\n\n                        Virtual Tour\n                        \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBrown Application Deadline\n\n\n\nFirst-Year Applications are Due\n\nJan 5\n\nTransfer Applications are Due\n\nMar 1\n\n\n\n            \n                The deadline for Fall first-year applications to Brown is \n                Jan 5. \n                \n            \n          \n\n            \n                The deadline for Fall transfer applications to Brown is \n                Mar 1. \n                \n            \n          \n\n            \n            Check the school website \n            for more information about deadlines for specific programs


for more information about deadlines for specific programs or special admissions programs\n            \n          \n\n\n\n\n\n\nBrown ACT Scores\n\n\n\n\nic_reflect\n\n\n\n\n\n\n\n\nACT Range\n\n\n                  \n                    33 - 35\n                  \n                \n\n\n\nEstimated Chance of Acceptance by ACT Score\n\n\nACT Score\nEstimated Chance\n\n\n35 and Above\nGood\n\n\n33 to 35\nAvg\n\n\n33 and Less\nLow\n\n\n\n\n\n\nStand out on your college application\n\n• Qualify for scholarships\n• Most students who retest improve their score\n\nSponsored by ACT\n\n\n            Take the Next ACT Test\n        \n\n\n\n\n\nBrown SAT Scores\n\n\n\n\nic_reflect\n\n\n\n\n\n\n\n\nComposite SAT Range\n\n\n                    \n                        720 -


720 - 770\n                    \n                \n\n\n\nic_reflect\n\n\n\n\n\n\n\n\nMath SAT Range\n\n\n                    \n                        Not available\n                    \n                \n\n\n\nic_reflect\n\n\n\n\n\n\n\n\nReading SAT Range\n\n\n                    \n                        740 - 800\n                    \n                \n\n\n\n\n\n\n        Brown Tuition & Fees\n    \n\n\n\nTuition & Fees\n\n\n\n                        $82,286\n                    \nIn State\n\n\n\n\n


$82,286\n                    \nOut-of-State\n\n\n\n\n\n\n\nCost Breakdown\n\n\nIn State\n\n\nOut-of-State\n\n\n\n\nState Tuition\n\n\n\n                            $62,680\n                        \n\n\n\n                            $62,680\n                        \n\n\n\n\nFees\n\n\n\n                            $2,466\n                        \n\n\n\n                            $2,466\n                        \n\n\n\n\nHousing\n\n\n\n                            $15,840\n


\n\n\n\n                            $15,840\n                        \n\n\n\n\nBooks\n\n\n\n                            $1,300\n                        \n\n\n\n                            $1,300\n                        \n\n\n\n\n\n                            Total (Before Financial Aid):\n                        \n\n\n\n                            $82,286\n                        \n\n\n\n                            $82,286\n


\n\n\n\n\n\n\n\n\n\n\n\nStudent Life\n\n        Wondering what life at Brown is like? There are approximately \n        10,696 students enrolled at \n        Brown, \n        including 7,349 undergraduate students and \n        3,347  graduate students.\n        96% percent of students attend school \n        full-time, \n        6% percent are from RI and \n            94% percent of students are from other states.\n    \n\n\n\n\n\n                        None\n                    \n\n\n\n\nUndergraduate Enrollment\n\n\n\n                        96%\n                    \nFull Time\n\n\n\n\n                        4%\n


4%\n                    \nPart Time\n\n\n\n\n\n\n\n                        94%\n                    \n\n\n\n\nResidency\n\n\n\n                        6%\n                    \nIn State\n\n\n\n\n                        94%\n                    \nOut-of-State\n\n\n\n\n\n\n\n                Data Source: IPEDs and Peterson\'s Databases © 2022 Peterson\'s LLC All rights reserved\n            \n', lookup_str='', metadata={'source': 'https://www.collegeconfidential.com/colleges/brown-university/'}, lookup_index=0)]


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BiliBili
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Gutenberg
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Copy Paste
 Contents
Metadata
Copy Paste#
This notebook covers how to load a document object from something you just want to copy and paste. In this case, you don’t even need to use a DocumentLoader, but rather can just construct the Document directly.
from langchain.docstore.document import Document
text = "..... put the text you copy pasted here......"
doc = Document(page_content=text)
Metadata#
If you want to add metadata about the where you got this piece of text, you easily can with the metadata key.
metadata = {"source": "internet", "date": "Friday"}
doc = Document(page_content=text, metadata=metadata)
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CoNLL-U
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CSV
 Contents

Metadata
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Pandas DataFrame
Pandas DataFrame#
This notebook goes over how to load data from a pandas DataFrame.
#!pip install pandas
import pandas as pd
df = pd.read_csv('example_data/mlb_teams_2012.csv')
df.head()
Team
"Payroll (millions)"
"Wins"
0
Nationals
81.34
98
1
Reds
82.20
97
2
Yankees
197.96
95
3
Giants
117.62
94
4
Braves
83.31
94
from langchain.document_loaders import DataFrameLoader
loader = DataFrameLoader(df, page_content_column="Team")
loader.load()
[Document(page_content='Nationals', metadata={' "Payroll (millions)"': 81.34, ' "Wins"': 98}),
 Document(page_content='Reds', metadata={' "Payroll (millions)"': 82.2, ' "Wins"': 97}),
 Document(page_content='Yankees', metadata={' "Payroll (millions)"': 197.96, ' "Wins"': 95}),
 Document(page_content='Giants', metadata={' "Payroll (millions)"': 117.62, ' "Wins"': 94}),
 Document(page_content='Braves', metadata={' "Payroll (millions)"': 83.31, ' "Wins"': 94}),
 Document(page_content='Athletics', metadata={' "Payroll (millions)"': 55.37, ' "Wins"': 94}),
 Document(page_content='Rangers', metadata={' "Payroll (millions)"': 120.51, ' "Wins"': 93}),


Document(page_content='Orioles', metadata={' "Payroll (millions)"': 81.43, ' "Wins"': 93}),
 Document(page_content='Rays', metadata={' "Payroll (millions)"': 64.17, ' "Wins"': 90}),
 Document(page_content='Angels', metadata={' "Payroll (millions)"': 154.49, ' "Wins"': 89}),
 Document(page_content='Tigers', metadata={' "Payroll (millions)"': 132.3, ' "Wins"': 88}),
 Document(page_content='Cardinals', metadata={' "Payroll (millions)"': 110.3, ' "Wins"': 88}),
 Document(page_content='Dodgers', metadata={' "Payroll (millions)"': 95.14, ' "Wins"': 86}),
 Document(page_content='White Sox', metadata={' "Payroll (millions)"': 96.92, ' "Wins"': 85}),
 Document(page_content='Brewers', metadata={' "Payroll (millions)"': 97.65, ' "Wins"': 83}),
 Document(page_content='Phillies', metadata={' "Payroll (millions)"': 174.54, ' "Wins"': 81}),
 Document(page_content='Diamondbacks', metadata={' "Payroll (millions)"': 74.28, ' "Wins"': 81}),
 Document(page_content='Pirates', metadata={' "Payroll (millions)"': 63.43, ' "Wins"': 79}),
 Document(page_content='Padres', metadata={' "Payroll (millions)"': 55.24, ' "Wins"': 76}),


Document(page_content='Mariners', metadata={' "Payroll (millions)"': 81.97, ' "Wins"': 75}),
 Document(page_content='Mets', metadata={' "Payroll (millions)"': 93.35, ' "Wins"': 74}),
 Document(page_content='Blue Jays', metadata={' "Payroll (millions)"': 75.48, ' "Wins"': 73}),
 Document(page_content='Royals', metadata={' "Payroll (millions)"': 60.91, ' "Wins"': 72}),
 Document(page_content='Marlins', metadata={' "Payroll (millions)"': 118.07, ' "Wins"': 69}),
 Document(page_content='Red Sox', metadata={' "Payroll (millions)"': 173.18, ' "Wins"': 69}),
 Document(page_content='Indians', metadata={' "Payroll (millions)"': 78.43, ' "Wins"': 68}),
 Document(page_content='Twins', metadata={' "Payroll (millions)"': 94.08, ' "Wins"': 66}),
 Document(page_content='Rockies', metadata={' "Payroll (millions)"': 78.06, ' "Wins"': 64}),
 Document(page_content='Cubs', metadata={' "Payroll (millions)"': 88.19, ' "Wins"': 61}),
 Document(page_content='Astros', metadata={' "Payroll (millions)"': 60.65, ' "Wins"': 55})]
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Open Document Format (ODT)
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PDF
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
HuggingFace dataset
 Contents
Example
HuggingFace dataset#
The Hugging Face Hub is home to over 5,000 datasets in more than 100 languages that can be used for a broad range of tasks across NLP, Computer Vision, and Audio. They used for a diverse range of tasks such as translation,
automatic speech recognition, and image classification.
This notebook shows how to load Hugging Face Hub datasets to LangChain.
from langchain.document_loaders import HuggingFaceDatasetLoader
dataset_name="imdb"
page_content_column="text"
loader=HuggingFaceDatasetLoader(dataset_name,page_content_column)
data = loader.load()
data[:15]


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

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

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

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


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


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

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

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

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

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

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


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

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

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


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

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

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

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

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


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

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


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

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


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

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

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


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

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


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

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


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

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

All in all, though, the movie is harmless,


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


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


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

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

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

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

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


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


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


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


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


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

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


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

Example
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Markdown
 Contents
Retain Elements
Markdown#
Markdown is a lightweight markup language for creating formatted text using a plain-text editor.
This covers how to load markdown documents into a document format that we can use downstream.
# !pip install unstructured > /dev/null
from langchain.document_loaders import UnstructuredMarkdownLoader
markdown_path = "../../../../../README.md"
loader = UnstructuredMarkdownLoader(markdown_path)
data = loader.load()
data


[Document(page_content="ð\x9f¦\x9cï¸\x8fð\x9f”\x97 LangChain\n\nâ\x9a¡ Building applications with LLMs through composability â\x9a¡\n\nLooking for the JS/TS version? Check out LangChain.js.\n\nProduction Support: As you move your LangChains into production, we'd love to offer more comprehensive support.\nPlease fill out this form and we'll set up a dedicated support Slack channel.\n\nQuick Install\n\npip install langchain\nor\nconda install langchain -c conda-forge\n\nð\x9f¤” What is this?\n\nLarge language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.\n\nThis library aims to assist in the development of those types of applications. Common examples of these applications include:\n\nâ\x9d“ Question Answering over specific documents\n\nDocumentation\n\nEnd-to-end Example: Question Answering over Notion


Example: Question Answering over Notion Database\n\nð\x9f’¬ Chatbots\n\nDocumentation\n\nEnd-to-end Example: Chat-LangChain\n\nð\x9f¤\x96 Agents\n\nDocumentation\n\nEnd-to-end Example: GPT+WolframAlpha\n\nð\x9f“\x96 Documentation\n\nPlease see here for full documentation on:\n\nGetting started (installation, setting up the environment, simple examples)\n\nHow-To examples (demos, integrations, helper functions)\n\nReference (full API docs)\n\nResources (high-level explanation of core concepts)\n\nð\x9f\x9a\x80 What can this help with?\n\nThere are six main areas that LangChain is designed to help with.\nThese are, in increasing order of complexity:\n\nð\x9f“\x83 LLMs and Prompts:\n\nThis includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.\n\nð\x9f”\x97 Chains:\n\nChains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and


for chains, lots of integrations with other tools, and end-to-end chains for common applications.\n\nð\x9f“\x9a Data Augmented Generation:\n\nData Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.\n\nð\x9f¤\x96 Agents:\n\nAgents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.\n\nð\x9f§\xa0 Memory:\n\nMemory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.\n\nð\x9f§\x90 Evaluation:\n\n[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language


One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\n\nFor more information on these concepts, please see our full documentation.\n\nð\x9f’\x81 Contributing\n\nAs an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.\n\nFor detailed information on how to contribute, see here.", metadata={'source': '../../../../../README.md'})]


Retain Elements#
Under the hood, Unstructured creates different “elements” for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".
loader = UnstructuredMarkdownLoader(markdown_path, mode="elements")
data = loader.load()
data[0]
Document(page_content='ð\x9f¦\x9cï¸\x8fð\x9f”\x97 LangChain', metadata={'source': '../../../../../README.md', 'page_number': 1, 'category': 'Title'})
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JSON
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Microsoft PowerPoint
 Contents

Retain Elements
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Microsoft OneDrive
 Contents
Prerequisites
🧑 Instructions for ingesting your documents from OneDrive
🔑 Authentication
🗂 Documents loader
📑 Loading documents from a OneDrive Directory
📑 Loading documents from a list of Documents IDs
Microsoft OneDrive#
Microsoft OneDrive (formerly SkyDrive) is a file hosting service operated by Microsoft.
This notebook covers how to load documents from OneDrive. Currently, only docx, doc, and pdf files are supported.
Prerequisites#
Register an application with the Microsoft identity platform instructions.
When registration finishes, the Azure portal displays the app registration’s Overview pane. You see the Application (client) ID. Also called the client ID, this value uniquely identifies your application in the Microsoft identity platform.
During the steps you will be following at item 1, you can set the redirect URI as http://localhost:8000/callback
During the steps you will be following at item 1, generate a new password (client_secret) under Application Secrets section.
Follow the instructions at this document to add the following SCOPES (offline_access and Files.Read.All) to your application.
Visit the Graph Explorer Playground to obtain your OneDrive ID. The first step is to ensure you are logged in with the account associated your OneDrive account. Then you need to make a request to https://graph.microsoft.com/v1.0/me/drive and the response will return a payload with a field id that holds the ID of your OneDrive account.
You need to install the o365 package using the command pip install o365.
At the end of the steps you must have the following values:
CLIENT_ID
CLIENT_SECRET
DRIVE_ID
🧑 Instructions for ingesting your documents from OneDrive#
🔑 Authentication#


🧑 Instructions for ingesting your documents from OneDrive#
🔑 Authentication#
By default, the OneDriveLoader expects that the values of CLIENT_ID and CLIENT_SECRET must be stored as environment variables named O365_CLIENT_ID and O365_CLIENT_SECRET respectively. You could pass those environment variables through a .env file at the root of your application or using the following command in your script.
os.environ['O365_CLIENT_ID'] = "YOUR CLIENT ID"
os.environ['O365_CLIENT_SECRET'] = "YOUR CLIENT SECRET"
This loader uses an authentication called on behalf of a user. It is a 2 step authentication with user consent. When you instantiate the loader, it will call will print a url that the user must visit to give consent to the app on the required permissions. The user must then visit this url and give consent to the application. Then the user must copy the resulting page url and paste it back on the console. The method will then return True if the login attempt was succesful.
from langchain.document_loaders.onedrive import OneDriveLoader
loader = OneDriveLoader(drive_id="YOUR DRIVE ID")
Once the authentication has been done, the loader will store a token (o365_token.txt) at ~/.credentials/ folder. This token could be used later to authenticate without the copy/paste steps explained earlier. To use this token for authentication, you need to change the auth_with_token parameter to True in the instantiation of the loader.
from langchain.document_loaders.onedrive import OneDriveLoader
loader = OneDriveLoader(drive_id="YOUR DRIVE ID", auth_with_token=True)
🗂 Documents loader#
📑 Loading documents from a OneDrive Directory#
OneDriveLoader can load documents from a specific folder within your OneDrive. For instance, you want to load all documents that are stored at Documents/clients folder within your OneDrive.


from langchain.document_loaders.onedrive import OneDriveLoader
loader = OneDriveLoader(drive_id="YOUR DRIVE ID", folder_path="Documents/clients", auth_with_token=True)
documents = loader.load()
📑 Loading documents from a list of Documents IDs#
Another possibility is to provide a list of object_id for each document you want to load. For that, you will need to query the Microsoft Graph API to find all the documents ID that you are interested in. This link provides a list of endpoints that will be helpful to retrieve the documents ID.
For instance, to retrieve information about all objects that are stored at the root of the Documents folder, you need make a request to: https://graph.microsoft.com/v1.0/drives/{YOUR DRIVE ID}/root/children. Once you have the list of IDs that you are interested in, then you can instantiate the loader with the following parameters.
from langchain.document_loaders.onedrive import OneDriveLoader
loader = OneDriveLoader(drive_id="YOUR DRIVE ID", object_ids=["ID_1", "ID_2"], auth_with_token=True)
documents = loader.load()
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Joplin
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Modern Treasury
 Contents

Prerequisites
🧑 Instructions for ingesting your documents from OneDrive
🔑 Authentication
🗂 Documents loader
📑 Loading documents from a OneDrive Directory
📑 Loading documents from a list of Documents IDs
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Sitemap
 Contents
Filtering sitemap URLs
Local Sitemap
Sitemap#
Extends from the WebBaseLoader, SitemapLoader loads a sitemap from a given URL, and then scrape and load all pages in the sitemap, returning each page as a Document.
The scraping is done 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 scrapped server, or 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 it may cause the server to block you.  Be careful!
!pip install nest_asyncio
Requirement already satisfied: nest_asyncio in /Users/tasp/Code/projects/langchain/.venv/lib/python3.10/site-packages (1.5.6)
[notice] A new release of pip available: 22.3.1 -> 23.0.1
[notice] To update, run: pip install --upgrade pip
# fixes a bug with asyncio and jupyter
import nest_asyncio
nest_asyncio.apply()
from langchain.document_loaders.sitemap import SitemapLoader
sitemap_loader = SitemapLoader(web_path="https://langchain.readthedocs.io/sitemap.xml")
docs = sitemap_loader.load()
docs[0]


Document(page_content='\n\n\n\n\n\nWelcome to LangChain — 🦜🔗 LangChain 0.0.123\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\n\n\n\n\n\nCtrl+K\n\n\n\n\n\n\n\n\n\n\n\n\n🦜🔗 LangChain 0.0.123\n\n\n\nGetting Started\n\nQuickstart Guide\n\nModules\n\nPrompt Templates\nGetting Started\nKey Concepts\nHow-To Guides\nCreate a custom prompt template\nCreate a custom example selector\nProvide few shot examples to a prompt\nPrompt Serialization\nExample Selectors\nOutput Parsers\n\n\nReference\nPromptTemplates\nExample Selector\n\n\n\n\nLLMs\nGetting Started\nKey Concepts\nHow-To Guides\nGeneric Functionality\nCustom LLM\nFake LLM\nLLM Caching\nLLM Serialization\nToken Usage Tracking\n\n\nIntegrations\nAI21\nAleph Alpha\nAnthropic\nAzure OpenAI LLM Example\nBanana\nCerebriumAI LLM Example\nCohere\nDeepInfra LLM Example\nForefrontAI LLM Example\nGooseAI LLM Example\nHugging Face Hub\nManifest\nModal\nOpenAI\nPetals LLM Example\nPromptLayer OpenAI\nSageMakerEndpoint\nSelf-Hosted Models via


OpenAI\nSageMakerEndpoint\nSelf-Hosted Models via Runhouse\nStochasticAI\nWriter\n\n\nAsync API for LLM\nStreaming with LLMs\n\n\nReference\n\n\nDocument Loaders\nKey Concepts\nHow To Guides\nCoNLL-U\nAirbyte JSON\nAZLyrics\nBlackboard\nCollege Confidential\nCopy Paste\nCSV Loader\nDirectory Loader\nEmail\nEverNote\nFacebook Chat\nFigma\nGCS Directory\nGCS File Storage\nGitBook\nGoogle Drive\nGutenberg\nHacker News\nHTML\niFixit\nImages\nIMSDb\nMarkdown\nNotebook\nNotion\nObsidian\nPDF\nPowerPoint\nReadTheDocs Documentation\nRoam\ns3 Directory\ns3 File\nSubtitle Files\nTelegram\nUnstructured File Loader\nURL\nWeb Base\nWord Documents\nYouTube\n\n\n\n\nUtils\nKey Concepts\nGeneric Utilities\nBash\nBing Search\nGoogle Search\nGoogle Serper API\nIFTTT WebHooks\nPython REPL\nRequests\nSearxNG Search API\nSerpAPI\nWolfram Alpha\nZapier Natural Language Actions API\n\n\nReference\nPython REPL\nSerpAPI\nSearxNG Search\nDocstore\nText Splitter\nEmbeddings\nVectorStores\n\n\n\n\nIndexes\nGetting Started\nKey Concepts\nHow To Guides\nEmbeddings\nHypothetical Document Embeddings\nText Splitter\nVectorStores\nAtlasDB\nChroma\nDeep


Document Embeddings\nText Splitter\nVectorStores\nAtlasDB\nChroma\nDeep Lake\nElasticSearch\nFAISS\nMilvus\nOpenSearch\nPGVector\nPinecone\nQdrant\nRedis\nWeaviate\nChatGPT Plugin Retriever\nVectorStore Retriever\nAnalyze Document\nChat Index\nGraph QA\nQuestion Answering with Sources\nQuestion Answering\nSummarization\nRetrieval Question/Answering\nRetrieval Question Answering with Sources\nVector DB Text Generation\n\n\n\n\nChains\nGetting Started\nHow-To Guides\nGeneric Chains\nLoading from LangChainHub\nLLM Chain\nSequential Chains\nSerialization\nTransformation Chain\n\n\nUtility Chains\nAPI Chains\nSelf-Critique Chain with Constitutional AI\nBashChain\nLLMCheckerChain\nLLM Math\nLLMRequestsChain\nLLMSummarizationCheckerChain\nModeration\nPAL\nSQLite example\n\n\nAsync API for Chain\n\n\nKey Concepts\nReference\n\n\nAgents\nGetting Started\nKey Concepts\nHow-To Guides\nAgents and Vectorstores\nAsync API for Agent\nConversation Agent (for Chat Models)\nChatGPT Plugins\nCustom Agent\nDefining Custom Tools\nHuman as a tool\nIntermediate Steps\nLoading from LangChainHub\nMax Iterations\nMulti Input Tools\nSearch Tools\nSerialization\nAdding SharedMemory to an Agent and its Tools\nCSV Agent\nJSON Agent\nOpenAPI Agent\nPandas Dataframe Agent\nPython


Agent\nJSON Agent\nOpenAPI Agent\nPandas Dataframe Agent\nPython Agent\nSQL Database Agent\nVectorstore Agent\nMRKL\nMRKL Chat\nReAct\nSelf Ask With Search\n\n\nReference\n\n\nMemory\nGetting Started\nKey Concepts\nHow-To Guides\nConversationBufferMemory\nConversationBufferWindowMemory\nEntity Memory\nConversation Knowledge Graph Memory\nConversationSummaryMemory\nConversationSummaryBufferMemory\nConversationTokenBufferMemory\nAdding Memory To an LLMChain\nAdding Memory to a Multi-Input Chain\nAdding Memory to an Agent\nChatGPT Clone\nConversation Agent\nConversational Memory Customization\nCustom Memory\nMultiple Memory\n\n\n\n\nChat\nGetting Started\nKey Concepts\nHow-To Guides\nAgent\nChat Vector DB\nFew Shot Examples\nMemory\nPromptLayer ChatOpenAI\nStreaming\nRetrieval Question/Answering\nRetrieval Question Answering with Sources\n\n\n\n\n\nUse Cases\n\nAgents\nChatbots\nGenerate Examples\nData Augmented Generation\nQuestion Answering\nSummarization\nQuerying Tabular Data\nExtraction\nEvaluation\nAgent Benchmarking: Search + Calculator\nAgent VectorDB Question Answering Benchmarking\nBenchmarking Template\nData Augmented Question Answering\nUsing Hugging Face Datasets\nLLM Math\nQuestion Answering Benchmarking: Paul Graham Essay\nQuestion Answering Benchmarking: State of the Union Address\nQA Generation\nQuestion Answering\nSQL Question Answering Benchmarking:


Generation\nQuestion Answering\nSQL Question Answering Benchmarking: Chinook\n\n\nModel Comparison\n\nReference\n\nInstallation\nIntegrations\nAPI References\nPrompts\nPromptTemplates\nExample Selector\n\n\nUtilities\nPython REPL\nSerpAPI\nSearxNG Search\nDocstore\nText Splitter\nEmbeddings\nVectorStores\n\n\nChains\nAgents\n\n\n\nEcosystem\n\nLangChain Ecosystem\nAI21 Labs\nAtlasDB\nBanana\nCerebriumAI\nChroma\nCohere\nDeepInfra\nDeep Lake\nForefrontAI\nGoogle Search Wrapper\nGoogle Serper Wrapper\nGooseAI\nGraphsignal\nHazy Research\nHelicone\nHugging Face\nMilvus\nModal\nNLPCloud\nOpenAI\nOpenSearch\nPetals\nPGVector\nPinecone\nPromptLayer\nQdrant\nRunhouse\nSearxNG Search API\nSerpAPI\nStochasticAI\nUnstructured\nWeights & Biases\nWeaviate\nWolfram Alpha Wrapper\nWriter\n\n\n\nAdditional Resources\n\nLangChainHub\nGlossary\nLangChain Gallery\nDeployments\nTracing\nDiscord\nProduction Support\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.rst\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWelcome to LangChain\n\n\n\n\n Contents \n\n\n\nGetting Started\nModules\nUse Cases\nReference Docs\nLangChain Ecosystem\nAdditional


Started\nModules\nUse Cases\nReference Docs\nLangChain Ecosystem\nAdditional Resources\n\n\n\n\n\n\n\n\nWelcome to LangChain#\nLarge language models (LLMs) are emerging as a transformative technology, enabling\ndevelopers to build applications that they previously could not.\nBut using these LLMs in isolation is often not enough to\ncreate a truly powerful app - the real power comes when you are able to\ncombine them with other sources of computation or knowledge.\nThis library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:\n❓ Question Answering over specific documents\n\nDocumentation\nEnd-to-end Example: Question Answering over Notion Database\n\n💬 Chatbots\n\nDocumentation\nEnd-to-end Example: Chat-LangChain\n\n🤖 Agents\n\nDocumentation\nEnd-to-end Example: GPT+WolframAlpha\n\n\nGetting Started#\nCheckout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.\n\nGetting Started Documentation\n\n\n\n\n\nModules#\nThere are several main modules that LangChain provides support for.\nFor each module we provide some examples to


support for.\nFor each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.\nThese modules are, in increasing order of complexity:\n\nPrompts: This includes prompt management, prompt optimization, and prompt serialization.\nLLMs: This includes a generic interface for all LLMs, and common utilities for working with LLMs.\nDocument Loaders: This includes a standard interface for loading documents, as well as specific integrations to all types of text data sources.\nUtils: Language models are often more powerful when interacting with other sources of knowledge or computation. This can include Python REPLs, embeddings, search engines, and more. LangChain provides a large collection of common utils to use in your application.\nChains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\nIndexes: Language models are often more powerful when combined with your own


models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.\nAgents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.\nMemory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.\nChat: Chat models are a variation on Language Models that expose a different API - rather than working with raw text, they work with messages. LangChain provides a standard interface for working with them and doing all the same things as above.\n\n\n\n\n\nUse Cases#\nThe above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.\n\nAgents: Agents


the common use cases LangChain supports.\n\nAgents: Agents are systems that use a language model to interact with other tools. These can be used to do more grounded question/answering, interact with APIs, or even take actions.\nChatbots: Since language models are good at producing text, that makes them ideal for creating chatbots.\nData Augmented Generation: Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.\nQuestion Answering: Answering questions over specific documents, only utilizing the information in those documents to construct an answer. A type of Data Augmented Generation.\nSummarization: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.\nQuerying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this


SQL, dataframes, etc) you should read this page.\nEvaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\nGenerate similar examples: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this.\nCompare models: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.\n\n\n\n\n\nReference Docs#\nAll of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\n\nReference Documentation\n\n\n\n\n\nLangChain Ecosystem#\nGuides for how other companies/products can be used with LangChain\n\nLangChain Ecosystem\n\n\n\n\n\nAdditional Resources#\nAdditional collection of resources we think may be useful as you develop your application!\n\nLangChainHub: The LangChainHub is a place


application!\n\nLangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.\nGlossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!\nGallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.\nDeployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.\nDiscord: Join us on our Discord to discuss all things LangChain!\nTracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.\nProduction Support: As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.\n\n\n\n\n\n\n\n\n\n\n\nnext\nQuickstart Guide\n\n\n\n\n\n\n\n\n\n Contents\n  \n\n\nGetting Started\nModules\nUse Cases\nReference Docs\nLangChain Ecosystem\nAdditional Resources\n\n\n\n\n\n\n\n\n\nBy Harrison Chase\n\n\n\n\n    \n


Harrison Chase\n\n\n\n\n    \n      © Copyright 2023, Harrison Chase.\n      \n\n\n\n\n  Last updated on Mar 24, 2023.\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n', lookup_str='', metadata={'source': 'https://python.langchain.com/en/stable/', 'loc': 'https://python.langchain.com/en/stable/', 'lastmod': '2023-03-24T19:30:54.647430+00:00', 'changefreq': 'weekly', 'priority': '1'}, lookup_index=0)


Filtering sitemap URLs#
Sitemaps can be massive files, with thousands of URLs.  Often you don’t need every single one of them.  You can filter the URLs by passing a list of strings or regex patterns to the url_filter parameter.  Only URLs that match one of the patterns will be loaded.
loader = SitemapLoader(
    "https://langchain.readthedocs.io/sitemap.xml",
    filter_urls=["https://python.langchain.com/en/latest/"]
)
documents = loader.load()
documents[0]


Document(page_content='\n\n\n\n\n\nWelcome to LangChain — 🦜🔗 LangChain 0.0.123\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\n\n\n\n\n\nCtrl+K\n\n\n\n\n\n\n\n\n\n\n\n\n🦜🔗 LangChain 0.0.123\n\n\n\nGetting Started\n\nQuickstart Guide\n\nModules\n\nModels\nLLMs\nGetting Started\nGeneric Functionality\nHow to use the async API for LLMs\nHow to write a custom LLM wrapper\nHow (and why) to use the fake LLM\nHow to cache LLM calls\nHow to serialize LLM classes\nHow to stream LLM responses\nHow to track token usage\n\n\nIntegrations\nAI21\nAleph Alpha\nAnthropic\nAzure OpenAI LLM Example\nBanana\nCerebriumAI LLM Example\nCohere\nDeepInfra LLM Example\nForefrontAI LLM Example\nGooseAI LLM Example\nHugging Face Hub\nManifest\nModal\nOpenAI\nPetals LLM Example\nPromptLayer OpenAI\nSageMakerEndpoint\nSelf-Hosted Models via Runhouse\nStochasticAI\nWriter\n\n\nReference\n\n\nChat Models\nGetting Started\nHow-To Guides\nHow


Models\nGetting Started\nHow-To Guides\nHow to use few shot examples\nHow to stream responses\n\n\nIntegrations\nAzure\nOpenAI\nPromptLayer ChatOpenAI\n\n\n\n\nText Embedding Models\nAzureOpenAI\nCohere\nFake Embeddings\nHugging Face Hub\nInstructEmbeddings\nOpenAI\nSageMaker Endpoint Embeddings\nSelf Hosted Embeddings\nTensorflowHub\n\n\n\n\nPrompts\nPrompt Templates\nGetting Started\nHow-To Guides\nHow to create a custom prompt template\nHow to create a prompt template that uses few shot examples\nHow to work with partial Prompt Templates\nHow to serialize prompts\n\n\nReference\nPromptTemplates\nExample Selector\n\n\n\n\nChat Prompt Template\nExample Selectors\nHow to create a custom example selector\nLengthBased ExampleSelector\nMaximal Marginal Relevance ExampleSelector\nNGram Overlap ExampleSelector\nSimilarity ExampleSelector\n\n\nOutput Parsers\nOutput Parsers\nCommaSeparatedListOutputParser\nOutputFixingParser\nPydanticOutputParser\nRetryOutputParser\nStructured Output Parser\n\n\n\n\nIndexes\nGetting Started\nDocument Loaders\nCoNLL-U\nAirbyte JSON\nAZLyrics\nBlackboard\nCollege Confidential\nCopy Paste\nCSV Loader\nDirectory Loader\nEmail\nEverNote\nFacebook Chat\nFigma\nGCS Directory\nGCS File Storage\nGitBook\nGoogle Drive\nGutenberg\nHacker


File Storage\nGitBook\nGoogle Drive\nGutenberg\nHacker News\nHTML\niFixit\nImages\nIMSDb\nMarkdown\nNotebook\nNotion\nObsidian\nPDF\nPowerPoint\nReadTheDocs Documentation\nRoam\ns3 Directory\ns3 File\nSubtitle Files\nTelegram\nUnstructured File Loader\nURL\nWeb Base\nWord Documents\nYouTube\n\n\nText Splitters\nGetting Started\nCharacter Text Splitter\nHuggingFace Length Function\nLatex Text Splitter\nMarkdown Text Splitter\nNLTK Text Splitter\nPython Code Text Splitter\nRecursiveCharacterTextSplitter\nSpacy Text Splitter\ntiktoken (OpenAI) Length Function\nTiktokenText Splitter\n\n\nVectorstores\nGetting Started\nAtlasDB\nChroma\nDeep Lake\nElasticSearch\nFAISS\nMilvus\nOpenSearch\nPGVector\nPinecone\nQdrant\nRedis\nWeaviate\n\n\nRetrievers\nChatGPT Plugin Retriever\nVectorStore Retriever\n\n\n\n\nMemory\nGetting Started\nHow-To Guides\nConversationBufferMemory\nConversationBufferWindowMemory\nEntity Memory\nConversation Knowledge Graph Memory\nConversationSummaryMemory\nConversationSummaryBufferMemory\nConversationTokenBufferMemory\nHow to add Memory to an LLMChain\nHow to add memory to a Multi-Input Chain\nHow to add Memory to an Agent\nHow to customize conversational memory\nHow to create a custom Memory class\nHow to use multiple memroy classes in the


Memory class\nHow to use multiple memroy classes in the same chain\n\n\n\n\nChains\nGetting Started\nHow-To Guides\nAsync API for Chain\nLoading from LangChainHub\nLLM Chain\nSequential Chains\nSerialization\nTransformation Chain\nAnalyze Document\nChat Index\nGraph QA\nHypothetical Document Embeddings\nQuestion Answering with Sources\nQuestion Answering\nSummarization\nRetrieval Question/Answering\nRetrieval Question Answering with Sources\nVector DB Text Generation\nAPI Chains\nSelf-Critique Chain with Constitutional AI\nBashChain\nLLMCheckerChain\nLLM Math\nLLMRequestsChain\nLLMSummarizationCheckerChain\nModeration\nPAL\nSQLite example\n\n\nReference\n\n\nAgents\nGetting Started\nTools\nGetting Started\nDefining Custom Tools\nMulti Input Tools\nBash\nBing Search\nChatGPT Plugins\nGoogle Search\nGoogle Serper API\nHuman as a tool\nIFTTT WebHooks\nPython REPL\nRequests\nSearch Tools\nSearxNG Search API\nSerpAPI\nWolfram Alpha\nZapier Natural Language Actions API\n\n\nAgents\nAgent Types\nCustom Agent\nConversation Agent (for Chat Models)\nConversation Agent\nMRKL\nMRKL Chat\nReAct\nSelf Ask With Search\n\n\nToolkits\nCSV Agent\nJSON Agent\nOpenAPI Agent\nPandas Dataframe Agent\nPython Agent\nSQL Database Agent\nVectorstore


Dataframe Agent\nPython Agent\nSQL Database Agent\nVectorstore Agent\n\n\nAgent Executors\nHow to combine agents and vectorstores\nHow to use the async API for Agents\nHow to create ChatGPT Clone\nHow to access intermediate steps\nHow to cap the max number of iterations\nHow to add SharedMemory to an Agent and its Tools\n\n\n\n\n\nUse Cases\n\nPersonal Assistants\nQuestion Answering over Docs\nChatbots\nQuerying Tabular Data\nInteracting with APIs\nSummarization\nExtraction\nEvaluation\nAgent Benchmarking: Search + Calculator\nAgent VectorDB Question Answering Benchmarking\nBenchmarking Template\nData Augmented Question Answering\nUsing Hugging Face Datasets\nLLM Math\nQuestion Answering Benchmarking: Paul Graham Essay\nQuestion Answering Benchmarking: State of the Union Address\nQA Generation\nQuestion Answering\nSQL Question Answering Benchmarking: Chinook\n\n\n\nReference\n\nInstallation\nIntegrations\nAPI References\nPrompts\nPromptTemplates\nExample Selector\n\n\nUtilities\nPython REPL\nSerpAPI\nSearxNG Search\nDocstore\nText Splitter\nEmbeddings\nVectorStores\n\n\nChains\nAgents\n\n\n\nEcosystem\n\nLangChain Ecosystem\nAI21 Labs\nAtlasDB\nBanana\nCerebriumAI\nChroma\nCohere\nDeepInfra\nDeep


Lake\nForefrontAI\nGoogle Search Wrapper\nGoogle Serper Wrapper\nGooseAI\nGraphsignal\nHazy Research\nHelicone\nHugging Face\nMilvus\nModal\nNLPCloud\nOpenAI\nOpenSearch\nPetals\nPGVector\nPinecone\nPromptLayer\nQdrant\nRunhouse\nSearxNG Search API\nSerpAPI\nStochasticAI\nUnstructured\nWeights & Biases\nWeaviate\nWolfram Alpha Wrapper\nWriter\n\n\n\nAdditional Resources\n\nLangChainHub\nGlossary\nLangChain Gallery\nDeployments\nTracing\nDiscord\nProduction Support\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n.rst\n\n\n\n\n\n\n\n.pdf\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWelcome to LangChain\n\n\n\n\n Contents \n\n\n\nGetting Started\nModules\nUse Cases\nReference Docs\nLangChain Ecosystem\nAdditional Resources\n\n\n\n\n\n\n\n\nWelcome to LangChain#\nLangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:\n\nBe data-aware: connect a language model to other sources of data\nBe agentic: allow a language model to interact


data\nBe agentic: allow a language model to interact with its environment\n\nThe LangChain framework is designed with the above principles in mind.\nThis is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see here. For the JavaScript documentation, see here.\n\nGetting Started#\nCheckout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.\n\nGetting Started Documentation\n\n\n\n\n\nModules#\nThere are several main modules that LangChain provides support for.\nFor each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.\nThese modules are, in increasing order of complexity:\n\nModels: The various model types and model integrations LangChain supports.\nPrompts: This includes prompt management, prompt optimization, and prompt serialization.\nMemory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.\nIndexes: Language models are often


that use memory.\nIndexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.\nChains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\nAgents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.\n\n\n\n\n\nUse Cases#\nThe above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.\n\nPersonal Assistants: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data.\nQuestion Answering: The second


have knowledge about your data.\nQuestion Answering: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.\nChatbots: Since language models are good at producing text, that makes them ideal for creating chatbots.\nQuerying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.\nInteracting with APIs: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions.\nExtraction: Extract structured information from text.\nSummarization: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.\nEvaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\n\n\n\n\n\nReference Docs#\nAll


assisting in this.\n\n\n\n\n\nReference Docs#\nAll of LangChain’s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\n\nReference Documentation\n\n\n\n\n\nLangChain Ecosystem#\nGuides for how other companies/products can be used with LangChain\n\nLangChain Ecosystem\n\n\n\n\n\nAdditional Resources#\nAdditional collection of resources we think may be useful as you develop your application!\n\nLangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.\nGlossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!\nGallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.\nDeployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.\nTracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.\nModel Laboratory: Experimenting with different prompts, models, and chains is a big part of


prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.\nDiscord: Join us on our Discord to discuss all things LangChain!\nProduction Support: As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.\n\n\n\n\n\n\n\n\n\n\n\nnext\nQuickstart Guide\n\n\n\n\n\n\n\n\n\n Contents\n  \n\n\nGetting Started\nModules\nUse Cases\nReference Docs\nLangChain Ecosystem\nAdditional Resources\n\n\n\n\n\n\n\n\n\nBy Harrison Chase\n\n\n\n\n    \n      © Copyright 2023, Harrison Chase.\n      \n\n\n\n\n  Last updated on Mar 27, 2023.\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n', lookup_str='', metadata={'source': 'https://python.langchain.com/en/latest/', 'loc': 'https://python.langchain.com/en/latest/', 'lastmod': '2023-03-27T22:50:49.790324+00:00', 'changefreq': 'daily', 'priority': '0.9'}, lookup_index=0)


Local Sitemap#
The sitemap loader can also be used to load local files.
sitemap_loader = SitemapLoader(web_path="example_data/sitemap.xml", is_local=True)
docs = sitemap_loader.load()
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Filtering sitemap URLs
Local Sitemap
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
CSV
 Contents
Customizing the csv parsing and loading
Specify a column to identify the document source
CSV#
A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.
Load csv data with a single row per document.
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')
data = loader.load()
print(data)


[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


lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='Team:


'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0),


'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0),


'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0)]


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)


[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':


'./example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='MLB Team: Athletics\nPayroll in millions: 55.37\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='MLB Team: Rangers\nPayroll in millions: 120.51\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='MLB Team: Orioles\nPayroll in millions: 81.43\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='MLB Team: Rays\nPayroll in millions: 64.17\nWins: 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='MLB Team: Angels\nPayroll in millions: 154.49\nWins: 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='MLB Team: Tigers\nPayroll in millions: 132.30\nWins: 88', lookup_str='',


in millions: 132.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='MLB Team: Cardinals\nPayroll in millions: 110.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='MLB Team: Dodgers\nPayroll in millions: 95.14\nWins: 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='MLB Team: White Sox\nPayroll in millions: 96.92\nWins: 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='MLB Team: Brewers\nPayroll in millions: 97.65\nWins: 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='MLB Team: Phillies\nPayroll in millions: 174.54\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='MLB Team:


16}, lookup_index=0), Document(page_content='MLB Team: Diamondbacks\nPayroll in millions: 74.28\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='MLB Team: Pirates\nPayroll in millions: 63.43\nWins: 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='MLB Team: Padres\nPayroll in millions: 55.24\nWins: 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='MLB Team: Mariners\nPayroll in millions: 81.97\nWins: 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='MLB Team: Mets\nPayroll in millions: 93.35\nWins: 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='MLB Team: Blue Jays\nPayroll in millions: 75.48\nWins: 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv',


metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='MLB Team: Royals\nPayroll in millions: 60.91\nWins: 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='MLB Team: Marlins\nPayroll in millions: 118.07\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='MLB Team: Red Sox\nPayroll in millions: 173.18\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='MLB Team: Indians\nPayroll in millions: 78.43\nWins: 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='MLB Team: Twins\nPayroll in millions: 94.08\nWins: 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='MLB Team: Rockies\nPayroll in millions: 78.06\nWins: 64',


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)]


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)


[Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': 'Nationals', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': 'Reds', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', lookup_str='', metadata={'source': 'Yankees', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', lookup_str='', metadata={'source': 'Giants', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', lookup_str='', metadata={'source': 'Braves', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', lookup_str='', metadata={'source': 'Athletics', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': 'Rangers', 'row': 6}, lookup_index=0), Document(page_content='Team:


'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': 'Orioles', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': 'Rays', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': 'Angels', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': 'Tigers', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': 'Cardinals', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': 'Dodgers', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': 'White Sox', 'row':


lookup_str='', metadata={'source': 'White Sox', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': 'Brewers', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': 'Phillies', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': 'Diamondbacks', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': 'Pirates', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': 'Padres', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': 'Mariners', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='',


(millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': 'Mets', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': 'Blue Jays', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': 'Royals', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': 'Marlins', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': 'Red Sox', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': 'Indians', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': 'Twins', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll


lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': 'Rockies', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': 'Cubs', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': 'Astros', 'row': 29}, lookup_index=0)]


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Email
 Contents

Customizing the csv parsing and loading
Specify a column to identify the document source
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
URL
 Contents
URL
Selenium URL Loader
Setup
Playwright URL Loader
Setup
URL#
This covers how to load HTML documents from a list of URLs into a document format that we can use downstream.
 from langchain.document_loaders import UnstructuredURLLoader
urls = [
    "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023",
    "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023"
]
loader = UnstructuredURLLoader(urls=urls)
data = loader.load()
Selenium URL Loader#
This covers how to load HTML documents from a list of URLs using the SeleniumURLLoader.
Using selenium allows us to load pages that require JavaScript to render.
Setup#
To use the SeleniumURLLoader, you will need to install selenium and unstructured.
from langchain.document_loaders import SeleniumURLLoader
urls = [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "https://goo.gl/maps/NDSHwePEyaHMFGwh8"
]
loader = SeleniumURLLoader(urls=urls)
data = loader.load()
Playwright URL Loader#
This covers how to load HTML documents from a list of URLs using the PlaywrightURLLoader.
As in the Selenium case, Playwright allows us to load pages that need JavaScript to render.
Setup#
To use the PlaywrightURLLoader, you will need to install playwright and unstructured. Additionally, you will need to install the Playwright Chromium browser:
# Install playwright
!pip install "playwright"
!pip install "unstructured"
!playwright install


!pip install "playwright"
!pip install "unstructured"
!playwright install
from langchain.document_loaders import PlaywrightURLLoader
urls = [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "https://goo.gl/maps/NDSHwePEyaHMFGwh8"
]
loader = PlaywrightURLLoader(urls=urls, remove_selectors=["header", "footer"])
data = loader.load()
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WebBaseLoader
 Contents

URL
Selenium URL Loader
Setup
Playwright URL Loader
Setup
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Gutenberg
Gutenberg#
Project Gutenberg is an online library of free eBooks.
This notebook covers how to load links to Gutenberg e-books into a document format that we can use downstream.
from langchain.document_loaders import GutenbergLoader
loader = GutenbergLoader('https://www.gutenberg.org/cache/epub/69972/pg69972.txt')
data = loader.load()
data[0].page_content[:300]
'The Project Gutenberg eBook of The changed brides, by Emma Dorothy\r\n\n\nEliza Nevitte Southworth\r\n\n\n\r\n\n\nThis eBook is for the use of anyone anywhere in the United States and\r\n\n\nmost other parts of the world at no cost and with almost no restrictions\r\n\n\nwhatsoever. You may copy it, give it away or re-u'
data[0].metadata
{'source': 'https://www.gutenberg.org/cache/epub/69972/pg69972.txt'}
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Hacker News
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
Airbyte JSON
Airbyte JSON#
Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
This covers how to load any source from Airbyte into a local JSON file that can be read in as a document
Prereqs:
Have docker desktop installed
Steps:
Clone Airbyte from GitHub - git clone https://github.com/airbytehq/airbyte.git
Switch into Airbyte directory - cd airbyte
Start Airbyte - docker compose up
In your browser, just visit http://localhost:8000. You will be asked for a username and password. By default, that’s username airbyte and password password.
Setup any source you wish.
Set destination as Local JSON, with specified destination path - lets say /json_data. Set up manual sync.
Run the connection.
To see what files are create, you can navigate to: file:///tmp/airbyte_local
Find your data and copy path. That path should be saved in the file variable below. It should start with /tmp/airbyte_local
from langchain.document_loaders import AirbyteJSONLoader
!ls /tmp/airbyte_local/json_data/
_airbyte_raw_pokemon.jsonl
loader = AirbyteJSONLoader('/tmp/airbyte_local/json_data/_airbyte_raw_pokemon.jsonl')
data = loader.load()
print(data[0].page_content[:500])
abilities:
ability:
name: blaze
url: https://pokeapi.co/api/v2/ability/66/
is_hidden: False
slot: 1
ability:
name: solar-power
url: https://pokeapi.co/api/v2/ability/94/
is_hidden: True
slot: 3


is_hidden: True
slot: 3
base_experience: 267
forms:
name: charizard
url: https://pokeapi.co/api/v2/pokemon-form/6/
game_indices:
game_index: 180
version:
name: red
url: https://pokeapi.co/api/v2/version/1/
game_index: 180
version:
name: blue
url: https://pokeapi.co/api/v2/version/2/
game_index: 180
version:
n
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Apify Dataset
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.ipynb
.pdf
File Directory
 Contents
Show a progress bar
Use multithreading
Change loader class
Auto detect file encodings with TextLoader
A. Default Behavior
B. Silent fail
C. Auto detect encodings
File Directory#
This covers how to use the DirectoryLoader to load all documents in a directory. Under the hood, by default this uses the UnstructuredLoader
from langchain.document_loaders import DirectoryLoader
We can use the glob parameter to control which files to load. Note that here it doesn’t load the .rst file or the .ipynb files.
loader = DirectoryLoader('../', glob="**/*.md")
docs = loader.load()
len(docs)
1
Show a progress bar#
By default a progress bar will not be shown. To show a progress bar, install the tqdm library (e.g. pip install tqdm), and set the show_progress parameter to True.
%pip install tqdm
loader = DirectoryLoader('../', glob="**/*.md", show_progress=True)
docs = loader.load()
Requirement already satisfied: tqdm in /Users/jon/.pyenv/versions/3.9.16/envs/microbiome-app/lib/python3.9/site-packages (4.65.0)
0it [00:00, ?it/s]
Use multithreading#
By default the loading happens in one thread. In order to utilize several threads set the use_multithreading flag to true.
loader = DirectoryLoader('../', glob="**/*.md", use_multithreading=True)
docs = loader.load()
Change loader class#
By default this uses the UnstructuredLoader class. However, you can change up the type of loader pretty easily.
from langchain.document_loaders import TextLoader
loader = DirectoryLoader('../', glob="**/*.md", loader_cls=TextLoader)
docs = loader.load()


docs = loader.load()
len(docs)
1
If you need to load Python source code files, use the PythonLoader.
from langchain.document_loaders import PythonLoader
loader = DirectoryLoader('../../../../../', glob="**/*.py", loader_cls=PythonLoader)
docs = loader.load()
len(docs)
691
Auto detect file encodings with TextLoader#
In this example we will see some strategies that can be useful when loading a big list of arbitrary files from a directory using the TextLoader class.
First to illustrate the problem, let’s try to load multiple text with arbitrary encodings.
path = '../../../../../tests/integration_tests/examples'
loader = DirectoryLoader(path, glob="**/*.txt", loader_cls=TextLoader)
A. Default Behavior#
loader.load()
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /data/source/langchain/langchain/document_loaders/text.py:29 in load                             │
│                                                                                │
│   26 │   │   text = ""                                                                           │
│   27 │   │   with open(self.file_path, encoding=self.encoding) as f:                             │
│   28 │   │   │   try:                                                                            │
│ ❱ 29 │   │   │   │   text = f.read()                                                             │
│   30 │   │   │   except UnicodeDecodeError as e:                                                 │
│   31 │   │   │   │   if self.autodetect_encoding:                                                │
│   32 │   │   │   │   │   detected_encodings = self.detect_file_encodings()                       │
│                                                                                │
│ /home/spike/.pyenv/versions/3.9.11/lib/python3.9/codecs.py:322 in decode                         │


│                                                                                │
│    319 │   def decode(self, input, final=False):                                                 │
│    320 │   │   # decode input (taking the buffer into account)                                   │
│    321 │   │   data = self.buffer + input                                                        │
│ ❱  322 │   │   (result, consumed) = self._buffer_decode(data, self.errors, final)                │
│    323 │   │   # keep undecoded input until the next call                                        │
│    324 │   │   self.buffer = data[consumed:]                                                     │
│    325 │   │   return result                                                                     │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xca in position 0: invalid continuation byte
The above exception was the direct cause of the following exception:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <module>:1                                                                                │
│                                                                                │
│ ❱ 1 loader.load()                                                                                │
│   2                                                                                │
│                                                                                │
│ /data/source/langchain/langchain/document_loaders/directory.py:84 in load                        │
│                                                                                │
│   81 │   │   │   │   │   │   if self.silent_errors:                                              │
│   82 │   │   │   │   │   │   │   logger.warning(e)                                               │
│   83 │   │   │   │   │   │   else:                                                               │
│ ❱ 84 │   │   │   │   │   │   │   raise e                                                         │


│   85 │   │   │   │   │   finally:                                                                │
│   86 │   │   │   │   │   │   if pbar:                                                            │
│   87 │   │   │   │   │   │   │   pbar.update(1)                                                  │
│                                                                                │
│ /data/source/langchain/langchain/document_loaders/directory.py:78 in load                        │
│                                                                                │
│   75 │   │   │   if i.is_file():                                                                 │
│   76 │   │   │   │   if _is_visible(i.relative_to(p)) or self.load_hidden:                       │
│   77 │   │   │   │   │   try:                                                                    │
│ ❱ 78 │   │   │   │   │   │   sub_docs = self.loader_cls(str(i), **self.loader_kwargs).load()     │
│   79 │   │   │   │   │   │   docs.extend(sub_docs)                                               │
│   80 │   │   │   │   │   except Exception as e:                                                  │
│   81 │   │   │   │   │   │   if self.silent_errors:                                              │
│                                                                                │
│ /data/source/langchain/langchain/document_loaders/text.py:44 in load                             │
│                                                                                │
│   41 │   │   │   │   │   │   except UnicodeDecodeError:                                          │
│   42 │   │   │   │   │   │   │   continue                                                        │
│   43 │   │   │   │   else:                                                                       │
│ ❱ 44 │   │   │   │   │   raise RuntimeError(f"Error loading {self.file_path}") from e            │


│   45 │   │   │   except Exception as e:                                                          │
│   46 │   │   │   │   raise RuntimeError(f"Error loading {self.file_path}") from e                │
│   47                                                                                │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: Error loading ../../../../../tests/integration_tests/examples/example-non-utf8.txt
The file example-non-utf8.txt uses a different encoding the load() function fails with a helpful message indicating which file failed decoding.
With the default behavior of TextLoader any failure to load any of the documents will fail the whole loading process and no documents are loaded.
B. Silent fail#
We can pass the parameter silent_errors to the DirectoryLoader to skip the files which could not be loaded and continue the load process.
loader = DirectoryLoader(path, glob="**/*.txt", loader_cls=TextLoader, silent_errors=True)
docs = loader.load()
Error loading ../../../../../tests/integration_tests/examples/example-non-utf8.txt
doc_sources = [doc.metadata['source']  for doc in docs]
doc_sources
['../../../../../tests/integration_tests/examples/whatsapp_chat.txt',
 '../../../../../tests/integration_tests/examples/example-utf8.txt']
C. Auto detect encodings#
We can also ask TextLoader to auto detect the file encoding before failing, by passing the autodetect_encoding to the loader class.
text_loader_kwargs={'autodetect_encoding': True}
loader = DirectoryLoader(path, glob="**/*.txt", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)
docs = loader.load()
doc_sources = [doc.metadata['source']  for doc in docs]
doc_sources
['../../../../../tests/integration_tests/examples/example-non-utf8.txt',
 '../../../../../tests/integration_tests/examples/whatsapp_chat.txt',