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Langchain Is Pointless

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Re: Langchain Is Pointless

#11

Post by arpowers »

Proper term is that it's a "false abstraction"...

It abstracts some work only to introduce introduce it's own API (which is ultimately more complicated, less documented, introduces limits and constraints, and comes with its own bugs and dev politics)...

But nobody asked me, keep using it

Re: Langchain Is Pointless

#12

Post by chpmrc »

Der_Einzige wrote: post #3

This guy's analysis is terrible. I literally built a retrieval augmented generation with memory in langchain last night (comments claim you can't do this) Sure, langchain has poor documentation and useless helper functions. That doesn't mean that it's pointless.

Could you please share the code? I tried doing the same and, like the people in that thread, could not. TIA!

Re: Langchain Is Pointless

#13

Post by ibains »

It is pointless - LlamaIndex and LangChain are re-inventing ETL - why use them when you have robust technology already?

1. You ETL your documents into a vector database - you run this pipeline everyday to keep it up to date. You can run scalable, robust pipelines on Spark for this.

2. You have a streaming inference pipeline that has components that make API calls (agents) and between them transform data. This is Spark streaming.

Prophecy is working with large enterprises to implement generative AI use cases, but they don’t talk so much on HN.

Here’s our talk from Data+AI Summit: Build a Generative AI App on Enterprise Data in 13 Minutes https://www.youtube.com/watch?v=1exLfT-b-GM

Here’s a blog/demo https://www.prophecy.io/blog/prophecy-generative-ai-platform...

Re: Langchain Is Pointless

#14

Post by tehsauce »

Der_Einzige wrote: post #3

This guy's analysis is terrible. I literally built a retrieval augmented generation with memory in langchain last night (comments claim you can't do this) Sure, langchain has poor documentation and useless helper functions. That doesn't mean that it's pointless.

Just fyi, if you use a standard knn library like faiss and pretty much any embedding + language model raw from huggingface or an API, it will require ~15 lines of code to do what you describe. I’m not sure how much shorter langchain made your implementation but I can’t imagine it saving too much.

Re: Langchain Is Pointless

#16

Post by lmeyerov »

lmeyerov wrote: post #5

I've definitely got heartache here, and they merit criticism, but it is real We need a lot of pluggability to support diff vendor LLMs and BYO LLMs in Louie.ai, so having langchain has been nice for helping code to interfaces vs vendor lockin. It definitely has growing pains - ex: sync & multithreading is important for us so we are generally coding around langchain while that smooths out. Likewise, we ended up buildi…

Also, the thread title is unintentionally funny: I'd like the interface to be more functional so we can write truly 'point-free' pipelines, especially around areas like memory. Ex: When dealing with multithreading, that makes it a lot safer. There are projects exploring that, but langchain is winning as a pluggable interface for many new LLM providers.

Edit: link - https://en.wikipedia.org/wiki/Tacit_programming

Re: Langchain Is Pointless

#17

Post by lmeyerov »

ibains wrote: post #13

It is pointless - LlamaIndex and LangChain are re-inventing ETL - why use them when you have robust technology already? 1. You ETL your documents into a vector database - you run this pipeline everyday to keep it up to date. You can run scalable, robust pipelines on Spark for this. 2. You have a streaming inference pipeline that has components that make API calls (agents) and between them transform data. This is Spar…

We also do platform & customer work there (cool pipelines to feed louie.ai or real-time headless versions), and agreed those pipelines have simple uses of LLM where langchain is mostly useful just for a vendor neutrality. Think BYO LLM as it is now a zoo. Basically apache nifi or spark streaming with simple LLM & vector DB call outs. Our harder work here is more at the data engineering level.

But....a lot of our louie.ai work happens for less trivial scenarios where it isn't just the ETL NLP 2.0 tier . That logic is much more complicated, so structured programming abstractions matter a LOT more for AI-style business logic. Think talk to your data and generate on-the-fly analytics pushdown with an interactive data viz UI. That's.. a lot of code.

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