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Re-implementing LangChain in 100 lines of code

blog.scottlogic.com

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Re: Re-implementing LangChain in 100 lines of code

#81

Post by theptrk »

rcme wrote: post #16

LangChain has been so frequently discussed that I thought it must be this amazing piece of software. I was recently reading about vector databases and how they can be used to provide context to LLMs. I came across a LangChain class called RetrievalQA, which takes in a vector database and a question and produces and answer based on documents stored in the vector db. My curiosity was piqued! How did it work? Well... it…

I tried to make a youtube video exploring the code and it was fairly short https://www.youtube.com/watch?v=Joby-58DuBE. I think if the prompts were put front and center in the documentation it would be clear up a lot of mystery.

Re: Re-implementing LangChain in 100 lines of code

#82

Post by sia_m »

There are a few ways to use Langchain. Firstly, the docs are a mess. What I personally did, I followed the notebook from OpenAI cookbook on embedding a code base, and one on embedding the docs, and querying over that with GPT-4.

After a while of doing that, I realised like many others that it's too high of an abstraction. In the end I think you're better off just looking at their source code, and just looking at how they've implemented the stuff in normal python and then adapting it for your own needs.

Re: Re-implementing LangChain in 100 lines of code

#84

Post by msikora »

kozikow wrote: post #65
fbrncci wrote: post #14

I work with Langchain on a daily basis now, and so often I find myself asking; do I really need a whole LLM framework for this? At this point, the assistant I am writing, will likely be more stable rewritten in pure Python. The deeper and more complex the application becomes, the more of a risk Langchain seems to become to keeping it maintainable. But even at less complex levels, if I want to do this: 1. Have a huge…

I've experimented with LangChain for my chatbot as well, but ultimately, I resorted to using custom Python. Here are a few issues I faced with LangChain: - By developing your own solutions, you can engineer specific components that would be provided by LangChain to better suit your use case. For example, by fine-tuning to your use case you can have better results with converation history, context and summarization be…

This, I just pretty much ended up using the basic LLMChain and do my own custom flow. The built in agents are close to useless for anything but a toy project; it is simply way too unreliable.

Re: Re-implementing LangChain in 100 lines of code

#85

Post by dpbrinkm »

KevinBenSmith wrote: post #30

As someone who has created several LLM-based applications running in production, my personal experience with langchain has been that it is too high of an abstraction for steps that in the end are actually fairly simple. And as soon as you want to slightly modify something to better accomodate your use-case, you are trapped in layers & layers of Python boiler plate code and unnecessary abstractions. Maybe our llm appl…

>someone who has created several LLM-based applications running in production

Come and talk about what you are doing and challenges of it at our LLM in production virtual conference? https://home.mlops.community/home/events/llm-in-prod-part-ii...

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