A basic implementation will return the top, let's say 1000, documents and then do the more expensive access check on each of them. Most of the time, you've now eliminated all of your search results.
Your search must be access aware to do a reasonable job of pre-filtering the content to documents the user has access to, at which point you then can apply post-filtering with the "100% sure" access check.
If the docs were indexed by groups/roles and you had some form of RBAC then this wouldn't happen.
Right, but this compare this to the original proposal:
> A basic implementation will return the top, let's say 1000, documents and then do the more expensive access check on each of them
Using an index is much better than that.
And it should be possible to update the index without a substantial cost, since most of the 100000 documents likely aren't changing their role access very often. You only have to reindex a document's metadata when that changes.
This is also far less costly than updating the actual content index (the vector embeddings) when the document content changes, which you have to do regardless of your permissions model.
If you use your index to get search results, then you will have a mix of roles that you then have to filter.
If you want to filter first, then you need to make a whole new search index from scratch with the documents that came out of the filter.
You can't use the same indexing information from the full corpus to search a subset, your classical search will have undefined IDF terms and your vector search will find empty clusters.
If you want quality search results and a filter, you have to commit to reindexing your data live at query time after the filter step and before the search step.
I don't think Elastic supports this (last time I used it it was being managed in a bizarre way, so I may be wrong). Azure AI Search does this by default. I don't know about others.
It's a separate index.
You store document access rules in the metadata. These metadata fields can be indexed and then use as a pre-filter before the vector search.
> I don't think Elastic supports this
https://www.elastic.co/docs/solutions/search/vector/knn#knn-...
I've seen a list of what was supposed to be 20 items of something, it only showed 2, plus a comment "18 results were omitted to insufficient permissions".
(Servicenow has at least three different ways to do permissions, I don't know if this applies to all of them).
But yes, one could probably also construct a series of queries that reveal properties of hidden objects.
@pxt.query def search_documents(query_text: str, user_id: str): sim = chunks.text.similarity(query_text) return ( chunks.where( (chunks.user_id == user_id) # Metadata filtering & (sim > 0.5) # Filter by similarity threshold & (pxt_str.len(chunks.text) > 30) # Additional filter/transformation ) .order_by(sim, asc=False) .select( chunks.text, source_doc=chunks.document, # Ref to the original document sim=sim, title=chunks.title, heading=chunks.heading, page_number=chunks.page ) .limit(20) )
For instance in https://github.com/pixeltable/pixeltable
Index your ACLs, index your users, index your docs. Your database can handle it.
Vector Databases intended for this purpose filter this way by default for exactly this reason. It doesn't matter how many documents are in the master index, it could be 100000 or 100000000,doesn't matter. Once you filter down to the 10 that your user is allowed to see, it takes the same tenth of a second or whatever to whip up a new bespoke index just for them for this query.
Pre-search filtering is only a problem when your filter captures a large portion of the original corpus, which is rare. How often are you querying "all documents that Joe Schmoe isn't allowed to view"?
But I guess they want something like training the chatbot as a LLM once with all the confidential data - and then indeed you could never separate it again.
Sometimes the potentially relevant list of documents itself is a leak all by itself.
Depending on the context it could be relevant.
If Joe's search is faster than Sally's because Sally has higher permissions, that's hardly a revelation.
Allow me to try to inject my understanding of how these agents work vs regular applications.
A regular SaaS will have an API endpoint that has permissions attached. Before the endpoint processes anything, the user making the request has their permissions checked against the endpoint itself. Once this request succeeds, anything that endpoint collects is considered "ok" ship to the user.
AI Agents, instead, directly access the database, completely bypassing this layer. That means you need to embed the access permissions into the individual rows, rather than at the URL/API layer. It's much more complex as a result.
For your bank analogy: they actually work in a similar way to how I described above. A temporary access is granted to the resources but, once it's granted, any data included in those screens is assumed to be ok. They won't see something like a blank box somewhere because there's info they're not supposed to see.
DISCLAIMER: I'm making an assumption on how these AI Agents work, I could be wrong.
If so, then as the wise man says: "well, there‘s your problem!"
I don't doubt there are implementations like that out there, but we should not judge the potential of a technology by the mistakes of the most boneheaded implementation.
Doing the same in the bank analogy would be like giving root SQL access to the phone operators and then asking them pretty please to be careful with it.
Of course, I wouldn't defend this! To be clear, it's not possible to know how every AI Agent works, I just go off what I've seen when a company promises to unlock analytics insights on your data: usually by plugging directly into the Prod DB and having your data analysts complain whenever the engineers change the schema.
> we should not judge the potential of a technology by the mistakes of the most boneheaded implementation.
I agree.
That's what the bank agent analogy was meant to tell you. The agent has a direct line to the prod DB through their computer terminal, but every session they open is automatically constrained to the account details if the person on the phone right now and nobody else.
It depends on how it's plugged-in. If you just hand it a connection and query access then what exactly stops it? In a lot of SaaS systems, there's only the "application" user, which is restricted via queries within the API.
You can create a user in the DB per user of your application but this isn't free. Now you have the operational problem of managing your permissions, not via application logic, but subject to the rules and restrictions of your DBMS.
You can also create your own API layer on top, however this also comes with constraints of your API and adding protections on your query language.
None of this is impossible but, given what I've seen happen in the data analytics space, I can tell you that I know which option business leaders opt for.
I don't understand the desire - borderline need - of folks on HN to just make stuff up. That is likely why you're being downvoted. I know we all love to do stuff "frOM fIRsT PRiNcIPlEs" around here but "let me just imagine how I think AI agents work then pass that off as truth" is taking it a bit far IMO.
This is the human equivalent of an AI hallucination. You are just making stuff up, passing it off as truth ("injecting your understanding"), then adding a one-line throwaway "this might be completely wrong lol" at the end.
Hacker News is addictive. This forum is designed to reward engagement with imaginary internet points and that operant conditioning works just as well here as everywhere else.
(To your point, >15 of them would have had different answers and the majority would have been materially wrong, but still.)
> AI Agents, instead, directly access the database
However, I don't think I'd be too far off the mark given many systems work like this (analytics tools typically hook into your DB, to the chagrin of many an SRE/DevOps) and it's usually marketed as the easy solution. Also, I've since read a few comments and it appears I'm pretty fucking close: the agents here read a search index, so pretty tightly hooked into a DB system.
Everything else, I know I'm right (I've built plenty of systems like this), and someone was making a point that permissions access does scale. I pointed out that it appears to scale because of the way they're designed.
I'd say most of my comment is substantively correct, with a disclaimer on an (important) point, where I'd be happy to be corrected.
> I'd say most of my comment is substantively correct, with a disclaimer on an (important) point, where I'd be happy to be corrected.
I read this and feel that you still want imaginary internet points for something that is, at best, directionally correct. To me it seems your desire for internet points urged you to post a statement and not a question. I imagine most of HN is just statements that are overconfident bluster by only directionally correct statements which create the cacophony of this site.
That doesn’t solve the problem of changing schemas causing issues for your data team at all. Something I see regularly. If you setup an AI Agent the same way you still give it full access, so you still haven’t fixed the problem at hand.
> I read this and feel that you still want imaginary internet points for something that is, at best, directionally correct.
And you’ve yet to substantiate your objection to what I posited (alongside everyone else), so instead you continue to talk about something unrelated in the hope of… what, exactly?
Searching the whole index and then filtering is possible, but infeasible for large indexes where a specific user only has access to a few docs. And for diverse data sources (as we want to access), this would be really slow, many systems would need to be checked.
So, access rights should be part of the index. In that case, we are just storing a copy of the access rights, so this is prone to races. Besides that, we have multiple systems with different authorization systems, groups, roles, whatever. To homogenize this, we would need to store the info down to each individual user. Besides this, not all systems even support asking which users have access to resource Y, they only allow to ask „has X access to Y“.
I don't understand why you think tracking user access rights would be infeasible and would not scale. There is a query. You search for matching documents in your vector database / index. Once you have found the potentially relevant list of documents you check which ones can the current user access. You only pass the ones over to the LLM which the user can see.
This is very similar to how banks provide phone based services. The operator on the other side of the line can only see your account details once you have authenticated yourself. They can't accidentally tell you someone else's account balance, because they themselves don't have access to it unless they typed in all the information you provide them to authenticate yourself. You can't trick the operator to provide you with someone else's account balance because they can't see the account balance of anyone without authenticating first.