We're building a data framework to unlock the full capabilities of LLMs on top of your private data. We can’t wait for the future - this space is moving so rapidly and there’s so many things we want to do on both the open-source and enterprise side.
Feel free to shoot me a personal note on Twitter/Discord as well.
that said, while there's some clear crossover between the two, i find myself using langchain for things like huggingface embeddings for local models, and other helpers that work well with llamaindex.
somewhat akin to a data warehouse and all the techniques and abstractions that go into modeling it for non-technical end users, llamaindex makes a lot of that much easier to work with as a developer. structured and unstructured data can be indexed side by side, and the auto retriever functions they've recently built out work really well once you've got data indexed in a sensible way. our next step is to put a simple UI on top of it all with filters (like a dashboard) that pass metadata filters to the llamaindex autoretriever.
these patterns may not be exactly right today, but I don't see any others focusing on this area. just throwing all of your docs haphazardly into an index and calling it a day is no different than tossing all your data into a single database schema without any rhyme or reason, and hoping your dashboards can do 'magic' on top of it.
the more folks experiment with this stuff, i think they'll see where it all comes together, and where some of the crossover is. but given how quickly everything changes in this space, i'm glad there's a clear focus from each team on their core strengths rather than throwing the kitchen sink of new papers at it.
