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Introducing Agents in Haystack: Make LLMs resolve complex tasks

haystack.deepset.ai

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Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#81

Post by antti909 »

wyldfire wrote: post #42
yosito wrote: post #10

I 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.

Maybe we should just assume that LLMs "know" very little and if you want to build an oracle you should teach the LLM how to access an ontology.

With a real-life application it's often about making the LLM work on top of your actual (private) data most reliably. By definition a proprietary hosted LLM can't know about it unless you bridge it somehow in a reliable manner.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#82

Post by antti909 »

j0e1 wrote: post #3

Could someone in the know compare this with LangChain ( https://github.com/hwchase17/langchain )?

Haystack has been around for a while now, and we've been mostly specializing in the extractive QA. The focus has been indeed on making the use of local Transformer models most easy and convenient for a backend application builder. You can build very reliable and sometimes quite elaborate NLP pipelines with Haystack (e.g., extractive or generative QA, summarization, document similarity, semantic search, FAQ-style search, etc. etc.) with either Transformer models, LLMs, or both. With the Agents you can also put an Agent on top of your pipelines and use a prompt-defined control to find the best underlying tool and pipeline for the task. Haystack has always included all the necessary 'infrastructure' components - pre-processing, indexing, several document stores to choose from (ES/OS, Pinecone, Weavite, Milvus, now Qdrant, etc.) and the means to evaluate and fine-tune Transformer models.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#84

Post by antti909 »

teruakohatu wrote: post #4
j0e1 wrote: post #3

Could someone in the know compare this with LangChain ( https://github.com/hwchase17/langchain )?

Looks like the same general idea: https://github.com/deepset-ai/haystack

See above - Haystack started a few years ago as a result of us working with some large enterprise clients on implementing extractive QA at scale. Now evolving to also allow the backend builders to mimic what's available from, e.g. OpenAI+plugins, but with their own set of models, and being able to mix&match best available components and technology.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#85

Post by antti909 »

thomashop wrote: post #77
ActorNightly wrote: post #64

Sure, lets build more and more applications that all have a single point of failure that is Open AI.

The framework let's you plugin many different LLMs not just OpenAI's

Very accurate observation :) So basically, a bit more freedom in picking the right tools for the job, connecting an LLM to proprietary data in a safe way, using multiple models simultaneously, and leveraging custom extractive/generative pipelines.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#86

Post by antti909 »

jcims wrote: post #7

From 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…

Thanks for the emphasis :) Accurate!

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#87

Post by [deleted] »

nico wrote: post #21
Art9681 wrote: post #12

Earlier quoted context omitted.

I've been tinkering with LangChain for a few days and I agree. Is there a resource that collects the agents so we can experiment? I'd love to see an aggregated list of the most impressive agents and use cases if anyone knows of any. This stuff is the future of computing no doubt.

There is a LangChainHub mentioned in their docs, but the repo for it seems dead. Is there any sort of marketplace/AppStore for agents/tools/plug-ins for LLMs via LangChain? Or some other library like haystack?

[deleted]

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#88

Post by antti909 »

nextworddev wrote: post #5
j0e1 wrote: post #3

Could 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

For the Agents? Yes, indeed. Referred in the article.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#89

Post by antti909 »

ukuina wrote: post #15
j0e1 wrote: post #3

Could someone in the know compare this with LangChain ( https://github.com/hwchase17/langchain )?

I would consider Haystack to be the more batteries-included, easier to use (but harder to customize) of the two. They have a good emphasis on local model use.

Thanks :) Working on it. Re local models - indeed, all started with using the Transformer models for extractive QA and semantic search. With the Promptnode, and/or the Agents it's also now possible to combine local models/pipelines & 'LLMs' freely.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#90

Post by tholor »

Sol- wrote: post #57
sr-latch wrote: post #37

Earlier quoted context omitted.

This looks similar to the WebGPT paper, is that referenced in any of langchain or haystack's publications? Introducing the mechanism of internal thought is very interesting, I wonder if there's a way to make it implicit in the model's architecture.

I think the ReAct paper also popularized this approach: https://arxiv.org/abs/2210.03629

Haystack's agent is indeed using the approach suggested in the ReAct paper
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