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Use kimi 2.6 for planning and a cheap model (preferably local) for execution, and then kimi once again for reviewing it. Then finally I review the code. Saves a lot on tokens.

You can sign up for a plan on the kimi code platform and use it via the pi.dev coding agent, or opencode. In planning, I’d say it’s almost on par with Claude Opus.

Thanks Philippe! You guys have been super helpful on slack!

So it depends on the app - e.g., Google has domain-wide delegation where the workspace admin can provide service account creds that allow us to impersonate all users in the workspace and index all their files/email. During indexing, we determine the users/groups who have permissions file and persist that in the db. (It's not perfect, because Google Drive permission model is a bit complex, but I'm working on it.) This model is much simpler than doing per-user OAuth.

In general, the goal is to use an org-wide installation method wherever possible, and record the identify of the user we are impersonating when ingesting data in the ACL. There are some gaps in the permission-gathering step in some of the connectors, I'm still working on fixing those.

Fair point, although I think we have OpenAI to blame for that - for buying chat.com and pointing it to the most popular textual AI interface of them all :)

So far both projects are quite similar… the only major difference being the search index. Onyx uses vespa.ai for BM25 and vector search, I decided to go down the Postgres-only route.

That's a good point, it might make sense to clarify that for individuals who want to self-host. I'll make the change, thanks!

Exactly, enterprise customers almost always use private model endpoints on their cloud provider for any serious deployments. Data stays within the customer's VPC, data security and privacy is guaranteed by the cloud providers.

Thank you!

Typical RAG implementations I’ve seen take the user query and directly run it against the full-text search and embedding indexes. This produces sub-par results because the query embedding doesn’t really capture fully what the user is really looking for.

A better solution is to send the user query to the LLM, and let it construct and run queries against the index via tool calling. Nothing too ground-breaking tbh, pretty much every AI search agent does this now. But it produces much better results.

Not yet, there’s a Microsoft connector implementation, but it only does Sharepoint, OneDrive, Outlook etc. and I haven’t tested it thoroughly yet. Teams required some special setup to work IIRC, so I skipped it. Will keep it on the roadmap though!

Thanks for sharing! Big part of the reason why I decided on postgres, everything I've read about people using it in prod tells me that most organizations never really grow beyond requiring anything more than what it offers.

Yeah, Omni uses Postgres and pgvector for search. ParadeDB is essentially just Postgres with the pgsearch extension that brings in Tantivy, a full-text search engine (like Apache Lucene).

Oops, sorry! That page is still a WIP, haven't pushed it yet. The plan was to expose the main search and chat APIs so that users can build integrations with third-party messaging apps (e.g. Slack), but haven't gotten around to properly documenting all the APIs yet.