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Docugami
 Contents 
Prerequisites
Quick start
Advantages vs Other Chunking Techniques
Load Documents
Basic Use: Docugami Loader for Document QA
Using Docugami to Add Metadata to Chunks for High Accuracy Document QA
Docugami#
This notebook covers how to load documents from Docugami. It provides the advantages of using this system over alternative data loaders.
Prerequisites#
Install 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
!pip install lxml
Quick start#
Create 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 instructions
Explore 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#
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/docugami.html