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b4a6bf503031-0 | .ipynb
.pdf
Fauna
Contents
Query data example
Query with Pagination
Fauna#
Fauna is a Document Database.
Query Fauna documents
#!pip install fauna
Query data example#
from langchain.document_loaders.fauna import FaunaLoader
secret = "<enter-valid-fauna-secret>"
query = "Item.all()" # Fauna query. Assumes that the collection is called "Item"
field = "text" # The field that contains the page content. Assumes that the field is called "text"
loader = FaunaLoader(query, field, secret)
docs = loader.lazy_load()
for value in docs:
print(value)
Query with Pagination#
You get a after value if there are more data. You can get values after the curcor by passing in the after string in query.
To learn more following this link
query = """
Item.paginate("hs+DzoPOg ... aY1hOohozrV7A")
Item.all()
"""
loader = FaunaLoader(query, field, secret)
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DuckDB
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Figma
Contents
Query data example
Query with Pagination
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/fauna.html |
ca0757c8b2da-0 | .ipynb
.pdf
Apify Dataset
Contents
Prerequisites
An example with question answering
Apify Dataset#
Apify Dataset is a scaleable append-only storage with sequential access built for storing structured web scraping results, such as a list of products or Google SERPs, and then export them to various formats like JSON, CSV, or Excel. Datasets are mainly used to save results of Apify Actors—serverless cloud programs for varius web scraping, crawling, and data extraction use cases.
This notebook shows how to load Apify datasets to LangChain.
Prerequisites#
You need to have an existing dataset on the Apify platform. If you don’t have one, please first check out this notebook on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs.
#!pip install apify-client
First, import ApifyDatasetLoader into your source code:
from langchain.document_loaders import ApifyDatasetLoader
from langchain.document_loaders.base import Document
Then provide a function that maps Apify dataset record fields to LangChain Document format.
For example, if your dataset items are structured like this:
{
"url": "https://apify.com",
"text": "Apify is the best web scraping and automation platform."
}
The mapping function in the code below will convert them to LangChain Document format, so that you can use them further with any LLM model (e.g. for question answering).
loader = ApifyDatasetLoader(
dataset_id="your-dataset-id",
dataset_mapping_function=lambda dataset_item: Document(
page_content=dataset_item["text"], metadata={"source": dataset_item["url"]}
),
)
data = loader.load()
An example with question answering#
In this example, we use data from a dataset to answer a question. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/apify_dataset.html |
ca0757c8b2da-1 | In this example, we use data from a dataset to answer a question.
from langchain.docstore.document import Document
from langchain.document_loaders import ApifyDatasetLoader
from langchain.indexes import VectorstoreIndexCreator
loader = ApifyDatasetLoader(
dataset_id="your-dataset-id",
dataset_mapping_function=lambda item: Document(
page_content=item["text"] or "", metadata={"source": item["url"]}
),
)
index = VectorstoreIndexCreator().from_loaders([loader])
query = "What is Apify?"
result = index.query_with_sources(query)
print(result["answer"])
print(result["sources"])
Apify is a platform for developing, running, and sharing serverless cloud programs. It enables users to create web scraping and automation tools and publish them on the Apify platform.
https://docs.apify.com/platform/actors, https://docs.apify.com/platform/actors/running/actors-in-store, https://docs.apify.com/platform/security, https://docs.apify.com/platform/actors/examples
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Airbyte JSON
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AWS S3 Directory
Contents
Prerequisites
An example with question answering
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/apify_dataset.html |
26981f610e66-0 | .ipynb
.pdf
Stripe
Stripe#
Stripe is an Irish-American financial services and software as a service (SaaS) company. It offers payment-processing software and application programming interfaces for e-commerce websites and mobile applications.
This notebook covers how to load data from the Stripe REST API into a format that can be ingested into LangChain, along with example usage for vectorization.
import os
from langchain.document_loaders import StripeLoader
from langchain.indexes import VectorstoreIndexCreator
The Stripe API requires an access token, which can be found inside of the Stripe dashboard.
This document loader also requires a resource option which defines what data you want to load.
Following resources are available:
balance_transations Documentation
charges Documentation
customers Documentation
events Documentation
refunds Documentation
disputes Documentation
stripe_loader = StripeLoader("charges")
# Create a vectorstore retriver from the loader
# see https://python.langchain.com/en/latest/modules/indexes/getting_started.html for more details
index = VectorstoreIndexCreator().from_loaders([stripe_loader])
stripe_doc_retriever = index.vectorstore.as_retriever()
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Spreedly
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2Markdown
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/stripe.html |
b8d166bf1553-0 | .ipynb
.pdf
Pandas DataFrame
Pandas DataFrame#
This notebook goes over how to load data from a pandas DataFrame.
#!pip install pandas
import pandas as pd
df = pd.read_csv('example_data/mlb_teams_2012.csv')
df.head()
Team
"Payroll (millions)"
"Wins"
0
Nationals
81.34
98
1
Reds
82.20
97
2
Yankees
197.96
95
3
Giants
117.62
94
4
Braves
83.31
94
from langchain.document_loaders import DataFrameLoader
loader = DataFrameLoader(df, page_content_column="Team")
loader.load()
[Document(page_content='Nationals', metadata={' "Payroll (millions)"': 81.34, ' "Wins"': 98}),
Document(page_content='Reds', metadata={' "Payroll (millions)"': 82.2, ' "Wins"': 97}),
Document(page_content='Yankees', metadata={' "Payroll (millions)"': 197.96, ' "Wins"': 95}),
Document(page_content='Giants', metadata={' "Payroll (millions)"': 117.62, ' "Wins"': 94}),
Document(page_content='Braves', metadata={' "Payroll (millions)"': 83.31, ' "Wins"': 94}),
Document(page_content='Athletics', metadata={' "Payroll (millions)"': 55.37, ' "Wins"': 94}),
Document(page_content='Rangers', metadata={' "Payroll (millions)"': 120.51, ' "Wins"': 93}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pandas_dataframe.html |
b8d166bf1553-1 | Document(page_content='Orioles', metadata={' "Payroll (millions)"': 81.43, ' "Wins"': 93}),
Document(page_content='Rays', metadata={' "Payroll (millions)"': 64.17, ' "Wins"': 90}),
Document(page_content='Angels', metadata={' "Payroll (millions)"': 154.49, ' "Wins"': 89}),
Document(page_content='Tigers', metadata={' "Payroll (millions)"': 132.3, ' "Wins"': 88}),
Document(page_content='Cardinals', metadata={' "Payroll (millions)"': 110.3, ' "Wins"': 88}),
Document(page_content='Dodgers', metadata={' "Payroll (millions)"': 95.14, ' "Wins"': 86}),
Document(page_content='White Sox', metadata={' "Payroll (millions)"': 96.92, ' "Wins"': 85}),
Document(page_content='Brewers', metadata={' "Payroll (millions)"': 97.65, ' "Wins"': 83}),
Document(page_content='Phillies', metadata={' "Payroll (millions)"': 174.54, ' "Wins"': 81}),
Document(page_content='Diamondbacks', metadata={' "Payroll (millions)"': 74.28, ' "Wins"': 81}),
Document(page_content='Pirates', metadata={' "Payroll (millions)"': 63.43, ' "Wins"': 79}),
Document(page_content='Padres', metadata={' "Payroll (millions)"': 55.24, ' "Wins"': 76}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pandas_dataframe.html |
b8d166bf1553-2 | Document(page_content='Mariners', metadata={' "Payroll (millions)"': 81.97, ' "Wins"': 75}),
Document(page_content='Mets', metadata={' "Payroll (millions)"': 93.35, ' "Wins"': 74}),
Document(page_content='Blue Jays', metadata={' "Payroll (millions)"': 75.48, ' "Wins"': 73}),
Document(page_content='Royals', metadata={' "Payroll (millions)"': 60.91, ' "Wins"': 72}),
Document(page_content='Marlins', metadata={' "Payroll (millions)"': 118.07, ' "Wins"': 69}),
Document(page_content='Red Sox', metadata={' "Payroll (millions)"': 173.18, ' "Wins"': 69}),
Document(page_content='Indians', metadata={' "Payroll (millions)"': 78.43, ' "Wins"': 68}),
Document(page_content='Twins', metadata={' "Payroll (millions)"': 94.08, ' "Wins"': 66}),
Document(page_content='Rockies', metadata={' "Payroll (millions)"': 78.06, ' "Wins"': 64}),
Document(page_content='Cubs', metadata={' "Payroll (millions)"': 88.19, ' "Wins"': 61}),
Document(page_content='Astros', metadata={' "Payroll (millions)"': 60.65, ' "Wins"': 55})]
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Open Document Format (ODT)
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PDF
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pandas_dataframe.html |
f0202d2c0106-0 | .ipynb
.pdf
Subtitle
Subtitle#
The SubRip file format is described on the Matroska multimedia container format website as “perhaps the most basic of all subtitle formats.” SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hours:minutes:seconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (00:00:00,000). The fractional separator used is the comma, since the program was written in France.
How to load data from subtitle (.srt) files
Please, download the example .srt file from here.
!pip install pysrt
from langchain.document_loaders import SRTLoader
loader = SRTLoader("example_data/Star_Wars_The_Clone_Wars_S06E07_Crisis_at_the_Heart.srt")
docs = loader.load()
docs[0].page_content[:100]
'<i>Corruption discovered\nat the core of the Banking Clan!</i> <i>Reunited, Rush Clovis\nand Senator A'
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Sitemap
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Telegram
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/subtitle.html |
fbbfb116561e-0 | .ipynb
.pdf
Airbyte JSON
Airbyte JSON#
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This covers how to load any source from Airbyte into a local JSON file that can be read in as a document
Prereqs:
Have docker desktop installed
Steps:
Clone Airbyte from GitHub - git clone https://github.com/airbytehq/airbyte.git
Switch into Airbyte directory - cd airbyte
Start Airbyte - docker compose up
In your browser, just visit http://localhost:8000. You will be asked for a username and password. By default, that’s username airbyte and password password.
Setup any source you wish.
Set destination as Local JSON, with specified destination path - lets say /json_data. Set up manual sync.
Run the connection.
To see what files are create, you can navigate to: file:///tmp/airbyte_local
Find your data and copy path. That path should be saved in the file variable below. It should start with /tmp/airbyte_local
from langchain.document_loaders import AirbyteJSONLoader
!ls /tmp/airbyte_local/json_data/
_airbyte_raw_pokemon.jsonl
loader = AirbyteJSONLoader('/tmp/airbyte_local/json_data/_airbyte_raw_pokemon.jsonl')
data = loader.load()
print(data[0].page_content[:500])
abilities:
ability:
name: blaze
url: https://pokeapi.co/api/v2/ability/66/
is_hidden: False
slot: 1
ability:
name: solar-power
url: https://pokeapi.co/api/v2/ability/94/
is_hidden: True
slot: 3 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/airbyte_json.html |
fbbfb116561e-1 | is_hidden: True
slot: 3
base_experience: 267
forms:
name: charizard
url: https://pokeapi.co/api/v2/pokemon-form/6/
game_indices:
game_index: 180
version:
name: red
url: https://pokeapi.co/api/v2/version/1/
game_index: 180
version:
name: blue
url: https://pokeapi.co/api/v2/version/2/
game_index: 180
version:
n
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YouTube transcripts
next
Apify Dataset
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/airbyte_json.html |
a9dc2b45b5e7-0 | .ipynb
.pdf
Blackboard
Blackboard#
Blackboard Learn (previously the Blackboard Learning Management System) is a web-based virtual learning environment and learning management system developed by Blackboard Inc. The software features course management, customizable open architecture, and scalable design that allows integration with student information systems and authentication protocols. It may be installed on local servers, hosted by Blackboard ASP Solutions, or provided as Software as a Service hosted on Amazon Web Services. Its main purposes are stated to include the addition of online elements to courses traditionally delivered face-to-face and development of completely online courses with few or no face-to-face meetings
This covers how to load data from a Blackboard Learn instance.
This loader is not compatible with all Blackboard courses. It is only
compatible with courses that use the new Blackboard interface.
To use this loader, you must have the BbRouter cookie. You can get this
cookie by logging into the course and then copying the value of the
BbRouter cookie from the browser’s developer tools.
from langchain.document_loaders import BlackboardLoader
loader = BlackboardLoader(
blackboard_course_url="https://blackboard.example.com/webapps/blackboard/execute/announcement?method=search&context=course_entry&course_id=_123456_1",
bbrouter="expires:12345...",
load_all_recursively=True,
)
documents = loader.load()
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Azure Blob Storage File
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Blockchain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/blackboard.html |
ff7feea85cdd-0 | .ipynb
.pdf
URL
Contents
URL
Selenium URL Loader
Setup
Playwright URL Loader
Setup
URL#
This covers how to load HTML documents from a list of URLs into a document format that we can use downstream.
from langchain.document_loaders import UnstructuredURLLoader
urls = [
"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023",
"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023"
]
loader = UnstructuredURLLoader(urls=urls)
data = loader.load()
Selenium URL Loader#
This covers how to load HTML documents from a list of URLs using the SeleniumURLLoader.
Using selenium allows us to load pages that require JavaScript to render.
Setup#
To use the SeleniumURLLoader, you will need to install selenium and unstructured.
from langchain.document_loaders import SeleniumURLLoader
urls = [
"https://www.youtube.com/watch?v=dQw4w9WgXcQ",
"https://goo.gl/maps/NDSHwePEyaHMFGwh8"
]
loader = SeleniumURLLoader(urls=urls)
data = loader.load()
Playwright URL Loader#
This covers how to load HTML documents from a list of URLs using the PlaywrightURLLoader.
As in the Selenium case, Playwright allows us to load pages that need JavaScript to render.
Setup#
To use the PlaywrightURLLoader, you will need to install playwright and unstructured. Additionally, you will need to install the Playwright Chromium browser:
# Install playwright
!pip install "playwright"
!pip install "unstructured"
!playwright install | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/url.html |
ff7feea85cdd-1 | !pip install "unstructured"
!playwright install
from langchain.document_loaders import PlaywrightURLLoader
urls = [
"https://www.youtube.com/watch?v=dQw4w9WgXcQ",
"https://goo.gl/maps/NDSHwePEyaHMFGwh8"
]
loader = PlaywrightURLLoader(urls=urls, remove_selectors=["header", "footer"])
data = loader.load()
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Unstructured File
next
WebBaseLoader
Contents
URL
Selenium URL Loader
Setup
Playwright URL Loader
Setup
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/url.html |
81a91c62b252-0 | .ipynb
.pdf
Gutenberg
Gutenberg#
Project Gutenberg is an online library of free eBooks.
This notebook covers how to load links to Gutenberg e-books into a document format that we can use downstream.
from langchain.document_loaders import GutenbergLoader
loader = GutenbergLoader('https://www.gutenberg.org/cache/epub/69972/pg69972.txt')
data = loader.load()
data[0].page_content[:300]
'The Project Gutenberg eBook of The changed brides, by Emma Dorothy\r\n\n\nEliza Nevitte Southworth\r\n\n\n\r\n\n\nThis eBook is for the use of anyone anywhere in the United States and\r\n\n\nmost other parts of the world at no cost and with almost no restrictions\r\n\n\nwhatsoever. You may copy it, give it away or re-u'
data[0].metadata
{'source': 'https://www.gutenberg.org/cache/epub/69972/pg69972.txt'}
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College Confidential
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Hacker News
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/gutenberg.html |
f0795d15fb2c-0 | .ipynb
.pdf
PDF
Contents
Using PyPDF
Using MathPix
Using Unstructured
Retain Elements
Fetching remote PDFs using Unstructured
Using PyPDFium2
Using PDFMiner
Using PDFMiner to generate HTML text
Using PyMuPDF
PyPDF Directory
Using pdfplumber
PDF#
Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.
This covers how to load PDF documents into the Document format that we use downstream.
Using PyPDF#
Load PDF using pypdf into array of documents, where each document contains the page content and metadata with page number.
!pip install pypdf
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("example_data/layout-parser-paper.pdf")
pages = loader.load_and_split()
pages[0] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-1 | Document(page_content='LayoutParser : A Uni\x0ced Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1( \x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1Allen Institute for AI\nshannons@allenai.org\n2Brown University\nruochen zhang@brown.edu\n3Harvard University\nfmelissadell,jacob carlson g@fas.harvard.edu\n4University of Washington\nbcgl@cs.washington.edu\n5University of Waterloo\nw422li@uwaterloo.ca\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model con\x0cgurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\ne\x0borts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser , an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-2 | also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io .\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\n·Character Recognition ·Open Source library ·Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classi\x0ccation [ 11,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0}) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-3 | An advantage of this approach is that documents can be retrieved with page numbers.
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
OpenAI API Key: ········
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())
docs = faiss_index.similarity_search("How will the community be engaged?", k=2)
for doc in docs:
print(str(doc.metadata["page"]) + ":", doc.page_content[:300])
9: 10 Z. Shen et al.
Fig. 4: Illustration of (a) the original historical Japanese document with layout
detection results and (b) a recreated version of the document image that achieves
much better character recognition recall. The reorganization algorithm rearranges
the tokens based on the their detect
3: 4 Z. Shen et al.
Efficient Data AnnotationC u s t o m i z e d M o d e l T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images
T h e C o r e L a y o u t P a r s e r L i b r a r yOCR ModuleSt or age & VisualizationLa y ou
Using MathPix#
Inspired by Daniel Gross’s https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21
from langchain.document_loaders import MathpixPDFLoader
loader = MathpixPDFLoader("example_data/layout-parser-paper.pdf")
data = loader.load() | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-4 | data = loader.load()
Using Unstructured#
from langchain.document_loaders import UnstructuredPDFLoader
loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf")
data = loader.load()
Retain Elements#
Under the hood, Unstructured creates different “elements” for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".
loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf", mode="elements")
data = loader.load()
data[0] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-5 | Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\nshannons@allenai.org\n2 Brown University\nruochen zhang@brown.edu\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\nbcgl@cs.washington.edu\n5 University of Waterloo\nw422li@uwaterloo.ca\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-6 | for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\n· Character Recognition · Open Source library · Toolkit.\n1\nIntroduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-7 | Fetching remote PDFs using Unstructured#
This covers how to load online pdfs into a document format that we can use downstream. This can be used for various online pdf sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/
Note: all other pdf loaders can also be used to fetch remote PDFs, but OnlinePDFLoader is a legacy function, and works specifically with UnstructuredPDFLoader.
from langchain.document_loaders import OnlinePDFLoader
loader = OnlinePDFLoader("https://arxiv.org/pdf/2302.03803.pdf")
data = loader.load()
print(data) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-8 | [Document(page_content='A WEAK ( k, k ) -LEFSCHETZ THEOREM FOR PROJECTIVE TORIC ORBIFOLDS\n\nWilliam D. Montoya\n\nInstituto de Matem´atica, Estat´ıstica e Computa¸c˜ao Cient´ıfica,\n\nIn [3] we proved that, under suitable conditions, on a very general codimension s quasi- smooth intersection subvariety X in a projective toric orbifold P d Σ with d + s = 2 ( k + 1 ) the Hodge conjecture holds, that is, every ( p, p ) -cohomology class, under the Poincar´e duality is a rational linear combination of fundamental classes of algebraic subvarieties of X . The proof of the above-mentioned result relies, for p ≠ d + 1 − s , on a Lefschetz\n\nKeywords: (1,1)- Lefschetz theorem, Hodge conjecture, toric varieties, complete intersection Email: wmontoya@ime.unicamp.br\n\ntheorem ([7]) and the Hard Lefschetz theorem for projective orbifolds ([11]). When p = d + 1 − s the proof relies on the Cayley trick, a trick which associates to X a quasi-smooth hypersurface Y in a projective vector bundle, and the Cayley Proposition (4.3) which gives an isomorphism of some primitive cohomologies (4.2) of X and Y . The Cayley trick, following the philosophy of Mavlyutov in [7], reduces results known for quasi-smooth hypersurfaces to quasi-smooth intersection subvarieties. The idea in this paper goes the other way around, we translate some results for quasi-smooth intersection subvarieties to\n\nAcknowledgement. I thank | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-9 | we translate some results for quasi-smooth intersection subvarieties to\n\nAcknowledgement. I thank Prof. Ugo Bruzzo and Tiago Fonseca for useful discus- sions. I also acknowledge support from FAPESP postdoctoral grant No. 2019/23499-7.\n\nLet M be a free abelian group of rank d , let N = Hom ( M, Z ) , and N R = N ⊗ Z R .\n\nif there exist k linearly independent primitive elements e\n\n, . . . , e k ∈ N such that σ = { µ\n\ne\n\n+ ⋯ + µ k e k } . • The generators e i are integral if for every i and any nonnegative rational number µ the product µe i is in N only if µ is an integer. • Given two rational simplicial cones σ , σ ′ one says that σ ′ is a face of σ ( σ ′ < σ ) if the set of integral generators of σ ′ is a subset of the set of integral generators of σ . • A finite set Σ = { σ\n\n, . . . , σ t } of rational simplicial cones is called a rational simplicial complete d -dimensional fan if:\n\nall faces of cones in Σ are in Σ ;\n\nif σ, σ ′ ∈ Σ then σ ∩ σ ′ < σ and σ ∩ σ ′ < σ ′ ;\n\nN R = σ\n\n∪ ⋅ ⋅ ⋅ ∪ σ t .\n\nA rational simplicial complete d -dimensional fan Σ defines a d -dimensional toric variety P d Σ having only orbifold singularities which we assume to be projective. Moreover, T ∶ = N ⊗ Z C | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-10 | which we assume to be projective. Moreover, T ∶ = N ⊗ Z C ∗ ≃ ( C ∗ ) d is the torus action on P d Σ . We denote by Σ ( i ) the i -dimensional cones\n\nFor a cone σ ∈ Σ, ˆ σ is the set of 1-dimensional cone in Σ that are not contained in σ\n\nand x ˆ σ ∶ = ∏ ρ ∈ ˆ σ x ρ is the associated monomial in S .\n\nDefinition 2.2. The irrelevant ideal of P d Σ is the monomial ideal B Σ ∶ =< x ˆ σ ∣ σ ∈ Σ > and the zero locus Z ( Σ ) ∶ = V ( B Σ ) in the affine space A d ∶ = Spec ( S ) is the irrelevant locus.\n\nProposition 2.3 (Theorem 5.1.11 [5]) . The toric variety P d Σ is a categorical quotient A d ∖ Z ( Σ ) by the group Hom ( Cl ( Σ ) , C ∗ ) and the group action is induced by the Cl ( Σ ) - grading of S .\n\nNow we give a brief introduction to complex orbifolds and we mention the needed theorems for the next section. Namely: de Rham theorem and Dolbeault theorem for complex orbifolds.\n\nDefinition 2.4. A complex orbifold of complex dimension d is a singular complex space whose singularities are locally isomorphic to quotient singularities C d / G , for finite sub- groups G ⊂ Gl ( d, C ) .\n\nDefinition 2.5. A differential form | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-11 | 2.5. A differential form on a complex orbifold Z is defined locally at z ∈ Z as a G -invariant differential form on C d where G ⊂ Gl ( d, C ) and Z is locally isomorphic to d\n\nRoughly speaking the local geometry of orbifolds reduces to local G -invariant geometry.\n\nWe have a complex of differential forms ( A ● ( Z ) , d ) and a double complex ( A ● , ● ( Z ) , ∂, ¯ ∂ ) of bigraded differential forms which define the de Rham and the Dolbeault cohomology groups (for a fixed p ∈ N ) respectively:\n\n(1,1)-Lefschetz theorem for projective toric orbifolds\n\nDefinition 3.1. A subvariety X ⊂ P d Σ is quasi-smooth if V ( I X ) ⊂ A #Σ ( 1 ) is smooth outside\n\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub-\n\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub- varieties are quasi-smooth subvarieties (see [2] or [7] for more details).\n\nRemark 3.3 . Quasi-smooth subvarieties are suborbifolds of P d Σ in the sense of Satake in [8]. Intuitively speaking they are subvarieties whose only singularities come from the ambient\n\nProof. From the exponential short exact sequence\n\nwe have a long exact sequence in cohomology\n\nH 1 (O ∗ X ) → H 2 ( X, Z ) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-12 | 1 (O ∗ X ) → H 2 ( X, Z ) → H 2 (O X ) ≃ H 0 , 2 ( X )\n\nwhere the last isomorphisms is due to Steenbrink in [9]. Now, it is enough to prove the commutativity of the next diagram\n\nwhere the last isomorphisms is due to Steenbrink in [9]. Now,\n\nH 2 ( X, Z ) / / H 2 ( X, O X ) ≃ Dolbeault H 2 ( X, C ) deRham ≃ H 2 dR ( X, C ) / / H 0 , 2 ¯ ∂ ( X )\n\nof the proof follows as the ( 1 , 1 ) -Lefschetz theorem in [6].\n\nRemark 3.5 . For k = 1 and P d Σ as the projective space, we recover the classical ( 1 , 1 ) - Lefschetz theorem.\n\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we\n\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we get an isomorphism of cohomologies :\n\ngiven by the Lefschetz morphism and since it is a morphism of Hodge structures, we have:\n\nH 1 , 1 ( X, Q ) ≃ H dim X − 1 , dim X − 1 ( X, Q )\n\nCorollary 3.6. If the dimension of X is 1 , 2 or 3 . The Hodge conjecture holds on X\n\nProof. If the dim C X = 1 the result is clear by the | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-13 | on X\n\nProof. If the dim C X = 1 the result is clear by the Hard Lefschetz theorem for projective orbifolds. The dimension 2 and 3 cases are covered by Theorem 3.5 and the Hard Lefschetz.\n\nCayley trick and Cayley proposition\n\nThe Cayley trick is a way to associate to a quasi-smooth intersection subvariety a quasi- smooth hypersurface. Let L 1 , . . . , L s be line bundles on P d Σ and let π ∶ P ( E ) → P d Σ be the projective space bundle associated to the vector bundle E = L 1 ⊕ ⋯ ⊕ L s . It is known that P ( E ) is a ( d + s − 1 ) -dimensional simplicial toric variety whose fan depends on the degrees of the line bundles and the fan Σ. Furthermore, if the Cox ring, without considering the grading, of P d Σ is C [ x 1 , . . . , x m ] then the Cox ring of P ( E ) is\n\nMoreover for X a quasi-smooth intersection subvariety cut off by f 1 , . . . , f s with deg ( f i ) = [ L i ] we relate the hypersurface Y cut off by F = y 1 f 1 + ⋅ ⋅ ⋅ + y s f s which turns out to be quasi-smooth. For more details see Section 2 in [7].\n\nWe will denote P ( E ) as P d + s − 1 Σ ,X to keep track of its relation with X and P d Σ .\n\nThe following is a key remark.\n\nRemark 4.1 . There is a morphism ι ∶ X → Y | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-14 | 4.1 . There is a morphism ι ∶ X → Y ⊂ P d + s − 1 Σ ,X . Moreover every point z ∶ = ( x, y ) ∈ Y with y ≠ 0 has a preimage. Hence for any subvariety W = V ( I W ) ⊂ X ⊂ P d Σ there exists W ′ ⊂ Y ⊂ P d + s − 1 Σ ,X such that π ( W ′ ) = W , i.e., W ′ = { z = ( x, y ) ∣ x ∈ W } .\n\nFor X ⊂ P d Σ a quasi-smooth intersection variety the morphism in cohomology induced by the inclusion i ∗ ∶ H d − s ( P d Σ , C ) → H d − s ( X, C ) is injective by Proposition 1.4 in [7].\n\nDefinition 4.2. The primitive cohomology of H d − s prim ( X ) is the quotient H d − s ( X, C )/ i ∗ ( H d − s ( P d Σ , C )) and H d − s prim ( X, Q ) with rational coefficients.\n\nH d − s ( P d Σ , C ) and H d − s ( X, C ) have pure Hodge structures, and the morphism i ∗ is com- patible with them, so that H d − s prim ( X ) gets a pure Hodge structure.\n\nThe next Proposition is the Cayley proposition.\n\nProposition 4.3. [Proposition 2.3 in [3] ] Let X = X 1 ∩⋅ ⋅ ⋅∩ X s be a quasi-smooth intersec- tion | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-15 | ⋅ ⋅∩ X s be a quasi-smooth intersec- tion subvariety in P d Σ cut off by homogeneous polynomials f 1 . . . f s . Then for p ≠ d + s − 1 2 , d + s − 3 2\n\nRemark 4.5 . The above isomorphisms are also true with rational coefficients since H ● ( X, C ) = H ● ( X, Q ) ⊗ Q C . See the beginning of Section 7.1 in [10] for more details.\n\nTheorem 5.1. Let Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to the quasi-smooth intersection surface X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f k ⊂ P k + 2 Σ . Then on Y the Hodge conjecture holds.\n\nthe Hodge conjecture holds.\n\nProof. If H k,k prim ( X, Q ) = 0 we are done. So let us assume H k,k prim ( X, Q ) ≠ 0. By the Cayley proposition H k,k prim ( Y, Q ) ≃ H 1 , 1 prim ( X, Q ) and by the ( 1 , 1 ) -Lefschetz theorem for projective\n\ntoric orbifolds there is a non-zero algebraic basis λ C 1 , . . . , λ C n with rational coefficients of H 1 , 1 prim ( X, Q ) , that is, there are n ∶ = h 1 , 1 prim ( X, Q ) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-16 | is, there are n ∶ = h 1 , 1 prim ( X, Q ) algebraic curves C 1 , . . . , C n in X such that under the Poincar´e duality the class in homology [ C i ] goes to λ C i , [ C i ] ↦ λ C i . Recall that the Cox ring of P k + 2 is contained in the Cox ring of P 2 k + 1 Σ ,X without considering the grading. Considering the grading we have that if α ∈ Cl ( P k + 2 Σ ) then ( α, 0 ) ∈ Cl ( P 2 k + 1 Σ ,X ) . So the polynomials defining C i ⊂ P k + 2 Σ can be interpreted in P 2 k + 1 X, Σ but with different degree. Moreover, by Remark 4.1 each C i is contained in Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } and\n\nfurthermore it has codimension k .\n\nClaim: { C i } ni = 1 is a basis of prim ( ) . It is enough to prove that λ C i is different from zero in H k,k prim ( Y, Q ) or equivalently that the cohomology classes { λ C i } ni = 1 do not come from the ambient space. By contradiction, let us assume that there exists a j and C ⊂ P 2 k + 1 Σ ,X such that λ C ∈ H k,k ( P 2 k + 1 Σ ,X , Q ) with i ∗ ( λ C ) = λ C j or in terms of homology there exists a ( k + 2 ) -dimensional algebraic subvariety | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-17 | of homology there exists a ( k + 2 ) -dimensional algebraic subvariety V ⊂ P 2 k + 1 Σ ,X such that V ∩ Y = C j so they are equal as a homology class of P 2 k + 1 Σ ,X ,i.e., [ V ∩ Y ] = [ C j ] . It is easy to check that π ( V ) ∩ X = C j as a subvariety of P k + 2 Σ where π ∶ ( x, y ) ↦ x . Hence [ π ( V ) ∩ X ] = [ C j ] which is equivalent to say that λ C j comes from P k + 2 Σ which contradicts the choice of [ C j ] .\n\nRemark 5.2 . Into the proof of the previous theorem, the key fact was that on X the Hodge conjecture holds and we translate it to Y by contradiction. So, using an analogous argument we have:\n\nargument we have:\n\nProposition 5.3. Let Y = { F = y 1 f s +⋯+ y s f s = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to a quasi-smooth intersection subvariety X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f s ⊂ P d Σ such that d + s = 2 ( k + 1 ) . If the Hodge conjecture holds on X then it holds as well on Y .\n\nCorollary 5.4. If the dimension of Y is 2 s − 1 , 2 s or 2 s + 1 then the Hodge conjecture holds on Y .\n\nProof. By Proposition 5.3 and | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-18 | Hodge conjecture holds on Y .\n\nProof. By Proposition 5.3 and Corollary 3.6.\n\n[\n\n] Angella, D. Cohomologies of certain orbifolds. Journal of Geometry and Physics\n\n(\n\n),\n\n–\n\n[\n\n] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal\n\n,\n\n(Aug\n\n). [\n\n] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S˜ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (\n\n). [\n\n] Caramello Jr, F. C. Introduction to orbifolds. a\n\niv:\n\nv\n\n(\n\n). [\n\n] Cox, D., Little, J., and Schenck, H. Toric varieties, vol.\n\nAmerican Math- ematical Soc.,\n\n[\n\n] Griffiths, P., and Harris, J. Principles of Algebraic Geometry. John Wiley & Sons, Ltd,\n\n[\n\n] Mavlyutov, A. R. Cohomology of complete intersections in toric varieties. Pub- lished in Pacific J. of Math.\n\nNo.\n\n(\n\n),\n\n–\n\n[\n\n] Satake, I. On a Generalization of the Notion of Manifold. Proceedings of the National Academy of Sciences of the United States of America\n\n,\n\n(\n\n),\n\n–\n\n[\n\n] Steenbrink, J. H. M. Intersection form for | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-19 | Steenbrink, J. H. M. Intersection form for quasi-homogeneous singularities. Com- positio Mathematica\n\n,\n\n(\n\n),\n\n–\n\n[\n\n] Voisin, C. Hodge Theory and Complex Algebraic Geometry I, vol.\n\nof Cambridge Studies in Advanced Mathematics . Cambridge University Press,\n\n[\n\n] Wang, Z. Z., and Zaffran, D. A remark on the Hard Lefschetz theorem for K¨ahler orbifolds. Proceedings of the American Mathematical Society\n\n,\n\n(Aug\n\n).\n\n[2] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal 75, 2 (Aug 1994).\n\n[\n\n] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S˜ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (\n\n).\n\n[3] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S˜ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (2021).\n\nA. R. Cohomology of complete intersections in toric varieties. Pub-', lookup_str='', metadata={'source': '/var/folders/ph/hhm7_zyx4l13k3v8z02dwp1w0000gn/T/tmpgq0ckaja/online_file.pdf'}, lookup_index=0)] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-20 | Using PyPDFium2#
from langchain.document_loaders import PyPDFium2Loader
loader = PyPDFium2Loader("example_data/layout-parser-paper.pdf")
data = loader.load()
Using PDFMiner#
from langchain.document_loaders import PDFMinerLoader
loader = PDFMinerLoader("example_data/layout-parser-paper.pdf")
data = loader.load()
Using PDFMiner to generate HTML text#
This can be helpful for chunking texts semantically into sections as the output html content can be parsed via BeautifulSoup to get more structured and rich information about font size, page numbers, pdf headers/footers, etc.
from langchain.document_loaders import PDFMinerPDFasHTMLLoader
loader = PDFMinerPDFasHTMLLoader("example_data/layout-parser-paper.pdf")
data = loader.load()[0] # entire pdf is loaded as a single Document
from bs4 import BeautifulSoup
soup = BeautifulSoup(data.page_content,'html.parser')
content = soup.find_all('div')
import re
cur_fs = None
cur_text = ''
snippets = [] # first collect all snippets that have the same font size
for c in content:
sp = c.find('span')
if not sp:
continue
st = sp.get('style')
if not st:
continue
fs = re.findall('font-size:(\d+)px',st)
if not fs:
continue
fs = int(fs[0])
if not cur_fs:
cur_fs = fs
if fs == cur_fs:
cur_text += c.text
else:
snippets.append((cur_text,cur_fs))
cur_fs = fs
cur_text = c.text
snippets.append((cur_text,cur_fs)) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-21 | cur_text = c.text
snippets.append((cur_text,cur_fs))
# Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as
# headers/footers in a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant info)
from langchain.docstore.document import Document
cur_idx = -1
semantic_snippets = []
# Assumption: headings have higher font size than their respective content
for s in snippets:
# if current snippet's font size > previous section's heading => it is a new heading
if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']:
metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}
metadata.update(data.metadata)
semantic_snippets.append(Document(page_content='',metadata=metadata))
cur_idx += 1
continue
# if current snippet's font size <= previous section's content => content belongs to the same section (one can also create
# a tree like structure for sub sections if needed but that may require some more thinking and may be data specific)
if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']:
semantic_snippets[cur_idx].page_content += s[0]
semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font'])
continue
# if current snippet's font size > previous section's content but less tha previous section's heading than also make a new | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-22 | # section (e.g. title of a pdf will have the highest font size but we don't want it to subsume all sections)
metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}
metadata.update(data.metadata)
semantic_snippets.append(Document(page_content='',metadata=metadata))
cur_idx += 1
semantic_snippets[4] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-23 | Document(page_content='Recently, various DL models and datasets have been developed for layout analysis\ntasks. The dhSegment [22] utilizes fully convolutional networks [20] for segmen-\ntation tasks on historical documents. Object detection-based methods like Faster\nR-CNN [28] and Mask R-CNN [12] are used for identifying document elements [38]\nand detecting tables [30, 26]. Most recently, Graph Neural Networks [29] have also\nbeen used in table detection [27]. However, these models are usually implemented\nindividually and there is no unified framework to load and use such models.\nThere has been a surge of interest in creating open-source tools for document\nimage processing: a search of document image analysis in Github leads to 5M\nrelevant code pieces 6; yet most of them rely on traditional rule-based methods\nor provide limited functionalities. The closest prior research to our work is the\nOCR-D project7, which also tries to build a complete toolkit for DIA. However,\nsimilar to the platform developed by Neudecker et al. [21], it is designed for\nanalyzing historical documents, and provides no supports for recent DL models.\nThe DocumentLayoutAnalysis project8 focuses on processing born-digital PDF\ndocuments via analyzing the stored PDF data. Repositories like DeepLayout9\nand Detectron2-PubLayNet10 are individual deep learning models trained on\nlayout analysis datasets without support for the full DIA pipeline. The Document\nAnalysis and Exploitation (DAE) platform [15] and the DeepDIVA project [2]\naim to improve the reproducibility of DIA methods (or DL models), yet they\nare not actively maintained. OCR engines like Tesseract [14], easyOCR11 and\npaddleOCR12 usually do not come with comprehensive functionalities for other\nDIA tasks like layout analysis.\nRecent | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-24 | usually do not come with comprehensive functionalities for other\nDIA tasks like layout analysis.\nRecent years have also seen numerous efforts to create libraries for promoting\nreproducibility and reusability in the field of DL. Libraries like Dectectron2 [35],\n6 The number shown is obtained by specifying the search type as ‘code’.\n7 https://ocr-d.de/en/about\n8 https://github.com/BobLd/DocumentLayoutAnalysis\n9 https://github.com/leonlulu/DeepLayout\n10 https://github.com/hpanwar08/detectron2\n11 https://github.com/JaidedAI/EasyOCR\n12 https://github.com/PaddlePaddle/PaddleOCR\n4\nZ. Shen et al.\nFig. 1: The overall architecture of LayoutParser. For an input document image,\nthe core LayoutParser library provides a set of off-the-shelf tools for layout\ndetection, OCR, visualization, and storage, backed by a carefully designed layout\ndata structure. LayoutParser also supports high level customization via efficient\nlayout annotation and model training functions. These improve model accuracy\non the target samples. The community platform enables the easy sharing of DIA\nmodels and whole digitization pipelines to promote reusability and reproducibility.\nA collection of detailed documentation, tutorials and exemplar projects make\nLayoutParser easy to learn and use.\nAllenNLP [8] and transformers [34] have provided the community with complete\nDL-based support for developing and deploying models for general computer\nvision and natural language processing problems. LayoutParser, on the other\nhand, specializes specifically in DIA tasks. LayoutParser is also equipped with a\ncommunity platform inspired by established model hubs such as Torch Hub [23]\nand TensorFlow Hub [1]. It enables the sharing of pretrained models as | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-25 | Torch Hub [23]\nand TensorFlow Hub [1]. It enables the sharing of pretrained models as well as\nfull document processing pipelines that are unique to DIA tasks.\nThere have been a variety of document data collections to facilitate the\ndevelopment of DL models. Some examples include PRImA [3](magazine layouts),\nPubLayNet [38](academic paper layouts), Table Bank [18](tables in academic\npapers), Newspaper Navigator Dataset [16, 17](newspaper figure layouts) and\nHJDataset [31](historical Japanese document layouts). A spectrum of models\ntrained on these datasets are currently available in the LayoutParser model zoo\nto support different use cases.\n', metadata={'heading': '2 Related Work\n', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'}) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-26 | Using PyMuPDF#
This is the fastest of the PDF parsing options, and contains detailed metadata about the PDF and its pages, as well as returns one document per page.
from langchain.document_loaders import PyMuPDFLoader
loader = PyMuPDFLoader("example_data/layout-parser-paper.pdf")
data = loader.load()
data[0] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-27 | Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\nshannons@allenai.org\n2 Brown University\nruochen zhang@brown.edu\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\nbcgl@cs.washington.edu\n5 University of Waterloo\nw422li@uwaterloo.ca\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-28 | for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\n· Character Recognition · Open Source library · Toolkit.\n1\nIntroduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-29 | Additionally, you can pass along any of the options from the PyMuPDF documentation as keyword arguments in the load call, and it will be pass along to the get_text() call.
PyPDF Directory#
Load PDFs from directory
from langchain.document_loaders import PyPDFDirectoryLoader
loader = PyPDFDirectoryLoader("example_data/")
docs = loader.load()
Using pdfplumber#
Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.
from langchain.document_loaders import PDFPlumberLoader
loader = PDFPlumberLoader("example_data/layout-parser-paper.pdf")
data = loader.load()
data[0] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-30 | Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\n1202 shannons@allenai.org\n2 Brown University\nruochen zhang@brown.edu\n3 Harvard University\nnuJ {melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\nbcgl@cs.washington.edu\n12 5 University of Waterloo\nw422li@uwaterloo.ca\n]VC.sc[\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\nprimarily driven by the application of neural networks. Ideally, research\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\n2v84351.3012:viXra portantinnovationsbyawideaudience.Thoughtherehavebeenon-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\nTo promote extensibility, | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-31 | promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\n· Character Recognition · Open Source library · Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,', metadata={'source': 'example_data/layout-parser-paper.pdf', 'file_path': 'example_data/layout-parser-paper.pdf', 'page': 1, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
f0795d15fb2c-32 | previous
Pandas DataFrame
next
Sitemap
Contents
Using PyPDF
Using MathPix
Using Unstructured
Retain Elements
Fetching remote PDFs using Unstructured
Using PyPDFium2
Using PDFMiner
Using PDFMiner to generate HTML text
Using PyMuPDF
PyPDF Directory
Using pdfplumber
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
29d469b34894-0 | .ipynb
.pdf
DuckDB
Contents
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
DuckDB#
DuckDB is an in-process SQL OLAP database management system.
Load a DuckDB query with one document per row.
#!pip install duckdb
from langchain.document_loaders import DuckDBLoader
%%file example.csv
Team,Payroll
Nationals,81.34
Reds,82.20
Writing example.csv
loader = DuckDBLoader("SELECT * FROM read_csv_auto('example.csv')")
data = loader.load()
print(data)
[Document(page_content='Team: Nationals\nPayroll: 81.34', metadata={}), Document(page_content='Team: Reds\nPayroll: 82.2', metadata={})]
Specifying Which Columns are Content vs Metadata#
loader = DuckDBLoader(
"SELECT * FROM read_csv_auto('example.csv')",
page_content_columns=["Team"],
metadata_columns=["Payroll"]
)
data = loader.load()
print(data)
[Document(page_content='Team: Nationals', metadata={'Payroll': 81.34}), Document(page_content='Team: Reds', metadata={'Payroll': 82.2})]
Adding Source to Metadata#
loader = DuckDBLoader(
"SELECT Team, Payroll, Team As source FROM read_csv_auto('example.csv')",
metadata_columns=["source"]
)
data = loader.load()
print(data)
[Document(page_content='Team: Nationals\nPayroll: 81.34\nsource: Nationals', metadata={'source': 'Nationals'}), Document(page_content='Team: Reds\nPayroll: 82.2\nsource: Reds', metadata={'source': 'Reds'})]
previous
Docugami
next
Fauna
Contents | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/duckdb.html |
29d469b34894-1 | previous
Docugami
next
Fauna
Contents
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/duckdb.html |
8abf990c09cf-0 | .ipynb
.pdf
Google BigQuery
Contents
Basic Usage
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
Google BigQuery#
Google BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.
BigQuery is a part of the Google Cloud Platform.
Load a BigQuery query with one document per row.
#!pip install google-cloud-bigquery
from langchain.document_loaders import BigQueryLoader
BASE_QUERY = '''
SELECT
id,
dna_sequence,
organism
FROM (
SELECT
ARRAY (
SELECT
AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp. (strain GC14_75)." AS organism
UNION ALL
SELECT
AS STRUCT 2 AS id, "AGGCGA" AS dna_sequence, "Heimdallarchaeota archaeon (strain LC_2)." AS organism
UNION ALL
SELECT
AS STRUCT 3 AS id, "TCCGGA" AS dna_sequence, "Acidianus hospitalis (strain W1)." AS organism) AS new_array),
UNNEST(new_array)
'''
Basic Usage#
loader = BigQueryLoader(BASE_QUERY)
data = loader.load()
print(data) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_bigquery.html |
8abf990c09cf-1 | loader = BigQueryLoader(BASE_QUERY)
data = loader.load()
print(data)
[Document(page_content='id: 1\ndna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).', lookup_str='', metadata={}, lookup_index=0), Document(page_content='id: 2\ndna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).', lookup_str='', metadata={}, lookup_index=0), Document(page_content='id: 3\ndna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).', lookup_str='', metadata={}, lookup_index=0)]
Specifying Which Columns are Content vs Metadata#
loader = BigQueryLoader(BASE_QUERY, page_content_columns=["dna_sequence", "organism"], metadata_columns=["id"])
data = loader.load()
print(data)
[Document(page_content='dna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).', lookup_str='', metadata={'id': 1}, lookup_index=0), Document(page_content='dna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).', lookup_str='', metadata={'id': 2}, lookup_index=0), Document(page_content='dna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).', lookup_str='', metadata={'id': 3}, lookup_index=0)]
Adding Source to Metadata#
# Note that the `id` column is being returned twice, with one instance aliased as `source`
ALIASED_QUERY = '''
SELECT
id,
dna_sequence,
organism,
id as source
FROM (
SELECT
ARRAY (
SELECT | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_bigquery.html |
8abf990c09cf-2 | id as source
FROM (
SELECT
ARRAY (
SELECT
AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp. (strain GC14_75)." AS organism
UNION ALL
SELECT
AS STRUCT 2 AS id, "AGGCGA" AS dna_sequence, "Heimdallarchaeota archaeon (strain LC_2)." AS organism
UNION ALL
SELECT
AS STRUCT 3 AS id, "TCCGGA" AS dna_sequence, "Acidianus hospitalis (strain W1)." AS organism) AS new_array),
UNNEST(new_array)
'''
loader = BigQueryLoader(ALIASED_QUERY, metadata_columns=["source"])
data = loader.load()
print(data)
[Document(page_content='id: 1\ndna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).\nsource: 1', lookup_str='', metadata={'source': 1}, lookup_index=0), Document(page_content='id: 2\ndna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).\nsource: 2', lookup_str='', metadata={'source': 2}, lookup_index=0), Document(page_content='id: 3\ndna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).\nsource: 3', lookup_str='', metadata={'source': 3}, lookup_index=0)]
previous
Git
next
Google Cloud Storage Directory
Contents
Basic Usage
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
By Harrison Chase
© Copyright 2023, Harrison Chase. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_bigquery.html |
8abf990c09cf-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/google_bigquery.html |
5a0d2c405fcc-0 | .ipynb
.pdf
CSV
Contents
Customizing the csv parsing and loading
Specify a column to identify the document source
UnstructuredCSVLoader
CSV#
A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.
Load csv data with a single row per document.
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')
data = loader.load()
print(data) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-1 | [Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-2 | 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='Team: | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-3 | 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-4 | 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-5 | 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0)] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-6 | Customizing the csv parsing and loading#
See the csv module documentation for more information of what csv args are supported.
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', csv_args={
'delimiter': ',',
'quotechar': '"',
'fieldnames': ['MLB Team', 'Payroll in millions', 'Wins']
})
data = loader.load()
print(data) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-7 | [Document(page_content='MLB Team: Team\nPayroll in millions: "Payroll (millions)"\nWins: "Wins"', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='MLB Team: Nationals\nPayroll in millions: 81.34\nWins: 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='MLB Team: Reds\nPayroll in millions: 82.20\nWins: 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='MLB Team: Yankees\nPayroll in millions: 197.96\nWins: 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='MLB Team: Giants\nPayroll in millions: 117.62\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='MLB Team: Braves\nPayroll in millions: 83.31\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='MLB Team: Athletics\nPayroll in millions: 55.37\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='MLB Team: Rangers\nPayroll in millions: | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-8 | lookup_index=0), Document(page_content='MLB Team: Rangers\nPayroll in millions: 120.51\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='MLB Team: Orioles\nPayroll in millions: 81.43\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='MLB Team: Rays\nPayroll in millions: 64.17\nWins: 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='MLB Team: Angels\nPayroll in millions: 154.49\nWins: 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='MLB Team: Tigers\nPayroll in millions: 132.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='MLB Team: Cardinals\nPayroll in millions: 110.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='MLB Team: Dodgers\nPayroll in millions: 95.14\nWins: 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='MLB Team: White Sox\nPayroll in millions: | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-9 | Document(page_content='MLB Team: White Sox\nPayroll in millions: 96.92\nWins: 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='MLB Team: Brewers\nPayroll in millions: 97.65\nWins: 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='MLB Team: Phillies\nPayroll in millions: 174.54\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='MLB Team: Diamondbacks\nPayroll in millions: 74.28\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='MLB Team: Pirates\nPayroll in millions: 63.43\nWins: 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='MLB Team: Padres\nPayroll in millions: 55.24\nWins: 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='MLB Team: Mariners\nPayroll in millions: 81.97\nWins: 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='MLB Team: Mets\nPayroll in millions: | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-10 | lookup_index=0), Document(page_content='MLB Team: Mets\nPayroll in millions: 93.35\nWins: 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='MLB Team: Blue Jays\nPayroll in millions: 75.48\nWins: 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='MLB Team: Royals\nPayroll in millions: 60.91\nWins: 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='MLB Team: Marlins\nPayroll in millions: 118.07\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='MLB Team: Red Sox\nPayroll in millions: 173.18\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='MLB Team: Indians\nPayroll in millions: 78.43\nWins: 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='MLB Team: Twins\nPayroll in millions: 94.08\nWins: 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='MLB Team: Rockies\nPayroll in millions: | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-11 | lookup_index=0), Document(page_content='MLB Team: Rockies\nPayroll in millions: 78.06\nWins: 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='MLB Team: Cubs\nPayroll in millions: 88.19\nWins: 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0), Document(page_content='MLB Team: Astros\nPayroll in millions: 60.65\nWins: 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 30}, lookup_index=0)] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-12 | Specify a column to identify the document source#
Use the source_column argument to specify a source for the document created from each row. Otherwise file_path will be used as the source for all documents created from the CSV file.
This is useful when using documents loaded from CSV files for chains that answer questions using sources.
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', source_column="Team")
data = loader.load()
print(data) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-13 | [Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': 'Nationals', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': 'Reds', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', lookup_str='', metadata={'source': 'Yankees', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', lookup_str='', metadata={'source': 'Giants', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', lookup_str='', metadata={'source': 'Braves', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', lookup_str='', metadata={'source': 'Athletics', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': 'Rangers', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': 'Orioles', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-14 | 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': 'Rays', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': 'Angels', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': 'Tigers', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': 'Cardinals', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': 'Dodgers', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': 'White Sox', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': 'Brewers', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': 'Phillies', 'row': 15}, lookup_index=0), Document(page_content='Team: | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-15 | 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': 'Diamondbacks', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': 'Pirates', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': 'Padres', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': 'Mariners', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': 'Mets', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': 'Blue Jays', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': 'Royals', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': 'Marlins', 'row': 23}, lookup_index=0), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-16 | metadata={'source': 'Marlins', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': 'Red Sox', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': 'Indians', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': 'Twins', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': 'Rockies', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': 'Cubs', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': 'Astros', 'row': 29}, lookup_index=0)] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-17 | UnstructuredCSVLoader#
You can also load the table using the UnstructuredCSVLoader. One advantage of using UnstructuredCSVLoader is that if you use it in "elements" mode, an HTML representation of the table will be available in the metadata.
from langchain.document_loaders.csv_loader import UnstructuredCSVLoader
loader = UnstructuredCSVLoader(file_path='example_data/mlb_teams_2012.csv', mode="elements")
docs = loader.load()
print(docs[0].metadata["text_as_html"])
<table border="1" class="dataframe">
<tbody>
<tr>
<td>Nationals</td>
<td>81.34</td>
<td>98</td>
</tr>
<tr>
<td>Reds</td>
<td>82.20</td>
<td>97</td>
</tr>
<tr>
<td>Yankees</td>
<td>197.96</td>
<td>95</td>
</tr>
<tr>
<td>Giants</td>
<td>117.62</td>
<td>94</td>
</tr>
<tr>
<td>Braves</td>
<td>83.31</td>
<td>94</td>
</tr>
<tr>
<td>Athletics</td>
<td>55.37</td>
<td>94</td>
</tr>
<tr>
<td>Rangers</td>
<td>120.51</td>
<td>93</td> | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-18 | <td>120.51</td>
<td>93</td>
</tr>
<tr>
<td>Orioles</td>
<td>81.43</td>
<td>93</td>
</tr>
<tr>
<td>Rays</td>
<td>64.17</td>
<td>90</td>
</tr>
<tr>
<td>Angels</td>
<td>154.49</td>
<td>89</td>
</tr>
<tr>
<td>Tigers</td>
<td>132.30</td>
<td>88</td>
</tr>
<tr>
<td>Cardinals</td>
<td>110.30</td>
<td>88</td>
</tr>
<tr>
<td>Dodgers</td>
<td>95.14</td>
<td>86</td>
</tr>
<tr>
<td>White Sox</td>
<td>96.92</td>
<td>85</td>
</tr>
<tr>
<td>Brewers</td>
<td>97.65</td>
<td>83</td>
</tr>
<tr>
<td>Phillies</td>
<td>174.54</td>
<td>81</td>
</tr>
<tr>
<td>Diamondbacks</td> | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-19 | </tr>
<tr>
<td>Diamondbacks</td>
<td>74.28</td>
<td>81</td>
</tr>
<tr>
<td>Pirates</td>
<td>63.43</td>
<td>79</td>
</tr>
<tr>
<td>Padres</td>
<td>55.24</td>
<td>76</td>
</tr>
<tr>
<td>Mariners</td>
<td>81.97</td>
<td>75</td>
</tr>
<tr>
<td>Mets</td>
<td>93.35</td>
<td>74</td>
</tr>
<tr>
<td>Blue Jays</td>
<td>75.48</td>
<td>73</td>
</tr>
<tr>
<td>Royals</td>
<td>60.91</td>
<td>72</td>
</tr>
<tr>
<td>Marlins</td>
<td>118.07</td>
<td>69</td>
</tr>
<tr>
<td>Red Sox</td>
<td>173.18</td>
<td>69</td>
</tr>
<tr>
<td>Indians</td>
<td>78.43</td>
<td>68</td> | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
5a0d2c405fcc-20 | <td>78.43</td>
<td>68</td>
</tr>
<tr>
<td>Twins</td>
<td>94.08</td>
<td>66</td>
</tr>
<tr>
<td>Rockies</td>
<td>78.06</td>
<td>64</td>
</tr>
<tr>
<td>Cubs</td>
<td>88.19</td>
<td>61</td>
</tr>
<tr>
<td>Astros</td>
<td>60.65</td>
<td>55</td>
</tr>
</tbody>
</table>
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Contents
Customizing the csv parsing and loading
Specify a column to identify the document source
UnstructuredCSVLoader
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
a546852f0d85-0 | .ipynb
.pdf
Wikipedia
Contents
Installation
Examples
Wikipedia#
Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.
This notebook shows how to load wiki pages from wikipedia.org into the Document format that we use downstream.
Installation#
First, you need to install wikipedia python package.
#!pip install wikipedia
Examples#
WikipediaLoader has these arguments:
query: free text which used to find documents in Wikipedia
optional lang: default=”en”. Use it to search in a specific language part of Wikipedia
optional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.
optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), title, Summary. If True, other fields also downloaded.
from langchain.document_loaders import WikipediaLoader
docs = WikipediaLoader(query='HUNTER X HUNTER', load_max_docs=2).load()
len(docs)
docs[0].metadata # meta-information of the Document
docs[0].page_content[:400] # a content of the Document
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Contents
Installation
Examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/wikipedia.html |
661ffa15e3f9-0 | .ipynb
.pdf
HuggingFace dataset
Contents
Example
HuggingFace dataset#
The Hugging Face Hub is home to over 5,000 datasets in more than 100 languages that can be used for a broad range of tasks across NLP, Computer Vision, and Audio. They used for a diverse range of tasks such as translation,
automatic speech recognition, and image classification.
This notebook shows how to load Hugging Face Hub datasets to LangChain.
from langchain.document_loaders import HuggingFaceDatasetLoader
dataset_name="imdb"
page_content_column="text"
loader=HuggingFaceDatasetLoader(dataset_name,page_content_column)
data = loader.load()
data[:15] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-1 | data = loader.load()
data[:15]
[Document(page_content='I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered "controversial" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\'t have much of a plot.', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-2 | Document(page_content='"I Am Curious: Yellow" is a risible and pretentious steaming pile. It doesn\'t matter what one\'s political views are because this film can hardly be taken seriously on any level. As for the claim that frontal male nudity is an automatic NC-17, that isn\'t true. I\'ve seen R-rated films with male nudity. Granted, they only offer some fleeting views, but where are the R-rated films with gaping vulvas and flapping labia? Nowhere, because they don\'t exist. The same goes for those crappy cable shows: schlongs swinging in the breeze but not a clitoris in sight. And those pretentious indie movies like The Brown Bunny, in which we\'re treated to the site of Vincent Gallo\'s throbbing johnson, but not a trace of pink visible on Chloe Sevigny. Before crying (or implying) "double-standard" in matters of nudity, the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and women: there are no genitals on display when actresses appears nude, and the same cannot be said for a man. In fact, you generally won\'t see female genitals in an American film in anything short of porn or explicit erotica. This alleged double-standard is less a double standard than an admittedly depressing ability to come to terms culturally with the insides of women\'s bodies.', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-3 | Document(page_content="If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.<br /><br />One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).<br /><br />One might better spend one's time staring out a window at a tree growing.<br /><br />", metadata={'label': 0}),
Document(page_content="This film was probably inspired by Godard's Masculin, féminin and I urge you to see that film instead.<br /><br />The film has two strong elements and those are, (1) the realistic acting (2) the impressive, undeservedly good, photo. Apart from that, what strikes me most is the endless stream of silliness. Lena Nyman has to be most annoying actress in the world. She acts so stupid and with all the nudity in this film,...it's unattractive. Comparing to Godard's film, intellectuality has been replaced with stupidity. Without going too far on this subject, I would say that follows from the difference in ideals between the French and the Swedish society.<br /><br />A movie of its time, and place. 2/10.", metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-4 | Document(page_content='Oh, brother...after hearing about this ridiculous film for umpteen years all I can think of is that old Peggy Lee song..<br /><br />"Is that all there is??" ...I was just an early teen when this smoked fish hit the U.S. I was too young to get in the theater (although I did manage to sneak into "Goodbye Columbus"). Then a screening at a local film museum beckoned - Finally I could see this film, except now I was as old as my parents were when they schlepped to see it!!<br /><br />The ONLY reason this film was not condemned to the anonymous sands of time was because of the obscenity case sparked by its U.S. release. MILLIONS of people flocked to this stinker, thinking they were going to see a sex film...Instead, they got lots of closeups of gnarly, repulsive Swedes, on-street interviews in bland shopping malls, asinie political pretension...and feeble who-cares simulated sex scenes with saggy, pale actors.<br /><br />Cultural icon, holy grail, historic artifact..whatever this thing was, shred it, burn it, then stuff the ashes in a lead box!<br /><br />Elite esthetes still scrape to find value in its boring pseudo revolutionary political spewings..But if it weren\'t for the censorship scandal, it would have been ignored, then forgotten.<br /><br />Instead, the "I Am Blank, Blank" rhythymed title was repeated endlessly for years as a titilation for porno films (I am Curious, Lavender - for gay films, I Am Curious, Black - for blaxploitation films, etc..) and every ten years or so the thing rises from the dead, to be viewed by a new generation of suckers who want to see that "naughty sex film" that "revolutionized | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-5 | new generation of suckers who want to see that "naughty sex film" that "revolutionized the film industry"...<br /><br />Yeesh, avoid like the plague..Or if you MUST see it - rent the video and fast forward to the "dirty" parts, just to get it over with.<br /><br />', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-6 | Document(page_content="I would put this at the top of my list of films in the category of unwatchable trash! There are films that are bad, but the worst kind are the ones that are unwatchable but you are suppose to like them because they are supposed to be good for you! The sex sequences, so shocking in its day, couldn't even arouse a rabbit. The so called controversial politics is strictly high school sophomore amateur night Marxism. The film is self-consciously arty in the worst sense of the term. The photography is in a harsh grainy black and white. Some scenes are out of focus or taken from the wrong angle. Even the sound is bad! And some people call this art?<br /><br />", metadata={'label': 0}),
Document(page_content="Whoever wrote the screenplay for this movie obviously never consulted any books about Lucille Ball, especially her autobiography. I've never seen so many mistakes in a biopic, ranging from her early years in Celoron and Jamestown to her later years with Desi. I could write a whole list of factual errors, but it would go on for pages. In all, I believe that Lucille Ball is one of those inimitable people who simply cannot be portrayed by anyone other than themselves. If I were Lucie Arnaz and Desi, Jr., I would be irate at how many mistakes were made in this film. The filmmakers tried hard, but the movie seems awfully sloppy to me.", metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-7 | Document(page_content='When I first saw a glimpse of this movie, I quickly noticed the actress who was playing the role of Lucille Ball. Rachel York\'s portrayal of Lucy is absolutely awful. Lucille Ball was an astounding comedian with incredible talent. To think about a legend like Lucille Ball being portrayed the way she was in the movie is horrendous. I cannot believe out of all the actresses in the world who could play a much better Lucy, the producers decided to get Rachel York. She might be a good actress in other roles but to play the role of Lucille Ball is tough. It is pretty hard to find someone who could resemble Lucille Ball, but they could at least find someone a bit similar in looks and talent. If you noticed York\'s portrayal of Lucy in episodes of I Love Lucy like the chocolate factory or vitavetavegamin, nothing is similar in any way-her expression, voice, or movement.<br /><br />To top it all off, Danny Pino playing Desi Arnaz is horrible. Pino does not qualify to play as Ricky. He\'s small and skinny, his accent is unreal, and once again, his acting is unbelievable. Although Fred and Ethel were not similar either, they were not as bad as the characters of Lucy and Ricky.<br /><br />Overall, extremely horrible casting and the story is badly told. If people want to understand the real life situation of Lucille Ball, I suggest watching A&E Biography of Lucy and Desi, read the book from Lucille Ball herself, or PBS\' American Masters: Finding Lucy. If you want to see a docudrama, "Before the Laughter" would be a better choice. The casting of Lucille Ball and Desi Arnaz in "Before the Laughter" is much better compared to this. At least, a similar aspect is shown rather than nothing.', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-8 | Document(page_content='Who are these "They"- the actors? the filmmakers? Certainly couldn\'t be the audience- this is among the most air-puffed productions in existence. It\'s the kind of movie that looks like it was a lot of fun to shoot\x97 TOO much fun, nobody is getting any actual work done, and that almost always makes for a movie that\'s no fun to watch.<br /><br />Ritter dons glasses so as to hammer home his character\'s status as a sort of doppleganger of the bespectacled Bogdanovich; the scenes with the breezy Ms. Stratten are sweet, but have an embarrassing, look-guys-I\'m-dating-the-prom-queen feel to them. Ben Gazzara sports his usual cat\'s-got-canary grin in a futile attempt to elevate the meager plot, which requires him to pursue Audrey Hepburn with all the interest of a narcoleptic at an insomnia clinic. In the meantime, the budding couple\'s respective children (nepotism alert: Bogdanovich\'s daughters) spew cute and pick up some fairly disturbing pointers on \'love\' while observing their parents. (Ms. Hepburn, drawing on her dignity, manages to rise above the proceedings- but she has the monumental challenge of playing herself, ostensibly.) Everybody looks great, but so what? It\'s a movie and we can expect that much, if that\'s what you\'re looking for you\'d be better off picking up a copy of Vogue.<br /><br />Oh- and it has to be mentioned that Colleen Camp thoroughly annoys, even apart from her singing, which, while competent, is wholly unconvincing... the country and western numbers are woefully mismatched with the standards on the soundtrack. Surely this is NOT what Gershwin (who wrote the song from which the movie\'s title is derived) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-9 | NOT what Gershwin (who wrote the song from which the movie\'s title is derived) had in mind; his stage musicals of the 20\'s may have been slight, but at least they were long on charm. "They All Laughed" tries to coast on its good intentions, but nobody- least of all Peter Bogdanovich - has the good sense to put on the brakes.<br /><br />Due in no small part to the tragic death of Dorothy Stratten, this movie has a special place in the heart of Mr. Bogdanovich- he even bought it back from its producers, then distributed it on his own and went bankrupt when it didn\'t prove popular. His rise and fall is among the more sympathetic and tragic of Hollywood stories, so there\'s no joy in criticizing the film... there _is_ real emotional investment in Ms. Stratten\'s scenes. But "Laughed" is a faint echo of "The Last Picture Show", "Paper Moon" or "What\'s Up, Doc"- following "Daisy Miller" and "At Long Last Love", it was a thundering confirmation of the phase from which P.B. has never emerged.<br /><br />All in all, though, the movie is harmless, only a waste of rental. I want to watch people having a good time, I\'ll go to the park on a sunny day. For filmic expressions of joy and love, I\'ll stick to Ernest Lubitsch and Jaques Demy...', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-10 | Document(page_content="This is said to be a personal film for Peter Bogdonavitch. He based it on his life but changed things around to fit the characters, who are detectives. These detectives date beautiful models and have no problem getting them. Sounds more like a millionaire playboy filmmaker than a detective, doesn't it? This entire movie was written by Peter, and it shows how out of touch with real people he was. You're supposed to write what you know, and he did that, indeed. And leaves the audience bored and confused, and jealous, for that matter. This is a curio for people who want to see Dorothy Stratten, who was murdered right after filming. But Patti Hanson, who would, in real life, marry Keith Richards, was also a model, like Stratten, but is a lot better and has a more ample part. In fact, Stratten's part seemed forced; added. She doesn't have a lot to do with the story, which is pretty convoluted to begin with. All in all, every character in this film is somebody that very few people can relate with, unless you're millionaire from Manhattan with beautiful supermodels at your beckon call. For the rest of us, it's an irritating snore fest. That's what happens when you're out of touch. You entertain your few friends with inside jokes, and bore all the rest.", metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-11 | Document(page_content='It was great to see some of my favorite stars of 30 years ago including John Ritter, Ben Gazarra and Audrey Hepburn. They looked quite wonderful. But that was it. They were not given any characters or good lines to work with. I neither understood or cared what the characters were doing.<br /><br />Some of the smaller female roles were fine, Patty Henson and Colleen Camp were quite competent and confident in their small sidekick parts. They showed some talent and it is sad they didn\'t go on to star in more and better films. Sadly, I didn\'t think Dorothy Stratten got a chance to act in this her only important film role.<br /><br />The film appears to have some fans, and I was very open-minded when I started watching it. I am a big Peter Bogdanovich fan and I enjoyed his last movie, "Cat\'s Meow" and all his early ones from "Targets" to "Nickleodeon". So, it really surprised me that I was barely able to keep awake watching this one.<br /><br />It is ironic that this movie is about a detective agency where the detectives and clients get romantically involved with each other. Five years later, Bogdanovich\'s ex-girlfriend, Cybil Shepherd had a hit television series called "Moonlighting" stealing the story idea from Bogdanovich. Of course, there was a great difference in that the series relied on tons of witty dialogue, while this tries to make do with slapstick and a few screwball lines.<br /><br />Bottom line: It ain\'t no "Paper Moon" and only a very pale version of "What\'s Up, Doc".', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-12 | Document(page_content="I can't believe that those praising this movie herein aren't thinking of some other film. I was prepared for the possibility that this would be awful, but the script (or lack thereof) makes for a film that's also pointless. On the plus side, the general level of craft on the part of the actors and technical crew is quite competent, but when you've got a sow's ear to work with you can't make a silk purse. Ben G fans should stick with just about any other movie he's been in. Dorothy S fans should stick to Galaxina. Peter B fans should stick to Last Picture Show and Target. Fans of cheap laughs at the expense of those who seem to be asking for it should stick to Peter B's amazingly awful book, Killing of the Unicorn.", metadata={'label': 0}),
Document(page_content='Never cast models and Playboy bunnies in your films! Bob Fosse\'s "Star 80" about Dorothy Stratten, of whom Bogdanovich was obsessed enough to have married her SISTER after her murder at the hands of her low-life husband, is a zillion times more interesting than Dorothy herself on the silver screen. Patty Hansen is no actress either..I expected to see some sort of lost masterpiece a la Orson Welles but instead got Audrey Hepburn cavorting in jeans and a god-awful "poodlesque" hair-do....Very disappointing...."Paper Moon" and "The Last Picture Show" I could watch again and again. This clunker I could barely sit through once. This movie was reputedly not released because of the brouhaha surrounding Ms. Stratten\'s tawdry death; I think the real reason was because it was so bad!', metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-13 | Document(page_content="Its not the cast. A finer group of actors, you could not find. Its not the setting. The director is in love with New York City, and by the end of the film, so are we all! Woody Allen could not improve upon what Bogdonovich has done here. If you are going to fall in love, or find love, Manhattan is the place to go. No, the problem with the movie is the script. There is none. The actors fall in love at first sight, words are unnecessary. In the director's own experience in Hollywood that is what happens when they go to work on the set. It is reality to him, and his peers, but it is a fantasy to most of us in the real world. So, in the end, the movie is hollow, and shallow, and message-less.", metadata={'label': 0}), | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-14 | Document(page_content='Today I found "They All Laughed" on VHS on sale in a rental. It was a really old and very used VHS, I had no information about this movie, but I liked the references listed on its cover: the names of Peter Bogdanovich, Audrey Hepburn, John Ritter and specially Dorothy Stratten attracted me, the price was very low and I decided to risk and buy it. I searched IMDb, and the User Rating of 6.0 was an excellent reference. I looked in "Mick Martin & Marsha Porter Video & DVD Guide 2003" and \x96 wow \x96 four stars! So, I decided that I could not waste more time and immediately see it. Indeed, I have just finished watching "They All Laughed" and I found it a very boring overrated movie. The characters are badly developed, and I spent lots of minutes to understand their roles in the story. The plot is supposed to be funny (private eyes who fall in love for the women they are chasing), but I have not laughed along the whole story. The coincidences, in a huge city like New York, are ridiculous. Ben Gazarra as an attractive and very seductive man, with the women falling for him as if her were a Brad Pitt, Antonio Banderas or George Clooney, is quite ridiculous. In the end, the greater attractions certainly are the presence of the Playboy centerfold and playmate of the year Dorothy Stratten, murdered by her husband pretty after the release of this movie, and whose life was showed in "Star 80" and "Death of a Centerfold: The Dorothy Stratten Story"; the amazing beauty of the sexy Patti Hansen, the future Mrs. Keith Richards; the always wonderful, even being fifty-two years old, Audrey Hepburn; and the song "Amigo", from Roberto Carlos. Although I do not like him, Roberto Carlos has been | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-15 | song "Amigo", from Roberto Carlos. Although I do not like him, Roberto Carlos has been the most popular Brazilian singer since the end of the 60\'s and is called by his fans as "The King". I will keep this movie in my collection only because of these attractions (manly Dorothy Stratten). My vote is four.<br /><br />Title (Brazil): "Muito Riso e Muita Alegria" ("Many Laughs and Lots of Happiness")', metadata={'label': 0})] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
661ffa15e3f9-16 | Example#
In this example, we use data from a dataset to answer a question
from langchain.indexes import VectorstoreIndexCreator
from langchain.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
dataset_name="tweet_eval"
page_content_column="text"
name="stance_climate"
loader=HuggingFaceDatasetLoader(dataset_name,page_content_column,name)
index = VectorstoreIndexCreator().from_loaders([loader])
Found cached dataset tweet_eval
Using embedded DuckDB without persistence: data will be transient
query = "What are the most used hashtag?"
result = index.query(query)
result
' The most used hashtags in this context are #UKClimate2015, #Sustainability, #TakeDownTheFlag, #LoveWins, #CSOTA, #ClimateSummitoftheAmericas, #SM, and #SocialMedia.'
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Example
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hugging_face_dataset.html |
534b4c7edced-0 | .ipynb
.pdf
Obsidian
Obsidian#
Obsidian is a powerful and extensible knowledge base
that works on top of your local folder of plain text files.
This notebook covers how to load documents from an Obsidian database.
Since Obsidian is just stored on disk as a folder of Markdown files, the loader just takes a path to this directory.
Obsidian files also sometimes contain metadata which is a YAML block at the top of the file. These values will be added to the document’s metadata. (ObsidianLoader can also be passed a collect_metadata=False argument to disable this behavior.)
from langchain.document_loaders import ObsidianLoader
loader = ObsidianLoader("<path-to-obsidian>")
docs = loader.load()
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Psychic
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/obsidian.html |
40ba3d7d7368-0 | .ipynb
.pdf
iFixit
Contents
Searching iFixit using /suggest
iFixit#
iFixit is the largest, open repair community on the web. The site contains nearly 100k repair manuals, 200k Questions & Answers on 42k devices, and all the data is licensed under CC-BY-NC-SA 3.0.
This loader will allow you to download the text of a repair guide, text of Q&A’s and wikis from devices on iFixit using their open APIs. It’s incredibly useful for context related to technical documents and answers to questions about devices in the corpus of data on iFixit.
from langchain.document_loaders import IFixitLoader
loader = IFixitLoader("https://www.ifixit.com/Teardown/Banana+Teardown/811")
data = loader.load()
data | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html |
40ba3d7d7368-1 | data = loader.load()
data
[Document(page_content="# Banana Teardown\nIn this teardown, we open a banana to see what's inside. Yellow and delicious, but most importantly, yellow.\n\n\n###Tools Required:\n\n - Fingers\n\n - Teeth\n\n - Thumbs\n\n\n###Parts Required:\n\n - None\n\n\n## Step 1\nTake one banana from the bunch.\nDon't squeeze too hard!\n\n\n## Step 2\nHold the banana in your left hand and grip the stem between your right thumb and forefinger.\n\n\n## Step 3\nPull the stem downward until the peel splits.\n\n\n## Step 4\nInsert your thumbs into the split of the peel and pull the two sides apart.\nExpose the top of the banana. It may be slightly squished from pulling on the stem, but this will not affect the flavor.\n\n\n## Step 5\nPull open the peel, starting from your original split, and opening it along the length of the banana.\n\n\n## Step 6\nRemove fruit from peel.\n\n\n## Step 7\nEat and enjoy!\nThis is where you'll need your teeth.\nDo not choke on banana!\n", lookup_str='', metadata={'source': 'https://www.ifixit.com/Teardown/Banana+Teardown/811', 'title': 'Banana Teardown'}, lookup_index=0)]
loader = IFixitLoader("https://www.ifixit.com/Answers/View/318583/My+iPhone+6+is+typing+and+opening+apps+by+itself")
data = loader.load()
data | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html |
40ba3d7d7368-2 | [Document(page_content='# My iPhone 6 is typing and opening apps by itself\nmy iphone 6 is typing and opening apps by itself. How do i fix this. I just bought it last week.\nI restored as manufactures cleaned up the screen\nthe problem continues\n\n## 27 Answers\n\nFilter by: \n\nMost Helpful\nNewest\nOldest\n\n### Accepted Answer\nHi,\nWhere did you buy it? If you bought it from Apple or from an official retailer like Carphone warehouse etc. Then you\'ll have a year warranty and can get it replaced free.\nIf you bought it second hand, from a third part repair shop or online, then it may still have warranty, unless it is refurbished and has been repaired elsewhere.\nIf this is the case, it may be the screen that needs replacing to solve your issue.\nEither way, wherever you got it, it\'s best to return it and get a refund or a replacement device. :-)\n\n\n\n### Most Helpful Answer\nI had the same issues, screen freezing, opening apps by itself, selecting the screens and typing on it\'s own. I first suspected aliens and then ghosts and then hackers.\niPhone 6 is weak physically and tend to bend on pressure. And my phone had no case or cover.\nI took the phone to apple stores and they said sensors need to be replaced and possibly screen replacement as well. My phone is just 17 months old.\nHere is what I did two days ago and since then it is working like a charm..\nHold the phone in portrait (as if watching a movie). Twist it very very gently. do it few times.Rest the phone for 10 mins (put it on a flat surface). You can now notice those self typing things gone and screen getting stabilized.\nThen, reset the hardware (hold the power and home button till the screen goes off and comes back with apple | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html |
40ba3d7d7368-3 | reset the hardware (hold the power and home button till the screen goes off and comes back with apple logo). release the buttons when you see this.\nThen, connect to your laptop and log in to iTunes and reset your phone completely. (please take a back-up first).\nAnd your phone should be good to use again.\nWhat really happened here for me is that the sensors might have stuck to the screen and with mild twisting, they got disengaged/released.\nI posted this in Apple Community and the moderators deleted it, for the best reasons known to them.\nInstead of throwing away your phone (or selling cheaply), try this and you could be saving your phone.\nLet me know how it goes.\n\n\n\n### Other Answer\nIt was the charging cord! I bought a gas station braided cord and it was the culprit. Once I plugged my OEM cord into the phone the GHOSTS went away.\n\n\n\n### Other Answer\nI\'ve same issue that I just get resolved. I first tried to restore it from iCloud back, however it was not a software issue or any virus issue, so after restore same problem continues. Then I get my phone to local area iphone repairing lab, and they detected that it is an LCD issue. LCD get out of order without any reason (It was neither hit or nor slipped, but LCD get out of order all and sudden, while using it) it started opening things at random. I get LCD replaced with new one, that cost me $80.00 in total ($70.00 LCD charges + $10.00 as labor charges to fix it). iPhone is back to perfect mode now. It was iphone 6s. Thanks.\n\n\n\n### Other Answer\nI was having the same issue with my 6 plus, I took it to a repair shop, they opened the phone, disconnected the three ribbons the screen has, blew up | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html |
40ba3d7d7368-4 | a repair shop, they opened the phone, disconnected the three ribbons the screen has, blew up and cleaned the connectors and connected the screen again and it solved the issue… it’s hardware, not software.\n\n\n\n### Other Answer\nHey.\nJust had this problem now. As it turns out, you just need to plug in your phone. I use a case and when I took it off I noticed that there was a lot of dust and dirt around the areas that the case didn\'t cover. I shined a light in my ports and noticed they were filled with dust. Tomorrow I plan on using pressurized air to clean it out and the problem should be solved. If you plug in your phone and unplug it and it stops the issue, I recommend cleaning your phone thoroughly.\n\n\n\n### Other Answer\nI simply changed the power supply and problem was gone. The block that plugs in the wall not the sub cord. The cord was fine but not the block.\n\n\n\n### Other Answer\nSomeone ask! I purchased my iPhone 6s Plus for 1000 from at&t. Before I touched it, I purchased a otter defender case. I read where at&t said touch desease was due to dropping! Bullshit!! I am 56 I have never dropped it!! Looks brand new! Never dropped or abused any way! I have my original charger. I am going to clean it and try everyone’s advice. It really sucks! I had 40,000,000 on my heart of Vegas slots! I play every day. I would be spinning and my fingers were no where max buttons and it would light up and switch to max. It did it 3 times before I caught it light up by its self. It sucks. Hope I can fix it!!!!\n\n\n\n### Other Answer\nNo answer, but same | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html |
40ba3d7d7368-5 | Hope I can fix it!!!!\n\n\n\n### Other Answer\nNo answer, but same problem with iPhone 6 plus--random, self-generated jumping amongst apps and typing on its own--plus freezing regularly (aha--maybe that\'s what the "plus" in "6 plus" refers to?). An Apple Genius recommended upgrading to iOS 11.3.1 from 11.2.2, to see if that fixed the trouble. If it didn\'t, Apple will sell me a new phone for $168! Of couese the OS upgrade didn\'t fix the problem. Thanks for helping me figure out that it\'s most likely a hardware problem--which the "genius" probably knows too.\nI\'m getting ready to go Android.\n\n\n\n### Other Answer\nI experienced similar ghost touches. Two weeks ago, I changed my iPhone 6 Plus shell (I had forced the phone into it because it’s pretty tight), and also put a new glass screen protector (the edges of the protector don’t stick to the screen, weird, so I brushed pressure on the edges at times to see if they may smooth out one day miraculously). I’m not sure if I accidentally bend the phone when I installed the shell, or, if I got a defective glass protector that messes up the touch sensor. Well, yesterday was the worse day, keeps dropping calls and ghost pressing keys for me when I was on a call. I got fed up, so I removed the screen protector, and so far problems have not reoccurred yet. I’m crossing my fingers that problems indeed solved.\n\n\n\n### Other Answer\nthank you so much for this post! i was struggling doing the reset because i cannot type userids and passwords correctly because the iphone 6 plus i have kept on typing letters incorrectly. I have been doing it for a day until i come across this article. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html |
40ba3d7d7368-6 | on typing letters incorrectly. I have been doing it for a day until i come across this article. Very helpful! God bless you!!\n\n\n\n### Other Answer\nI just turned it off, and turned it back on.\n\n\n\n### Other Answer\nMy problem has not gone away completely but its better now i changed my charger and turned off prediction ....,,,now it rarely happens\n\n\n\n### Other Answer\nI tried all of the above. I then turned off my home cleaned it with isopropyl alcohol 90%. Then I baked it in my oven on warm for an hour and a half over foil. Took it out and set it cool completely on the glass top stove. Then I turned on and it worked.\n\n\n\n### Other Answer\nI think at& t should man up and fix your phone for free! You pay a lot for a Apple they should back it. I did the next 30 month payments and finally have it paid off in June. My iPad sept. Looking forward to a almost 100 drop in my phone bill! Now this crap!!! Really\n\n\n\n### Other Answer\nIf your phone is JailBroken, suggest downloading a virus. While all my symptoms were similar, there was indeed a virus/malware on the phone which allowed for remote control of my iphone (even while in lock mode). My mistake for buying a third party iphone i suppose. Anyway i have since had the phone restored to factory and everything is working as expected for now. I will of course keep you posted if this changes. Thanks to all for the helpful posts, really helped me narrow a few things down.\n\n\n\n### Other Answer\nWhen my phone was doing this, it ended up being the screen protector that i got from 5 below. I took it off and it stopped. I ordered more protectors from amazon and replaced | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html |
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