Haystack 1.0 – open-source NLP framework to build NLProc back end applications (opens in new tab)

Semantic document search is one of the core use cases we see in the community (besides Question Answering) and Haystack was pretty much started because we saw that you need much more than just models. It's so much pain to integrate models properly with document storage (e.g. elasticsearch), route requests effectively in larger pipelines or track user feedback in production. Have you tried using DPR or sentence transformers for your case?

Disclaimer: I am one of the maintainers of Haystack:)

I had a client who was using sentence transformers with elasticsearch already. My colleague suggested switching to haystack to enable a larger number of model architectures. Switching over to haystack was pretty straightforward because we just used it as a wrapper around sentence transformers, but I do remember some inconvenience around all the other dependencies that haystack pulled in.

Haystack does a lot more besides just wrapping sentence transformers, and we weren't using the rest of it, so it was just a lot of extra dependencies sitting around taking up disk space and memory (I think we had to go up to a larger instance size). I remember feeling a bit frustrated that the dependencies weren't split up into "core" and "optional" in a more fine-grained way, but maybe most users don't mind and so it doesn't make sense for them to prioritize that?

[edit: looks like there's an open issue related to this: https://github.com/deepset-ai/haystack/issues/1070]

[edit 2: 'JPKab happy to share more about using huggingface and elasticsearch. email is in my profile]

Yeah I’m using their FAISS document store and QA pipeline to run semantic search over a set of YouTube transcripts. Was easier to set up than Jina AI in my specific use case, and the search results are actually useful. Only real constraint for me is GPU access, creating the embeddings to store in the FAISS index sans a GPU takes an unreasonable amount of time.

Happy to help too - feel free to ask in the community channels as well :)

Not the author, but not a reasonable request to make. It's not like the concept of "finding a needle in a haystack" is novel. There are dozens of projects with the name "haystack".

There is also https://django-haystack.readthedocs.io/en/master/ a search library for Python+Django... much closer to the same space

This is perhaps just an innocent question for the community - but it's simultaneously an ingenious approach to inbound marketing.

Whoa, this is cool :) I could think of a ton of marketing (or maybe even 'devrel') applications for it.

The demo corpus there just contains documents about countries and capital cities. So you could try asking questions like "What's the climate of Beijing?" or "How many people live in the capital of the US?".

Haystack supports abstractive summarization and you have access to all models here: https://huggingface.co/models?pipeline_tag=summarization&sor.... you can build them into a pipeline e.g. with a retriever to filter out irrelevant docs (e.g. those not containing a certain word)

Probably you are interested in extractive summarization for explainability reasons? To overcome this, the summarizer module will show you the passages that were used for creating the abstractive summarization. Hope this is a potential solution for your case!

Re structured data - in theory, yes :) We have to work a bit more in that direction. Here's the first step - querying table data, which could be really helpful for reports, financial data, etc. In regards to the storage backend - it's currently Elasticsearch, OpenSearch, SQL+FAISS/Milvus/Weaviate (when using dense vectors/dense passage retrieval). There is also an in-memory datastore using python primitives for fast prototyping.

(Also, latest features highlights here https://www.deepset.ai/blog/new-features-in-haystack-v1.0)

Happy to help! - we've got Slack and everything :)

Depends! When using 'DPR' (dense passage retrieval, potentially for more accurate results), a re-indexing is required.