LlamaIndex raises $8.5M seed round, led by Greylock Partners
medium.comHi all - Jerry (co-founder/CEO) here, here to help answer any questions you might have!
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.
Github link https://github.com/jerryjliu/llama_index
Congrats Jerry! Amazing community and excited to see you all continue to build.
alright HN - langchain and llamaindex - they are not-competitors in that you can use either standalone or you can use both together - who is using which and why?
I'm using LlamaIndex way more these days, mostly because the big use cases from enterprise orgs is around using their own data in novel ways. it makes much more sense for backend tasks.
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.
AFAIK llamaindex is built on langchain to save you a lot of time and lines of code, and also constantly adding new stuff (as does langchain) as dataloaders, different indeces, all kinds of cool stuff like nodes pre and post processing, reranking, query optimization, query decomposition, custom query engines and whatnot. you can still use langchain, for example when using agents or more realistically when going the route of textsplitting by token size. I'd say its complementary, do whatever you can do with llama_index and whatever you cant use langchain.
after using both of them for awhile, i see them as complementary as well. i'm more likely to use langchain for execution of tasks, and llamaindex for backend optimization and building out 'embeddings data pipelines'. if i need something from langchain in llamaindex, i can bring it in, and vice versa.
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.
Congrats Jerry! Tackling a hard but very real problem that every organization is going to run into eventually.
Congratulations Jerry and the LlamaIndex team! Excited to see what’s to come.
Congrats! Very cool. I also love the collaboration with Weaviate!
Congrats Jerry and the LlamaIndex Team!
Here for the "I could build this in a weekend" comment so I can bookmark for the IPO :)
Congrats from Recursion Venture Capital. Excited to be part of this journey!
This is such a great milestone. Congrats to the llama-index team!
Excellent news! Congratulations!
Congrats from the Qdrant team!
Congrats! Exciting news!!