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llama-index-packs-docugami-kg-rag
Advanced tools
This template contains a reference architecture for Retrieval Augmented Generation against a set of documents using Docugami's XML Knowledge Graph (KG-RAG).
This LlamaPack provides an end-to-end Knowledge Graph Retrieval Augmented Generation flow using Docugami.
Before you use this llamapack, you must have some documents already processed in Docugami. Here's what you need to get started:
Once your documents are in Docugami, they are processed and organized into sets of similar documents, e.g. NDAs, Lease Agreements, and Service Agreements. Docugami is not limited to any particular types of documents, and the clusters created depend on your particular documents. You can change the docset assignments later if you wish. You can monitor file status in the simple Docugami webapp, or use a webhook to be informed when your documents are done processing.
You need to set some required environment variables before using your new app based on this template. These are used to index as well as run the application, and exceptions are raised if the following required environment variables are not set:
OPENAI_API_KEY
: from the OpenAI platform.DOCUGAMI_API_KEY
: from the Docugami Developer Playgroundexport OPENAI_API_KEY=...
export DOCUGAMI_API_KEY=...
Once your documents are finished processing, you can build and use the agent by adding the following code
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
DocugamiKgRagPack = download_llama_pack(
"DocugamiKgRagPack", "./docugami_kg_rag"
)
docset_id = ...
pack = DocugamiKgRagPack()
pack.build_agent_for_docset(docset_id)
pack.run(...)
FAQs
This template contains a reference architecture for Retrieval Augmented Generation against a set of documents using Docugami's XML Knowledge Graph (KG-RAG).
We found that llama-index-packs-docugami-kg-rag demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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