The tricky part is when we receive embeddings to insert: we parse them from JSON and store them into our vector store as raw and optimal floating points, using a write transaction; they're therefore only visible to that write transaction, but we need to unlock the power of parallelization: being able to read the HNSW graph in parallel. As I described above, our previous method was to store all key-value entry pointers (pointers into the memory map), since we didn't know in advance which ones we would need to update the HNSW layers. LMDB scales linearly with the number of cores used to read a database.
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