Ask HN: Semantic Vector Searching in WASM?

I'm working on a simple markdown-driven CMS, in Python, that outputs to static HTML files that can be served directly from GitHub (as an example).

I'm interested in implementing local-only search of this content, w/o using a backend, and preferably w/o cobbling together some NLP algorithms in JavaScript.

I've also been training ML models for other use cases and experimenting around w/ vector databases.

I started looking into creating embeddings using Python, from my markdown content, potentially using word2vec or finalfusion to help me get those embeddings into a Rust environment that I can compile w/ Rust into WASM, and use cosine similarity for my search.

Now, I neither have any Rust nor WASM experience, but that doesn't really deter me much. I'm just curious if the overhead would be significant, or if I should skip the semantic embeddings and do a different type of embedding such as TD-IDF.