Source code for langchain_community.document_loaders.telegram

from __future__ import annotations

import asyncio
import json
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Union

from langchain_core.documents import Document

from langchain_community.document_loaders.base import BaseLoader

    import pandas as pd
    from telethon.hints import EntityLike

[docs]def concatenate_rows(row: dict) -> str: """Combine message information in a readable format ready to be used.""" date = row["date"] sender = row["from"] text = row["text"] return f"{sender} on {date}: {text}\n\n"
[docs]class TelegramChatFileLoader(BaseLoader): """Load from `Telegram chat` dump."""
[docs] def __init__(self, path: Union[str, Path]): """Initialize with a path.""" self.file_path = path
[docs] def load(self) -> List[Document]: """Load documents.""" p = Path(self.file_path) with open(p, encoding="utf8") as f: d = json.load(f) text = "".join( concatenate_rows(message) for message in d["messages"] if message["type"] == "message" and isinstance(message["text"], str) ) metadata = {"source": str(p)} return [Document(page_content=text, metadata=metadata)]
[docs]def text_to_docs(text: Union[str, List[str]]) -> List[Document]: """Convert a string or list of strings to a list of Documents with metadata.""" from langchain_text_splitters import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter( chunk_size=800, separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""], chunk_overlap=20, ) if isinstance(text, str): # Take a single string as one page text = [text] page_docs = [Document(page_content=page) for page in text] # Add page numbers as metadata for i, doc in enumerate(page_docs): doc.metadata["page"] = i + 1 # Split pages into chunks doc_chunks = [] for doc in page_docs: chunks = text_splitter.split_text(doc.page_content) for i, chunk in enumerate(chunks): doc = Document( page_content=chunk, metadata={"page": doc.metadata["page"], "chunk": i} ) # Add sources a metadata doc.metadata["source"] = f"{doc.metadata['page']}-{doc.metadata['chunk']}" doc_chunks.append(doc) return doc_chunks
[docs]class TelegramChatApiLoader(BaseLoader): """Load `Telegram` chat json directory dump."""
[docs] def __init__( self, chat_entity: Optional[EntityLike] = None, api_id: Optional[int] = None, api_hash: Optional[str] = None, username: Optional[str] = None, file_path: str = "telegram_data.json", ): """Initialize with API parameters. Args: chat_entity: The chat entity to fetch data from. api_id: The API ID. api_hash: The API hash. username: The username. file_path: The file path to save the data to. Defaults to "telegram_data.json". """ self.chat_entity = chat_entity self.api_id = api_id self.api_hash = api_hash self.username = username self.file_path = file_path
[docs] async def fetch_data_from_telegram(self) -> None: """Fetch data from Telegram API and save it as a JSON file.""" from telethon.sync import TelegramClient data = [] async with TelegramClient(self.username, self.api_id, self.api_hash) as client: async for message in client.iter_messages(self.chat_entity): is_reply = message.reply_to is not None reply_to_id = message.reply_to.reply_to_msg_id if is_reply else None data.append( { "sender_id": message.sender_id, "text": message.text, "date":, "":, "is_reply": is_reply, "reply_to_id": reply_to_id, } ) with open(self.file_path, "w", encoding="utf-8") as f: json.dump(data, f, ensure_ascii=False, indent=4)
def _get_message_threads(self, data: pd.DataFrame) -> dict: """Create a dictionary of message threads from the given data. Args: data (pd.DataFrame): A DataFrame containing the conversation \ data with columns: - message.sender_id - text - date - - is_reply - reply_to_id Returns: dict: A dictionary where the key is the parent message ID and \ the value is a list of message IDs in ascending order. """ def find_replies(parent_id: int, reply_data: pd.DataFrame) -> List[int]: """ Recursively find all replies to a given parent message ID. Args: parent_id (int): The parent message ID. reply_data (pd.DataFrame): A DataFrame containing reply messages. Returns: list: A list of message IDs that are replies to the parent message ID. """ # Find direct replies to the parent message ID direct_replies = reply_data[reply_data["reply_to_id"] == parent_id][ "" ].tolist() # Recursively find replies to the direct replies all_replies = [] for reply_id in direct_replies: all_replies += [reply_id] + find_replies(reply_id, reply_data) return all_replies # Filter out parent messages parent_messages = data[~data["is_reply"]] # Filter out reply messages and drop rows with NaN in 'reply_to_id' reply_messages = data[data["is_reply"]].dropna(subset=["reply_to_id"]) # Convert 'reply_to_id' to integer reply_messages["reply_to_id"] = reply_messages["reply_to_id"].astype(int) # Create a dictionary of message threads with parent message IDs as keys and \ # lists of reply message IDs as values message_threads = { parent_id: [parent_id] + find_replies(parent_id, reply_messages) for parent_id in parent_messages[""] } return message_threads def _combine_message_texts( self, message_threads: Dict[int, List[int]], data: pd.DataFrame ) -> str: """ Combine the message texts for each parent message ID based \ on the list of message threads. Args: message_threads (dict): A dictionary where the key is the parent message \ ID and the value is a list of message IDs in ascending order. data (pd.DataFrame): A DataFrame containing the conversation data: - message.sender_id - text - date - - is_reply - reply_to_id Returns: str: A combined string of message texts sorted by date. """ combined_text = "" # Iterate through sorted parent message IDs for parent_id, message_ids in message_threads.items(): # Get the message texts for the message IDs and sort them by date message_texts = ( data[data[""].isin(message_ids)] .sort_values(by="date")["text"] .tolist() ) message_texts = [str(elem) for elem in message_texts] # Combine the message texts combined_text += " ".join(message_texts) + ".\n" return combined_text.strip()
[docs] def load(self) -> List[Document]: """Load documents.""" if self.chat_entity is not None: try: import nest_asyncio nest_asyncio.apply() except ImportError: raise ImportError( """`nest_asyncio` package not found. please install with `pip install nest_asyncio` """ ) p = Path(self.file_path) with open(p, encoding="utf8") as f: d = json.load(f) try: import pandas as pd except ImportError: raise ImportError( """`pandas` package not found. please install with `pip install pandas` """ ) normalized_messages = pd.json_normalize(d) df = pd.DataFrame(normalized_messages) message_threads = self._get_message_threads(df) combined_texts = self._combine_message_texts(message_threads, df) return text_to_docs(combined_texts)
# For backwards compatibility TelegramChatLoader = TelegramChatFileLoader