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/ex...
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
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).
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
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.
Very useful article explaining this approach: https://dagster.io/blog/chatgpt-langchain
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.
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)