Somehow the first time I see such pop up in my feed. Glad that someone (judging by the comments that is not the only one project) is working on this, of course I am rather far from the field but to me this feels like a step in the right direction for advancing AI past the hyperadvanced parrot stage that is the current "AI" is (at least per my perception).
KAG – Knowledge Graph RAG Framework
61–70 of 81 posts
Re: KAG – Knowledge Graph RAG Framework
#62Yet another RAG/knowledge graph implementation. At this point, the onus is on the developer to prove it's value through AB comparisons versus traditional RAG. No person/team has the bandwidth to try out this (n + 1) solution.
I enjoy the explosion of tools. Only time will tell which ones stand the test of time. But this is my day job so I never get tired of new tools but I can see how non-industry folks can find it overwhelming
But unfortunately its like a game of musical chairs or whoever is pushing their wares the hardest that we may get stuck with rather than the actual best solution.
In fact, im wondering if thats what happened in the early noughts and we had the misfortune of Java, and still have the misfortune of Javascript.
Re: KAG – Knowledge Graph RAG Framework
#63Earlier quoted context omitted.
I enjoy the explosion of tools. Only time will tell which ones stand the test of time. But this is my day job so I never get tired of new tools but I can see how non-industry folks can find it overwhelming
Can you expand on that? Where do big enterprise orgs products fit in, eg Microsoft, Google? What are the leading providers as you see them? As an outsider it is bewildering. First I hear that llama_index is good, then I hear that its overcomplicating slop. What sources or resources are reliable on this? How can we develop anything that will still stand in 12 months time?
haha I had heard that langchain was overcomplicated, self contradictory slop and that llama index was better. I dont doubt its bad as well.
Both are cut from the same cloth of typical inexperienced devs who made something cool in a new space and posted on GitHub but then immediately morphed into a companies trying to trap users etc. without going through an organic lifecycle of growing, improving, refactoring with the community.
Re: KAG – Knowledge Graph RAG Framework
#64Fancy, 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.
This has always been the Hard Problem. For one, constructing an ontology that is comprehensive, flexible, and stable is huge effort. Then, taking the unstructured mess of documents and categorizing them is an entire industry in itself. Librarians have cataloging as a sub-specialty of library sciences devoted to this.
So yes, there's a huge pile of tools and software for working with knowledge graphs, but to date populating the graph is still the realm of human experts.
Re: KAG – Knowledge Graph RAG Framework
#65Earlier quoted context omitted.
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 LLM…
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.
Granted they use the `openai` python library (or other library/implementation that uses that same env var), hence my question in the previous-previous comment...
Re: KAG – Knowledge Graph RAG Framework
#66advice 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.
If, on the other hand, it were a long, drawn-out animation of moving the mouse pointer to the button, hovering for a few seconds, and then slowing clicking while dragging the mouse away so that the button didn't select and they had to repeat the task again-- that would be art.
Re: KAG – Knowledge Graph RAG Framework
#67advice 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.
If, on the other hand, it were a long, drawn-out animation of moving the mouse pointer to the button, hovering for a few seconds, and then slowing clicking while dragging the mouse away so that the button didn't select and they had to repeat the task again-- that would be art.
sounds agentic
Re: KAG – Knowledge Graph RAG Framework
#68What do other HNers make out of this? Would you use this? Responsible for a legaltech startup here.
Fellow legal tech founder here. The first thing I look at in projects like this are the prompts: https://github.com/OpenSPG/KAG/blob/master/kag/builder/promp... All you’re doing here is “front loading” AI: Imstead of running slow and expensive LLMs at query time, you run them at index time. It’s a method for data augmentation or, in database lingo, index building. You use LLMs to add context to chunks that doesn’t ex…
>I found the result to be an absolute mess. That is because these systems are built to cover a broad range of applications and are not adapted at all to your problem domain.
Same findings here, re: legal text. Basic hybrid search performs better. In this use case the user knows what to look for, so the queries are specific. The advantage of graph RAG is when you need to integrate disparate sources for a holistic overview.
Re: KAG – Knowledge Graph RAG Framework
#69Earlier quoted context omitted.
Fellow legal tech founder here. The first thing I look at in projects like this are the prompts: https://github.com/OpenSPG/KAG/blob/master/kag/builder/promp... All you’re doing here is “front loading” AI: Imstead of running slow and expensive LLMs at query time, you run them at index time. It’s a method for data augmentation or, in database lingo, index building. You use LLMs to add context to chunks that doesn’t ex…
Prompts are a great place to look for these, but the part you linked too isn't very important for knowledge graph generation. It is doing an initial semantic breakdown into more manageable chunks. The actual entity and fact extraction that actually turns this into a knowledge graph is this one: https://github.com/OpenSPG/KAG/blob/master/kag/builder/promp... GraphRAG and a lot of the semantic indexes are simply vector…
To my knowledge most graph RAG implementations, including the Microsoft research project, rely on LLM entity extraction (subject-predicate-object triplets) to build the graph.
Re: KAG – Knowledge Graph RAG Framework
#70Fancy, 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.
This has always been the Hard Problem. For one, constructing an ontology that is comprehensive, flexible, and stable is huge effort. Then, taking the unstructured mess of documents and categorizing them is an entire industry in itself. Librarians have cataloging as a sub-specialty of library sciences devoted to this. So yes, there's a huge pile of tools and software for working with knowledge graphs, but to date popu…
When you boil it down, the current LLMs could work effectively if a prompt engineer could figure out a converging loop of a librarian tasked with generating a hypertext web ring crossed with a wikipedia.
Perhaps one needs to manually create a starting point then ask the LLM to propse links to various documents or follow an existing one.
Sufficiently loopable transversal should create a KG