alt Hacker NewsWe (Team of 3) built a retrieval engine from scratch in .NET for investigative search, with AI doing most of the backend implementation while humans handled the design and architecture and front end. It was developed using a task based, incremental approach. Includes more than 3,000 tests and extensive documentation, and is optimized for asynchronous ingestion using NATS and a disk backed WAL.
It includes a custom WAL, structure aware semantic chunking, ingestion and indexing pipelines, hybrid (rrf), text and vector retrieval, ACL aware filtering at query level, query classifier, citation tracking, and a CrateDB backend that performs fused retrieval over a single table. total LOC: 180 K. Status: evaluation by State agencies for investigative search on legacy documents. tool used - Codex, Luna, and earlier codex 5.3, and gpt 4 mini.
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"we have more than 3,000 tests"...
Great, but since no human has looked at them, how do you know that they are relevant and actually test undesired behavior?
Also, would you be able to point at which tests should be added in the future?
How long did it take you to review things for correctness? Like an incremental query engine is a few thousand lines of reasonably difficult code on its own, a reliable WAL is another few thousand, etc, and each of those might take me days or weeks of thinking to have any serious confidence in them. I guess you can get provenance and ACL tracking "for free" from a query engine, but it still seems like a very ambitious project.