Yet 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.
replies(2):
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.
1. langchain, llamaindex, etc are the equivalent of jquery or ORMs for calling third-party LLMs. They're thin adapter layers with a bit of consistency and common tasks across. Arguably like React, where they are thin composition layers. So complaints of being leaky abstractions is in the sense of an ORM getting in the way vs helping.
2. KG/graph RAG libraries are the LLM equivalent of, when regex + LIKE sql statements aren't enough, graduating to a full-blown lucene/solr engine. These are intelligence engines that address index-time, query-time, and likely, both. Thin libraries and those lacking standard benchmarks are a sign of experiments vs production-relevant: unless you're just talking to 1 pdf, not likely what you want. IMO, no 'winners' here yet: llamaindex was part of an early wave of preprocessors that feed PDFs etc to the KG, but not winning the actual 'smart' KG/RAG. In contrast, MSR Graph RAG is popular and benchmarks well, but if you read the github & paper, not intended for use -- ex: it addresses 1 family of infrequent query you'd do in a RAG system ("n-hop"), but not the primary kinds like mixing semantic+keyword search with query rewriting, and struggles with basics like updates.
Most VC infra/DB $ goes to a layer below the KG. For example, vector databases -- but vector DBs are relatively dumb blackboxes, you can think of them more like S3 or a DB index, while the LLM KG/AI quality work is generally a layer above. (We do train & tune our embedding models, but that's a tiny % of the ultimate win, mostly for smarter compression for handling scaling costs, not the bigger smarts.)
+ 1 to presentation being confusing! VC $ on agents, vector DB co's, etc, and well-meaning LLM enthusiasts are cranking out articles on small uses of LLMs, but in reality, these end up being pretty crappy in quality if you'd actually ship them. So once quality matters, you get into things like the KG/graph RAG work & evals, which is a lot more effort & grinding => smaller % of the infotainment & marketing going around.
(We do this stuff at real-time & data-intensive scales as part of Louie.AI, and are always looking for design partners, esp on graph rag, so happy to chat.)
imo, none. Unfortunately, the landscape is changing too fast. May be things will stabilize, but for now I find experimentation a time-consuming but essential part of maintaining any ML stack.
But it's okay not to experiment with every new tool (it can be overwhelming to do this). The key is in understanding one's own stack and filtering out anything that doesn't fit into it.
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 good. So, if this helps, skip the details, understand the basics, and go for your own implementation. One thing to look out for is new SOTA LLM releases, and the jumps in capability. Eg: 4o did not announce it, but they started doing very well on vision. (GPT-4 was okay, 4o is empirically quite better). These things help when you update your pipeline.
It’s not hard for a product to swap the underlying LLM for a given task.
Then Google did.
Then llava.
The issue is that this technology has no most (other than the cost to create models and datasets)
There’s not a lot of secret sauce you can use that someone else can’t trivially replicate, given the resources.
It’s going to come down to good ol product design and engineering.
The issue is openai doesn’t seem to care about what their users want. (I don’t think their users know what they want either, but that’s another discussion)
They want more money to make bigger models in the hope that nobody else can or will.
They want to achieve regulatory capture as their moat.
For all their technical abilities at scaling LLM training and inference, I don’t get the feeling that they have great product direction.
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.