201–210 of 307 posts
Damn I built a RAG agent during the past 3 months and a half for my internship. And literally everyone in my company was asking me why I wasn't using llangchain or llamaindex like I was a lunatic. Everyone else that built a rag in my company used llangchain, one even went into prod. I kept telling them that it works well if you have a standard usage case but the second you need to something a little original you have…
Damn I built a RAG agent during the past 3 months and a half for my internship. And literally everyone in my company was asking me why I wasn't using llangchain or llamaindex like I was a lunatic. Everyone else that built a rag in my company used llangchain, one even went into prod. I kept telling them that it works well if you have a standard usage case but the second you need to something a little original you have…
Damn I built a RAG agent during the past 3 months and a half for my internship. And literally everyone in my company was asking me why I wasn't using llangchain or llamaindex like I was a lunatic. Everyone else that built a rag in my company used llangchain, one even went into prod. I kept telling them that it works well if you have a standard usage case but the second you need to something a little original you have…
Could someone point me towards a good resource for learning how to build a RAG app without llangchain or llamaindex? It's hard to find good information.
LLM frameworks like LangChain are causing a java-fication or Python . Do you want a banana? You should first create the universe and the jungle and use dependency injection to provide every tree one at a time, then create the monkey that will grab and eat the banana.
I appreciate Fabian and the Octomind team sharing their experience in a level-headed and precise way. I don't think this is trying to be click-baity at all which I appreciate. I want to share a bit about how we are thinking about things because I think it aligns with some of the points here (although this may be worth a longer post)
> But frameworks are typically designed for enforcing structure based on well-established patterns of usage - something LLM-powered applications don’t yet have.
I think this is the key point. I agree with their sentiment that frameworks are useful when there are clear patterns. I also agree that it is super early on and super fast moving field.
The initial version of LangChain was pretty high level and absolutely abstracted away too much. We're moving more and more to low level abstractions, while also trying to figure out what some of these high level patterns are.
For moving to lower level abstractions - we're investing a lot in LangGraph (and hearing very good feedback). It's a very low-level, controllable framework for building agentic applications. All nodes/edges are just Python functions, you can use with/without LangChain. It's intended to replace the LangChain AgentExecutor (which as they noted was opaque)
I think there are a few patterns that are emerging, and we're trying to invest heavily there. Generating structured output and tool calling are two of those, and we're trying to standardize our interfaces there
Again, this is probably a longer discussion but I just wanted to share some of the directions we're taking to address some of the valid criticisms here. Happy to answer any questions!
I think LangChain basically tried to do a land grab, insert itself between developers and LLM's. But it didn't add significant value and seemed to dress it up by adding abstractions that didn't really make sense. It was that abstraction gobbledygook smell that made me cautious.
Damn I built a RAG agent during the past 3 months and a half for my internship. And literally everyone in my company was asking me why I wasn't using llangchain or llamaindex like I was a lunatic. Everyone else that built a rag in my company used llangchain, one even went into prod. I kept telling them that it works well if you have a standard usage case but the second you need to something a little original you have…
The OpenAI api and others are quite raw, and it’s hard as a developer to resist building abstractions on top of it.
Some people are comparing libraries like Langchain to ORMs in this conversation, but I think maybe the better comparison would be web frameworks. Like, yeah the web/HTML/JSON are “just text” too, but you probably don’t want to reinvent a bunch of string and header parsing libraries every time you spin up a new project.
Coming from the JS ecosystem, I imagine a lot of people would like a lighter weight library like Express that handles the boring parts but doesn’t get in the way.
LLM frameworks like LangChain are causing a java-fication or Python . Do you want a banana? You should first create the universe and the jungle and use dependency injection to provide every tree one at a time, then create the monkey that will grab and eat the banana.
Earlier quoted context omitted.
Yup, I meant "Markov chain" as a way to say state. The idea was that it was extremely complex to control state. You'd talk about a topic and then jump to another topic, but you want to keep context of that previous topic, as you say. Was RAG popular on release? Google Trends indicates it started appearing around April 2023. To be honest, I'm trying to reverse engineer its popularity, and I think there are better solu…
I don't think this is a sensible use of Markov chain because that has historic connotations in NLP for text prediction models and would not include external resources in that. RAG has been popular for years including in models like BERT and T5 which can also make use of contextual content (either in the prompt, or through biasing output logits which GPT also supports). You can see the earliest formal work that gained…
There was a meme "Markov chain" framework going around at the time around these parts and I figured the name was a nod to it.
It was to solve the AI Dungeon problem: You lived in a village. The prince was captured by a dragon in the cave. You go to the blacksmith to get a sword. But now the village, cave, dragon, prince no longer exist. Context was tiny and expensive, so the idea was to chain locations like village - blacksmith - cave, and then link dragon to cave, prince to dragon, so the context only unfolds when relevant.
This really sucked to do with JS and Promises, but Langchain made it manageable. Today, we'd probably do RAG for that in some form, it just wasn't apparent to us coming from AI Dungeon.