mirror of
https://github.com/hwchase17/langchain
synced 2024-11-16 06:13:16 +00:00
265 lines
8.8 KiB
Python
265 lines
8.8 KiB
Python
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
|
|
|
|
if TYPE_CHECKING:
|
|
import pandas as pd
|
|
from telethon.hints import EntityLike
|
|
|
|
|
|
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"
|
|
|
|
|
|
class TelegramChatFileLoader(BaseLoader):
|
|
"""Load from `Telegram chat` dump."""
|
|
|
|
def __init__(self, path: str):
|
|
"""Initialize with a path."""
|
|
self.file_path = path
|
|
|
|
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)]
|
|
|
|
|
|
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
|
|
|
|
|
|
class TelegramChatApiLoader(BaseLoader):
|
|
"""Load `Telegram` chat json directory dump."""
|
|
|
|
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
|
|
|
|
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": message.date.isoformat(),
|
|
"message.id": message.id,
|
|
"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
|
|
- message.id
|
|
- 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][
|
|
"message.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["message.id"]
|
|
}
|
|
|
|
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
|
|
- message.id
|
|
- 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["message.id"].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()
|
|
|
|
def load(self) -> List[Document]:
|
|
"""Load documents."""
|
|
|
|
if self.chat_entity is not None:
|
|
try:
|
|
import nest_asyncio
|
|
|
|
nest_asyncio.apply()
|
|
asyncio.run(self.fetch_data_from_telegram())
|
|
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)
|