91–100 of 106 posts
Anyone have requests for an agent framework that adds some of the capabilities Yohei on Twitter has been tinkering with? Longer term planning, memory, etc?
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.
With tools like this, you basically assume the LLM doesn't know, and teach it to always defer to a tool, so its response is basically summarization over the tool output.
Thanks for the spotlight :) We've spent quite a lot of time working on the Agents lately, and it's definitely a big focus. Couple of extra points to reflect on some of the comments here. It's quite straightforward to build a hybrid NLP backend with Haystack combining either hosted LLM (e.g., OpenAI or Cohere), or local, smaller Transformer models, or both. Agents add another level of control on top of that, as descri…
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 sear…
Also it seems like the functionality of haystack subsumes those of langchain and llama-index (fka GPT-index) ?
Thanks for the spotlight :) We've spent quite a lot of time working on the Agents lately, and it's definitely a big focus. Couple of extra points to reflect on some of the comments here. It's quite straightforward to build a hybrid NLP backend with Haystack combining either hosted LLM (e.g., OpenAI or Cohere), or local, smaller Transformer models, or both. Agents add another level of control on top of that, as descri…
Haystack looks very interesting, just found out about it today :) Is there some overlap with the functionality of langchain? Could you highlight some differences? Thanks
Earlier quoted context omitted.
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 sear…
Thanks for clarifying. The support for local LLMs seems very interesting — would a haystack agent call out to a separately “running” self-hosted LLM via an API (REST, etc) or would it need to actually load up the model and directly query it (e.g model.generate( ) ) ? Also it seems like the functionality of haystack subsumes those of langchain and llama-index (fka GPT-index) ?
Earlier quoted context omitted.
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 sear…
Thanks for clarifying. The support for local LLMs seems very interesting — would a haystack agent call out to a separately “running” self-hosted LLM via an API (REST, etc) or would it need to actually load up the model and directly query it (e.g model.generate( ) ) ? Also it seems like the functionality of haystack subsumes those of langchain and llama-index (fka GPT-index) ?
So back to your question: We will enable both ways in Haystack: 1) Loading a local model directly via Haystack AND 2) quering self-hosted models via REST (e.g. Huggingface running on AWS SageMaker). Our philosophy here: The model provider should be independent from your application logic and easy to switch.
In the current version, we support for local models only option 1. This works for many of the provided models provided by HuggingFace, e.g. flan-t5. We are already working on adding support for more open-source models (e.g. alpaca) as models like Flan-T5 don't perform great when used in Agents. The support for sagemaker endpoints is also on our list. Any options you'd like to see here?
Doesn't this remind anyone else of Bitcoin autonomous agents? https://en.bitcoin.it/wiki/Agent#:~:text=An%20agent%20is%20a... . It would be interesting if you could somehow combine a wallet plugin to give the GPT something to work to increase. That + that AutoGPT repo from a day ago + maybe Mitogen for self-replication might be a cool combo to really kick off a fun "dumb" AI on the loose
This is cool. Some of the weaknesses they point out in software agents can be obviated with the latest AI advancements—give a chatbot the tools of the courts, and maybe the autonomous agent won’t be at a disadvantage in scams anymore. Interesting to see if an AI agent given the objective to generate profit would resort to running scams. Encoding moral boundaries becomes important. What if you asked it to raise money…
And now we have the beginnings to a plot point from Accelerando. Self propelled AI businesses as a distinct entity.