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KAG – Knowledge Graph RAG Framework

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Re: KAG – Knowledge Graph RAG Framework

#41

Post by kergonath »

isoprophlex wrote: post #14
kergonath wrote: post #12

Earlier quoted context omitted.

> 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. I have heard good things about Graphrag [1] (but what a stupid name). I did not have the time to try it properly, but it is supposed to build the knowledge graph itself somewhat transparently, using LLMs. This is a big stumbling block. At least vector stores are easy to understand and t…

Indeed they seem to actually know/show how the sausage is made... but still, no fire and forget approach for any random dataset. check out what you need to do if the default isnt working for you (scroll down to eg. entity_extraction settings). there is so much complexity there to deal with that i'd just roll my own extraction pipeline from the start, rather than learning someone elses complex setup (that you have to…

> i'd just roll my own extraction pipeline from the start, rather than learning someone elses complex setup

I have to agree. It’s actually quite a good summary of hacking with AI-related libraries these days. A lot of them get complex fast once you get slightly out of the intended path. I hope it’ll get better, but unfortunately it is where we are.

Re: KAG – Knowledge Graph RAG Framework

#42

Post by ankit219 »

dartos wrote: post #35
ankit219 wrote: post #27

Earlier quoted context omitted.

> How can we develop anything that will still stand in 12 months time? The pace at which things are moving, likely none. You will have to keep making changes as and when you see newer things. One thing in your favor (arguably) is that every technique is very dependent on the dataset and problem you are solving. So, if you do not have the latest one implemented, you would be okay, as long as your evals and metrics are…

Well the rate of new LLMs keep coming out, but since they’re all trying to model language, they should all be fairly interchangeable and potentially will converge. It’s not hard for a product to swap the underlying LLM for a given task.

I meant not a jump in text generation ability, but more like adding a completely new modality and the likes. With 4o, you can have a multimodal embedding space and provide more relevant context to a model for fewer tokens (and higher accuracy). Ideally everyone would get there, but upgrading your pipeline is more about getting the latest functionality faster rather than just a slightly better generation.

Re: KAG – Knowledge Graph RAG Framework

#43

Post by axpy906 »

isoprophlex wrote: post #7

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.

Came here to say this and glad I am not the only one. Building out an ontology seems like quite an expensive process. It would be hard to convince my stakeholders to do this.

Re: KAG – Knowledge Graph RAG Framework

#44

Post by veggieroll »

isoprophlex wrote: post #14
kergonath wrote: post #12

Earlier quoted context omitted.

> 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. I have heard good things about Graphrag [1] (but what a stupid name). I did not have the time to try it properly, but it is supposed to build the knowledge graph itself somewhat transparently, using LLMs. This is a big stumbling block. At least vector stores are easy to understand and t…

Indeed they seem to actually know/show how the sausage is made... but still, no fire and forget approach for any random dataset. check out what you need to do if the default isnt working for you (scroll down to eg. entity_extraction settings). there is so much complexity there to deal with that i'd just roll my own extraction pipeline from the start, rather than learning someone elses complex setup (that you have to…

IMO like with most other out-of-the-box LLM frameworks, the value is in looking at their prompts and then doing it yourself.

[1] https://github.com/microsoft/graphrag/tree/main/graphrag/pro...

Re: KAG – Knowledge Graph RAG Framework

#45

Post by roseway4 »

isoprophlex wrote: post #7

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.

Re: KAG – Knowledge Graph RAG Framework

#47

Post by OJFord »

swyx wrote: post #20

advice to OP - that gif showing how you zoom in and star the repo is a giant turnoff. i closed my tab when i saw that.

> Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases

That's not even correct, starring isn't going to do that. You'd need to smash that subscribe button and not forget the bell icon (metaphorically), not ~like~ star it.

Re: KAG – Knowledge Graph RAG Framework

#48

Post by jeromechoo »

isoprophlex wrote: post #7

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.

There are two paths to KG generation today and both are problematic in their own ways. 1. Natural Language Processing (NLP) 2. LLM

NLP is fast but requires a model that is trained on an ontology that works with your data. Once you do, it’s a matter of simply feeling the model your bazillion CSVs and PDFs.

LLMs are slow but way easier to start as ontologies can be generated on the fly. This is a double edged sword however as LLMs have a tendency to lose fidelity and consistency on edge naming.

I work in NLP, which is the most used in practice as it’s far more consistent and explainable in very large corpora. But the difficulty in starting a fresh ontology dead ends many projects.

Re: KAG – Knowledge Graph RAG Framework

#49

Post by diggan »

roseway4 wrote: post #45
isoprophlex wrote: post #7

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.

Re: KAG – Knowledge Graph RAG Framework

#50

Post by TrueDuality »

kergonath wrote: post #12
isoprophlex wrote: post #7

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.

> 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. I have heard good things about Graphrag [1] (but what a stupid name). I did not have the time to try it properly, but it is supposed to build the knowledge graph itself somewhat transparently, using LLMs. This is a big stumbling block. At least vector stores are easy to understand and t…

GraphRAG isn't quite a knowledge graph. It is a graph of document snippets with semantic relations but is not doing fact extraction nor can you do any reasoning over the structure itself.

This is a common issue I've seen from LLM projects that only kind-of understand what is going on here and try and separate their vector database w/ semantic edge information into something that has a formal name.

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