KAG – Knowledge Graph RAG Framework

Fancy, I think, but again no word on the actual work of turning a few bazillion csv files and pdf's into a knowledge graph.

I see a lot of these KG tools pop up, but they never solve the first problem I have, which is actually constructing the KG itself.

You may want to take a look at Graphiti, which accepts plaintext or JSON input and automatically constructs a KG. While it’s primarily designed to enable temporal use cases (where data changes over time), it works just as well with static content.

https://github.com/getzep/graphiti

I’m one of the authors. Happy to answer any questions.

> Graphiti uses OpenAI for LLM inference and embedding. Ensure that an OPENAI_API_KEY is set in your environment. Support for Anthropic and Groq LLM inferences is available, too.

Don't have time to scan the source code myself, but are you using the OpenAI python library, so the server URL can easily be changed? Didn't see it exposed by your library, so hoping it can at least be overridden with a env var, so we could use local LLMs instead.

On second look, it seems like you've already rejected a PR trying to add local LLM support: https://github.com/getzep/graphiti/pull/184

> We recommend that you put this on a local fork as we really want the service to be as lightweight and simple as possible as we see this asa good entry point into new developers.

Sadly, it seems like you're recommending forking the library instead of allowing people to use local LLMs. You were smart enough to lock the PR from any further conversation at least :)

You can override the default OpenAI url using an environment variable (iirc, OPENAI_API_BASE). Any LLM provider / inference server offering an OpenAI-compatible API will work.