Hmm… This seems more like a problem related to C or the operating system than Python…
Since I’m a Windows user, I don’t really know much about Linux issues…

My view is that your latest test shows two different phenomena :

1. The machine is still healthy at the system level right now.
2. The Uvicorn process is retaining memory across requests.

Those are not the same…

Apart from the following hypothesis, there are simply so many cases where using a dataset containing media files rather than text consumes more RAM than expected that I’m not sure if pinpointing the problem will be easy:

The most likely cause in your case is host RAM growth from the request path itself , then whole-file Wav2Vec2 inference on long audio , then **allocator…

for now:

The right next step is to turn your current metadata lookup system into a routed retrieval system with offline document preparation and grounded answer generation.

Your current design already proves one important thing: the corpus is structured enough to answer exact questions like “What is the Gazette for date X?” The part that is missing is not “more RAG.” It is a…

for now:

The right next step is to turn your current metadata lookup system into a routed retrieval system with offline document preparation and grounded answer generation.

Your current design already proves one important thing: the corpus is structured enough to answer exact questions like “What is the Gazette for date X?” The part that is missing is not “more RAG.” It is a…

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