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15e70240f00f-577 | his Gun pointed at\r\n him. Then looks left finding OFFICER MYERS, also White, 30\'s,\r\n has his revolver aimed at him.\r\n \r\n CSPD OFFICER BRICKHOUSE (CONT\'D)\r\n Get off her!\r\n \r\n Ron slowly rises up off Connie, gradually turning to them.\r\n With his hands raised you can see Ron\'s shoulder holster and\r\n 38 CALIBER SNUB-NOSE. Officer Myers sees it!\r\n \r\n CSPD OFFICER | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-578 | CSPD OFFICER MYERS\r\n He\'s got a Gun!\r\n \r\n RON STALLWORTH\r\n I\'m a Cop! I\'m a COP!!!\r\n \r\n Connie springs up from the lawn! Pleading like crazy to the\r\n cops!\r\n \r\n CONNIE\r\n He attacked me! That Nigger attacked\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-579 | That Nigger attacked\r\n me, he tried to Rape me! Arrest him!\r\n \r\n Myers and Brickhouse look at each other, unsure.\r\n \r\n RON STALLWORTH\r\n I\'m Undercover!!!\r\n \r\n CSPD OFFICER BRICKHOUSE\r\n Show me your badge!\r\n \r\n Ron goes to reach in his pocket but the two Officers | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-580 | to reach in his pocket but the two Officers make\r\n aggressive moves with their Guns! Ron catches himself! He\r\n doesn\'t want to get shot! He decides to just tell them.\r\n \r\n RON STALLWORTH\r\n It\'s in my pocket.\r\n CONNIE\r\n You gonna believe this lying Nigger\r\n or me?\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-581 | CSPD OFFICER MYERS\r\n Get on the ground!\r\n \r\n RON STALLWORTH\r\n I\'m a Cop goddammit! She\'s got a\r\n Bomb! She\'s a Terrorist!\r\n \r\n CSPD OFFICER MYERS\r\n Get on the ground NOW!!!\r\n \r\n Ron slowly lowers down to his knees and | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-582 | Ron slowly lowers down to his knees and the two Cops push him\r\n face down on the street! Felix drives up with Ivanhoe and\r\n Walker in the back seat.\r\n \r\n ANGLE - STREET\r\n Felix has pulled up next to Patrice\'s Volkswagen Beetle.\r\n \r\n INT./EXT. CAR - DAY\r\n \r\n FELIX\r\n Gimme\' a detonator.\r\n \r\n Walker unzips his Bag quickly handing a | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-583 | Walker unzips his Bag quickly handing a Detonator to Felix.\r\n \r\n ANGLE - DOWN THE STREET\r\n \r\n Ron yells at the Cops trying to explain!\r\n \r\n RON STALLWORTH\r\n THAT WOMAN HAS A BOMB SHE\'S TRYING TO\r\n BLOW THAT HOUSE UP!\r\n \r\n ANGLE - PATRICE\'S HOUSE\r\n \r\n Patrice hearing the | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-584 | \r\n Patrice hearing the commotion steps out on the porch with\r\n Odetta.\r\n \r\n Ivanhoe sees Patrice on the porch.\r\n \r\n IVANHOE\r\n There she is! Do it!\r\n \r\n ANGLE - DOWN THE STREET\r\n \r\n RON STALLWORTH\r\n PATRICE!\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-585 | PATRICE!\r\n \r\n Officer Myers jabs Ron in the Belly with his Nightstick. Ron\r\n doubles over.\r\n CLOSE - PATRICE\r\n \r\n PATRICE\r\n Ron???\r\n \r\n CLOSE - FELIX\r\n \r\n FELIX\r\n You\'re Dead Black Bitch.\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-586 | You\'re Dead Black Bitch.\r\n \r\n ANGLE - PATRICE\'S HOUSE\r\n \r\n Patrice looks at Felix.\r\n \r\n CLOSE - RON\r\n \r\n recovering from the blow SCREAMS to her!\r\n \r\n RON STALLWORTH\r\n RUN!!! RUN!!! RUN!!!\r\n \r\n ANGLE - STREET\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-587 | ANGLE - STREET\r\n \r\n Connie finally sees Felix in the car. Felix sees her, nods.\r\n She then sees that they are parked... NEXT TO PATRICE\'S\r\n CAR!!! Connie runs to Felix, screaming!\r\n \r\n CONNIE\r\n NO!!! FELIX!!! NO!!! FELIX!!!\r\n \r\n Felix pushes the Button!\r\n \r\n THE BOMB\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-588 | \r\n is attached to the inside of the wheel well of Patrice\'s car.\r\n \r\n PATRICE\'S CAR\r\n \r\n EXPLODES! THEN IT BLOWS UP FELIX\'S CAR NEXT TO IT!!! A double\r\n explosion!!! THE IMPACT BLOWS OUT WINDOWS EVERYWHERE! Patrice\r\n and Odetta are knocked to the ground. Connie is hurled to the\r\n street! Glass and car parts flying! Ron and the Cops are\r\n ROCKED by the force of the HUGE BLAST!\r\n \r\n THE TWO CARS TOTALLY DESTROYED! | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-589 | THE TWO CARS TOTALLY DESTROYED! ENGULFED IN FLAMES!!!\r\n \r\n Connie on her knees on the street, weeping!\r\n \r\n RON STILL HANDCUFFED\r\n \r\n through the smoke and flames is able to make eye contact with\r\n Patrice, on the steps of her porch. She is shaken but all\r\n right. SIRENS in the distance heading toward them!\r\n \r\n ANGLE - STREET\r\n Flip drives up in a fury and jumps out and holds up his\r\n BADGE.\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-590 | \r\n FLIP\r\n Hey, you fucking idiots!!! We\'re\r\n undercover.\r\n \r\n Officers Brickhouse and Myers lower their guns.\r\n \r\n CLOSE - RON STALLWORTH\r\n \r\n RON STALLWORTH\r\n YOU\'RE LATE.\r\n \r\n CLOSE | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-591 | \r\n CLOSE - FLIP\r\n Flip smiles.\r\n \r\n OMITTED\r\n \r\n OMITTED\r\n INT. DIVE BAR - NIGHT\r\n \r\n The place is full of Off Duty Cops and their Girlfriends, a\r\n few Wives but mainly Cops drinking and having a good time.\r\n Ron is in the corner talking with Patrice. They are sharing a\r\n drink looking very intimate. Ron sees something.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-592 | \r\n RON STALLWORTH\r\n Jeezus Christ.\r\n \r\n PATRICE\r\n What?\r\n \r\n RON STALLWORTH\r\n Your Boyfriend.\r\n \r\n Patrice turns and sees.\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-593 | \r\n PATRICE\r\n Oh My God.\r\n \r\n Master Patrolman Landers nears them with a Beer in his hand.\r\n \r\n LANDERS\r\n Who\'s da\' Soul Sistah, Stallworth?\r\n You been holding out on me.\r\n \r\n Patrice stares at him with contempt.\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-594 | stares at him with contempt.\r\n \r\n PATRICE\r\n You don\'t remember me do you?\r\n \r\n Landers stares at her.\r\n PATRICE (CONT\'D)\r\n Kwame Ture.\r\n \r\n Landers doesn\'t know who that is.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-595 | PATRICE (CONT\'D)\r\n Stokely Carmichael.\r\n \r\n LANDERS\r\n Oh Yeah, Yeah, you looked good that\r\n night but you look even better now.\r\n \r\n PATRICE\r\n How often do you do that to Black\r\n People?\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-596 | \r\n LANDERS\r\n Do what?\r\n \r\n PATRICE\r\n Pull us over for nothing. Harass us.\r\n Put your hands all over a Woman in\r\n the guise of searching her. Call us\r\n everything but A Child of God.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-597 | LANDERS\r\n I don\'t know what you\'re talking\r\n about.\r\n \r\n RON STALLWORTH\r\n It\'s like what I told you. He just\r\n likes taking advantage but in the end\r\n he\'s All Hat and No Cattle.\r\n \r\n Landers looks around then leans in close to Patrice and Ron.\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-598 | to Patrice and Ron.\r\n He speaks softly issuing a deadly threat.\r\n \r\n LANDERS\r\n Let me tell you both something, I\'ve\r\n been keeping you People in line in\r\n this City for years. What I did to\r\n your Girl that night, I can do to any\r\n of you, Anytime, Anyplace. That\'s my\r\n prerogative. I can even Bust a Cap in\r\n ya Black | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-599 | ya Black Ass if I feel like it and\r\n nuthin\' will be done about it. Get\r\n it? Wish the both of you got blown up\r\n instead of Good White Folks.\r\n \r\n Master Patrolman Landers raises up.\r\n \r\n RON STALLWORTH\r\n Ohhh, I get it.\r\n \r\n Ron looks at Patrice.\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-600 | RON STALLWORTH (CONT\'D)\r\n You get it, Patrice?\r\n \r\n PATRICE\r\n Oh, I totally and completely get it.\r\n \r\n Landers looks confused with their response.\r\n \r\n RON STALLWORTH\r\n Good.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-601 | \r\n Ron turns toward the Bar and shouts.\r\n \r\n RON STALLWORTH (CONT\'D)\r\n You get it, Flip?\r\n \r\n Behind the Bar, Flip leans out from the back room waving to\r\n Ron wearing Headphones recording The Conversation.\r\n \r\n FLIP\r\n Oh, We got it! We got it all!\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-602 | \r\n Ron stands removing his Shirt revealing The Wire he is\r\n wearing. Master Patrolman Landers is in shock.\r\n \r\n RON STALLWORTH\r\n You get it, Chief?\r\n \r\n Sgt. Trapp appears taking the Beer from Landers\' hand turning\r\n him around putting Handcuffs on him. Chief Bridges comes from\r\n the back nearing Landers. The two lock eyes.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-603 | CHIEF BRIDGES\r\n Oh, I really, really get it. You\'re\r\n under arrest for Police Misconduct,\r\n Sexual Misconduct and Police\r\n Brutality.\r\n \r\n Sgt. Trapp and the Chief usher Master Patrolman Landers, who\r\n is babbling like a Fool out of The Bar reading him his\r\n rights.\r\n \r\n INT. INTELLIGENCE UNIT - CSPD - DAY\r\n \r\n Ron, walking taller | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-604 | \r\n Ron, walking taller than usual, steps inside The Unit. Some\r\n of his Colleagues notice and give him a Low-Key Ovation. At\r\n his Desk is Flip, who is in Great Spirits.\r\n \r\n FLIP\r\n There he is... Man of the Minute.\r\n \r\n RON STALLWORTH\r\n ... not an Hour?\r\n \r\n Ron smiles, gives Fives all around. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-605 | Ron smiles, gives Fives all around. They all share a laugh.\r\n FLIP (CONT\'D)\r\n That Polaroid Stunt you pulled? When\r\n you threw your Arms around them, I\r\n swear to God I almost Shit myself!\r\n \r\n RON STALLWORTH\r\n Told you, Ron was born ready.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-606 | FLIP\r\n Born ready is Ron.\r\n \r\n Sgt. Trapp steps out of his Office.\r\n \r\n SGT. TRAPP\r\n There\'s The Crazy Son of a Bitch!!!\r\n \r\n Trapp gives Ron a Bear Hug.\r\n \r\n SGT. TRAPP (CONT\'D)\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-607 | You did good.\r\n \r\n RON STALLWORTH\r\n Sarge. We did good.\r\n \r\n Ron and Flip eyes meet, bonded.\r\n \r\n SGT. TRAPP\r\n Chief wants to see you Guys.\r\n \r\n Flip nudges Ron.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-608 | \r\n FLIP\r\n Hey... early promotion?\r\n \r\n Ron smiles.\r\n \r\n INT. OFFICE OF THE CHIEF OF POLICE - DAY\r\n \r\n Ron, Flip, and Sgt. Trapp sit opposite Chief Bridges.\r\n \r\n CHIEF BRIDGES\r\n Again, I can\'t commend you enough for\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-609 | I can\'t commend you enough for\r\n what you\'ve achieved. You know there\r\n was not a Single Cross Burning the\r\n entire time you were involved?\r\n \r\n RON STALLWORTH\r\n I\'m aware.\r\n \r\n CHIEF BRIDGES\r\n But all good things must come to an\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-610 | end...\r\n \r\n Sgt. Trapp shakes his head, resigned.\r\n RON STALLWORTH\r\n What does that mean?\r\n \r\n Ron and Flip look at each other, stunned.\r\n \r\n CHIEF BRIDGES\r\n Budget Cuts.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-611 | FLIP\r\n Budget Cuts?\r\n \r\n CHIEF BRIDGES\r\n Inflation... I wish I had a choice.\r\n My hands are tied. Besides, it looks\r\n like there are no longer any tangible\r\n Threats...\r\n \r\n RON | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-612 | RON STALLWORTH\r\n ...Sounds like we did too good a job.\r\n \r\n CHIEF BRIDGES\r\n Not a Bad Legacy to leave.\r\n \r\n Bridges takes a deliberate pause. Then, THE Sucker Punch...\r\n \r\n CHIEF BRIDGES (CONT\'D)\r\n And I need you, Ron Stallworth, to\r\n destroy all | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-613 | destroy all Evidence of this\r\n Investigation.\r\n \r\n RON STALLWORTH\r\n Excuse me?\r\n \r\n FLIP\r\n This is total Horseshit.\r\n \r\n CHIEF BRIDGES\r\n We | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-614 | We prefer that The Public never knew\r\n about this Investigation.\r\n \r\n Ron and Flip are heated. Sgt. Trapp is silent but gutted.\r\n \r\n RON STALLWORTH\r\n If they found out...\r\n \r\n CHIEF BRIDGES\r\n ...Cease all further contact with The\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-615 | Ku Klux Klan. Effective immediately.\r\n That goes for Flip too. Ron\r\n Stallworth...\r\n \r\n RON STALLWORTH\r\n This is some Fucked up Bullshit.\r\n CHIEF BRIDGES\r\n Take a week off. Go on vacation with\r\n your Girlfriend. We\'ll hold down The\r\n Fort until you get | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-616 | Fort until you get back. Get you\r\n another assignment...Narcotics.\r\n \r\n Ron storms out.\r\n \r\n INT. INTELLIGENCE UNIT - CSPD - DAY\r\n \r\n Ron reflects as he feeds Investigation documents in a\r\n Shredder. The documents shred into pieces. Just then, the\r\n Undercover Phone Line rings on Ron\'s desk.\r\n \r\n Ron stares at the Phone, still ringing. He looks at The\r\n Documents in his hand, about to feed them into The Shredder.\r\n Ron | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-617 | The Shredder.\r\n Ron stops. Throws The Documents in a Folder. Sweeps some\r\n Folders into his Briefcase. Leaves as The Phone still rings.\r\n \r\n EXT. COLORADO SPRINGS POLICE DEPARTMENT BUILDING - DAY\r\n \r\n Ron is walking fast now, trying to make it out of The\r\n Building with The Evidence but he remembers something.\r\n He stops, turns back.\r\n \r\n INT. INTELLIGENCE DIVISION - CSPD - DAY\r\n \r\n Ron sits at his Desk, on The Undercover Phone Line. Flip,\r\n Jimmy and Sgt. Trapp are | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-618 | Jimmy and Sgt. Trapp are behind, both close enough to listen,\r\n giggling.\r\n \r\n RON STALLWORTH\r\n I\'m sorry we didn\'t get to spend more\r\n One-on-One time together.\r\n \r\n INT. DEVIN DAVIS OFFICE - DAY\r\n \r\n INTERCUT RON, FLIP, AND TRAPP WITH DEVIN DAVIS:\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-619 | DEVIN DAVIS\r\n Well, that tragic event. I had just\r\n met those Fine Brothers in the cause.\r\n \r\n RON STALLWORTH\r\n Our Chapter is just shaken to the\r\n core. And poor Connie not only does\r\n she lose her Husband but she\'s facing\r\n a healthy Prison Sentence.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-620 | \r\n DEVIN DAVIS\r\n My God. And then there was that one\r\n Nigger Detective who threatened me.\r\n RON STALLWORTH\r\n Goddamn Coloreds sure know how to\r\n spoil a Celebration.\r\n \r\n Flip and Jimmy snort. Ron holds in a Belly-Laugh.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-621 | DEVIN DAVIS\r\n Christ. You can say that again.\r\n \r\n Ron cracks up into his Hand. Sgt. Trapp is wheezing-- his\r\n Face Bright Pink. Flip is laughing hard in the background.\r\n \r\n RON STALLWORTH\r\n Can I ask you something? That Nigger\r\n Detective who gave you a hard time?\r\n Ever get his name?\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-622 | \r\n DEVIN DAVIS\r\n No, I...\r\n \r\n RON STALLWORTH\r\n ...Are-uh you sure you don\'t know who\r\n he is? Are-uh you absolutely sure?\r\n \r\n Davis looks at his Phone. Ron takes out his SMALL NOTE PAD\r\n out revealing a list of Racial epitaphs he had written down\r\n being on this | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-623 | written down\r\n being on this Investigation. He reads from it to Davis on the\r\n phone.\r\n \r\n ANGLE - SPLIT SCREEN\r\n \r\n Ron Stallworth and Devin Davis.\r\n \r\n RON STALLWORTH (CONT\'D)\r\n Cuz\' dat Niggah Coon, Gator Bait,\r\n Spade, Spook, Sambo, Spear Flippin\',\r\n Jungle Bunny, Mississippi Wind\r\n Chime...Detective is Ron Stallworth\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-624 | is Ron Stallworth\r\n you Redneck, Racist Peckerwood Small\r\n Dick Motherfucker!!!\r\n \r\n CLICK. Ron SLAM DUNKS THE RECEIVER LIKE SHAQ.\r\n \r\n CLOSE - DEVIN DAVIS\r\n \r\n Devin Davis\'s Jaw Drops.\r\n \r\n INT. INTELLIGENCE DIVISION - CSPD - DAY\r\n \r\n THE WHOLE OFFICE EXPLODES IN LAUGHTER. COPS ARE ROLLING ON\r\n THE OFFICE FLOOR.\r\n INT. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-625 | OFFICE FLOOR.\r\n INT. RON\'S APARTMENT - KITCHEN - NIGHT\r\n \r\n Folders of Evidence sit on The Kitchen Table in a stack in\r\n front of Ron. He sips his Lipton Tea and removes from the\r\n FILES THE\r\n \r\n CLOSE - POLAROID\r\n Ron hugged up, between Devin Davis and Jesse Nayyar. He then\r\n looks at The Klan Membership Card shifting in his hands, his\r\n gaze fixated on the words.\r\n \r\n CLOSE - Ron Stallworth\r\n KKK Member in Good Standing\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-626 | Member in Good Standing\r\n \r\n Patrice comes up from behind.\r\n CLOSE - PATRICE\r\n She pulls out a small handgun from her pocketbook.\r\n \r\n 2 - SHOT - PATRICE AND RON\r\n \r\n PATRICE (O.S.)\r\n Have you Resigned from The KKK?\r\n \r\n RON STALLWORTH\r\n Affirmative.\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-627 | Affirmative.\r\n \r\n PATRICE\r\n Have you handed in your Resignation\r\n as a Undercover Detective for The\r\n Colorado Springs Police Department?\r\n \r\n RON STALLWORTH\r\n Negative. Truth be told I\'ve always\r\n wanted to be a Cop...and I\'m still\r\n for The | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-628 | for The Liberation for My People.\r\n \r\n PATRICE\r\n My Conscience won\'t let me Sleep with\r\n The Enemy.\r\n \r\n RON STALLWORTH\r\n Enemy? I\'m a Black Man that saved\r\n your life.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-629 | PATRICE\r\n You\'re absolutely right, and I Thank\r\n you for it.\r\n \r\n Patrice Kisses Ron on the cheek. Good Bye. WE HEAR a KNOCK on\r\n Ron\'s DOOR. Ron, who is startled, slowly rises. We HEAR\r\n another KNOCK.\r\n \r\n QUICK FLASHES - of a an OLD TIME KLAN RALLY. Ron moves\r\n quietly to pull out his SERVICE REVOLVER from the COUNTER\r\n DRAWER. WE HEAR ANOTHER KNOCK on the DOOR. Patrice stands\r\n behind | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-630 | Patrice stands\r\n behind him.\r\n \r\n QUICK FLASHES - BLACK BODY HANGING FROM A TREE (STRANGE\r\n FRUIT) Ron slowly moves to the DOOR. Ron has his SERVICE\r\n REVOLVER up and aimed ready to fire. Ron swings open the\r\n DOOR.\r\n ANGLE - HALLWAY\r\n \r\n CU - RON\'S POV\r\n \r\n WE TRACK DOWN THE EMPTY HALLWAY PANNING OUT THE WINDOW.\r\n \r\n CLOSE - RON AND PATRICE\r\n \r\n Looking in | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-631 | \r\n Looking in the distance: The Rolling Hills surrounding The\r\n Neighborhood lead towards Pike\'s Peak, which sits on the\r\n horizon like a King on A Throne.\r\n \r\n WE SEE: Something Burning.\r\n \r\n CLOSER-- WE SEE a CROSS, its Flames dancing, sending embers\r\n into The BLACK, Colorado Sky.\r\n OMITTED\r\n \r\n EXT. UVA CAMPUS - NIGHT\r\n \r\n WE SEE FOOTAGE of NEO-NAZIS, ALT RIGHT, THE KLAN, NEO-\r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-632 | ALT RIGHT, THE KLAN, NEO-\r\n CONFEDERATES AND WHITE NATIONALISTS MARCHING, HOLDING UP\r\n THEIR TIKI TORCHES, CHANTING.\r\n \r\n AMERICAN TERRORISTS\r\n YOU WILL NOT REPLACE US!!!\r\n JEWS WILL NOT REPLACE US!!!\r\n BLOOD AND SOIL!!!\r\n \r\n CUT TO BLACK.\r\n \r\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-633 | \r\n FINI.\r\n\r\n\r\n\n\n\n\nBlacKkKlansman\nWriters : \xa0\xa0Charlie Wachtel\xa0\xa0David Rabinowitz\xa0\xa0Kevin Willmott\xa0\xa0Spike Lee\nGenres : \xa0\xa0Crime\xa0\xa0Drama\nUser Comments\n\n\n\n\n\r\nBack to IMSDb\n\n\n', lookup_str='', metadata={'source': 'https://imsdb.com/scripts/BlacKkKlansman.html'}, lookup_index=0)] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
15e70240f00f-634 | previous
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Markdown
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/imsdb.html |
2794eaf96e0c-0 | .ipynb
.pdf
College Confidential
College Confidential#
This covers how to load College Confidential webpages into a document format that we can use downstream.
from langchain.document_loaders import CollegeConfidentialLoader
loader = CollegeConfidentialLoader("https://www.collegeconfidential.com/colleges/brown-university/")
data = loader.load()
data | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-1 | [Document(page_content='\n\n\n\n\n\n\n\nA68FEB02-9D19-447C-B8BC-818149FD6EAF\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Media (2)\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nE45B8B13-33D4-450E-B7DB-F66EFE8F2097\n\n\n\n\n\n\n\n\n\nE45B8B13-33D4-450E-B7DB-F66EFE8F2097\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAbout Brown\n\n\n\n\n\n\nBrown University Overview\nBrown University is a private, nonprofit school in the urban setting of Providence, Rhode Island. Brown was founded in 1764 and the school currently enrolls around 10,696 students a year, including 7,349 undergraduates. Brown provides on-campus housing for students. Most students live in off campus housing.\n📆 Mark your calendar! January 5, 2023 is the final deadline to submit an application for the Fall 2023 semester. \nThere are many ways for students to get involved at Brown! \nLove music or | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-2 | students to get involved at Brown! \nLove music or performing? Join a campus band, sing in a chorus, or perform with one of the school\'s theater groups.\nInterested in journalism or communications? Brown students can write for the campus newspaper, host a radio show or be a producer for the student-run television channel.\nInterested in joining a fraternity or sorority? Brown has fraternities and sororities.\nPlanning to play sports? Brown has many options for athletes. See them all and learn more about life at Brown on the Student Life page.\n\n\n\n2022 Brown Facts At-A-Glance\n\n\n\n\n\nAcademic Calendar\nOther\n\n\nOverall Acceptance Rate\n6%\n\n\nEarly Decision Acceptance Rate\n16%\n\n\nEarly Action Acceptance Rate\nEA not offered\n\n\nApplicants Submitting SAT scores\n51%\n\n\nTuition\n$62,680\n\n\nPercent of Need Met\n100%\n\n\nAverage First-Year Financial Aid Package\n$59,749\n\n\n\n\nIs Brown a Good School?\n\nDifferent people have different ideas about what makes a "good" school. Some factors that can help you | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-3 | "good" school. Some factors that can help you determine what a good school for you might be include admissions criteria, acceptance rate, tuition costs, and more.\nLet\'s take a look at these factors to get a clearer sense of what Brown offers and if it could be the right college for you.\nBrown Acceptance Rate 2022\nIt is extremely difficult to get into Brown. Around 6% of applicants get into Brown each year. In 2022, just 2,568 out of the 46,568 students who applied were accepted.\nRetention and Graduation Rates at Brown\nRetention refers to the number of students that stay enrolled at a school over time. This is a way to get a sense of how satisfied students are with their school experience, and if they have the support necessary to succeed in college. \nApproximately 98% of first-year, full-time undergrads who start at Browncome back their sophomore year. 95% of Brown undergrads graduate within six years. The average six-year graduation rate for U.S. colleges and | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-4 | six-year graduation rate for U.S. colleges and universities is 61% for public schools, and 67% for private, non-profit schools.\nJob Outcomes for Brown Grads\nJob placement stats are a good resource for understanding the value of a degree from Brown by providing a look on how job placement has gone for other grads. \nCheck with Brown directly, for information on any information on starting salaries for recent grads.\nBrown\'s Endowment\nAn endowment is the total value of a school\'s investments, donations, and assets. Endowment is not necessarily an indicator of the quality of a school, but it can give you a sense of how much money a college can afford to invest in expanding programs, improving facilities, and support students. \nAs of 2022, the total market value of Brown University\'s endowment was $4.7 billion. The average college endowment was $905 million in 2021. The school spends $34,086 for each full-time student enrolled. \nTuition and Financial Aid at Brown\nTuition is another important factor | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-5 | Financial Aid at Brown\nTuition is another important factor when choose a college. Some colleges may have high tuition, but do a better job at meeting students\' financial need.\nBrown meets 100% of the demonstrated financial need for undergraduates. The average financial aid package for a full-time, first-year student is around $59,749 a year. \nThe average student debt for graduates in the class of 2022 was around $24,102 per student, not including those with no debt. For context, compare this number with the average national debt, which is around $36,000 per borrower. \nThe 2023-2024 FAFSA Opened on October 1st, 2022\nSome financial aid is awarded on a first-come, first-served basis, so fill out the FAFSA as soon as you can. Visit the FAFSA website to apply for student aid. Remember, the first F in FAFSA stands for FREE! You should never have to pay to submit the Free Application for Federal Student Aid (FAFSA), so be very wary of anyone asking you | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-6 | so be very wary of anyone asking you for money.\nLearn more about Tuition and Financial Aid at Brown.\nBased on this information, does Brown seem like a good fit? Remember, a school that is perfect for one person may be a terrible fit for someone else! So ask yourself: Is Brown a good school for you?\nIf Brown University seems like a school you want to apply to, click the heart button to save it to your college list.\n\nStill Exploring Schools?\nChoose one of the options below to learn more about Brown:\nAdmissions\nStudent Life\nAcademics\nTuition & Aid\nBrown Community Forums\nThen use the college admissions predictor to take a data science look at your chances of getting into some of the best colleges and universities in the U.S.\nWhere is Brown?\nBrown is located in the urban setting of Providence, Rhode Island, less than an hour from Boston. \nIf you would like to see Brown for yourself, plan a visit. The best way to reach campus is to take Interstate | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-7 | best way to reach campus is to take Interstate 95 to Providence, or book a flight to the nearest airport, T.F. Green.\nYou can also take a virtual campus tour to get a sense of what Brown and Providence are like without leaving home.\nConsidering Going to School in Rhode Island?\nSee a full list of colleges in Rhode Island and save your favorites to your college list.\n\n\n\nCollege Info\n\n\n\n\n\n\n\n\n\n Providence, RI 02912\n \n\n\n\n Campus Setting: Urban\n \n\n\n\n\n\n\n\n (401) 863-2378\n \n\n Website\n \n\n | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-8 | \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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-9 | 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 - | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-10 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-11 | $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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-12 | \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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-13 | \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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
2794eaf96e0c-14 | 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)] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
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Blackboard
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Confluence
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/college_confidential.html |
58036cc2ff5a-0 | .ipynb
.pdf
Telegram
Telegram#
This notebook covers how to load data from Telegram into a format that can be ingested into LangChain.
from langchain.document_loaders import TelegramChatLoader
loader = TelegramChatLoader("example_data/telegram.json")
loader.load()
[Document(page_content="Henry on 2020-01-01T00:00:02: It's 2020...\n\nHenry on 2020-01-01T00:00:04: Fireworks!\n\nGrace 🧤 ðŸ\x8d’ on 2020-01-01T00:00:05: You're a minute late!\n\n", lookup_str='', metadata={'source': 'example_data/telegram.json'}, lookup_index=0)]
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Twitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/telegram.html |
7c24b5b8badb-0 | .ipynb
.pdf
ReadTheDocs Documentation
ReadTheDocs Documentation#
This notebook covers how to load content from html that was generated as part of a Read-The-Docs build.
For an example of this in the wild, see here.
This assumes that the html has already been scraped into a folder. This can be done by uncommenting and running the following command
#!wget -r -A.html -P rtdocs https://langchain.readthedocs.io/en/latest/
from langchain.document_loaders import ReadTheDocsLoader
loader = ReadTheDocsLoader("rtdocs")
docs = loader.load()
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PowerPoint
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Roam
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/readthedocs_documentation.html |
a52016bb5801-0 | .ipynb
.pdf
Sitemap Loader
Contents
Filtering sitemap URLs
Sitemap Loader#
Extends from the WebBaseLoader, this will load a sitemap from a given URL, and then scrape and load all the pages in the sitemap, returning each page as a document.
The scraping is done concurrently, using WebBaseLoader. There are reasonable limits to concurrent requests, defaulting to 2 per second. If you aren’t concerned about being a good citizen, or you control the server you are scraping and don’t care about load, you can change the requests_per_second parameter to increase the max concurrent requests. Note, while this will speed up the scraping process, but may cause the server to block you. Be careful!
!pip install nest_asyncio
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] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-1 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-2 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-3 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-4 | 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: | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-5 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-6 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-7 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-8 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-9 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-10 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-11 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-12 | 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) | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-13 | 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] | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-14 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-15 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-16 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-17 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-18 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-19 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-20 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-21 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-22 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
a52016bb5801-23 | 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 | https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/sitemap.html |
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