langchain_graphrag library for - "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" paper

[ksachdeva]

Please find the implementation here:

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

or install it to take it for spin

pip install langchain-graphrag

Why re-implementation 🤔?

Personal Preference

While I generally prefer utilizing and refining existing implementations, as re-implementation often isn't optimal, I decided to take a different approach after encountering several challenges with the official version.

Issues with the Official Implementation

• Lacks integration with popular frameworks like LangChain, LlamaIndex, etc.
• Complexity due to dependence on the datashaper package, making it harder to understand.
• Limited to OpenAI and AzureOpenAI models, with no support for other providers.

Why reling on established frameworks like LangChain?

Using an established foundation like LangChain offers numerous benefits. It abstracts various providers, whether related to LLMs, embeddings, vector stores, etc., allowing for easy component swapping without altering core logic or adding complex support. More importantly, a solid foundation like this lets you focus on the problem's core logic rather than reinventing the wheel.

LangChain also supports advanced features like batching and streaming, provided your components align with the framework’s guidelines. For instance, using chains (LCEL) allows you to take full advantage of these capabilities.

Modularity & Extensibility focused design

The APIs are designed to be modular and extensible. You can replace any component with your own implementation as long as it implements the required interface.

Given the nature of the domain, this is important for conducting experiments by swapping out various components.