langchain-graphrag 0.0.2b5

Implementation of GraphRAG (https://arxiv.org/pdf/2404.16130)

GraphRAG

** WORK IN PROGRESS **

This is an implementation of GraphRAG as described in

https://arxiv.org/pdf/2404.16130

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Official implementation by the authors of the paper is available at:

https://github.com/microsoft/graphrag/

Why re-implementation 🤔?

The primary reasons for re-implementing:

• Develop better understanding of the intricacies of the paper by implementing it
• Official implementation
  • is not built upon popular frameworks like langchain, llamaIndex etc
  • is bit difficult to understand because of reliance on datashaper package
  • does not support models other than OpenAI or AzureOpenAI

Install (Not Recommended yet!)

Note - this is work in progress so installing the package is not recommended yet. It would be better to clone the repo and try out current state of the code. See below for more details.

I published the package so as to reserve the name. Clone the repo and install the package locally.

pip install langchain-graphrag

Projects

There are 2 projects in the repo:

langchain_graphrag

This is the core library that implements the GraphRAG paper. It is built on top of the langchain library.

The concepts described in GraphRAG paper are implemented in a modular fashion with easy extensibility and replacement in mind.

To use the development version (Recommended as it is under active development):

Clone the repo

git clone https://github.com/ksachdeva/langchain-graphrag.git

Open in VSCode devcontainer (Recommended)

Devcontainer will install all the dependencies

If not using devcontainer

Make sure you have rye installed. See https://rye.astral.sh/

# sync all the dependencies
rye sync

examples/simple-app

This is a simple typer based CLI app.

In terms of configuration it is limited by the number of command line options exposed.

That said, the way core library is written you can easily replace any component by your own implementation i.e. your choice of LLM, embedding models etc. Even some of the classes as long as they implement the required interface.

# To generate the index
# default set azure_openai/gpt4-o/text-embedding-3-small
# you can change the model and other parameters from command line 
rye run simple-app-indexer 

# To see more options
rye run simple-app-indexer --help

# To do global search/query
# defaults are azure_openai/gpt4-o/text-embedding-3-small
# you can change the model and other parameters from command line 
rye run simple-app-global-search --query "What are the top themes in this story?"

# To do local search/query
# defaults are azure_openai/gpt4-o/text-embedding-3-small
# you can change the model and other parameters from command line 
rye run simple-app-local-search --query "Who is Scrooge, and what are his main relationships?"

See examples/simple-app/README.md for more details.