161–170 of 192 posts
Langchain hasn’t invested enough in quality of lib. A good lib needs good deliberation to build right abstractions that add value, then “get out of the way“.
HTMLElement root = new HTMLElement(); // serialize as text and send root to output stream
I was like why not send the string “” to the output stream.
Earlier quoted context omitted.
The rumored valuation was around $200M, and the product is pre-revenue right now. So that seems pretty ridiculous.
I’m curious - who gets screwed over the most here? Is it investors who got tricked into over valuing? Or langchain who now can’t meet their expected revenue targets and will be forced to pivot? Speaking in hypothetical terms of course. I’m assuming the langchain folks are probably paying themselves pretty well and not working super hard (at least not on engineering stuff)?
Since last year, before I heard about langchain, I've been building my own stack of tooling for my own LLM projects that probably now covers about 10-20% of Langchain's functionality. I heard about Langchain earlier this year and groaned, thinking that I did a lot of work for nothing.. ..Then I actually used langchain. I was shocked at how poorly performant the code is. Some operations took 10x longer than how I did…
Using an LLM framework at this moment doesn’t make sense and can be damaging, in my humble opinion. Ways to extract value from LLMs are in early exploration stage. Look at research in prompting: chain of thought, react, reflection, tree of thoughts, zero vs few hot etc. Then completion vs conversational interfacing. Then memory management via vector databases and prompt expansion vs compression vs progressive summari…
> Look at research in prompting: chain of thought, react, reflection, tree of thoughts, zero vs few hot etc. Then completion vs conversational interfacing. Then memory management via vector databases and prompt expansion vs compression vs progressive summarization etc. Wow, that's a list of things that's completely new to me - thanks! Do you have any particular resources that you'd recommend for learning about these…
The reason why Langchain is pointless is that it's trying to solve problems on top of technical foundations that just cannot support it. The #1 learning is that there is no reusability with the current generation of LLMs. We're using GPT-4 and 3.5T exclusively. Over the last several months, my team has been building several features using highly sophisticated LLM chains that do all manner of reasoning. The ultimate o…
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100% this! What is worse is that LangChain hides their prompts away, I had to read the source code and mess with private variables of nested classes just to change a single prompt from something like RetrievalQA, and not only that, the default prompt they use is actually bad, they are lucky things work because GPT-3.5 and GPT-4 are damn smart machines, with any other open LLM, things break. I was hoping for good defa…
Can you share some insights/examples, if you can, on how you improved the prompts? One I feel is particularly poor is the next question generation/past question condensation prompts which are used to refine the user's input based on the history, so that the query includes all the context required for the question, and hence, incorporating "memory".
It's a lot of instructions for an LLM, they seem to forget an LLM is an auto-completion machine, and which data it is trained on. Using > for sections is not a normal thing, it's not markdown, which probably the thing read way more often on the internet, instead of open json comments, why not type signatures, instead of so many rules, why not give it examples? It is an autocomplete machine!
They are relying too much on the LLM being smart because they probably only test stuff in GPT-4 and 3.5, but with GPT4All models this prompt was not working at all, so I had to rewrite it, for simple routing, we don't even need json, carying the `next_inputs` here is weird if you don't need it.
So this is my version of it: https://gist.github.com/rogeriochaves/b67676977eebb1936b9b5c...
It's so basic it's dumb, yet it is more powerful, as it does not rely on GPT-4 level intelligence, it's just what I needed
Using an LLM framework at this moment doesn’t make sense and can be damaging, in my humble opinion. Ways to extract value from LLMs are in early exploration stage. Look at research in prompting: chain of thought, react, reflection, tree of thoughts, zero vs few hot etc. Then completion vs conversational interfacing. Then memory management via vector databases and prompt expansion vs compression vs progressive summari…
> Look at research in prompting: chain of thought, react, reflection, tree of thoughts, zero vs few hot etc. Then completion vs conversational interfacing. Then memory management via vector databases and prompt expansion vs compression vs progressive summarization etc. Wow, that's a list of things that's completely new to me - thanks! Do you have any particular resources that you'd recommend for learning about these…
Good video on "Tree of thoughts" which also reviews / puts it in the context of other methods: https://www.youtube.com/watch?v=ut5kp56wW_4
Completion vs conversational interface is something you can read about in the OpenAI API documentation.
For the remaining things I don't have single specific pointer at hand.
I have a full-on "The Problem With LangChain" blog post in the pipeline, and the reason I made a simple alternative ( https://news.ycombinator.com/item?id=36393782 ) because I spent a month working with LangChain and coming to the conclusion that it's just easier to make my own Python package than it is to hack LangChain to fit my needs. A few bullet points: - LangChain encourages tool lock-in for little developer be…
> Part of the reason I'm hesitant to release said blog post is because I don't want to be that asshole who criticizes open source software that's operating in good faith. I agree with your restraint, this feels like it might be more productive in another format. Ultimately this either needs to be broached with the maintainers or an alternative should be started.
Using an LLM framework at this moment doesn’t make sense and can be damaging, in my humble opinion. Ways to extract value from LLMs are in early exploration stage. Look at research in prompting: chain of thought, react, reflection, tree of thoughts, zero vs few hot etc. Then completion vs conversational interfacing. Then memory management via vector databases and prompt expansion vs compression vs progressive summari…
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