[ChatSession] = list(
    map_ai_messages(merged_messages, sender="Jiminy Cricket")
)

API Reference:map_ai_messages | merge_chat_runs | ChatSession

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

I said, "You better trust your conscience."