'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