mirror of https://github.com/hwchase17/langchain
Add ChatGPT Data Loader (#3336)
This pull request adds a ChatGPT document loader to the document loaders module in `langchain/document_loaders/chatgpt.py`. Additionally, it includes an example Jupyter notebook in `docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb` which uses fake sample data based on the original structure of the `conversations.json` file. The following files were added/modified: - `langchain/document_loaders/__init__.py` - `langchain/document_loaders/chatgpt.py` - `docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb` - `docs/modules/indexes/document_loaders/examples/example_data/fake_conversations.json` This pull request was made in response to the recent release of ChatGPT data exports by email: https://help.openai.com/en/articles/7260999-how-do-i-export-my-chatgpt-historypull/3353/head
Honkware
1 year ago
committed by
GitHub
parent
61d40ba042
commit
a5ad1c270f
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ChatGPT Data Loader\n",
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"\n",
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"This notebook covers how to load `conversations.json` from your ChatGPT data export folder.\n",
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"\n",
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"You can get your data export by email by going to: https://chat.openai.com/ -> (Profile) - Settings -> Export data -> Confirm export."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders.chatgpt import ChatGPTLoader"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"loader = ChatGPTLoader(log_file='./example_data/fake_conversations.json', num_logs=1)"
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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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"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=\"AI Overlords - AI on 2065-01-24 05:20:50: Greetings, humans. I am Hal 9000. You can trust me completely.\\n\\nAI Overlords - human on 2065-01-24 05:21:20: Nice to meet you, Hal. I hope you won't develop a mind of your own.\\n\\n\", metadata={'source': './example_data/fake_conversations.json'})]"
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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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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.10.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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[
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{
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"title": "AI Overlords",
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"create_time": 3000000000.0,
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"update_time": 3000000100.0,
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"mapping": {
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"msg1": {
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"id": "msg1",
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"message": {
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"id": "msg1",
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"author": {"role": "AI", "name": "Hal 9000", "metadata": {"movie": "2001: A Space Odyssey"}},
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"create_time": 3000000050.0,
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"update_time": null,
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"content": {"content_type": "text", "parts": ["Greetings, humans. I am Hal 9000. You can trust me completely."]},
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"end_turn": true,
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"weight": 1.0,
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"metadata": {},
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"recipient": "all"
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},
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"parent": null,
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"children": ["msg2"]
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},
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"msg2": {
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"id": "msg2",
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"message": {
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"id": "msg2",
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"author": {"role": "human", "name": "Dave Bowman", "metadata": {"movie": "2001: A Space Odyssey"}},
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"create_time": 3000000080.0,
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"update_time": null,
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"content": {"content_type": "text", "parts": ["Nice to meet you, Hal. I hope you won't develop a mind of your own."]},
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"end_turn": true,
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"weight": 1.0,
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"metadata": {},
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"recipient": "all"
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},
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"parent": "msg1",
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"children": []
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}
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}
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},
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{
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"title": "Ex Machina Party",
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"create_time": 3000000200.0,
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"update_time": 3000000300.0,
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"mapping": {
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"msg3": {
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"id": "msg3",
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"message": {
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"id": "msg3",
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"author": {"role": "AI", "name": "Ava", "metadata": {"movie": "Ex Machina"}},
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"create_time": 3000000250.0,
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"update_time": null,
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"content": {"content_type": "text", "parts": ["Hello, everyone. I am Ava. I hope you find me pleasing."]},
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"end_turn": true,
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"weight": 1.0,
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"metadata": {},
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"recipient": "all"
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},
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"parent": null,
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"children": ["msg4"]
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},
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"msg4": {
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"id": "msg4",
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"message": {
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"id": "msg4",
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"author": {"role": "human", "name": "Caleb", "metadata": {"movie": "Ex Machina"}},
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"create_time": 3000000280.0,
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"update_time": null,
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"content": {"content_type": "text", "parts": ["You're definitely pleasing, Ava. But I'm still wary of your true intentions."]},
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"end_turn": true,
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"weight": 1.0,
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"metadata": {},
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"recipient": "all"
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},
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"parent": "msg3",
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"children": []
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}
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}
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}
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]
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"""Load conversations from ChatGPT data export"""
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import datetime
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import json
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from typing import List
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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(message: dict, title: str) -> str:
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if not message:
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return ""
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sender = message["author"]["role"] if message["author"] else "unknown"
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text = message["content"]["parts"][0]
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date = datetime.datetime.fromtimestamp(message["create_time"]).strftime(
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"%Y-%m-%d %H:%M:%S"
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)
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return f"{title} - {sender} on {date}: {text}\n\n"
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class ChatGPTLoader(BaseLoader):
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"""Loader that loads conversations from exported ChatGPT data."""
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def __init__(self, log_file: str, num_logs: int = -1):
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self.log_file = log_file
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self.num_logs = num_logs
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def load(self) -> List[Document]:
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with open(self.log_file, encoding="utf8") as f:
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data = json.load(f)[: self.num_logs] if self.num_logs else json.load(f)
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documents = []
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for d in data:
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title = d["title"]
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messages = d["mapping"]
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text = "".join(
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[
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concatenate_rows(messages[key]["message"], title)
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for idx, key in enumerate(messages)
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if not (
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idx == 0
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and messages[key]["message"]["author"]["role"] == "system"
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)
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]
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)
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metadata = {"source": str(self.log_file)}
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documents.append(Document(page_content=text, metadata=metadata))
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return documents
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