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andreybavt
searching PlanetScale…
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3 ms
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by
andreybavt
4mo ago
Hi, thanks for trying it! 19m for ~20 tables is slower that I'd expect, I assume you activated historic queries, right? As Luca said we're focusing on performance improvements right now. 99% of the latency is LLM calls, so we'
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andreybavt
4mo ago
good point! yes, there's a feedback mcp tool that agents are instructed to use when they want to save/update ktx "memories". This is a way to keep the wiki+semantic layer up to date beyond the initial ingestion. Plus eve
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andreybavt
4mo ago
Hopefully more agents and fewer PowerPoints and McKinsey folks in the future :)
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andreybavt
4mo ago
yes, exactly
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andreybavt
4mo ago
When you install ktx, you'll have to initialize the project directory and ktx initializes a git repo there to assure the version controls. ktx project directory is self-contained. The main 2 parts inside are : - wiki: a collection of .
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andreybavt
4mo ago
Thanks! ktx maintainer here, related to the queries sandbox question, we're attacking this problem from a different angle: instead of catching bad queries at runtime we try to make it impossible to generate a bad query. ktx allows agen
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andreybavt
4mo ago
Thanks! yes, exactly. But what you described is just half of what ktx does, we call it "wiki" - self organizing collection of ingested markdown files. The second (equally important) part of ktx is similarly self-organizing executa
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andreybavt
4mo ago
yep, happy to share more details with you @modus-tollens if you're interested
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andreybavt
4mo ago
Great questions! we're currently working on Spider 2 submission, hope to have first results soon. It's true that we took file-first approach and not a graph DB. Main reason is that the ktx wiki and semantic layer entities while be
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andreybavt
4mo ago
Agreed, in addition to this I'd say it's better to spend tokens once while ingesting the sources and storing the canonical reusable definition rather than having agents do the full exploration over and over again
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andreybavt
4mo ago
Hey, ktx maintainer here! Great question, we actually have quite a bit in common with Wren, they're in the same category. The main difference is that with Wren you author the semantic model (write MDL) and it executes SQL through its
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gRPC tunnel to reverse the client-server communication
(giskard.ai)
3 points
by
andreybavt
4y ago
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1 comments