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d926d452363d-0 | .rst
.pdf
Document Loaders
Document Loaders#
Note
Conceptual Guide
Combining language models with your own text data is a powerful way to differentiate them.
The first step in doing this is to load the data into “documents” - a fancy way of say some pieces of text.
This module is aimed at making this easy.
A primary dr... | https://python.langchain.com/en/latest/modules/indexes/document_loaders.html |
d926d452363d-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders.html |
5a7261ebb916-0 | .ipynb
.pdf
Getting Started
Contents
One Line Index Creation
Walkthrough
Getting Started#
LangChain primary focuses on constructing indexes with the goal of using them as a Retriever. In order to best understand what this means, it’s worth highlighting what the base Retriever interface is. The BaseRetriever class in ... | https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
5a7261ebb916-1 | Create a Retriever from that index
Create a question answering chain
Ask questions!
Each of the steps has multiple sub steps and potential configurations. In this notebook we will primarily focus on (1). We will start by showing the one-liner for doing so, but then break down what is actually going on.
First, let’s imp... | https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
5a7261ebb916-2 | index.query_with_sources(query)
{'question': 'What did the president say about Ketanji Brown Jackson',
'answer': " The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, one of the nation's top legal minds, to continue Justice Breyer's legacy of excellence, and that she has received... | https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
5a7261ebb916-3 | We will then select which embeddings we want to use.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
We now create the vectorstore to use as the index.
from langchain.vectorstores import Chroma
db = Chroma.from_documents(texts, embeddings)
Running Chroma using direct local API.
Using D... | https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
5a7261ebb916-4 | )
Hopefully this highlights what is going on under the hood of VectorstoreIndexCreator. While we think it’s important to have a simple way to create indexes, we also think it’s important to understand what’s going on under the hood.
previous
Indexes
next
Document Loaders
Contents
One Line Index Creation
Walkthrough... | https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
6b18ab40dee4-0 | .rst
.pdf
Vectorstores
Vectorstores#
Note
Conceptual Guide
Vectorstores are one of the most important components of building indexes.
For an introduction to vectorstores and generic functionality see:
Getting Started
We also have documentation for all the types of vectorstores that are supported.
Please see below for t... | https://python.langchain.com/en/latest/modules/indexes/vectorstores.html |
2c63ab6e979c-0 | .rst
.pdf
Text Splitters
Text Splitters#
Note
Conceptual Guide
When you want to deal with long pieces of text, it is necessary to split up that text into chunks.
As simple as this sounds, there is a lot of potential complexity here. Ideally, you want to keep the semantically related pieces of text together. What “seman... | https://python.langchain.com/en/latest/modules/indexes/text_splitters.html |
9891479292d6-0 | .rst
.pdf
Retrievers
Retrievers#
Note
Conceptual Guide
The retriever interface is a generic interface that makes it easy to combine documents with
language models. This interface exposes a get_relevant_documents method which takes in a query
(a string) and returns a list of documents.
Please see below for a list of all... | https://python.langchain.com/en/latest/modules/indexes/retrievers.html |
210b46549c3b-0 | .ipynb
.pdf
s3 Directory
Contents
Specifying a prefix
s3 Directory#
This covers how to load document objects from an s3 directory object.
from langchain.document_loaders import S3DirectoryLoader
#!pip install boto3
loader = S3DirectoryLoader("testing-hwc")
loader.load()
[Document(page_content='Lorem ipsum dolor sit a... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/s3_directory.html |
f3da393524db-0 | .ipynb
.pdf
Telegram
Telegram#
This notebook covers how to load data from Telegram into a format that can be ingested into LangChain.
from langchain.document_loaders import TelegramChatLoader
loader = TelegramChatLoader("example_data/telegram.json")
loader.load()
[Document(page_content="Henry on 2020-01-01T00:00:02: It... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/telegram.html |
d8c5ad934266-0 | .ipynb
.pdf
Copy Paste
Contents
Metadata
Copy Paste#
This notebook covers how to load a document object from something you just want to copy and paste. In this case, you don’t even need to use a DocumentLoader, but rather can just construct the Document directly.
from langchain.docstore.document import Document
text ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/copypaste.html |
9a76d59e269e-0 | .ipynb
.pdf
YouTube
Contents
Add video info
YouTube loader from Google Cloud
Prerequisites
🧑 Instructions for ingesting your Google Docs data
YouTube#
How to load documents from YouTube transcripts.
from langchain.document_loaders import YoutubeLoader
# !pip install youtube-transcript-api
loader = YoutubeLoader.from... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/youtube.html |
9a76d59e269e-1 | # Use a Channel
youtube_loader_channel = GoogleApiYoutubeLoader(google_api_client=google_api_client, channel_name="Reducible",captions_language="en")
# Use Youtube Ids
youtube_loader_ids = GoogleApiYoutubeLoader(google_api_client=google_api_client, video_ids=["TrdevFK_am4"], add_video_info=True)
# returns a list of Doc... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/youtube.html |
7743c3706e86-0 | .ipynb
.pdf
Hacker News
Hacker News#
How to pull page data and comments from Hacker News
from langchain.document_loaders import HNLoader
loader = HNLoader("https://news.ycombinator.com/item?id=34817881")
data = loader.load()
data
[Document(page_content="delta_p_delta_x 18 hours ago \n | next [–] \n\nAstrop... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hn.html |
7743c3706e86-1 | Document(page_content="andrewflnr 19 hours ago \n | prev | next [–] \n\nWhoa. I didn't know the accretion theory of Ia supernovae was dead, much less that it had been since 2011.\n \nreply", lookup_str='', metadata={'source': 'https://news.ycombinator.com/item?id=34817881', 'title': 'What Lights the Univer... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hn.html |
ddb077e13f23-0 | .ipynb
.pdf
CSV Loader
Contents
Customizing the csv parsing and loading
Specify a column to be used identify the document source
CSV Loader#
Load csv files 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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-1 | [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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-2 | 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 (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': './exam... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-3 | '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={'so... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-4 | '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={'sour... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-5 | '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={'sou... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-6 | 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 = ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-7 | [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': './e... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-8 | './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: 120.51\nW... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-9 | 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), Do... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-10 | 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={'sour... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-11 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-12 | 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(... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-13 | Specify a column to be used identify the document source#
Use the source_column argument to specify a column to be set as the 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 fil... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-14 | [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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-15 | '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 (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': 'Rays', 'row': 8}, lookup_... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-16 | 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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-17 | (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)... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-18 | 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),... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
ddb077e13f23-19 | previous
Copy Paste
next
DataFrame Loader
Contents
Customizing the csv parsing and loading
Specify a column to be used identify the document source
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
6cac92fcc702-0 | .ipynb
.pdf
PDF
Contents
Using PyPDF
Using Unstructured
Retain Elements
Fetching remote PDFs using Unstructured
Using PDFMiner
Using PDFMiner to generate HTML text
Using PyMuPDF
PDF#
This covers how to load pdfs into a document format that we can use downstream.
Using PyPDF#
Load PDF using pypdf into array of documen... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-1 | 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\n3Ha... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-2 | 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\nl... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-3 | 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', lookup_str='', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': '0'}, lookup_index=0) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-4 | An advantage of this approach is that documents can be retrieved with page numbers.
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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-5 | For each shared pipeline, it has a dedicated project page, with links to the source
code, documentation, and an outline of the approaches. A discussion panel is
provided for exchanging ideas. Combined with the core LayoutParser library,
users can easily build reusable components based on the shared pipelines and
apply ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-6 | vision and natural language processing problems. LayoutParser , on the other
hand, specializes speci
cally in DIA tasks. LayoutParser is also equipped with a
community platform inspired by established model hubs such as Torch Hub [23]
andTensorFlow Hub [1]. It enables the sharing of pretrained models as well as
full do... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-7 | 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 Harvar... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-8 | 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\nli... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-9 | 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, 'fo... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-10 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-11 | [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 projectiv... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-12 | 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, fol... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-13 | 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 ′ ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-14 | < 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 ∗ ≃ ( C ∗ ) d is the torus action on P d Σ . We denote by Σ ( i ) the i -d... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-15 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-16 | 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 ) a... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-17 | . 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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-18 | / 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 projectiv... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-19 | 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 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 wa... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-20 | 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-... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-21 | 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 , ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-22 | − 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] ] L... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-23 | 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 conjec... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-24 | 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 ) 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 r... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-25 | 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 } n... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-26 | ,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 ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-27 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-28 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-29 | 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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-30 | 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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-31 | 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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-32 | 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_snip... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-33 | 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 identif... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-34 | 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 ... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-35 | 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... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-36 | 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 s... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-37 | 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 |
6cac92fcc702-38 | 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 Harvar... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-39 | 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\nli... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-40 | 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, 'fo... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
6cac92fcc702-41 | 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.
previous
Obsidian
next
PowerPoint
Contents
Using PyPDF
Using Unstructured
Retain Elements
Fetching remote PDFs using Unstructured
Using PDFMiner... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
9afdaea84afa-0 | .ipynb
.pdf
CoNLL-U
CoNLL-U#
This is an example of how to load a file in CoNLL-U format. The whole file is treated as one document. The example data (conllu.conllu) is based on one of the standard UD/CoNLL-U examples.
from langchain.document_loaders import CoNLLULoader
loader = CoNLLULoader("example_data/conllu.conllu"... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/CoNLL-U.html |
97dfdede6c81-0 | .ipynb
.pdf
Markdown
Contents
Retain Elements
Markdown#
This covers how to load markdown documents into a document format that we can use downstream.
from langchain.document_loaders import UnstructuredMarkdownLoader
loader = UnstructuredMarkdownLoader("../../../../README.md")
data = loader.load()
data | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
97dfdede6c81-1 | [Document(page_content="ð\x9f¦\x9cï¸\x8fð\x9f”\x97 LangChain\n\nâ\x9a¡ Building applications with LLMs through composability â\x9a¡\n\nProduction Support: As you move your LangChains into production, we'd love to offer more comprehensive support.\nPlease fill out this form and we'll set up a dedicated support Slack cha... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
97dfdede6c81-2 | Chatbots\n\nDocumentation\n\nEnd-to-end Example: Chat-LangChain\n\nð\x9f¤\x96 Agents\n\nDocumentation\n\nEnd-to-end Example: GPT+WolframAlpha\n\nð\x9f“\x96 Documentation\n\nPlease see here for full documentation on:\n\nGetting started (installation, setting up the environment, simple examples)\n\nHow-To examples (demos... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
97dfdede6c81-3 | chains for common applications.\n\nð\x9f“\x9a Data Augmented Generation:\n\nData Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over s... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
97dfdede6c81-4 | is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\n\nFor more information on these concepts, please see our full documentation.\n\nð\x9f’\x81 Contributing\n\nAs an open source project in a rapidly developing field, we are extremely open to contributi... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
97dfdede6c81-5 | 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 = UnstructuredMarkdownLoader("../../../../README.md", mode="elements")
data = loader.load()
data[0]... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
710af5899c8b-0 | .ipynb
.pdf
Image captions
Contents
Prepare a list of image urls from Wikimedia
Create the loader
Create the index
Query
Image captions#
This notebook shows how to use the ImageCaptionLoader tutorial to generate a query-able index of image captions
from langchain.document_loaders import ImageCaptionLoader
Prepare a l... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
710af5899c8b-1 | 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/2022-01-22_Men%27s_World_Cup_at_2021-22_St._Moritz%E2%80%93Celerina_Luge_World_Cup_and_European_Championships_by_Sandro_Halank%E2%80%93257.jpg/288px-2022-01-22_Men%27s_World_Cup_at_2021-22_St._Moritz%E2%80%93Celerina_Luge_World_Cup_and_European_Championships_by... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
710af5899c8b-2 | Document(page_content='an image of a shark swimming in the ocean [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/7/71/Tibur%C3%B3n_azul_%28Prionace_glauca%29%2C_canal_Fayal-Pico%2C_islas_Azores%2C_Portugal%2C_2020-07-27%2C_DD_14.jpg/270px-Tibur%C3%B3n_azul_%28Prionace_glauca%29%2C_... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
710af5899c8b-3 | Document(page_content='an image of a man on skis in the snow [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/2022-01-22_Men%27s_World_Cup_at_2021-22_St._Moritz%E2%80%93Celerina_Luge_World_Cup_and_European_Championships_by_Sandro_Halank%E2%80%93257.jpg/288px-2022-01-22_Men%27s_... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
710af5899c8b-4 | from .autonotebook import tqdm as notebook_tqdm
/Users/saitosean/dev/langchain/.venv/lib/python3.10/site-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
155408c9b4a6-0 | .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/backgrounde... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/url.html |
155408c9b4a6-1 | !pip install "playwright"
!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"])
da... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/url.html |
fcaa2cc3aa1c-0 | .ipynb
.pdf
GCS File Storage
GCS File Storage#
This covers how to load document objects from an Google Cloud Storage (GCS) file object.
from langchain.document_loaders import GCSFileLoader
# !pip install google-cloud-storage
loader = GCSFileLoader(project_name="aist", bucket="testing-hwc", blob="fake.docx")
loader.load... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/gcs_file.html |
e0dcc9dea2b3-0 | .ipynb
.pdf
College Confidential
College Confidential#
This covers how to load College Confidential webpages into a document format that we can use downstream.
from langchain.document_loaders import CollegeConfidentialLoader
loader = CollegeConfidentialLoader("https://www.collegeconfidential.com/colleges/brown-universi... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
e0dcc9dea2b3-1 | [Document(page_content='\n\n\n\n\n\n\n\nA68FEB02-9D19-447C-B8BC-818149FD6EAF\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Media (2)\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nE45B8B13-33D4-450E-B7DB-F66EFE8F2097\n\n\n\n\n\n\n\n\n\nE45B8B13-33D4-450E-B7DB-F66EFE8F2097\n\n\n\n\n\n\n\n\n\n\n\n\n\... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
e0dcc9dea2b3-2 | students to get involved at Brown! \nLove music or performing? Join a campus band, sing in a chorus, or perform with one of the school\'s theater groups.\nInterested in journalism or communications? Brown students can write for the campus newspaper, host a radio show or be a producer for the student-run television chan... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
e0dcc9dea2b3-3 | "good" school. Some factors that can help you determine what a good school for you might be include admissions criteria, acceptance rate, tuition costs, and more.\nLet\'s take a look at these factors to get a clearer sense of what Brown offers and if it could be the right college for you.\nBrown Acceptance Rate 2022\nI... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
e0dcc9dea2b3-4 | six-year graduation rate for U.S. colleges and universities is 61% for public schools, and 67% for private, non-profit schools.\nJob Outcomes for Brown Grads\nJob placement stats are a good resource for understanding the value of a degree from Brown by providing a look on how job placement has gone for other grads. \nC... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
e0dcc9dea2b3-5 | Financial Aid at Brown\nTuition is another important factor when choose a college. Some colleges may have high tuition, but do a better job at meeting students\' financial need.\nBrown meets 100% of the demonstrated financial need for undergraduates. The average financial aid package for a full-time, first-year studen... | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
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