4 ms·

LangChain is great for finding out who has a clue.

pkkkzip 2y ago
i think this is an excellent litmus test. Anyone caught pushing langchain signals pedantry and should be completely ignored.
- a_bonobo 2y ago
  Could you both expand on what you mean? I've built some useful stuff with langchain v1 but gave up on porting it to v2, looked like too much work.
  - pkkkzip 2y ago
    [flagged]
  - tonyoconnell 2y ago
    Langchain abstracts too much and you can't really see what's going on or control the flow of data with real precision. They are fixing that though and now you have much better visibility into what's being inferred. I think Langchain is pretty useful though, especially if you want to integrate with something quickly.
    - _neil 2y ago
      I think this is the reasonable answer. Langchain gets a lot of derision and rightly so but it does have uses for prototyping. It’s also a good resource for learning the landscape specifically because of the integrations. I haven’t used it in a while so I’m not familiar with the most recent updates.
    - mavelikara 2y ago
      Thanks for explaining.
- 3abiton 2y ago
  It's unreasonable to expect that everyone is on top of every latest LLM advancement.
- d4rkp4ttern 2y ago
  Indeed, it has become the LLM equivalent of IBM, as in -- "No one ever got fired for choosing LangChain". A certain well-known ML person even runs a course on LangChain, as if it's a "fundamental" thing to know about LLMs. I was also surprised/disappointed to see that the otherwise excellent "Hands-on Large Language Models" book from O'Reilly has extensive examples using this library. In Apr 2023 we (CMU/UW-Madison researchers) looked into this lib to build a simple RAG workflow that was slightly different from the canned "chains" like RetrievalQAConversation or others, and ended up wasting time hunting docs and notebooks all over the place and going up and down class hierarchies to find out what exactly was going on. We decided it shouldn't have to be this hard, and started building Langroid as an agent-oriented LLM programming framework. In Langroid you set up a ChatAgent class which encapsulates an LLM-interface plus any state you'd like. There's a Task class that wraps an Agent and allows inter-agent communication and tool-handling. We have devs who've found our framework easy to understand and extend for their purposes, and some companies are using it in production (some have endorsed us publicly). A quick tour gives a flavor of Langroid: https://langroid.github.io/langroid/tutorials/langroid-tour/ https://langroid.github.io/langroid/tutorials/langroid-tour/
factormeta 2y ago
From a developer with extensive database ETL experience, is it still necceary to learn or use LangChain? Would it be just easier to load directly to vector db?
- gr3ml1n 2y ago
  LangChain was the first big attempt at a cohesive LLM application framework. As a result, it's terrible. If someone is seriously suggesting using it, they aren't keeping up.
  - Uehreka 2y ago
    So what are the people who are “keeping up” using? No one ITT is saying what the thing that replaced LangChain is.
    - jackmpcollins 2y ago
      If you are using Python, check out the package I've been building, magentic https://github.com/jackmpcollins/magentic https://github.com/jackmpcollins/magentic It supports structured outputs and streaming, and aims to avoid making unnecessary abstractions (but might require some more understanding of LLM patterns as a result). Also recently released is pydantic-ai, which is also based around pydantic / structured outputs, though works at level of "agents". https://github.com/pydantic/pydantic-ai https://github.com/pydantic/pydantic-ai
    - oersted 2y ago
      Frankly, just Python. LLM generation is just a function call, fetching from a vector db is just a function call. LLMs are hard to tame at scale, the focus is on tightly controlling the LLM inputs, making sure it has the information it needs to be accurate, and having detailed observability over outputs and costs. For that last part this new wave of AI observability tools can help (Helicone, Langsmith, W&B Weave...). Frameworks like LangChain obscure the exact inputs and outputs and when the LLM is called. Fancy agentic patterns and one-size-fits-all RAG are expensive and their effectiveness in general is dubious. It's important to tightly engineer the prompt for every individual use-case and to think of it as a low-level input-output call, just like coding a good function, rather than a magical abstract intelligent being. In practice, I prefer to keep the control and simplicity of vanilla Python so I can focus on the actually difficult part of prompting the LLM well.
WesleyJohnson 2y ago
How is this helpful? Not everyone can keep up with the rapidly shifting world of AI; it's worse than JavaScript. People have day jobs that don't revolve around AI and don't have the bandwidth to devote endless hours trying to keep up, while still getting work done that makes money for the business they're in. I read through the article and it provided a lot of helpful information on the pitfalls with LLMs which, based on the title, is its intended purpose. I didn't take it as a shining recommendation for LangChain. If your point is that suggesting LangChain means they're not keeping up, as someone else stated, and because of that the other information is probably dated as well - that's far more helpful. I wouldn't mind seeing a current guide on pitfalls and practical examples of interacting with LLMs with raw python - from someone who "has a clue".
knowsuchagency 2y ago
Agreed. I wrote my own LLM abstraction library in a few hundred lines of code: https://github.com/knowsuchagency/promptic https://github.com/knowsuchagency/promptic

i think this is an excellent litmus test. Anyone caught pushing langchain signals pedantry and should be completely ignored.

a_bonobo 2y ago
Could you both expand on what you mean? I've built some useful stuff with langchain v1 but gave up on porting it to v2, looked like too much work.
- pkkkzip 2y ago
  [flagged]
- tonyoconnell 2y ago
  Langchain abstracts too much and you can't really see what's going on or control the flow of data with real precision. They are fixing that though and now you have much better visibility into what's being inferred. I think Langchain is pretty useful though, especially if you want to integrate with something quickly.
  - _neil 2y ago
    I think this is the reasonable answer. Langchain gets a lot of derision and rightly so but it does have uses for prototyping. It’s also a good resource for learning the landscape specifically because of the integrations. I haven’t used it in a while so I’m not familiar with the most recent updates.
  - mavelikara 2y ago
    Thanks for explaining.
3abiton 2y ago
It's unreasonable to expect that everyone is on top of every latest LLM advancement.
d4rkp4ttern 2y ago
Indeed, it has become the LLM equivalent of IBM, as in -- "No one ever got fired for choosing LangChain". A certain well-known ML person even runs a course on LangChain, as if it's a "fundamental" thing to know about LLMs. I was also surprised/disappointed to see that the otherwise excellent "Hands-on Large Language Models" book from O'Reilly has extensive examples using this library. In Apr 2023 we (CMU/UW-Madison researchers) looked into this lib to build a simple RAG workflow that was slightly different from the canned "chains" like RetrievalQAConversation or others, and ended up wasting time hunting docs and notebooks all over the place and going up and down class hierarchies to find out what exactly was going on. We decided it shouldn't have to be this hard, and started building Langroid as an agent-oriented LLM programming framework. In Langroid you set up a ChatAgent class which encapsulates an LLM-interface plus any state you'd like. There's a Task class that wraps an Agent and allows inter-agent communication and tool-handling. We have devs who've found our framework easy to understand and extend for their purposes, and some companies are using it in production (some have endorsed us publicly). A quick tour gives a flavor of Langroid: https://langroid.github.io/langroid/tutorials/langroid-tour/ https://langroid.github.io/langroid/tutorials/langroid-tour/

factormeta 2y ago

From a developer with extensive database ETL experience, is it still necceary to learn or use LangChain? Would it be just easier to load directly to vector db?

gr3ml1n 2y ago
LangChain was the first big attempt at a cohesive LLM application framework. As a result, it's terrible. If someone is seriously suggesting using it, they aren't keeping up.
- Uehreka 2y ago
  So what are the people who are “keeping up” using? No one ITT is saying what the thing that replaced LangChain is.
  - jackmpcollins 2y ago
    If you are using Python, check out the package I've been building, magentic https://github.com/jackmpcollins/magentic https://github.com/jackmpcollins/magentic It supports structured outputs and streaming, and aims to avoid making unnecessary abstractions (but might require some more understanding of LLM patterns as a result). Also recently released is pydantic-ai, which is also based around pydantic / structured outputs, though works at level of "agents". https://github.com/pydantic/pydantic-ai https://github.com/pydantic/pydantic-ai
  - oersted 2y ago
    Frankly, just Python. LLM generation is just a function call, fetching from a vector db is just a function call. LLMs are hard to tame at scale, the focus is on tightly controlling the LLM inputs, making sure it has the information it needs to be accurate, and having detailed observability over outputs and costs. For that last part this new wave of AI observability tools can help (Helicone, Langsmith, W&B Weave...). Frameworks like LangChain obscure the exact inputs and outputs and when the LLM is called. Fancy agentic patterns and one-size-fits-all RAG are expensive and their effectiveness in general is dubious. It's important to tightly engineer the prompt for every individual use-case and to think of it as a low-level input-output call, just like coding a good function, rather than a magical abstract intelligent being. In practice, I prefer to keep the control and simplicity of vanilla Python so I can focus on the actually difficult part of prompting the LLM well.

WesleyJohnson 2y ago

How is this helpful? Not everyone can keep up with the rapidly shifting world of AI; it's worse than JavaScript. People have day jobs that don't revolve around AI and don't have the bandwidth to devote endless hours trying to keep up, while still getting work done that makes money for the business they're in. I read through the article and it provided a lot of helpful information on the pitfalls with LLMs which, based on the title, is its intended purpose. I didn't take it as a shining recommendation for LangChain. If your point is that suggesting LangChain means they're not keeping up, as someone else stated, and because of that the other information is probably dated as well - that's far more helpful. I wouldn't mind seeing a current guide on pitfalls and practical examples of interacting with LLMs with raw python - from someone who "has a clue".

knowsuchagency 2y ago

Agreed. I wrote my own LLM abstraction library in a few hundred lines of code: https://github.com/knowsuchagency/promptic https://github.com/knowsuchagency/promptic