71–80 of 85 posts
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> I can write integrations for what my app needs in less than an hour myself, Or just ask ChatGPT to do it... Joking aside, I think 'npm "left-pad"' describes it perfectly.
Most of the code my OpenAI API experiments run on was written by ChatGPT.
To put together a basic question/answer demo that didn't quite fit the LangChain templates, I had to hunt a bunch of doc pages and and cobble together snippets from multiple notebooks. Sure, the final result was under 30 lines of code, BUT: It uses fns/classes like `load_qa_with_sources_chain` and `ConversationalRetrievalChain`, and to know what these do under the hood, I tried stepping into the debugger, and it was a nightmare of call after call up and down the object hierarchy. They have verbose mode so you can see what prompts are being generated, but there is more to it than just the prompts. I had to spend several hours piecing together a simple flat recipe based on this object hierarchy hunting.
It very much feels like what happened with PyTorch Lightning -- sure, you can accomplish things with "just a few lines of code", but now everything is in one giant function, and you have to understand all the settings. If you ever want to do something different, good luck digging into their code -- I've been there, for example trying to implement a version of k-fold cross-validation: again, an object-hierarchy mess.
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The most important aspect of langchain is NOT using OpenAI for the LM. The most useful aspect of using langchain is to use it with Galpaca (or vicuna/koala/etc) to spin up an assistant for your home. This way, you can push all of your files through it - even petabytes or terabytes of files, at a fraction of the cost - and have it organize things for you. No privacy problems, no extreme costs, just ease of use, low la…
The vast majority of people building LM apps with (or without) LangChain are using OpenAI. I sincerely hope local LM tech like Galpaca (or vicuna/koala/etc) succeed but I don't understand why we are collectively pretending they are currently anywhere near gpt-3.5-turbo both in terms of speed and quality. Honestly the local models feel more like first generation BERT/GPT-1 models that have been fine-tuned for QA using…
There are some issues that arise from not having decent priors due to that lack of knowledge, which may or may not be important for the given task. A (perhaps somewhat bad) example may be: if you ask me "what is a good one-liner in bash for parsing xml from a stream", I may give you an answer using xmlstarlet. However, this may not be the best answer - since Xalan can handle XSLT version 3, but xmlstarlet can't (XSLTv3 handles streams).
So if looking up information in the database, some things may be slightly missed like that - but this behavior would be close to what ChatGPT offers (ChatGPT is quite awful in this way most of the time).
You are right that it would miss GPT-4 by a good bit though in these cases, but most people aren't using GPT-4 for this anyway.
Ultimately both can be used. OpenAI can do things you really want it for, such as a programming assistant, or things that may require much more "reasoning" (not a proper word, but conveys the message) capabilities.
Local models can do the really useful base work of completely re-organizing or re-encoding files to free up space if you set it to do so, integrating with a HomeAssistant system, setting up a HomeAssistant system if you want one, answering your vocal 'Alexa/Siri'-like questions completely offline, setting up backup solutions for all your computers, setting up servers that perform more tasks that you may want - essentially a complete personal assistant. OpenAI shouldn't be needed for this, and it is highly desired to not have them do any of this (due to costs and the number of credentials it would give to them).
Earlier quoted context omitted.
Most of the code my OpenAI API experiments run on was written by ChatGPT.
…which implies that a library of pre written shims is not needed…
Am I the only one who is not convinced by the value proposition of langchain? 99% of it are interface definitions and implementations for external tools, most of which are super straightforward. I can write integrations for what my app needs in less than an hour myself, why bring in a heavily opinionated external framework? It kind of feels like the npm "left-pad" to me. Everyone just uses it because it seems popular…
It would probably be much better to start with the basic OpenAI API and then build on top of it.
What I find particularly frustrating is the difficulty in easily interfacing with my existing python tools (not like add two numbers, but somewhat complex analytics on top of structured data). If anybody has any success with interfacing with existing tools/scripts, would love to know how people are going about doing it.
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…
Given that the company has $200 million valuation, that is $2 million per line of code! just kidding. Still, I would like to understand $200 million valuation of langchain.ai.
VC's are in full blown FOMO mode for things they barely understand. Even the engineering backgrounds are pretty lost; imagine the finance backgrounds that have barely wrote a lick of code.
If you want to develop a real LLM application, you're probably better off skipping the library completely, or at least fully understand each abstraction to make sure it does everything you want before you decide you want to incorporate it.
Am I the only one who is not convinced by the value proposition of langchain? 99% of it are interface definitions and implementations for external tools, most of which are super straightforward. I can write integrations for what my app needs in less than an hour myself, why bring in a heavily opinionated external framework? It kind of feels like the npm "left-pad" to me. Everyone just uses it because it seems popular…
Langchain is absolutely perfect though, it's bad enough that you'll be driven to write something better out of pure frustration but gives you enough good ideas and breadcrumbs to actually do it. It's probably the best on-ramp for "practical uses of llms" because it scratches just the right developer itch.
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…
It basically gathers text that are similar to what you are asking and feed it into the prompt, yes. No magic. The worse part is that if you ask “please get me the summary to this doc” it will actually search the vector db using the entire question. It’s not very smart. Depending on how you split the embedding you could end up with a bunch of crap