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from langchain.document_loaders import UnstructuredFileLoader
loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", strategy="fast", mode="elements")
docs = loader.load()
docs[:5]
[Document(page_content='1', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),
Document(page_content='2', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),
Document(page_content='0', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),
Document(page_content='2', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),
Document(page_content='n', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'Title'}, lookup_index=0)]
PDF Example#
Processing PDF documents works exactly the same way. Unstructured detects the file type and extracts the same types of elements.
!wget https://raw.githubusercontent.com/Unstructured-IO/unstructured/main/example-docs/layout-parser-paper.pdf -P "../../"
loader = UnstructuredFileLoader("./example_data/layout-parser-paper.pdf", mode="elements")
docs = loader.load()
docs[:5]
|
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docs = loader.load()
docs[:5]
[Document(page_content='LayoutParser : A Unified Toolkit for Deep Learning Based Document Image Analysis', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),
Document(page_content='Zejiang Shen 1 ( (ea)\n ), Ruochen Zhang 2 , Melissa Dell 3 , Benjamin Charles Germain Lee 4 , Jacob Carlson 3 , and Weining Li 5', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),
Document(page_content='Allen Institute for AI [email protected]', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),
Document(page_content='Brown University ruochen [email protected]', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),
Document(page_content='Harvard University { melissadell,jacob carlson } @fas.harvard.edu', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0)]
Unstructured API#
If you want to get up and running with less set up, you can simply run pip install unstructured and use UnstructuredAPIFileLoader or UnstructuredAPIFileIOLoader. That will process your document using the hosted Unstructured API. Note that currently (as of 11 May 2023) the Unstructured API is open, but it will soon require an API. The Unstructured documentation page will have instructions on how to generate an API key once they’re available. Check out the instructions here if you’d like to self-host the Unstructured API or run it locally.
from langchain.document_loaders import UnstructuredAPIFileLoader
filenames = ["example_data/fake.docx", "example_data/fake-email.eml"]
loader = UnstructuredAPIFileLoader(
|
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loader = UnstructuredAPIFileLoader(
file_path=filenames[0],
api_key="FAKE_API_KEY",
)
docs = loader.load()
docs[0]
Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': 'example_data/fake.docx'})
You can also batch multiple files through the Unstructured API in a single API using UnstructuredAPIFileLoader.
loader = UnstructuredAPIFileLoader(
file_path=filenames,
api_key="FAKE_API_KEY",
)
docs = loader.load()
docs[0]
Document(page_content='Lorem ipsum dolor sit amet.\n\nThis is a test email to use for unit tests.\n\nImportant points:\n\nRoses are red\n\nViolets are blue', metadata={'source': ['example_data/fake.docx', 'example_data/fake-email.eml']})
previous
TOML
next
URL
Contents
Retain Elements
Define a Partitioning Strategy
PDF Example
Unstructured API
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/unstructured_file.html
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1058459285c7-0
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.ipynb
.pdf
Open Document Format (ODT)
Open Document Format (ODT)#
The Open Document Format for Office Applications (ODF), also known as OpenDocument, is an open file format for word processing documents, spreadsheets, presentations and graphics and using ZIP-compressed XML files. It was developed with the aim of providing an open, XML-based file format specification for office applications.
The standard is developed and maintained by a technical committee in the Organization for the Advancement of Structured Information Standards (OASIS) consortium. It was based on the Sun Microsystems specification for OpenOffice.org XML, the default format for OpenOffice.org and LibreOffice. It was originally developed for StarOffice “to provide an open standard for office documents.”
The UnstructuredODTLoader is used to load Open Office ODT files.
from langchain.document_loaders import UnstructuredODTLoader
loader = UnstructuredODTLoader("example_data/fake.odt", mode="elements")
docs = loader.load()
docs[0]
Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': 'example_data/fake.odt', 'filename': 'example_data/fake.odt', 'category': 'Title'})
previous
Microsoft Word
next
Pandas DataFrame
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/odt.html
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cfa88f27b228-0
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.ipynb
.pdf
College Confidential
College Confidential#
College Confidential gives information on 3,800+ colleges and universities.
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-university/")
data = loader.load()
data
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[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\n\n\nAbout Brown\n\n\n\n\n\n\nBrown University Overview\nBrown University is a private, nonprofit school in the urban setting of Providence, Rhode Island. Brown was founded in 1764 and the school currently enrolls around 10,696 students a year, including 7,349 undergraduates. Brown provides on-campus housing for students. Most students live in off campus housing.\n📆 Mark your calendar! January 5, 2023 is the final deadline to submit an application for the Fall 2023 semester. \nThere are many ways for students to get involved at Brown! \nLove music or
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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 channel.\nInterested in joining a fraternity or sorority? Brown has fraternities and sororities.\nPlanning to play sports? Brown has many options for athletes. See them all and learn more about life at Brown on the Student Life page.\n\n\n\n2022 Brown Facts At-A-Glance\n\n\n\n\n\nAcademic Calendar\nOther\n\n\nOverall Acceptance Rate\n6%\n\n\nEarly Decision Acceptance Rate\n16%\n\n\nEarly Action Acceptance Rate\nEA not offered\n\n\nApplicants Submitting SAT scores\n51%\n\n\nTuition\n$62,680\n\n\nPercent of Need Met\n100%\n\n\nAverage First-Year Financial Aid Package\n$59,749\n\n\n\n\nIs Brown a Good School?\n\nDifferent people have different ideas about what makes a "good" school. Some factors that can help you
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"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\nIt is extremely difficult to get into Brown. Around 6% of applicants get into Brown each year. In 2022, just 2,568 out of the 46,568 students who applied were accepted.\nRetention and Graduation Rates at Brown\nRetention refers to the number of students that stay enrolled at a school over time. This is a way to get a sense of how satisfied students are with their school experience, and if they have the support necessary to succeed in college. \nApproximately 98% of first-year, full-time undergrads who start at Browncome back their sophomore year. 95% of Brown undergrads graduate within six years. The average six-year graduation rate for U.S. colleges and
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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. \nCheck with Brown directly, for information on any information on starting salaries for recent grads.\nBrown\'s Endowment\nAn endowment is the total value of a school\'s investments, donations, and assets. Endowment is not necessarily an indicator of the quality of a school, but it can give you a sense of how much money a college can afford to invest in expanding programs, improving facilities, and support students. \nAs of 2022, the total market value of Brown University\'s endowment was $4.7 billion. The average college endowment was $905 million in 2021. The school spends $34,086 for each full-time student enrolled. \nTuition and Financial Aid at Brown\nTuition is another important factor
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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 student is around $59,749 a year. \nThe average student debt for graduates in the class of 2022 was around $24,102 per student, not including those with no debt. For context, compare this number with the average national debt, which is around $36,000 per borrower. \nThe 2023-2024 FAFSA Opened on October 1st, 2022\nSome financial aid is awarded on a first-come, first-served basis, so fill out the FAFSA as soon as you can. Visit the FAFSA website to apply for student aid. Remember, the first F in FAFSA stands for FREE! You should never have to pay to submit the Free Application for Federal Student Aid (FAFSA), so be very wary of anyone asking you
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so be very wary of anyone asking you for money.\nLearn more about Tuition and Financial Aid at Brown.\nBased on this information, does Brown seem like a good fit? Remember, a school that is perfect for one person may be a terrible fit for someone else! So ask yourself: Is Brown a good school for you?\nIf Brown University seems like a school you want to apply to, click the heart button to save it to your college list.\n\nStill Exploring Schools?\nChoose one of the options below to learn more about Brown:\nAdmissions\nStudent Life\nAcademics\nTuition & Aid\nBrown Community Forums\nThen use the college admissions predictor to take a data science look at your chances of getting into some of the best colleges and universities in the U.S.\nWhere is Brown?\nBrown is located in the urban setting of Providence, Rhode Island, less than an hour from Boston. \nIf you would like to see Brown for yourself, plan a visit. The best way to reach campus is to take Interstate
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best way to reach campus is to take Interstate 95 to Providence, or book a flight to the nearest airport, T.F. Green.\nYou can also take a virtual campus tour to get a sense of what Brown and Providence are like without leaving home.\nConsidering Going to School in Rhode Island?\nSee a full list of colleges in Rhode Island and save your favorites to your college list.\n\n\n\nCollege Info\n\n\n\n\n\n\n\n\n\n Providence, RI 02912\n \n\n\n\n Campus Setting: Urban\n \n\n\n\n\n\n\n\n (401) 863-2378\n \n\n Website\n \n\n
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\n\n Virtual Tour\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBrown Application Deadline\n\n\n\nFirst-Year Applications are Due\n\nJan 5\n\nTransfer Applications are Due\n\nMar 1\n\n\n\n \n The deadline for Fall first-year applications to Brown is \n Jan 5. \n \n \n \n\n \n The deadline for Fall transfer applications to Brown is \n Mar 1. \n \n \n \n\n \n Check the school website \n for more information about deadlines for specific programs
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for more information about deadlines for specific programs or special admissions programs\n \n \n\n\n\n\n\n\nBrown ACT Scores\n\n\n\n\nic_reflect\n\n\n\n\n\n\n\n\nACT Range\n\n\n \n 33 - 35\n \n \n\n\n\nEstimated Chance of Acceptance by ACT Score\n\n\nACT Score\nEstimated Chance\n\n\n35 and Above\nGood\n\n\n33 to 35\nAvg\n\n\n33 and Less\nLow\n\n\n\n\n\n\nStand out on your college application\n\n• Qualify for scholarships\n• Most students who retest improve their score\n\nSponsored by ACT\n\n\n Take the Next ACT Test\n \n\n\n\n\n\nBrown SAT Scores\n\n\n\n\nic_reflect\n\n\n\n\n\n\n\n\nComposite SAT Range\n\n\n \n 720 -
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720 - 770\n \n \n\n\n\nic_reflect\n\n\n\n\n\n\n\n\nMath SAT Range\n\n\n \n Not available\n \n \n\n\n\nic_reflect\n\n\n\n\n\n\n\n\nReading SAT Range\n\n\n \n 740 - 800\n \n \n\n\n\n\n\n\n Brown Tuition & Fees\n \n\n\n\nTuition & Fees\n\n\n\n $82,286\n \nIn State\n\n\n\n\n
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$82,286\n \nOut-of-State\n\n\n\n\n\n\n\nCost Breakdown\n\n\nIn State\n\n\nOut-of-State\n\n\n\n\nState Tuition\n\n\n\n $62,680\n \n\n\n\n $62,680\n \n\n\n\n\nFees\n\n\n\n $2,466\n \n\n\n\n $2,466\n \n\n\n\n\nHousing\n\n\n\n $15,840\n
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\n\n\n\n $15,840\n \n\n\n\n\nBooks\n\n\n\n $1,300\n \n\n\n\n $1,300\n \n\n\n\n\n\n Total (Before Financial Aid):\n \n\n\n\n $82,286\n \n\n\n\n $82,286\n
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\n\n\n\n\n\n\n\n\n\n\n\nStudent Life\n\n Wondering what life at Brown is like? There are approximately \n 10,696 students enrolled at \n Brown, \n including 7,349 undergraduate students and \n 3,347 graduate students.\n 96% percent of students attend school \n full-time, \n 6% percent are from RI and \n 94% percent of students are from other states.\n \n\n\n\n\n\n None\n \n\n\n\n\nUndergraduate Enrollment\n\n\n\n 96%\n \nFull Time\n\n\n\n\n 4%\n
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4%\n \nPart Time\n\n\n\n\n\n\n\n 94%\n \n\n\n\n\nResidency\n\n\n\n 6%\n \nIn State\n\n\n\n\n 94%\n \nOut-of-State\n\n\n\n\n\n\n\n Data Source: IPEDs and Peterson\'s Databases © 2022 Peterson\'s LLC All rights reserved\n \n', lookup_str='', metadata={'source': 'https://www.collegeconfidential.com/colleges/brown-university/'}, lookup_index=0)]
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previous
BiliBili
next
Gutenberg
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html
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31115a2d1804-0
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.ipynb
.pdf
WhatsApp Chat
WhatsApp Chat#
WhatsApp (also called WhatsApp Messenger) is a freeware, cross-platform, centralized instant messaging (IM) and voice-over-IP (VoIP) service. It allows users to send text and voice messages, make voice and video calls, and share images, documents, user locations, and other content.
This notebook covers how to load data from the WhatsApp Chats into a format that can be ingested into LangChain.
from langchain.document_loaders import WhatsAppChatLoader
loader = WhatsAppChatLoader("example_data/whatsapp_chat.txt")
loader.load()
previous
Weather
next
Arxiv
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/whatsapp_chat.html
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025d71efaaa3-0
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.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()
previous
Spreedly
next
2Markdown
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/stripe.html
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5f0a3c4b2f84-0
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.ipynb
.pdf
CSV
Contents
Customizing the csv parsing and loading
Specify a column to identify the document source
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)
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[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
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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': './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:
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'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: 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),
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5f0a3c4b2f84-4
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'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), 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),
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-5
|
'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={'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), 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
|
5f0a3c4b2f84-6
|
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
|
5f0a3c4b2f84-7
|
[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':
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-8
|
'./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\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='',
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-9
|
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), 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: 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:
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-10
|
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={'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: 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',
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-11
|
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 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: 78.06\nWins: 64',
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-12
|
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(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
|
5f0a3c4b2f84-13
|
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
|
5f0a3c4b2f84-14
|
[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_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:
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-15
|
'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_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':
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-16
|
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_str='', metadata={'source': 'Phillies', '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='',
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-17
|
(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)": 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)": 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
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
5f0a3c4b2f84-18
|
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), 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
|
5f0a3c4b2f84-19
|
previous
Copy Paste
next
Email
Contents
Customizing the csv parsing and loading
Specify a column to identify the document source
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html
|
d57f5de99721-0
|
.ipynb
.pdf
Microsoft PowerPoint
Contents
Retain Elements
Microsoft PowerPoint#
Microsoft PowerPoint is a presentation program by Microsoft.
This covers how to load Microsoft PowerPoint documents into a document format that we can use downstream.
from langchain.document_loaders import UnstructuredPowerPointLoader
loader = UnstructuredPowerPointLoader("example_data/fake-power-point.pptx")
data = loader.load()
data
[Document(page_content='Adding a Bullet Slide\n\nFind the bullet slide layout\n\nUse _TextFrame.text for first bullet\n\nUse _TextFrame.add_paragraph() for subsequent bullets\n\nHere is a lot of text!\n\nHere is some text in a text box!', metadata={'source': 'example_data/fake-power-point.pptx'})]
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 = UnstructuredPowerPointLoader("example_data/fake-power-point.pptx", mode="elements")
data = loader.load()
data[0]
Document(page_content='Adding a Bullet Slide', lookup_str='', metadata={'source': 'example_data/fake-power-point.pptx'}, lookup_index=0)
previous
Markdown
next
Microsoft Word
Contents
Retain Elements
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/microsoft_powerpoint.html
|
6079e7ff7d41-0
|
.ipynb
.pdf
Roam
Contents
🧑 Instructions for ingesting your own dataset
Roam#
ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base.
This notebook covers how to load documents from a Roam database. This takes a lot of inspiration from the example repo here.
🧑 Instructions for ingesting your own dataset#
Export your dataset from Roam Research. You can do this by clicking on the three dots in the upper right hand corner and then clicking Export.
When exporting, make sure to select the Markdown & CSV format option.
This will produce a .zip file in your Downloads folder. Move the .zip file into this repository.
Run the following command to unzip the zip file (replace the Export... with your own file name as needed).
unzip Roam-Export-1675782732639.zip -d Roam_DB
from langchain.document_loaders import RoamLoader
loader = RoamLoader("Roam_DB")
docs = loader.load()
previous
Reddit
next
Slack
Contents
🧑 Instructions for ingesting your own dataset
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/roam.html
|
4154765bdad1-0
|
.ipynb
.pdf
AZLyrics
AZLyrics#
AZLyrics is a large, legal, every day growing collection of lyrics.
This covers how to load AZLyrics webpages into a document format that we can use downstream.
from langchain.document_loaders import AZLyricsLoader
loader = AZLyricsLoader("https://www.azlyrics.com/lyrics/mileycyrus/flowers.html")
data = loader.load()
data
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html
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4154765bdad1-1
|
[Document(page_content="Miley Cyrus - Flowers Lyrics | AZLyrics.com\n\r\nWe were good, we were gold\nKinda dream that can't be sold\nWe were right till we weren't\nBuilt a home and watched it burn\n\nI didn't wanna leave you\nI didn't wanna lie\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than you can\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\n\nPaint my nails, cherry red\nMatch the roses that you left\nNo remorse, no regret\nI forgive every word you said\n\nI didn't wanna leave you, baby\nI didn't wanna fight\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours, yeah\nSay things you don't
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html
|
4154765bdad1-2
|
to myself for hours, yeah\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than you can\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby\nCan love me better\nI\n\nI didn't wanna wanna leave you\nI didn't wanna fight\nStarted to cry but then remembered I\n\nI can buy myself flowers\nWrite my name in the sand\nTalk to myself for hours (Yeah)\nSay things you don't understand\nI can take myself dancing\nAnd I can hold my own hand\nYeah, I can love me better than\nYeah, I can love me better than you can, uh\n\nCan love me better\nI can love me better, baby\nCan love me better\nI can love me better, baby (Than you can)\nCan love me better\nI can love me better, baby\nCan love me better\nI\n", lookup_str='', metadata={'source':
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html
|
4154765bdad1-3
|
love me better\nI\n", lookup_str='', metadata={'source': 'https://www.azlyrics.com/lyrics/mileycyrus/flowers.html'}, lookup_index=0)]
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html
|
4154765bdad1-4
|
previous
Arxiv
next
BiliBili
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azlyrics.html
|
dba02fd26a65-0
|
.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
|
dba02fd26a65-1
|
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
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dba02fd26a65-2
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[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,
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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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 logo). release the buttons when you see this.\nThen, connect to your laptop and log in to iTunes and reset your phone completely. (please
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-4
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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
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-5
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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 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
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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
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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 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
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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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
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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. 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
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-10
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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
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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below. I took it off and it stopped. I ordered more protectors from amazon and replaced it\n\n\n\n### Other Answer\niPhone 6 Plus first generation….I had the same issues as all above, apps opening by themselves, self typing, ultra sensitive screen, items jumping around all over….it even called someone on FaceTime twice by itself when I was not in the room…..I thought the phone was toast and i’d have to buy a new one took me a while to figure out but it was the extra cheap block plug I bought at a dollar store for convenience of an extra charging station when I move around the house from den to living room…..cord was fine but bought a new Apple brand block plug…no more problems works just fine now. This issue was a recent event so had to narrow things down to what had changed recently to my phone so I could figure it out.\nI even had the same problem on a laptop with documents opening up by themselves…..a laptop that was plugged in to the same
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-12
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laptop that was plugged in to the same wall plug as my phone charger with the dollar store block plug….until I changed the block plug.\n\n\n\n### Other Answer\nHad the problem: Inherited a 6s Plus from my wife. She had no problem with it.\nLooks like it was merely the cheap phone case I purchased on Amazon. It was either pinching the edges or torquing the screen/body of the phone. Problem solved.\n\n\n\n### Other Answer\nI bought my phone on march 6 and it was a brand new, but It sucks me uo because it freezing, shaking and control by itself. I went to the store where I bought this and I told them to replacr it, but they told me I have to pay it because Its about lcd issue. Please help me what other ways to fix it. Or should I try to remove the screen or should I follow your step above.\n\n\n\n### Other Answer\nI tried everything and it seems to come back to needing the original iPhone cable…or
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-13
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come back to needing the original iPhone cable…or at least another 1 that would have come with another iPhone…not the $5 Store fast charging cables. My original cable is pretty beat up - like most that I see - but I’ve been beaten up much MUCH less by sticking with its use! I didn’t find that the casing/shell around it or not made any diff.\n\n\n\n### Other Answer\ngreat now I have to wait one more hour to reset my phone and while I was tryin to connect my phone to my computer the computer also restarted smh does anyone else knows how I can get my phone to work… my problem is I have a black dot on the bottom left of my screen an it wont allow me to touch a certain part of my screen unless I rotate my phone and I know the password but the first number is a 2 and it won\'t let me touch 1,2, or 3 so now I have to find a way to get rid of my password and all of
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-14
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way to get rid of my password and all of a sudden my phone wants to touch stuff on its own which got my phone disabled many times to the point where I have to wait a whole hour and I really need to finish something on my phone today PLEASE HELPPPP\n\n\n\n### Other Answer\nIn my case , iphone 6 screen was faulty. I got it replaced at local repair shop, so far phone is working fine.\n\n\n\n### Other Answer\nthis problem in iphone 6 has many different scenarios and solutions, first try to reconnect the lcd screen to the motherboard again, if didnt solve, try to replace the lcd connector on the motherboard, if not solved, then remains two issues, lcd screen it self or touch IC. in my country some repair shops just change them all for almost 40$ since they dont want to troubleshoot one by one. readers of this comment also should know that partial screen not responding in other iphone models might also have an issue in LCD connector on the motherboard, specially if you
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-15
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in LCD connector on the motherboard, specially if you lock/unlock screen and screen works again for sometime. lcd connectors gets disconnected lightly from the motherboard due to multiple falls and hits after sometime. best of luck for all\n\n\n\n### Other Answer\nI am facing the same issue whereby these ghost touches type and open apps , I am using an original Iphone cable , how to I fix this issue.\n\n\n\n### Other Answer\nThere were two issues with the phone I had troubles with. It was my dads and turns out he carried it in his pocket. The phone itself had a little bend in it as a result. A little pressure in the opposite direction helped the issue. But it also had a tiny crack in the screen which wasnt obvious, once we added a screen protector this fixed the issues entirely.\n\n\n\n### Other Answer\nI had the same problem with my 64Gb iPhone 6+. Tried a lot of things and eventually downloaded all my images and videos to my PC and restarted
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-16
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all my images and videos to my PC and restarted the phone - problem solved. Been working now for two days.', lookup_str='', metadata={'source': 'https://www.ifixit.com/Answers/View/318583/My+iPhone+6+is+typing+and+opening+apps+by+itself', 'title': 'My iPhone 6 is typing and opening apps by itself'}, lookup_index=0)]
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dba02fd26a65-17
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loader = IFixitLoader("https://www.ifixit.com/Device/Standard_iPad")
data = loader.load()
data
[Document(page_content="Standard iPad\nThe standard edition of the tablet computer made by Apple.\n== Background Information ==\n\nOriginally introduced in January 2010, the iPad is Apple's standard edition of their tablet computer. In total, there have been ten generations of the standard edition of the iPad.\n\n== Additional Information ==\n\n* [link|https://www.apple.com/ipad-select/|Official Apple Product Page]\n* [link|https://en.wikipedia.org/wiki/IPad#iPad|Official iPad Wikipedia]", lookup_str='', metadata={'source': 'https://www.ifixit.com/Device/Standard_iPad', 'title': 'Standard iPad'}, lookup_index=0)]
Searching iFixit using /suggest#
If you’re looking for a more general way to search iFixit based on a keyword or phrase, the /suggest endpoint will return content related to the search term, then the loader will load the content from each of the suggested items and prep and return the documents.
data = IFixitLoader.load_suggestions("Banana")
data
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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dba02fd26a65-18
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data = IFixitLoader.load_suggestions("Banana")
data
[Document(page_content='Banana\nTasty fruit. Good source of potassium. Yellow.\n== Background Information ==\n\nCommonly misspelled, this wildly popular, phone shaped fruit serves as nutrition and an obstacle to slow down vehicles racing close behind you. Also used commonly as a synonym for “crazy” or “insane”.\n\nBotanically, the banana is considered a berry, although it isn’t included in the culinary berry category containing strawberries and raspberries. Belonging to the genus Musa, the banana originated in Southeast Asia and Australia. Now largely cultivated throughout South and Central America, bananas are largely available throughout the world. They are especially valued as a staple food group in developing countries due to the banana tree’s ability to produce fruit year round.\n\nThe banana can be easily opened. Simply remove the outer yellow shell by cracking the top of the stem. Then, with the broken piece, peel downward on each side until the fruity components on the inside are exposed. Once the shell has been removed it cannot be put back together.\n\n== Technical Specifications ==\n\n* Dimensions: Variable depending on genetics of the parent tree\n* Color: Variable depending on ripeness, region, and season\n\n== Additional Information ==\n\n[link|https://en.wikipedia.org/wiki/Banana|Wiki: Banana]', lookup_str='', metadata={'source': 'https://www.ifixit.com/Device/Banana', 'title': 'Banana'}, lookup_index=0),
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dba02fd26a65-19
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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)]
previous
HuggingFace dataset
next
IMSDb
Contents
Searching iFixit using /suggest
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/ifixit.html
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7bf109ac43b1-0
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.ipynb
.pdf
Azure Blob Storage Container
Contents
Specifying a prefix
Azure Blob Storage Container#
Azure Blob Storage is Microsoft’s object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn’t adhere to a particular data model or definition, such as text or binary data.
Azure Blob Storage is designed for:
Serving images or documents directly to a browser.
Storing files for distributed access.
Streaming video and audio.
Writing to log files.
Storing data for backup and restore, disaster recovery, and archiving.
Storing data for analysis by an on-premises or Azure-hosted service.
This notebook covers how to load document objects from a container on Azure Blob Storage.
#!pip install azure-storage-blob
from langchain.document_loaders import AzureBlobStorageContainerLoader
loader = AzureBlobStorageContainerLoader(conn_str="<conn_str>", container="<container>")
loader.load()
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpaa9xl6ch/fake.docx'}, lookup_index=0)]
Specifying a prefix#
You can also specify a prefix for more finegrained control over what files to load.
loader = AzureBlobStorageContainerLoader(conn_str="<conn_str>", container="<container>", prefix="<prefix>")
loader.load()
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpujbkzf_l/fake.docx'}, lookup_index=0)]
previous
AWS S3 File
next
Azure Blob Storage File
Contents
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https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azure_blob_storage_container.html
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7bf109ac43b1-1
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previous
AWS S3 File
next
Azure Blob Storage File
Contents
Specifying a prefix
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/azure_blob_storage_container.html
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fb5094209836-0
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.rst
.pdf
Indexes
Indexes#
Indexes refer to ways to structure documents so that LLMs can best interact with them.
LangChain has a number of modules that help you load, structure, store, and retrieve documents.
Docstore
Text Splitter
Document Loaders
Vector Stores
Retrievers
Document Compressors
Document Transformers
previous
Embeddings
next
Docstore
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/reference/indexes.html
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6cf1e98e9e40-0
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.rst
.pdf
Prompts
Prompts#
The reference guides here all relate to objects for working with Prompts.
PromptTemplates
Example Selector
Output Parsers
previous
How to serialize prompts
next
PromptTemplates
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/reference/prompts.html
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da935d0fe1d3-0
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.rst
.pdf
Agents
Agents#
Reference guide for Agents and associated abstractions.
Agents
Tools
Agent Toolkits
previous
Memory
next
Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/reference/agents.html
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4cea7f8de31a-0
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.md
.pdf
Installation
Contents
Official Releases
Installing from source
Installation#
Official Releases#
LangChain is available on PyPi, so to it is easily installable with:
pip install langchain
That will install the bare minimum requirements of LangChain.
A lot of the value of LangChain comes when integrating it with various model providers, datastores, etc.
By default, the dependencies needed to do that are NOT installed.
However, there are two other ways to install LangChain that do bring in those dependencies.
To install modules needed for the common LLM providers, run:
pip install langchain[llms]
To install all modules needed for all integrations, run:
pip install langchain[all]
Note that if you are using zsh, you’ll need to quote square brackets when passing them as an argument to a command, for example:
pip install 'langchain[all]'
Installing from source#
If you want to install from source, you can do so by cloning the repo and running:
pip install -e .
previous
SQL Question Answering Benchmarking: Chinook
next
API References
Contents
Official Releases
Installing from source
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/reference/installation.html
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adf66b8da154-0
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.rst
.pdf
Models
Models#
LangChain provides interfaces and integrations for a number of different types of models.
LLMs
Chat Models
Embeddings
previous
API References
next
Chat Models
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/reference/models.html
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df087f296616-0
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.rst
.pdf
Chat Models
Chat Models#
pydantic model langchain.chat_models.AzureChatOpenAI[source]#
Wrapper around Azure OpenAI Chat Completion API. To use this class you
must have a deployed model on Azure OpenAI. Use deployment_name in the
constructor to refer to the “Model deployment name” in the Azure portal.
In addition, you should have the openai python package installed, and the
following environment variables set or passed in constructor in lower case:
- OPENAI_API_TYPE (default: azure)
- OPENAI_API_KEY
- OPENAI_API_BASE
- OPENAI_API_VERSION
- OPENAI_PROXY
For exmaple, if you have gpt-35-turbo deployed, with the deployment name
35-turbo-dev, the constructor should look like:
Be aware the API version may change.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
field deployment_name: str = ''#
field openai_api_base: str = ''#
field openai_api_key: str = ''#
Base URL path for API requests,
leave blank if not using a proxy or service emulator.
field openai_api_type: str = 'azure'#
field openai_api_version: str = ''#
field openai_organization: str = ''#
field openai_proxy: str = ''#
pydantic model langchain.chat_models.ChatAnthropic[source]#
Wrapper around Anthropic’s large language model.
To use, you should have the anthropic python package installed, and the
environment variable ANTHROPIC_API_KEY set with your API key, or pass
it as a named parameter to the constructor.
Example
get_num_tokens(text: str) → int[source]#
Calculate number of tokens.
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https://python.langchain.com/en/latest/reference/modules/chat_models.html
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Example
get_num_tokens(text: str) → int[source]#
Calculate number of tokens.
pydantic model langchain.chat_models.ChatGooglePalm[source]#
Wrapper around Google’s PaLM Chat API.
To use you must have the google.generativeai Python package installed and
either:
The GOOGLE_API_KEY` environment varaible set with your API key, or
Pass your API key using the google_api_key kwarg to the ChatGoogle
constructor.
Example
from langchain.chat_models import ChatGooglePalm
chat = ChatGooglePalm()
field google_api_key: Optional[str] = None#
field model_name: str = 'models/chat-bison-001'#
Model name to use.
field n: int = 1#
Number of chat completions to generate for each prompt. Note that the API may
not return the full n completions if duplicates are generated.
field temperature: Optional[float] = None#
Run inference with this temperature. Must by in the closed
interval [0.0, 1.0].
field top_k: Optional[int] = None#
Decode using top-k sampling: consider the set of top_k most probable tokens.
Must be positive.
field top_p: Optional[float] = None#
Decode using nucleus sampling: consider the smallest set of tokens whose
probability sum is at least top_p. Must be in the closed interval [0.0, 1.0].
pydantic model langchain.chat_models.ChatOpenAI[source]#
Wrapper around OpenAI Chat large language models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
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in, even if not explicitly saved on this class.
Example
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
field max_retries: int = 6#
Maximum number of retries to make when generating.
field max_tokens: Optional[int] = None#
Maximum number of tokens to generate.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not explicitly specified.
field model_name: str = 'gpt-3.5-turbo' (alias 'model')#
Model name to use.
field n: int = 1#
Number of chat completions to generate for each prompt.
field openai_api_base: Optional[str] = None#
field openai_api_key: Optional[str] = None#
Base URL path for API requests,
leave blank if not using a proxy or service emulator.
field openai_organization: Optional[str] = None#
field openai_proxy: Optional[str] = None#
field request_timeout: Optional[Union[float, Tuple[float, float]]] = None#
Timeout for requests to OpenAI completion API. Default is 600 seconds.
field streaming: bool = False#
Whether to stream the results or not.
field temperature: float = 0.7#
What sampling temperature to use.
completion_with_retry(**kwargs: Any) → Any[source]#
Use tenacity to retry the completion call.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int[source]#
Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
Official documentation: openai/openai-cookbook
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
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get_token_ids(text: str) → List[int][source]#
Get the tokens present in the text with tiktoken package.
pydantic model langchain.chat_models.ChatVertexAI[source]#
Wrapper around Vertex AI large language models.
field model_name: str = 'chat-bison'#
Model name to use.
pydantic model langchain.chat_models.PromptLayerChatOpenAI[source]#
Wrapper around OpenAI Chat large language models and PromptLayer.
To use, you should have the openai and promptlayer python
package installed, and the environment variable OPENAI_API_KEY
and PROMPTLAYER_API_KEY set with your openAI API key and
promptlayer key respectively.
All parameters that can be passed to the OpenAI LLM can also
be passed here. The PromptLayerChatOpenAI adds to optional
:param pl_tags: List of strings to tag the request with.
:param return_pl_id: If True, the PromptLayer request ID will be
returned in the generation_info field of the
Generation object.
Example
from langchain.chat_models import PromptLayerChatOpenAI
openai = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo")
field pl_tags: Optional[List[str]] = None#
field return_pl_id: Optional[bool] = False#
previous
Models
next
Embeddings
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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Python REPL
Python REPL#
For backwards compatibility.
pydantic model langchain.python.PythonREPL[source]#
Simulates a standalone Python REPL.
field globals: Optional[Dict] [Optional] (alias '_globals')#
field locals: Optional[Dict] [Optional] (alias '_locals')#
run(command: str) → str[source]#
Run command with own globals/locals and returns anything printed.
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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Example Selector
Example Selector#
Logic for selecting examples to include in prompts.
pydantic model langchain.prompts.example_selector.LengthBasedExampleSelector[source]#
Select examples based on length.
Validators
calculate_example_text_lengths » example_text_lengths
field example_prompt: langchain.prompts.prompt.PromptTemplate [Required]#
Prompt template used to format the examples.
field examples: List[dict] [Required]#
A list of the examples that the prompt template expects.
field get_text_length: Callable[[str], int] = <function _get_length_based>#
Function to measure prompt length. Defaults to word count.
field max_length: int = 2048#
Max length for the prompt, beyond which examples are cut.
add_example(example: Dict[str, str]) → None[source]#
Add new example to list.
select_examples(input_variables: Dict[str, str]) → List[dict][source]#
Select which examples to use based on the input lengths.
pydantic model langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector[source]#
ExampleSelector that selects examples based on Max Marginal Relevance.
This was shown to improve performance in this paper:
https://arxiv.org/pdf/2211.13892.pdf
field fetch_k: int = 20#
Number of examples to fetch to rerank.
classmethod from_examples(examples: List[dict], embeddings: langchain.embeddings.base.Embeddings, vectorstore_cls: Type[langchain.vectorstores.base.VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, fetch_k: int = 20, **vectorstore_cls_kwargs: Any) → langchain.prompts.example_selector.semantic_similarity.MaxMarginalRelevanceExampleSelector[source]#
Create k-shot example selector using example list and embeddings.
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Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples – List of examples to use in the prompt.
embeddings – An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g. FAISS.
k – Number of examples to select
input_keys – If provided, the search is based on the input variables
instead of all variables.
vectorstore_cls_kwargs – optional kwargs containing url for vector store
Returns
The ExampleSelector instantiated, backed by a vector store.
select_examples(input_variables: Dict[str, str]) → List[dict][source]#
Select which examples to use based on semantic similarity.
pydantic model langchain.prompts.example_selector.SemanticSimilarityExampleSelector[source]#
Example selector that selects examples based on SemanticSimilarity.
field example_keys: Optional[List[str]] = None#
Optional keys to filter examples to.
field input_keys: Optional[List[str]] = None#
Optional keys to filter input to. If provided, the search is based on
the input variables instead of all variables.
field k: int = 4#
Number of examples to select.
field vectorstore: langchain.vectorstores.base.VectorStore [Required]#
VectorStore than contains information about examples.
add_example(example: Dict[str, str]) → str[source]#
Add new example to vectorstore.
classmethod from_examples(examples: List[dict], embeddings: langchain.embeddings.base.Embeddings, vectorstore_cls: Type[langchain.vectorstores.base.VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, **vectorstore_cls_kwargs: Any) → langchain.prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector[source]#
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Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples – List of examples to use in the prompt.
embeddings – An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g. FAISS.
k – Number of examples to select
input_keys – If provided, the search is based on the input variables
instead of all variables.
vectorstore_cls_kwargs – optional kwargs containing url for vector store
Returns
The ExampleSelector instantiated, backed by a vector store.
select_examples(input_variables: Dict[str, str]) → List[dict][source]#
Select which examples to use based on semantic similarity.
previous
PromptTemplates
next
Output Parsers
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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Text Splitter
Text Splitter#
Functionality for splitting text.
class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class langchain.text_splitter.LatexTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Latex-formatted layout elements.
class langchain.text_splitter.MarkdownTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Markdown-formatted headings.
class langchain.text_splitter.NLTKTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at sentences using NLTK.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class langchain.text_splitter.PythonCodeTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Python syntax.
class langchain.text_splitter.RecursiveCharacterTextSplitter(separators: Optional[List[str]] = None, **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
Recursively tries to split by different characters to find one
that works.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class langchain.text_splitter.SpacyTextSplitter(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', **kwargs: Any)[source]#
Implementation of splitting text that looks at sentences using Spacy.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
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Split incoming text and return chunks.
class langchain.text_splitter.TextSplitter(chunk_size: int = 4000, chunk_overlap: int = 200, length_function: typing.Callable[[str], int] = <built-in function len>)[source]#
Interface for splitting text into chunks.
async atransform_documents(documents: Sequence[langchain.schema.Document], **kwargs: Any) → Sequence[langchain.schema.Document][source]#
Asynchronously transform a sequence of documents by splitting them.
create_documents(texts: List[str], metadatas: Optional[List[dict]] = None) → List[langchain.schema.Document][source]#
Create documents from a list of texts.
classmethod from_huggingface_tokenizer(tokenizer: Any, **kwargs: Any) → langchain.text_splitter.TextSplitter[source]#
Text splitter that uses HuggingFace tokenizer to count length.
classmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any) → langchain.text_splitter.TS[source]#
Text splitter that uses tiktoken encoder to count length.
split_documents(documents: Iterable[langchain.schema.Document]) → List[langchain.schema.Document][source]#
Split documents.
abstract split_text(text: str) → List[str][source]#
Split text into multiple components.
transform_documents(documents: Sequence[langchain.schema.Document], **kwargs: Any) → Sequence[langchain.schema.Document][source]#
Transform sequence of documents by splitting them.
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Transform sequence of documents by splitting them.
class langchain.text_splitter.TokenTextSplitter(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any)[source]#
Implementation of splitting text that looks at tokens.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
previous
Docstore
next
Document Loaders
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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SearxNG Search
Contents
Quick Start
Searching
Engine Parameters
Search Tips
SearxNG Search#
Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
multiple search engines and databases and
supports the OpenSearch
specification.
More detailes on the installtion instructions here.
For the search API refer to https://docs.searxng.org/dev/search_api.html
Quick Start#
In order to use this utility you need to provide the searx host. This can be done
by passing the named parameter searx_host
or exporting the environment variable SEARX_HOST.
Note: this is the only required parameter.
Then create a searx search instance like this:
from langchain.utilities import SearxSearchWrapper
# when the host starts with `http` SSL is disabled and the connection
# is assumed to be on a private network
searx_host='http://self.hosted'
search = SearxSearchWrapper(searx_host=searx_host)
You can now use the search instance to query the searx API.
Searching#
Use the run() and
results() methods to query the searx API.
Other methods are are available for convenience.
SearxResults is a convenience wrapper around the raw json result.
Example usage of the run method to make a search:
s.run(query="what is the best search engine?")
Engine Parameters#
You can pass any accepted searx search API parameters to the
SearxSearchWrapper instance.
In the following example we are using the
engines and the language parameters:
# assuming the searx host is set as above or exported as an env variable
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# assuming the searx host is set as above or exported as an env variable
s = SearxSearchWrapper(engines=['google', 'bing'],
language='es')
Search Tips#
Searx offers a special
search syntax
that can also be used instead of passing engine parameters.
For example the following query:
s = SearxSearchWrapper("langchain library", engines=['github'])
# can also be written as:
s = SearxSearchWrapper("langchain library !github")
# or even:
s = SearxSearchWrapper("langchain library !gh")
In some situations you might want to pass an extra string to the search query.
For example when the run() method is called by an agent. The search suffix can
also be used as a way to pass extra parameters to searx or the underlying search
engines.
# select the github engine and pass the search suffix
s = SearchWrapper("langchain library", query_suffix="!gh")
s = SearchWrapper("langchain library")
# select github the conventional google search syntax
s.run("large language models", query_suffix="site:github.com")
NOTE: A search suffix can be defined on both the instance and the method level.
The resulting query will be the concatenation of the two with the former taking
precedence.
See SearxNG Configured Engines and
SearxNG Search Syntax
for more details.
Notes
This wrapper is based on the SearxNG fork searxng/searxng which is
better maintained than the original Searx project and offers more features.
Public searxNG instances often use a rate limiter for API usage, so you might want to
use a self hosted instance and disable the rate limiter.
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use a self hosted instance and disable the rate limiter.
If you are self-hosting an instance you can customize the rate limiter for your
own network as described here.
For a list of public SearxNG instances see https://searx.space/
class langchain.utilities.searx_search.SearxResults(data: str)[source]#
Dict like wrapper around search api results.
property answers: Any#
Helper accessor on the json result.
pydantic model langchain.utilities.searx_search.SearxSearchWrapper[source]#
Wrapper for Searx API.
To use you need to provide the searx host by passing the named parameter
searx_host or exporting the environment variable SEARX_HOST.
In some situations you might want to disable SSL verification, for example
if you are running searx locally. You can do this by passing the named parameter
unsecure. You can also pass the host url scheme as http to disable SSL.
Example
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://localhost:8888")
Example with SSL disabled:from langchain.utilities import SearxSearchWrapper
# note the unsecure parameter is not needed if you pass the url scheme as
# http
searx = SearxSearchWrapper(searx_host="http://localhost:8888",
unsecure=True)
Validators
disable_ssl_warnings » unsecure
validate_params » all fields
field aiosession: Optional[Any] = None#
field categories: Optional[List[str]] = []#
field engines: Optional[List[str]] = []#
field headers: Optional[dict] = None#
field k: int = 10#
field params: dict [Optional]#
field query_suffix: Optional[str] = ''#
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field params: dict [Optional]#
field query_suffix: Optional[str] = ''#
field searx_host: str = ''#
field unsecure: bool = False#
async aresults(query: str, num_results: int, engines: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → List[Dict][source]#
Asynchronously query with json results.
Uses aiohttp. See results for more info.
async arun(query: str, engines: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → str[source]#
Asynchronously version of run.
results(query: str, num_results: int, engines: Optional[List[str]] = None, categories: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → List[Dict][source]#
Run query through Searx API and returns the results with metadata.
Parameters
query – The query to search for.
query_suffix – Extra suffix appended to the query.
num_results – Limit the number of results to return.
engines – List of engines to use for the query.
categories – List of categories to use for the query.
**kwargs – extra parameters to pass to the searx API.
Returns
{snippet: The description of the result.
title: The title of the result.
link: The link to the result.
engines: The engines used for the result.
category: Searx category of the result.
}
Return type
Dict with the following keys
run(query: str, engines: Optional[List[str]] = None, categories: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → str[source]#
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Run query through Searx API and parse results.
You can pass any other params to the searx query API.
Parameters
query – The query to search for.
query_suffix – Extra suffix appended to the query.
engines – List of engines to use for the query.
categories – List of categories to use for the query.
**kwargs – extra parameters to pass to the searx API.
Returns
The result of the query.
Return type
str
Raises
ValueError – If an error occured with the query.
Example
This will make a query to the qwant engine:
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://my.searx.host")
searx.run("what is the weather in France ?", engine="qwant")
# the same result can be achieved using the `!` syntax of searx
# to select the engine using `query_suffix`
searx.run("what is the weather in France ?", query_suffix="!qwant")
Contents
Quick Start
Searching
Engine Parameters
Search Tips
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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Utilities
Utilities#
General utilities.
pydantic model langchain.utilities.ApifyWrapper[source]#
Wrapper around Apify.
To use, you should have the apify-client python package installed,
and the environment variable APIFY_API_TOKEN set with your API key, or pass
apify_api_token as a named parameter to the constructor.
field apify_client: Any = None#
field apify_client_async: Any = None#
async acall_actor(actor_id: str, run_input: Dict, dataset_mapping_function: Callable[[Dict], langchain.schema.Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None) → langchain.document_loaders.apify_dataset.ApifyDatasetLoader[source]#
Run an Actor on the Apify platform and wait for results to be ready.
Parameters
actor_id (str) – The ID or name of the Actor on the Apify platform.
run_input (Dict) – The input object of the Actor that you’re trying to run.
dataset_mapping_function (Callable) – A function that takes a single
dictionary (an Apify dataset item) and converts it to
an instance of the Document class.
build (str, optional) – Optionally specifies the actor build to run.
It can be either a build tag or build number.
memory_mbytes (int, optional) – Optional memory limit for the run,
in megabytes.
timeout_secs (int, optional) – Optional timeout for the run, in seconds.
Returns
A loader that will fetch the records from theActor run’s default dataset.
Return type
ApifyDatasetLoader
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Return type
ApifyDatasetLoader
call_actor(actor_id: str, run_input: Dict, dataset_mapping_function: Callable[[Dict], langchain.schema.Document], *, build: Optional[str] = None, memory_mbytes: Optional[int] = None, timeout_secs: Optional[int] = None) → langchain.document_loaders.apify_dataset.ApifyDatasetLoader[source]#
Run an Actor on the Apify platform and wait for results to be ready.
Parameters
actor_id (str) – The ID or name of the Actor on the Apify platform.
run_input (Dict) – The input object of the Actor that you’re trying to run.
dataset_mapping_function (Callable) – A function that takes a single
dictionary (an Apify dataset item) and converts it to an
instance of the Document class.
build (str, optional) – Optionally specifies the actor build to run.
It can be either a build tag or build number.
memory_mbytes (int, optional) – Optional memory limit for the run,
in megabytes.
timeout_secs (int, optional) – Optional timeout for the run, in seconds.
Returns
A loader that will fetch the records from theActor run’s default dataset.
Return type
ApifyDatasetLoader
pydantic model langchain.utilities.ArxivAPIWrapper[source]#
Wrapper around ArxivAPI.
To use, you should have the arxiv python package installed.
https://lukasschwab.me/arxiv.py/index.html
This wrapper will use the Arxiv API to conduct searches and
fetch document summaries. By default, it will return the document summaries
of the top-k results.
It limits the Document content by doc_content_chars_max.
Set doc_content_chars_max=None if you don’t want to limit the content size.
Parameters
top_k_results – number of the top-scored document used for the arxiv tool
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Parameters
top_k_results – number of the top-scored document used for the arxiv tool
ARXIV_MAX_QUERY_LENGTH – the cut limit on the query used for the arxiv tool.
load_max_docs – a limit to the number of loaded documents
load_all_available_meta –
if True: the metadata of the loaded Documents gets all available meta info(see https://lukasschwab.me/arxiv.py/index.html#Result),
if False: the metadata gets only the most informative fields.
field arxiv_exceptions: Any = None#
field doc_content_chars_max: int = 4000#
field load_all_available_meta: bool = False#
field load_max_docs: int = 100#
field top_k_results: int = 3#
load(query: str) → List[langchain.schema.Document][source]#
Run Arxiv search and get the article texts plus the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
Returns: a list of documents with the document.page_content in text format
run(query: str) → str[source]#
Run Arxiv search and get the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
See https://lukasschwab.me/arxiv.py/index.html#Result
It uses only the most informative fields of article meta information.
class langchain.utilities.BashProcess(strip_newlines: bool = False, return_err_output: bool = False, persistent: bool = False)[source]#
Executes bash commands and returns the output.
process_output(output: str, command: str) → str[source]#
run(commands: Union[str, List[str]]) → str[source]#
Run commands and return final output.
pydantic model langchain.utilities.BingSearchAPIWrapper[source]#
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pydantic model langchain.utilities.BingSearchAPIWrapper[source]#
Wrapper for Bing Search API.
In order to set this up, follow instructions at:
https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e
field bing_search_url: str [Required]#
field bing_subscription_key: str [Required]#
field k: int = 10#
results(query: str, num_results: int) → List[Dict][source]#
Run query through BingSearch and return metadata.
Parameters
query – The query to search for.
num_results – The number of results to return.
Returns
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
Return type
A list of dictionaries with the following keys
run(query: str) → str[source]#
Run query through BingSearch and parse result.
pydantic model langchain.utilities.DuckDuckGoSearchAPIWrapper[source]#
Wrapper for DuckDuckGo Search API.
Free and does not require any setup
field k: int = 10#
field max_results: int = 5#
field region: Optional[str] = 'wt-wt'#
field safesearch: str = 'moderate'#
field time: Optional[str] = 'y'#
get_snippets(query: str) → List[str][source]#
Run query through DuckDuckGo and return concatenated results.
results(query: str, num_results: int) → List[Dict[str, str]][source]#
Run query through DuckDuckGo and return metadata.
Parameters
query – The query to search for.
num_results – The number of results to return.
Returns
snippet - The description of the result.
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https://python.langchain.com/en/latest/reference/modules/utilities.html
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Returns
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
Return type
A list of dictionaries with the following keys
run(query: str) → str[source]#
pydantic model langchain.utilities.GooglePlacesAPIWrapper[source]#
Wrapper around Google Places API.
To use, you should have the googlemaps python package installed,an API key for the google maps platform,
and the enviroment variable ‘’GPLACES_API_KEY’’
set with your API key , or pass ‘gplaces_api_key’
as a named parameter to the constructor.
By default, this will return the all the results on the input query.You can use the top_k_results argument to limit the number of results.
Example
from langchain import GooglePlacesAPIWrapper
gplaceapi = GooglePlacesAPIWrapper()
field gplaces_api_key: Optional[str] = None#
field top_k_results: Optional[int] = None#
fetch_place_details(place_id: str) → Optional[str][source]#
format_place_details(place_details: Dict[str, Any]) → Optional[str][source]#
run(query: str) → str[source]#
Run Places search and get k number of places that exists that match.
pydantic model langchain.utilities.GoogleSearchAPIWrapper[source]#
Wrapper for Google Search API.
Adapted from: Instructions adapted from https://stackoverflow.com/questions/
37083058/
programmatically-searching-google-in-python-using-custom-search
TODO: DOCS for using it
1. Install google-api-python-client
- If you don’t already have a Google account, sign up.
- If you have never created a Google APIs Console project,
read the Managing Projects page and create a project in the Google API Console.
- Install the library using pip install google-api-python-client
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- Install the library using pip install google-api-python-client
The current version of the library is 2.70.0 at this time
2. To create an API key:
- Navigate to the APIs & Services→Credentials panel in Cloud Console.
- Select Create credentials, then select API key from the drop-down menu.
- The API key created dialog box displays your newly created key.
- You now have an API_KEY
3. Setup Custom Search Engine so you can search the entire web
- Create a custom search engine in this link.
- In Sites to search, add any valid URL (i.e. www.stackoverflow.com).
- That’s all you have to fill up, the rest doesn’t matter.
In the left-side menu, click Edit search engine → {your search engine name}
→ Setup Set Search the entire web to ON. Remove the URL you added from
the list of Sites to search.
Under Search engine ID you’ll find the search-engine-ID.
4. Enable the Custom Search API
- Navigate to the APIs & Services→Dashboard panel in Cloud Console.
- Click Enable APIs and Services.
- Search for Custom Search API and click on it.
- Click Enable.
URL for it: https://console.cloud.google.com/apis/library/customsearch.googleapis
.com
field google_api_key: Optional[str] = None#
field google_cse_id: Optional[str] = None#
field k: int = 10#
field siterestrict: bool = False#
results(query: str, num_results: int) → List[Dict][source]#
Run query through GoogleSearch and return metadata.
Parameters
query – The query to search for.
num_results – The number of results to return.
Returns
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
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title - The title of the result.
link - The link to the result.
Return type
A list of dictionaries with the following keys
run(query: str) → str[source]#
Run query through GoogleSearch and parse result.
pydantic model langchain.utilities.GoogleSerperAPIWrapper[source]#
Wrapper around the Serper.dev Google Search API.
You can create a free API key at https://serper.dev.
To use, you should have the environment variable SERPER_API_KEY
set with your API key, or pass serper_api_key as a named parameter
to the constructor.
Example
from langchain import GoogleSerperAPIWrapper
google_serper = GoogleSerperAPIWrapper()
field aiosession: Optional[aiohttp.client.ClientSession] = None#
field gl: str = 'us'#
field hl: str = 'en'#
field k: int = 10#
field serper_api_key: Optional[str] = None#
field tbs: Optional[str] = None#
field type: Literal['news', 'search', 'places', 'images'] = 'search'#
async aresults(query: str, **kwargs: Any) → Dict[source]#
Run query through GoogleSearch.
async arun(query: str, **kwargs: Any) → str[source]#
Run query through GoogleSearch and parse result async.
results(query: str, **kwargs: Any) → Dict[source]#
Run query through GoogleSearch.
run(query: str, **kwargs: Any) → str[source]#
Run query through GoogleSearch and parse result.
pydantic model langchain.utilities.GraphQLAPIWrapper[source]#
Wrapper around GraphQL API.
To use, you should have the gql python package installed.
This wrapper will use the GraphQL API to conduct queries.
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This wrapper will use the GraphQL API to conduct queries.
field custom_headers: Optional[Dict[str, str]] = None#
field graphql_endpoint: str [Required]#
run(query: str) → str[source]#
Run a GraphQL query and get the results.
pydantic model langchain.utilities.LambdaWrapper[source]#
Wrapper for AWS Lambda SDK.
Docs for using:
pip install boto3
Create a lambda function using the AWS Console or CLI
Run aws configure and enter your AWS credentials
field awslambda_tool_description: Optional[str] = None#
field awslambda_tool_name: Optional[str] = None#
field function_name: Optional[str] = None#
run(query: str) → str[source]#
Invoke Lambda function and parse result.
pydantic model langchain.utilities.MetaphorSearchAPIWrapper[source]#
Wrapper for Metaphor Search API.
field k: int = 10#
field metaphor_api_key: str [Required]#
results(query: str, num_results: int) → List[Dict][source]#
Run query through Metaphor Search and return metadata.
Parameters
query – The query to search for.
num_results – The number of results to return.
Returns
title - The title of the
url - The url
author - Author of the content, if applicable. Otherwise, None.
date_created - Estimated date created,
in YYYY-MM-DD format. Otherwise, None.
Return type
A list of dictionaries with the following keys
async results_async(query: str, num_results: int) → List[Dict][source]#
Get results from the Metaphor Search API asynchronously.
pydantic model langchain.utilities.OpenWeatherMapAPIWrapper[source]#
Wrapper for OpenWeatherMap API using PyOWM.
Docs for using:
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https://python.langchain.com/en/latest/reference/modules/utilities.html
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