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600 | the index​from langchain.indexes import VectorstoreIndexCreatorindex = VectorstoreIndexCreator().from_loaders([loader])Query​query = "What's the painting about?"index.query(query)query = "What kind of images are there?"index.query(query)PreviousImagesNextIMSDbPrepare a list of image urls from WikimediaCreate the loaderCreate the indexQueryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | By default, the loader utilizes the pre-trained Salesforce BLIP image captioning model. | By default, the loader utilizes the pre-trained Salesforce BLIP image captioning model. ->: the index​from langchain.indexes import VectorstoreIndexCreatorindex = VectorstoreIndexCreator().from_loaders([loader])Query​query = "What's the painting about?"index.query(query)query = "What kind of images are there?"index.query(query)PreviousImagesNextIMSDbPrepare a list of image urls from WikimediaCreate the loaderCreate the indexQueryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
601 | Docugami | ü¶úÔ∏èüîó Langchain | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: Docugami | ü¶úÔ∏èüîó Langchain |
602 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersDocugamiOn this pageDocugamiThis notebook covers how to load documents from Docugami. It | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersDocugamiOn this pageDocugamiThis notebook covers how to load documents from Docugami. It |
603 | covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders.Prerequisites‚ÄãInstall necessary python packages.Grab an access token for your workspace, and make sure it is set as the DOCUGAMI_API_KEY environment variable.Grab some docset and document IDs for your processed documents, as described here: https://help.docugami.com/home/docugami-api# You need the lxml package to use the DocugamiLoader (run pip install directly without "poetry run" if you are not using poetry)poetry run pip install lxml --quietQuick start‚ÄãCreate a Docugami workspace (free trials available)Add your documents (PDF, DOCX or DOC) and allow Docugami to ingest and cluster them into sets of similar documents, e.g. NDAs, Lease Agreements, and Service Agreements. There is no fixed set of document types supported by the system, the clusters created depend on your particular documents, and you can change the docset assignments later.Create an access token via the Developer Playground for your workspace. Detailed instructionsExplore the Docugami API to get a list of your processed docset IDs, or just the document IDs for a particular docset. Use the DocugamiLoader as detailed below, to get rich semantic chunks for your documents.Optionally, build and publish one or more reports or abstracts. This helps Docugami improve the semantic XML with better tags based on your preferences, which are then added to the DocugamiLoader output as metadata. Use techniques like self-querying retriever to do high accuracy Document QA.Advantages vs Other Chunking Techniques‚ÄãAppropriate chunking of your documents is critical for retrieval from documents. Many chunking techniques exist, including simple ones that rely on whitespace and recursive chunk splitting based on character length. Docugami offers a different approach:Intelligent Chunking: Docugami breaks down every document into a hierarchical semantic XML tree of chunks of varying sizes, from | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders.Prerequisites‚ÄãInstall necessary python packages.Grab an access token for your workspace, and make sure it is set as the DOCUGAMI_API_KEY environment variable.Grab some docset and document IDs for your processed documents, as described here: https://help.docugami.com/home/docugami-api# You need the lxml package to use the DocugamiLoader (run pip install directly without "poetry run" if you are not using poetry)poetry run pip install lxml --quietQuick start‚ÄãCreate a Docugami workspace (free trials available)Add your documents (PDF, DOCX or DOC) and allow Docugami to ingest and cluster them into sets of similar documents, e.g. NDAs, Lease Agreements, and Service Agreements. There is no fixed set of document types supported by the system, the clusters created depend on your particular documents, and you can change the docset assignments later.Create an access token via the Developer Playground for your workspace. Detailed instructionsExplore the Docugami API to get a list of your processed docset IDs, or just the document IDs for a particular docset. Use the DocugamiLoader as detailed below, to get rich semantic chunks for your documents.Optionally, build and publish one or more reports or abstracts. This helps Docugami improve the semantic XML with better tags based on your preferences, which are then added to the DocugamiLoader output as metadata. Use techniques like self-querying retriever to do high accuracy Document QA.Advantages vs Other Chunking Techniques‚ÄãAppropriate chunking of your documents is critical for retrieval from documents. Many chunking techniques exist, including simple ones that rely on whitespace and recursive chunk splitting based on character length. Docugami offers a different approach:Intelligent Chunking: Docugami breaks down every document into a hierarchical semantic XML tree of chunks of varying sizes, from |
604 | XML tree of chunks of varying sizes, from single words or numerical values to entire sections. These chunks follow the semantic contours of the document, providing a more meaningful representation than arbitrary length or simple whitespace-based chunking.Structured Representation: In addition, the XML tree indicates the structural contours of every document, using attributes denoting headings, paragraphs, lists, tables, and other common elements, and does that consistently across all supported document formats, such as scanned PDFs or DOCX files. It appropriately handles long-form document characteristics like page headers/footers or multi-column flows for clean text extraction.Semantic Annotations: Chunks are annotated with semantic tags that are coherent across the document set, facilitating consistent hierarchical queries across multiple documents, even if they are written and formatted differently. For example, in set of lease agreements, you can easily identify key provisions like the Landlord, Tenant, or Renewal Date, as well as more complex information such as the wording of any sub-lease provision or whether a specific jurisdiction has an exception section within a Termination Clause.Additional Metadata: Chunks are also annotated with additional metadata, if a user has been using Docugami. This additional metadata can be used for high-accuracy Document QA without context window restrictions. See detailed code walk-through below.import osfrom langchain.document_loaders import DocugamiLoaderLoad Documents‚ÄãIf the DOCUGAMI_API_KEY environment variable is set, there is no need to pass it in to the loader explicitly otherwise you can pass it in as the access_token parameter.DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")# To load all docs in the given docset ID, just don't provide document_idsloader = DocugamiLoader(docset_id="ecxqpipcoe2p", document_ids=["43rj0ds7s0ur"])docs = loader.load()docs [Document(page_content='MUTUAL NON-DISCLOSURE AGREEMENT | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: XML tree of chunks of varying sizes, from single words or numerical values to entire sections. These chunks follow the semantic contours of the document, providing a more meaningful representation than arbitrary length or simple whitespace-based chunking.Structured Representation: In addition, the XML tree indicates the structural contours of every document, using attributes denoting headings, paragraphs, lists, tables, and other common elements, and does that consistently across all supported document formats, such as scanned PDFs or DOCX files. It appropriately handles long-form document characteristics like page headers/footers or multi-column flows for clean text extraction.Semantic Annotations: Chunks are annotated with semantic tags that are coherent across the document set, facilitating consistent hierarchical queries across multiple documents, even if they are written and formatted differently. For example, in set of lease agreements, you can easily identify key provisions like the Landlord, Tenant, or Renewal Date, as well as more complex information such as the wording of any sub-lease provision or whether a specific jurisdiction has an exception section within a Termination Clause.Additional Metadata: Chunks are also annotated with additional metadata, if a user has been using Docugami. This additional metadata can be used for high-accuracy Document QA without context window restrictions. See detailed code walk-through below.import osfrom langchain.document_loaders import DocugamiLoaderLoad Documents‚ÄãIf the DOCUGAMI_API_KEY environment variable is set, there is no need to pass it in to the loader explicitly otherwise you can pass it in as the access_token parameter.DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")# To load all docs in the given docset ID, just don't provide document_idsloader = DocugamiLoader(docset_id="ecxqpipcoe2p", document_ids=["43rj0ds7s0ur"])docs = loader.load()docs [Document(page_content='MUTUAL NON-DISCLOSURE AGREEMENT |
605 | NON-DISCLOSURE AGREEMENT This Mutual Non-Disclosure Agreement (this “ Agreement ”) is entered into and made effective as of April 4 , 2018 between Docugami Inc. , a Delaware corporation , whose address is 150 Lake Street South , Suite 221 , Kirkland , Washington 98033 , and Caleb Divine , an individual, whose address is 1201 Rt 300 , Newburgh NY 12550 .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:ThisMutualNon-disclosureAgreement', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'ThisMutualNon-disclosureAgreement'}), Document(page_content='The above named parties desire to engage in discussions regarding a potential agreement or other transaction between the parties (the “Purpose”). In connection with such discussions, it may be necessary for the parties to disclose to each other certain confidential information or materials to enable them to evaluate whether to enter into such agreement or transaction.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Discussions', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Discussions'}), Document(page_content='In consideration of the foregoing, the parties agree as follows:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Consideration', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Consideration'}), Document(page_content='1. Confidential Information . For purposes of this Agreement , “ Confidential Information ” means any information or materials disclosed by one party to the other party | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: NON-DISCLOSURE AGREEMENT This Mutual Non-Disclosure Agreement (this “ Agreement ”) is entered into and made effective as of April 4 , 2018 between Docugami Inc. , a Delaware corporation , whose address is 150 Lake Street South , Suite 221 , Kirkland , Washington 98033 , and Caleb Divine , an individual, whose address is 1201 Rt 300 , Newburgh NY 12550 .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:ThisMutualNon-disclosureAgreement', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'ThisMutualNon-disclosureAgreement'}), Document(page_content='The above named parties desire to engage in discussions regarding a potential agreement or other transaction between the parties (the “Purpose”). In connection with such discussions, it may be necessary for the parties to disclose to each other certain confidential information or materials to enable them to evaluate whether to enter into such agreement or transaction.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Discussions', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Discussions'}), Document(page_content='In consideration of the foregoing, the parties agree as follows:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Consideration', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Consideration'}), Document(page_content='1. Confidential Information . For purposes of this Agreement , “ Confidential Information ” means any information or materials disclosed by one party to the other party |
606 | disclosed by one party to the other party that: (i) if disclosed in writing or in the form of tangible materials, is marked “confidential” or “proprietary” at the time of such disclosure; (ii) if disclosed orally or by visual presentation, is identified as “confidential” or “proprietary” at the time of such disclosure, and is summarized in a writing sent by the disclosing party to the receiving party within thirty ( 30 ) days after any such disclosure; or (iii) due to its nature or the circumstances of its disclosure, a person exercising reasonable business judgment would understand to be confidential or proprietary.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Purposes/docset:ConfidentialInformation-section/docset:ConfidentialInformation[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ConfidentialInformation'}), Document(page_content="2. Obligations and Restrictions . Each party agrees: (i) to maintain the other party's Confidential Information in strict confidence; (ii) not to disclose such Confidential Information to any third party; and (iii) not to use such Confidential Information for any purpose except for the Purpose. Each party may disclose the other party’s Confidential Information to its employees and consultants who have a bona fide need to know such Confidential Information for the Purpose, but solely to the extent necessary to pursue the Purpose and for no other purpose; provided, that each such employee and consultant first executes a written agreement (or is otherwise already bound by a written agreement) that contains use and nondisclosure restrictions at least as protective of the other party’s Confidential Information as those set forth in this Agreement .", metadata={'xpath': | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: disclosed by one party to the other party that: (i) if disclosed in writing or in the form of tangible materials, is marked “confidential” or “proprietary” at the time of such disclosure; (ii) if disclosed orally or by visual presentation, is identified as “confidential” or “proprietary” at the time of such disclosure, and is summarized in a writing sent by the disclosing party to the receiving party within thirty ( 30 ) days after any such disclosure; or (iii) due to its nature or the circumstances of its disclosure, a person exercising reasonable business judgment would understand to be confidential or proprietary.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Purposes/docset:ConfidentialInformation-section/docset:ConfidentialInformation[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ConfidentialInformation'}), Document(page_content="2. Obligations and Restrictions . Each party agrees: (i) to maintain the other party's Confidential Information in strict confidence; (ii) not to disclose such Confidential Information to any third party; and (iii) not to use such Confidential Information for any purpose except for the Purpose. Each party may disclose the other party’s Confidential Information to its employees and consultants who have a bona fide need to know such Confidential Information for the Purpose, but solely to the extent necessary to pursue the Purpose and for no other purpose; provided, that each such employee and consultant first executes a written agreement (or is otherwise already bound by a written agreement) that contains use and nondisclosure restrictions at least as protective of the other party’s Confidential Information as those set forth in this Agreement .", metadata={'xpath': |
607 | forth in this Agreement .", metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Obligations/docset:ObligationsAndRestrictions-section/docset:ObligationsAndRestrictions', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ObligationsAndRestrictions'}), Document(page_content='3. Exceptions. The obligations and restrictions in Section 2 will not apply to any information or materials that:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Exceptions/docset:Exceptions-section/docset:Exceptions[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Exceptions'}), Document(page_content='(i) were, at the date of disclosure, or have subsequently become, generally known or available to the public through no act or failure to act by the receiving party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheDate/docset:TheDate', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheDate'}), Document(page_content='(ii) were rightfully known by the receiving party prior to receiving such information or materials from the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:SuchInformation/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: forth in this Agreement .", metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Obligations/docset:ObligationsAndRestrictions-section/docset:ObligationsAndRestrictions', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ObligationsAndRestrictions'}), Document(page_content='3. Exceptions. The obligations and restrictions in Section 2 will not apply to any information or materials that:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Exceptions/docset:Exceptions-section/docset:Exceptions[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Exceptions'}), Document(page_content='(i) were, at the date of disclosure, or have subsequently become, generally known or available to the public through no act or failure to act by the receiving party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheDate/docset:TheDate', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheDate'}), Document(page_content='(ii) were rightfully known by the receiving party prior to receiving such information or materials from the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:SuchInformation/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': |
608 | simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}), Document(page_content='(iii) are rightfully acquired by the receiving party from a third party who has the right to disclose such information or materials without breach of any confidentiality obligation to the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheReceivingParty/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}), Document(page_content='4. Compelled Disclosure . Nothing in this Agreement will be deemed to restrict a party from disclosing the other party’s Confidential Information to the extent required by any order, subpoena, law, statute or regulation; provided, that the party required to make such a disclosure uses reasonable efforts to give the other party reasonable advance notice of such required disclosure in order to enable the other party to prevent or limit such disclosure.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Disclosure/docset:CompelledDisclosure-section/docset:CompelledDisclosure', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'CompelledDisclosure'}), Document(page_content='5. Return of Confidential Information . Upon the completion or abandonment of the Purpose, and in any event upon the disclosing party’s request, the receiving party will promptly return to the disclosing party all tangible items and embodiments containing or consisting of the disclosing party’s Confidential Information and all copies thereof (including electronic copies), and any notes, analyses, compilations, | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}), Document(page_content='(iii) are rightfully acquired by the receiving party from a third party who has the right to disclose such information or materials without breach of any confidentiality obligation to the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheReceivingParty/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}), Document(page_content='4. Compelled Disclosure . Nothing in this Agreement will be deemed to restrict a party from disclosing the other party’s Confidential Information to the extent required by any order, subpoena, law, statute or regulation; provided, that the party required to make such a disclosure uses reasonable efforts to give the other party reasonable advance notice of such required disclosure in order to enable the other party to prevent or limit such disclosure.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Disclosure/docset:CompelledDisclosure-section/docset:CompelledDisclosure', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'CompelledDisclosure'}), Document(page_content='5. Return of Confidential Information . Upon the completion or abandonment of the Purpose, and in any event upon the disclosing party’s request, the receiving party will promptly return to the disclosing party all tangible items and embodiments containing or consisting of the disclosing party’s Confidential Information and all copies thereof (including electronic copies), and any notes, analyses, compilations, |
609 | copies), and any notes, analyses, compilations, studies, interpretations, memoranda or other documents (regardless of the form thereof) prepared by or on behalf of the receiving party that contain or are based upon the disclosing party’s Confidential Information .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheCompletion/docset:ReturnofConfidentialInformation-section/docset:ReturnofConfidentialInformation', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ReturnofConfidentialInformation'}), Document(page_content='6. No Obligations . Each party retains the right to determine whether to disclose any Confidential Information to the other party.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoObligations/docset:NoObligations-section/docset:NoObligations[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoObligations'}), Document(page_content='7. No Warranty. ALL CONFIDENTIAL INFORMATION IS PROVIDED BY THE DISCLOSING PARTY “AS IS ”.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoWarranty/docset:NoWarranty-section/docset:NoWarranty[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoWarranty'}), Document(page_content='8. Term. This Agreement will remain in effect for a period of seven ( 7 ) years from the date of last disclosure of Confidential Information by either party, at which time it will terminate.', metadata={'xpath': | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: copies), and any notes, analyses, compilations, studies, interpretations, memoranda or other documents (regardless of the form thereof) prepared by or on behalf of the receiving party that contain or are based upon the disclosing party’s Confidential Information .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheCompletion/docset:ReturnofConfidentialInformation-section/docset:ReturnofConfidentialInformation', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ReturnofConfidentialInformation'}), Document(page_content='6. No Obligations . Each party retains the right to determine whether to disclose any Confidential Information to the other party.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoObligations/docset:NoObligations-section/docset:NoObligations[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoObligations'}), Document(page_content='7. No Warranty. ALL CONFIDENTIAL INFORMATION IS PROVIDED BY THE DISCLOSING PARTY “AS IS ”.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoWarranty/docset:NoWarranty-section/docset:NoWarranty[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoWarranty'}), Document(page_content='8. Term. This Agreement will remain in effect for a period of seven ( 7 ) years from the date of last disclosure of Confidential Information by either party, at which time it will terminate.', metadata={'xpath': |
610 | time it will terminate.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:ThisAgreement/docset:Term-section/docset:Term', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Term'}), Document(page_content='9. Equitable Relief . Each party acknowledges that the unauthorized use or disclosure of the disclosing party’s Confidential Information may cause the disclosing party to incur irreparable harm and significant damages, the degree of which may be difficult to ascertain. Accordingly, each party agrees that the disclosing party will have the right to seek immediate equitable relief to enjoin any unauthorized use or disclosure of its Confidential Information , in addition to any other rights and remedies that it may have at law or otherwise.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:EquitableRelief/docset:EquitableRelief-section/docset:EquitableRelief[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'EquitableRelief'}), Document(page_content='10. Non-compete. To the maximum extent permitted by applicable law, during the Term of this Agreement and for a period of one ( 1 ) year thereafter, Caleb Divine may not market software products or do business that directly or indirectly competes with Docugami software products .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheMaximumExtent/docset:Non-compete-section/docset:Non-compete', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: time it will terminate.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:ThisAgreement/docset:Term-section/docset:Term', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Term'}), Document(page_content='9. Equitable Relief . Each party acknowledges that the unauthorized use or disclosure of the disclosing party’s Confidential Information may cause the disclosing party to incur irreparable harm and significant damages, the degree of which may be difficult to ascertain. Accordingly, each party agrees that the disclosing party will have the right to seek immediate equitable relief to enjoin any unauthorized use or disclosure of its Confidential Information , in addition to any other rights and remedies that it may have at law or otherwise.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:EquitableRelief/docset:EquitableRelief-section/docset:EquitableRelief[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'EquitableRelief'}), Document(page_content='10. Non-compete. To the maximum extent permitted by applicable law, during the Term of this Agreement and for a period of one ( 1 ) year thereafter, Caleb Divine may not market software products or do business that directly or indirectly competes with Docugami software products .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheMaximumExtent/docset:Non-compete-section/docset:Non-compete', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': |
611 | 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Non-compete'}), Document(page_content='11. Miscellaneous. This Agreement will be governed and construed in accordance with the laws of the State of Washington , excluding its body of law controlling conflict of laws. This Agreement is the complete and exclusive understanding and agreement between the parties regarding the subject matter of this Agreement and supersedes all prior agreements, understandings and communications, oral or written, between the parties regarding the subject matter of this Agreement . If any provision of this Agreement is held invalid or unenforceable by a court of competent jurisdiction, that provision of this Agreement will be enforced to the maximum extent permissible and the other provisions of this Agreement will remain in full force and effect. Neither party may assign this Agreement , in whole or in part, by operation of law or otherwise, without the other party’s prior written consent, and any attempted assignment without such consent will be void. This Agreement may be executed in counterparts, each of which will be deemed an original, but all of which together will constitute one and the same instrument.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Accordance/docset:Miscellaneous-section/docset:Miscellaneous', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Miscellaneous'}), Document(page_content='[SIGNATURE PAGE FOLLOWS] IN WITNESS WHEREOF, the parties hereto have executed this Mutual Non-Disclosure Agreement by their duly authorized officers or representatives as of the date first set forth above.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:TheParties', 'id': '43rj0ds7s0ur', 'source': 'NDA | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Non-compete'}), Document(page_content='11. Miscellaneous. This Agreement will be governed and construed in accordance with the laws of the State of Washington , excluding its body of law controlling conflict of laws. This Agreement is the complete and exclusive understanding and agreement between the parties regarding the subject matter of this Agreement and supersedes all prior agreements, understandings and communications, oral or written, between the parties regarding the subject matter of this Agreement . If any provision of this Agreement is held invalid or unenforceable by a court of competent jurisdiction, that provision of this Agreement will be enforced to the maximum extent permissible and the other provisions of this Agreement will remain in full force and effect. Neither party may assign this Agreement , in whole or in part, by operation of law or otherwise, without the other party’s prior written consent, and any attempted assignment without such consent will be void. This Agreement may be executed in counterparts, each of which will be deemed an original, but all of which together will constitute one and the same instrument.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Accordance/docset:Miscellaneous-section/docset:Miscellaneous', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Miscellaneous'}), Document(page_content='[SIGNATURE PAGE FOLLOWS] IN WITNESS WHEREOF, the parties hereto have executed this Mutual Non-Disclosure Agreement by their duly authorized officers or representatives as of the date first set forth above.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:TheParties', 'id': '43rj0ds7s0ur', 'source': 'NDA |
612 | 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheParties'}), Document(page_content='DOCUGAMI INC . : \n\n Caleb Divine : \n\n Signature: Signature: Name: \n\n Jean Paoli Name: Title: \n\n CEO Title:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:DocugamiInc/docset:DocugamiInc/xhtml:table', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': '', 'tag': 'table'})]The metadata for each Document (really, a chunk of an actual PDF, DOC or DOCX) contains some useful additional information:id and source: ID and Name of the file (PDF, DOC or DOCX) the chunk is sourced from within Docugami.xpath: XPath inside the XML representation of the document, for the chunk. Useful for source citations directly to the actual chunk inside the document XML.structure: Structural attributes of the chunk, e.g. h1, h2, div, table, td, etc. Useful to filter out certain kinds of chunks if needed by the caller.tag: Semantic tag for the chunk, using various generative and extractive techniques. More details here: https://github.com/docugami/DFM-benchmarksBasic Use: Docugami Loader for Document QA‚ÄãYou can use the Docugami Loader like a standard loader for Document QA over multiple docs, albeit with much better chunks that follow the natural contours of the document. There are many great tutorials on how to do this, e.g. this one. We can just use the same code, but use the DocugamiLoader for better chunking, instead of loading text or PDF files directly with basic splitting techniques.poetry run pip -q install openai tiktoken chromadbfrom langchain.schema import Documentfrom langchain.vectorstores import Chromafrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.llms import OpenAIfrom langchain.chains import RetrievalQA# For this example, we already have a processed docset for a set of lease documentsloader = DocugamiLoader(docset_id="wh2kned25uqm")documents = loader.load()The | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheParties'}), Document(page_content='DOCUGAMI INC . : \n\n Caleb Divine : \n\n Signature: Signature: Name: \n\n Jean Paoli Name: Title: \n\n CEO Title:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:DocugamiInc/docset:DocugamiInc/xhtml:table', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': '', 'tag': 'table'})]The metadata for each Document (really, a chunk of an actual PDF, DOC or DOCX) contains some useful additional information:id and source: ID and Name of the file (PDF, DOC or DOCX) the chunk is sourced from within Docugami.xpath: XPath inside the XML representation of the document, for the chunk. Useful for source citations directly to the actual chunk inside the document XML.structure: Structural attributes of the chunk, e.g. h1, h2, div, table, td, etc. Useful to filter out certain kinds of chunks if needed by the caller.tag: Semantic tag for the chunk, using various generative and extractive techniques. More details here: https://github.com/docugami/DFM-benchmarksBasic Use: Docugami Loader for Document QA‚ÄãYou can use the Docugami Loader like a standard loader for Document QA over multiple docs, albeit with much better chunks that follow the natural contours of the document. There are many great tutorials on how to do this, e.g. this one. We can just use the same code, but use the DocugamiLoader for better chunking, instead of loading text or PDF files directly with basic splitting techniques.poetry run pip -q install openai tiktoken chromadbfrom langchain.schema import Documentfrom langchain.vectorstores import Chromafrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.llms import OpenAIfrom langchain.chains import RetrievalQA# For this example, we already have a processed docset for a set of lease documentsloader = DocugamiLoader(docset_id="wh2kned25uqm")documents = loader.load()The |
613 | = loader.load()The documents returned by the loader are already split, so we don't need to use a text splitter. Optionally, we can use the metadata on each document, for example the structure or tag attributes, to do any post-processing we want.We will just use the output of the DocugamiLoader as-is to set up a retrieval QA chain the usual way.embedding = OpenAIEmbeddings()vectordb = Chroma.from_documents(documents=documents, embedding=embedding)retriever = vectordb.as_retriever()qa_chain = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True)# Try out the retriever with an example queryqa_chain("What can tenants do with signage on their properties?") {'query': 'What can tenants do with signage on their properties?', 'result': " Tenants can place or attach signs (digital or otherwise) to their premises with written permission from the landlord. The signs must conform to all applicable laws, ordinances, etc. governing the same. Tenants can also have their name listed in the building's directory at the landlord's cost.", 'source_documents': [Document(page_content='ARTICLE VI SIGNAGE 6.01 Signage . Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant ’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises.', metadata={'Landlord': 'BUBBA CENTER PARTNERSHIP', 'Lease Date': 'April 24 \n\n ,', 'Lease Parties': 'This OFFICE LEASE AGREEMENT (this "Lease") is made | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: = loader.load()The documents returned by the loader are already split, so we don't need to use a text splitter. Optionally, we can use the metadata on each document, for example the structure or tag attributes, to do any post-processing we want.We will just use the output of the DocugamiLoader as-is to set up a retrieval QA chain the usual way.embedding = OpenAIEmbeddings()vectordb = Chroma.from_documents(documents=documents, embedding=embedding)retriever = vectordb.as_retriever()qa_chain = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True)# Try out the retriever with an example queryqa_chain("What can tenants do with signage on their properties?") {'query': 'What can tenants do with signage on their properties?', 'result': " Tenants can place or attach signs (digital or otherwise) to their premises with written permission from the landlord. The signs must conform to all applicable laws, ordinances, etc. governing the same. Tenants can also have their name listed in the building's directory at the landlord's cost.", 'source_documents': [Document(page_content='ARTICLE VI SIGNAGE 6.01 Signage . Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant ’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises.', metadata={'Landlord': 'BUBBA CENTER PARTNERSHIP', 'Lease Date': 'April 24 \n\n ,', 'Lease Parties': 'This OFFICE LEASE AGREEMENT (this "Lease") is made |
614 | OFFICE LEASE AGREEMENT (this "Lease") is made and entered into by and between BUBBA CENTER PARTNERSHIP (" Landlord "), and Truetone Lane LLC , a Delaware limited liability company (" Tenant ").', 'Tenant': 'Truetone Lane LLC', 'id': 'v1bvgaozfkak', 'source': 'TruTone Lane 2.docx', 'structure': 'div', 'tag': '_601Signage', 'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:Article/docset:ARTICLEVISIGNAGE-section/docset:_601Signage-section/docset:_601Signage'}), Document(page_content='Signage. Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant ’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises. \n\n ARTICLE VII UTILITIES 7.01', metadata={'Landlord': 'GLORY ROAD LLC', 'Lease Date': 'April 30 , 2020', 'Lease Parties': 'This OFFICE LEASE AGREEMENT (this "Lease") is made and entered into by and between GLORY ROAD LLC (" Landlord "), and Truetone Lane LLC , a Delaware limited liability company (" Tenant ").', 'Tenant': 'Truetone Lane LLC', 'id': 'g2fvhekmltza', 'source': 'TruTone Lane 6.pdf', 'structure': 'lim', 'tag': 'chunk', 'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:Article/docset:ArticleIiiUse/docset:ARTICLEIIIUSEANDCAREOFPREMISES-section/docset:ARTICLEIIIUSEANDCAREOFPREMISES/docset:AnyTime/docset:Addition/dg:chunk'}), Document(page_content='Landlord , its agents, | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: OFFICE LEASE AGREEMENT (this "Lease") is made and entered into by and between BUBBA CENTER PARTNERSHIP (" Landlord "), and Truetone Lane LLC , a Delaware limited liability company (" Tenant ").', 'Tenant': 'Truetone Lane LLC', 'id': 'v1bvgaozfkak', 'source': 'TruTone Lane 2.docx', 'structure': 'div', 'tag': '_601Signage', 'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:Article/docset:ARTICLEVISIGNAGE-section/docset:_601Signage-section/docset:_601Signage'}), Document(page_content='Signage. Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant ’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises. \n\n ARTICLE VII UTILITIES 7.01', metadata={'Landlord': 'GLORY ROAD LLC', 'Lease Date': 'April 30 , 2020', 'Lease Parties': 'This OFFICE LEASE AGREEMENT (this "Lease") is made and entered into by and between GLORY ROAD LLC (" Landlord "), and Truetone Lane LLC , a Delaware limited liability company (" Tenant ").', 'Tenant': 'Truetone Lane LLC', 'id': 'g2fvhekmltza', 'source': 'TruTone Lane 6.pdf', 'structure': 'lim', 'tag': 'chunk', 'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:Article/docset:ArticleIiiUse/docset:ARTICLEIIIUSEANDCAREOFPREMISES-section/docset:ARTICLEIIIUSEANDCAREOFPREMISES/docset:AnyTime/docset:Addition/dg:chunk'}), Document(page_content='Landlord , its agents, |
615 | Document(page_content='Landlord , its agents, servants, employees, licensees, invitees, and contractors during the last year of the term of this Lease at any and all times during regular business hours, after 24 hour notice to tenant, to pass and repass on and through the Premises, or such portion thereof as may be necessary, in order that they or any of them may gain access to the Premises for the purpose of showing the Premises to potential new tenants or real estate brokers. In addition, Landlord shall be entitled to place a "FOR RENT " or "FOR LEASE" sign (not exceeding 8.5 ” x 11 ”) in the front window of the Premises during the last six months of the term of this Lease .', metadata={'Landlord': 'BIRCH STREET , LLC', 'Lease Date': 'October 15 , 2021', 'Lease Parties': 'The provisions of this rider are hereby incorporated into and made a part of the Lease dated as of October 15 , 2021 between BIRCH STREET , LLC , having an address at c/o Birch Palace , 6 Grace Avenue Suite 200 , Great Neck , New York 11021 (" Landlord "), and Trutone Lane LLC , having an address at 4 Pearl Street , New York , New York 10012 (" Tenant ") of Premises known as the ground floor space and lower level space, as per floor plan annexed hereto and made a part hereof as Exhibit A (“Premises”) at 4 Pearl Street , New York , New York 10012 in the City of New York , Borough of Manhattan , to which this rider is annexed. If there is any conflict between the provisions of this rider and the remainder of this Lease , the provisions of this rider shall govern.', 'Tenant': 'Trutone Lane LLC', 'id': 'omvs4mysdk6b', 'source': 'TruTone Lane 1.docx', 'structure': 'p', 'tag': 'Landlord', 'xpath': '/docset:Rider/docset:RIDERTOLEASE-section/docset:RIDERTOLEASE/docset:FixedRent/docset:TermYearPeriod/docset:Lease/docset:_42FLandlordSAccess-section/docset:_42FLandlordSAccess/docset:LandlordsRights/docset:Landlord'}), | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: Document(page_content='Landlord , its agents, servants, employees, licensees, invitees, and contractors during the last year of the term of this Lease at any and all times during regular business hours, after 24 hour notice to tenant, to pass and repass on and through the Premises, or such portion thereof as may be necessary, in order that they or any of them may gain access to the Premises for the purpose of showing the Premises to potential new tenants or real estate brokers. In addition, Landlord shall be entitled to place a "FOR RENT " or "FOR LEASE" sign (not exceeding 8.5 ” x 11 ”) in the front window of the Premises during the last six months of the term of this Lease .', metadata={'Landlord': 'BIRCH STREET , LLC', 'Lease Date': 'October 15 , 2021', 'Lease Parties': 'The provisions of this rider are hereby incorporated into and made a part of the Lease dated as of October 15 , 2021 between BIRCH STREET , LLC , having an address at c/o Birch Palace , 6 Grace Avenue Suite 200 , Great Neck , New York 11021 (" Landlord "), and Trutone Lane LLC , having an address at 4 Pearl Street , New York , New York 10012 (" Tenant ") of Premises known as the ground floor space and lower level space, as per floor plan annexed hereto and made a part hereof as Exhibit A (“Premises”) at 4 Pearl Street , New York , New York 10012 in the City of New York , Borough of Manhattan , to which this rider is annexed. If there is any conflict between the provisions of this rider and the remainder of this Lease , the provisions of this rider shall govern.', 'Tenant': 'Trutone Lane LLC', 'id': 'omvs4mysdk6b', 'source': 'TruTone Lane 1.docx', 'structure': 'p', 'tag': 'Landlord', 'xpath': '/docset:Rider/docset:RIDERTOLEASE-section/docset:RIDERTOLEASE/docset:FixedRent/docset:TermYearPeriod/docset:Lease/docset:_42FLandlordSAccess-section/docset:_42FLandlordSAccess/docset:LandlordsRights/docset:Landlord'}), |
616 | Document(page_content="24. SIGNS . No signage shall be placed by Tenant on any portion of the Project . However, Tenant shall be permitted to place a sign bearing its name in a location approved by Landlord near the entrance to the Premises (at Tenant's cost ) and will be furnished a single listing of its name in the Building's directory (at Landlord 's cost ), all in accordance with the criteria adopted from time to time by Landlord for the Project . Any changes or additional listings in the directory shall be furnished (subject to availability of space) for the then Building Standard charge .", metadata={'Landlord': 'Perry & Blair LLC', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'dsyfhh4vpeyf', 'source': 'Shorebucks LLC_CO.pdf', 'structure': 'div', 'tag': 'SIGNS', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:ThisLease-section/docset:ThisLease/docset:Guaranty-section/docset:Guaranty[2]/docset:TheTransfer/docset:TheTerms/docset:Indemnification/docset:INDEMNIFICATION-section/docset:INDEMNIFICATION/docset:Waiver/docset:Waiver/docset:Signs/docset:SIGNS-section/docset:SIGNS'})]}Using Docugami to Add Metadata to Chunks for High Accuracy Document QA‚ÄãOne issue with large documents is that the correct answer to your question may depend on chunks that are far apart in the document. Typical chunking techniques, even with overlap, will struggle with providing the LLM sufficent context to answer such questions. With upcoming very large context LLMs, it may be possible to stuff a lot of tokens, | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: Document(page_content="24. SIGNS . No signage shall be placed by Tenant on any portion of the Project . However, Tenant shall be permitted to place a sign bearing its name in a location approved by Landlord near the entrance to the Premises (at Tenant's cost ) and will be furnished a single listing of its name in the Building's directory (at Landlord 's cost ), all in accordance with the criteria adopted from time to time by Landlord for the Project . Any changes or additional listings in the directory shall be furnished (subject to availability of space) for the then Building Standard charge .", metadata={'Landlord': 'Perry & Blair LLC', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'dsyfhh4vpeyf', 'source': 'Shorebucks LLC_CO.pdf', 'structure': 'div', 'tag': 'SIGNS', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:ThisLease-section/docset:ThisLease/docset:Guaranty-section/docset:Guaranty[2]/docset:TheTransfer/docset:TheTerms/docset:Indemnification/docset:INDEMNIFICATION-section/docset:INDEMNIFICATION/docset:Waiver/docset:Waiver/docset:Signs/docset:SIGNS-section/docset:SIGNS'})]}Using Docugami to Add Metadata to Chunks for High Accuracy Document QA‚ÄãOne issue with large documents is that the correct answer to your question may depend on chunks that are far apart in the document. Typical chunking techniques, even with overlap, will struggle with providing the LLM sufficent context to answer such questions. With upcoming very large context LLMs, it may be possible to stuff a lot of tokens, |
617 | it may be possible to stuff a lot of tokens, perhaps even entire documents, inside the context but this will still hit limits at some point with very long documents, or a lot of documents.For example, if we ask a more complex question that requires the LLM to draw on chunks from different parts of the document, even OpenAI's powerful LLM is unable to answer correctly.chain_response = qa_chain("What is rentable area for the property owned by DHA Group?")chain_response["result"] # correct answer should be 13,500 sq ft ' 9,753 square feet.'At first glance the answer may seem reasonable, but if you review the source chunks carefully for this answer, you will see that the chunking of the document did not end up putting the Landlord name and the rentable area in the same context, since they are far apart in the document. The retriever therefore ends up finding unrelated chunks from other documents not even related to the DHA Group landlord. That landlord happens to be mentioned on the first page of the file Shorebucks LLC_NJ.pdf file, and while one of the source chunks used by the chain is indeed from that doc that contains the correct answer (13,500), other source chunks from different docs are included, and the answer is therefore incorrect.chain_response["source_documents"] [Document(page_content='1.1 Landlord . DHA Group , a Delaware limited liability company authorized to transact business in New Jersey .', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'DhaGroup', 'xpath': | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: it may be possible to stuff a lot of tokens, perhaps even entire documents, inside the context but this will still hit limits at some point with very long documents, or a lot of documents.For example, if we ask a more complex question that requires the LLM to draw on chunks from different parts of the document, even OpenAI's powerful LLM is unable to answer correctly.chain_response = qa_chain("What is rentable area for the property owned by DHA Group?")chain_response["result"] # correct answer should be 13,500 sq ft ' 9,753 square feet.'At first glance the answer may seem reasonable, but if you review the source chunks carefully for this answer, you will see that the chunking of the document did not end up putting the Landlord name and the rentable area in the same context, since they are far apart in the document. The retriever therefore ends up finding unrelated chunks from other documents not even related to the DHA Group landlord. That landlord happens to be mentioned on the first page of the file Shorebucks LLC_NJ.pdf file, and while one of the source chunks used by the chain is indeed from that doc that contains the correct answer (13,500), other source chunks from different docs are included, and the answer is therefore incorrect.chain_response["source_documents"] [Document(page_content='1.1 Landlord . DHA Group , a Delaware limited liability company authorized to transact business in New Jersey .', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'DhaGroup', 'xpath': |
618 | 'structure': 'div', 'tag': 'DhaGroup', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:DhaGroup/docset:Landlord-section/docset:DhaGroup'}), Document(page_content='WITNESSES: LANDLORD: DHA Group , a Delaware limited liability company', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'DhaGroup', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Guaranty-section/docset:Guaranty[2]/docset:SIGNATURESONNEXTPAGE-section/docset:INWITNESSWHEREOF-section/docset:INWITNESSWHEREOF/docset:Behalf/docset:Witnesses/xhtml:table/xhtml:tbody/xhtml:tr[3]/xhtml:td[2]/docset:DhaGroup'}), Document(page_content="1.16 Landlord 's Notice Address . DHA Group , Suite 1010 , 111 Bauer Dr , Oakland , New Jersey , 07436 , with a copy to the Building Management Office at the Project , Attention: On - Site Property Manager .", metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: 'structure': 'div', 'tag': 'DhaGroup', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:DhaGroup/docset:Landlord-section/docset:DhaGroup'}), Document(page_content='WITNESSES: LANDLORD: DHA Group , a Delaware limited liability company', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'DhaGroup', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Guaranty-section/docset:Guaranty[2]/docset:SIGNATURESONNEXTPAGE-section/docset:INWITNESSWHEREOF-section/docset:INWITNESSWHEREOF/docset:Behalf/docset:Witnesses/xhtml:table/xhtml:tbody/xhtml:tr[3]/xhtml:td[2]/docset:DhaGroup'}), Document(page_content="1.16 Landlord 's Notice Address . DHA Group , Suite 1010 , 111 Bauer Dr , Oakland , New Jersey , 07436 , with a copy to the Building Management Office at the Project , Attention: On - Site Property Manager .", metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', |
619 | Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'LandlordsNoticeAddress', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:NoticeAddress[2]/docset:LandlordsNoticeAddress-section/docset:LandlordsNoticeAddress[2]'}), Document(page_content='1.6 Rentable Area of the Premises. 9,753 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'Perry & Blair LLC', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'dsyfhh4vpeyf', 'source': 'Shorebucks LLC_CO.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:PerryBlair/docset:PerryBlair/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'})]Docugami can help here. Chunks are annotated with additional metadata created using different techniques if a user has been using Docugami. More technical approaches will be added later.Specifically, let's look at the additional | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'LandlordsNoticeAddress', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:NoticeAddress[2]/docset:LandlordsNoticeAddress-section/docset:LandlordsNoticeAddress[2]'}), Document(page_content='1.6 Rentable Area of the Premises. 9,753 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'Perry & Blair LLC', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'dsyfhh4vpeyf', 'source': 'Shorebucks LLC_CO.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:PerryBlair/docset:PerryBlair/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'})]Docugami can help here. Chunks are annotated with additional metadata created using different techniques if a user has been using Docugami. More technical approaches will be added later.Specifically, let's look at the additional |
620 | later.Specifically, let's look at the additional metadata that is returned on the documents returned by docugami, in the form of some simple key/value pairs on all the text chunks:loader = DocugamiLoader(docset_id="wh2kned25uqm")documents = loader.load()documents[0].metadata {'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:LeaseParties', 'id': 'v1bvgaozfkak', 'source': 'TruTone Lane 2.docx', 'structure': 'p', 'tag': 'LeaseParties', 'Lease Date': 'April 24 \n\n ,', 'Landlord': 'BUBBA CENTER PARTNERSHIP', 'Tenant': 'Truetone Lane LLC', 'Lease Parties': 'This OFFICE LEASE AGREEMENT (this "Lease") is made and entered into by and between BUBBA CENTER PARTNERSHIP (" Landlord "), and Truetone Lane LLC , a Delaware limited liability company (" Tenant ").'}We can use a self-querying retriever to improve our query accuracy, using this additional metadata:from langchain.chains.query_constructor.schema import AttributeInfofrom langchain.retrievers.self_query.base import SelfQueryRetrieverEXCLUDE_KEYS = ["id", "xpath", "structure"]metadata_field_info = [ AttributeInfo( name=key, description=f"The {key} for this chunk", type="string", ) for key in documents[0].metadata if key.lower() not in EXCLUDE_KEYS]document_content_description = "Contents of this chunk"llm = OpenAI(temperature=0)vectordb = Chroma.from_documents(documents=documents, embedding=embedding)retriever = SelfQueryRetriever.from_llm( llm, vectordb, document_content_description, metadata_field_info, verbose=True)qa_chain = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True)Let's run the same question again. It returns the correct result since all the chunks have metadata key/value pairs on them carrying key information about the document even if this information is physically very far away from the source chunk used to generate the | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: later.Specifically, let's look at the additional metadata that is returned on the documents returned by docugami, in the form of some simple key/value pairs on all the text chunks:loader = DocugamiLoader(docset_id="wh2kned25uqm")documents = loader.load()documents[0].metadata {'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:LeaseParties', 'id': 'v1bvgaozfkak', 'source': 'TruTone Lane 2.docx', 'structure': 'p', 'tag': 'LeaseParties', 'Lease Date': 'April 24 \n\n ,', 'Landlord': 'BUBBA CENTER PARTNERSHIP', 'Tenant': 'Truetone Lane LLC', 'Lease Parties': 'This OFFICE LEASE AGREEMENT (this "Lease") is made and entered into by and between BUBBA CENTER PARTNERSHIP (" Landlord "), and Truetone Lane LLC , a Delaware limited liability company (" Tenant ").'}We can use a self-querying retriever to improve our query accuracy, using this additional metadata:from langchain.chains.query_constructor.schema import AttributeInfofrom langchain.retrievers.self_query.base import SelfQueryRetrieverEXCLUDE_KEYS = ["id", "xpath", "structure"]metadata_field_info = [ AttributeInfo( name=key, description=f"The {key} for this chunk", type="string", ) for key in documents[0].metadata if key.lower() not in EXCLUDE_KEYS]document_content_description = "Contents of this chunk"llm = OpenAI(temperature=0)vectordb = Chroma.from_documents(documents=documents, embedding=embedding)retriever = SelfQueryRetriever.from_llm( llm, vectordb, document_content_description, metadata_field_info, verbose=True)qa_chain = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=True)Let's run the same question again. It returns the correct result since all the chunks have metadata key/value pairs on them carrying key information about the document even if this information is physically very far away from the source chunk used to generate the |
621 | away from the source chunk used to generate the answer.qa_chain( "What is rentable area for the property owned by DHA Group?") # correct answer should be 13,500 sq ft /root/Source/github/docugami.langchain/libs/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain. warnings.warn( query='rentable area' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='Landlord', value='DHA Group') limit=None {'query': 'What is rentable area for the property owned by DHA Group?', 'result': ' The rentable area for the property owned by DHA Group is 13,500 square feet.', 'source_documents': [Document(page_content='1.6 Rentable Area of the Premises. 13,500 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'}), Document(page_content='1.6 Rentable Area of the Premises. 13,500 square feet . This square footage figure | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: away from the source chunk used to generate the answer.qa_chain( "What is rentable area for the property owned by DHA Group?") # correct answer should be 13,500 sq ft /root/Source/github/docugami.langchain/libs/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain. warnings.warn( query='rentable area' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='Landlord', value='DHA Group') limit=None {'query': 'What is rentable area for the property owned by DHA Group?', 'result': ' The rentable area for the property owned by DHA Group is 13,500 square feet.', 'source_documents': [Document(page_content='1.6 Rentable Area of the Premises. 13,500 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'}), Document(page_content='1.6 Rentable Area of the Premises. 13,500 square feet . This square footage figure |
622 | 13,500 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'}), Document(page_content='1.11 Percentage Rent . (a) 55 % of Gross Revenue to Landlord until Landlord receives Percentage Rent in an amount equal to the Annual Market Rent Hurdle (as escalated); and', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'GrossRevenue', 'xpath': | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: 13,500 square feet . This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'}), Document(page_content='1.11 Percentage Rent . (a) 55 % of Gross Revenue to Landlord until Landlord receives Percentage Rent in an amount equal to the Annual Market Rent Hurdle (as escalated); and', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'GrossRevenue', 'xpath': |
623 | 'structure': 'p', 'tag': 'GrossRevenue', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent-section/docset:PercentageRent[2]/docset:PercentageRent/docset:GrossRevenue[1]/docset:GrossRevenue'}), Document(page_content='1.11 Percentage Rent . (a) 55 % of Gross Revenue to Landlord until Landlord receives Percentage Rent in an amount equal to the Annual Market Rent Hurdle (as escalated); and', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'GrossRevenue', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent-section/docset:PercentageRent[2]/docset:PercentageRent/docset:GrossRevenue[1]/docset:GrossRevenue'})]}This time the answer is correct, since the self-querying retriever created a filter on the landlord attribute of the metadata, correctly filtering to document that specifically is about the DHA Group landlord. The resulting source chunks are all relevant to this landlord, and this improves answer accuracy even though the landlord is not directly mentioned in the specific chunk that contains the correct | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: 'structure': 'p', 'tag': 'GrossRevenue', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent-section/docset:PercentageRent[2]/docset:PercentageRent/docset:GrossRevenue[1]/docset:GrossRevenue'}), Document(page_content='1.11 Percentage Rent . (a) 55 % of Gross Revenue to Landlord until Landlord receives Percentage Rent in an amount equal to the Annual Market Rent Hurdle (as escalated); and', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March 29th , 2019', 'Lease Parties': 'THIS OFFICE LEASE (the "Lease") is made and entered into as of March 29th , 2019 , by and between Landlord and Tenant . "Date of this Lease" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'GrossRevenue', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent-section/docset:PercentageRent[2]/docset:PercentageRent/docset:GrossRevenue[1]/docset:GrossRevenue'})]}This time the answer is correct, since the self-querying retriever created a filter on the landlord attribute of the metadata, correctly filtering to document that specifically is about the DHA Group landlord. The resulting source chunks are all relevant to this landlord, and this improves answer accuracy even though the landlord is not directly mentioned in the specific chunk that contains the correct |
624 | in the specific chunk that contains the correct answer.PreviousDiscordNextDropboxPrerequisitesQuick startAdvantages vs Other Chunking TechniquesLoad DocumentsBasic Use: Docugami Loader for Document QAUsing Docugami to Add Metadata to Chunks for High Accuracy Document QACommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. | This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders. ->: in the specific chunk that contains the correct answer.PreviousDiscordNextDropboxPrerequisitesQuick startAdvantages vs Other Chunking TechniquesLoad DocumentsBasic Use: Docugami Loader for Document QAUsing Docugami to Add Metadata to Chunks for High Accuracy Document QACommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
625 | Self-querying retriever | 🦜�🔗 Langchain | Learn about how the self-querying retriever works here. | Learn about how the self-querying retriever works here. ->: Self-querying retriever | 🦜�🔗 Langchain |
626 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverSelf-querying retrieverLearn about how the self-querying retriever works here.📄� Deep LakeDeep Lake is a multimodal database for building AI applications📄� ChromaChroma is a database for building AI applications with embeddings.📄� DashVectorDashVector is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.📄� ElasticsearchElasticsearch is a distributed, RESTful search and analytics engine.📄� MilvusMilvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.📄� MyScaleMyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain.📄� OpenSearchOpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on | Learn about how the self-querying retriever works here. | Learn about how the self-querying retriever works here. ->: Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverSelf-querying retrieverLearn about how the self-querying retriever works here.📄� Deep LakeDeep Lake is a multimodal database for building AI applications📄� ChromaChroma is a database for building AI applications with embeddings.📄� DashVectorDashVector is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.📄� ElasticsearchElasticsearch is a distributed, RESTful search and analytics engine.📄� MilvusMilvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.📄� MyScaleMyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain.📄� OpenSearchOpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on |
627 | distributed search and analytics engine based on Apache Lucene.📄� PineconePinecone is a vector database with broad functionality.📄� QdrantQdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.📄� RedisRedis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.📄� SupabaseSupabase is an open-source Firebase alternative.📄� Timescale Vector (Postgres) self-queryingTimescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.📄� VectaraVectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation📄� WeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings fromPreviousSEC filingNextDeep LakeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Learn about how the self-querying retriever works here. | Learn about how the self-querying retriever works here. ->: distributed search and analytics engine based on Apache Lucene.📄� PineconePinecone is a vector database with broad functionality.📄� QdrantQdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.📄� RedisRedis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.📄� SupabaseSupabase is an open-source Firebase alternative.📄� Timescale Vector (Postgres) self-queryingTimescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.📄� VectaraVectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation📄� WeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings fromPreviousSEC filingNextDeep LakeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
628 | Self-querying retriever | 🦜�🔗 Langchain | Learn about how the self-querying retriever works here. | Learn about how the self-querying retriever works here. ->: Self-querying retriever | 🦜�🔗 Langchain |
629 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverSelf-querying retrieverLearn about how the self-querying retriever works here.📄� Deep LakeDeep Lake is a multimodal database for building AI applications📄� ChromaChroma is a database for building AI applications with embeddings.📄� DashVectorDashVector is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.📄� ElasticsearchElasticsearch is a distributed, RESTful search and analytics engine.📄� MilvusMilvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.📄� MyScaleMyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain.📄� OpenSearchOpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on | Learn about how the self-querying retriever works here. | Learn about how the self-querying retriever works here. ->: Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverSelf-querying retrieverLearn about how the self-querying retriever works here.📄� Deep LakeDeep Lake is a multimodal database for building AI applications📄� ChromaChroma is a database for building AI applications with embeddings.📄� DashVectorDashVector is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.📄� ElasticsearchElasticsearch is a distributed, RESTful search and analytics engine.📄� MilvusMilvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.📄� MyScaleMyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain.📄� OpenSearchOpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on |
630 | distributed search and analytics engine based on Apache Lucene.📄� PineconePinecone is a vector database with broad functionality.📄� QdrantQdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.📄� RedisRedis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.📄� SupabaseSupabase is an open-source Firebase alternative.📄� Timescale Vector (Postgres) self-queryingTimescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.📄� VectaraVectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation📄� WeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings fromPreviousSEC filingNextDeep LakeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Learn about how the self-querying retriever works here. | Learn about how the self-querying retriever works here. ->: distributed search and analytics engine based on Apache Lucene.📄� PineconePinecone is a vector database with broad functionality.📄� QdrantQdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.📄� RedisRedis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.📄� SupabaseSupabase is an open-source Firebase alternative.📄� Timescale Vector (Postgres) self-queryingTimescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.📄� VectaraVectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation📄� WeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings fromPreviousSEC filingNextDeep LakeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
631 | Etherscan | ü¶úÔ∏èüîó Langchain | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, ->: Etherscan | ü¶úÔ∏èüîó Langchain |
632 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersEtherscanOn this pageEtherscanEtherscan is the leading blockchain explorer, search, API and | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersEtherscanOn this pageEtherscanEtherscan is the leading blockchain explorer, search, API and |
633 | the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, ->: the leading blockchain explorer, search, API and analytics platform for Ethereum, |
634 | a decentralized smart contracts platform.Overview‚ÄãThe Etherscan loader use Etherscan API to load transactions 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 functionalities:Retrieve normal transactions under specific account on Ethereum MainetRetrieve internal transactions under specific account on Ethereum MainetRetrieve erc20 transactions under specific account on Ethereum MainetRetrieve erc721 transactions under specific account on Ethereum MainetRetrieve erc1155 transactions under specific account on Ethereum MainetRetrieve ethereum balance in wei under specific 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 different filters to loader to access different functionalities we mentioned above:"normal_transaction""internal_transaction""erc20_transaction""eth_balance""erc721_transaction""erc1155_transaction" | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, ->: a decentralized smart contracts platform.Overview‚ÄãThe Etherscan loader use Etherscan API to load transactions 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 functionalities:Retrieve normal transactions under specific account on Ethereum MainetRetrieve internal transactions under specific account on Ethereum MainetRetrieve erc20 transactions under specific account on Ethereum MainetRetrieve erc721 transactions under specific account on Ethereum MainetRetrieve erc1155 transactions under specific account on Ethereum MainetRetrieve ethereum balance in wei under specific 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 different filters to loader to access different functionalities we mentioned above:"normal_transaction""internal_transaction""erc20_transaction""eth_balance""erc721_transaction""erc1155_transaction" |
635 | The filter is default to normal_transactionIf you have any questions, you can access Etherscan API Doc or contact me via [email protected] 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 pagination.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‚Äã%pip install langchain -qfrom langchain.document_loaders import EtherscanLoaderimport osos.environ["ETHERSCAN_API_KEY"] = etherscanAPIKeyCreate a ERC20 transaction loader‚Äãaccount_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': '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 parameters‚Äãloader = EtherscanLoader( account_address, page=2, offset=20, start_block=10000, end_block=8888888888, sort="asc",)result = | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, ->: The filter is default to normal_transactionIf you have any questions, you can access Etherscan API Doc or contact me via [email protected] 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 pagination.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‚Äã%pip install langchain -qfrom langchain.document_loaders import EtherscanLoaderimport osos.environ["ETHERSCAN_API_KEY"] = etherscanAPIKeyCreate a ERC20 transaction loader‚Äãaccount_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': '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 parameters‚Äãloader = EtherscanLoader( account_address, page=2, offset=20, start_block=10000, end_block=8888888888, sort="asc",)result = |
636 | end_block=8888888888, sort="asc",)result = loader.load()result 20 [Document(page_content="{'blockNumber': '1723771', 'timeStamp': '1466213371', 'hash': '0xe00abf5fa83a4b23ee1cc7f07f9dda04ab5fa5efe358b315df8b76699a83efc4', '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': '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': '1730337', 'timeStamp': '1466308222', 'hash': '0xceaffdb3766d2741057d402738eb41e1d1941939d9d438c102fb981fd47a87a4', 'nonce': '3344', 'blockHash': | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, ->: end_block=8888888888, sort="asc",)result = loader.load()result 20 [Document(page_content="{'blockNumber': '1723771', 'timeStamp': '1466213371', 'hash': '0xe00abf5fa83a4b23ee1cc7f07f9dda04ab5fa5efe358b315df8b76699a83efc4', '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': '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': '1730337', 'timeStamp': '1466308222', 'hash': '0xceaffdb3766d2741057d402738eb41e1d1941939d9d438c102fb981fd47a87a4', 'nonce': '3344', 'blockHash': |
637 | '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'}), 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'}), Document(page_content="{'blockNumber': '1734172', 'timeStamp': '1466362463', 'hash': '0x7a062d25b83bafc9fe6b22bc6f5718bca333908b148676e1ac66c0adeccef647', 'nonce': '1016', 'blockHash': '0x8a8afe2b446713db88218553cfb5dd202422928e5e0bc00475ed2f37d95649de', 'transactionIndex': '4', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '6322276709000000000', 'gas': | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, ->: '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'}), 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'}), Document(page_content="{'blockNumber': '1734172', 'timeStamp': '1466362463', 'hash': '0x7a062d25b83bafc9fe6b22bc6f5718bca333908b148676e1ac66c0adeccef647', 'nonce': '1016', 'blockHash': '0x8a8afe2b446713db88218553cfb5dd202422928e5e0bc00475ed2f37d95649de', 'transactionIndex': '4', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '6322276709000000000', 'gas': |
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644 | '1777057', 'timeStamp': '1466976422', 'hash': '0xe76ca3603d2f4e7134bdd7a1c3fd553025fc0b793f3fd2a75cd206b8049e74ab', 'nonce': '1248', 'blockHash': '0xc7cacda0ac38c99f1b9bccbeee1562a41781d2cfaa357e8c7b4af6a49584b968', 'transactionIndex': '7', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '4556173496000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '168000', 'gasUsed': '21000', 'confirmations': '15958195', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0xe76ca3603d2f4e7134bdd7a1c3fd553025fc0b793f3fd2a75cd206b8049e74ab', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1780120', 'timeStamp': '1467020353', 'hash': '0xc5ec8cecdc9f5ed55a5b8b0ad79c964fb5c49dc1136b6a49e981616c3e70bbe6', 'nonce': '1266', 'blockHash': '0xfc0e066e5b613239e1a01e6d582e7ab162ceb3ca4f719dfbd1a0c965adcfe1c5', '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'})]PreviousEPubNextEverNoteOverviewSetupCreate a ERC20 transaction loaderCreate a normal transaction loader with customized parametersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, | Etherscan is the leading blockchain explorer, search, API and analytics platform for Ethereum, ->: '1777057', 'timeStamp': '1466976422', 'hash': '0xe76ca3603d2f4e7134bdd7a1c3fd553025fc0b793f3fd2a75cd206b8049e74ab', 'nonce': '1248', 'blockHash': '0xc7cacda0ac38c99f1b9bccbeee1562a41781d2cfaa357e8c7b4af6a49584b968', 'transactionIndex': '7', 'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b', 'value': '4556173496000000000', 'gas': '90000', 'gasPrice': '20000000000', 'isError': '0', 'txreceipt_status': '', 'input': '0x', 'contractAddress': '', 'cumulativeGasUsed': '168000', 'gasUsed': '21000', 'confirmations': '15958195', 'methodId': '0x', 'functionName': ''}", metadata={'from': '0x16545fb79dbee1ad3a7f868b7661c023f372d5de', 'tx_hash': '0xe76ca3603d2f4e7134bdd7a1c3fd553025fc0b793f3fd2a75cd206b8049e74ab', 'to': '0x9dd134d14d1e65f84b706d6f205cd5b1cd03a46b'}), Document(page_content="{'blockNumber': '1780120', 'timeStamp': '1467020353', 'hash': '0xc5ec8cecdc9f5ed55a5b8b0ad79c964fb5c49dc1136b6a49e981616c3e70bbe6', 'nonce': '1266', 'blockHash': '0xfc0e066e5b613239e1a01e6d582e7ab162ceb3ca4f719dfbd1a0c965adcfe1c5', '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'})]PreviousEPubNextEverNoteOverviewSetupCreate a ERC20 transaction loaderCreate a normal transaction loader with customized parametersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
645 | URL | ü¶úÔ∏èüîó Langchain | This covers how to load HTML documents from a list of URLs into a document format that we can use downstream. | This covers how to load HTML documents from a list of URLs into a document format that we can use downstream. ->: URL | ü¶úÔ∏èüîó Langchain |
646 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersURLOn this pageURLThis covers how to load HTML documents from a list of URLs into a document | This covers how to load HTML documents from a list of URLs into a document format that we can use downstream. | This covers how to load HTML documents from a list of URLs into a document format that we can use downstream. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersURLOn this pageURLThis covers how to load HTML documents from a list of URLs into a document |
647 | documents from a list of URLs into a document format that we can use downstream.from langchain.document_loaders import UnstructuredURLLoaderurls = [ "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023", "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023",]Pass in ssl_verify=False with headers=headers to get past ssl_verification error.loader = UnstructuredURLLoader(urls=urls)data = loader.load()Selenium URL LoaderThis covers how to load HTML documents from a list of URLs using the SeleniumURLLoader.Using selenium allows us to load pages that require JavaScript to render.Setup​To use the SeleniumURLLoader, you will need to install selenium and unstructured.from langchain.document_loaders import SeleniumURLLoaderurls = [ "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "https://goo.gl/maps/NDSHwePEyaHMFGwh8",]loader = SeleniumURLLoader(urls=urls)data = loader.load()Playwright URL LoaderThis covers how to load HTML documents from a list of URLs using the PlaywrightURLLoader.As in the Selenium case, Playwright allows us to load pages that need JavaScript to render.Setup​To use the PlaywrightURLLoader, you will need to install playwright and unstructured. Additionally, you will need to install the Playwright Chromium browser:# Install playwrightpip install "playwright"pip install "unstructured"playwright installfrom langchain.document_loaders import PlaywrightURLLoaderurls = [ "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "https://goo.gl/maps/NDSHwePEyaHMFGwh8",]loader = PlaywrightURLLoader(urls=urls, remove_selectors=["header", "footer"])data = loader.load()PreviousUnstructured FileNextWeatherSetupSetupCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This covers how to load HTML documents from a list of URLs into a document format that we can use downstream. | This covers how to load HTML documents from a list of URLs into a document format that we can use downstream. ->: documents from a list of URLs into a document format that we can use downstream.from langchain.document_loaders import UnstructuredURLLoaderurls = [ "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023", "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023",]Pass in ssl_verify=False with headers=headers to get past ssl_verification error.loader = UnstructuredURLLoader(urls=urls)data = loader.load()Selenium URL LoaderThis covers how to load HTML documents from a list of URLs using the SeleniumURLLoader.Using selenium allows us to load pages that require JavaScript to render.Setup​To use the SeleniumURLLoader, you will need to install selenium and unstructured.from langchain.document_loaders import SeleniumURLLoaderurls = [ "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "https://goo.gl/maps/NDSHwePEyaHMFGwh8",]loader = SeleniumURLLoader(urls=urls)data = loader.load()Playwright URL LoaderThis covers how to load HTML documents from a list of URLs using the PlaywrightURLLoader.As in the Selenium case, Playwright allows us to load pages that need JavaScript to render.Setup​To use the PlaywrightURLLoader, you will need to install playwright and unstructured. Additionally, you will need to install the Playwright Chromium browser:# Install playwrightpip install "playwright"pip install "unstructured"playwright installfrom langchain.document_loaders import PlaywrightURLLoaderurls = [ "https://www.youtube.com/watch?v=dQw4w9WgXcQ", "https://goo.gl/maps/NDSHwePEyaHMFGwh8",]loader = PlaywrightURLLoader(urls=urls, remove_selectors=["header", "footer"])data = loader.load()PreviousUnstructured FileNextWeatherSetupSetupCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
648 | Subtitle | ü¶úÔ∏èüîó Langchain | The SubRip file format is described on the Matroska multimedia container format website as "perhaps the most basic of all subtitle formats." SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hoursseconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (0000,000). The fractional separator used is the comma, since the program was written in France. | The SubRip file format is described on the Matroska multimedia container format website as "perhaps the most basic of all subtitle formats." SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hoursseconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (0000,000). The fractional separator used is the comma, since the program was written in France. ->: Subtitle | ü¶úÔ∏èüîó Langchain |
649 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersSubtitleSubtitleThe SubRip file format is described on the Matroska multimedia container | The SubRip file format is described on the Matroska multimedia container format website as "perhaps the most basic of all subtitle formats." SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hoursseconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (0000,000). The fractional separator used is the comma, since the program was written in France. | The SubRip file format is described on the Matroska multimedia container format website as "perhaps the most basic of all subtitle formats." SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hoursseconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (0000,000). The fractional separator used is the comma, since the program was written in France. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersSubtitleSubtitleThe SubRip file format is described on the Matroska multimedia container |
650 | is described on the Matroska multimedia container format website as "perhaps the most basic of all subtitle formats." SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hours:minutes:seconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (00:00:00,000). The fractional separator used is the comma, since the program was written in France.How to load data from subtitle (.srt) filesPlease, download the example .srt file from here.pip install pysrtfrom langchain.document_loaders import SRTLoaderloader = SRTLoader( "example_data/Star_Wars_The_Clone_Wars_S06E07_Crisis_at_the_Heart.srt")docs = loader.load()docs[0].page_content[:100] '<i>Corruption discovered\nat the core of the Banking Clan!</i> <i>Reunited, Rush Clovis\nand Senator A'PreviousStripeNextTelegramCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | The SubRip file format is described on the Matroska multimedia container format website as "perhaps the most basic of all subtitle formats." SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hoursseconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (0000,000). The fractional separator used is the comma, since the program was written in France. | The SubRip file format is described on the Matroska multimedia container format website as "perhaps the most basic of all subtitle formats." SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hoursseconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (0000,000). The fractional separator used is the comma, since the program was written in France. ->: is described on the Matroska multimedia container format website as "perhaps the most basic of all subtitle formats." SubRip (SubRip Text) files are named with the extension .srt, and contain formatted lines of plain text in groups separated by a blank line. Subtitles are numbered sequentially, starting at 1. The timecode format used is hours:minutes:seconds,milliseconds with time units fixed to two zero-padded digits and fractions fixed to three zero-padded digits (00:00:00,000). The fractional separator used is the comma, since the program was written in France.How to load data from subtitle (.srt) filesPlease, download the example .srt file from here.pip install pysrtfrom langchain.document_loaders import SRTLoaderloader = SRTLoader( "example_data/Star_Wars_The_Clone_Wars_S06E07_Crisis_at_the_Heart.srt")docs = loader.load()docs[0].page_content[:100] '<i>Corruption discovered\nat the core of the Banking Clan!</i> <i>Reunited, Rush Clovis\nand Senator A'PreviousStripeNextTelegramCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
651 | Telegram | ü¶úÔ∏èüîó Langchain | Telegram 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 and video calling, VoIP, file sharing and several other features. | Telegram 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 and video calling, VoIP, file sharing and several other features. ->: Telegram | ü¶úÔ∏èüîó Langchain |
652 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersTelegramTelegramTelegram Messenger is a globally accessible freemium, cross-platform, | Telegram 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 and video calling, VoIP, file sharing and several other features. | Telegram 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 and video calling, VoIP, file sharing and several other features. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersTelegramTelegramTelegram Messenger is a globally accessible freemium, cross-platform, |
653 | a globally accessible freemium, cross-platform, 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. | Telegram 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 and video calling, VoIP, file sharing and several other features. | Telegram 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 and video calling, VoIP, file sharing and several other features. ->: a globally accessible freemium, cross-platform, 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. |
654 | XML | ü¶úÔ∏èüîó Langchain | The UnstructuredXMLLoader is used to load XML files. The loader works with .xml files. The page content will be the text extracted from the XML tags. | The UnstructuredXMLLoader is used to load XML files. The loader works with .xml files. The page content will be the text extracted from the XML tags. ->: XML | ü¶úÔ∏èüîó Langchain |
655 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersXMLXMLThe UnstructuredXMLLoader is used to load XML files. The loader works with .xml files. | The UnstructuredXMLLoader is used to load XML files. The loader works with .xml files. The page content will be the text extracted from the XML tags. | The UnstructuredXMLLoader is used to load XML files. The loader works with .xml files. The page content will be the text extracted from the XML tags. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersXMLXMLThe UnstructuredXMLLoader is used to load XML files. The loader works with .xml files. |
656 | load XML files. The loader works with .xml files. The page content will be the text extracted from the XML tags.from langchain.document_loaders import UnstructuredXMLLoaderloader = UnstructuredXMLLoader( "example_data/factbook.xml",)docs = loader.load()docs[0] Document(page_content='United States\n\nWashington, DC\n\nJoe Biden\n\nBaseball\n\nCanada\n\nOttawa\n\nJustin Trudeau\n\nHockey\n\nFrance\n\nParis\n\nEmmanuel Macron\n\nSoccer\n\nTrinidad & Tobado\n\nPort of Spain\n\nKeith Rowley\n\nTrack & Field', metadata={'source': 'example_data/factbook.xml'})PreviousWikipediaNextXorbits Pandas DataFrameCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | The UnstructuredXMLLoader is used to load XML files. The loader works with .xml files. The page content will be the text extracted from the XML tags. | The UnstructuredXMLLoader is used to load XML files. The loader works with .xml files. The page content will be the text extracted from the XML tags. ->: load XML files. The loader works with .xml files. The page content will be the text extracted from the XML tags.from langchain.document_loaders import UnstructuredXMLLoaderloader = UnstructuredXMLLoader( "example_data/factbook.xml",)docs = loader.load()docs[0] Document(page_content='United States\n\nWashington, DC\n\nJoe Biden\n\nBaseball\n\nCanada\n\nOttawa\n\nJustin Trudeau\n\nHockey\n\nFrance\n\nParis\n\nEmmanuel Macron\n\nSoccer\n\nTrinidad & Tobado\n\nPort of Spain\n\nKeith Rowley\n\nTrack & Field', metadata={'source': 'example_data/factbook.xml'})PreviousWikipediaNextXorbits Pandas DataFrameCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
657 | Airbyte Gong | ü¶úÔ∏èüîó Langchain | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: Airbyte Gong | ü¶úÔ∏èüîó Langchain |
658 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersAirbyte GongOn this pageAirbyte GongAirbyte is a data integration platform for ELT pipelines | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersAirbyte GongOn this pageAirbyte GongAirbyte is a data integration platform for ELT pipelines |
659 | is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.This loader exposes the Gong connector as a document loader, allowing you to load various Gong objects as documents.Installation‚ÄãFirst, you need to install the airbyte-source-gong python package.#!pip install airbyte-source-gongExample‚ÄãCheck out the Airbyte documentation page for details about how to configure the reader. | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.This loader exposes the Gong connector as a document loader, allowing you to load various Gong objects as documents.Installation‚ÄãFirst, you need to install the airbyte-source-gong python package.#!pip install airbyte-source-gongExample‚ÄãCheck out the Airbyte documentation page for details about how to configure the reader. |
660 | The JSON schema the config object should adhere to can be found on Github: https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-gong/source_gong/spec.yaml.The general shape looks like this:{ "access_key": "<access key name>", "access_key_secret": "<access key secret>", "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",}By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader.from langchain.document_loaders.airbyte import AirbyteGongLoaderconfig = { # your gong configuration}loader = AirbyteGongLoader(config=config, stream_name="calls") # check the documentation linked above for a list of all streamsNow you can load documents the usual waydocs = loader.load()As load returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the lazy_load method which returns an iterator instead:docs_iterator = loader.lazy_load()Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To process documents, create a class inheriting from the base loader and implement the _handle_records method yourself:from langchain.docstore.document import Documentdef handle_record(record, id): return Document(page_content=record.data["title"], metadata=record.data)loader = AirbyteGongLoader(config=config, record_handler=handle_record, stream_name="calls")docs = loader.load()Incremental loads‚ÄãSome streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.To take advantage of this, store the last_state property of the loader and pass it in when creating the loader again. This will ensure that only new | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: The JSON schema the config object should adhere to can be found on Github: https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-gong/source_gong/spec.yaml.The general shape looks like this:{ "access_key": "<access key name>", "access_key_secret": "<access key secret>", "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",}By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader.from langchain.document_loaders.airbyte import AirbyteGongLoaderconfig = { # your gong configuration}loader = AirbyteGongLoader(config=config, stream_name="calls") # check the documentation linked above for a list of all streamsNow you can load documents the usual waydocs = loader.load()As load returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the lazy_load method which returns an iterator instead:docs_iterator = loader.lazy_load()Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To process documents, create a class inheriting from the base loader and implement the _handle_records method yourself:from langchain.docstore.document import Documentdef handle_record(record, id): return Document(page_content=record.data["title"], metadata=record.data)loader = AirbyteGongLoader(config=config, record_handler=handle_record, stream_name="calls")docs = loader.load()Incremental loads‚ÄãSome streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.To take advantage of this, store the last_state property of the loader and pass it in when creating the loader again. This will ensure that only new |
661 | the loader again. This will ensure that only new records are loaded.last_state = loader.last_state # store safelyincremental_loader = AirbyteGongLoader(config=config, stream_name="calls", state=last_state)new_docs = incremental_loader.load()PreviousAirbyte CDKNextAirbyte HubspotInstallationExampleIncremental loadsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. | Airbyte is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases. ->: the loader again. This will ensure that only new records are loaded.last_state = loader.last_state # store safelyincremental_loader = AirbyteGongLoader(config=config, stream_name="calls", state=last_state)new_docs = incremental_loader.load()PreviousAirbyte CDKNextAirbyte HubspotInstallationExampleIncremental loadsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
662 | RST | ü¶úÔ∏èüîó Langchain | A reStructured Text (RST) file is a file format for textual data used primarily in the Python programming language community for technical documentation. | A reStructured Text (RST) file is a file format for textual data used primarily in the Python programming language community for technical documentation. ->: RST | ü¶úÔ∏èüîó Langchain |
663 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersRSTOn this pageRSTA reStructured Text (RST) file is a file format for textual data used | A reStructured Text (RST) file is a file format for textual data used primarily in the Python programming language community for technical documentation. | A reStructured Text (RST) file is a file format for textual data used primarily in the Python programming language community for technical documentation. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersRSTOn this pageRSTA reStructured Text (RST) file is a file format for textual data used |
664 | (RST) file is a file format for textual data used primarily in the Python programming language community for technical documentation.UnstructuredRSTLoader​You can load data from RST files with UnstructuredRSTLoader using the following workflow.from langchain.document_loaders import UnstructuredRSTLoaderloader = UnstructuredRSTLoader(file_path="example_data/README.rst", mode="elements")docs = loader.load()print(docs[0]) page_content='Example Docs' metadata={'source': 'example_data/README.rst', 'filename': 'README.rst', 'file_directory': 'example_data', 'filetype': 'text/x-rst', 'page_number': 1, 'category': 'Title'}PreviousRSS FeedsNextSitemapUnstructuredRSTLoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | A reStructured Text (RST) file is a file format for textual data used primarily in the Python programming language community for technical documentation. | A reStructured Text (RST) file is a file format for textual data used primarily in the Python programming language community for technical documentation. ->: (RST) file is a file format for textual data used primarily in the Python programming language community for technical documentation.UnstructuredRSTLoader​You can load data from RST files with UnstructuredRSTLoader using the following workflow.from langchain.document_loaders import UnstructuredRSTLoaderloader = UnstructuredRSTLoader(file_path="example_data/README.rst", mode="elements")docs = loader.load()print(docs[0]) page_content='Example Docs' metadata={'source': 'example_data/README.rst', 'filename': 'README.rst', 'file_directory': 'example_data', 'filetype': 'text/x-rst', 'page_number': 1, 'category': 'Title'}PreviousRSS FeedsNextSitemapUnstructuredRSTLoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
665 | BibTeX | ü¶úÔ∏èüîó Langchain | BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents. | BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents. ->: BibTeX | ü¶úÔ∏èüîó Langchain |
666 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersBibTeXOn this pageBibTeXBibTeX is a file format and reference management system commonly used | BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents. | BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersBibTeXOn this pageBibTeXBibTeX is a file format and reference management system commonly used |
667 | and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents.BibTeX files have a .bib extension and consist of plain text entries representing references to various publications, such as books, articles, conference papers, theses, and more. Each BibTeX entry follows a specific structure and contains fields for different bibliographic details like author names, publication title, journal or book title, year of publication, page numbers, and more.Bibtex files can also store the path to documents, such as .pdf files that can be retrieved.Installation‚ÄãFirst, you need to install bibtexparser and PyMuPDF.#!pip install bibtexparser pymupdfExamples‚ÄãBibtexLoader has these arguments:file_path: the path the the .bib bibtex fileoptional max_docs: default=None, i.e. not limit. Use it to limit number of retrieved documents.optional max_content_chars: default=4000. Use it to limit the number of characters in a single document.optional load_extra_meta: default=False. By default only the most important fields from the bibtex entries: Published (publication year), Title, Authors, Summary, Journal, Keywords, and URL. If True, it will also try to load return entry_id, note, doi, and links fields. optional file_pattern: default=r'[^:]+\.pdf'. Regex pattern to find files in the file entry. Default pattern supports Zotero flavour bibtex style and bare file path.from langchain.document_loaders import BibtexLoader# Create a dummy bibtex file and download a pdf.import urllib.requesturllib.request.urlretrieve( "https://www.fourmilab.ch/etexts/einstein/specrel/specrel.pdf", "einstein1905.pdf")bibtex_text = """ @article{einstein1915, title={Die Feldgleichungen der Gravitation}, abstract={Die Grundgleichungen der Gravitation, die ich hier entwickeln werde, wurden von mir in einer Abhandlung: ,,Die formale Grundlage der allgemeinen | BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents. | BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents. ->: and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents.BibTeX files have a .bib extension and consist of plain text entries representing references to various publications, such as books, articles, conference papers, theses, and more. Each BibTeX entry follows a specific structure and contains fields for different bibliographic details like author names, publication title, journal or book title, year of publication, page numbers, and more.Bibtex files can also store the path to documents, such as .pdf files that can be retrieved.Installation‚ÄãFirst, you need to install bibtexparser and PyMuPDF.#!pip install bibtexparser pymupdfExamples‚ÄãBibtexLoader has these arguments:file_path: the path the the .bib bibtex fileoptional max_docs: default=None, i.e. not limit. Use it to limit number of retrieved documents.optional max_content_chars: default=4000. Use it to limit the number of characters in a single document.optional load_extra_meta: default=False. By default only the most important fields from the bibtex entries: Published (publication year), Title, Authors, Summary, Journal, Keywords, and URL. If True, it will also try to load return entry_id, note, doi, and links fields. optional file_pattern: default=r'[^:]+\.pdf'. Regex pattern to find files in the file entry. Default pattern supports Zotero flavour bibtex style and bare file path.from langchain.document_loaders import BibtexLoader# Create a dummy bibtex file and download a pdf.import urllib.requesturllib.request.urlretrieve( "https://www.fourmilab.ch/etexts/einstein/specrel/specrel.pdf", "einstein1905.pdf")bibtex_text = """ @article{einstein1915, title={Die Feldgleichungen der Gravitation}, abstract={Die Grundgleichungen der Gravitation, die ich hier entwickeln werde, wurden von mir in einer Abhandlung: ,,Die formale Grundlage der allgemeinen |
668 | ,,Die formale Grundlage der allgemeinen Relativit{\"a}tstheorie`` in den Sitzungsberichten der Preu{\ss}ischen Akademie der Wissenschaften 1915 ver{\"o}ffentlicht.}, author={Einstein, Albert}, journal={Sitzungsberichte der K{\"o}niglich Preu{\ss}ischen Akademie der Wissenschaften}, volume={1915}, number={1}, pages={844--847}, year={1915}, doi={10.1002/andp.19163540702}, link={https://onlinelibrary.wiley.com/doi/abs/10.1002/andp.19163540702}, file={einstein1905.pdf} } """# save bibtex_text to biblio.bib filewith open("./biblio.bib", "w") as file: file.write(bibtex_text)docs = BibtexLoader("./biblio.bib").load()docs[0].metadata {'id': 'einstein1915', 'published_year': '1915', 'title': 'Die Feldgleichungen der Gravitation', 'publication': 'Sitzungsberichte der K{"o}niglich Preu{\\ss}ischen Akademie der Wissenschaften', 'authors': 'Einstein, Albert', 'abstract': 'Die Grundgleichungen der Gravitation, die ich hier entwickeln werde, wurden von mir in einer Abhandlung: ,,Die formale Grundlage der allgemeinen Relativit{"a}tstheorie`` in den Sitzungsberichten der Preu{\\ss}ischen Akademie der Wissenschaften 1915 ver{"o}ffentlicht.', 'url': 'https://doi.org/10.1002/andp.19163540702'}print(docs[0].page_content[:400]) # all pages of the pdf content ON THE ELECTRODYNAMICS OF MOVING BODIES By A. EINSTEIN June 30, 1905 It is known that Maxwell’s electrodynamics—as usually understood at the present time—when applied to moving bodies, leads to asymmetries which do not appear to be inherent in the phenomena. Take, for example, the recipro- cal electrodynamic action of a magnet and a conductor. The observable phe- nomenon here depends only on the rPreviousAzure Document IntelligenceNextBiliBiliInstallationExamplesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents. | BibTeX is a file format and reference management system commonly used in conjunction with LaTeX typesetting. It serves as a way to organize and store bibliographic information for academic and research documents. ->: ,,Die formale Grundlage der allgemeinen Relativit{\"a}tstheorie`` in den Sitzungsberichten der Preu{\ss}ischen Akademie der Wissenschaften 1915 ver{\"o}ffentlicht.}, author={Einstein, Albert}, journal={Sitzungsberichte der K{\"o}niglich Preu{\ss}ischen Akademie der Wissenschaften}, volume={1915}, number={1}, pages={844--847}, year={1915}, doi={10.1002/andp.19163540702}, link={https://onlinelibrary.wiley.com/doi/abs/10.1002/andp.19163540702}, file={einstein1905.pdf} } """# save bibtex_text to biblio.bib filewith open("./biblio.bib", "w") as file: file.write(bibtex_text)docs = BibtexLoader("./biblio.bib").load()docs[0].metadata {'id': 'einstein1915', 'published_year': '1915', 'title': 'Die Feldgleichungen der Gravitation', 'publication': 'Sitzungsberichte der K{"o}niglich Preu{\\ss}ischen Akademie der Wissenschaften', 'authors': 'Einstein, Albert', 'abstract': 'Die Grundgleichungen der Gravitation, die ich hier entwickeln werde, wurden von mir in einer Abhandlung: ,,Die formale Grundlage der allgemeinen Relativit{"a}tstheorie`` in den Sitzungsberichten der Preu{\\ss}ischen Akademie der Wissenschaften 1915 ver{"o}ffentlicht.', 'url': 'https://doi.org/10.1002/andp.19163540702'}print(docs[0].page_content[:400]) # all pages of the pdf content ON THE ELECTRODYNAMICS OF MOVING BODIES By A. EINSTEIN June 30, 1905 It is known that Maxwell’s electrodynamics—as usually understood at the present time—when applied to moving bodies, leads to asymmetries which do not appear to be inherent in the phenomena. Take, for example, the recipro- cal electrodynamic action of a magnet and a conductor. The observable phe- nomenon here depends only on the rPreviousAzure Document IntelligenceNextBiliBiliInstallationExamplesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
669 | TensorFlow Datasets | ü¶úÔ∏èüîó Langchain | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. ->: TensorFlow Datasets | ü¶úÔ∏èüîó Langchain |
670 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersTensorFlow DatasetsOn this pageTensorFlow DatasetsTensorFlow Datasets is a collection of | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersTensorFlow DatasetsOn this pageTensorFlow DatasetsTensorFlow Datasets is a collection of |
671 | DatasetsTensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets.This notebook shows how to load TensorFlow Datasets into a Document format that we can use downstream.Installation‚ÄãYou need to install tensorflow and tensorflow-datasets python packages.pip install tensorflowpip install tensorflow-datasetsExample‚ÄãAs an example, we use the mlqa/en dataset.MLQA (Multilingual Question Answering Dataset) is a benchmark dataset for evaluating multilingual question answering performance. The dataset consists of 7 languages: Arabic, German, Spanish, English, Hindi, Vietnamese, Chinese.Homepage: https://github.com/facebookresearch/MLQASource code: tfds.datasets.mlqa.BuilderDownload size: 72.21 MiB# Feature structure of `mlqa/en` dataset:FeaturesDict({ 'answers': Sequence({ 'answer_start': int32, 'text': Text(shape=(), dtype=string), }), 'context': Text(shape=(), dtype=string), 'id': string, 'question': Text(shape=(), dtype=string), 'title': Text(shape=(), dtype=string),})import tensorflow as tfimport tensorflow_datasets as tfds# try directly access this dataset:ds = tfds.load('mlqa/en', split='test')ds = ds.take(1) # Only take a single exampleds <_TakeDataset element_spec={'answers': {'answer_start': TensorSpec(shape=(None,), dtype=tf.int32, name=None), 'text': TensorSpec(shape=(None,), dtype=tf.string, name=None)}, 'context': TensorSpec(shape=(), dtype=tf.string, name=None), 'id': TensorSpec(shape=(), dtype=tf.string, name=None), 'question': TensorSpec(shape=(), dtype=tf.string, name=None), 'title': TensorSpec(shape=(), dtype=tf.string, name=None)}>Now we have to create a custom function to convert dataset sample into a Document.This is a requirement. There is no standard format for the TF datasets that's why we | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. ->: DatasetsTensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets.This notebook shows how to load TensorFlow Datasets into a Document format that we can use downstream.Installation‚ÄãYou need to install tensorflow and tensorflow-datasets python packages.pip install tensorflowpip install tensorflow-datasetsExample‚ÄãAs an example, we use the mlqa/en dataset.MLQA (Multilingual Question Answering Dataset) is a benchmark dataset for evaluating multilingual question answering performance. The dataset consists of 7 languages: Arabic, German, Spanish, English, Hindi, Vietnamese, Chinese.Homepage: https://github.com/facebookresearch/MLQASource code: tfds.datasets.mlqa.BuilderDownload size: 72.21 MiB# Feature structure of `mlqa/en` dataset:FeaturesDict({ 'answers': Sequence({ 'answer_start': int32, 'text': Text(shape=(), dtype=string), }), 'context': Text(shape=(), dtype=string), 'id': string, 'question': Text(shape=(), dtype=string), 'title': Text(shape=(), dtype=string),})import tensorflow as tfimport tensorflow_datasets as tfds# try directly access this dataset:ds = tfds.load('mlqa/en', split='test')ds = ds.take(1) # Only take a single exampleds <_TakeDataset element_spec={'answers': {'answer_start': TensorSpec(shape=(None,), dtype=tf.int32, name=None), 'text': TensorSpec(shape=(None,), dtype=tf.string, name=None)}, 'context': TensorSpec(shape=(), dtype=tf.string, name=None), 'id': TensorSpec(shape=(), dtype=tf.string, name=None), 'question': TensorSpec(shape=(), dtype=tf.string, name=None), 'title': TensorSpec(shape=(), dtype=tf.string, name=None)}>Now we have to create a custom function to convert dataset sample into a Document.This is a requirement. There is no standard format for the TF datasets that's why we |
672 | standard format for the TF datasets that's why we need to make a custom transformation function.Let's use context field as the Document.page_content and place other fields in the Document.metadata.def decode_to_str(item: tf.Tensor) -> str: return item.numpy().decode('utf-8')def mlqaen_example_to_document(example: dict) -> Document: return Document( page_content=decode_to_str(example["context"]), metadata={ "id": decode_to_str(example["id"]), "title": decode_to_str(example["title"]), "question": decode_to_str(example["question"]), "answer": decode_to_str(example["answers"]["text"][0]), }, ) for example in ds: doc = mlqaen_example_to_document(example) print(doc) break page_content='After completing the journey around South America, on 23 February 2006, Queen Mary 2 met her namesake, the original RMS Queen Mary, which is permanently docked at Long Beach, California. Escorted by a flotilla of smaller ships, the two Queens exchanged a "whistle salute" which was heard throughout the city of Long Beach. Queen Mary 2 met the other serving Cunard liners Queen Victoria and Queen Elizabeth 2 on 13 January 2008 near the Statue of Liberty in New York City harbour, with a celebratory fireworks display; Queen Elizabeth 2 and Queen Victoria made a tandem crossing of the Atlantic for the meeting. This marked the first time three Cunard Queens have been present in the same location. Cunard stated this would be the last time these three ships would ever meet, due to Queen Elizabeth 2\'s impending retirement from service in late 2008. However this would prove not to be the case, as the three Queens met in Southampton on 22 April 2008. Queen Mary 2 rendezvoused with Queen Elizabeth 2 in Dubai on Saturday 21 March 2009, after the latter ship\'s retirement, while both ships were berthed at Port Rashid. With the withdrawal of Queen Elizabeth 2 from Cunard\'s fleet and its docking in Dubai, | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. ->: standard format for the TF datasets that's why we need to make a custom transformation function.Let's use context field as the Document.page_content and place other fields in the Document.metadata.def decode_to_str(item: tf.Tensor) -> str: return item.numpy().decode('utf-8')def mlqaen_example_to_document(example: dict) -> Document: return Document( page_content=decode_to_str(example["context"]), metadata={ "id": decode_to_str(example["id"]), "title": decode_to_str(example["title"]), "question": decode_to_str(example["question"]), "answer": decode_to_str(example["answers"]["text"][0]), }, ) for example in ds: doc = mlqaen_example_to_document(example) print(doc) break page_content='After completing the journey around South America, on 23 February 2006, Queen Mary 2 met her namesake, the original RMS Queen Mary, which is permanently docked at Long Beach, California. Escorted by a flotilla of smaller ships, the two Queens exchanged a "whistle salute" which was heard throughout the city of Long Beach. Queen Mary 2 met the other serving Cunard liners Queen Victoria and Queen Elizabeth 2 on 13 January 2008 near the Statue of Liberty in New York City harbour, with a celebratory fireworks display; Queen Elizabeth 2 and Queen Victoria made a tandem crossing of the Atlantic for the meeting. This marked the first time three Cunard Queens have been present in the same location. Cunard stated this would be the last time these three ships would ever meet, due to Queen Elizabeth 2\'s impending retirement from service in late 2008. However this would prove not to be the case, as the three Queens met in Southampton on 22 April 2008. Queen Mary 2 rendezvoused with Queen Elizabeth 2 in Dubai on Saturday 21 March 2009, after the latter ship\'s retirement, while both ships were berthed at Port Rashid. With the withdrawal of Queen Elizabeth 2 from Cunard\'s fleet and its docking in Dubai, |
673 | 2 from Cunard\'s fleet and its docking in Dubai, Queen Mary 2 became the only ocean liner left in active passenger service.' metadata={'id': '5116f7cccdbf614d60bcd23498274ffd7b1e4ec7', 'title': 'RMS Queen Mary 2', 'question': 'What year did Queen Mary 2 complete her journey around South America?', 'answer': '2006'} 2023-08-03 14:27:08.482983: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.from langchain.schema import Documentfrom langchain.document_loaders import TensorflowDatasetLoaderloader = TensorflowDatasetLoader( dataset_name="mlqa/en", split_name="test", load_max_docs=3, sample_to_document_function=mlqaen_example_to_document, )TensorflowDatasetLoader has these parameters:dataset_name: the name of the dataset to loadsplit_name: the name of the split to load. Defaults to "train".load_max_docs: a limit to the number of loaded documents. Defaults to 100.sample_to_document_function: a function that converts a dataset sample to a Documentdocs = loader.load()len(docs) 2023-08-03 14:27:22.998964: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. 3docs[0].page_content 'After completing the journey around South America, on 23 February 2006, Queen Mary 2 met her namesake, the original RMS Queen Mary, which is permanently docked at Long Beach, | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. ->: 2 from Cunard\'s fleet and its docking in Dubai, Queen Mary 2 became the only ocean liner left in active passenger service.' metadata={'id': '5116f7cccdbf614d60bcd23498274ffd7b1e4ec7', 'title': 'RMS Queen Mary 2', 'question': 'What year did Queen Mary 2 complete her journey around South America?', 'answer': '2006'} 2023-08-03 14:27:08.482983: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.from langchain.schema import Documentfrom langchain.document_loaders import TensorflowDatasetLoaderloader = TensorflowDatasetLoader( dataset_name="mlqa/en", split_name="test", load_max_docs=3, sample_to_document_function=mlqaen_example_to_document, )TensorflowDatasetLoader has these parameters:dataset_name: the name of the dataset to loadsplit_name: the name of the split to load. Defaults to "train".load_max_docs: a limit to the number of loaded documents. Defaults to 100.sample_to_document_function: a function that converts a dataset sample to a Documentdocs = loader.load()len(docs) 2023-08-03 14:27:22.998964: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. 3docs[0].page_content 'After completing the journey around South America, on 23 February 2006, Queen Mary 2 met her namesake, the original RMS Queen Mary, which is permanently docked at Long Beach, |
674 | Mary, which is permanently docked at Long Beach, California. Escorted by a flotilla of smaller ships, the two Queens exchanged a "whistle salute" which was heard throughout the city of Long Beach. Queen Mary 2 met the other serving Cunard liners Queen Victoria and Queen Elizabeth 2 on 13 January 2008 near the Statue of Liberty in New York City harbour, with a celebratory fireworks display; Queen Elizabeth 2 and Queen Victoria made a tandem crossing of the Atlantic for the meeting. This marked the first time three Cunard Queens have been present in the same location. Cunard stated this would be the last time these three ships would ever meet, due to Queen Elizabeth 2\'s impending retirement from service in late 2008. However this would prove not to be the case, as the three Queens met in Southampton on 22 April 2008. Queen Mary 2 rendezvoused with Queen Elizabeth 2 in Dubai on Saturday 21 March 2009, after the latter ship\'s retirement, while both ships were berthed at Port Rashid. With the withdrawal of Queen Elizabeth 2 from Cunard\'s fleet and its docking in Dubai, Queen Mary 2 became the only ocean liner left in active passenger service.'docs[0].metadata {'id': '5116f7cccdbf614d60bcd23498274ffd7b1e4ec7', 'title': 'RMS Queen Mary 2', 'question': 'What year did Queen Mary 2 complete her journey around South America?', 'answer': '2006'}PreviousTencent COS FileNext2MarkdownInstallationExampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets, enabling easy-to-use and high-performance input pipelines. To get started see the guide and the list of datasets. ->: Mary, which is permanently docked at Long Beach, California. Escorted by a flotilla of smaller ships, the two Queens exchanged a "whistle salute" which was heard throughout the city of Long Beach. Queen Mary 2 met the other serving Cunard liners Queen Victoria and Queen Elizabeth 2 on 13 January 2008 near the Statue of Liberty in New York City harbour, with a celebratory fireworks display; Queen Elizabeth 2 and Queen Victoria made a tandem crossing of the Atlantic for the meeting. This marked the first time three Cunard Queens have been present in the same location. Cunard stated this would be the last time these three ships would ever meet, due to Queen Elizabeth 2\'s impending retirement from service in late 2008. However this would prove not to be the case, as the three Queens met in Southampton on 22 April 2008. Queen Mary 2 rendezvoused with Queen Elizabeth 2 in Dubai on Saturday 21 March 2009, after the latter ship\'s retirement, while both ships were berthed at Port Rashid. With the withdrawal of Queen Elizabeth 2 from Cunard\'s fleet and its docking in Dubai, Queen Mary 2 became the only ocean liner left in active passenger service.'docs[0].metadata {'id': '5116f7cccdbf614d60bcd23498274ffd7b1e4ec7', 'title': 'RMS Queen Mary 2', 'question': 'What year did Queen Mary 2 complete her journey around South America?', 'answer': '2006'}PreviousTencent COS FileNext2MarkdownInstallationExampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
675 | Twitter | ü¶úÔ∏èüîó Langchain | Twitter is an online social media and social networking service. | Twitter is an online social media and social networking service. ->: Twitter | ü¶úÔ∏èüîó Langchain |
676 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersTwitterTwitterTwitter is an online social media and social networking service.This loader | Twitter is an online social media and social networking service. | Twitter is an online social media and social networking service. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersTwitterTwitterTwitter is an online social media and social networking service.This loader |
677 | media and social networking service.This loader fetches the text from the Tweets of a list of Twitter users, using the tweepy Python package. | Twitter is an online social media and social networking service. | Twitter is an online social media and social networking service. ->: media and social networking service.This loader fetches the text from the Tweets of a list of Twitter users, using the tweepy Python package. |
678 | You must initialize the loader with your Twitter API token, and you need to pass in the Twitter username you want to extract.from langchain.document_loaders import TwitterTweetLoader#!pip install tweepyloader = TwitterTweetLoader.from_bearer_token( oauth2_bearer_token="YOUR BEARER TOKEN", twitter_users=["elonmusk"], number_tweets=50, # Default value is 100)# Or load from access token and consumer keys# loader = TwitterTweetLoader.from_secrets(# access_token='YOUR ACCESS TOKEN',# access_token_secret='YOUR ACCESS TOKEN SECRET',# consumer_key='YOUR CONSUMER KEY',# consumer_secret='YOUR CONSUMER SECRET',# twitter_users=['elonmusk'],# number_tweets=50,# )documents = loader.load()documents[:5] [Document(page_content='@MrAndyNgo @REI One store after another shutting down', metadata={'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ng√¥ üè≥Ô∏è\u200düåà', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter | Twitter is an online social media and social networking service. | Twitter is an online social media and social networking service. ->: You must initialize the loader with your Twitter API token, and you need to pass in the Twitter username you want to extract.from langchain.document_loaders import TwitterTweetLoader#!pip install tweepyloader = TwitterTweetLoader.from_bearer_token( oauth2_bearer_token="YOUR BEARER TOKEN", twitter_users=["elonmusk"], number_tweets=50, # Default value is 100)# Or load from access token and consumer keys# loader = TwitterTweetLoader.from_secrets(# access_token='YOUR ACCESS TOKEN',# access_token_secret='YOUR ACCESS TOKEN SECRET',# consumer_key='YOUR CONSUMER KEY',# consumer_secret='YOUR CONSUMER SECRET',# twitter_users=['elonmusk'],# number_tweets=50,# )documents = loader.load()documents[:5] [Document(page_content='@MrAndyNgo @REI One store after another shutting down', metadata={'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ng√¥ üè≥Ô∏è\u200düåà', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter |
679 | rel="nofollow">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}}), Document(page_content='@KanekoaTheGreat @joshrogin @glennbeck Large ships are fundamentally vulnerable to ballistic (hypersonic) missiles', metadata={'created_at': 'Tue Apr 18 03:43:25 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, | Twitter is an online social media and social networking service. | Twitter is an online social media and social networking service. ->: rel="nofollow">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}}), Document(page_content='@KanekoaTheGreat @joshrogin @glennbeck Large ships are fundamentally vulnerable to ballistic (hypersonic) missiles', metadata={'created_at': 'Tue Apr 18 03:43:25 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, |
680 | 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ng√¥ üè≥Ô∏è\u200düåà', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': | Twitter is an online social media and social networking service. | Twitter is an online social media and social networking service. ->: 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ng√¥ üè≥Ô∏è\u200düåà', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, 'lang': 'en'}, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/44196397/1576183471', 'profile_link_color': '0084B4', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': |
681 | 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}}), Document(page_content='@KanekoaTheGreat The Golden Rule', metadata={'created_at': 'Tue Apr 18 03:37:17 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ng√¥ üè≥Ô∏è\u200düåà', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, | Twitter is an online social media and social networking service. | Twitter is an online social media and social networking service. ->: 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': None, 'follow_request_sent': None, 'notifications': None, 'translator_type': 'none', 'withheld_in_countries': []}}), Document(page_content='@KanekoaTheGreat The Golden Rule', metadata={'created_at': 'Tue Apr 18 03:37:17 +0000 2023', 'user_info': {'id': 44196397, 'id_str': '44196397', 'name': 'Elon Musk', 'screen_name': 'elonmusk', 'location': 'A Shortfall of Gravitas', 'profile_location': None, 'description': 'nothing', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 135528327, 'friends_count': 220, 'listed_count': 120478, 'created_at': 'Tue Jun 02 20:12:29 +0000 2009', 'favourites_count': 21285, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 24795, 'lang': None, 'status': {'created_at': 'Tue Apr 18 03:45:50 +0000 2023', 'id': 1648170947541704705, 'id_str': '1648170947541704705', 'text': '@MrAndyNgo @REI One store after another shutting down', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'MrAndyNgo', 'name': 'Andy Ng√¥ üè≥Ô∏è\u200düåà', 'id': 2835451658, 'id_str': '2835451658', 'indices': [0, 10]}, {'screen_name': 'REI', 'name': 'REI', 'id': 16583846, 'id_str': '16583846', 'indices': [11, 15]}], 'urls': []}, 'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>', 'in_reply_to_status_id': 1648134341678051328, 'in_reply_to_status_id_str': '1648134341678051328', 'in_reply_to_user_id': 2835451658, 'in_reply_to_user_id_str': '2835451658', 'in_reply_to_screen_name': 'MrAndyNgo', 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 118, 'favorite_count': 1286, 'favorited': False, 'retweeted': False, |
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686 | Jupyter Notebook | ü¶úÔ∏èüîó Langchain | Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. | Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. ->: Jupyter Notebook | ü¶úÔ∏èüîó Langchain |
687 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersJupyter NotebookJupyter NotebookJupyter Notebook (formerly IPython Notebook) is a web-based | Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. | Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersJupyter NotebookJupyter NotebookJupyter Notebook (formerly IPython Notebook) is a web-based |
688 | (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents.This notebook covers how to load data from a Jupyter notebook (.html) into a format suitable by LangChain.from langchain.document_loaders import NotebookLoaderloader = NotebookLoader( "example_data/notebook.html", include_outputs=True, max_output_length=20, remove_newline=True,)NotebookLoader.load() loads the .html notebook file into a Document object.Parameters:include_outputs (bool): whether to include cell outputs in the resulting document (default is False).max_output_length (int): the maximum number of characters to include from each cell output (default is 10).remove_newline (bool): whether to remove newline characters from the cell sources and outputs (default is False).traceback (bool): whether to include full traceback (default is False).loader.load() [Document(page_content='\'markdown\' cell: \'[\'# Notebook\', \'\', \'This notebook covers how to load data from an .html notebook into a format suitable by LangChain.\']\'\n\n \'code\' cell: \'[\'from langchain.document_loaders import NotebookLoader\']\'\n\n \'code\' cell: \'[\'loader = NotebookLoader("example_data/notebook.html")\']\'\n\n \'markdown\' cell: \'[\'`NotebookLoader.load()` loads the `.html` notebook file into a `Document` object.\', \'\', \'**Parameters**:\', \'\', \'* `include_outputs` (bool): whether to include cell outputs in the resulting document (default is False).\', \'* `max_output_length` (int): the maximum number of characters to include from each cell output (default is 10).\', \'* `remove_newline` (bool): whether to remove newline characters from the cell sources and outputs (default is False).\', \'* `traceback` (bool): whether to include full traceback (default is False).\']\'\n\n \'code\' cell: \'[\'loader.load(include_outputs=True, max_output_length=20, remove_newline=True)\']\'\n\n', metadata={'source': | Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. | Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. ->: (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents.This notebook covers how to load data from a Jupyter notebook (.html) into a format suitable by LangChain.from langchain.document_loaders import NotebookLoaderloader = NotebookLoader( "example_data/notebook.html", include_outputs=True, max_output_length=20, remove_newline=True,)NotebookLoader.load() loads the .html notebook file into a Document object.Parameters:include_outputs (bool): whether to include cell outputs in the resulting document (default is False).max_output_length (int): the maximum number of characters to include from each cell output (default is 10).remove_newline (bool): whether to remove newline characters from the cell sources and outputs (default is False).traceback (bool): whether to include full traceback (default is False).loader.load() [Document(page_content='\'markdown\' cell: \'[\'# Notebook\', \'\', \'This notebook covers how to load data from an .html notebook into a format suitable by LangChain.\']\'\n\n \'code\' cell: \'[\'from langchain.document_loaders import NotebookLoader\']\'\n\n \'code\' cell: \'[\'loader = NotebookLoader("example_data/notebook.html")\']\'\n\n \'markdown\' cell: \'[\'`NotebookLoader.load()` loads the `.html` notebook file into a `Document` object.\', \'\', \'**Parameters**:\', \'\', \'* `include_outputs` (bool): whether to include cell outputs in the resulting document (default is False).\', \'* `max_output_length` (int): the maximum number of characters to include from each cell output (default is 10).\', \'* `remove_newline` (bool): whether to remove newline characters from the cell sources and outputs (default is False).\', \'* `traceback` (bool): whether to include full traceback (default is False).\']\'\n\n \'code\' cell: \'[\'loader.load(include_outputs=True, max_output_length=20, remove_newline=True)\']\'\n\n', metadata={'source': |
689 | metadata={'source': 'example_data/notebook.html'})]PreviousJoplinNextLarkSuite (FeiShu)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. | Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. ->: metadata={'source': 'example_data/notebook.html'})]PreviousJoplinNextLarkSuite (FeiShu)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
690 | Azure Document Intelligence | ü¶úÔ∏èüîó Langchain | Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning | Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning ->: Azure Document Intelligence | ü¶úÔ∏èüîó Langchain |
691 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersAzure Document IntelligenceOn this pageAzure Document IntelligenceAzure Document Intelligence | Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning | Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersAzure Document IntelligenceOn this pageAzure Document IntelligenceAzure Document Intelligence |
692 | Document IntelligenceAzure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning | Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning | Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning ->: Document IntelligenceAzure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning |
693 | based service that extracts text (including handwriting), tables or key-value-pairs from
scanned documents or images.This current implementation of a loader using Document Intelligence is able to incorporate content page-wise and turn it into LangChain documents.Document Intelligence supports PDF, JPEG, PNG, BMP, or TIFF.Further documentation is available at https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/?view=doc-intel-3.1.0.%pip install langchain azure-ai-formrecognizer -qExample 1​The first example uses a local file which will be sent to Azure Document Intelligence.First, an instance of a DocumentAnalysisClient is created with endpoint and key for the Azure service. from azure.ai.formrecognizer import DocumentAnalysisClientfrom azure.core.credentials import AzureKeyCredentialdocument_analysis_client = DocumentAnalysisClient( endpoint="<service_endpoint>", credential=AzureKeyCredential("<service_key>") )With the initialized document analysis client, we can proceed to create an instance of the DocumentIntelligenceLoader:from langchain.document_loaders.pdf import DocumentIntelligenceLoaderloader = DocumentIntelligenceLoader( "<Local_filename>", client=document_analysis_client, model="<model_name>") # e.g. prebuilt-documentdocuments = loader.load()The output contains each page of the source document as a LangChain document: documents [Document(page_content='...', metadata={'source': '...', 'page': 1})]PreviousAzure Blob Storage FileNextBibTeXExample 1CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning | Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning ->: based service that extracts text (including handwriting), tables or key-value-pairs from
scanned documents or images.This current implementation of a loader using Document Intelligence is able to incorporate content page-wise and turn it into LangChain documents.Document Intelligence supports PDF, JPEG, PNG, BMP, or TIFF.Further documentation is available at https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/?view=doc-intel-3.1.0.%pip install langchain azure-ai-formrecognizer -qExample 1​The first example uses a local file which will be sent to Azure Document Intelligence.First, an instance of a DocumentAnalysisClient is created with endpoint and key for the Azure service. from azure.ai.formrecognizer import DocumentAnalysisClientfrom azure.core.credentials import AzureKeyCredentialdocument_analysis_client = DocumentAnalysisClient( endpoint="<service_endpoint>", credential=AzureKeyCredential("<service_key>") )With the initialized document analysis client, we can proceed to create an instance of the DocumentIntelligenceLoader:from langchain.document_loaders.pdf import DocumentIntelligenceLoaderloader = DocumentIntelligenceLoader( "<Local_filename>", client=document_analysis_client, model="<model_name>") # e.g. prebuilt-documentdocuments = loader.load()The output contains each page of the source document as a LangChain document: documents [Document(page_content='...', metadata={'source': '...', 'page': 1})]PreviousAzure Blob Storage FileNextBibTeXExample 1CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
694 | Facebook Chat | ü¶úÔ∏èüîó Langchain | Messenger) is an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010. | Messenger) is an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010. ->: Facebook Chat | ü¶úÔ∏èüîó Langchain |
695 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersFacebook ChatFacebook ChatMessenger is an American proprietary instant messaging app and | Messenger) is an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010. | Messenger) is an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersFacebook ChatFacebook ChatMessenger is an American proprietary instant messaging app and |
696 | an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010.This notebook covers how to load data from the Facebook Chats into a format that can be ingested into LangChain.# pip install pandasfrom langchain.document_loaders import FacebookChatLoaderloader = FacebookChatLoader("example_data/facebook_chat.json")loader.load() [Document(page_content='User 2 on 2023-02-05 03:46:11: Bye!\n\nUser 1 on 2023-02-05 03:43:55: Oh no worries! Bye\n\nUser 2 on 2023-02-05 03:24:37: No Im sorry it was my mistake, the blue one is not for sale\n\nUser 1 on 2023-02-05 03:05:40: I thought you were selling the blue one!\n\nUser 1 on 2023-02-05 03:05:09: Im not interested in this bag. Im interested in the blue one!\n\nUser 2 on 2023-02-05 03:04:28: Here is $129\n\nUser 2 on 2023-02-05 03:04:05: Online is at least $100\n\nUser 1 on 2023-02-05 02:59:59: How much do you want?\n\nUser 2 on 2023-02-04 22:17:56: Goodmorning! $50 is too low.\n\nUser 1 on 2023-02-04 14:17:02: Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!\n\n', metadata={'source': 'example_data/facebook_chat.json'})]PreviousMicrosoft ExcelNextFaunaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Messenger) is an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010. | Messenger) is an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010. ->: an American proprietary instant messaging app and platform developed by Meta Platforms. Originally developed as Facebook Chat in 2008, the company revamped its messaging service in 2010.This notebook covers how to load data from the Facebook Chats into a format that can be ingested into LangChain.# pip install pandasfrom langchain.document_loaders import FacebookChatLoaderloader = FacebookChatLoader("example_data/facebook_chat.json")loader.load() [Document(page_content='User 2 on 2023-02-05 03:46:11: Bye!\n\nUser 1 on 2023-02-05 03:43:55: Oh no worries! Bye\n\nUser 2 on 2023-02-05 03:24:37: No Im sorry it was my mistake, the blue one is not for sale\n\nUser 1 on 2023-02-05 03:05:40: I thought you were selling the blue one!\n\nUser 1 on 2023-02-05 03:05:09: Im not interested in this bag. Im interested in the blue one!\n\nUser 2 on 2023-02-05 03:04:28: Here is $129\n\nUser 2 on 2023-02-05 03:04:05: Online is at least $100\n\nUser 1 on 2023-02-05 02:59:59: How much do you want?\n\nUser 2 on 2023-02-04 22:17:56: Goodmorning! $50 is too low.\n\nUser 1 on 2023-02-04 14:17:02: Hi! Im interested in your bag. Im offering $50. Let me know if you are interested. Thanks!\n\n', metadata={'source': 'example_data/facebook_chat.json'})]PreviousMicrosoft ExcelNextFaunaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
697 | ChatGPT Data | ü¶úÔ∏èüîó Langchain | ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. | ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. ->: ChatGPT Data | ü¶úÔ∏èüîó Langchain |
698 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersChatGPT DataChatGPT DataChatGPT is an artificial intelligence (AI) chatbot developed by | ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. | ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersacreomAirbyte CDKAirbyte GongAirbyte HubspotAirbyte JSONAirbyte SalesforceAirbyte ShopifyAirbyte StripeAirbyte TypeformAirbyte Zendesk SupportAirtableAlibaba Cloud MaxComputeApify DatasetArcGISArxivAssemblyAI Audio TranscriptsAsync ChromiumAsyncHtmlAWS S3 DirectoryAWS S3 FileAZLyricsAzure Blob Storage ContainerAzure Blob Storage FileAzure Document IntelligenceBibTeXBiliBiliBlackboardBlockchainBrave SearchBrowserlessChatGPT DataCollege ConfidentialConcurrent LoaderConfluenceCoNLL-UCopy PasteCSVCube Semantic LayerDatadog LogsDiffbotDiscordDocugamiDropboxDuckDBEmailEmbaasEPubEtherscanEverNoteexample_dataMicrosoft ExcelFacebook ChatFaunaFigmaGeopandasGitGitBookGitHubGoogle BigQueryGoogle Cloud Storage DirectoryGoogle Cloud Storage FileGoogle DriveGrobidGutenbergHacker NewsHuawei OBS DirectoryHuawei OBS FileHuggingFace datasetiFixitImagesImage captionsIMSDbIuguJoplinJupyter NotebookLarkSuite (FeiShu)MastodonMediaWiki DumpMerge Documents LoadermhtmlMicrosoft OneDriveMicrosoft PowerPointMicrosoft SharePointMicrosoft WordModern TreasuryMongoDBNews URLNotion DB 1/2Notion DB 2/2NucliaObsidianOpen Document Format (ODT)Open City DataOrg-modePandas DataFrameAmazon TextractPolars DataFramePsychicPubMedPySparkReadTheDocs DocumentationRecursive URLRedditRoamRocksetrspaceRSS FeedsRSTSitemapSlackSnowflakeSource CodeSpreedlyStripeSubtitleTelegramTencent COS DirectoryTencent COS FileTensorFlow Datasets2MarkdownTOMLTrelloTSVTwitterUnstructured FileURLWeatherWebBaseLoaderWhatsApp ChatWikipediaXMLXorbits Pandas DataFrameYouTube audioYouTube transcriptsDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsDocument loadersChatGPT DataChatGPT DataChatGPT is an artificial intelligence (AI) chatbot developed by |
699 | artificial intelligence (AI) chatbot developed by OpenAI.This notebook covers how to load conversations.json from your ChatGPT data export folder.You can get your data export by email by going to: https://chat.openai.com/ -> (Profile) - Settings -> Export data -> Confirm export.from langchain.document_loaders.chatgpt import ChatGPTLoaderloader = ChatGPTLoader(log_file="./example_data/fake_conversations.json", num_logs=1)loader.load() [Document(page_content="AI Overlords - AI on 2065-01-24 05:20:50: Greetings, humans. I am Hal 9000. You can trust me completely.\n\nAI Overlords - human on 2065-01-24 05:21:20: Nice to meet you, Hal. I hope you won't develop a mind of your own.\n\n", metadata={'source': './example_data/fake_conversations.json'})]PreviousBrowserlessNextCollege ConfidentialCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. | ChatGPT is an artificial intelligence (AI) chatbot developed by OpenAI. ->: artificial intelligence (AI) chatbot developed by OpenAI.This notebook covers how to load conversations.json from your ChatGPT data export folder.You can get your data export by email by going to: https://chat.openai.com/ -> (Profile) - Settings -> Export data -> Confirm export.from langchain.document_loaders.chatgpt import ChatGPTLoaderloader = ChatGPTLoader(log_file="./example_data/fake_conversations.json", num_logs=1)loader.load() [Document(page_content="AI Overlords - AI on 2065-01-24 05:20:50: Greetings, humans. I am Hal 9000. You can trust me completely.\n\nAI Overlords - human on 2065-01-24 05:21:20: Nice to meet you, Hal. I hope you won't develop a mind of your own.\n\n", metadata={'source': './example_data/fake_conversations.json'})]PreviousBrowserlessNextCollege ConfidentialCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
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