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f6104a1b5c3a-0 | CSV | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersCSVOn this pageCSVA 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 | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-2 | 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 CSVLoaderloader = 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': | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-3 | 55.37\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-4 | (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), 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": | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-5 | 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), 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)": | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-6 | 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}, | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-7 | './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='MLB Team: Nationals\nPayroll in millions: 81.34\nWins: 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='MLB Team: Reds\nPayroll in millions: 82.20\nWins: 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='MLB Team: Yankees\nPayroll in millions: 197.96\nWins: 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='MLB Team: Giants\nPayroll in millions: 117.62\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='MLB Team: Braves\nPayroll in millions: 83.31\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='MLB Team: Athletics\nPayroll in millions: 55.37\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='MLB Team: Rangers\nPayroll in millions: 120.51\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-8 | 'row': 7}, lookup_index=0), Document(page_content='MLB Team: Orioles\nPayroll in millions: 81.43\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='MLB Team: Rays\nPayroll in millions: 64.17\nWins: 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='MLB Team: Angels\nPayroll in millions: 154.49\nWins: 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='MLB Team: Tigers\nPayroll in millions: 132.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='MLB Team: Cardinals\nPayroll in millions: 110.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='MLB Team: Dodgers\nPayroll in millions: 95.14\nWins: 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='MLB Team: White Sox\nPayroll in millions: 96.92\nWins: 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='MLB Team: | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-9 | 'row': 14}, lookup_index=0), Document(page_content='MLB Team: Brewers\nPayroll in millions: 97.65\nWins: 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='MLB Team: Phillies\nPayroll in millions: 174.54\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='MLB Team: Diamondbacks\nPayroll in millions: 74.28\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='MLB Team: Pirates\nPayroll in millions: 63.43\nWins: 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='MLB Team: Padres\nPayroll in millions: 55.24\nWins: 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='MLB Team: Mariners\nPayroll in millions: 81.97\nWins: 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='MLB Team: Mets\nPayroll in millions: 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 | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-10 | 'row': 21}, lookup_index=0), Document(page_content='MLB Team: Blue Jays\nPayroll in millions: 75.48\nWins: 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='MLB Team: Royals\nPayroll in millions: 60.91\nWins: 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='MLB Team: Marlins\nPayroll in millions: 118.07\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='MLB Team: Red Sox\nPayroll in millions: 173.18\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='MLB Team: Indians\nPayroll in millions: 78.43\nWins: 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='MLB Team: Twins\nPayroll in millions: 94.08\nWins: 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='MLB Team: Rockies\nPayroll in millions: 78.06\nWins: 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='MLB Team: | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-11 | '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', | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-12 | 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: 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', | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-13 | 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': 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': | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-14 | 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='', 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 (millions)": 78.06\n"Wins": 64', | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-15 | 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)]UnstructuredCSVLoader​You can also load the table using the UnstructuredCSVLoader. One advantage of using UnstructuredCSVLoader is that if you use it in "elements" mode, an HTML representation of the table will be available in the metadata.from langchain.document_loaders.csv_loader import UnstructuredCSVLoaderloader = UnstructuredCSVLoader( file_path="example_data/mlb_teams_2012.csv", mode="elements")docs = loader.load()print(docs[0].metadata["text_as_html"]) <table border="1" class="dataframe"> <tbody> <tr> <td>Nationals</td> <td>81.34</td> <td>98</td> </tr> <tr> <td>Reds</td> <td>82.20</td> | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-16 | <td>82.20</td> <td>97</td> </tr> <tr> <td>Yankees</td> <td>197.96</td> <td>95</td> </tr> <tr> <td>Giants</td> <td>117.62</td> <td>94</td> </tr> <tr> <td>Braves</td> <td>83.31</td> <td>94</td> </tr> <tr> <td>Athletics</td> <td>55.37</td> <td>94</td> </tr> <tr> <td>Rangers</td> <td>120.51</td> <td>93</td> </tr> | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-17 | </tr> <tr> <td>Orioles</td> <td>81.43</td> <td>93</td> </tr> <tr> <td>Rays</td> <td>64.17</td> <td>90</td> </tr> <tr> <td>Angels</td> <td>154.49</td> <td>89</td> </tr> <tr> <td>Tigers</td> <td>132.30</td> <td>88</td> </tr> <tr> <td>Cardinals</td> <td>110.30</td> <td>88</td> </tr> <tr> <td>Dodgers</td> | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-18 | <td>Dodgers</td> <td>95.14</td> <td>86</td> </tr> <tr> <td>White Sox</td> <td>96.92</td> <td>85</td> </tr> <tr> <td>Brewers</td> <td>97.65</td> <td>83</td> </tr> <tr> <td>Phillies</td> <td>174.54</td> <td>81</td> </tr> <tr> <td>Diamondbacks</td> <td>74.28</td> <td>81</td> </tr> <tr> <td>Pirates</td> <td>63.43</td> <td>79</td> | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-19 | <td>79</td> </tr> <tr> <td>Padres</td> <td>55.24</td> <td>76</td> </tr> <tr> <td>Mariners</td> <td>81.97</td> <td>75</td> </tr> <tr> <td>Mets</td> <td>93.35</td> <td>74</td> </tr> <tr> <td>Blue Jays</td> <td>75.48</td> <td>73</td> </tr> <tr> <td>Royals</td> <td>60.91</td> <td>72</td> </tr> <tr> | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-20 | <tr> <td>Marlins</td> <td>118.07</td> <td>69</td> </tr> <tr> <td>Red Sox</td> <td>173.18</td> <td>69</td> </tr> <tr> <td>Indians</td> <td>78.43</td> <td>68</td> </tr> <tr> <td>Twins</td> <td>94.08</td> <td>66</td> </tr> <tr> <td>Rockies</td> <td>78.06</td> <td>64</td> </tr> <tr> <td>Cubs</td> <td>88.19</td> | https://python.langchain.com/docs/integrations/document_loaders/csv |
f6104a1b5c3a-21 | <td>88.19</td> <td>61</td> </tr> <tr> <td>Astros</td> <td>60.65</td> <td>55</td> </tr> </tbody> </table>PreviousCopy PasteNextCube Semantic LayerCustomizing the csv parsing and loadingSpecify a column to identify the document sourceUnstructuredCSVLoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/csv |
fd94cb3e0a8a-0 | Slack | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/slack |
fd94cb3e0a8a-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersSlackOn this pageSlackSlack is an instant messaging program.This notebook covers how to load documents from a Zipfile generated from a Slack export.In order to get this | https://python.langchain.com/docs/integrations/document_loaders/slack |
fd94cb3e0a8a-2 | notebook covers how to load documents from a Zipfile generated from a Slack export.In order to get this Slack export, follow these instructions:🧑 Instructions for ingesting your own dataset​Export your Slack data. You can do this by going to your Workspace Management page and clicking the Import/Export option ({your_slack_domain}.slack.com/services/export). Then, choose the right date range and click Start export. Slack will send you an email and a DM when the export is ready.The download will produce a .zip file in your Downloads folder (or wherever your downloads can be found, depending on your OS configuration).Copy the path to the .zip file, and assign it as LOCAL_ZIPFILE below.from langchain.document_loaders import SlackDirectoryLoader# Optionally set your Slack URL. This will give you proper URLs in the docs sources.SLACK_WORKSPACE_URL = "https://xxx.slack.com"LOCAL_ZIPFILE = "" # Paste the local paty to your Slack zip file here.loader = SlackDirectoryLoader(LOCAL_ZIPFILE, SLACK_WORKSPACE_URL)docs = loader.load()docsPreviousSitemapNextSnowflake🧑 Instructions for ingesting your own datasetCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/slack |
064030fdde6f-0 | Telegram | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/telegram |
064030fdde6f-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersTelegramTelegramTelegram Messenger is a globally accessible freemium, cross-platform, encrypted, cloud-based and centralized instant messaging service. The application also provides optional end-to-end encrypted chats | https://python.langchain.com/docs/integrations/document_loaders/telegram |
064030fdde6f-2 | encrypted, cloud-based and centralized instant messaging service. The application also provides optional end-to-end encrypted chats and video calling, VoIP, file sharing and several other features.This notebook covers how to load data from Telegram into a format that can be ingested into LangChain.from langchain.document_loaders import TelegramChatFileLoader, TelegramChatApiLoaderloader = TelegramChatFileLoader("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", metadata={'source': 'example_data/telegram.json'})]TelegramChatApiLoader loads data directly from any specified chat from Telegram. In order to export the data, you will need to authenticate your Telegram account. You can get the API_HASH and API_ID from https://my.telegram.org/auth?to=appschat_entity – recommended to be the entity of a channel.loader = TelegramChatApiLoader( chat_entity="<CHAT_URL>", # recommended to use Entity here api_hash="<API HASH >", api_id="<API_ID>", user_name="", # needed only for caching the session.)loader.load()PreviousSubtitleNextTencent COS DirectoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/telegram |
aaa1eaa8bc27-0 | Obsidian | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/obsidian |
aaa1eaa8bc27-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersObsidianObsidianObsidian is a powerful and extensible knowledge base | https://python.langchain.com/docs/integrations/document_loaders/obsidian |
aaa1eaa8bc27-2 | that works on top of your local folder of plain text files.This notebook covers how to load documents from an Obsidian database.Since Obsidian is just stored on disk as a folder of Markdown files, the loader just takes a path to this directory.Obsidian files also sometimes contain metadata which is a YAML block at the top of the file. These values will be added to the document's metadata. (ObsidianLoader can also be passed a collect_metadata=False argument to disable this behavior.)from langchain.document_loaders import ObsidianLoaderloader = ObsidianLoader("<path-to-obsidian>")docs = loader.load()PreviousNotion DB 2/2NextOpen Document Format (ODT)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/obsidian |
00e6fb67e039-0 | Xorbits Pandas DataFrame | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersXorbits Pandas DataFrameXorbits Pandas DataFrameThis notebook goes over how to load data from a xorbits.pandas DataFrame.#!pip install xorbitsimport | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-2 | goes over how to load data from a xorbits.pandas DataFrame.#!pip install xorbitsimport xorbits.pandas as pddf = pd.read_csv("example_data/mlb_teams_2012.csv")df.head() 0%| | 0.00/100 [00:00<?, ?it/s]<div><style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }</style><table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>Team</th> <th>"Payroll (millions)"</th> <th>"Wins"</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>Nationals</td> <td>81.34</td> <td>98</td> </tr> <tr> <th>1</th> <td>Reds</td> <td>82.20</td> <td>97</td> </tr> <tr> | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-3 | </tr> <tr> <th>2</th> <td>Yankees</td> <td>197.96</td> <td>95</td> </tr> <tr> <th>3</th> <td>Giants</td> <td>117.62</td> <td>94</td> </tr> <tr> <th>4</th> <td>Braves</td> <td>83.31</td> <td>94</td> </tr> </tbody></table></div>from langchain.document_loaders import XorbitsLoaderloader = XorbitsLoader(df, page_content_column="Team")loader.load() 0%| | 0.00/100 [00:00<?, ?it/s] [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, ' | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-4 | 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}), | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-5 | (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}), | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-6 | 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})]# Use lazy load for larger table, which won't read the full table into memoryfor i in loader.lazy_load(): print(i) 0%| | 0.00/100 [00:00<?, ?it/s] page_content='Nationals' metadata={' "Payroll (millions)"': 81.34, ' "Wins"': 98} page_content='Reds' metadata={' "Payroll (millions)"': 82.2, ' "Wins"': 97} page_content='Yankees' metadata={' "Payroll (millions)"': 197.96, ' "Wins"': 95} page_content='Giants' metadata={' "Payroll (millions)"': 117.62, ' "Wins"': 94} page_content='Braves' metadata={' "Payroll (millions)"': 83.31, ' "Wins"': 94} page_content='Athletics' metadata={' "Payroll (millions)"': | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-7 | page_content='Athletics' metadata={' "Payroll (millions)"': 55.37, ' "Wins"': 94} page_content='Rangers' metadata={' "Payroll (millions)"': 120.51, ' "Wins"': 93} page_content='Orioles' metadata={' "Payroll (millions)"': 81.43, ' "Wins"': 93} page_content='Rays' metadata={' "Payroll (millions)"': 64.17, ' "Wins"': 90} page_content='Angels' metadata={' "Payroll (millions)"': 154.49, ' "Wins"': 89} page_content='Tigers' metadata={' "Payroll (millions)"': 132.3, ' "Wins"': 88} page_content='Cardinals' metadata={' "Payroll (millions)"': 110.3, ' "Wins"': 88} page_content='Dodgers' metadata={' "Payroll (millions)"': 95.14, ' "Wins"': 86} page_content='White Sox' metadata={' "Payroll (millions)"': 96.92, ' "Wins"': 85} page_content='Brewers' metadata={' "Payroll (millions)"': 97.65, ' "Wins"': 83} page_content='Phillies' metadata={' "Payroll (millions)"': 174.54, ' "Wins"': 81} page_content='Diamondbacks' metadata={' "Payroll (millions)"': 74.28, ' "Wins"': 81} page_content='Pirates' metadata={' "Payroll | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-8 | 81} page_content='Pirates' metadata={' "Payroll (millions)"': 63.43, ' "Wins"': 79} page_content='Padres' metadata={' "Payroll (millions)"': 55.24, ' "Wins"': 76} page_content='Mariners' metadata={' "Payroll (millions)"': 81.97, ' "Wins"': 75} page_content='Mets' metadata={' "Payroll (millions)"': 93.35, ' "Wins"': 74} page_content='Blue Jays' metadata={' "Payroll (millions)"': 75.48, ' "Wins"': 73} page_content='Royals' metadata={' "Payroll (millions)"': 60.91, ' "Wins"': 72} page_content='Marlins' metadata={' "Payroll (millions)"': 118.07, ' "Wins"': 69} page_content='Red Sox' metadata={' "Payroll (millions)"': 173.18, ' "Wins"': 69} page_content='Indians' metadata={' "Payroll (millions)"': 78.43, ' "Wins"': 68} page_content='Twins' metadata={' "Payroll (millions)"': 94.08, ' "Wins"': 66} page_content='Rockies' metadata={' "Payroll (millions)"': 78.06, ' "Wins"': 64} page_content='Cubs' metadata={' "Payroll (millions)"': 88.19, ' "Wins"': 61} page_content='Astros' metadata={' | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
00e6fb67e039-9 | ' "Wins"': 61} page_content='Astros' metadata={' "Payroll (millions)"': 60.65, ' "Wins"': 55}PreviousXMLNextLoading documents from a YouTube urlCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/xorbits |
5237d644c048-0 | Microsoft Word | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/microsoft_word |
5237d644c048-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersMicrosoft WordOn this pageMicrosoft WordMicrosoft Word is a word processor developed by Microsoft.This covers how to load Word documents into a document format that we can use downstream.Using | https://python.langchain.com/docs/integrations/document_loaders/microsoft_word |
5237d644c048-2 | by Microsoft.This covers how to load Word documents into a document format that we can use downstream.Using Docx2txt​Load .docx using Docx2txt into a document.pip install docx2txtfrom langchain.document_loaders import Docx2txtLoaderloader = Docx2txtLoader("example_data/fake.docx")data = loader.load()data [Document(page_content='Lorem ipsum dolor sit amet.', metadata={'source': 'example_data/fake.docx'})]Using Unstructured​from langchain.document_loaders import UnstructuredWordDocumentLoaderloader = UnstructuredWordDocumentLoader("example_data/fake.docx")data = loader.load()data [Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 'fake.docx'}, lookup_index=0)]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 = UnstructuredWordDocumentLoader("example_data/fake.docx", mode="elements")data = loader.load()data[0] Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 'fake.docx', 'filename': 'fake.docx', 'category': 'Title'}, lookup_index=0)PreviousMicrosoft PowerPointNextModern TreasuryUsing Docx2txtUsing UnstructuredRetain ElementsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/microsoft_word |
79e53a7ac20a-0 | Google BigQuery | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/google_bigquery |
79e53a7ac20a-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersGoogle BigQueryOn this pageGoogle BigQueryGoogle BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. | https://python.langchain.com/docs/integrations/document_loaders/google_bigquery |
79e53a7ac20a-2 | BigQuery is a part of the Google Cloud Platform.Load a BigQuery query with one document per row.#!pip install google-cloud-bigqueryfrom langchain.document_loaders import BigQueryLoaderBASE_QUERY = """SELECT id, dna_sequence, organismFROM ( SELECT ARRAY ( SELECT AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp. (strain GC14_75)." AS organism UNION ALL SELECT AS STRUCT 2 AS id, "AGGCGA" AS dna_sequence, "Heimdallarchaeota archaeon (strain LC_2)." AS organism UNION ALL SELECT AS STRUCT 3 AS id, "TCCGGA" AS dna_sequence, "Acidianus hospitalis (strain W1)." AS organism) AS new_array), UNNEST(new_array)"""Basic Usage​loader = BigQueryLoader(BASE_QUERY)data = loader.load()print(data) [Document(page_content='id: 1\ndna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).', lookup_str='', metadata={}, lookup_index=0), Document(page_content='id: 2\ndna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).', lookup_str='', metadata={}, lookup_index=0), Document(page_content='id: 3\ndna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).', lookup_str='', metadata={}, lookup_index=0)]Specifying Which Columns are Content vs Metadata​loader = BigQueryLoader( BASE_QUERY, page_content_columns=["dna_sequence", | https://python.langchain.com/docs/integrations/document_loaders/google_bigquery |
79e53a7ac20a-3 | BigQueryLoader( BASE_QUERY, page_content_columns=["dna_sequence", "organism"], metadata_columns=["id"],)data = loader.load()print(data) [Document(page_content='dna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).', lookup_str='', metadata={'id': 1}, lookup_index=0), Document(page_content='dna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).', lookup_str='', metadata={'id': 2}, lookup_index=0), Document(page_content='dna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).', lookup_str='', metadata={'id': 3}, lookup_index=0)]Adding Source to Metadata​# Note that the `id` column is being returned twice, with one instance aliased as `source`ALIASED_QUERY = """SELECT id, dna_sequence, organism, id as sourceFROM ( SELECT ARRAY ( SELECT AS STRUCT 1 AS id, "ATTCGA" AS dna_sequence, "Lokiarchaeum sp. (strain GC14_75)." AS organism UNION ALL SELECT AS STRUCT 2 AS id, "AGGCGA" AS dna_sequence, "Heimdallarchaeota archaeon (strain LC_2)." AS organism UNION ALL SELECT AS STRUCT 3 AS id, "TCCGGA" AS dna_sequence, "Acidianus hospitalis (strain W1)." AS organism) AS new_array), UNNEST(new_array)"""loader = BigQueryLoader(ALIASED_QUERY, metadata_columns=["source"])data = | https://python.langchain.com/docs/integrations/document_loaders/google_bigquery |
79e53a7ac20a-4 | = BigQueryLoader(ALIASED_QUERY, metadata_columns=["source"])data = loader.load()print(data) [Document(page_content='id: 1\ndna_sequence: ATTCGA\norganism: Lokiarchaeum sp. (strain GC14_75).\nsource: 1', lookup_str='', metadata={'source': 1}, lookup_index=0), Document(page_content='id: 2\ndna_sequence: AGGCGA\norganism: Heimdallarchaeota archaeon (strain LC_2).\nsource: 2', lookup_str='', metadata={'source': 2}, lookup_index=0), Document(page_content='id: 3\ndna_sequence: TCCGGA\norganism: Acidianus hospitalis (strain W1).\nsource: 3', lookup_str='', metadata={'source': 3}, lookup_index=0)]PreviousGitHubNextGoogle Cloud Storage DirectoryBasic UsageSpecifying Which Columns are Content vs MetadataAdding Source to MetadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/google_bigquery |
b7faf45077fb-0 | Figma | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/figma |
b7faf45077fb-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersFigmaFigmaFigma is a collaborative web application for interface design.This notebook covers how to load data from the Figma REST API into a format that can be ingested | https://python.langchain.com/docs/integrations/document_loaders/figma |
b7faf45077fb-2 | notebook covers how to load data from the Figma REST API into a format that can be ingested into LangChain, along with example usage for code generation.import osfrom langchain.document_loaders.figma import FigmaFileLoaderfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.chat_models import ChatOpenAIfrom langchain.indexes import VectorstoreIndexCreatorfrom langchain.chains import ConversationChain, LLMChainfrom langchain.memory import ConversationBufferWindowMemoryfrom langchain.prompts.chat import ( ChatPromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemplate,)The Figma API Requires an access token, node_ids, and a file key.The file key can be pulled from the URL. https://www.figma.com/file/{filekey}/sampleFilenameNode IDs are also available in the URL. Click on anything and look for the '?node-id={node_id}' param.Access token instructions are in the Figma help center article: https://help.figma.com/hc/en-us/articles/8085703771159-Manage-personal-access-tokensfigma_loader = FigmaFileLoader( os.environ.get("ACCESS_TOKEN"), os.environ.get("NODE_IDS"), os.environ.get("FILE_KEY"),)# see https://python.langchain.com/en/latest/modules/data_connection/getting_started.html for more detailsindex = VectorstoreIndexCreator().from_loaders([figma_loader])figma_doc_retriever = index.vectorstore.as_retriever()def generate_code(human_input): # I have no idea if the Jon Carmack thing makes for better code. YMMV. # See https://python.langchain.com/en/latest/modules/models/chat/getting_started.html for chat info system_prompt_template = """You are expert coder Jon Carmack. Use the provided design context to | https://python.langchain.com/docs/integrations/document_loaders/figma |
b7faf45077fb-3 | system_prompt_template = """You are expert coder Jon Carmack. Use the provided design context to create idomatic HTML/CSS code as possible based on the user request. Everything must be inline in one file and your response must be directly renderable by the browser. Figma file nodes and metadata: {context}""" human_prompt_template = "Code the {text}. Ensure it's mobile responsive" system_message_prompt = SystemMessagePromptTemplate.from_template( system_prompt_template ) human_message_prompt = HumanMessagePromptTemplate.from_template( human_prompt_template ) # delete the gpt-4 model_name to use the default gpt-3.5 turbo for faster results gpt_4 = ChatOpenAI(temperature=0.02, model_name="gpt-4") # Use the retriever's 'get_relevant_documents' method if needed to filter down longer docs relevant_nodes = figma_doc_retriever.get_relevant_documents(human_input) conversation = [system_message_prompt, human_message_prompt] chat_prompt = ChatPromptTemplate.from_messages(conversation) response = gpt_4( chat_prompt.format_prompt( context=relevant_nodes, text=human_input ).to_messages() ) return responseresponse = generate_code("page top header")Returns the following in response.content:<!DOCTYPE html>\n<html lang="en">\n<head>\n <meta charset="UTF-8">\n <meta name="viewport" content="width=device-width, initial-scale=1.0">\n | https://python.langchain.com/docs/integrations/document_loaders/figma |
b7faf45077fb-4 | name="viewport" content="width=device-width, initial-scale=1.0">\n <style>\n @import url(\'https://fonts.googleapis.com/css2?family=DM+Sans:wght@500;700&family=Inter:wght@600&display=swap\');\n\n body {\n margin: 0;\n font-family: \'DM Sans\', sans-serif;\n }\n\n .header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n padding: 20px;\n background-color: #fff;\n box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);\n }\n\n .header h1 {\n font-size: 16px;\n font-weight: 700;\n margin: 0;\n }\n\n .header nav {\n display: flex;\n align-items: center;\n | https://python.langchain.com/docs/integrations/document_loaders/figma |
b7faf45077fb-5 | align-items: center;\n }\n\n .header nav a {\n font-size: 14px;\n font-weight: 500;\n text-decoration: none;\n color: #000;\n margin-left: 20px;\n }\n\n @media (max-width: 768px) {\n .header nav {\n display: none;\n }\n }\n </style>\n</head>\n<body>\n <header class="header">\n <h1>Company Contact</h1>\n <nav>\n <a href="#">Lorem Ipsum</a>\n <a href="#">Lorem Ipsum</a>\n <a href="#">Lorem Ipsum</a>\n </nav>\n </header>\n</body>\n</html>PreviousFaunaNextGeopandasCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/figma |
8253fc3f5642-0 | AWS S3 File | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/aws_s3_file |
8253fc3f5642-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersAWS S3 FileAWS S3 FileAmazon Simple Storage Service (Amazon S3) is an object storage service.AWS S3 BucketsThis covers how to load document objects from | https://python.langchain.com/docs/integrations/document_loaders/aws_s3_file |
8253fc3f5642-2 | is an object storage service.AWS S3 BucketsThis covers how to load document objects from an AWS S3 File object.from langchain.document_loaders import S3FileLoader#!pip install boto3loader = S3FileLoader("testing-hwc", "fake.docx")loader.load() [Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpxvave6wl/fake.docx'}, lookup_index=0)]PreviousAWS S3 DirectoryNextAZLyricsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/aws_s3_file |
8435959b917a-0 | Fauna | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/fauna |
8435959b917a-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersFaunaOn this pageFaunaFauna is a Document Database.Query Fauna documents#!pip install faunaQuery data example​from | https://python.langchain.com/docs/integrations/document_loaders/fauna |
8435959b917a-2 | is a Document Database.Query Fauna documents#!pip install faunaQuery data example​from langchain.document_loaders.fauna import FaunaLoadersecret = "<enter-valid-fauna-secret>"query = "Item.all()" # Fauna query. Assumes that the collection is called "Item"field = "text" # The field that contains the page content. Assumes that the field is called "text"loader = FaunaLoader(query, field, secret)docs = loader.lazy_load()for value in docs: print(value)Query with Pagination​You get a after value if there are more data. You can get values after the curcor by passing in the after string in query. To learn more following this linkquery = """Item.paginate("hs+DzoPOg ... aY1hOohozrV7A")Item.all()"""loader = FaunaLoader(query, field, secret)PreviousFacebook ChatNextFigmaQuery data exampleQuery with PaginationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/fauna |
40d0c2b925aa-0 | Grobid | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/grobid |
40d0c2b925aa-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersGrobidGrobidGROBID is a machine learning library for extracting, parsing, and re-structuring raw documents.It is particularly good for sturctured | https://python.langchain.com/docs/integrations/document_loaders/grobid |
40d0c2b925aa-2 | for extracting, parsing, and re-structuring raw documents.It is particularly good for sturctured PDFs, like academic papers.This loader uses GROBIB to parse PDFs into Documents that retain metadata associated with the section of text.For users on Mac - (Note: additional instructions can be found here.)Install Java (Apple Silicon):$ arch -arm64 brew install openjdk@11$ brew --prefix openjdk@11/opt/homebrew/opt/openjdk@ 11In ~/.zshrc:export JAVA_HOME=/opt/homebrew/opt/openjdk@11export PATH=$JAVA_HOME/bin:$PATHThen, in Terminal:$ source ~/.zshrcConfirm install:$ which java/opt/homebrew/opt/openjdk@11/bin/java$ java -version openjdk version "11.0.19" 2023-04-18OpenJDK Runtime Environment Homebrew (build 11.0.19+0)OpenJDK 64-Bit Server VM Homebrew (build 11.0.19+0, mixed mode)Then, get Grobid:$ curl -LO https://github.com/kermitt2/grobid/archive/0.7.3.zip$ unzip 0.7.3.zipBuild$ ./gradlew clean installThen, run the server:get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')Now, we can use the data loader.from langchain.document_loaders.parsers import GrobidParserfrom langchain.document_loaders.generic import GenericLoaderloader = GenericLoader.from_filesystem( "../Papers/", glob="*", suffixes=[".pdf"], parser=GrobidParser(segment_sentences=False),)docs = loader.load()docs[3].page_content 'Unlike Chinchilla, PaLM, or GPT-3, we only use publicly available data, making our work compatible with | https://python.langchain.com/docs/integrations/document_loaders/grobid |
40d0c2b925aa-3 | or GPT-3, we only use publicly available data, making our work compatible with open-sourcing, while most existing models rely on data which is either not publicly available or undocumented (e.g."Books -2TB" or "Social media conversations").There exist some exceptions, notably OPT (Zhang et al., 2022), GPT-NeoX (Black et al., 2022), BLOOM (Scao et al., 2022) and GLM (Zeng et al., 2022), but none that are competitive with PaLM-62B or Chinchilla.'docs[3].metadata {'text': 'Unlike Chinchilla, PaLM, or GPT-3, we only use publicly available data, making our work compatible with open-sourcing, while most existing models rely on data which is either not publicly available or undocumented (e.g."Books -2TB" or "Social media conversations").There exist some exceptions, notably OPT (Zhang et al., 2022), GPT-NeoX (Black et al., 2022), BLOOM (Scao et al., 2022) and GLM (Zeng et al., 2022), but none that are competitive with PaLM-62B or Chinchilla.', 'para': '2', 'bboxes': "[[{'page': '1', 'x': '317.05', 'y': '509.17', 'h': '207.73', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '522.72', 'h': '220.08', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '536.27', 'h': '218.27', 'w': | https://python.langchain.com/docs/integrations/document_loaders/grobid |
40d0c2b925aa-4 | 'y': '536.27', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '549.82', 'h': '218.65', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '563.37', 'h': '136.98', 'w': '9.46'}], [{'page': '1', 'x': '446.49', 'y': '563.37', 'h': '78.11', 'w': '9.46'}, {'page': '1', 'x': '304.69', 'y': '576.92', 'h': '138.32', 'w': '9.46'}], [{'page': '1', 'x': '447.75', 'y': '576.92', 'h': '76.66', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '590.47', 'h': '219.63', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '604.02', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '617.56', 'h': '218.27', 'w': '9.46'}, {'page': '1', 'x': '306.14', 'y': '631.11', 'h': '220.18', 'w': '9.46'}]]", 'pages': "('1', '1')", 'section_title': 'Introduction', | https://python.langchain.com/docs/integrations/document_loaders/grobid |
40d0c2b925aa-5 | '1')", 'section_title': 'Introduction', 'section_number': '1', 'paper_title': 'LLaMA: Open and Efficient Foundation Language Models', 'file_path': '/Users/31treehaus/Desktop/Papers/2302.13971.pdf'}PreviousGoogle DriveNextGutenbergCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/grobid |
89529bbb3b72-0 | Weather | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/weather |
89529bbb3b72-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersWeatherWeatherOpenWeatherMap is an open source weather service providerThis loader fetches the weather data from the OpenWeatherMap's OneCall API, using the pyowm Python | https://python.langchain.com/docs/integrations/document_loaders/weather |
89529bbb3b72-2 | the weather data from the OpenWeatherMap's OneCall API, using the pyowm Python package. You must initialize the loader with your OpenWeatherMap API token and the names of the cities you want the weather data for.from langchain.document_loaders import WeatherDataLoader#!pip install pyowm# Set API key either by passing it in to constructor directly# or by setting the environment variable "OPENWEATHERMAP_API_KEY".from getpass import getpassOPENWEATHERMAP_API_KEY = getpass()loader = WeatherDataLoader.from_params( ["chennai", "vellore"], openweathermap_api_key=OPENWEATHERMAP_API_KEY)documents = loader.load()documentsPreviousURLNextWebBaseLoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/weather |
ab7c2fa7e248-0 | Etherscan Loader | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersEtherscan LoaderOn this pageEtherscan LoaderOverview​The Etherscan loader use etherscan api to load transacactions histories under specific account on | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-2 | Etherscan loader use etherscan api to load transacactions histories under specific account on Ethereum Mainnet.You will need a Etherscan api key to proceed. The free api key has 5 calls per seconds quota.The loader supports the following six functinalities:Retrieve normal transactions under specifc account on Ethereum MainetRetrieve internal transactions under specifc account on Ethereum MainetRetrieve erc20 transactions under specifc account on Ethereum MainetRetrieve erc721 transactions under specifc account on Ethereum MainetRetrieve erc1155 transactions under specifc account on Ethereum MainetRetrieve ethereum balance in wei under specifc account on Ethereum MainetIf the account does not have corresponding transactions, the loader will a list with one document. The content of document is ''.You can pass differnt filters to loader to access different functionalities we mentioned above:"normal_transaction""internal_transaction""erc20_transaction""eth_balance""erc721_transaction""erc1155_transaction" | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-3 | The filter is default to normal_transactionIf you have any questions, you can access Etherscan API Doc or contact me via i@inevitable.tech.All functions related to transactions histories are restricted 1000 histories maximum because of Etherscan limit. You can use the following parameters to find the transaction histories you need:offset: default to 20. Shows 20 transactions for one timepage: default to 1. This controls pagenation.start_block: Default to 0. The transaction histories starts from 0 block.end_block: Default to 99999999. The transaction histories starts from 99999999 blocksort: "desc" or "asc". Set default to "desc" to get latest transactions.Setup%pip install langchain -qfrom langchain.document_loaders import EtherscanLoaderimport osos.environ["ETHERSCAN_API_KEY"] = etherscanAPIKeyCreate a ERC20 transaction loaderaccount_address = "0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b"loader = EtherscanLoader(account_address, filter="erc20_transaction")result = loader.load()eval(result[0].page_content) {'blockNumber': '13242975', 'timeStamp': '1631878751', 'hash': '0x366dda325b1a6570928873665b6b418874a7dedf7fee9426158fa3536b621788', 'nonce': '28', 'blockHash': '0x5469dba1b1e1372962cf2be27ab2640701f88c00640c4d26b8cc2ae9ac256fb6', 'from': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-4 | 'from': '0x2ceee24f8d03fc25648c68c8e6569aa0512f6ac3', 'contractAddress': '0x2ceee24f8d03fc25648c68c8e6569aa0512f6ac3', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '298131000000000', 'tokenName': 'ABCHANGE.io', 'tokenSymbol': 'XCH', 'tokenDecimal': '9', 'transactionIndex': '71', 'gas': '15000000', 'gasPrice': '48614996176', 'gasUsed': '5712724', 'cumulativeGasUsed': '11507920', 'input': 'deprecated', 'confirmations': '4492277'}Create a normal transaction loader with customized parametersloader = EtherscanLoader( account_address, page=2, offset=20, start_block=10000, end_block=8888888888, sort="asc",)result = loader.load()result 20 [Document(page_content="{'blockNumber': '1723771', 'timeStamp': '1466213371', 'hash': '0xe00abf5fa83a4b23ee1cc7f07f9dda04ab5fa5efe358b315df8b76699a83efc4', 'nonce': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-5 | 'nonce': '3155', 'blockHash': '0xc2c2207bcaf341eed07f984c9a90b3f8e8bdbdbd2ac6562f8c2f5bfa4b51299d', 'transactionIndex': '5', 'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '13149213761000000000', 'gas': '90000', 'gasPrice': '22655598156', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '126000', 'gasUsed': '21000', 'confirmations': '16011481', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'tx_hash': '0xe00abf5fa83a4b23ee1cc7f07f9dda04ab5fa5efe358b315df8b76699a83efc4', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1727090', 'timeStamp': '1466262018', 'hash': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-6 | '1727090', 'timeStamp': '1466262018', 'hash': '0xd5a779346d499aa722f72ffe7cd3c8594a9ddd91eb7e439e8ba92ceb7bc86928', 'nonce': '3267', 'blockHash': '0xc0cff378c3446b9b22d217c2c5f54b1c85b89a632c69c55b76cdffe88d2b9f4d', 'transactionIndex': '20', 'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '11521979886000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '3806725', 'gasUsed': '21000', 'confirmations': '16008162', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'tx_hash': '0xd5a779346d499aa722f72ffe7cd3c8594a9ddd91eb7e439e8ba92ceb7bc86928', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-7 | Document(page_content="{'blockNumber': '1730337', 'timeStamp': '1466308222', 'hash': '0xceaffdb3766d2741057d402738eb41e1d1941939d9d438c102fb981fd47a87a4', 'nonce': '3344', 'blockHash': '0x3a52d28b8587d55c621144a161a0ad5c37dd9f7d63b629ab31da04fa410b2cfa', 'transactionIndex': '1', 'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '9783400526000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '60788', 'gasUsed': '21000', 'confirmations': '16004915', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'tx_hash': '0xceaffdb3766d2741057d402738eb41e1d1941939d9d438c102fb981fd47a87a4', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-8 | Document(page_content="{'blockNumber': '1733479', 'timeStamp': '1466352351', 'hash': '0x720d79bf78775f82b40280aae5abfc347643c5f6708d4bf4ec24d65cd01c7121', 'nonce': '3367', 'blockHash': '0x9928661e7ae125b3ae0bcf5e076555a3ee44c52ae31bd6864c9c93a6ebb3f43e', 'transactionIndex': '0', 'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '1570706444000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '21000', 'gasUsed': '21000', 'confirmations': '16001773', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x3763e6e1228bfeab94191c856412d1bb0a8e6996', 'tx_hash': '0x720d79bf78775f82b40280aae5abfc347643c5f6708d4bf4ec24d65cd01c7121', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-9 | Document(page_content="{'blockNumber': '1734172', 'timeStamp': '1466362463', 'hash': '0x7a062d25b83bafc9fe6b22bc6f5718bca333908b148676e1ac66c0adeccef647', 'nonce': '1016', 'blockHash': '0x8a8afe2b446713db88218553cfb5dd202422928e5e0bc00475ed2f37d95649de', 'transactionIndex': '4', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '6322276709000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '105333', 'gasUsed': '21000', 'confirmations': '16001080', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0x7a062d25b83bafc9fe6b22bc6f5718bca333908b148676e1ac66c0adeccef647', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-10 | Document(page_content="{'blockNumber': '1737276', 'timeStamp': '1466406037', 'hash': '0xa4e89bfaf075abbf48f96700979e6c7e11a776b9040113ba64ef9c29ac62b19b', 'nonce': '1024', 'blockHash': '0xe117cad73752bb485c3bef24556e45b7766b283229180fcabc9711f3524b9f79', 'transactionIndex': '35', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '9976891868000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '3187163', 'gasUsed': '21000', 'confirmations': '15997976', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0xa4e89bfaf075abbf48f96700979e6c7e11a776b9040113ba64ef9c29ac62b19b', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-11 | Document(page_content="{'blockNumber': '1740314', 'timeStamp': '1466450262', 'hash': '0x6e1a22dcc6e2c77a9451426fb49e765c3c459dae88350e3ca504f4831ec20e8a', 'nonce': '1051', 'blockHash': '0x588d17842819a81afae3ac6644d8005c12ce55ddb66c8d4c202caa91d4e8fdbe', 'transactionIndex': '6', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '8060633765000000000', 'gas': '90000', 'gasPrice': '22926905859', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '153077', 'gasUsed': '21000', 'confirmations': '15994938', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0x6e1a22dcc6e2c77a9451426fb49e765c3c459dae88350e3ca504f4831ec20e8a', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-12 | Document(page_content="{'blockNumber': '1743384', 'timeStamp': '1466494099', 'hash': '0xdbfcc15f02269fc3ae27f69e344a1ac4e08948b12b76ebdd78a64d8cafd511ef', 'nonce': '1068', 'blockHash': '0x997245108c84250057fda27306b53f9438ad40978a95ca51d8fd7477e73fbaa7', 'transactionIndex': '2', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '9541921352000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '119650', 'gasUsed': '21000', 'confirmations': '15991868', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0xdbfcc15f02269fc3ae27f69e344a1ac4e08948b12b76ebdd78a64d8cafd511ef', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-13 | Document(page_content="{'blockNumber': '1746405', 'timeStamp': '1466538123', 'hash': '0xbd4f9602f7fff4b8cc2ab6286efdb85f97fa114a43f6df4e6abc88e85b89e97b', 'nonce': '1092', 'blockHash': '0x3af3966cdaf22e8b112792ee2e0edd21ceb5a0e7bf9d8c168a40cf22deb3690c', 'transactionIndex': '0', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '8433783799000000000', 'gas': '90000', 'gasPrice': '25689279306', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '21000', 'gasUsed': '21000', 'confirmations': '15988847', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0xbd4f9602f7fff4b8cc2ab6286efdb85f97fa114a43f6df4e6abc88e85b89e97b', 'to': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-14 | 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1749459', 'timeStamp': '1466582044', 'hash': '0x28c327f462cc5013d81c8682c032f014083c6891938a7bdeee85a1c02c3e9ed4', 'nonce': '1096', 'blockHash': '0x5fc5d2a903977b35ce1239975ae23f9157d45d7bd8a8f6205e8ce270000797f9', 'transactionIndex': '1', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '10269065805000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '42000', 'gasUsed': '21000', 'confirmations': '15985793', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-15 | 'tx_hash': '0x28c327f462cc5013d81c8682c032f014083c6891938a7bdeee85a1c02c3e9ed4', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1752614', 'timeStamp': '1466626168', 'hash': '0xc3849e550ca5276d7b3c51fa95ad3ae62c1c164799d33f4388fe60c4e1d4f7d8', 'nonce': '1118', 'blockHash': '0x88ef054b98e47504332609394e15c0a4467f84042396717af6483f0bcd916127', 'transactionIndex': '11', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '11325836780000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '252000', 'gasUsed': '21000', 'confirmations': '15982638', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-16 | 'tx_hash': '0xc3849e550ca5276d7b3c51fa95ad3ae62c1c164799d33f4388fe60c4e1d4f7d8', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1755659', 'timeStamp': '1466669931', 'hash': '0xb9f891b7c3d00fcd64483189890591d2b7b910eda6172e3bf3973c5fd3d5a5ae', 'nonce': '1133', 'blockHash': '0x2983972217a91343860415d1744c2a55246a297c4810908bbd3184785bc9b0c2', 'transactionIndex': '14', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '13226475343000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '2674679', 'gasUsed': '21000', 'confirmations': '15979593', 'methodId': '0x', 'functionName': ''}", metadata={'from': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
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ab7c2fa7e248-20 | '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '168000', 'gasUsed': '21000', 'confirmations': '15970357', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0x620b91b12af7aac75553b47f15742e2825ea38919cfc8082c0666f404a0db28b', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1767936', 'timeStamp': '1466845682', 'hash': '0x758efa27576cd17ebe7b842db4892eac6609e3962a4f9f57b7c84b7b1909512f', 'nonce': '1211', 'blockHash': '0xb01d8fd47b3554a99352ac3e5baf5524f314cfbc4262afcfbea1467b2d682898', 'transactionIndex': '0', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '11914401843000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
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ab7c2fa7e248-22 | 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '21000', 'gasUsed': '21000', 'confirmations': '15964341', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0x9d84470b54ab44b9074b108a0e506cd8badf30457d221e595bb68d63e926b865', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1774044', 'timeStamp': '1466932983', 'hash': '0x958d85270b58b80f1ad228f716bbac8dd9da7c5f239e9f30d8edeb5bb9301d20', 'nonce': '1240', 'blockHash': '0x69cee390378c3b886f9543fb3a1cb2fc97621ec155f7884564d4c866348ce539', 'transactionIndex': '2', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '9979637283000000000', 'gas': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
ab7c2fa7e248-23 | 'value': '9979637283000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '63000', 'gasUsed': '21000', 'confirmations': '15961208', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0x958d85270b58b80f1ad228f716bbac8dd9da7c5f239e9f30d8edeb5bb9301d20', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1777057', 'timeStamp': '1466976422', 'hash': '0xe76ca3603d2f4e7134bdd7a1c3fd553025fc0b793f3fd2a75cd206b8049e74ab', 'nonce': '1248', 'blockHash': '0xc7cacda0ac38c99f1b9bccbeee1562a41781d2cfaa357e8c7b4af6a49584b968', 'transactionIndex': '7', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
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ab7c2fa7e248-25 | 'transactionIndex': '1', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '11890330240000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '42000', 'gasUsed': '21000', 'confirmations': '15955132', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0xc5ec8cecdc9f5ed55a5b8b0ad79c964fb5c49dc1136b6a49e981616c3e70bbe6', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'})]PreviousDocument loadersNextacreomOverviewCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/Etherscan |
b48d76a3c0e0-0 | Microsoft PowerPoint | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/document_loaders/microsoft_powerpoint |
b48d76a3c0e0-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersEtherscan LoaderacreomAirbyte JSONAirtableAlibaba Cloud MaxComputeApify DatasetArxivAsyncHtmlLoaderAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlesschatgpt_loaderCollege ConfidentialConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDuckDBEmailEmbaasEPubEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWikiDumpMergeDocLoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordModern TreasuryNotion DB 1/2Notion DB 2/2ObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFramePsychicPySpark DataFrame LoaderReadTheDocs DocumentationRecursive URL LoaderRedditRoamRocksetRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS File2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameLoading documents from a YouTube urlYouTube transcriptsDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsDocument loadersMicrosoft PowerPointOn this pageMicrosoft PowerPointMicrosoft PowerPoint is a presentation program by Microsoft.This covers how to load Microsoft PowerPoint documents into a document format that we can use downstream.from | https://python.langchain.com/docs/integrations/document_loaders/microsoft_powerpoint |
b48d76a3c0e0-2 | by Microsoft.This covers how to load Microsoft PowerPoint documents into a document format that we can use downstream.from langchain.document_loaders import UnstructuredPowerPointLoaderloader = UnstructuredPowerPointLoader("example_data/fake-power-point.pptx")data = loader.load()data [Document(page_content='Adding a Bullet Slide\n\nFind the bullet slide layout\n\nUse _TextFrame.text for first bullet\n\nUse _TextFrame.add_paragraph() for subsequent bullets\n\nHere is a lot of text!\n\nHere is some text in a text box!', metadata={'source': 'example_data/fake-power-point.pptx'})]Retain Elements​Under the hood, Unstructured creates different "elements" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".loader = UnstructuredPowerPointLoader( "example_data/fake-power-point.pptx", mode="elements")data = loader.load()data[0] Document(page_content='Adding a Bullet Slide', lookup_str='', metadata={'source': 'example_data/fake-power-point.pptx'}, lookup_index=0)PreviousMicrosoft OneDriveNextMicrosoft WordRetain ElementsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/document_loaders/microsoft_powerpoint |