This template contains a reference architecture for Retrieval Augmented Generation against a set of documents using Docugami's XML Knowledge Graph (KG-RAG).

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:

1. OPENAI_API_KEY: from the OpenAI platform.
2. DOCUGAMI_API_KEY: from the Docugami Developer Playground

export OPENAI_API_KEY=...
export DOCUGAMI_API_KEY=...

Finally, make sure that you run poetry install --all-extras (or select a specific set of extras, see pyproject.toml) to install dependencies.

Before you use this template, you must have some documents already processed in Docugami. Here's what you need to get started:

1. Create a Docugami workspace (free trials available)
2. Create an access token via the Developer Playground for your workspace. Detailed instructions.
3. Add your documents to Docugami for processing. There are two ways to do this:
   • Upload via the simple Docugami web experience. Detailed instructions.
   • Upload via the Docugami API, specifically the documents endpoint. Code samples are available for python and JavaScript or you can use the docugami python library.

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. The Docugami RAG over XML Knowledge Graphs (KG-RAG) Cookbook has end to end code to upload docs and wait for them to be processed, if you are interested.

Once your documents are finished processing, you can index them in the following step.

Before you can run your app, you need to build your vector index. See index.py which you can run via poetry run python index.py after setting the environment variables as specified above. The CLI will query docsets in the workspace corresponding to your DOCUGAMI_API_KEY and let you pick which one(s) you want to index.

Indexing in this template uses the Docugami Loader for LangChain to create semantic chunks out of your documents. Refer to this documentation for details.

Note that if you previously ran indexing for the same docset, the index will not be recreated. If you want to force recreate the index (e.g. if you have new docs in the docset or changed your chunking config parameters) please specify poetry run python index.py

To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package git+https://github.com/docugami/langchain-template-docugami-kg-rag.git

If you want to add this to an existing project, you can just run:

langchain app add git+https://github.com/docugami/langchain-template-docugami-kg-rag.git

And add the following code to your server.py file:

from docugami_kg_rag import chain as docugami_kg_rag_chain

add_routes(app, docugami_kg_rag, path="/docugami-kg-rag")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up here.

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

If you are inside the app directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at https://localhost:8000

We can see all templates at https://127.0.0.1:8000/docs We can access the playground at https://127.0.0.1:8000/docugami-kg-rag/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("https://localhost:8000/docugami-kg-rag")

By default, this template uses OpenAI LLMs and Embeddings, plus ChromaDB as the vector database. This is specified in docugami_kg_rag/config/__init__.py in the following import lines at the top of the file:

from docugami_kg_rag.config.openai import *
from docugami_kg_rag.config.chromadb import *

You can change these defaults by changing which config you import, e.g. you can import the fireworksai.py config to run OSS models in fireworksai (or create your own config)

Optionally, if using local embeddings or llms, make sure your local CUDA runtime is updated. You can run torch.cuda.is_available() in a python REPL to make sure, and if you need to install a specific version for your local CUDA driver you can run something like poetry run pip3 install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117 to update it.

If using redis as a vector database in your config, you can configure the vector store to use Redis. See documentation here: https://python.langchain.com/docs/integrations/vectorstores/redis

One of the things you need to specify is the REDIS_URL. You may have an instance already running that you can point to, or for development you may want to deploy Redis locally:

docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest

See documentation here for how to configure the REDIS_URL.

If working on the template, you can run poetry run langchain template serve --chat-playground to test improvements. Make sure you run the /playground route of the localhost endpoint created.