11–20 of 85 posts
I've been working with langchain and llamaindex and did notice that it's a pretty hefty abstraction on top of pretty simple concepts and I also eventually ended up dropping both and simply write the underlying code without the framework on top.
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.
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…
1. Have a huge dataset of documents.
2. Want to ask questions and have an LLM chat conversation based on these documents.
3. Be able to implement tools like math, wiki or Google search on top of the retrieval.
4. Implement memory management for longer conversations.
Its still a lot more straightforward to maintain it in Python. The only thing where it becomes interesting is having agents execute async, which is not that easy replicate, but at the moment agents are not that helpful. Not trying to diss Langchain too much here, because its such an awesome framework, but I can't help seeing past it other than just being a helpful tool to understand LLM's and LLM programming for now.
I work with Langchain on a daily basis now, and so often I find myself asking; do I really need a whole LLM framework for this? At this point, the assistant I am writing, will likely be more stable rewritten in pure Python. The deeper and more complex the application becomes, the more of a risk Langchain seems to become to keeping it maintainable. But even at less complex levels, if I want to do this: 1. Have a huge…
The tools you’re talking about like math, wiki, or search are already built as plugins on ChatGPT.
I see so many AI apps being built, but I think ChatGPT will be general enough to cover 85-90% of use-cases using the chat UI.
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Helpful Answer:"""
I work with Langchain on a daily basis now, and so often I find myself asking; do I really need a whole LLM framework for this? At this point, the assistant I am writing, will likely be more stable rewritten in pure Python. The deeper and more complex the application becomes, the more of a risk Langchain seems to become to keeping it maintainable. But even at less complex levels, if I want to do this: 1. Have a huge…
Won’t ChatGPT eventually eat your lunch? Once ChatGPT allows uploading documents (embeddings), what good will your app be? The tools you’re talking about like math, wiki, or search are already built as plugins on ChatGPT. I see so many AI apps being built, but I think ChatGPT will be general enough to cover 85-90% of use-cases using the chat UI.
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…
There’s some useful parts though
It’s a very different experience from the hand-holding of LangChain, but it packs reusable magic in generic constructs like annotate, compile, etc that work with arbitrary programs.
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 got a lot of traction on non-coders thinking it’s doing some magic, but if you read the source it boils down to some brittle prompts and lots of Python class boilerplate. There’s some useful parts though