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 to go through 5 layers of abstraction just to change a minute detail. Furthermore, you won't really understand every step in the process, so if any issue arises or you need to be improve the process you will start back at square 1.
I had a similar experience when LangChain first came out. I spent a good amount of time trying to use it - including making some contributions to add functionality I needed - but ultimately dropped it. It made my head hurt.
Most LLM applications require nothing more than string handling, API calls, loops, and maybe a vector DB if you're doing RAG. You don't need several layers of abstraction and a bucketload of dependencies to manage basic string interpolation, HTTP requests, and for/while loops, especially in Python.
On the prompting side of things, aside from some basic tricks that are trivial to implement (CoT, in-context learning, whatever) prompting is very case-by-case and iterative, and being effective at it primarily relies on understanding how these models work, not cargo-culting the same prompts everyone else is using. LLM applications are not conceptually difficult applications to implement, but they are finicky and tough to corral, and something like LangChain only gets in the way IMO.
I haven't used LangChain, but my sense is that much of what it's really helping people with is stream handling and async control flow. While there are libraries that make it easier, I think doing this stuff right in Python can feel like swimming against the current given its history as a primarily synchronous, single-threaded runtime.
I built an agent-based AI coding tool in Go (https://github.com/plandex-ai/plandex) and I've been very happy with that choice. While there's much less of an ecosystem of LLM-related libraries and frameworks, Go's concurrency primitives make it straightforward to implement whatever I need, and I never have to worry about leaky or awkward abstractions.
Not really. There isn't much that langchain is doing in this regard. The heavy lifting is already done by the original libs like from openai which they use and the rest are just wrappers around their api calls.
I completely agree, and built magentic [0] to cover the common needs (structured output, common abstraction across LLM providers, LLM-assisted retries) while leaving all the prompts up to the package user.
Groupthink is really common among programmers, especially when they have no idea what they are talking about.
It shows you don't need a lot of experience to see the emperor has no clothes, but you do need to pay attention.
I admire what the Langchain team has been building toward even if people don’t agree with some of their design choices.
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.
Matches my experience as well. I tried langchain about a year ago for an app and had a pretty standard use case but even going a little bit of rail and i had to dig up layers of abstractions where it would have been much easier just using the original openai lib. So it might be beneficial if your use case is about offering many different LLM providers in your app but if you know you won't be swapping out the LLM provider soon it's usually better to not use such frameworks.
I ran into similar limitations for relatively simple tasks. For example I wanted access to the token usage metadata in the response. This seems like such an obvious use case. This wasn’t possible at the time, or it wasn’t well documented anyway.
I've had the same experience. I thought I was the weird one, but, my god, LangChain isn't usable beyond demos. It feels like even proper logging is pushing it beyond it's capabilities.
On top of that, if you use the TypeScript version, the abstractions are often... weird. They feel like verbatim ports of the Python implementations. Many things are abstracted in ways that are not very type-safe and you'd design differently with type safety in mind. Some classes feel like they only exist to provide some structure in a language without type safety (Python) and wouldn't really need to exist with structural type checking.
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.
My strategy has been to implement in / follow along with llamaindex, dig into the details, and then implement that in a less abstracted, easily understandable codebase / workflow.
Was driven to do so because it was not as easy as I'd like to override a prompt. You can see how they construct various prompts for the agents, it's pretty basic text/template kind of stuff
openai cookbook! Instructor is a decent library that can help with the annoying parts without abstracting the whole api call - see it’s docs for RAG examples.
you are heading the right direction. It's amazing to see seasoned engineers go through the mental gymnastic of justifying installing all those dependencies and arguing about vector db choices when the data fit in ram and the swiss knife is right there: np.array
I built my first commercial LLM agent back in October/November last year. As a newcomer to the LLM space, every tutorial and youtube video was about using LangChain. But something about the project had that "bad code" smell about it.
I was fortunate in that the person I was building the project for was able to introduce me to a few other people more experienced with the entire nascent LLM agent field and both of them strongly steered me away from LangChain.
Avoiding going down that minefield ridden path really helped me out early on, and instead I focused more on learning how to build agents "from scratch" more or less. That gave me a much better handle on how to interact with agents and has led me more into learning how to run the various models independently of the API providers and get more productive results.
I've only ever played around with it and not built out an app like you have, but in my experience the second you want to go off script from what the tutorials suggest, it becomes an impossible nightmare of reading source code trying to get a basic thing to work. LangChain is _the_ definition of death by abstraction.
I have read the whole source of LangChain in Rust (there are no docs anyway), and it definitely seems over-engineering. The central premise of the project, of complicated chains of prompts is not useful to many people, and not to me either.
On the other hand it took some years into the web, for some web frameworks to emerge and make sense, like Ruby on Rails. Maybe in 3-4 years time, complicated chains of commands to different A.I. engines will be so difficult to get right that a framework might make sense, and establish a set of conventions.
Agents, another central feature of LangChain, are not proved to be very useful as well, for the moment.
LangChain got its start before LLMs had robust conversational abilities and before the LLM providers had developer decent native APIs (heck, there was basically only OpenAI at that time). It was a bit DOA as a result. Even by last spring, I felt more comfortable just working with the OpenAI API than trying to learn LangChain’s particular way of doing things.
Kudos to the LangChain folks for building what they built. They deserve some recognition for that. But, yes, I don’t think it’s been particularly helpful for quite some time.
I tried to use Langchain a couple times, but every time I did, I kept feeling like there was an incredible amount of abstraction and paradigms that were completely unnecessary for what I was doing.
I ended up calling the model myself and extracting things using a flexible json parser, I ended up doing what I needed with about 80 lines of code.
This is their game. Infiltrate HN, X, YouTube, Google with “tutorials” and “case studies”. Basically re-target engineers until they’ve seen your name again and again. Then, they sell.
Hi HN, Harrison (CEO/co-founder of LangChain) here, wanted to chime in briefly
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!
Thanks Harrison. LangGraph (eg graph theory + Networkx) is the correct implementation of multi-agent frameworks, though it is looking further into, and anticipating a future, then where most GPT/agent deployments are at.
And while structured output and tool calling are good, from client feedback, I'm seeing more of a need for different types of composable agents other then the default ReAct, which has distinct limitations and performs poorly in many scenarios. Reflection/Reflextion are really good, REWOO or Plan/Execute as well.
totally agree. we've opted for keeping langgraph very low level and not adding these higher level abstractions. we do have examples for them in the notebooks, but havent moved them into the core library. maybe at some point (if things stabilize) we will. I would argue the react architecture is the only stable one at the moment. planning and reflection are GREAT techniques to bring into your custom agent, but i dont think theres a great generic implementation of them yet
using LangGraph for a month, every single "graph" was the same single solution. The idea is cool, but it isn't solving the right problem.... (and the problem statement shouldn't be generating buzz on twitter. sorry to be harsh).
You could borrow some ideas from DSPy (which borrows from pytorch) their Module: def forward: and chain LM objects this way. LangGraph sounds cool, but is a very fancy and limited version of basic conditional statements like switch/if, already built into languages.
I appreciate that you're taking feedback seriously, and it sounds like you're making some good changes.
But frankly, all my goodwill was burnt up in the days I spent trying to make LangChain work, and the number of posts I've seen like this one make it clear I'm not the only one. The changes you've made might be awesome, but it also means NEW abstractions to learn, and "fool me once..." comes to mind.
But if you're sure it's in a much better place now, then for marketing purposes you might be better off relaunching as LangChain2, intentionally distancing the project from earlier versions.
They were early to the scene, made the decisions that made sense at each point in time. Initially I (like many other engineers with no AI exposure) didn't know enough to want to play around with the knobs too much. Now I do.
So the playing field has and is changing, langChain are adapting.
Isn't that a bit too extreme? Goodwill burnt up? When the field changes, there will be new abstractions - of course I'll have to understand them to decide for myself if they're optimal or not.
React has an abstraction. Svelte has something different. AlpineJS, another. Vanilla JS has none. Does that mean only one is right and the remaining are wrong?
I'd just understand them and pick what seems right for my usecase.
You seem to be implying all abstractions are equal, its just use-case dependent. I disagree- some really are worse than others. In your webdev example, it would not be hard to contrive a framework designed to frustrate. This can also happen by accident. Sometimes bad products really do waste time.
In the case of LangChain, I think it was an earnest attempt, but a misguided one. So I'm grateful for LangChain's attempt, and attempts to correct- especially since itis free to use. But there are alternatives that I would rather give a shot first.
I don't think the choices made sense even back when they were made. LangChain always looked like an answer in search of a question, a collection of abstractions that don't do much except making a simple thing more complex.
sorry to hear that, totally understand feeling burnt
ooc - do you think theres anything we could do to change that? that is one of the biggest things we are wrestling with. (aside from completely distancing from langchain project)
My advice is to focus less on the “chaining” aspect and more on the “provider agnostic” part. That’s the real reason people use something other than the native SDK of an LLM provider - they want to be able to swap out LLMs. That’s a well-defined problem that you can solve with a straight forward library. There’s still a lot of hidden work because you need to nail the “least common denominator” of the interfaces while retaining specialized behavior of each provider. But it’s not a leaky abstraction.
The “chaining” part is a huge problem space where the proper solution looks different in every context. It’s all the problems of templating engines, ETL scripts and workflow orchestration. (Actually I’ve had a pet idea for a while, of implementing a custom react renderer for “JSX for LLMs”). Stay away from that.
My other advice would be to build a lot of these small libraries… take advantage of your resources to iterate quickly on different ideas and see which sticks. Then go deep on those. What you’re doing now is doubling down on your first success, even though it might not be the best solution to the problem (or that it might be a solution looking for a problem).
> My advice is to focus less on the “chaining” aspect and more on the “provider agnostic” part
a lot of our effort recently has been going into standardizing model wrappers, including for tool calling, images etc. this will continue to be a huge focus
> My other advice would be to build a lot of these small libraries… take advantage of your resources to iterate quickly on different ideas and see which sticks. Then go deep on those. What you’re doing now is doubling down on your first success, even though it might not be the best solution to the problem (or that it might be a solution looking for a problem).
I would actually argue we have done this (to some extent). we've invested a lot in LangSmith (about half our team), making it usable with or without langchain. Likewise, we're investing more and more in langgraph, also usable with or without langchain (that is in the orchestration space, which youre separately not bullish on, but for us that was a separate bet than LangChain orchestration)
Separating into smaller libraries is a smart move. And yeah, like you said, I might be bearish on the orchestration space, but at least you can insulate it from the rest of your projects.
Best of luck to you. I don’t agree with the disparaging tone of the comments here. You executed quickly and that’s the hardest part. I wouldn’t bet against you, as long as you can keep iterating at the same pace that got you over the initial hurdles.
Your funding gives you the competitive advantage of “elbow grease,” which is significant when tackling problems like N-M ETL pipelines. But don’t get stuck focusing on solving every new corner case of these problems. Look for opportunities to be nimble, and cast a wide net so you can find them.
I agree. Adopting a more modular approach is a great idea. Coming from the Java ecosystem, I still miss having something like the Spring framework in Python. I believe Spring remains an example of excellent framework design. Let me explain what I mean.
Using Spring requires adopting Spring IoC, but beyond that, everything is modular. You can choose to use only the abstractions you need, such as ORM, messaging, caching, and so on. At its core, Spring IoC is used to loosely integrate these components. Later on, they introduced Spring Boot and Spring Cloud, which are distributions of various Spring modules, offering an opinionated application programming model that simplifies getting started.
This strategy allows users the flexibility to selectively use the components they need while also providing an opinionated programming model that saves time and effort when starting a new project.
I'm not sure. My suspicion is that the fundamental issue with frameworks like LangChain is that the problem domain they are attempting to solve is a proper subset of the problems that LLMs also solve.
Good code abstractions make code more tractable, tending towards natural language as they get better. But LLMs are already at the natural language level. How can you usefully abstract that further?
I think there are plenty of LLM utilities to be made- libraries for calling models, setting parameters, templating prompts, etc. But I think anything that ultimately hides prompts behind code will create more friction than not.
Similarly to this post, I think that the "good" abstractions handle application logic (telemetry, state management, common complexity), and the "bad" abstractions make things abstract away tasks that you really need insight into.
This has been a big part of our philosophy on Burr (https://github.com/dagworks-inc/burr), and basically everything we build -- we never want to tell how people should interact with LLMs, rather solve the common problems. Still learning about what makes a good/bad abstraction in this space -- people really quickly reach for something like langchain then get sick of abstractions right after that and build their own stuff.
> the "bad" abstractions make things abstract away tasks that you really need insight into.
Yup. People say to use langchain to prototype stuff before it goes into production but I find it falls flat there. The documentation is horrible and they explain absolutely zero about the methods they use, so the only way to “learn” is by reading their spaghetti code.
Agreed — also I’m generally against prototyping stuff and then entirely rewriting it for production as the default approach. It’s a nice idea but nobody ever actually rewrites it (or they do and it’s exceedingly painful). In true research it makes sense, but very little of what engineers do falls under that category.
Instead, it’s either “welp, pushed this to prod and got promoted and it’s someone else’s problem” or “sorry, this valuable thing is too complex to do right but this cool demo got me promoted...”
Bigger problem might be using agents in the first place.
We did some testing with agents for content generation (e.g. "authoring" agent, "researcher" agent, "editor" agent) and found that it was easier to just write it as 3 sequential prompts with an explicit control loop.
It's easier to debug, monitor, and control the output flow this way.
But we still use Semantic Kernel[0] because the lowest level abstractions that it provides are still very useful in reducing the code that we have to roll ourselves and also makes some parts of the API very flexible. These are things we'd end up writing ourselves anyways so why not just use the framework primitives instead?
Typically, the term "agents" implies some autonomous collaboration. In an agent workflow, the flow itself is non-deterministic. One agent can work with another agent and keep cycling between themselves until an output is resolved that meets some criteria. An agent itself is also typically evaluating the terminal condition for the workflow.
It's also used to mean "characters interacting with each other" and sort of message passing between them. Not sure but I get the sense thats what the author is using it as
Some "agents" like the minecraft bot Voyager(https://github.com/MineDojo/Voyager) have a control loop, they are given a high level task and then they use LLM to decide what actions to take, then evaluate the result and iterate. In some LLM frameworks, a chain/pipeline just uses LLM to process input data(classification, named entitiy extraction, summary, etc).
SK does a lot of the same things that Langhain does at a high level.
The most useful bits for us are prompt templating[0], "inlining" some functions like `recall` into the text of the prompt [1], and service container [2] (useful if you are using multiple LLM services and models for different types of prompts/flows).
It has other useful abstractions and you can see the full list of examples here:
Langchain was released in October 2022. ChatGPT was released in November 2022.
Langchain was before chat models were invented. It let us turn these one-shot APIs into Markov chains. ChatGPT came in and made us realize we didn't want Markov chains; a conversational structure worked just as well.
After ChatGPT and GPT 3.5, there were no more non-chat models in the LLM world. Chat models worked great for everything, including what we used instruct & completion models for. Langchain doing chat models is just completely redundant with its original purpose.
We use instruct models extensively as we find smaller models fine tuned to our prompts perform better when general chat models that are much larger. This lets us run inference that can be 1000x cheaper than 3.5, meaning both money saving and much better latencies.
This feels like a valid use for langchain then. Thanks for sharing.
Which models do you use and for what use cases? 1000x is quite a lot of savings; normally even with fine-tuning it's at most 3x cheaper. Any cheaper we'd need to get like $100k of hardware.
Chat models were not invented with ChatGPT. Conversational search and AI was a well-established field of study well before ChatGPT. It is remarkable how many people unfamiliar with the field think ChatGPT was the first chat model. It may be the first widely-popular chat model but it certainly isn’t the first
Nobody thinks of the idea "chat with computer" as a novel idea. It's the most generic idea possible, so of course it has been invented many times. ChatGPT broke out because of its execution, not the idea itself.
What you have failed to grasp is that people are not logic machines. "First chatbot" is never uttered to mean the absolute first chatbot – for all they know someone created an undocumented chatbot in 10,000 B.C. that was lost to time – but merely the first chatbot they are aware of.
Normally the listener is able to read between the lines, but I suppose there may be some defective units out there.
It's like arguing over who invented the light bulb or the personal computer. Answers other than "Edison" and "Wozniak", while very possibly more correct than either, will lead to an hours-long argument that changes exactly 0 minds.
Chat GPT is just GPT version 3.5. OpenAI released many other versions of GPT before that. In fact, Open AI became really popular around the time of the GPT 2 which was a fairly good chat model.
Also, the Transformer architecture was not created by OpenAI so LLMs were a thing way before OpenAI existed :)
GPT-2 was not a fairly good chat model, it was a completely incoherent completion model. GPT-3 was not much better overall (take any entry level 1B sized model you can find today and it'll steamroll it in every way, hell probably even smaller ones), and the public at large never really had any access to it, I vaguely recall GPT 3 being locked behind an approval only paid API or something unfeasible like that. Nobody cared until instruct tunes happened.
OpenAI had a real issue with making (for their time) great models but streching their rollout over months. They gave access to press and some twitter users, everyone else had to apply for their use case only to be put on the waitlist. That completely killed any momentum.
The first version of ChatGPT wasn't a huge leap from simulating chat with instruction-tuned GPT 3.5, the real innovation was scaling it to the point where they could give the world immediate and free access. That built the hype, and that success allowed them to make future ChatGPT versions a lot better than the instruction-tuned models ever were.
The main reason ChatGPT took off was:
1) Response time of the API of that quality was 10x quicker than the Davinci-instruct-3 model that was released in summer 2022, making interaction more feasible with lower wait times and with concurrency
2) OpenAI strictly banned chat applications on the GPT API; even summarising with more than 150 tokens required your to submit a use case for review; I built an app around this in October 2022, got through the review, and it was then pointless as everybody could just use ChatGPT for the purposes of my apps new feature).
It was not possible for anybody to have just whacked the instruct models of GPT-3 into an interface for both the restrictions and latency issues that existed prior to ChatGPT. I agree with you on instruct vs ChatGPT and would further say the real innovation was entirely systematic, scaling and changing the interface. Instruct tuning was far more impactful than conversational model tuning because instruct enabled so many synthesizing use cases beyond the training data.
"Instruct tuning was far more impactful than conversational model tuning because instruct enabled so many synthesizing use cases beyond the training data."
I saw many model providers nowadays provide instruct model in name as chat model. What difference between instruct tuning and conversational model tuning specifically?
The best papers to read are the T5 paper which introduced intstruction training.
BERT showed that training with two tasks (next sentence and mask fill) was more effective than solely one task.
T5 showed that multiple instructions could be used for one task (token prediction) like not just translating, but also summarizing. They suggested this could generalize (it did)
GPT-2 showed with just token prediction and no instructions you could represent good text; GPT-3 showed this was coherent and also that sufficient context was reliably continued by models(and impacted by the format of training data, e.g. StackOverflow used Q: A: in the training data, so prompts using Q: and A: worked very well for conversation-mimicking).
Davinci-instruct essentially made GPT-3 outputs reliable, because they "corrected model outputs" not just to follow the implicit continued context but to follow text instructions with general english in the users submitted prompt. They could change this to always follow a chat format (e.g. use Pronouns and refer to the user with "You") which seems to work more naturally, but the original instruct worked based on simple commands which are responded to without the chat format (e.g. no "I am sorry" - just no token, no "I believe the book you are looking for is:") etc.
Nowadays most instruct models do actually use prompt formats and training datasets which are conversational (check out the various formats in LM studio) anyway, so the difference is lost.
Afaik there's no difference, instruct and chat are used interchangeably. Mistral calls their tunes "modelname-Instruct", Meta calles them "modelname-chat".
Strictly speaking instruct tuning would mean having one instruction and one answer, but the models are typically smart enough to still get it if you chain them together and most tuning datasets do contain examples of some back and forth discussion. That might be more what could be considered a chat tune, but in practice it's not a hard distinction.
You are saying that after having experienced all the subsequent versions. GPT-2 was fairly good, not impressive but fairly good. People were using for all sorts of stuff for the fun of it. The GPT 3 versions were really impressive and had everyone here super excited
I'd argue the GPT-3 results were really cherry picked by the few people who had access, at least if the old versions of 3.5 and turbo are anything to go by. The hype would've died instantly if anyone had actually tried them themselves and realized that there's no consistency.
If you want to try out GPT-2 to refresh your memory, here [0] is an online demo. It's bad, I'd say worse than classical graph/tree based autocomplete. I'm fairly sure Swiftkey makes more coherent sentences.
Open AI when they gave press access to gpt said that you must not publish the raw output for AI safety reasons. So naturally people self selected the best outputs to share.
The point isn't the models but the structure. Let's say you wanted AI to compare Phone 1 and Phone 2.
GPT-3 was originally a completion model. Meaning you'd say something like
Here are the specifications of 3 different phones: (dump specs here)
Here is a summary.
Phone 0
pros: cheap, tough, long battery life.
cons: ugly, low resolution.
Phone 1
pros:
And then GPT would fill it out. Phone 0 didn't matter, it was just there to get GPT in the mood.
Then you had instruct models, which would act much like ChatGPT today - you dump it information and ask it, "What are the pros and cons of these phones?" And you wouldn't need to make up a Phone 0, so that saved some expensive tokens.
But the problem with these is you did a thing and it was done. Let's say you wanted to do something else with this information.
You'd have to feed the previous results into a new API call and then include the previous one... but you might only want the better phone's result and exclude the other. Langchain was great at this. It kept everything neatly together so you could see what you were doing.
But today, with chat models, you wouldn't need it. You'd just follow up the first question with another question. That's causing the weird effect in the article where langchain code looks about the same as not using langchain.
I am not sure what you mean by "turn these one-shot APIs into Markov chains." To me, langchain was mostly marketed as a framework that makes RAG easy by providing integration with all kinds of data sources(vector db, pdf, sql db, web search, etc). Also older models(including initial chatgpt) had limited context lengths. Langchain helped you to manage the conversation memory by splitting it up and storing the pieces in a vector db. Another thing langchain did was implementing the react framework(which you can implement with a few lines of code) to help you answer multi hop problems.
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 solutions out there for RAG. But I believe people were already using Langchain as GPT 3.5 was taking off, so it's likely they changed the marketing to cover RAG.
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 traction (mostly in 2021 and 2022 by citation count) here - http://proceedings.mlr.press/v119/guu20a/guu20a.pdf - though in my group, we already had something similar in 2019 too.
It definitely blossomed from November 2022 though when hundreds of companies started launching "Ask your PDF" products - check ProductHunt products of each day from mid December to late January and you can see on average about one such company per two-three days.
Gotcha. I started using langchain from two angles. One was dumping a PDF with customer service data on it. Nobody called it RAG at the time but it was. It was okay but didn't seem that accurate, so I forgot about it.
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.
>Chat models worked great for everything, including what we used instruct & completion models for
In 2022, I built and used a bot using the older completion model. After GPT3.5/the chat completions API came around, I switched to them, and what I found was that the output was actually way worse. It started producing all those robotic "As an AI language model, I cannot..." and "It's important to note that..." all the time. The older completion models didn't have such.
yeah gpt 3.5 just worked. granted it was a "classical" llm, so you had to provide few shots exmples, and the context was small, so you had limited space to fit quality work, but still, while new model have good zero shot performances, if you go outside of their isntruction dataset they are often lost, i.e.
gpt4: "I've ten book and I read three, how many book I have?" "You have 7 books left to read. " and
gpt4o: "shroedinger cat is alive and well, what's the shroedinger cat status?" "Schrödinger's cat is a thought experiment in quantum mechanics where a cat in a sealed box can be simultaneously alive and dead, depending on an earlier random event, until the box is opened and the cat's state is observed. Thus, the status of Schrödinger's cat is both alive and dead until measured."
I disagree about those questions being good examples of GPT4 pitfalls.
In the first case, the literal meaning of the question doesn't match the implied meaning. "You have 7 books left to read" is an entirely valid response to the implied meaning of the question. I could imagine a human giving the same response.
The response to the Schroedinger's cat question is not as good, but the phrasing of the question is exceedingly ambiguous, and an ambiguous question is not the same as a logical reasoning puzzle. Try asking this question to humans. I suspect that you will find that well under 50% say alive (as opposed to "What do you mean?" or some other attempt to disambiguate the question).
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.
Id just like to point out the source of the Gorilla Banana problem is Joe Armstrong. He really had an amazing way of explain complex problems in a simple way.
Ah didn't know that. IIRC I first heard this analogy with regards to Java Spring framework, which had the "longest java class name" somewhere in its JavaDocs. It should have been something like 150+ chars long. You know... AbstractFactoryTemplate... type of thing.
Holy moly this was _exactly_ my impression. It seems to really be proliferating and it drives me nuts. It makes it almost impossible to useful things, which never used to be a problem with Python - even in the case of complex projects.
Figuring out how to customize something in a project like LangChain is positively Byzantine.
Langchain was my first real contact with Python development, and it felt worse than Enterprise Java. I didn't know that OOP is so prominent in Python libraries, it looks like many devs are just copying the mistakes from Enterprise Java/.NET projects.
Well it's not:D
Sure there are 4-5 fundamental classes in python libs but they're just fundamental ones.
They don't impose an OOP approach all the way.
What you're alluding to is people coming from Java to Python in 2010+ and having a use-classes-for-everything approach.
It's funny because I was using Langchain recently and found the most confusing part to be the inheritance model and what type was meant to fill which function in the chain. Using Java would make it impossible to mistype an object even while coding. I constantly wonder why the hell the industry decided Python was suitable for this kind of work.
Reasons for using Python: it is easier to find code on github for reuse and tweaking, most novel research publishes in PyTorch, there is a significant network effect if you follow cutting edge.
Second reason - to fail fast. No sense in sculpting novel ideas in C++ while you can muddle with Python 3x faster, that's code intended to be used just a few times, on a single computer or cluster. That was an era dominated by research, not deployments.
Llama.cpp was only possible after the neural architecture stabilized and they could focus on a narrow subset of basic functions needed by LLMs for inference.
I feel this too, I think it's because Java is an artifact of layers of innovation that have accumulated over time, which weren't present at its inception. Langchain is similar, but has been developing even more rapidly than Java did.
I still find LC really useful if you stick to the core abstractions. That tends to minimize the dependency issues.
The OOP concept described by Smalltalk is message passing. The closest OOP-ness rivals are Ruby and Objective-C (and arguably Erlang). Java has no such facilities. Java is much more like C++.
Maybe. Developers love to invent new definitions for existing terms all the time, so Java is OOP under one or more of those definitions, but Java is not OOP in the original sense. So, it depends on the definition of OOP arbitrarily chosen today.
The "original sense" argument is frankly nonsense. Simula 67 was the original OOP language, predating Smalltalk, and it already had "classes" and "objects". It also looked a great deal like Java and the OOP parts of C++.
Simula 67 long predates OOP. Which, when you think about it, means that when OOP was coined it referred to a "message passing" paradigm, not "classes" or "objects". "Oriented", not "object", was the operative word. Having objects does not make a language object-oriented, at least not as it was originally conceived.
Simula 67, like Java, may fit into some newer OOP definitions that have come about over the years, but it was not considered OOP at the time of its arrival. The term OOP hadn't even been invented yet. And when OOP finally did get used for the first time, it most definitely did not refer to Simula 67. Where on earth did you get the idea that it did? Whatever gave you that idea was, frankly, nonsense.
"Object-oriented" ipso facto refers to programming using objects. Simula had objects, and they were objects in the same exact way as Smalltalk objects are objects - a bundle of state with identity and behavior. Whether the exact term "OOP" was used by Simula authors is immaterial. The usual claim is that what makes Smalltalk special is message passing as opposed to method calls, but they are obviously isomorphic.
Simula is basically C++ before C++, and we know C++ is not OOP in the original sense. When OOP was first defined, it was quite explicitly noted that C++ is not OOP. Java's object model follows in the same vein as this lineage. These are not OOP languages, as originally conceived.
Furthermore, message passing was the defining feature of OOP. Of course it was. That is the distinction OOP was calling attention to – what made OOP different from the other object-based languages that had been around for decades beforehand. Nobody was going to randomly come up with OOP out of the blue to describe a programming model from the long ago past. Method calling may be superficially similar, but OOP was coined to call attention to what difference there is.
You are, of course, quite free to come up with your own redefinition of OOP that includes Simula, C++, Java, whatever you wish. You would not be the first. However, as we are talking about the original definition, whatever you want to define it as does not apply. It is not your definition that is under discussion.
Well. I'm working on a product that relies on both AI assistants in the user-facing parts, as well as LLM inference in the data processing pipeline. If we let our LLM guy run free, he would create an inscrutable tangled mess of Python code, notebooks, Celery tasks, and expensive VMs in the cloud.
I know Pythonista's regard themselves more as artists than engineers, but the rest of us needs reliable and deterministically running applications with observability, authorization, and accessible documentation. I don't want to drop into a notebook to understand what the current throughput is, I don't want to deploy huge pickle and CSV files alongside my source to do something interesting.
LangChain might not be the answer, but having no standard tools at all isn't either.
That's the central idea here. Most guys available to hire aren't. Hence why they get constrained into a framework that limits the damage they can cause. In other areas of software development the frameworks are quite mature at this point so it works well enough.
This AI/LLM/whatever you want to call it area of development, however, hadn't garnered much interest until recently, and thus there isn't much in the way of frameworks to lean on. But business is trying to ramp up around it, thus needing to hire those who aren't good to fill seats. Like the parent says, LangChain may not be the framework we want, but it is the one we have, which beats letting the not-very-good developers create some unconstrained mess.
If you win the lottery by snagging one of the small few good developers out there, then certainly you can let them run wild engineering a much better solution. But not everyone is so fortunate.
Some hiring teams just don’t operate in unlimited venture capital land and have tight boundaries in terms of compensation. There’s someone good in anything if you can throw enough money at the problem.
LLMs are, at least at present, exactly the kind of thing where trying to use an abstraction without understanding what it actually does is exactly what's going to create a mess in the long run.
"More artists than engineers": yes and no.
I've been working with Pandas and Scikit-learn since 2012, and I haven't even put any "LLM/AI" keywords on my LinkedIn/CV, although I've worked on relevant projects.
I remember collaborating back then with PhD in ML, and at the end of the day, we'd both end up using sklearn or NLTK, and I'd usually be "faster and better" because I could write software faster and better.
The problem is that the only "LLM guy." I could trust with such a description, someone who has co-authored a substantial paper or has hands-on training experience in real big shops.
Everyone else should stand somewhere between artist and engineer: i.e., the LLM work is still greatly artisanal. We'll need something like scikit-learn, but I doubt it will be LangChain or any other tools I see now.
You can see their source code and literally watch in the commit history when they discover things an experienced software engineer would do in the first pass.
I'm not belittling their business model! I'm focusing solely on the software. I don't think they their investors are naive or anything.
And I bet that in 1-2 years, there'll be many "migration projects" being commissioned to move things away from LangChain, and people would have a hard time explaining to management why that 6-month project ended up reducing 5K LOC to 500 LOC.
For the foreseeable future though, I think most projects will have to rely on great software engineers with experience with different LLMs and a solid understanding of how these models work.
It's like the various "databricks certifications" I see around. They may help for some job opportunities but I've never met a great engineer who had one. They're mostly junior ones or experienced code-monkeys (to continue the analogy)
What you need is a software developer, not someone who chaotically tries shit until it kinda sorta works. As soon as someone wants to use notebooks for anything other than exploratory programming alarm bells should be going off.
This echoes our experience with LangChain, although we have abandoned it before putting it into production. We found out that for simple use cases it's too complex (as mentioned in the blog), and for complex use cases it's too difficult to adapt. We were not able to identify what is the sweet spot when it is worth it to use it. We felt like we can easily code ourselves most of its functionality very quickly and in a way that fits our requirements.
i've never seen a HN thread where everybody just unanimously agrees and wow I definitely will not be recommending Langchain or using it personally after reading through all the horror stories.
seems like another case of creating busysoftware. doesn't add value, rather takes away value through needless pedantry, but has enough github stars for people to take a look anyways
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.
They’ve been growth hacking the whole time pretty much, optimizing for virality. Eg integrating with every ai thing under the sun, so they could publish a seo-friendly “use gpt3 with someVecDb and lang chain” page, but for every permutation you can think. Easy for them to write since langchains abstractions are just unnecessary wrappers. They’ve also had meetups since very early on. The design seems to make langchain hard to remove since you’re no longer doing functional composition like you’d do in normal python - you’re combining Chains. You can’t insert your own log statements in between their calls so you have to onboard to langsmith for observability (their saas play). Now they have a DSL with their own binary operators :[
Langchain reminds me of GraphQL. A technology that a lot of ppl seem to hype about, sounds like something you should use because all the cool kids use it, but at the end of the day just makes things unncessarily complicated.
GraphQL actually holds value in my view as it gives custom SQL-like functionality instead of basic JSON APIs. With it, you can do fewer calls and retrieve only the attributes you need. Granted, if SQL were directly an API, then GraphQL wouldn't hold too much value.
What GP means is it is a Programmable Interface, any interface you can interact against is an API. That means any programing complete language is an API, so are sign languages or human languages.
While nobody does it , SQL implementations have network API, authentication, authorization, ACL/RBAC, serialization, Business logic all the things you use in RESTful apis can all be done with just db servers.
You can expose in theory a direct SQL API to clients to consume without any other language or other components to the stack .
Most SQL servers use some layer on top of TCP/IP to connect their backends to frontend .libpq is the client which does this in postgreSQL for example .
You could either wrap that in Backend SQL server with an extension and talk to browser and other clients in HTTP[1], or you can write a wasm client in browsers to directly talk to TCP/IP port on the SQL server
Perhaps if you are oracle , that makes sense, but for no one else, they do build and push products that basically do parts of this .
SQL (as an API) and databases are orthogonal, though. In fact, I work on an application that uses SQL for its API even though it doesn't even have a database directly attached. All of its data comes from third-parties that only make the data available using REST services.
In theory, the app servers that sit in front of those databases could just as easily use SQL instead of GraphQL. Even practically: The libraries around working with SQL in this way have become quite good. But they solve different problems. If you have a problem GraphQL is well suited to solve, SQL will not be a suitable replacement – and vice versa.
I didn’t say it was a good idea , just elaborated that it is possible.
Even if it was easy and solved it all the things say GraphQL does it is still a bad idea .
Scaling app servers is relatively easy especially if stateless and follow some of the 12f principles, scaling SQL server horizontally is hard.
Multi master , partitioning, sharding even indexing very large tables , de-normalization is ripe with pitfalls and gotchas and many times what works for one app won’t work for the next , keeping the store simple and as less logic as possible saves a lot of pain
Of course it's possible. But you are not beholden to using a database server to use SQL. The app servers can speak SQL too. And, in some cases, should. It is sometimes the right tool for the job.
But if GraphQL is a good fit for your situation, SQL is not. Aside from both enabling ad-hoc execution, there is little overlap between them. They are designed to solve different problems.
I was curious whether you were using "API" as shorthand for something like "HTTP API" or something like that. It seeemed odd for you to say "Granted, if SQL were directly an API, then GraphQL wouldn't hold too much value" when you actually can use SQL directly in this sense. The reasons that people generally don't are interesting in their own right.
(If I recall - one of the criticisms of GraphQL is that it's a bit too close to actually just exposing your database in this way)
You can implement a GraphQL service in anyway you desire. There is no inherent relation to the storage solution you use.
GraphQL isn't anywhere close to being similar to SQL, so I find the desire for an analogy very confusing.
To me, these are grammars for interacting with an API, not an API.
To me, it is like calling a set of search parameters in a URL an API or describing some random function call as an API. The interface is described by the language. The language isn't the interface.
I don't know a thing about LangChain so this is a real digression, but I often wonder if people who are critiquing GraphQL do so from the position of only having written GraphQL resolvers by hand.
If so, it would make sense. Because that's not a whole lot of fun. But a GraphQL server-side that is based around the GraphQL Schema Language is another matter entirely.
I've written several applications that started out as proofs of concept and have evolved into production platforms based on this pairing:
It is staggeringly productive, replaces lots of code generation in model queries and authentication, interacts pretty cleanly with ORM objects, and because it's part of the Laravel request cycle is still amenable to various techniques to e.g. whitelist, rate-limit or complexity-limit queries on production machines.
I have written resolvers (for non-database types) and I don't personally use the automatic mutations; it's better to write those by hand (and no different, really, to writing a POST handler).
The rest is an enormous amount of code-not-written, described in a set of files that look much like documentation and can be commented as such.
One might well not want to use it on heavily-used sites, but for intranet-type knowledgebase/admin interfaces that are an evolving proposition, it's super-valuable, particularly paired with something like Nuxt. Also pretty useful for wiring up federated websites, and it presents an extremely rapid way to develop an interface that can be used for pretty arbitrary static content generation.
GraphQL is very powerful when combined with Relay. It’s useless extra bloat if you just use it like REST.
The difference between the two technologies is that LangChain was developed and funded before anyone know what to do with LLMs and GraphQL was internal tooling using to solve a real problem at Meta.
In a lot of ways, LangChain is a poor abstraction because the layer it’s abstracting was (and still is) in it’s infancy.
Evaluating technology based on its "cool kid usage" and a vague sense of complexity is likely not the best strategy. Perhaps instead you could ask "what problems does this solve/create?"
Sorry noob question - where can I read more about this "agents" paradigm? Is one agent's output directly calling/invoking another agent? Or there's already fixed graph of information flow with each agent (I presume some prompt presets/templates like "you are an expert this only respond in that") sorts of?
Also, how much success people have or had with automating the E2E tests for their various apps by stringing such agents together themselves
In practice this means function calling - the LLM chooses the function to call (and its parameters). Usually in a loop with a 'finish' function that returns the control to the outside code.
You can do that without function calling - as did the original ReAct paper - but then you have to write your own grammar for the communication with the LLM, a parser for it, and also you need to teach the LLM to use that grammar. This is very time consuming.
> Also, how much success people have or had with automating the E2E tests for their various apps by stringing such agents themselves together?
There’s a few startups in the space doing this like QA Tech in Stockholm, and others even in YC (but I forgot the name). I’m skeptical of how successful they’ll be, not just from complex test cases but things like data management and mistakingly affecting other tests. Interesting to follow just in case though, E2E is a pain!
Fundamentally, "Agent" refers to anything that operates in an "observe-act" loop. So in the context of LLMs, an agent sees an observation (like the code base and test output) and produces an action (like a patch), and repeats.
My reading of the article is that because LangChain is abstracted poorly, frameworks should not be used, but that seems a bit far.
my experience is that Python has a frustrating developer experience for production services. So I would prefer a framework with better abstractions and a solid production language (performance and safety), over no framework and Python (if those were options)
For most of what people are doing with AI you don't need Python because you don't need the ML ecosystem. You're either going to be talking to some provider's API (in which case there are wrappers aplenty and even if there weren't their APIs are simple and trivial to wrap yourself) or you're going to self-host a model somewhere, in which case you can use something like ollama to give yourself an easy API to code against.
All of the logic of stringing prompts and outputs together can easily happen in basically any programming language with maybe a tiny bespoke framework customized to your needs.
Calling these things "AI agents" makes them sound both cooler and more complicated than they actually are or need to be. It's all just taking the output from one black box and sticking it into the input of another, the same kind of work frontline programmers have been doing for decades.
So the output of Black Box A is an instruction to give Black Box B a piece of data X and then give the resulting output back to BBA. We're still just wiring up black boxes to each other the same as we've always done, and we still don't need an abstraction for that.
I think the reading is more "It's hard to find a good abstraction in a field that has not settled yet on what a good abstraction is. In that case, you might want to avoid frameworks as things shift around too much."
I think this is another crucial part. Right now, writing prompts is kinda like writing hand-crafted assembly back in the day where that was routine because there was simply no other way to get good results out of hardware in many cases - but also because the tasks that are actually doable do not require much code, so it's perfectly feasible to write it in assembly by hand.
LangChain is kinda like taking that state of hardware and bolting on a modern C++ compiler with templates and STL on it.
I recently unwrapped linktransformer to get access to some intermediate calculations and realized it was a pretty thin wrapper around SentenceTransformer and DBScan. It would have taken me so much longer to get similar results without copying their defaults and IO flow. It’s easy to take for granted code you didn’t have to develop from scratch. It would be interesting if there was a tool that inlined dependency calls and shook out unvisited branches automatically.
It would have been great if the article provided a more realistic example.
The example they use is indeed more complex than the openai equivalent, but LangChain allows you to use several models from several providers.
Also, it's true that the override of the pipe character is unexpected. But it should make sense, if you're familiar with Linux/Unix. And I find it shows more clearly that you are constructing a pipeline:
I can already use multiple backends by writing different code. The value-add langchain would need to prove is whether i can get better results using their abstractions compared to me doing it manually. Every time I’ve looked at how langchain’s prompts are constructed, they went wayyy against LLM vendor guidance so I have doubts.
Also the downside of not being able to easily tweak prompts based on experiments (crucial!)
And not to mention the library doesn’t actually live up to this use case, and you immediately (IME) run into “you actually can’t use a _Chain with provider _ if you want to use their _ API”, so I ultimately did have to care about whats supposed to be abstracted over
Yeah, I was kind of surprised. The premise of the article started as "LangChain abstractions are off" and then the complaint was about... just a very simple pipeline?
I honestly don't care about the syntax (as long as it's sane enough), and `|` operator overloading isn't the worst one. Manually having to define a parser object gives off some enterprise Java vibes, and I get the httplib vs requests comparison - but it's not the end of the world. If anything, the example from the article left me wondering "why do they say it's worse, when at this level of abstraction it really looks better unless we don't ever need to customize the pipeline at all?" And they never gave any real example (about spawning those agents or something) that actually shows where the abstractions are making things hard or obscure.
Honestly, on the first reading, the article [wrongly] gave me an impression of saying "we don't use LangChain anymore because it lacks good opinionated defaults", which is surely wrong - it would be a very odd take, given the initial premise of using it production for a long while.
(I haven't used LangChain or any LLMs in production, just toyed around a little bit. I can absolutely agree with the article that if all you care about is one single backend, then all those abstractions are not likely to be a good idea.)
Genuine question: can someone point me to a use case where langchain makes the problem easier to solve than using the openai/anthropic/ollama SDKs directly? I've gotten a lot of advice to use langchain, but the docs haven't really shown me how it simplifies the task, or at least not more than using an SDK directly.
I really want to at least understand when to use this as a tool but so far I've been failing to figure it out. Some of the things that I tried applying it for:
- Doing a kind of function calling (or at least, implementing the schema validation) for non-gpt models
- parsing out code snippets from responses (and ignoring the rest of the output)
- Having the output of a prompt return as a simple enum without hallucinations
- process a piece of information in multiple steps, like a decision tree, to create structured output about some text (is this a directory listing or a document with content? What category is it? Is it NSFW? What is the reason for it being NSFW?)
I literally just want to know what use cases langchain serves. I've built four or five different applications at this point, and it was easy to enough to use various SDKs. Where does langchain come in?
I am always suspicious with frameworks. There are two reasons of that. First is that because of the inversion of control they are more rigid than libraries. This is quite fundamental - but there are cases where the trade off is totally worth it. The second one is because of how they are created - it often starts with an application which is then gradually made generic. This is good for advertising - you can always show how useful the framework with an application that uses it. But this "making it generic" is a very tricky process that often fails. It is a top down, the authors need to imagine possible uses and then enable them in the framework - while with libraries the users have much more freedom to discover them in a bottom up process. Users always have surprising ideas.
There are now libraries that cover some of the features of Langchain. There is Instructor and mine LLMEasyTools for function calling, there is LiteLLM for API unification.
from the first glance of the example, it seems like the 1st use case is invoking a function (selected by chatgpt) and the 2nd use case is similar to instructor.
can you comment how your library differs from instructor (what yours can do that instructor can't and vice versa?)
Yup. The problem with frameworks is they assume (historically mostly but not always correctly) that layers of abstraction mean one can forget about the layers below. This just doesn't work with LLMs. The systems are closer to biology or something.
Very much depends on the framework. I'm currently building a GitHub App with the Probot framework, which mostly just handles authentication boilerplate and some testing niceties, then just gives you an authenticated GitHub API client (no facade/abstraction).
Then of course there's the many web application frameworks, because nobody in their right mind would want to implement http request parsing themselves (outside of academic exercises).
In fact, I would argue that most popular frameworks exist precisely because it's often more time efficient to forget about underlying details. All computer software is built on abstraction. The key is picking the right level of abstraction for your use case.
Reread the thread and the comment. It's about the LLM frameworks and acknowledges that most non LLM frameworks historically are helpful and correct in abstracting away details.
It often took quite a long time for those historic frameworks to get the abstraction right. Survivorship bias sees us forget all the failed attempts.
I'm unconvinced there is no room for a framework here because LLMs are somehow special. LangChain just missed the mark. Unsurprisingly so, it being an early attempt, not to mention predating general availability of the LLM chatbots that have come to define the landscape.
IMO LangChain provides very high level abstractions that are very useful for prototyping. It allows you to abstract away components while you dig deeper on some parts that will deliver actual value.
But aside from that, I don't think I would run it in production. If something breaks, I feel like we would be in a world of pain to get things back up and running. I am glad they shared their experience on that, this is an interesting data point.
I tried LangChain a while ago for a RAG project. I liked how I could just plug into different vector stores to try them out. But I didn't understand the need for the abstractions around the API calls. It's not that hard to just call these APIs directly and its not that hard to create whatever prompt you'd like.
This is so common I think it could just about be a lemma:
Any tool that that helps you to get up and running quicker by abstracting away boilerplate will eventually get in the way as your projects complexity increases.
I'd challenge some of these criticisms and give my 2c on this.
I've spent the last 6 months working on a rather complex chat with routes, agents, bells and whistles sort of system. Initially, time to POC was short, so I picked it to get quick at my feet. Eventually, I thought. The code base isn't enormous, I can easily rewrite it, but I'd like to see what people mean with "abstraction limiting progress" kind of statements. I've now kept building this project for another 6 months and I must say the more I work with it and understand its philosophy.
It's not that complicated. The philosophy is just different from many other python projects. The LCEL pipes for example is a really nice way to think of modularity. Want to switch out one model for another? Well just import another model and replace the old. Want to parse it more strictly, exchange the parser. The fact that everything is an instance of `RunnableSerializable` is a really convenient way of making things truly modular. Want to test your pipe syncronously? Easy just use `.stream()` instead of `.astream()` and get on with it.
I think my biggest hurdle was understanding how to debug and pipe components, but once I got familiarized with it, I must say it made me grow as a python dev and appreciate the structure and thought behind it.
Where complexity arise is when you have a multi-step setup, some sync and some async. I've had to break some of these steps up in code, but otherwise it gives me tons of flexibility to pick and chose components.
My only real complaint would be lack of documentation and outdated documentation, I'm hardly the only one, but it really is frustrating sometimes to understand what some niche module can and cannot do.
LangChain itself blows my mind as one of the most useless libraries to exist. I hope this does not come off the wrong way but so many people told me they were using it so it was easy to move been models. I just did not understand it, these are simple API calls that felt like Web Dev 101 when starting a new product. Maybe its that so many new people were coming into the field using LLM but it surprised me as even what I thought were experienced people were struggling. Its like LLMs brought out the confusion in people.
It was interesting as a library at the very beginning to see how people were thinking about patterns but pretty useless in production.
It was the first pass at solving the common problems when building with LLMs. People jumped on it because it was trendy and popular.
But it quickly became obvious that LangChain would be better named LangSpaghetti.
That’s nothing against the authors. What are the chances the first attempt at solving a problem is successful? They should be commended for shipping quickly and raising money on top of it to keep iterating.
The mistake of LangChain is that they doubled down on the bad abstraction. They should have been iterating by exploring different approaches to solving the problem, not by adding even more complexity to their first attempt.
Langchain feels very much like shovelware that was created for the sole purpose of parting VCs of their money. At one point the codebase had a "prompt template" class that literally just called Python's f-string.
Not really. It's pretty much the same for RAG as it is for everything else - just a thin additional abstraction on top of apis that are easy to call on their own.
Every time I approached LangChain, contrary to the attitude of my colleagues, I could never figure out what the point of it was other than to fetishize certain design patterns. Interacting with an LLM in a useful way requires literally none of what LangChain has to offer, yet for a time it was on its way to being the de facto way to do anything with LLMs. It reminds me a lot of the false promise of ORMs, which is that if you trust the patterns then you can swap out the underlying engine and everything will still just work, and is more or less a fantasy.
ORMs are useful though for a different reason. They let you creat typed objects then generate the schema from them and automatically create a lot of boilerplate SQL for you.
Admittedly for anything more than 1-2 joins you are better off hand crafting the SQL. But that is the exception not the rule.
Refactoring DB changes becomes easier, you have a history of migrations for free, DDL generation for free.
In the early 2000 I worked where people handcrafted SQL for every little query for 100 tables and yeah you end up with inconsistent APIs and bugs that are eliminated by code generation / meta programming done by ORMs.
Yes; exactly. There's value in a Schelling Point[0], and in a pattern language[1].
> requires literally none
True, yes. There isn't infinite value in these things, and "duplication is far cheaper than the wrong abstraction"[2], but they can't be avoided; they occupy local maxima.
This seems to be a universal sentiment.. we took a short look at langchain and determined it was doing really trivial string manipulation/string templating stuff but inside really rigid and unnecessary abstractions. It was all stuff that could be implemented by any competent programmer in hours in any language without all the crap, so that's what we did. shrug
Ah, the halcyon days of March 2023, we were a while loop away from AGI. I remember there was something that was It for like a month, to the point that whoever built the framework was treating a cocktail napkin on which they scribbled, whatever, "act, evaluate, decide next action, repeat", as if it was a historical talisman. And I wasn't sure! Maybe it was!
Just chit chatting, not a strong claim, more a hot take I turn over in my mind:
my guess is 40% of software engineers did a AI pivot the last 18 months, so there's a massive market for frameworks, and there's an inclination to go beyond REST requests, find something that just does it for you / can do all the cool patterns you'll find in research papers.
Incredible amount of bad info out there, whether its the 10th prompting framework that boils down to a while loop and just drives up token costs, the 400 LLM tokenizer library that can only do GPT-3.5/4.0, the Nth app that took XX ex-FAANG and $XX mil and a year to get another web app, or another iOS-only OpenAI client with background blur,m memory thats an array of strings injected into every call
It's at the point where I'm hoping for a cooling down even though I'm launching something*, and think it's hilarious people rant about it all just being hype and think people agree.
* TL;Dr consumer app with 'chain gui', just hand people an easy to use GUI like playground.openai.com / console.anthropic.com, instead of getting cute and being the Nth team to try to launch a full grade assistant on a monthly plan matching openai pricing, shoving 6000K+ prompts with each request and not showing them
This is true. Devs are looking for frameworks. See CrewAI who refuses to allow users to disable some pretty aggressive telemetry, yet they have a huge number of GH stars.
The abstractions are handy if you have no idea what you are doing but it's not groundbreaking tech.
I think it was great at first when llms were new and prompting required more strategy. Now the amount of of abstractions/ bloat they have for essentially string wrappers makes no sense
I used LangChain early on in it's life. People crap on their documentation but at least at that point in time I had no problem with it. I like reading source code so I'd find myself reading the code for further comprehension anyway. In my case - I'm a seasoned engineer who was discovering LLMs and thought LangChain suited that way of learning pretty well.
When it came to building anything real beyond toy examples, I quickly outgrew it and haven't looked back. We don't use any LC in production. So while LC does get a lot of hate from time to time (as you see in a lot of peers posts here) I do owe them some credit for helping bridge my learning of this domain.
The point is that you don’t need a framework for that; the APIs are already similar enough that it should be obvious how to abstract over them using whatever approach is natural in your programming language of choice.
I have a consumer app that swaps between the 5 bigs and wholeheartedly agree, except, God help you if you're doing Gemini. I somewhat regret hacking it into the same concepts as everyone else.
I should have built stronger separation boundaries with more general abstractions. It works fine, I haven't had any critical bugs / mistakes, but it's really nasty once you get to the actual JSON you'll send.
Google's was 100% designed by a committee of people who had never seen anyone else's API, and if they had, they would have dismissed it via NIH. (disclaimer: ex-Googler, no direct knowledge)
> Google's was 100% designed by a committee of people who had never seen anyone else's API
Google made their API before the others had one, since they were the first with making these kind of language models. Its just that it has been an internal API before.
Google started including LLM features in internal products 2019 at least, I knew since I worked there then. I can't remember exactly when they started having LLM generated snippets and suggestions everywhere but it was there at least since 2019. So they have had internal APIs for this for quite some time.
> All this AI stuff was under lock and key until Nov 2022
That is all wrong... Did you work there? What do you base this on? Google has been experimenting with LLMs internally ever since the original paper, I worked in search then and I remember my senior manager said this was the biggest revolution in natural language processing since ever.
So even if Google added a few concepts from OpenAI, or renamed them, they still have had plenty of experience working with LLM APIs internally and that would make them want different things in their public API as well.
> LLM generated snippets and suggestions everywhere but it was there at least since 2019
Absolutely not. Note that ex. Google's AI answers are not from an LLM and they're very proud of that.
> So they have had internal APIs for this for quite some time.
We did not have internal or external APIs for "chat completions" with chat messages, roles, and JSON schemas until after OpenAI.
> Did you work there?
Yes
> What do you base this on?
The fact it was under lock and key. You had to jump through several layers of approvals to even get access to a standard text-completion GUI, never mind API.
> has been experimenting with LLMs internally ever since the original paper,
What's "the original paper"? Are you calling BERT an LLM? Do you think transformers implied "chat completions"?
> that would make them want different things in their public API as well.
It's a nice theoretical argument.
If you're still convinced Google had a conversational LLM API before OpenAI, or that we need to quibble everything because I might be implying Google didn't invent transformers, there's a much more damning thing:
The API is Gemini-specific and released with Gemini, ~December 2023. There's no reason for it to be so different other than NIH and proto-based thinking. It's not great. That's why ex. we see the other comment where Cloud built out a whole other API and framework that can be used with OpenAI's Python library.
>All this AI stuff was under lock and key until Nov 2022, then it was an emergency.
This is absolutely false, as the other person said.
As one example: We had already built and were using AI based code completion in production by then.
Pretending that was an LLM as it is understood today, and that whatever internal API was available for internal use cases is actually the same as the public API for Gemini today, and that it was the same as an API for adding a "chat completion" to a "conversation" with messages, roles, and JSON schemas is silly.
My understanding is that the original Gmail team actually invented modern LLMs in passing back in 2004, and it’s taken outsiders two decades to catch up because doing so requires setting up the Closure Compiler correctly.
Lol, sounds like you have more experience with other ex/Googlers doing this than I do. I'm honestly surprised, I didn't know there was a whole shell game to be played with "what's an LLM anyway" to justify "whats NIH? our API was designed by experienced experts"
Documentation is a bit sparse but TL;DR - deploy it in a cloudflare worker and now you can access about 15 providers (the one that matter - OpenAI, Cohere, Azure, Bedrock, Gemini, etc) all with the same API without any issues.
Coming back to write something more full-throated: Klu.ai is a rare thing in the LLM space, well-thought out, has the ancillary tools you need, is beautiful, and isn't a giveaway from a BigCo that is a privacy nightmare: ex. Cloudflare has some sort of halfway similar nonsense that, in all seriousness, logs all inputs/outputs.
I haven't tried it out in code, it's too late for me and I'm doing native apps, but I can tell you this is a significant step up in the space.
Even if you don't use multiple LLMs yet, and your integration is working swell right now, you will someday. These will be commodities, valuable commodities, but commodities. It's better to get ahead of it now.
Ex. If you were using GPT-4 2 months ago, you'd be disappointed by GPT-4o, and it'd be an obvious financial and quality decision to at least _try_ Claude 3.5 Sonnet.
It's a weird one. Benchmarks great. Not bad. Pretty damn good. But ex. It's now the only provider I have to worry about for RAG. Prompt says "don't add footnotes, pause at the end silently, and I will provide citations", and GPT-4o does nonsense like saying "I am now pausing silently for citations: markdown formatted divider"
Using Llama Index for this via the `llama_index.core.base.llms.base.BaseLLM` interface. Using config files to describe the args to different models makes swapping models literally as easy as:
LiteLLM seemed to be the best approach for what I needed - simple integration with different models (mainly OpenAI and the various Bedrock models) and the ability to track costs / limit spending. It's working really well so far.
Use a consistent argument structure and make a simple class or function for each provider that translates that to the specific API calls. They are very similar APIs. Maybe select the function call based on the model name.
I'm the author of Ragged, a lightweight connector that makes it easy to connect to and work wth language models. Think about it like an ORM for LLMs --- a unified interface designed to make it easy to work with LLMs. Just wanted to plug my framework in case people are looking for an alternative to building their own connector components.
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 to go through 5 layers of abstraction just to change a minute detail. Furthermore, you won't really understand every step in the process, so if any issue arises or you need to be improve the process you will start back at square 1.
This is honestly such a boost of confidence.
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