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Isn't this a chat interface to various apis? I guess that's the point. But it doesn't seem like that is utilizing the power of large language models. Maybe I'm

Isn't this a chat interface to various apis? I guess that's the point. But it doesn't seem like that is utilizing the power of large language models. Maybe I'm not understanding this. Using a Google search api + a calculator to answer a question is cool [0]. but... we could already do that? [0] https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html https://langchain.readthedocs.io/en/latest/modules/agents/ex...

KennyFromIT 4y ago
They have a dedicated chat bot for their docs that might be able to help you find answers to your questions... https://chat.langchain.dev/ https://chat.langchain.dev/
- user- 4y ago
  Me > can i use docker? AI > Yes, you can use Docker with LangChain. For more information, please see the Docker Installation Guide in the LangChain documentation. Then it links to a 404 lol. I checked the docs and there is nothing about docker in them. I wonder why its incorrect here
  - andreigheorghe 4y ago
    maybe it's trained on non-published documentation pages?
- sebastiennight 4y ago
  I'm very curious as to how they are able to limit the chat bot's knowledge to their own documentation. It seems as if it should also have the entire knowledge of the LLM, but > Who was Thomas Jefferson? Outputs > Hmm, I'm not sure. I'm an AI assistant for the open source library LangChain. You can find more information about LangChain at https://langchain.readthedocs.io https://langchain.readthedocs.io.
  - rafaquintanilha 4y ago
    You can actually train the model to have access only to a specific body of text, like a documentation. Very useful article explaining this approach: https://dagster.io/blog/chatgpt-langchain https://dagster.io/blog/chatgpt-langchain
    - sebastiennight 4y ago
      Wow that was a fascinating read! Thank you. It doesn't explain why the model only refers to the documentation though. Basically what they are doing is giving GPT-3 a prompt that includes the (semantically relevant) pieces of the documentation. But I don't see why a User can't ask about something that is tangentially relevant ("Who is Bill Gates?") and get an answer that really comes from GPT-3 pre-existing knowledge. And very clever way for these guys to go from "hey we found this cool problem" to "well, did you notice that it ends up super complex and slow? Well well well, we could make it so much better with... wait for it... a data pipeline!" ("...and don't we just happen to sell data pipeline software! What a coincidence!") lol. Great read, thank you for the recommendation.
mahastore 4y ago
Yes also the part I don't understand is that how langchain can be used to make the agents smarter for a particular domain? I did not see any interface to provide feedback of feed data. Maybe I am missing something?
- Ozzie_osman 4y ago
  It's usually either providing data to the LLM, or doing back-and-forth with the LLM (usually a mix of both). For instance, if you want it to answer questions about your code-base, the model doesn't know your code base. You can't feed the entire code-base into a prompt. So, you'd use langchain to: - preprocess your code-base, by chunking it and embedding it in some vector space - when you get a question, see where it is in the vector space and find the "k nearest neighbors" - pass those nearest neighbors, along with your question, to the LLM (because those neighbors are the contextually relevant pieces, and they'd fit in the prompt)
  - delijati 4y ago
    i would really love to give it a try but with an offline LLM :/
bob29 4y ago
>but... we could already do that? that sums up so much. the emperor has no clothes. few see it. its inferior to the modern purpose built tools. we act impressed a simple program can be created from a prompt, what has every programmer been doing already for the last 10 years with google search and stackoverflow?
- yunwal 4y ago
  I think building regexes is a good example of where chatgpt/copilot is useful. It’s not that it’s particularly difficult or requires much understanding, just very time consuming compared to writing an English language description and walking through an example.
  - bob29 4y ago
    Useful compared to writing a regex in notepad yeah. Last time I was challenged by regex I easily found very fancy web page with so many nice features. Actual documentation, ability to select a specific regex engine (or implementation or whatever you call it) , real-time results on test data, highlighting that shows how the regex works, etc. and that was years ago I’m sure there’s even better web apps now. I can’t imagine having a better experience asking AI chat than using a web app made for the purpose
    - yunwal 4y ago
      Do you have a link to the site? Being able give some examples and just state in plain English what you want the capture groups to be is pretty much my ideal regex experience (in other words, I don’t want to think about the semantics of regex ever).
      - sandkoan 4y ago
        This might be what they're referring to: https://regex101.com/ https://regex101.com/.
        4y ago

KennyFromIT 4y ago

They have a dedicated chat bot for their docs that might be able to help you find answers to your questions... https://chat.langchain.dev/ https://chat.langchain.dev/

user- 4y ago
Me > can i use docker? AI > Yes, you can use Docker with LangChain. For more information, please see the Docker Installation Guide in the LangChain documentation. Then it links to a 404 lol. I checked the docs and there is nothing about docker in them. I wonder why its incorrect here
- andreigheorghe 4y ago
  maybe it's trained on non-published documentation pages?
sebastiennight 4y ago
I'm very curious as to how they are able to limit the chat bot's knowledge to their own documentation. It seems as if it should also have the entire knowledge of the LLM, but > Who was Thomas Jefferson? Outputs > Hmm, I'm not sure. I'm an AI assistant for the open source library LangChain. You can find more information about LangChain at https://langchain.readthedocs.io https://langchain.readthedocs.io.
- rafaquintanilha 4y ago
  You can actually train the model to have access only to a specific body of text, like a documentation. Very useful article explaining this approach: https://dagster.io/blog/chatgpt-langchain https://dagster.io/blog/chatgpt-langchain
  - sebastiennight 4y ago
    Wow that was a fascinating read! Thank you. It doesn't explain why the model only refers to the documentation though. Basically what they are doing is giving GPT-3 a prompt that includes the (semantically relevant) pieces of the documentation. But I don't see why a User can't ask about something that is tangentially relevant ("Who is Bill Gates?") and get an answer that really comes from GPT-3 pre-existing knowledge. And very clever way for these guys to go from "hey we found this cool problem" to "well, did you notice that it ends up super complex and slow? Well well well, we could make it so much better with... wait for it... a data pipeline!" ("...and don't we just happen to sell data pipeline software! What a coincidence!") lol. Great read, thank you for the recommendation.

mahastore 4y ago

Yes also the part I don't understand is that how langchain can be used to make the agents smarter for a particular domain? I did not see any interface to provide feedback of feed data. Maybe I am missing something?

Ozzie_osman 4y ago
It's usually either providing data to the LLM, or doing back-and-forth with the LLM (usually a mix of both). For instance, if you want it to answer questions about your code-base, the model doesn't know your code base. You can't feed the entire code-base into a prompt. So, you'd use langchain to: - preprocess your code-base, by chunking it and embedding it in some vector space - when you get a question, see where it is in the vector space and find the "k nearest neighbors" - pass those nearest neighbors, along with your question, to the LLM (because those neighbors are the contextually relevant pieces, and they'd fit in the prompt)
- delijati 4y ago
  i would really love to give it a try but with an offline LLM :/

bob29 4y ago

>but... we could already do that? that sums up so much. the emperor has no clothes. few see it. its inferior to the modern purpose built tools. we act impressed a simple program can be created from a prompt, what has every programmer been doing already for the last 10 years with google search and stackoverflow?

yunwal 4y ago
I think building regexes is a good example of where chatgpt/copilot is useful. It’s not that it’s particularly difficult or requires much understanding, just very time consuming compared to writing an English language description and walking through an example.
- bob29 4y ago
  Useful compared to writing a regex in notepad yeah. Last time I was challenged by regex I easily found very fancy web page with so many nice features. Actual documentation, ability to select a specific regex engine (or implementation or whatever you call it) , real-time results on test data, highlighting that shows how the regex works, etc. and that was years ago I’m sure there’s even better web apps now. I can’t imagine having a better experience asking AI chat than using a web app made for the purpose
  - yunwal 4y ago
    Do you have a link to the site? Being able give some examples and just state in plain English what you want the capture groups to be is pretty much my ideal regex experience (in other words, I don’t want to think about the semantics of regex ever).
    - sandkoan 4y ago
      This might be what they're referring to: https://regex101.com/ https://regex101.com/.
      - 4y ago