Introducing Agents in Haystack: Make LLMs resolve complex tasks
haystack.deepset.ai
Introducing Agents in Haystack: Make LLMs resolve complex tasks
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Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#2[deleted]
Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#3Could someone in the know compare this with LangChain (https://github.com/hwchase17/langchain)?
Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#4Could someone in the know compare this with LangChain ( https://github.com/hwchase17/langchain )?
Looks like the same general idea:
Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#5Could someone in the know compare this with LangChain ( https://github.com/hwchase17/langchain )?
Most of the core ideas came from a paper called React, they all kind of riff on the idea of self-inspection / introspection to augment the context or plan action
Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#6Somewhat OT, but I feel like this is such an underappreciated aspect of recent LLMs: Not just their ability to generate text - but their apparent effectiveness in making use of arbitrary tools to interact with their environment to achieve some goal. It seems like we're just at the beginning of this with the MRKL and ReAct papers, there will be a ton more awesomeness coming in this area I'm sure.
Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#7From the Github project readme
>Agent: (since 1.15) An Agent is a component that is powered by an LLM, such as GPT-3. It can decide on the next best course of action so as to get to the result of a query. It uses the Tools available to it to achieve this. While a pipeline has a clear start and end, an Agent is able to decide whether the query has resolved or not. It may also make use of a Pipeline as a Tool.
Emphasis mine. Having tinkered with LangChain I think the idea of a recursive and/or graph-oriented model is going to yield interesting phenomena in the overall feel of these language models. LangChain agents are already super impressive.
Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#8Is there some way of holding the LLM response to a given prompt constant? It sounds like a lot of this relies on the LLM getting the right answer in sequence, so I'm guessing they do something like keep the temperature at 0? Otherwise you are going to wind up with possibly different behavior run-to-run. And even if they do have something like the above, don't we end up with potentially breaking changes once models are updated? Basically the issue is that even if you can guarantee response format X for prompt A, a slightly modified prompt A' has no guarantee that its response will be in the same format as X, even in the same model. You can also imagine that the more "Tools" are available, the lower the chance that the model will pick the right one based on its English text description. Would be interesting to know how this is being addressed.
Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#9Is there some way of holding the LLM response to a given prompt constant? It sounds like a lot of this relies on the LLM getting the right answer in sequence, so I'm guessing they do something like keep the temperature at 0? Otherwise you are going to wind up with possibly different behavior run-to-run. And even if they do have something like the above, don't we end up with potentially breaking changes once models ar…
There is a library called guardrails . I've not played with it extensively but that seems to address issues like this one.
Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks
#10I haven't yet figured out how to get an LLM to accurately determine whether it actually knows something or is making it up. I wonder how they handle that. They may get to that at some point in the article, but the page eventually breaks for me on mobile and I can't read past the first code block.