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Harrison/telegram loader (#1080)
Co-authored-by: Maxime Vidal <max.vidal@hotmail.fr>
This commit is contained in:
parent
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commit
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@ -93,7 +93,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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@ -0,0 +1,31 @@
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{
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"name": "Grace 🧤",
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"type": "personal_chat",
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"id": 2730825451,
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"messages": [
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{
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"id": 1980499,
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"type": "message",
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"date": "2020-01-01T00:00:02",
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"from": "Henry",
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"from_id": 4325636679,
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"text": "It's 2020..."
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},
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{
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"id": 1980500,
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"type": "message",
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"date": "2020-01-01T00:00:04",
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"from": "Henry",
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"from_id": 4325636679,
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"text": "Fireworks!"
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},
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{
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"id": 1980501,
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"type": "message",
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"date": "2020-01-01T00:00:05",
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"from": "Grace 🧤 ðŸ’",
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"from_id": 4720225552,
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"text": "You're a minute late!"
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}
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]
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}
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84
docs/modules/document_loaders/examples/telegram.ipynb
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84
docs/modules/document_loaders/examples/telegram.ipynb
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@ -0,0 +1,84 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "33205b12",
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"metadata": {},
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"source": [
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"# Telegram\n",
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"\n",
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"This notebook covers how to load data from Telegram into a format that can be ingested into LangChain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "90b69c94",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TelegramChatLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "13deb0f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = TelegramChatLoader(\"example_data/telegram.json\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9ccc1e2f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content=\"Henry on 2020-01-01T00:00:02: It's 2020...\\n\\nHenry on 2020-01-01T00:00:04: Fireworks!\\n\\nGrace 🧤 ðŸ\\x8d’ on 2020-01-01T00:00:05: You're a minute late!\\n\\n\", lookup_str='', metadata={'source': 'example_data/telegram.json'}, lookup_index=0)]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"loader.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3e64cac2",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -6,7 +6,7 @@ from langchain.document_loaders.college_confidential import CollegeConfidentialL
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from langchain.document_loaders.directory import DirectoryLoader
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from langchain.document_loaders.docx import UnstructuredDocxLoader
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from langchain.document_loaders.email import UnstructuredEmailLoader
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from langchain.document_loaders.everynote import EveryNoteLoader
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from langchain.document_loaders.evernote import EverNoteLoader
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from langchain.document_loaders.gcs_directory import GCSDirectoryLoader
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from langchain.document_loaders.gcs_file import GCSFileLoader
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from langchain.document_loaders.googledrive import GoogleDriveLoader
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@ -23,6 +23,7 @@ from langchain.document_loaders.readthedocs import ReadTheDocsLoader
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from langchain.document_loaders.roam import RoamLoader
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from langchain.document_loaders.s3_directory import S3DirectoryLoader
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from langchain.document_loaders.s3_file import S3FileLoader
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from langchain.document_loaders.telegram import TelegramChatLoader
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from langchain.document_loaders.text import TextLoader
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from langchain.document_loaders.unstructured import UnstructuredFileLoader
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from langchain.document_loaders.url import UnstructuredURLLoader
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@ -55,8 +56,9 @@ __all__ = [
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"CollegeConfidentialLoader",
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"GutenbergLoader",
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"PagedPDFSplitter",
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"EveryNoteLoader",
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"EverNoteLoader",
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"AirbyteJSONLoader",
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"OnlinePDFLoader",
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"PDFMinerLoader",
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"TelegramChatLoader",
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]
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49
langchain/document_loaders/telegram.py
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49
langchain/document_loaders/telegram.py
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"""Loader that loads Telegram chat json dump."""
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import json
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from pathlib import Path
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from typing import List
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import pandas as pd
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from langchain.docstore.document import Document
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from langchain.document_loaders.base import BaseLoader
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def concatenate_rows(row: dict) -> str:
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"""Combine message information in a readable format ready to be used."""
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date = row["date"]
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sender = row["from"]
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text = row["text"]
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return f"{sender} on {date}: {text}\n\n"
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class TelegramChatLoader(BaseLoader):
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"""Loader that loads Telegram chat json directory dump."""
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def __init__(self, path: str):
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"""Initialize with path."""
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self.file_path = path
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def load(self) -> List[Document]:
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"""Load documents."""
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p = Path(self.file_path)
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with open(p, encoding="utf8") as f:
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d = json.load(f)
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normalized_messages = pd.json_normalize(d["messages"])
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df_normalized_messages = pd.DataFrame(normalized_messages)
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# Only keep plain text messages (no services, links, hashtags, code, bold...)
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df_filtered = df_normalized_messages[
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(df_normalized_messages.type == "message")
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& (df_normalized_messages.text.apply(lambda x: type(x) == str))
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]
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df_filtered = df_filtered[["date", "text", "from"]]
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text = df_filtered.apply(concatenate_rows, axis=1).str.cat(sep="")
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metadata = {"source": str(p)}
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return [Document(page_content=text, metadata=metadata)]
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