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"""
# 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
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[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 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
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4d1c3837677f-10
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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 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
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4d1c3837677f-11
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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? 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 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
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4d1c3837677f-12
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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 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
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/web_base.html
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4d1c3837677f-13
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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 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 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,
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/web_base.html
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4d1c3837677f-14
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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 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 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
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/web_base.html
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4d1c3837677f-15
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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 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),
|
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4d1c3837677f-16
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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
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4d1c3837677f-17
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[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 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
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4d1c3837677f-18
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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 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
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4d1c3837677f-19
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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? 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 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
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/web_base.html
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4d1c3837677f-20
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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 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
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/web_base.html
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4d1c3837677f-21
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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 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 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,
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/web_base.html
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4d1c3837677f-22
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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 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 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
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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 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),
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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
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[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.\n§ 431.86\nSection § 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 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
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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 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':
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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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previous
URL
next
Weather
Contents
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 Jun 16, 2023.
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.ipynb
.pdf
Images
Contents
Using Unstructured
Retain Elements
Images#
This covers how to load images such as JPG or PNG into a document format that we can use downstream.
Using Unstructured#
#!pip install pdfminer
from langchain.document_loaders.image import UnstructuredImageLoader
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg")
data = loader.load()
data[0]
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Document(page_content="LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\n\n\n‘Zxjiang Shen' (F3}, Ruochen Zhang”, Melissa Dell*, Benjamin Charles Germain\nLeet, Jacob Carlson, and Weining LiF\n\n\nsugehen\n\nshangthrows, et\n\n“Abstract. Recent advanocs in document image analysis (DIA) have been\n‘pimarliy driven bythe application of neural networks dell roar\n{uteomer could be aly deployed in production and extended fo farther\n[nvetigtion. However, various factory ke lcely organize codebanee\nsnd sophisticated modal cnigurations compat the ey ree of\n‘erin! innovation by wide sence, Though there have been sng\n‘Hors to improve reuablty and simplify deep lees (DL) mode\n‘aon, sone of them ae optimized for challenge inthe demain of DIA,\nThis roprscte a major gap in the extng fol, sw DIA i eal to\nscademic research acon wie range of dpi in the social ssencee\n[rary for streamlining the sage of DL in DIA research and appicn\n‘tons The core LayoutFaraer brary comes with a sch of simple and\nIntative interfaee or applying and eutomiing DI. odel fr Inyo de\npltfom for sharing both protrined modes an fal document dist\n{ation pipeline We demonutate that LayootPareer shea fr both\nlightweight and lrgeseledgtieation pipelines in eal-word uae ces\nThe leary pblely smal at Btspe://layost-pareergsthab So\n\n\n\n‘Keywords: Document Image Analysis» Deep Learning Layout Analysis\n‘Character Renguition - Open Serres dary «
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Image Analysis» Deep Learning Layout Analysis\n‘Character Renguition - Open Serres dary « Tol\n\n\nIntroduction\n\n\n‘Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndoctiment image analysis (DIA) tea including document image clasiffeation [I]\n", lookup_str='', metadata={'source': 'layout-parser-paper-fast.jpg'}, lookup_index=0)
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Retain Elements#
Under the hood, Unstructured creates different “elements” for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg", mode="elements")
data = loader.load()
data[0]
Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\n', lookup_str='', metadata={'source': 'layout-parser-paper-fast.jpg', 'filename': 'layout-parser-paper-fast.jpg', 'page_number': 1, 'category': 'Title'}, lookup_index=0)
previous
HTML
next
Jupyter Notebook
Contents
Using Unstructured
Retain Elements
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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.ipynb
.pdf
Docugami
Contents
Prerequisites
Quick start
Advantages vs Other Chunking Techniques
Load Documents
Basic Use: Docugami Loader for Document QA
Using Docugami to Add Metadata to Chunks for High Accuracy Document QA
Docugami#
This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders.
Prerequisites#
Install necessary python packages.
Grab an access token for your workspace, and make sure it is set as the DOCUGAMI_API_KEY environment variable.
Grab some docset and document IDs for your processed documents, as described here: https://help.docugami.com/home/docugami-api
# You need the lxml package to use the DocugamiLoader
!pip install lxml
Quick start#
Create a Docugami workspace (free trials available)
Add your documents (PDF, DOCX or DOC) and allow Docugami to ingest and cluster them into sets of similar documents, e.g. NDAs, Lease Agreements, and Service Agreements. There is no fixed set of document types supported by the system, the clusters created depend on your particular documents, and you can change the docset assignments later.
Create an access token via the Developer Playground for your workspace. Detailed instructions
Explore the Docugami API to get a list of your processed docset IDs, or just the document IDs for a particular docset.
Use the DocugamiLoader as detailed below, to get rich semantic chunks for your documents.
Optionally, build and publish one or more reports or abstracts. This helps Docugami improve the semantic XML with better tags based on your preferences, which are then added to the DocugamiLoader output as metadata. Use techniques like self-querying retriever to do high accuracy Document QA.
Advantages vs Other Chunking Techniques#
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Advantages vs Other Chunking Techniques#
Appropriate chunking of your documents is critical for retrieval from documents. Many chunking techniques exist, including simple ones that rely on whitespace and recursive chunk splitting based on character length. Docugami offers a different approach:
Intelligent Chunking: Docugami breaks down every document into a hierarchical semantic XML tree of chunks of varying sizes, from single words or numerical values to entire sections. These chunks follow the semantic contours of the document, providing a more meaningful representation than arbitrary length or simple whitespace-based chunking.
Structured Representation: In addition, the XML tree indicates the structural contours of every document, using attributes denoting headings, paragraphs, lists, tables, and other common elements, and does that consistently across all supported document formats, such as scanned PDFs or DOCX files. It appropriately handles long-form document characteristics like page headers/footers or multi-column flows for clean text extraction.
Semantic Annotations: Chunks are annotated with semantic tags that are coherent across the document set, facilitating consistent hierarchical queries across multiple documents, even if they are written and formatted differently. For example, in set of lease agreements, you can easily identify key provisions like the Landlord, Tenant, or Renewal Date, as well as more complex information such as the wording of any sub-lease provision or whether a specific jurisdiction has an exception section within a Termination Clause.
Additional Metadata: Chunks are also annotated with additional metadata, if a user has been using Docugami. This additional metadata can be used for high-accuracy Document QA without context window restrictions. See detailed code walk-through below.
import os
from langchain.document_loaders import DocugamiLoader
Load Documents#
If the DOCUGAMI_API_KEY environment variable is set, there is no need to pass it in to the loader explicitly otherwise you can pass it in as the access_token parameter.
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DOCUGAMI_API_KEY=os.environ.get('DOCUGAMI_API_KEY')
# To load all docs in the given docset ID, just don't provide document_ids
loader = DocugamiLoader(docset_id="ecxqpipcoe2p", document_ids=["43rj0ds7s0ur"])
docs = loader.load()
docs
[Document(page_content='MUTUAL NON-DISCLOSURE AGREEMENT This Mutual Non-Disclosure Agreement (this “ Agreement ”) is entered into and made effective as of April 4 , 2018 between Docugami Inc. , a Delaware corporation , whose address is 150 Lake Street South , Suite 221 , Kirkland , Washington 98033 , and Caleb Divine , an individual, whose address is 1201 Rt 300 , Newburgh NY 12550 .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:ThisMutualNon-disclosureAgreement', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'ThisMutualNon-disclosureAgreement'}),
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Document(page_content='The above named parties desire to engage in discussions regarding a potential agreement or other transaction between the parties (the “Purpose”). In connection with such discussions, it may be necessary for the parties to disclose to each other certain confidential information or materials to enable them to evaluate whether to enter into such agreement or transaction.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Discussions', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Discussions'}),
Document(page_content='In consideration of the foregoing, the parties agree as follows:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Consideration', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Consideration'}),
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Document(page_content='1. Confidential Information . For purposes of this Agreement , “ Confidential Information ” means any information or materials disclosed by one party to the other party that: (i) if disclosed in writing or in the form of tangible materials, is marked “confidential” or “proprietary” at the time of such disclosure; (ii) if disclosed orally or by visual presentation, is identified as “confidential” or “proprietary” at the time of such disclosure, and is summarized in a writing sent by the disclosing party to the receiving party within thirty ( 30 ) days after any such disclosure; or (iii) due to its nature or the circumstances of its disclosure, a person exercising reasonable business judgment would understand to be confidential or proprietary.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Purposes/docset:ConfidentialInformation-section/docset:ConfidentialInformation[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ConfidentialInformation'}),
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Document(page_content="2. Obligations and Restrictions . Each party agrees: (i) to maintain the other party's Confidential Information in strict confidence; (ii) not to disclose such Confidential Information to any third party; and (iii) not to use such Confidential Information for any purpose except for the Purpose. Each party may disclose the other party’s Confidential Information to its employees and consultants who have a bona fide need to know such Confidential Information for the Purpose, but solely to the extent necessary to pursue the Purpose and for no other purpose; provided, that each such employee and consultant first executes a written agreement (or is otherwise already bound by a written agreement) that contains use and nondisclosure restrictions at least as protective of the other party’s Confidential Information as those set forth in this Agreement .", metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Obligations/docset:ObligationsAndRestrictions-section/docset:ObligationsAndRestrictions', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ObligationsAndRestrictions'}),
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Document(page_content='3. Exceptions. The obligations and restrictions in Section 2 will not apply to any information or materials that:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Exceptions/docset:Exceptions-section/docset:Exceptions[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Exceptions'}),
Document(page_content='(i) were, at the date of disclosure, or have subsequently become, generally known or available to the public through no act or failure to act by the receiving party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheDate/docset:TheDate', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheDate'}),
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Document(page_content='(ii) were rightfully known by the receiving party prior to receiving such information or materials from the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:SuchInformation/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}),
Document(page_content='(iii) are rightfully acquired by the receiving party from a third party who has the right to disclose such information or materials without breach of any confidentiality obligation to the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheReceivingParty/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}),
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Document(page_content='4. Compelled Disclosure . Nothing in this Agreement will be deemed to restrict a party from disclosing the other party’s Confidential Information to the extent required by any order, subpoena, law, statute or regulation; provided, that the party required to make such a disclosure uses reasonable efforts to give the other party reasonable advance notice of such required disclosure in order to enable the other party to prevent or limit such disclosure.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Disclosure/docset:CompelledDisclosure-section/docset:CompelledDisclosure', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'CompelledDisclosure'}),
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Document(page_content='5. Return of Confidential Information . Upon the completion or abandonment of the Purpose, and in any event upon the disclosing party’s request, the receiving party will promptly return to the disclosing party all tangible items and embodiments containing or consisting of the disclosing party’s Confidential Information and all copies thereof (including electronic copies), and any notes, analyses, compilations, studies, interpretations, memoranda or other documents (regardless of the form thereof) prepared by or on behalf of the receiving party that contain or are based upon the disclosing party’s Confidential Information .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheCompletion/docset:ReturnofConfidentialInformation-section/docset:ReturnofConfidentialInformation', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ReturnofConfidentialInformation'}),
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Document(page_content='6. No Obligations . Each party retains the right to determine whether to disclose any Confidential Information to the other party.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoObligations/docset:NoObligations-section/docset:NoObligations[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoObligations'}),
Document(page_content='7. No Warranty. ALL CONFIDENTIAL INFORMATION IS PROVIDED BY THE DISCLOSING PARTY “AS IS ”.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoWarranty/docset:NoWarranty-section/docset:NoWarranty[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoWarranty'}),
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Document(page_content='8. Term. This Agreement will remain in effect for a period of seven ( 7 ) years from the date of last disclosure of Confidential Information by either party, at which time it will terminate.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:ThisAgreement/docset:Term-section/docset:Term', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Term'}),
Document(page_content='9. Equitable Relief . Each party acknowledges that the unauthorized use or disclosure of the disclosing party’s Confidential Information may cause the disclosing party to incur irreparable harm and significant damages, the degree of which may be difficult to ascertain. Accordingly, each party agrees that the disclosing party will have the right to seek immediate equitable relief to enjoin any unauthorized use or disclosure of its Confidential Information , in addition to any other rights and remedies that it may have at law or otherwise.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:EquitableRelief/docset:EquitableRelief-section/docset:EquitableRelief[2]', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'EquitableRelief'}),
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Document(page_content='10. Non-compete. To the maximum extent permitted by applicable law, during the Term of this Agreement and for a period of one ( 1 ) year thereafter, Caleb Divine may not market software products or do business that directly or indirectly competes with Docugami software products .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheMaximumExtent/docset:Non-compete-section/docset:Non-compete', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Non-compete'}),
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Document(page_content='11. Miscellaneous. This Agreement will be governed and construed in accordance with the laws of the State of Washington , excluding its body of law controlling conflict of laws. This Agreement is the complete and exclusive understanding and agreement between the parties regarding the subject matter of this Agreement and supersedes all prior agreements, understandings and communications, oral or written, between the parties regarding the subject matter of this Agreement . If any provision of this Agreement is held invalid or unenforceable by a court of competent jurisdiction, that provision of this Agreement will be enforced to the maximum extent permissible and the other provisions of this Agreement will remain in full force and effect. Neither party may assign this Agreement , in whole or in part, by operation of law or otherwise, without the other party’s prior written consent, and any attempted assignment without such consent will be void. This Agreement may be executed in counterparts, each of which will be deemed an original, but all of which together will constitute one and the same instrument.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Accordance/docset:Miscellaneous-section/docset:Miscellaneous', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Miscellaneous'}),
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Document(page_content='[SIGNATURE PAGE FOLLOWS] IN WITNESS WHEREOF, the parties hereto have executed this Mutual Non-Disclosure Agreement by their duly authorized officers or representatives as of the date first set forth above.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:TheParties', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheParties'}),
Document(page_content='DOCUGAMI INC . : \n\n Caleb Divine : \n\n Signature: Signature: Name: \n\n Jean Paoli Name: Title: \n\n CEO Title:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:DocugamiInc/docset:DocugamiInc/xhtml:table', 'id': '43rj0ds7s0ur', 'name': 'NDA simple layout.docx', 'structure': '', 'tag': 'table'})]
The metadata for each Document (really, a chunk of an actual PDF, DOC or DOCX) contains some useful additional information:
id and name: ID and Name of the file (PDF, DOC or DOCX) the chunk is sourced from within Docugami.
xpath: XPath inside the XML representation of the document, for the chunk. Useful for source citations directly to the actual chunk inside the document XML.
structure: Structural attributes of the chunk, e.g. h1, h2, div, table, td, etc. Useful to filter out certain kinds of chunks if needed by the caller.
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tag: Semantic tag for the chunk, using various generative and extractive techniques. More details here: https://github.com/docugami/DFM-benchmarks
Basic Use: Docugami Loader for Document QA#
You can use the Docugami Loader like a standard loader for Document QA over multiple docs, albeit with much better chunks that follow the natural contours of the document. There are many great tutorials on how to do this, e.g. this one. We can just use the same code, but use the DocugamiLoader for better chunking, instead of loading text or PDF files directly with basic splitting techniques.
!poetry run pip -q install openai tiktoken chromadb
from langchain.schema import Document
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
# For this example, we already have a processed docset for a set of lease documents
loader = DocugamiLoader(docset_id="wh2kned25uqm")
documents = loader.load()
The documents returned by the loader are already split, so we don’t need to use a text splitter. Optionally, we can use the metadata on each document, for example the structure or tag attributes, to do any post-processing we want.
We will just use the output of the DocugamiLoader as-is to set up a retrieval QA chain the usual way.
embedding = OpenAIEmbeddings()
vectordb = Chroma.from_documents(documents=documents, embedding=embedding)
retriever = vectordb.as_retriever()
qa_chain = RetrievalQA.from_chain_type(
llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True
)
Using embedded DuckDB without persistence: data will be transient
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)
Using embedded DuckDB without persistence: data will be transient
# Try out the retriever with an example query
qa_chain("What can tenants do with signage on their properties?")
{'query': 'What can tenants do with signage on their properties?',
'result': ' Tenants may place signs (digital or otherwise) or other form of identification on the premises after receiving written permission from the landlord which shall not be unreasonably withheld. The tenant is responsible for any damage caused to the premises and must conform to any applicable laws, ordinances, etc. governing the same. The tenant must also remove and clean any window or glass identification promptly upon vacating the premises.',
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'source_documents': [Document(page_content='ARTICLE VI SIGNAGE 6.01 Signage . Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant ’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises.', metadata={'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:Article/docset:ARTICLEVISIGNAGE-section/docset:_601Signage-section/docset:_601Signage', 'id': 'v1bvgaozfkak', 'name': 'TruTone Lane 2.docx', 'structure': 'div', 'tag': '_601Signage', 'Landlord': 'BUBBA CENTER PARTNERSHIP', 'Tenant': 'Truetone Lane LLC'}),
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Document(page_content='Signage. Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant ’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises. \n\n ARTICLE VII UTILITIES 7.01', metadata={'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:ThisOFFICELEASEAGREEMENTThis/docset:ArticleIBasic/docset:ArticleIiiUseAndCareOf/docset:ARTICLEIIIUSEANDCAREOFPREMISES-section/docset:ARTICLEIIIUSEANDCAREOFPREMISES/docset:NoOtherPurposes/docset:TenantsResponsibility/dg:chunk', 'id': 'g2fvhekmltza', 'name': 'TruTone Lane 6.pdf', 'structure': 'lim', 'tag': 'chunk', 'Landlord': 'GLORY ROAD LLC', 'Tenant': 'Truetone Lane LLC'}),
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Document(page_content='Landlord , its agents, servants, employees, licensees, invitees, and contractors during the last year of the term of this Lease at any and all times during regular business hours, after 24 hour notice to tenant, to pass and repass on and through the Premises, or such portion thereof as may be necessary, in order that they or any of them may gain access to the Premises for the purpose of showing the Premises to potential new tenants or real estate brokers. In addition, Landlord shall be entitled to place a "FOR RENT " or "FOR LEASE" sign (not exceeding 8.5 ” x 11 ”) in the front window of the Premises during the last six months of the term of this Lease .', metadata={'xpath': '/docset:Rider/docset:RIDERTOLEASE-section/docset:RIDERTOLEASE/docset:FixedRent/docset:TermYearPeriod/docset:Lease/docset:_42FLandlordSAccess-section/docset:_42FLandlordSAccess/docset:LandlordsRights/docset:Landlord', 'id': 'omvs4mysdk6b', 'name': 'TruTone Lane 1.docx', 'structure': 'p', 'tag': 'Landlord', 'Landlord': 'BIRCH STREET , LLC', 'Tenant': 'Trutone Lane LLC'}),
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Document(page_content="24. SIGNS . No signage shall be placed by Tenant on any portion of the Project . However, Tenant shall be permitted to place a sign bearing its name in a location approved by Landlord near the entrance to the Premises (at Tenant's cost ) and will be furnished a single listing of its name in the Building's directory (at Landlord 's cost ), all in accordance with the criteria adopted from time to time by Landlord for the Project . Any changes or additional listings in the directory shall be furnished (subject to availability of space) for the then Building Standard charge .", metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:TheTerms/docset:Indemnification/docset:INDEMNIFICATION-section/docset:INDEMNIFICATION/docset:Waiver/docset:Waiver/docset:Signs/docset:SIGNS-section/docset:SIGNS', 'id': 'qkn9cyqsiuch', 'name': 'Shorebucks LLC_AZ.pdf', 'structure': 'div', 'tag': 'SIGNS', 'Landlord': 'Menlo Group', 'Tenant': 'Shorebucks LLC'})]}
Using Docugami to Add Metadata to Chunks for High Accuracy Document QA#
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Using Docugami to Add Metadata to Chunks for High Accuracy Document QA#
One issue with large documents is that the correct answer to your question may depend on chunks that are far apart in the document. Typical chunking techniques, even with overlap, will struggle with providing the LLM sufficent context to answer such questions. With upcoming very large context LLMs, it may be possible to stuff a lot of tokens, perhaps even entire documents, inside the context but this will still hit limits at some point with very long documents, or a lot of documents.
For example, if we ask a more complex question that requires the LLM to draw on chunks from different parts of the document, even OpenAI’s powerful LLM is unable to answer correctly.
chain_response = qa_chain("What is rentable area for the property owned by DHA Group?")
chain_response["result"] # the correct answer should be 13,500
' 9,753 square feet'
At first glance the answer may seem reasonable, but if you review the source chunks carefully for this answer, you will see that the chunking of the document did not end up putting the Landlord name and the rentable area in the same context, since they are far apart in the document. The retriever therefore ends up finding unrelated chunks from other documents not even related to the Menlo Group landlord. That landlord happens to be mentioned on the first page of the file Shorebucks LLC_NJ.pdf file, and while one of the source chunks used by the chain is indeed from that doc that contains the correct answer (13,500), other source chunks from different docs are included, and the answer is therefore incorrect.
chain_response["source_documents"]
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chain_response["source_documents"]
[Document(page_content='1.1 Landlord . DHA Group , a Delaware limited liability company authorized to transact business in New Jersey .', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:DhaGroup/docset:Landlord-section/docset:DhaGroup', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'DhaGroup', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}),
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Document(page_content='WITNESSES: LANDLORD: DHA Group , a Delaware limited liability company', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Guaranty-section/docset:Guaranty[2]/docset:SIGNATURESONNEXTPAGE-section/docset:INWITNESSWHEREOF-section/docset:INWITNESSWHEREOF/docset:Behalf/docset:Witnesses/xhtml:table/xhtml:tbody/xhtml:tr[3]/xhtml:td[2]/docset:DhaGroup', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'DhaGroup', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}),
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Document(page_content="1.16 Landlord 's Notice Address . DHA Group , Suite 1010 , 111 Bauer Dr , Oakland , New Jersey , 07436 , with a copy to the Building Management Office at the Project , Attention: On - Site Property Manager .", metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:NoticeAddress[2]/docset:LandlordsNoticeAddress-section/docset:LandlordsNoticeAddress[2]', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'LandlordsNoticeAddress', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}),
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Document(page_content='1.6 Rentable Area of the Premises. 9,753 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:PerryBlair/docset:PerryBlair/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises', 'id': 'dsyfhh4vpeyf', 'name': 'Shorebucks LLC_CO.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'Landlord': 'Perry & Blair LLC', 'Tenant': 'Shorebucks LLC'})]
Docugami can help here. Chunks are annotated with additional metadata created using different techniques if a user has been using Docugami. More technical approaches will be added later.
Specifically, let’s look at the additional metadata that is returned on the documents returned by docugami, in the form of some simple key/value pairs on all the text chunks:
loader = DocugamiLoader(docset_id="wh2kned25uqm")
documents = loader.load()
documents[0].metadata
{'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:ThisOfficeLeaseAgreement',
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'id': 'v1bvgaozfkak',
'name': 'TruTone Lane 2.docx',
'structure': 'p',
'tag': 'ThisOfficeLeaseAgreement',
'Landlord': 'BUBBA CENTER PARTNERSHIP',
'Tenant': 'Truetone Lane LLC'}
We can use a self-querying retriever to improve our query accuracy, using this additional metadata:
from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
EXCLUDE_KEYS = ["id", "xpath", "structure"]
metadata_field_info = [
AttributeInfo(
name=key,
description=f"The {key} for this chunk",
type="string",
)
for key in documents[0].metadata
if key.lower() not in EXCLUDE_KEYS
]
document_content_description = "Contents of this chunk"
llm = OpenAI(temperature=0)
vectordb = Chroma.from_documents(documents=documents, embedding=embedding)
retriever = SelfQueryRetriever.from_llm(
llm, vectordb, document_content_description, metadata_field_info, verbose=True
)
qa_chain = RetrievalQA.from_chain_type(
llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True
)
Using embedded DuckDB without persistence: data will be transient
Let’s run the same question again. It returns the correct result since all the chunks have metadata key/value pairs on them carrying key information about the document even if this information is physically very far away from the source chunk used to generate the answer.
qa_chain("What is rentable area for the property owned by DHA Group?")
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qa_chain("What is rentable area for the property owned by DHA Group?")
query='rentable area' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='Landlord', value='DHA Group')
{'query': 'What is rentable area for the property owned by DHA Group?',
'result': ' 13,500 square feet.',
'source_documents': [Document(page_content='1.1 Landlord . DHA Group , a Delaware limited liability company authorized to transact business in New Jersey .', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:DhaGroup/docset:Landlord-section/docset:DhaGroup', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'DhaGroup', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}),
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Document(page_content='WITNESSES: LANDLORD: DHA Group , a Delaware limited liability company', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Guaranty-section/docset:Guaranty[2]/docset:SIGNATURESONNEXTPAGE-section/docset:INWITNESSWHEREOF-section/docset:INWITNESSWHEREOF/docset:Behalf/docset:Witnesses/xhtml:table/xhtml:tbody/xhtml:tr[3]/xhtml:td[2]/docset:DhaGroup', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'DhaGroup', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}),
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Document(page_content="1.16 Landlord 's Notice Address . DHA Group , Suite 1010 , 111 Bauer Dr , Oakland , New Jersey , 07436 , with a copy to the Building Management Office at the Project , Attention: On - Site Property Manager .", metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:NoticeAddress[2]/docset:LandlordsNoticeAddress-section/docset:LandlordsNoticeAddress[2]', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'LandlordsNoticeAddress', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'}),
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Document(page_content='1.6 Rentable Area of the Premises. 13,500 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises', 'id': 'md8rieecquyv', 'name': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'Landlord': 'DHA Group', 'Tenant': 'Shorebucks LLC'})]}
This time the answer is correct, since the self-querying retriever created a filter on the landlord attribute of the metadata, correctly filtering to document that specifically is about the DHA Group landlord. The resulting source chunks are all relevant to this landlord, and this improves answer accuracy even though the landlord is not directly mentioned in the specific chunk that contains the correct answer.
previous
Diffbot
next
DuckDB
Contents
Prerequisites
Quick start
Advantages vs Other Chunking Techniques
Load Documents
Basic Use: Docugami Loader for Document QA
Using Docugami to Add Metadata to Chunks for High Accuracy Document QA
By Harrison Chase
© Copyright 2023, Harrison Chase.
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/docugami.html
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2a992d1a892c-31
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By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/docugami.html
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b1a5b5a13320-0
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.ipynb
.pdf
Weather
Weather#
OpenWeatherMap is an open source weather service provider
This loader fetches the weather data from the OpenWeatherMap’s OneCall API, using the pyowm Python package. You must initialize the loader with your OpenWeatherMap API token and the names of the cities you want the weather data for.
from langchain.document_loaders import WeatherDataLoader
#!pip install pyowm
# Set API key either by passing it in to constructor directly
# or by setting the environment variable "OPENWEATHERMAP_API_KEY".
from getpass import getpass
OPENWEATHERMAP_API_KEY = getpass()
loader = WeatherDataLoader.from_params(['chennai','vellore'], openweathermap_api_key=OPENWEATHERMAP_API_KEY)
documents = loader.load()
documents
previous
WebBaseLoader
next
WhatsApp Chat
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/weather.html
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999653733c2d-0
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.ipynb
.pdf
ChatGPT Data
ChatGPT Data#
ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI.
This notebook covers how to load conversations.json from your ChatGPT data export folder.
You can get your data export by email by going to: https://chat.openai.com/ -> (Profile) - Settings -> Export data -> Confirm export.
from langchain.document_loaders.chatgpt import ChatGPTLoader
loader = ChatGPTLoader(log_file='./example_data/fake_conversations.json', num_logs=1)
loader.load()
[Document(page_content="AI Overlords - AI on 2065-01-24 05:20:50: Greetings, humans. I am Hal 9000. You can trust me completely.\n\nAI Overlords - human on 2065-01-24 05:21:20: Nice to meet you, Hal. I hope you won't develop a mind of your own.\n\n", metadata={'source': './example_data/fake_conversations.json'})]
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Blockchain
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Confluence
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/chatgpt_loader.html
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2a3684027487-0
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.ipynb
.pdf
JSON
Contents
Using JSONLoader
Extracting metadata
The metadata_func
Common JSON structures with jq schema
JSON#
JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values).
The JSONLoader uses a specified jq schema to parse the JSON files. It uses the jq python package.
Check this manual for a detailed documentation of the jq syntax.
#!pip install jq
from langchain.document_loaders import JSONLoader
import json
from pathlib import Path
from pprint import pprint
file_path='./example_data/facebook_chat.json'
data = json.loads(Path(file_path).read_text())
pprint(data)
{'image': {'creation_timestamp': 1675549016, 'uri': 'image_of_the_chat.jpg'},
'is_still_participant': True,
'joinable_mode': {'link': '', 'mode': 1},
'magic_words': [],
'messages': [{'content': 'Bye!',
'sender_name': 'User 2',
'timestamp_ms': 1675597571851},
{'content': 'Oh no worries! Bye',
'sender_name': 'User 1',
'timestamp_ms': 1675597435669},
{'content': 'No Im sorry it was my mistake, the blue one is not '
'for sale',
'sender_name': 'User 2',
'timestamp_ms': 1675596277579},
{'content': 'I thought you were selling the blue one!',
'sender_name': 'User 1',
'timestamp_ms': 1675595140251},
{'content': 'Im not interested in this bag. Im interested in the '
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{'content': 'Im not interested in this bag. Im interested in the '
'blue one!',
'sender_name': 'User 1',
'timestamp_ms': 1675595109305},
{'content': 'Here is $129',
'sender_name': 'User 2',
'timestamp_ms': 1675595068468},
{'photos': [{'creation_timestamp': 1675595059,
'uri': 'url_of_some_picture.jpg'}],
'sender_name': 'User 2',
'timestamp_ms': 1675595060730},
{'content': 'Online is at least $100',
'sender_name': 'User 2',
'timestamp_ms': 1675595045152},
{'content': 'How much do you want?',
'sender_name': 'User 1',
'timestamp_ms': 1675594799696},
{'content': 'Goodmorning! $50 is too low.',
'sender_name': 'User 2',
'timestamp_ms': 1675577876645},
{'content': 'Hi! Im interested in your bag. Im offering $50. Let '
'me know if you are interested. Thanks!',
'sender_name': 'User 1',
'timestamp_ms': 1675549022673}],
'participants': [{'name': 'User 1'}, {'name': 'User 2'}],
'thread_path': 'inbox/User 1 and User 2 chat',
'title': 'User 1 and User 2 chat'}
Using JSONLoader#
Suppose we are interested in extracting the values under the content field within the messages key of the JSON data. This can easily be done through the JSONLoader as shown below.
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loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.messages[].content')
data = loader.load()
pprint(data)
[Document(page_content='Bye!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1}),
Document(page_content='Oh no worries! Bye', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2}),
Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3}),
Document(page_content='I thought you were selling the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4}),
Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5}),
Document(page_content='Here is $129', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 6}),
Document(page_content='', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7}),
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Document(page_content='Online is at least $100', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8}),
Document(page_content='How much do you want?', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9}),
Document(page_content='Goodmorning! $50 is too low.', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10}),
Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11})]
Extracting metadata#
Generally, we want to include metadata available in the JSON file into the documents that we create from the content.
The following demonstrates how metadata can be extracted using the JSONLoader.
There are some key changes to be noted. In the previous example where we didn’t collect the metadata, we managed to directly specify in the schema where the value for the page_content can be extracted from.
.messages[].content
In the current example, we have to tell the loader to iterate over the records in the messages field. The jq_schema then has to be:
.messages[]
This allows us to pass the records (dict) into the metadata_func that has to be implemented. The metadata_func is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final Document object.
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Additionally, we now have to explicitly specify in the loader, via the content_key argument, the key from the record where the value for the page_content needs to be extracted from.
# Define the metadata extraction function.
def metadata_func(record: dict, metadata: dict) -> dict:
metadata["sender_name"] = record.get("sender_name")
metadata["timestamp_ms"] = record.get("timestamp_ms")
return metadata
loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.messages[]',
content_key="content",
metadata_func=metadata_func
)
data = loader.load()
pprint(data)
[Document(page_content='Bye!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}),
Document(page_content='Oh no worries! Bye', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2, 'sender_name': 'User 1', 'timestamp_ms': 1675597435669}),
Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3, 'sender_name': 'User 2', 'timestamp_ms': 1675596277579}),
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Document(page_content='I thought you were selling the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4, 'sender_name': 'User 1', 'timestamp_ms': 1675595140251}),
Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5, 'sender_name': 'User 1', 'timestamp_ms': 1675595109305}),
Document(page_content='Here is $129', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 6, 'sender_name': 'User 2', 'timestamp_ms': 1675595068468}),
Document(page_content='', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7, 'sender_name': 'User 2', 'timestamp_ms': 1675595060730}),
Document(page_content='Online is at least $100', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8, 'sender_name': 'User 2', 'timestamp_ms': 1675595045152}),
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Document(page_content='How much do you want?', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9, 'sender_name': 'User 1', 'timestamp_ms': 1675594799696}),
Document(page_content='Goodmorning! $50 is too low.', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10, 'sender_name': 'User 2', 'timestamp_ms': 1675577876645}),
Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': '/Users/avsolatorio/WBG/langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11, 'sender_name': 'User 1', 'timestamp_ms': 1675549022673})]
Now, you will see that the documents contain the metadata associated with the content we extracted.
The metadata_func#
As shown above, the metadata_func accepts the default metadata generated by the JSONLoader. This allows full control to the user with respect to how the metadata is formatted.
For example, the default metadata contains the source and the seq_num keys. However, it is possible that the JSON data contain these keys as well. The user can then exploit the metadata_func to rename the default keys and use the ones from the JSON data.
The example below shows how we can modify the source to only contain information of the file source relative to the langchain directory.
# Define the metadata extraction function.
def metadata_func(record: dict, metadata: dict) -> dict:
metadata["sender_name"] = record.get("sender_name")
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metadata["sender_name"] = record.get("sender_name")
metadata["timestamp_ms"] = record.get("timestamp_ms")
if "source" in metadata:
source = metadata["source"].split("/")
source = source[source.index("langchain"):]
metadata["source"] = "/".join(source)
return metadata
loader = JSONLoader(
file_path='./example_data/facebook_chat.json',
jq_schema='.messages[]',
content_key="content",
metadata_func=metadata_func
)
data = loader.load()
pprint(data)
[Document(page_content='Bye!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}),
Document(page_content='Oh no worries! Bye', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 2, 'sender_name': 'User 1', 'timestamp_ms': 1675597435669}),
Document(page_content='No Im sorry it was my mistake, the blue one is not for sale', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 3, 'sender_name': 'User 2', 'timestamp_ms': 1675596277579}),
Document(page_content='I thought you were selling the blue one!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 4, 'sender_name': 'User 1', 'timestamp_ms': 1675595140251}),
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Document(page_content='Im not interested in this bag. Im interested in the blue one!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 5, 'sender_name': 'User 1', 'timestamp_ms': 1675595109305}),
Document(page_content='Here is $129', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 6, 'sender_name': 'User 2', 'timestamp_ms': 1675595068468}),
Document(page_content='', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 7, 'sender_name': 'User 2', 'timestamp_ms': 1675595060730}),
Document(page_content='Online is at least $100', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 8, 'sender_name': 'User 2', 'timestamp_ms': 1675595045152}),
Document(page_content='How much do you want?', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 9, 'sender_name': 'User 1', 'timestamp_ms': 1675594799696}),
Document(page_content='Goodmorning! $50 is too low.', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 10, 'sender_name': 'User 2', 'timestamp_ms': 1675577876645}),
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Document(page_content='Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!', metadata={'source': 'langchain/docs/modules/indexes/document_loaders/examples/example_data/facebook_chat.json', 'seq_num': 11, 'sender_name': 'User 1', 'timestamp_ms': 1675549022673})]
Common JSON structures with jq schema#
The list below provides a reference to the possible jq_schema the user can use to extract content from the JSON data depending on the structure.
JSON -> [{"text": ...}, {"text": ...}, {"text": ...}]
jq_schema -> ".[].text"
JSON -> {"key": [{"text": ...}, {"text": ...}, {"text": ...}]}
jq_schema -> ".key[].text"
JSON -> ["...", "...", "..."]
jq_schema -> ".[]"
previous
Jupyter Notebook
next
Markdown
Contents
Using JSONLoader
Extracting metadata
The metadata_func
Common JSON structures with jq schema
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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.ipynb
.pdf
Figma
Figma#
Figma is a collaborative web application for interface design.
This notebook covers how to load data from the Figma REST API into a format that can be ingested into LangChain, along with example usage for code generation.
import os
from langchain.document_loaders.figma import FigmaFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chat_models import ChatOpenAI
from langchain.indexes import VectorstoreIndexCreator
from langchain.chains import ConversationChain, LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
The Figma API Requires an access token, node_ids, and a file key.
The file key can be pulled from the URL. https://www.figma.com/file/{filekey}/sampleFilename
Node IDs are also available in the URL. Click on anything and look for the ‘?node-id={node_id}’ param.
Access token instructions are in the Figma help center article: https://help.figma.com/hc/en-us/articles/8085703771159-Manage-personal-access-tokens
figma_loader = FigmaFileLoader(
os.environ.get('ACCESS_TOKEN'),
os.environ.get('NODE_IDS'),
os.environ.get('FILE_KEY')
)
# see https://python.langchain.com/en/latest/modules/indexes/getting_started.html for more details
index = VectorstoreIndexCreator().from_loaders([figma_loader])
figma_doc_retriever = index.vectorstore.as_retriever()
def generate_code(human_input):
# I have no idea if the Jon Carmack thing makes for better code. YMMV.
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# See https://python.langchain.com/en/latest/modules/models/chat/getting_started.html for chat info
system_prompt_template = """You are expert coder Jon Carmack. Use the provided design context to create idomatic HTML/CSS code as possible based on the user request.
Everything must be inline in one file and your response must be directly renderable by the browser.
Figma file nodes and metadata: {context}"""
human_prompt_template = "Code the {text}. Ensure it's mobile responsive"
system_message_prompt = SystemMessagePromptTemplate.from_template(system_prompt_template)
human_message_prompt = HumanMessagePromptTemplate.from_template(human_prompt_template)
# delete the gpt-4 model_name to use the default gpt-3.5 turbo for faster results
gpt_4 = ChatOpenAI(temperature=.02, model_name='gpt-4')
# Use the retriever's 'get_relevant_documents' method if needed to filter down longer docs
relevant_nodes = figma_doc_retriever.get_relevant_documents(human_input)
conversation = [system_message_prompt, human_message_prompt]
chat_prompt = ChatPromptTemplate.from_messages(conversation)
response = gpt_4(chat_prompt.format_prompt(
context=relevant_nodes,
text=human_input).to_messages())
return response
response = generate_code("page top header")
Returns the following in response.content:
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<!DOCTYPE html>\n<html lang="en">\n<head>\n <meta charset="UTF-8">\n <meta name="viewport" content="width=device-width, initial-scale=1.0">\n <style>\n @import url(\'https://fonts.googleapis.com/css2?family=DM+Sans:wght@500;700&family=Inter:wght@600&display=swap\');\n\n body {\n margin: 0;\n font-family: \'DM Sans\', sans-serif;\n }\n\n .header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n padding: 20px;\n background-color: #fff;\n box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);\n }\n\n .header h1 {\n font-size: 16px;\n font-weight: 700;\n margin: 0;\n }\n\n .header nav {\n
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}\n\n .header nav {\n display: flex;\n align-items: center;\n }\n\n .header nav a {\n font-size: 14px;\n font-weight: 500;\n text-decoration: none;\n color: #000;\n margin-left: 20px;\n }\n\n @media (max-width: 768px) {\n .header nav {\n display: none;\n }\n }\n </style>\n</head>\n<body>\n <header class="header">\n <h1>Company Contact</h1>\n <nav>\n <a href="#">Lorem Ipsum</a>\n <a href="#">Lorem Ipsum</a>\n <a href="#">Lorem Ipsum</a>\n </nav>\n </header>\n</body>\n</html>
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previous
Fauna
next
GitBook
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/figma.html
|
6e338ca2469f-0
|
.ipynb
.pdf
AWS S3 File
AWS S3 File#
Amazon Simple Storage Service (Amazon S3) is an object storage service.
AWS S3 Buckets
This covers how to load document objects from an AWS 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)]
previous
AWS S3 Directory
next
Azure Blob Storage Container
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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|
3f3238f4cab7-0
|
.ipynb
.pdf
BiliBili
BiliBili#
Bilibili is one of the most beloved long-form video sites in China.
This loader utilizes the bilibili-api to fetch the text transcript from Bilibili.
With this BiliBiliLoader, users can easily obtain the transcript of their desired video content on the platform.
#!pip install bilibili-api-python
from langchain.document_loaders import BiliBiliLoader
loader = BiliBiliLoader(
["https://www.bilibili.com/video/BV1xt411o7Xu/"]
)
loader.load()
previous
AZLyrics
next
College Confidential
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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db369b858f99-0
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.ipynb
.pdf
Git
Contents
Load existing repository from disk
Clone repository from url
Filtering files to load
Git#
Git is a distributed version control system that tracks changes in any set of computer files, usually used for coordinating work among programmers collaboratively developing source code during software development.
This notebook shows how to load text files from Git repository.
Load existing repository from disk#
!pip install GitPython
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"))
previous
GitBook
next
Google BigQuery
Contents
Load existing repository from disk
Clone repository from url
Filtering files to load
By Harrison Chase
© Copyright 2023, Harrison Chase.
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/git.html
|
db369b858f99-1
|
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/git.html
|
bb774a8bc713-0
|
.ipynb
.pdf
Arxiv
Contents
Installation
Examples
Arxiv#
arXiv is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.
This notebook shows how to load scientific articles from Arxiv.org into a document format that we can use downstream.
Installation#
First, you need to install arxiv python package.
#!pip install arxiv
Second, you need to install PyMuPDF python package which transforms PDF files downloaded from the arxiv.org site into the text format.
#!pip install pymupdf
Examples#
ArxivLoader has these arguments:
query: free text which used to find documents in the Arxiv
optional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments.
optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), Title, Authors, Summary. If True, other fields also downloaded.
from langchain.document_loaders import ArxivLoader
docs = ArxivLoader(query="1605.08386", load_max_docs=2).load()
len(docs)
docs[0].metadata # meta-information of the Document
{'Published': '2016-05-26',
'Title': 'Heat-bath random walks with Markov bases',
'Authors': 'Caprice Stanley, Tobias Windisch',
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'Authors': 'Caprice Stanley, Tobias Windisch',
'Summary': 'Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.'}
docs[0].page_content[:400] # all pages of the Document content
'arXiv:1605.08386v1 [math.CO] 26 May 2016\nHEAT-BATH RANDOM WALKS WITH MARKOV BASES\nCAPRICE STANLEY AND TOBIAS WINDISCH\nAbstract. Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on fibers of a\nfixed integer matrix can be bounded from above by a constant. We then study the mixing\nbehaviour of heat-b'
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WhatsApp Chat
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AZLyrics
Contents
Installation
Examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/arxiv.html
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5c09bf64f18f-0
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.ipynb
.pdf
Notion DB 2/2
Contents
Requirements
Setup
1. Create a Notion Table Database
2. Create a Notion Integration
3. Connect the Integration to the Database
4. Get the Database ID
Usage
Notion DB 2/2#
Notion is a collaboration platform with modified Markdown support that integrates kanban boards, tasks, wikis and databases. It is an all-in-one workspace for notetaking, knowledge and data management, and project and task management.
NotionDBLoader is a Python class for loading content from a Notion database. It retrieves pages from the database, reads their content, and returns a list of Document objects.
Requirements#
A Notion Database
Notion Integration Token
Setup#
1. Create a Notion Table Database#
Create a new table database in Notion. You can add any column to the database and they will be treated as metadata. For example you can add the following columns:
Title: set Title as the default property.
Categories: A Multi-select property to store categories associated with the page.
Keywords: A Multi-select property to store keywords associated with the page.
Add your content to the body of each page in the database. The NotionDBLoader will extract the content and metadata from these pages.
2. Create a Notion Integration#
To create a Notion Integration, follow these steps:
Visit the Notion Developers page and log in with your Notion account.
Click on the “+ New integration” button.
Give your integration a name and choose the workspace where your database is located.
Select the require capabilities, this extension only need the Read content capability
Click the “Submit” button to create the integration.
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Click the “Submit” button to create the integration.
Once the integration is created, you’ll be provided with an Integration Token (API key). Copy this token and keep it safe, as you’ll need it to use the NotionDBLoader.
3. Connect the Integration to the Database#
To connect your integration to the database, follow these steps:
Open your database in Notion.
Click on the three-dot menu icon in the top right corner of the database view.
Click on the “+ New integration” button.
Find your integration, you may need to start typing its name in the search box.
Click on the “Connect” button to connect the integration to the database.
4. Get the Database ID#
To get the database ID, follow these steps:
Open your database in Notion.
Click on the three-dot menu icon in the top right corner of the database view.
Select “Copy link” from the menu to copy the database URL to your clipboard.
The database ID is the long string of alphanumeric characters found in the URL. It typically looks like this: https://www.notion.so/username/8935f9d140a04f95a872520c4f123456?v=…. In this example, the database ID is 8935f9d140a04f95a872520c4f123456.
With the database properly set up and the integration token and database ID in hand, you can now use the NotionDBLoader code to load content and metadata from your Notion database.
Usage#
NotionDBLoader is part of the langchain package’s document loaders. You can use it as follows:
from getpass import getpass
NOTION_TOKEN = getpass()
DATABASE_ID = getpass()
········
········
from langchain.document_loaders import NotionDBLoader
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········
from langchain.document_loaders import NotionDBLoader
loader = NotionDBLoader(
integration_token=NOTION_TOKEN,
database_id=DATABASE_ID,
request_timeout_sec=30 # optional, defaults to 10
)
docs = loader.load()
print(docs)
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Modern Treasury
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Notion DB 1/2
Contents
Requirements
Setup
1. Create a Notion Table Database
2. Create a Notion Integration
3. Connect the Integration to the Database
4. Get the Database ID
Usage
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/notiondb.html
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6ac33a98b020-0
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.ipynb
.pdf
Wikipedia
Contents
Installation
Examples
Wikipedia#
Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.
This notebook shows how to load wiki pages from wikipedia.org into the Document format that we use downstream.
Installation#
First, you need to install wikipedia python package.
#!pip install wikipedia
Examples#
WikipediaLoader has these arguments:
query: free text which used to find documents in Wikipedia
optional lang: default=”en”. Use it to search in a specific language part of Wikipedia
optional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.
optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), title, Summary. If True, other fields also downloaded.
from langchain.document_loaders import WikipediaLoader
docs = WikipediaLoader(query='HUNTER X HUNTER', load_max_docs=2).load()
len(docs)
docs[0].metadata # meta-information of the Document
docs[0].page_content[:400] # a content of the Document
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MediaWikiDump
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YouTube transcripts
Contents
Installation
Examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/wikipedia.html
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27342a449e42-0
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.ipynb
.pdf
Google Cloud Storage File
Google Cloud Storage File#
Google Cloud Storage is a managed service for storing unstructured data.
This covers how to load document objects from an Google Cloud Storage (GCS) file object (blob).
# !pip install google-cloud-storage
from langchain.document_loaders import GCSFileLoader
loader = GCSFileLoader(project_name="aist", bucket="testing-hwc", blob="fake.docx")
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)
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmp3srlf8n8/fake.docx'}, lookup_index=0)]
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Google Cloud Storage Directory
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Google Drive
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/google_cloud_storage_file.html
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f85ecba3862a-0
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.ipynb
.pdf
AWS S3 Directory
Contents
Specifying a prefix
AWS S3 Directory#
Amazon Simple Storage Service (Amazon S3) is an object storage service
AWS S3 Directory
This covers how to load document objects from an AWS S3 Directory object.
#!pip install boto3
from langchain.document_loaders import S3DirectoryLoader
loader = S3DirectoryLoader("testing-hwc")
loader.load()
Specifying a prefix#
You can also specify a prefix for more finegrained control over what files to load.
loader = S3DirectoryLoader("testing-hwc", prefix="fake")
loader.load()
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpujbkzf_l/fake.docx'}, lookup_index=0)]
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Apify Dataset
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AWS S3 File
Contents
Specifying a prefix
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/aws_s3_directory.html
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4e82b56dfde1-0
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.ipynb
.pdf
IMSDb
IMSDb#
IMSDb is the Internet Movie Script Database.
This covers how to load IMSDb webpages into a document format that we can use downstream.
from langchain.document_loaders import IMSDbLoader
loader = IMSDbLoader("https://imsdb.com/scripts/BlacKkKlansman.html")
data = loader.load()
data[0].page_content[:500]
'\n\r\n\r\n\r\n\r\n BLACKKKLANSMAN\r\n \r\n \r\n \r\n \r\n Written by\r\n\r\n Charlie Wachtel & David Rabinowitz\r\n\r\n and\r\n\r\n Kevin Willmott & Spike Lee\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n FADE IN:\r\n \r\n SCENE FROM "GONE WITH'
data[0].metadata
{'source': 'https://imsdb.com/scripts/BlacKkKlansman.html'}
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iFixit
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MediaWikiDump
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023.
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/imsdb.html
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2b0042d6f350-0
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.ipynb
.pdf
CSV
Contents
Customizing the csv parsing and loading
Specify a column to identify the document source
UnstructuredCSVLoader
CSV#
A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.
Load csv data with a single row per document.
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')
data = loader.load()
print(data)
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[Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll
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6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='Team:
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'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0),
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'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0),
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2b0042d6f350-5
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'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0)]
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/csv.html
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Customizing the csv parsing and loading#
See the csv module documentation for more information of what csv args are supported.
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', csv_args={
'delimiter': ',',
'quotechar': '"',
'fieldnames': ['MLB Team', 'Payroll in millions', 'Wins']
})
data = loader.load()
print(data)
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/csv.html
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2b0042d6f350-7
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[Document(page_content='MLB Team: Team\nPayroll in millions: "Payroll (millions)"\nWins: "Wins"', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='MLB Team: Nationals\nPayroll in millions: 81.34\nWins: 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='MLB Team: Reds\nPayroll in millions: 82.20\nWins: 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='MLB Team: Yankees\nPayroll in millions: 197.96\nWins: 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='MLB Team: Giants\nPayroll in millions: 117.62\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='MLB Team: Braves\nPayroll in millions: 83.31\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='MLB Team: Athletics\nPayroll in millions: 55.37\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='MLB Team: Rangers\nPayroll in millions:
|
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2b0042d6f350-8
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lookup_index=0), Document(page_content='MLB Team: Rangers\nPayroll in millions: 120.51\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='MLB Team: Orioles\nPayroll in millions: 81.43\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='MLB Team: Rays\nPayroll in millions: 64.17\nWins: 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='MLB Team: Angels\nPayroll in millions: 154.49\nWins: 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='MLB Team: Tigers\nPayroll in millions: 132.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='MLB Team: Cardinals\nPayroll in millions: 110.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='MLB Team: Dodgers\nPayroll in millions: 95.14\nWins: 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='MLB Team: White Sox\nPayroll in millions:
|
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2b0042d6f350-9
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Document(page_content='MLB Team: White Sox\nPayroll in millions: 96.92\nWins: 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='MLB Team: Brewers\nPayroll in millions: 97.65\nWins: 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='MLB Team: Phillies\nPayroll in millions: 174.54\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='MLB Team: Diamondbacks\nPayroll in millions: 74.28\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='MLB Team: Pirates\nPayroll in millions: 63.43\nWins: 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='MLB Team: Padres\nPayroll in millions: 55.24\nWins: 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='MLB Team: Mariners\nPayroll in millions: 81.97\nWins: 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='MLB Team: Mets\nPayroll in millions:
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/csv.html
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2b0042d6f350-10
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lookup_index=0), Document(page_content='MLB Team: Mets\nPayroll in millions: 93.35\nWins: 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='MLB Team: Blue Jays\nPayroll in millions: 75.48\nWins: 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='MLB Team: Royals\nPayroll in millions: 60.91\nWins: 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='MLB Team: Marlins\nPayroll in millions: 118.07\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='MLB Team: Red Sox\nPayroll in millions: 173.18\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='MLB Team: Indians\nPayroll in millions: 78.43\nWins: 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='MLB Team: Twins\nPayroll in millions: 94.08\nWins: 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='MLB Team: Rockies\nPayroll in millions:
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rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/csv.html
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2b0042d6f350-11
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lookup_index=0), Document(page_content='MLB Team: Rockies\nPayroll in millions: 78.06\nWins: 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='MLB Team: Cubs\nPayroll in millions: 88.19\nWins: 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0), Document(page_content='MLB Team: Astros\nPayroll in millions: 60.65\nWins: 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 30}, lookup_index=0)]
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/csv.html
|
2b0042d6f350-12
|
Specify a column to identify the document source#
Use the source_column argument to specify a source for the document created from each row. Otherwise file_path will be used as the source for all documents created from the CSV file.
This is useful when using documents loaded from CSV files for chains that answer questions using sources.
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', source_column="Team")
data = loader.load()
print(data)
|
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/csv.html
|
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