.ipynb
.pdf
Git
 Contents
Load existing repository from disk
Clone repository from url
Filtering files to load
Git#
This notebook shows how to load text files from Git repository.
Load existing repository from disk#
from git import Repo
repo = Repo.clone_from(
    "https://github.com/hwchase17/langchain", to_path="./example_data/test_repo1"
)
branch = repo.head.reference
from langchain.document_loaders import GitLoader
loader = GitLoader(repo_path="./example_data/test_repo1/", branch=branch)
data = loader.load()
len(data)
print(data[0])
page_content='.venv\n.github\n.git\n.mypy_cache\n.pytest_cache\nDockerfile' metadata={'file_path': '.dockerignore', 'file_name': '.dockerignore', 'file_type': ''}
Clone repository from url#
from langchain.document_loaders import GitLoader
loader = GitLoader(
    clone_url="https://github.com/hwchase17/langchain",
    repo_path="./example_data/test_repo2/",
    branch="master",
)
data = loader.load()
len(data)
1074
Filtering files to load#
from langchain.document_loaders import GitLoader
# eg. loading only python files
loader = GitLoader(repo_path="./example_data/test_repo1/", file_filter=lambda file_path: file_path.endswith(".py"))
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GCS File Storage
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GitBook
 Contents

Load existing repository from disk
Clone repository from url
Filtering files to load
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
GCS Directory
 Contents
Specifying a prefix
GCS Directory#
This covers how to load document objects from an Google Cloud Storage (GCS) directory.
from langchain.document_loaders import GCSDirectoryLoader
# !pip install google-cloud-storage
loader = GCSDirectoryLoader(project_name="aist", bucket="testing-hwc")
loader.load()
/Users/harrisonchase/workplace/langchain/.venv/lib/python3.10/site-packages/google/auth/_default.py:83: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/
  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)
/Users/harrisonchase/workplace/langchain/.venv/lib/python3.10/site-packages/google/auth/_default.py:83: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/
  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpz37njh7u/fake.docx'}, lookup_index=0)]
Specifying a prefix#


Specifying a prefix#
You can also specify a prefix for more finegrained control over what files to load.
loader = GCSDirectoryLoader(project_name="aist", bucket="testing-hwc", prefix="fake")
loader.load()
/Users/harrisonchase/workplace/langchain/.venv/lib/python3.10/site-packages/google/auth/_default.py:83: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/
  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)
/Users/harrisonchase/workplace/langchain/.venv/lib/python3.10/site-packages/google/auth/_default.py:83: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/
  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpylg6291i/fake.docx'}, lookup_index=0)]
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Figma
next
GCS File Storage
 Contents

Specifying a prefix
By Harrison Chase

      © Copyright 2023, Harrison Chase.


By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
Web Base
 Contents
Loading multiple webpages
Load multiple urls concurrently
Loading a xml file, or using a different BeautifulSoup parser
Web Base#
This covers how to load all text from webpages into a document format that we can use downstream. For more custom logic for loading webpages look at some child class examples such as IMSDbLoader, AZLyricsLoader, and CollegeConfidentialLoader
from langchain.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://www.espn.com/")
data = loader.load()
data


[Document(page_content="\n\n\n\n\n\n\n\n\nESPN - Serving Sports Fans. Anytime. Anywhere.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n        Skip to main content\n    \n\n        Skip to navigation\n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<\n\n>\n\n\n\n\n\n\n\n\n\nMenuESPN\n\n\nSearch\n\n\n\nscores\n\n\n\nNFLNBANCAAMNCAAWNHLSoccer…MLBNCAAFGolfTennisSports BettingBoxingCFLNCAACricketF1HorseLLWSMMANASCARNBA G LeagueOlympic SportsRacingRN BBRN FBRugbyWNBAWorld Baseball ClassicWWEX GamesXFLMore ESPNFantasyListenWatchESPN+\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  \n\nSUBSCRIBE NOW\n\n\n\n\n\nNHL: Select Games\n\n\n\n\n\n\n\nXFL\n\n\n\n\n\n\n\nMLB: Select Games\n\n\n\n\n\n\n\nNCAA


Select Games\n\n\n\n\n\n\n\nNCAA Baseball\n\n\n\n\n\n\n\nNCAA Softball\n\n\n\n\n\n\n\nCricket: Select Matches\n\n\n\n\n\n\n\nMel Kiper's NFL Mock Draft 3.0\n\n\nQuick Links\n\n\n\n\nMen's Tournament Challenge\n\n\n\n\n\n\n\nWomen's Tournament Challenge\n\n\n\n\n\n\n\nNFL Draft Order\n\n\n\n\n\n\n\nHow To Watch NHL Games\n\n\n\n\n\n\n\nFantasy Baseball: Sign Up\n\n\n\n\n\n\n\nHow To Watch PGA TOUR\n\n\n\n\n\n\nFavorites\n\n\n\n\n\n\n      Manage Favorites\n      \n\n\n\nCustomize ESPNSign UpLog InESPN Sites\n\n\n\n\nESPN Deportes\n\n\n\n\n\n\n\nAndscape\n\n\n\n\n\n\n\nespnW\n\n\n\n\n\n\n\nESPNFC\n\n\n\n\n\n\n\nX Games\n\n\n\n\n\n\n\nSEC Network\n\n\nESPN Apps\n\n\n\n\nESPN\n\n\n\n\n\n\n\nESPN Fantasy\n\n\nFollow ESPN\n\n\n\n\nFacebook\n\n\n\n\n\n\n\nTwitter\n\n\n\n\n\n\n\nInstagram\n\n\n\n\n\n\n\nSnapchat\n\n\n\n\n\n\n\nYouTube\n\n\n\n\n\n\n\nThe ESPN Daily Podcast\n\n\nAre you ready for Opening Day? Here's your guide to MLB's offseason


Opening Day? Here's your guide to MLB's offseason chaosWait, Jacob deGrom is on the Rangers now? Xander Bogaerts and Trea Turner signed where? And what about Carlos Correa? Yeah, you're going to need to read up before Opening Day.12hESPNIllustration by ESPNEverything you missed in the MLB offseason3h2:33World Series odds, win totals, props for every teamPlay fantasy baseball for free!TOP HEADLINESQB Jackson has requested trade from RavensSources: Texas hiring Terry as full-time coachJets GM: No rush on Rodgers; Lamar not optionLove to leave North Carolina, enter transfer portalBelichick to angsty Pats fans: See last 25 yearsEmbiid out, Harden due back vs. Jokic, NuggetsLynch: Purdy 'earned the right' to start for NinersMan Utd, Wrexham plan July friendly in San DiegoOn paper, Padres overtake DodgersLAMAR WANTS OUT OF BALTIMOREMarcus Spears identifies the two teams that need Lamar Jackson the most8h2:00Would Lamar sit out?


Jackson the most8h2:00Would Lamar sit out? Will Ravens draft a QB? Jackson trade request insightsLamar Jackson has asked Baltimore to trade him, but Ravens coach John Harbaugh hopes the QB will be back.3hJamison HensleyBallard, Colts will consider trading for QB JacksonJackson to Indy? Washington? Barnwell ranks the QB's trade fitsSNYDER'S TUMULTUOUS 24-YEAR RUNHow Washington’s NFL franchise sank on and off the field under owner Dan SnyderSnyder purchased one of the NFL's marquee franchises in 1999. Twenty-four years later, and with the team up for sale, he leaves a legacy of on-field futility and off-field scandal.13hJohn KeimESPNIOWA STAR STEPS UP AGAINJ-Will: Caitlin Clark is the biggest brand in college sports right now8h0:47'The better the opponent, the better she plays': Clark draws comparisons to TaurasiCaitlin Clark's performance on Sunday had longtime observers going back decades to find comparisons.16hKevin PeltonWOMEN'S ELITE


find comparisons.16hKevin PeltonWOMEN'S ELITE EIGHT SCOREBOARDMONDAY'S GAMESCheck your bracket!NBA DRAFTHow top prospects fared on the road to the Final FourThe 2023 NCAA tournament is down to four teams, and ESPN's Jonathan Givony recaps the players who saw their NBA draft stock change.11hJonathan GivonyAndy Lyons/Getty ImagesTALKING BASKETBALLWhy AD needs to be more assertive with LeBron on the court10h1:33Why Perk won't blame Kyrie for Mavs' woes8h1:48WHERE EVERY TEAM STANDSNew NFL Power Rankings: Post-free-agency 1-32 poll, plus underrated offseason movesThe free agent frenzy has come and gone. Which teams have improved their 2023 outlook, and which teams have taken a hit?12hNFL Nation reportersIllustration by ESPNTHE BUCK STOPS WITH BELICHICKBruschi: Fair to criticize Bill Belichick for Patriots' struggles10h1:27 Top HeadlinesQB Jackson has requested trade from RavensSources: Texas hiring Terry as full-time coachJets GM: No


Texas hiring Terry as full-time coachJets GM: No rush on Rodgers; Lamar not optionLove to leave North Carolina, enter transfer portalBelichick to angsty Pats fans: See last 25 yearsEmbiid out, Harden due back vs. Jokic, NuggetsLynch: Purdy 'earned the right' to start for NinersMan Utd, Wrexham plan July friendly in San DiegoOn paper, Padres overtake DodgersFavorites FantasyManage FavoritesFantasy HomeCustomize ESPNSign UpLog InMarch Madness LiveESPNMarch Madness LiveWatch every men's NCAA tournament game live! ICYMI1:42Austin Peay's coach, pitcher and catcher all ejected after retaliation pitchAustin Peay's pitcher, catcher and coach were all ejected after a pitch was thrown at Liberty's Nathan Keeter, who earlier in the game hit a home run and celebrated while running down the third-base line. Men's Tournament ChallengeIllustration by ESPNMen's Tournament ChallengeCheck your bracket(s) in the 2023 Men's Tournament Challenge, which you can follow throughout the Big Dance. Women's Tournament


can follow throughout the Big Dance. Women's Tournament ChallengeIllustration by ESPNWomen's Tournament ChallengeCheck your bracket(s) in the 2023 Women's Tournament Challenge, which you can follow throughout the Big Dance. Best of ESPN+AP Photo/Lynne SladkyFantasy Baseball ESPN+ Cheat Sheet: Sleepers, busts, rookies and closersYou've read their names all preseason long, it'd be a shame to forget them on draft day. The ESPN+ Cheat Sheet is one way to make sure that doesn't happen.Steph Chambers/Getty ImagesPassan's 2023 MLB season preview: Bold predictions and moreOpening Day is just over a week away -- and Jeff Passan has everything you need to know covered from every possible angle.Photo by Bob Kupbens/Icon Sportswire2023 NFL free agency: Best team fits for unsigned playersWhere could Ezekiel Elliott land? Let's match remaining free agents to teams and find fits for two trade candidates.Illustration by ESPN2023 NFL mock draft: Mel Kiper's first-round pick predictionsMel Kiper Jr. makes his predictions for Round


predictionsMel Kiper Jr. makes his predictions for Round 1 of the NFL draft, including projecting a trade in the top five. Trending NowAnne-Marie Sorvin-USA TODAY SBoston Bruins record tracker: Wins, points, milestonesThe B's are on pace for NHL records in wins and points, along with some individual superlatives as well. Follow along here with our updated tracker.Mandatory Credit: William Purnell-USA TODAY Sports2023 NFL full draft order: AFC, NFC team picks for all roundsStarting with the Carolina Panthers at No. 1 overall, here's the entire 2023 NFL draft broken down round by round. How to Watch on ESPN+Gregory Fisher/Icon Sportswire2023 NCAA men's hockey: Results, bracket, how to watchThe matchups in Tampa promise to be thrillers, featuring plenty of star power, high-octane offense and stellar defense.(AP Photo/Koji Sasahara, File)How to watch the PGA Tour, Masters, PGA Championship and FedEx Cup playoffs on ESPN, ESPN+Here's everything you need to know


on ESPN, ESPN+Here's everything you need to know about how to watch the PGA Tour, Masters, PGA Championship and FedEx Cup playoffs on ESPN and ESPN+.Hailie Lynch/XFLHow to watch the XFL: 2023 schedule, teams, players, news, moreEvery XFL game will be streamed on ESPN+. Find out when and where else you can watch the eight teams compete. Sign up to play the #1 Fantasy Baseball GameReactivate A LeagueCreate A LeagueJoin a Public LeaguePractice With a Mock DraftSports BettingAP Photo/Mike KropfMarch Madness betting 2023: Bracket odds, lines, tips, moreThe 2023 NCAA tournament brackets have finally been released, and we have everything you need to know to make a bet on all of the March Madness games. Sign up to play the #1 Fantasy game!Create A LeagueJoin Public LeagueReactivateMock Draft Now\n\nESPN+\n\n\n\n\nNHL: Select Games\n\n\n\n\n\n\n\nXFL\n\n\n\n\n\n\n\nMLB: Select Games\n\n\n\n\n\n\n\nNCAA Baseball\n\n\n\n\n\n\n\nNCAA


Baseball\n\n\n\n\n\n\n\nNCAA Softball\n\n\n\n\n\n\n\nCricket: Select Matches\n\n\n\n\n\n\n\nMel Kiper's NFL Mock Draft 3.0\n\n\nQuick Links\n\n\n\n\nMen's Tournament Challenge\n\n\n\n\n\n\n\nWomen's Tournament Challenge\n\n\n\n\n\n\n\nNFL Draft Order\n\n\n\n\n\n\n\nHow To Watch NHL Games\n\n\n\n\n\n\n\nFantasy Baseball: Sign Up\n\n\n\n\n\n\n\nHow To Watch PGA TOUR\n\n\nESPN Sites\n\n\n\n\nESPN Deportes\n\n\n\n\n\n\n\nAndscape\n\n\n\n\n\n\n\nespnW\n\n\n\n\n\n\n\nESPNFC\n\n\n\n\n\n\n\nX Games\n\n\n\n\n\n\n\nSEC Network\n\n\nESPN Apps\n\n\n\n\nESPN\n\n\n\n\n\n\n\nESPN Fantasy\n\n\nFollow ESPN\n\n\n\n\nFacebook\n\n\n\n\n\n\n\nTwitter\n\n\n\n\n\n\n\nInstagram\n\n\n\n\n\n\n\nSnapchat\n\n\n\n\n\n\n\nYouTube\n\n\n\n\n\n\n\nThe ESPN Daily Podcast\n\n\nTerms of UsePrivacy PolicyYour US State Privacy RightsChildren's Online Privacy PolicyInterest-Based AdsAbout Nielsen MeasurementDo Not Sell or Share My Personal InformationContact UsDisney Ad Sales SiteWork for ESPNCopyright: © ESPN Enterprises, Inc. All rights


ESPNCopyright: © ESPN Enterprises, Inc. All rights reserved.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", lookup_str='', metadata={'source': 'https://www.espn.com/'}, lookup_index=0)]


"""
# Use this piece of code for testing new custom BeautifulSoup parsers
import requests
from bs4 import BeautifulSoup
html_doc = requests.get("{INSERT_NEW_URL_HERE}")
soup = BeautifulSoup(html_doc.text, 'html.parser')
# Beautiful soup logic to be exported to langchain.document_loaders.webpage.py
# Example: transcript = soup.select_one("td[class='scrtext']").text
# BS4 documentation can be found here: https://www.crummy.com/software/BeautifulSoup/bs4/doc/
""";
Loading multiple webpages#
You can also load multiple webpages at once by passing in a list of urls to the loader. This will return a list of documents in the same order as the urls passed in.
loader = WebBaseLoader(["https://www.espn.com/", "https://google.com"])
docs = loader.load()
docs


Opening Day? Here's your guide to MLB's offseason chaosWait, Jacob deGrom is on the Rangers now? Xander Bogaerts and Trea Turner signed where? And what about Carlos Correa? Yeah, you're going to need to read up before Opening Day.12hESPNIllustration by ESPNEverything you missed in the MLB offseason3h2:33World Series odds, win totals, props for every teamPlay fantasy baseball for free!TOP HEADLINESQB Jackson has requested trade from RavensSources: Texas hiring Terry as full-time coachJets GM: No rush on Rodgers; Lamar not optionLove to leave North Carolina, enter transfer portalBelichick to angsty Pats fans: See last 25 yearsEmbiid out, Harden due back vs. Jokic, NuggetsLynch: Purdy 'earned the right' to start for NinersMan Utd, Wrexham plan July friendly in San DiegoOn paper, Padres overtake DodgersLAMAR WANTS OUT OF BALTIMOREMarcus Spears identifies the two teams that need Lamar Jackson the most7h2:00Would Lamar sit out?


Jackson the most7h2:00Would Lamar sit out? Will Ravens draft a QB? Jackson trade request insightsLamar Jackson has asked Baltimore to trade him, but Ravens coach John Harbaugh hopes the QB will be back.3hJamison HensleyBallard, Colts will consider trading for QB JacksonJackson to Indy? Washington? Barnwell ranks the QB's trade fitsSNYDER'S TUMULTUOUS 24-YEAR RUNHow Washington’s NFL franchise sank on and off the field under owner Dan SnyderSnyder purchased one of the NFL's marquee franchises in 1999. Twenty-four years later, and with the team up for sale, he leaves a legacy of on-field futility and off-field scandal.13hJohn KeimESPNIOWA STAR STEPS UP AGAINJ-Will: Caitlin Clark is the biggest brand in college sports right now8h0:47'The better the opponent, the better she plays': Clark draws comparisons to TaurasiCaitlin Clark's performance on Sunday had longtime observers going back decades to find comparisons.16hKevin PeltonWOMEN'S ELITE


find comparisons.16hKevin PeltonWOMEN'S ELITE EIGHT SCOREBOARDMONDAY'S GAMESCheck your bracket!NBA DRAFTHow top prospects fared on the road to the Final FourThe 2023 NCAA tournament is down to four teams, and ESPN's Jonathan Givony recaps the players who saw their NBA draft stock change.11hJonathan GivonyAndy Lyons/Getty ImagesTALKING BASKETBALLWhy AD needs to be more assertive with LeBron on the court9h1:33Why Perk won't blame Kyrie for Mavs' woes8h1:48WHERE EVERY TEAM STANDSNew NFL Power Rankings: Post-free-agency 1-32 poll, plus underrated offseason movesThe free agent frenzy has come and gone. Which teams have improved their 2023 outlook, and which teams have taken a hit?12hNFL Nation reportersIllustration by ESPNTHE BUCK STOPS WITH BELICHICKBruschi: Fair to criticize Bill Belichick for Patriots' struggles10h1:27 Top HeadlinesQB Jackson has requested trade from RavensSources: Texas hiring Terry as full-time coachJets GM: No


ESPNCopyright: © ESPN Enterprises, Inc. All rights reserved.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", lookup_str='', metadata={'source': 'https://www.espn.com/'}, lookup_index=0),


Document(page_content='GoogleSearch Images Maps Play YouTube News Gmail Drive More »Web History | Settings | Sign in\xa0Advanced searchAdvertisingBusiness SolutionsAbout Google© 2023 - Privacy - Terms   ', lookup_str='', metadata={'source': 'https://google.com'}, lookup_index=0)]
Load multiple urls concurrently#
You can speed up the scraping process by scraping and parsing multiple urls concurrently.
There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren’t concerned about being a good citizen, or you control the server you are scraping and don’t care about load, you can change the requests_per_second parameter to increase the max concurrent requests.  Note, while this will speed up the scraping process, but may cause the server to block you.  Be careful!
!pip install nest_asyncio
# fixes a bug with asyncio and jupyter
import nest_asyncio
nest_asyncio.apply()
Requirement already satisfied: nest_asyncio in /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages (1.5.6)
loader = WebBaseLoader(["https://www.espn.com/", "https://google.com"])
loader.requests_per_second = 1
docs = loader.aload()
docs


[Document(page_content="\n\n\n\n\n\n\n\n\nESPN - Serving Sports Fans. Anytime. Anywhere.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n        Skip to main content\n    \n\n        Skip to navigation\n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<\n\n>\n\n\n\n\n\n\n\n\n\nMenuESPN\n\n\nSearch\n\n\n\nscores\n\n\n\nNFLNBANCAAMNCAAWNHLSoccer…MLBNCAAFGolfTennisSports BettingBoxingCFLNCAACricketF1HorseLLWSMMANASCARNBA G LeagueOlympic SportsRacingRN BBRN FBRugbyWNBAWorld Baseball ClassicWWEX GamesXFLMore ESPNFantasyListenWatchESPN+\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  \n\nSUBSCRIBE NOW\n\n\n\n\n\nNHL: Select Games\n\n\n\n\n\n\n\nXFL\n\n\n\n\n\n\n\nMLB: Select Games\n\n\n\n\n\n\n\nNCAA


Select Games\n\n\n\n\n\n\n\nNCAA Baseball\n\n\n\n\n\n\n\nNCAA Softball\n\n\n\n\n\n\n\nCricket: Select Matches\n\n\n\n\n\n\n\nMel Kiper's NFL Mock Draft 3.0\n\n\nQuick Links\n\n\n\n\nMen's Tournament Challenge\n\n\n\n\n\n\n\nWomen's Tournament Challenge\n\n\n\n\n\n\n\nNFL Draft Order\n\n\n\n\n\n\n\nHow To Watch NHL Games\n\n\n\n\n\n\n\nFantasy Baseball: Sign Up\n\n\n\n\n\n\n\nHow To Watch PGA TOUR\n\n\n\n\n\n\nFavorites\n\n\n\n\n\n\n      Manage Favorites\n      \n\n\n\nCustomize ESPNSign UpLog InESPN Sites\n\n\n\n\nESPN Deportes\n\n\n\n\n\n\n\nAndscape\n\n\n\n\n\n\n\nespnW\n\n\n\n\n\n\n\nESPNFC\n\n\n\n\n\n\n\nX Games\n\n\n\n\n\n\n\nSEC Network\n\n\nESPN Apps\n\n\n\n\nESPN\n\n\n\n\n\n\n\nESPN Fantasy\n\n\nFollow ESPN\n\n\n\n\nFacebook\n\n\n\n\n\n\n\nTwitter\n\n\n\n\n\n\n\nInstagram\n\n\n\n\n\n\n\nSnapchat\n\n\n\n\n\n\n\nYouTube\n\n\n\n\n\n\n\nThe ESPN Daily Podcast\n\n\nAre you ready for Opening Day? Here's your guide to MLB's offseason


Document(page_content='GoogleSearch Images Maps Play YouTube News Gmail Drive More »Web History | Settings | Sign in\xa0Advanced searchAdvertisingBusiness SolutionsAbout Google© 2023 - Privacy - Terms   ', lookup_str='', metadata={'source': 'https://google.com'}, lookup_index=0)]
Loading a xml file, or using a different BeautifulSoup parser#
You can also look at SitemapLoader for an example of how to load a sitemap file, which is an example of using this feature.
loader = WebBaseLoader("https://www.govinfo.gov/content/pkg/CFR-2018-title10-vol3/xml/CFR-2018-title10-vol3-sec431-86.xml")
loader.default_parser = "xml"
docs = loader.load()
docs


[Document(page_content='\n\n10\nEnergy\n3\n2018-01-01\n2018-01-01\nfalse\nUniform test method for the measurement of energy efficiency of commercial packaged boilers.\nÂ§ 431.86\nSection Â§ 431.86\n\nEnergy\nDEPARTMENT OF ENERGY\nENERGY CONSERVATION\nENERGY EFFICIENCY PROGRAM FOR CERTAIN COMMERCIAL AND INDUSTRIAL EQUIPMENT\nCommercial Packaged Boilers\nTest Procedures\n\n\n\n\n§\u2009431.86\nUniform test method for the measurement of energy efficiency of commercial packaged boilers.\n(a) Scope. This section provides test procedures, pursuant to the Energy Policy and Conservation Act (EPCA), as amended, which must be followed for measuring the combustion efficiency and/or thermal efficiency of a gas- or oil-fired commercial packaged boiler.\n(b) Testing and Calculations. Determine the thermal efficiency or combustion efficiency of commercial packaged boilers by conducting the appropriate test procedure(s) indicated in Table 1 of this section.\n\nTable 1—Test Requirements for Commercial Packaged Boiler Equipment Classes\n\nEquipment category\nSubcategory\nCertified rated inputBtu/h\n\nStandards efficiency


rated inputBtu/h\n\nStandards efficiency metric(§\u2009431.87)\n\nTest procedure(corresponding to\nstandards efficiency\nmetric required\nby §\u2009431.87)\n\n\n\nHot Water\nGas-fired\n≥300,000 and ≤2,500,000\nThermal Efficiency\nAppendix A, Section 2.\n\n\nHot Water\nGas-fired\n>2,500,000\nCombustion Efficiency\nAppendix A, Section 3.\n\n\nHot Water\nOil-fired\n≥300,000 and ≤2,500,000\nThermal Efficiency\nAppendix A, Section 2.\n\n\nHot Water\nOil-fired\n>2,500,000\nCombustion Efficiency\nAppendix A, Section 3.\n\n\nSteam\nGas-fired (all*)\n≥300,000 and ≤2,500,000\nThermal Efficiency\nAppendix A, Section 2.\n\n\nSteam\nGas-fired (all*)\n>2,500,000 and ≤5,000,000\nThermal Efficiency\nAppendix A, Section 2.\n\n\n\u2003\n\n>5,000,000\nThermal Efficiency\nAppendix A, Section 2.OR\nAppendix A, Section 3 with Section 2.4.3.2.\n\n\n\nSteam\nOil-fired\n≥300,000 and ≤2,500,000\nThermal Efficiency\nAppendix A, Section


Efficiency\nAppendix A, Section 2.\n\n\nSteam\nOil-fired\n>2,500,000 and ≤5,000,000\nThermal Efficiency\nAppendix A, Section 2.\n\n\n\u2003\n\n>5,000,000\nThermal Efficiency\nAppendix A, Section 2.OR\nAppendix A, Section 3. with Section 2.4.3.2.\n\n\n\n*\u2009Equipment classes for commercial packaged boilers as of July 22, 2009 (74 FR 36355) distinguish between gas-fired natural draft and all other gas-fired (except natural draft).\n\n(c) Field Tests. The field test provisions of appendix A may be used only to test a unit of commercial packaged boiler with rated input greater than 5,000,000 Btu/h.\n[81 FR 89305, Dec. 9, 2016]\n\n\nEnergy Efficiency Standards\n\n', lookup_str='', metadata={'source': 'https://www.govinfo.gov/content/pkg/CFR-2018-title10-vol3/xml/CFR-2018-title10-vol3-sec431-86.xml'}, lookup_index=0)]


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Loading multiple webpages
Load multiple urls concurrently
Loading a xml file, or using a different BeautifulSoup parser
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
Hacker News
Hacker News#
How to pull page data and comments from Hacker News
from langchain.document_loaders import HNLoader
loader = HNLoader("https://news.ycombinator.com/item?id=34817881")
data = loader.load()
data
[Document(page_content="delta_p_delta_x 18 hours ago  \n             | next [–] \n\nAstrophysical and cosmological simulations are often insightful. They're also very cross-disciplinary; besides the obvious astrophysics, there's networking and sysadmin, parallel computing and algorithm theory (so that the simulation programs are actually fast but still accurate), systems design, and even a bit of graphic design for the visualisations.Some of my favourite simulation projects:- IllustrisTNG: https://www.tng-project.org/- SWIFT: https://swift.dur.ac.uk/- CO5BOLD: https://www.astro.uu.se/~bf/co5bold_main.html (which produced these animations of a red-giant star: https://www.astro.uu.se/~bf/movie/AGBmovie.html)- AbacusSummit: https://abacussummit.readthedocs.io/en/latest/And I can add the simulations in the article, too.\n \nreply", lookup_str='', metadata={'source': 'https://news.ycombinator.com/item?id=34817881', 'title': 'What Lights the Universe’s Standard Candles?'}, lookup_index=0),


Document(page_content="andrewflnr 19 hours ago  \n             | prev | next [–] \n\nWhoa. I didn't know the accretion theory of Ia supernovae was dead, much less that it had been since 2011.\n \nreply", lookup_str='', metadata={'source': 'https://news.ycombinator.com/item?id=34817881', 'title': 'What Lights the Universe’s Standard Candles?'}, lookup_index=0),
 Document(page_content='andreareina 18 hours ago  \n             | prev | next [–] \n\nThis seems  to be the paper https://academic.oup.com/mnras/article/517/4/5260/6779709\n \nreply', lookup_str='', metadata={'source': 'https://news.ycombinator.com/item?id=34817881', 'title': 'What Lights the Universe’s Standard Candles?'}, lookup_index=0),
 Document(page_content="andreareina 18 hours ago  \n             | prev [–] \n\nWouldn't double detonation show up as variance in the brightness?\n \nreply", lookup_str='', metadata={'source': 'https://news.ycombinator.com/item?id=34817881', 'title': 'What Lights the Universe’s Standard Candles?'}, lookup_index=0)]
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Gutenberg
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HTML
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
Subtitle Files
Subtitle Files#
How to load data from subtitle (.srt) files
from langchain.document_loaders import SRTLoader
loader = SRTLoader("example_data/Star_Wars_The_Clone_Wars_S06E07_Crisis_at_the_Heart.srt")
docs = loader.load()
docs[0].page_content[:100]
'<i>Corruption discovered\nat the core of the Banking Clan!</i> <i>Reunited, Rush Clovis\nand Senator A'
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Slack (Local Exported Zipfile)
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Telegram
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
s3 File
s3 File#
This covers how to load document objects from an s3 file object.
from langchain.document_loaders import S3FileLoader
#!pip install boto3
loader = S3FileLoader("testing-hwc", "fake.docx")
loader.load()
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpxvave6wl/fake.docx'}, lookup_index=0)]
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s3 Directory
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Sitemap Loader
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
YouTube
 Contents
Add video info
YouTube loader from Google Cloud
Prerequisites
🧑 Instructions for ingesting your Google Docs data
YouTube#
How to load documents from YouTube transcripts.
from langchain.document_loaders import YoutubeLoader
# !pip install youtube-transcript-api
loader = YoutubeLoader.from_youtube_url("https://www.youtube.com/watch?v=QsYGlZkevEg", add_video_info=True)
loader.load()
Add video info#
# ! pip install pytube
loader = YoutubeLoader.from_youtube_url("https://www.youtube.com/watch?v=QsYGlZkevEg", add_video_info=True)
loader.load()
YouTube loader from Google Cloud#
Prerequisites#
Create a Google Cloud project or use an existing project
Enable the Youtube Api
Authorize credentials for desktop app
pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib youtube-transcript-api
🧑 Instructions for ingesting your Google Docs data#
By default, the GoogleDriveLoader expects the credentials.json file to be ~/.credentials/credentials.json, but this is configurable using the credentials_file keyword argument. Same thing with token.json. Note that token.json will be created automatically the first time you use the loader.
GoogleApiYoutubeLoader can load from a list of Google Docs document ids or a folder id. You can obtain your folder and document id from the URL:
Note depending on your set up, the service_account_path needs to be set up. See here for more details.
from langchain.document_loaders import GoogleApiClient, GoogleApiYoutubeLoader
# Init the GoogleApiClient
from pathlib import Path
google_api_client = GoogleApiClient(credentials_path=Path("your_path_creds.json"))
# Use a Channel


# Use a Channel
youtube_loader_channel = GoogleApiYoutubeLoader(google_api_client=google_api_client, channel_name="Reducible",captions_language="en")
# Use Youtube Ids
youtube_loader_ids = GoogleApiYoutubeLoader(google_api_client=google_api_client, video_ids=["TrdevFK_am4"], add_video_info=True)
# returns a list of Documents
youtube_loader_channel.load()
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Word Documents
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Text Splitters
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Add video info
YouTube loader from Google Cloud
Prerequisites
🧑 Instructions for ingesting your Google Docs data
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
AZLyrics
AZLyrics#
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


[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


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':


love me better\nI\n", lookup_str='', metadata={'source': 'https://www.azlyrics.com/lyrics/mileycyrus/flowers.html'}, lookup_index=0)]


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Arxiv
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Azure Blob Storage Container
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
Facebook Chat
Facebook Chat#
This notebook covers how to load data from the Facebook Chats into a format that can be ingested into LangChain.
from langchain.document_loaders import FacebookChatLoader
loader = FacebookChatLoader("example_data/facebook_chat.json")
loader.load()
[Document(page_content='User 2 on 2023-02-05 12:46:11: Bye!\n\nUser 1 on 2023-02-05 12:43:55: Oh no worries! Bye\n\nUser 2 on 2023-02-05 12:24:37: No Im sorry it was my mistake, the blue one is not for sale\n\nUser 1 on 2023-02-05 12:05:40: I thought you were selling the blue one!\n\nUser 1 on 2023-02-05 12:05:09: Im not interested in this bag. Im interested in the blue one!\n\nUser 2 on 2023-02-05 12:04:28: Here is $129\n\nUser 2 on 2023-02-05 12:04:05: Online is at least $100\n\nUser 1 on 2023-02-05 11:59:59: How much do you want?\n\nUser 2 on 2023-02-05 07:17:56: Goodmorning! $50 is too low.\n\nUser 1 on 2023-02-04 23:17:02: Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!\n\n', lookup_str='', metadata={'source': 'docs/modules/document_loaders/examples/example_data/facebook_chat.json'}, lookup_index=0)]
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EverNote
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EverNote
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Figma
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
DuckDB Loader
 Contents
Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
DuckDB Loader#
Load a DuckDB query with one document per row.
from langchain.document_loaders import DuckDBLoader
%%file example.csv
Team,Payroll
Nationals,81.34
Reds,82.20
Writing example.csv
loader = DuckDBLoader("SELECT * FROM read_csv_auto('example.csv')")
data = loader.load()
print(data)
[Document(page_content='Team: Nationals\nPayroll: 81.34', metadata={}), Document(page_content='Team: Reds\nPayroll: 82.2', metadata={})]
Specifying Which Columns are Content vs Metadata#
loader = DuckDBLoader(
    "SELECT * FROM read_csv_auto('example.csv')",
    page_content_columns=["Team"],
    metadata_columns=["Payroll"]
)
data = loader.load()
print(data)
[Document(page_content='Team: Nationals', metadata={'Payroll': 81.34}), Document(page_content='Team: Reds', metadata={'Payroll': 82.2})]
Adding Source to Metadata#
loader = DuckDBLoader(
    "SELECT Team, Payroll, Team As source FROM read_csv_auto('example.csv')",
    metadata_columns=["source"]
)
data = loader.load()
print(data)
[Document(page_content='Team: Nationals\nPayroll: 81.34\nsource: Nationals', metadata={'source': 'Nationals'}), Document(page_content='Team: Reds\nPayroll: 82.2\nsource: Reds', metadata={'source': 'Reds'})]
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Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
By Harrison Chase


Specifying Which Columns are Content vs Metadata
Adding Source to Metadata
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
HuggingFace dataset loader
 Contents
Example
HuggingFace dataset loader#
This notebook shows how to load Hugging Face Hub datasets to LangChain.
The Hugging Face Hub hosts a large number of community-curated datasets for a diverse range of tasks such as translation, automatic speech recognition, and image classification.
from langchain.document_loaders import HuggingFaceDatasetLoader
dataset_name="imdb"
page_content_column="text"
loader=HuggingFaceDatasetLoader(dataset_name,page_content_column)
data = loader.load()
data[:15]


data = loader.load()
data[:15]
[Document(page_content='I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered "controversial" I really had to see this for myself.

The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.

What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.

I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\'t have much of a plot.', metadata={'label': 0}),


Document(page_content='"I Am Curious: Yellow" is a risible and pretentious steaming pile. It doesn\'t matter what one\'s political views are because this film can hardly be taken seriously on any level. As for the claim that frontal male nudity is an automatic NC-17, that isn\'t true. I\'ve seen R-rated films with male nudity. Granted, they only offer some fleeting views, but where are the R-rated films with gaping vulvas and flapping labia? Nowhere, because they don\'t exist. The same goes for those crappy cable shows: schlongs swinging in the breeze but not a clitoris in sight. And those pretentious indie movies like The Brown Bunny, in which we\'re treated to the site of Vincent Gallo\'s throbbing johnson, but not a trace of pink visible on Chloe Sevigny. Before crying (or implying) "double-standard" in matters of nudity, the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and women: there are no genitals on display when actresses appears nude, and the same cannot be said for a man. In fact, you generally won\'t see female genitals in an American film in anything short of porn or explicit erotica. This alleged double-standard is less a double standard than an admittedly depressing ability to come to terms culturally with the insides of women\'s bodies.', metadata={'label': 0}),


Document(page_content="If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.

One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).

One might better spend one's time staring out a window at a tree growing.

", metadata={'label': 0}),
 Document(page_content="This film was probably inspired by Godard's Masculin, féminin and I urge you to see that film instead.

The film has two strong elements and those are, (1) the realistic acting (2) the impressive, undeservedly good, photo. Apart from that, what strikes me most is the endless stream of silliness. Lena Nyman has to be most annoying actress in the world. She acts so stupid and with all the nudity in this film,...it's unattractive. Comparing to Godard's film, intellectuality has been replaced with stupidity. Without going too far on this subject, I would say that follows from the difference in ideals between the French and the Swedish society.

A movie of its time, and place. 2/10.", metadata={'label': 0}),


Document(page_content='Oh, brother...after hearing about this ridiculous film for umpteen years all I can think of is that old Peggy Lee song..

"Is that all there is??" ...I was just an early teen when this smoked fish hit the U.S. I was too young to get in the theater (although I did manage to sneak into "Goodbye Columbus"). Then a screening at a local film museum beckoned - Finally I could see this film, except now I was as old as my parents were when they schlepped to see it!!

The ONLY reason this film was not condemned to the anonymous sands of time was because of the obscenity case sparked by its U.S. release. MILLIONS of people flocked to this stinker, thinking they were going to see a sex film...Instead, they got lots of closeups of gnarly, repulsive Swedes, on-street interviews in bland shopping malls, asinie political pretension...and feeble who-cares simulated sex scenes with


pretension...and feeble who-cares simulated sex scenes with saggy, pale actors.

Cultural icon, holy grail, historic artifact..whatever this thing was, shred it, burn it, then stuff the ashes in a lead box!

Elite esthetes still scrape to find value in its boring pseudo revolutionary political spewings..But if it weren\'t for the censorship scandal, it would have been ignored, then forgotten.

Instead, the "I Am Blank, Blank" rhythymed title was repeated endlessly for years as a titilation for porno films (I am Curious, Lavender - for gay films, I Am Curious, Black - for blaxploitation films, etc..) and every ten years or so the thing rises from the dead, to be viewed by a new generation of suckers who want to see that "naughty sex film" that "revolutionized the film industry"...

Yeesh, avoid like the plague..Or if you MUST see it - rent the video and fast forward to the


it - rent the video and fast forward to the "dirty" parts, just to get it over with.

', metadata={'label': 0}),


Document(page_content="I would put this at the top of my list of films in the category of unwatchable trash! There are films that are bad, but the worst kind are the ones that are unwatchable but you are suppose to like them because they are supposed to be good for you! The sex sequences, so shocking in its day, couldn't even arouse a rabbit. The so called controversial politics is strictly high school sophomore amateur night Marxism. The film is self-consciously arty in the worst sense of the term. The photography is in a harsh grainy black and white. Some scenes are out of focus or taken from the wrong angle. Even the sound is bad! And some people call this art?

", metadata={'label': 0}),
 Document(page_content="Whoever wrote the screenplay for this movie obviously never consulted any books about Lucille Ball, especially her autobiography. I've never seen so many mistakes in a biopic, ranging from her early years in Celoron and Jamestown to her later years with Desi. I could write a whole list of factual errors, but it would go on for pages. In all, I believe that Lucille Ball is one of those inimitable people who simply cannot be portrayed by anyone other than themselves. If I were Lucie Arnaz and Desi, Jr., I would be irate at how many mistakes were made in this film. The filmmakers tried hard, but the movie seems awfully sloppy to me.", metadata={'label': 0}),


Document(page_content='When I first saw a glimpse of this movie, I quickly noticed the actress who was playing the role of Lucille Ball. Rachel York\'s portrayal of Lucy is absolutely awful. Lucille Ball was an astounding comedian with incredible talent. To think about a legend like Lucille Ball being portrayed the way she was in the movie is horrendous. I cannot believe out of all the actresses in the world who could play a much better Lucy, the producers decided to get Rachel York. She might be a good actress in other roles but to play the role of Lucille Ball is tough. It is pretty hard to find someone who could resemble Lucille Ball, but they could at least find someone a bit similar in looks and talent. If you noticed York\'s portrayal of Lucy in episodes of I Love Lucy like the chocolate factory or vitavetavegamin, nothing is similar in any way-her expression, voice, or movement.

To top it all off, Danny Pino playing Desi Arnaz is horrible. Pino does not qualify to play as Ricky. He\'s small and skinny, his accent is unreal, and once again, his acting is unbelievable. Although Fred and Ethel were not similar either, they were not as bad as the characters of Lucy and Ricky.

Overall, extremely horrible casting and the story is badly told. If people want to understand the real life situation of Lucille Ball, I suggest watching A&E Biography of Lucy and Desi, read the book from Lucille Ball herself, or PBS\' American Masters: Finding Lucy. If you want to see a docudrama, "Before the Laughter" would be a better choice. The casting of Lucille Ball and Desi Arnaz in "Before the Laughter" is much better compared to this. At least, a similar aspect is shown rather than nothing.', metadata={'label': 0}),


Document(page_content='Who are these "They"- the actors? the filmmakers? Certainly couldn\'t be the audience- this is among the most air-puffed productions in existence. It\'s the kind of movie that looks like it was a lot of fun to shoot\x97 TOO much fun, nobody is getting any actual work done, and that almost always makes for a movie that\'s no fun to watch.

Ritter dons glasses so as to hammer home his character\'s status as a sort of doppleganger of the bespectacled Bogdanovich; the scenes with the breezy Ms. Stratten are sweet, but have an embarrassing, look-guys-I\'m-dating-the-prom-queen feel to them. Ben Gazzara sports his usual cat\'s-got-canary grin in a futile attempt to elevate the meager plot, which requires him to pursue Audrey Hepburn with all the interest of a narcoleptic at an insomnia clinic. In the meantime, the budding couple\'s respective children (nepotism alert: Bogdanovich\'s


respective children (nepotism alert: Bogdanovich\'s daughters) spew cute and pick up some fairly disturbing pointers on \'love\' while observing their parents. (Ms. Hepburn, drawing on her dignity, manages to rise above the proceedings- but she has the monumental challenge of playing herself, ostensibly.) Everybody looks great, but so what? It\'s a movie and we can expect that much, if that\'s what you\'re looking for you\'d be better off picking up a copy of Vogue.

Oh- and it has to be mentioned that Colleen Camp thoroughly annoys, even apart from her singing, which, while competent, is wholly unconvincing... the country and western numbers are woefully mismatched with the standards on the soundtrack. Surely this is NOT what Gershwin (who wrote the song from which the movie\'s title is derived) had in mind; his stage musicals of the 20\'s may have been slight, but at least they were long on charm. "They All


but at least they were long on charm. "They All Laughed" tries to coast on its good intentions, but nobody- least of all Peter Bogdanovich - has the good sense to put on the brakes.

Due in no small part to the tragic death of Dorothy Stratten, this movie has a special place in the heart of Mr. Bogdanovich- he even bought it back from its producers, then distributed it on his own and went bankrupt when it didn\'t prove popular. His rise and fall is among the more sympathetic and tragic of Hollywood stories, so there\'s no joy in criticizing the film... there _is_ real emotional investment in Ms. Stratten\'s scenes. But "Laughed" is a faint echo of "The Last Picture Show", "Paper Moon" or "What\'s Up, Doc"- following "Daisy Miller" and "At Long Last Love", it was a thundering confirmation of the phase from which P.B. has never emerged.

All in all, though, the movie is harmless,


in all, though, the movie is harmless, only a waste of rental. I want to watch people having a good time, I\'ll go to the park on a sunny day. For filmic expressions of joy and love, I\'ll stick to Ernest Lubitsch and Jaques Demy...', metadata={'label': 0}),


Document(page_content="This is said to be a personal film for Peter Bogdonavitch. He based it on his life but changed things around to fit the characters, who are detectives. These detectives date beautiful models and have no problem getting them. Sounds more like a millionaire playboy filmmaker than a detective, doesn't it? This entire movie was written by Peter, and it shows how out of touch with real people he was. You're supposed to write what you know, and he did that, indeed. And leaves the audience bored and confused, and jealous, for that matter. This is a curio for people who want to see Dorothy Stratten, who was murdered right after filming. But Patti Hanson, who would, in real life, marry Keith Richards, was also a model, like Stratten, but is a lot better and has a more ample part. In fact, Stratten's part seemed forced; added. She doesn't have a lot to do with the story, which is pretty convoluted to begin with. All in all, every character in this film is somebody that very few people can relate with, unless you're millionaire from Manhattan with beautiful supermodels at your beckon call. For the rest of us, it's an irritating snore fest. That's what happens when you're out of touch. You entertain your few friends with inside jokes, and bore all the rest.", metadata={'label': 0}),


Document(page_content='It was great to see some of my favorite stars of 30 years ago including John Ritter, Ben Gazarra and Audrey Hepburn. They looked quite wonderful. But that was it. They were not given any characters or good lines to work with. I neither understood or cared what the characters were doing.

Some of the smaller female roles were fine, Patty Henson and Colleen Camp were quite competent and confident in their small sidekick parts. They showed some talent and it is sad they didn\'t go on to star in more and better films. Sadly, I didn\'t think Dorothy Stratten got a chance to act in this her only important film role.

The film appears to have some fans, and I was very open-minded when I started watching it. I am a big Peter Bogdanovich fan and I enjoyed his last movie, "Cat\'s Meow" and all his early ones from "Targets" to "Nickleodeon". So, it really surprised me that I was barely able to keep awake watching this one.

It is ironic that this movie is about a detective agency where the detectives and clients get romantically involved with each other. Five years later, Bogdanovich\'s ex-girlfriend, Cybil Shepherd had a hit television series called "Moonlighting" stealing the story idea from Bogdanovich. Of course, there was a great difference in that the series relied on tons of witty dialogue, while this tries to make do with slapstick and a few screwball lines.

Bottom line: It ain\'t no "Paper Moon" and only a very pale version of "What\'s Up, Doc".', metadata={'label': 0}),


Document(page_content="I can't believe that those praising this movie herein aren't thinking of some other film. I was prepared for the possibility that this would be awful, but the script (or lack thereof) makes for a film that's also pointless. On the plus side, the general level of craft on the part of the actors and technical crew is quite competent, but when you've got a sow's ear to work with you can't make a silk purse. Ben G fans should stick with just about any other movie he's been in. Dorothy S fans should stick to Galaxina. Peter B fans should stick to Last Picture Show and Target. Fans of cheap laughs at the expense of those who seem to be asking for it should stick to Peter B's amazingly awful book, Killing of the Unicorn.", metadata={'label': 0}),
 Document(page_content='Never cast models and Playboy bunnies in your films! Bob Fosse\'s "Star 80" about Dorothy Stratten, of whom Bogdanovich was obsessed enough to have married her SISTER after her murder at the hands of her low-life husband, is a zillion times more interesting than Dorothy herself on the silver screen. Patty Hansen is no actress either..I expected to see some sort of lost masterpiece a la Orson Welles but instead got Audrey Hepburn cavorting in jeans and a god-awful "poodlesque" hair-do....Very disappointing...."Paper Moon" and "The Last Picture Show" I could watch again and again. This clunker I could barely sit through once. This movie was reputedly not released because of the brouhaha surrounding Ms. Stratten\'s tawdry death; I think the real reason was because it was so bad!', metadata={'label': 0}),


Document(page_content="Its not the cast. A finer group of actors, you could not find. Its not the setting. The director is in love with New York City, and by the end of the film, so are we all! Woody Allen could not improve upon what Bogdonovich has done here. If you are going to fall in love, or find love, Manhattan is the place to go. No, the problem with the movie is the script. There is none. The actors fall in love at first sight, words are unnecessary. In the director's own experience in Hollywood that is what happens when they go to work on the set. It is reality to him, and his peers, but it is a fantasy to most of us in the real world. So, in the end, the movie is hollow, and shallow, and message-less.", metadata={'label': 0}),


Document(page_content='Today I found "They All Laughed" on VHS on sale in a rental. It was a really old and very used VHS, I had no information about this movie, but I liked the references listed on its cover: the names of Peter Bogdanovich, Audrey Hepburn, John Ritter and specially Dorothy Stratten attracted me, the price was very low and I decided to risk and buy it. I searched IMDb, and the User Rating of 6.0 was an excellent reference. I looked in "Mick Martin & Marsha Porter Video & DVD Guide 2003" and \x96 wow \x96 four stars! So, I decided that I could not waste more time and immediately see it. Indeed, I have just finished watching "They All Laughed" and I found it a very boring overrated movie. The characters are badly developed, and I spent lots of minutes to understand their roles in the story. The plot is supposed to be funny (private eyes who fall in love for the women they are


eyes who fall in love for the women they are chasing), but I have not laughed along the whole story. The coincidences, in a huge city like New York, are ridiculous. Ben Gazarra as an attractive and very seductive man, with the women falling for him as if her were a Brad Pitt, Antonio Banderas or George Clooney, is quite ridiculous. In the end, the greater attractions certainly are the presence of the Playboy centerfold and playmate of the year Dorothy Stratten, murdered by her husband pretty after the release of this movie, and whose life was showed in "Star 80" and "Death of a Centerfold: The Dorothy Stratten Story"; the amazing beauty of the sexy Patti Hansen, the future Mrs. Keith Richards; the always wonderful, even being fifty-two years old, Audrey Hepburn; and the song "Amigo", from Roberto Carlos. Although I do not like him, Roberto Carlos has been the most popular Brazilian singer since the end of the


most popular Brazilian singer since the end of the 60\'s and is called by his fans as "The King". I will keep this movie in my collection only because of these attractions (manly Dorothy Stratten). My vote is four.

Title (Brazil): "Muito Riso e Muita Alegria" ("Many Laughs and Lots of Happiness")', metadata={'label': 0})]


Example#
In this example, we use data from a dataset to answer a question
from langchain.indexes import VectorstoreIndexCreator
from langchain.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
dataset_name="tweet_eval"
page_content_column="text"
name="stance_climate"
loader=HuggingFaceDatasetLoader(dataset_name,page_content_column,name)
index = VectorstoreIndexCreator().from_loaders([loader])
Found cached dataset tweet_eval
Using embedded DuckDB without persistence: data will be transient
query = "What are the most used hashtag?"
result = index.query(query)
result
' The most used hashtags in this context are #UKClimate2015, #Sustainability, #TakeDownTheFlag, #LoveWins, #CSOTA, #ClimateSummitoftheAmericas, #SM, and #SocialMedia.'
previous
HTML
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iFixit
 Contents

Example
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.rst
.pdf
How-To Guides
How-To Guides#
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either prompts, models, arbitrary functions, or other chains.
The examples here are broken up into three sections:
Generic Functionality
Covers both generic chains (that are useful in a wide variety of applications) as well as generic functionality related to those chains.
Async API for Chain
Loading from LangChainHub
LLM Chain
Additional ways of running LLM Chain
Parsing the outputs
Initialize from string
Sequential Chains
Serialization
Transformation Chain
Index-related Chains
Chains related to working with indexes.
Analyze Document
Chat Over Documents with Chat History
Graph QA
Hypothetical Document Embeddings
Question Answering with Sources
Question Answering
Summarization
Retrieval Question/Answering
Retrieval Question Answering with Sources
Vector DB Text Generation
All other chains
All other types of chains!
API Chains
Self-Critique Chain with Constitutional AI
BashChain
LLMCheckerChain
LLM Math
LLMRequestsChain
LLMSummarizationCheckerChain
Moderation
OpenAPI Chain
PAL
SQL Chain example
previous
Getting Started
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Async API for Chain
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
Getting Started
 Contents
Why do we need chains?
Quick start: Using LLMChain
Different ways of calling chains
Add memory to chains
Debug Chain
Combine chains with the SequentialChain
Create a custom chain with the Chain class
Getting Started#
In this tutorial, we will learn about creating simple chains in LangChain. We will learn how to create a chain, add components to it, and run it.
In this tutorial, we will cover:
Using a simple LLM chain
Creating sequential chains
Creating a custom chain
Why do we need chains?#
Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.
Quick start: Using LLMChain#
The LLMChain is a simple chain that takes in a prompt template, formats it with the user input and returns the response from an LLM.
To use the LLMChain, first create a prompt template.
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)
We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM.
from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain only specifying the input variable.
print(chain.run("colorful socks"))
SockSplash!


print(chain.run("colorful socks"))
SockSplash!
You can use a chat model in an LLMChain as well:
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
)
human_message_prompt = HumanMessagePromptTemplate(
        prompt=PromptTemplate(
            template="What is a good name for a company that makes {product}?",
            input_variables=["product"],
        )
    )
chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
chat = ChatOpenAI(temperature=0.9)
chain = LLMChain(llm=chat, prompt=chat_prompt_template)
print(chain.run("colorful socks"))
Rainbow Sox Co.
Different ways of calling chains#
All classes inherited from Chain offer a few ways of running chain logic. The most direct one is by using __call__:
chat = ChatOpenAI(temperature=0)
prompt_template = "Tell me a {adjective} joke"
llm_chain = LLMChain(
    llm=chat,
    prompt=PromptTemplate.from_template(prompt_template)
)
llm_chain(inputs={"adjective":"corny"})
{'adjective': 'corny',
 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}
By default, __call__ returns both the input and output key values. You can configure it to only return output key values by setting return_only_outputs to True.
llm_chain("corny", return_only_outputs=True)
{'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}


{'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}
If the Chain only outputs one output key (i.e. only has one element in its output_keys), you can  use run method. Note that run outputs a string instead of a dictionary.
# llm_chain only has one output key, so we can use run
llm_chain.output_keys
['text']
llm_chain.run({"adjective":"corny"})
'Why did the tomato turn red? Because it saw the salad dressing!'
In the case of one input key, you can input the string directly without specifying the input mapping.
# These two are equivalent
llm_chain.run({"adjective":"corny"})
llm_chain.run("corny")
# These two are also equivalent
llm_chain("corny")
llm_chain({"adjective":"corny"})
{'adjective': 'corny',
 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}
Tips: You can easily integrate a Chain object as a Tool in your Agent via its run method. See an example here.
Add memory to chains#
Chain supports taking a BaseMemory object as its memory argument, allowing Chain object to persist data across multiple calls. In other words, it makes Chain a stateful object.
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
conversation = ConversationChain(
    llm=chat,
    memory=ConversationBufferMemory()
)
conversation.run("Answer briefly. What are the first 3 colors of a rainbow?")
# -> The first three colors of a rainbow are red, orange, and yellow.
conversation.run("And the next 4?")
# -> The next four colors of a rainbow are green, blue, indigo, and violet.


# -> The next four colors of a rainbow are green, blue, indigo, and violet.
'The next four colors of a rainbow are green, blue, indigo, and violet.'
Essentially, BaseMemory defines an interface of how langchain stores memory. It allows reading of stored data through load_memory_variables method and storing new data through save_context method. You can learn more about it in Memory section.
Debug Chain#
It can be hard to debug Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing. Setting verbose to True will print out some internal states of the Chain object while it is being ran.
conversation = ConversationChain(
    llm=chat,
    memory=ConversationBufferMemory(),
    verbose=True
)
conversation.run("What is ChatGPT?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Human: What is ChatGPT?
AI:
> Finished chain.
'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'
Combine chains with the SequentialChain#


Combine chains with the SequentialChain#
The next step after calling a language model is to make a series of calls to a language model. We can do this using sequential chains, which are chains that execute their links in a predefined order. Specifically, we will use the SimpleSequentialChain. This is the simplest type of a sequential chain, where each step has a single input/output, and the output of one step is the input to the next.
In this tutorial, our sequential chain will:
First, create a company name for a product. We will reuse the LLMChain we’d previously initialized to create this company name.
Then, create a catchphrase for the product. We will initialize a new LLMChain to create this catchphrase, as shown below.
second_prompt = PromptTemplate(
    input_variables=["company_name"],
    template="Write a catchphrase for the following company: {company_name}",
)
chain_two = LLMChain(llm=llm, prompt=second_prompt)
Now we can combine the two LLMChains, so that we can create a company name and a catchphrase in a single step.
from langchain.chains import SimpleSequentialChain
overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)
# Run the chain specifying only the input variable for the first chain.
catchphrase = overall_chain.run("colorful socks")
print(catchphrase)
> Entering new SimpleSequentialChain chain...
Rainbow Socks Co.
"Step into Color with Rainbow Socks!"
> Finished chain.
"Step into Color with Rainbow Socks!"
Create a custom chain with the Chain class#


"Step into Color with Rainbow Socks!"
Create a custom chain with the Chain class#
LangChain provides many chains out of the box, but sometimes you may want to create a custom chain for your specific use case. For this example, we will create a custom chain that concatenates the outputs of 2 LLMChains.
In order to create a custom chain:
Start by subclassing the Chain class,
Fill out the input_keys and output_keys properties,
Add the _call method that shows how to execute the chain.
These steps are demonstrated in the example below:
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from typing import Dict, List
class ConcatenateChain(Chain):
    chain_1: LLMChain
    chain_2: LLMChain
    @property
    def input_keys(self) -> List[str]:
        # Union of the input keys of the two chains.
        all_input_vars = set(self.chain_1.input_keys).union(set(self.chain_2.input_keys))
        return list(all_input_vars)
    @property
    def output_keys(self) -> List[str]:
        return ['concat_output']
    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        output_1 = self.chain_1.run(inputs)
        output_2 = self.chain_2.run(inputs)
        return {'concat_output': output_1 + output_2}
Now, we can try running the chain that we called.
prompt_1 = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)
chain_1 = LLMChain(llm=llm, prompt=prompt_1)
prompt_2 = PromptTemplate(
    input_variables=["product"],


prompt_2 = PromptTemplate(
    input_variables=["product"],
    template="What is a good slogan for a company that makes {product}?",
)
chain_2 = LLMChain(llm=llm, prompt=prompt_2)
concat_chain = ConcatenateChain(chain_1=chain_1, chain_2=chain_2)
concat_output = concat_chain.run("colorful socks")
print(f"Concatenated output:\n{concat_output}")
Concatenated output:
Socktastic Colors.
"Put Some Color in Your Step!"
That’s it! For more details about how to do cool things with Chains, check out the how-to guide for chains.
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Chains
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How-To Guides
 Contents

Why do we need chains?
Quick start: Using LLMChain
Different ways of calling chains
Add memory to chains
Debug Chain
Combine chains with the SequentialChain
Create a custom chain with the Chain class
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
BashChain
 Contents
Customize Prompt
Persistent Terminal
BashChain#
This notebook showcases using LLMs and a bash process to perform simple filesystem commands.
from langchain.chains import LLMBashChain
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
text = "Please write a bash script that prints 'Hello World' to the console."
bash_chain = LLMBashChain(llm=llm, verbose=True)
bash_chain.run(text)
> Entering new LLMBashChain chain...
Please write a bash script that prints 'Hello World' to the console.
```bash
echo "Hello World"
```
Code: ['echo "Hello World"']
Answer: Hello World
> Finished chain.
'Hello World\n'
Customize Prompt#
You can also customize the prompt that is used. Here is an example prompting to avoid using the ‘echo’ utility
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:
Question: "copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'"
I need to take the following actions:
- List all files in the directory
- Create a new directory
- Copy the files from the first directory into the second directory
```bash
ls
mkdir myNewDirectory
cp -r target/* myNewDirectory
```
Do not use 'echo' when writing the script.
That is the format. Begin!
Question: {question}"""


That is the format. Begin!
Question: {question}"""
PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)
bash_chain = LLMBashChain(llm=llm, prompt=PROMPT, verbose=True)
text = "Please write a bash script that prints 'Hello World' to the console."
bash_chain.run(text)
> Entering new LLMBashChain chain...
Please write a bash script that prints 'Hello World' to the console.
```bash
printf "Hello World\n"
```
Code: ['printf "Hello World\\n"']
Answer: Hello World
> Finished chain.
'Hello World\n'
Persistent Terminal#
By default, the chain will run in a separate subprocess each time it is called. This behavior can be changed by instantiating with a persistent bash process.
from langchain.utilities.bash import BashProcess
persistent_process = BashProcess(persistent=True)
bash_chain = LLMBashChain.from_bash_process(llm=llm, bash_process=persistent_process, verbose=True)
text = "List the current directory then move up a level."
bash_chain.run(text)
> Entering new LLMBashChain chain...
List the current directory then move up a level.
```bash
ls
cd ..
```
Code: ['ls', 'cd ..']
Answer: api.ipynb			llm_summarization_checker.ipynb
constitutional_chain.ipynb	moderation.ipynb
llm_bash.ipynb			openai_openapi.yaml
llm_checker.ipynb		openapi.ipynb
llm_math.ipynb			pal.ipynb
llm_requests.ipynb		sqlite.ipynb
> Finished chain.


llm_requests.ipynb		sqlite.ipynb
> Finished chain.
'api.ipynb\t\t\tllm_summarization_checker.ipynb\r\nconstitutional_chain.ipynb\tmoderation.ipynb\r\nllm_bash.ipynb\t\t\topenai_openapi.yaml\r\nllm_checker.ipynb\t\topenapi.ipynb\r\nllm_math.ipynb\t\t\tpal.ipynb\r\nllm_requests.ipynb\t\tsqlite.ipynb'
# Run the same command again and see that the state is maintained between calls
bash_chain.run(text)
> Entering new LLMBashChain chain...
List the current directory then move up a level.
```bash
ls
cd ..
```
Code: ['ls', 'cd ..']
Answer: examples		getting_started.ipynb	index_examples
generic			how_to_guides.rst
> Finished chain.
'examples\t\tgetting_started.ipynb\tindex_examples\r\ngeneric\t\t\thow_to_guides.rst'
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Self-Critique Chain with Constitutional AI
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LLMCheckerChain
 Contents

Customize Prompt
Persistent Terminal
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
LLM Math
 Contents
Customize Prompt
LLM Math#
This notebook showcases using LLMs and Python REPLs to do complex word math problems.
from langchain import OpenAI, LLMMathChain
llm = OpenAI(temperature=0)
llm_math = LLMMathChain(llm=llm, verbose=True)
llm_math.run("What is 13 raised to the .3432 power?")
> Entering new LLMMathChain chain...
What is 13 raised to the .3432 power?
```python
import math
print(math.pow(13, .3432))
```
Answer: 2.4116004626599237
> Finished chain.
'Answer: 2.4116004626599237\n'
Customize Prompt#
You can also customize the prompt that is used. Here is an example prompting it to use numpy
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.
You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers.
So we hooked you up to a Python 3 kernel, and now you can execute code. If you execute code, you must print out the final answer using the print function. You MUST use the python package numpy to answer your question. You must import numpy as np.
Question: ${{Question with hard calculation.}}
```python
${{Code that prints what you need to know}}
print(${{code}})
```
```output
${{Output of your code}}
```
Answer: ${{Answer}}
Begin.


${{Output of your code}}
```
Answer: ${{Answer}}
Begin.
Question: What is 37593 * 67?
```python
import numpy as np
print(np.multiply(37593, 67))
```
```output
2518731
```
Answer: 2518731
Question: {question}"""
PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)
llm_math = LLMMathChain(llm=llm, prompt=PROMPT, verbose=True)
llm_math.run("What is 13 raised to the .3432 power?")
> Entering new LLMMathChain chain...
What is 13 raised to the .3432 power?
```python
import numpy as np
print(np.power(13, .3432))
```
Answer: 2.4116004626599237
> Finished chain.
'Answer: 2.4116004626599237\n'
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LLMCheckerChain
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LLMRequestsChain
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Customize Prompt
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
OpenAPI Chain
 Contents
Load the spec
Select the Operation
Construct the chain
Return raw response
Example POST message
OpenAPI Chain#
This notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language
from langchain.tools import OpenAPISpec, APIOperation
from langchain.chains import OpenAPIEndpointChain
from langchain.requests import Requests
from langchain.llms import OpenAI
Load the spec#
Load a wrapper of the spec (so we can work with it more easily). You can load from a url or from a local file.
spec = OpenAPISpec.from_url("https://www.klarna.com/us/shopping/public/openai/v0/api-docs/")
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
# Alternative loading from file
# spec = OpenAPISpec.from_file("openai_openapi.yaml")
Select the Operation#
In order to provide a focused on modular chain, we create a chain specifically only for one of the endpoints. Here we get an API operation from a specified endpoint and method.
operation = APIOperation.from_openapi_spec(spec, '/public/openai/v0/products', "get")
Construct the chain#
We can now construct a chain to interact with it. In order to construct such a chain, we will pass in:
The operation endpoint
A requests wrapper (can be used to handle authentication, etc)
The LLM to use to interact with it
llm = OpenAI() # Load a Language Model
chain = OpenAPIEndpointChain.from_api_operation(
    operation,
    llm,
    requests=Requests(),
    verbose=True,


llm,
    requests=Requests(),
    verbose=True,
    return_intermediate_steps=True # Return request and response text
)
output = chain("whats the most expensive shirt?")
> Entering new OpenAPIEndpointChain chain...
> Entering new APIRequesterChain chain...
Prompt after formatting:
You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.
API_SCHEMA: ```typescript
/* API for fetching Klarna product information */
type productsUsingGET = (_: {
/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */
		q: string,
/* number of products returned */
		size?: number,
/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */
		min_price?: number,
/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */
		max_price?: number,
}) => any;
```
USER_INSTRUCTIONS: "whats the most expensive shirt?"
Your arguments must be plain json provided in a markdown block:
ARGS: ```json
{valid json conforming to API_SCHEMA}
```
Example


ARGS: ```json
{valid json conforming to API_SCHEMA}
```
Example
-----
ARGS: ```json
{"foo": "bar", "baz": {"qux": "quux"}}
```
The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.
You MUST strictly comply to the types indicated by the provided schema, including all required args.
If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:
Message: ```text
Concise response requesting the additional information that would make calling the function successful.
```
Begin
-----
ARGS:
> Finished chain.
{"q": "shirt", "size": 1, "max_price": null}
{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]}]}
> Entering new APIResponderChain chain...
Prompt after formatting:
You are a helpful AI assistant trained to answer user queries from API responses.
You attempted to call an API, which resulted in:


You attempted to call an API, which resulted in:
API_RESPONSE: {"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]}]}
USER_COMMENT: "whats the most expensive shirt?"
If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:
Response: ```json
{"response": "Human-understandable synthesis of the API_RESPONSE"}
```
Otherwise respond with the following markdown json block:
Response Error: ```json
{"response": "What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion."}
```
You MUST respond as a markdown json code block. The person you are responding to CANNOT see the API_RESPONSE, so if there is any relevant information there you must include it in your response.
Begin:
---
> Finished chain.
The most expensive shirt in the API response is the Burberry Check Poplin Shirt, which costs $360.00.
> Finished chain.
# View intermediate steps
output["intermediate_steps"]
{'request_args': '{"q": "shirt", "size": 1, "max_price": null}',


'response_text': '{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]}]}'}
Return raw response#
We can also run this chain without synthesizing the response. This will have the effect of just returning the raw API output.
chain = OpenAPIEndpointChain.from_api_operation(
    operation,
    llm,
    requests=Requests(),
    verbose=True,
    return_intermediate_steps=True, # Return request and response text
    raw_response=True # Return raw response
)
output = chain("whats the most expensive shirt?")
> Entering new OpenAPIEndpointChain chain...
> Entering new APIRequesterChain chain...
Prompt after formatting:
You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.
API_SCHEMA: ```typescript
/* API for fetching Klarna product information */
type productsUsingGET = (_: {
/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */
		q: string,


q: string,
/* number of products returned */
		size?: number,
/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */
		min_price?: number,
/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */
		max_price?: number,
}) => any;
```
USER_INSTRUCTIONS: "whats the most expensive shirt?"
Your arguments must be plain json provided in a markdown block:
ARGS: ```json
{valid json conforming to API_SCHEMA}
```
Example
-----
ARGS: ```json
{"foo": "bar", "baz": {"qux": "quux"}}
```
The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.
You MUST strictly comply to the types indicated by the provided schema, including all required args.
If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:
Message: ```text
Concise response requesting the additional information that would make calling the function successful.
```
Begin
-----
ARGS:
> Finished chain.
{"q": "shirt", "max_price": null}


{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]},{"name":"Burberry Vintage Check Cotton Shirt - Beige","url":"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin","price":"$229.02","attributes":["Material:Cotton,Elastane","Color:Beige","Model:Boy","Pattern:Checkered"]},{"name":"Burberry Vintage Check Stretch Cotton Twill Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$309.99","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Woman","Color:Beige","Properties:Stretch","Pattern:Checkered"]},{"name":"Burberry Somerton Check Shirt -


Somerton Check Shirt - Camel","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin","price":"$450.00","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Man","Color:Beige"]},{"name":"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$19.99","attributes":["Material:Polyester,Nylon","Target Group:Man","Color:Red,Pink,White,Blue,Purple,Beige,Black,Green","Properties:Pockets","Pattern:Solid Color"]}]}


> Finished chain.
output
{'instructions': 'whats the most expensive shirt?',


'output': '{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]},{"name":"Burberry Vintage Check Cotton Shirt - Beige","url":"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin","price":"$229.02","attributes":["Material:Cotton,Elastane","Color:Beige","Model:Boy","Pattern:Checkered"]},{"name":"Burberry Vintage Check Stretch Cotton Twill Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$309.99","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Woman","Color:Beige","Properties:Stretch","Pattern:Checkered"]},{"name":"Burberry Somerton Check Shirt -


Somerton Check Shirt - Camel","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin","price":"$450.00","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Man","Color:Beige"]},{"name":"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$19.99","attributes":["Material:Polyester,Nylon","Target Group:Man","Color:Red,Pink,White,Blue,Purple,Beige,Black,Green","Properties:Pockets","Pattern:Solid Color"]}]}',


'intermediate_steps': {'request_args': '{"q": "shirt", "max_price": null}',


'response_text': '{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]},{"name":"Burberry Vintage Check Cotton Shirt - Beige","url":"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin","price":"$229.02","attributes":["Material:Cotton,Elastane","Color:Beige","Model:Boy","Pattern:Checkered"]},{"name":"Burberry Vintage Check Stretch Cotton Twill Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$309.99","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Woman","Color:Beige","Properties:Stretch","Pattern:Checkered"]},{"name":"Burberry Somerton Check Shirt -


Somerton Check Shirt - Camel","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin","price":"$450.00","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Man","Color:Beige"]},{"name":"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$19.99","attributes":["Material:Polyester,Nylon","Target Group:Man","Color:Red,Pink,White,Blue,Purple,Beige,Black,Green","Properties:Pockets","Pattern:Solid Color"]}]}'}}


Example POST message#
For this demo, we will interact with the speak API.
spec = OpenAPISpec.from_url("https://api.speak.com/openapi.yaml")
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
operation = APIOperation.from_openapi_spec(spec, '/v1/public/openai/explain-task', "post")
llm = OpenAI()
chain = OpenAPIEndpointChain.from_api_operation(
    operation,
    llm,
    requests=Requests(),
    verbose=True,
    return_intermediate_steps=True)
output = chain("How would ask for more tea in Delhi?")
> Entering new OpenAPIEndpointChain chain...
> Entering new APIRequesterChain chain...
Prompt after formatting:
You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.
API_SCHEMA: ```typescript
type explainTask = (_: {
/* Description of the task that the user wants to accomplish or do. For example, "tell the waiter they messed up my order" or "compliment someone on their shirt" */
  task_description?: string,
/* The foreign language that the user is learning and asking about. The value can be inferred from question - for example, if the user asks "how do i ask a girl out in mexico city", the value should be "Spanish" because of Mexico City. Always use the full name of the language (e.g. Spanish, French). */
  learning_language?: string,


learning_language?: string,
/* The user's native language. Infer this value from the language the user asked their question in. Always use the full name of the language (e.g. Spanish, French). */
  native_language?: string,
/* A description of any additional context in the user's question that could affect the explanation - e.g. setting, scenario, situation, tone, speaking style and formality, usage notes, or any other qualifiers. */
  additional_context?: string,
/* Full text of the user's question. */
  full_query?: string,
}) => any;
```
USER_INSTRUCTIONS: "How would ask for more tea in Delhi?"
Your arguments must be plain json provided in a markdown block:
ARGS: ```json
{valid json conforming to API_SCHEMA}
```
Example
-----
ARGS: ```json
{"foo": "bar", "baz": {"qux": "quux"}}
```
The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.
You MUST strictly comply to the types indicated by the provided schema, including all required args.
If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:
Message: ```text
Concise response requesting the additional information that would make calling the function successful.
```
Begin
-----
ARGS:
> Finished chain.
{"task_description": "ask for more tea", "learning_language": "Hindi", "native_language": "English", "full_query": "How would I ask for more tea in Delhi?"}


{"explanation":"<what-to-say language=\"Hindi\" context=\"None\">\nऔर चाय लाओ। (Aur chai lao.) \n</what-to-say>\n\n<alternatives context=\"None\">\n1. \"चाय थोड़ी ज्यादा मिल सकती है?\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\n2. \"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\n3. \"क्या मुझे or cup में milk/tea powder मिल सकता है?\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or


tone, asking for an extra serving of milk or tea powder)*\n</alternatives>\n\n<usage-notes>\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\n</usage-notes>\n\n<example-convo language=\"Hindi\">\n<context>At home during breakfast.</context>\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\n</example-convo>\n\n*[Report an issue or leave


an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*","extra_response_instructions":"Use all information in the API response and fully render all Markdown.\nAlways end your response with a link to report an issue or leave feedback on the plugin."}


> Entering new APIResponderChain chain...
Prompt after formatting:
You are a helpful AI assistant trained to answer user queries from API responses.
You attempted to call an API, which resulted in:


API_RESPONSE: {"explanation":"<what-to-say language=\"Hindi\" context=\"None\">\nऔर चाय लाओ। (Aur chai lao.) \n</what-to-say>\n\n<alternatives context=\"None\">\n1. \"चाय थोड़ी ज्यादा मिल सकती है?\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\n2. \"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\n3. \"क्या मुझे or cup में milk/tea powder मिल सकता है?\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of


USER_COMMENT: "How would ask for more tea in Delhi?"
If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:
Response: ```json
{"response": "Concise response to USER_COMMENT based on API_RESPONSE."}
```
Otherwise respond with the following markdown json block:
Response Error: ```json
{"response": "What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion."}
```
You MUST respond as a markdown json code block.
Begin:
---
> Finished chain.
In Delhi you can ask for more tea by saying 'Chai thodi zyada mil sakti hai?'
> Finished chain.
# Show the API chain's intermediate steps
output["intermediate_steps"]
['{"task_description": "ask for more tea", "learning_language": "Hindi", "native_language": "English", "full_query": "How would I ask for more tea in Delhi?"}',


'{"explanation":"<what-to-say language=\\"Hindi\\" context=\\"None\\">\\nऔर चाय लाओ। (Aur chai lao.) \\n</what-to-say>\\n\\n<alternatives context=\\"None\\">\\n1. \\"चाय थोड़ी ज्यादा मिल सकती है?\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\n2. \\"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\n3. \\"क्या मुझे or cup में milk/tea powder मिल सकता है?\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an


- Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\n</alternatives>\\n\\n<usage-notes>\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\n</usage-notes>\\n\\n<example-convo language=\\"Hindi\\">\\n<context>At home during breakfast.</context>\\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as


add a little extra in the quantity of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*","extra_response_instructions":"Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin."}']


previous
Moderation
next
PAL
 Contents

Load the spec
Select the Operation
Construct the chain
Return raw response
Example POST message
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.ipynb
.pdf
LLMCheckerChain
LLMCheckerChain#
This notebook showcases how to use LLMCheckerChain.
from langchain.chains import LLMCheckerChain
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.7)
text = "What type of mammal lays the biggest eggs?"
checker_chain = LLMCheckerChain(llm=llm, verbose=True)
checker_chain.run(text)
> Entering new LLMCheckerChain chain...
> Entering new SequentialChain chain...
Chain 0:
{'statement': '\nNone. Mammals do not lay eggs.'}
Chain 1:
{'assertions': '\n• Mammals reproduce using live birth\n• Mammals do not lay eggs\n• Animals that lay eggs are not mammals'}
Chain 2:
{'checked_assertions': '\n1. True\n\n2. True\n\n3. False - Mammals are a class of animals that includes animals that lay eggs, such as monotremes (platypus and echidna).'}
Chain 3:
{'revised_statement': ' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'}
> Finished SequentialChain chain.
> Finished LLMCheckerChain chain.
' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'
previous
BashChain
next
LLM Math
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.