Vector diskann impl

[sivukhin]

Context

Third branch in the series for DiskANN implementation. This PR introduce DiskANN algorithm itself 🎉

The algorithm core based on the paper LM-DiskANN: Low Memory Footprint in
Disk-Native Dynamic Graph-Based ANN Indexing

Current implementation can be suboptimal in some places due to usage of very trivial data structures (lists / arrays). That's how it was intended - we will improve performance in the subsequent PRs but make this one simpler.

Nevertheless, this PR tries to address most heavy operation in the algorithm - reading blobs from disk - and aims to make as little reads as possible (utilizing sqlite3_blob_reopen if possible).

From the performance perspective rough hierarchy of operation cost looks like this:

1. read/write diskann node block
2. distance calculation between two vectors (for example, openai vectors are 1536 dimensions long - so this is clearly very slow operation)
3. operations with non-compressed vector payload
4. all other operations

Changes

• Implement DiskANN algorithm
• Adjust BlobSpot code for cases when previous call to open/reopen failed (and blob became useless as all subsequent operations will return SQLITE_ABORT error; read sqlite3_blob_* docstrings for more details)
• Add basic test in the test_libsql_diskann.c (it's a bit hacky for now - but it's fine since we soon will get merge full integration with SQLite and will no longer need these tests anymore, actually)