id stringlengths 14 16 | text stringlengths 31 3.14k | source stringlengths 58 124 |
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266d0daa8f0d-0 | .ipynb
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RetryOutputParser
RetryOutputParser#
While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it can’t. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example.
from langchain.prompts... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
266d0daa8f0d-1 | bad_response = '{"action": "search"}'
If we try to parse this response as is, we will get an error
parser.parse(bad_response)
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
File ~/workplace/langchain/langchain/outpu... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
266d0daa8f0d-2 | Cell In[6], line 1
----> 1 parser.parse(bad_response)
File ~/workplace/langchain/langchain/output_parsers/pydantic.py:29, in PydanticOutputParser.parse(self, text)
27 name = self.pydantic_object.__name__
28 msg = f"Failed to parse {name} from completion {text}. Got: {e}"
---> 29 raise OutputParserException(ms... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
266d0daa8f0d-3 | Action(action='search', action_input='who is leo di caprios gf?')
previous
PydanticOutputParser
next
Structured Output Parser
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/retry.html |
738ac689bec8-0 | .ipynb
.pdf
PydanticOutputParser
PydanticOutputParser#
This output parser allows users to specify an arbitrary JSON schema and query LLMs for JSON outputs that conform to that schema.
Keep in mind that large language models are leaky abstractions! You’ll have to use an LLM with sufficient capacity to generate well-form... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/pydantic.html |
738ac689bec8-1 | raise ValueError("Badly formed question!")
return field
# And a query intented to prompt a language model to populate the data structure.
joke_query = "Tell me a joke."
# Set up a parser + inject instructions into the prompt template.
parser = PydanticOutputParser(pydantic_object=Joke)
prompt = PromptTemplate(
... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/pydantic.html |
738ac689bec8-2 | )
_input = prompt.format_prompt(query=actor_query)
output = model(_input.to_string())
parser.parse(output)
Actor(name='Tom Hanks', film_names=['Forrest Gump', 'Saving Private Ryan', 'The Green Mile', 'Cast Away', 'Toy Story'])
previous
OutputFixingParser
next
RetryOutputParser
By Harrison Chase
© Copyright 2... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/pydantic.html |
1acb0a74c007-0 | .ipynb
.pdf
OutputFixingParser
OutputFixingParser#
This output parser wraps another output parser and tries to fix any mmistakes
The Pydantic guardrail simply tries to parse the LLM response. If it does not parse correctly, then it errors.
But we can do other things besides throw errors. Specifically, we can pass the m... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
1acb0a74c007-1 | JSONDecodeError Traceback (most recent call last)
File ~/workplace/langchain/langchain/output_parsers/pydantic.py:23, in PydanticOutputParser.parse(self, text)
22 json_str = match.group()
---> 23 json_object = json.loads(json_str)
24 return self.pydantic_object.parse_obj(json_obj... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
1acb0a74c007-2 | 338 end = _w(s, end).end()
File ~/.pyenv/versions/3.9.1/lib/python3.9/json/decoder.py:353, in JSONDecoder.raw_decode(self, s, idx)
352 try:
--> 353 obj, end = self.scan_once(s, idx)
354 except StopIteration as err:
JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
1acb0a74c007-3 | from langchain.output_parsers import OutputFixingParser
new_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())
new_parser.parse(misformatted)
Actor(name='Tom Hanks', film_names=['Forrest Gump'])
previous
CommaSeparatedListOutputParser
next
PydanticOutputParser
By Harrison Chase
© Copyright... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
729b68a30218-0 | .ipynb
.pdf
Structured Output Parser
Structured Output Parser#
While the Pydantic/JSON parser is more powerful, we initially experimented data structures having text fields only.
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import PromptTemplate, ChatPromptTemplate,... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/structured.html |
729b68a30218-1 | output = model(_input.to_string())
output_parser.parse(output)
{'answer': 'Paris', 'source': 'https://en.wikipedia.org/wiki/Paris'}
And here’s an example of using this in a chat model
chat_model = ChatOpenAI(temperature=0)
prompt = ChatPromptTemplate(
messages=[
HumanMessagePromptTemplate.from_template("ans... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/structured.html |
535b02a2dca1-0 | .ipynb
.pdf
CommaSeparatedListOutputParser
CommaSeparatedListOutputParser#
Here’s another parser strictly less powerful than Pydantic/JSON parsing.
from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langch... | /content/https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/comma_separated.html |
bd4199454a86-0 | .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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders.html |
61ad92cb255f-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 ... | /content/https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
61ad92cb255f-1 | Question answering over documents consists of four steps:
Create an index
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... | /content/https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
61ad92cb255f-2 | index.query(query)
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad... | /content/https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
61ad92cb255f-3 | index.vectorstore.as_retriever()
VectorStoreRetriever(vectorstore=<langchain.vectorstores.chroma.Chroma object at 0x119aa5940>, search_kwargs={})
Walkthrough#
Okay, so what’s actually going on? How is this index getting created?
A lot of the magic is being hid in this VectorstoreIndexCreator. What is this doing?
There ... | /content/https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
61ad92cb255f-4 | Then, as before, we create a chain and use it to answer questions!
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever)
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
" The President said that Judge Ketanji Brown Jackson is one of the nation's top legal... | /content/https://python.langchain.com/en/latest/modules/indexes/getting_started.html |
0e75cf4cf340-0 | .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... | /content/https://python.langchain.com/en/latest/modules/indexes/vectorstores.html |
8a3d34c3d6d1-0 | .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... | /content/https://python.langchain.com/en/latest/modules/indexes/text_splitters.html |
2fc5d215352b-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... | /content/https://python.langchain.com/en/latest/modules/indexes/retrievers.html |
26d822eb982f-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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/s3_directory.html |
90494dff95a1-0 | .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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/telegram.html |
4414d3cb1178-0 | .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 ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/copypaste.html |
a26c7bfe149a-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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/youtube.html |
a26c7bfe149a-1 | Note depending on your set up, the service_account_path needs to be set up. See here for more details.
from langchain.document_loaders import GoogleApiClient, GoogleApiYoutubeLoader
# Init the GoogleApiClient
from pathlib import Path
google_api_client = GoogleApiClient(credentials_path=Path("your_path_creds.json"))
# ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/youtube.html |
8c6bf65c23f0-0 | .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 | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hn.html |
8c6bf65c23f0-1 | data = loader.load()
data
[Document(page_content="delta_p_delta_x 18 hours ago \n | next [–] \n\nAstrophysical and cosmological simulations are often insightful. They're also very cross-disciplinary; besides the obvious astrophysics, there's networking and sysadmin, parallel computing and algorithm theory ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hn.html |
8c6bf65c23f0-2 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/hn.html |
7c891e54180d-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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-2 | 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: Rang... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-3 | 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': './e... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-4 | 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', '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: Phil... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-5 | 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': '.... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-6 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-7 | 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), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': './ex... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-8 | 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 = ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-9 | [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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-10 | '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={'sou... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-11 | '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={'sou... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-12 | 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:... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-13 | 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_te... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-14 | '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={'... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-15 | 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: As... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-16 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-17 | [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/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-18 | 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=... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-19 | (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 (mill... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-20 | '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': 'Padre... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-21 | (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), Document(page_content='Team: Red Sox\n"Payroll (millions)... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-22 | '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': ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7c891e54180d-23 | 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 26, 2023. | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html |
7b0dfe8ade09-0 | .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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-1 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-2 | 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, chara... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-3 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-4 | are mainly described in academic papers and implementations are often not pub-
licly available. To this end, the LayoutParser community platform also enables
the sharing of layout pipelines to promote the discussion and reuse of techniques.
For each shared pipeline, it has a dedicated project page, with links to the so... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-5 | models and whole digitization pipelines to promote reusability and reproducibility.
A collection of detailed documentation, tutorials and exemplar projects make
LayoutParser easy to learn and use.
AllenNLP [ 8] and transformers [ 34] have provided the community with complete
DL-based support for developing and deployin... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-6 | 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 ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-7 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-8 | 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 exten... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-9 | hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0) | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-10 | 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 ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-11 | [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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-12 | [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 Prof. Ugo Bruzzo and Tiago Fonseca for useful discus- sions. I also a... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-13 | < 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-14 | 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 differentia... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-15 | 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 e... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-16 | 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 ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-17 | ( 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 t... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-18 | 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 nex... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-19 | 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 alg... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-20 | 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 contradictio... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-21 | 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 = ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-22 | 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 Geom... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-23 | 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 p... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-24 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-25 | cur_fs = fs
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 ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-26 | 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 ne... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-27 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-28 | 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 ... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-29 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-30 | 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] | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-31 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-32 | 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 exten... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-33 | hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0) | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7b0dfe8ade09-34 | 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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/pdf.html |
7e3e629ee4a0-0 | .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"... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/CoNLL-U.html |
79bd01e7a10e-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 | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
79bd01e7a10e-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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
79bd01e7a10e-2 | Documentation\n\nPlease see here for full documentation on:\n\nGetting started (installation, setting up the environment, simple examples)\n\nHow-To examples (demos, integrations, helper functions)\n\nReference (full API docs)\n Resources (high-level explanation of core concepts)\n\nð\x9f\x9a\x80 What can this help wi... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
79bd01e7a10e-3 | which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.\n\nð\x9f§\xa0 Memory:\n\nMemory is the concept of persisting state between calls of a chain/agent.... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
79bd01e7a10e-4 | 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]... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/markdown.html |
056f81721b80-0 | .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... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
056f81721b80-1 | 'https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Passion_fruits_-_whole_and_halved.jpg/270px-Passion_fruits_-_whole_and_halved.jpg',
'https://upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Messier83_-_Heic1403a.jpg/277px-Messier83_-_Heic1403a.jpg',
'https://upload.wikimedia.org/wikipedia/commons/th... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
056f81721b80-2 | list_docs = loader.load()
list_docs
/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 -- we recom... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
056f81721b80-3 | Document(page_content='an image of a painting of a battle scene [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Thure_de_Thulstrup_-_Battle_of_Shiloh.jpg/251px-Thure_de_Thulstrup_-_Battle_of_Shiloh.jpg'}),
Document(page_content='an image of a passion fruit and a half cut pass... | /content/https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/image_captions.html |
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