It's crazy how people add bloat and complexity to their stuff just because they want to do medium scale RAG with ca. 2 million embeddings.

Here comes the punchline, you do not need a fancy vector database in this case. I stumbled over https://github.com/sqliteai/sqlite-vector which is a SQLite extension and I wonder why no one else did this before, but it simply implements a highly optimized brute force search over the vectors, so you get sub 100ms queries over millions of vectors with perfect recall. It uses dynamic runtime dispatch that makes use of the available SIMD instructions your CPU has. Turns out this might be all you need. No need for memory a memory hungry search index (like HNSW) or writing a huge index to disk (like DiskANN).