kohakuvault 0.7.1

SQLite-backed KV store (Rust+PyO3) with vector search, auto-packing, and efficient array storage

KohakuVault

One-file SQLite datastore with:

• KVault key-value storage inspired by BoringDB (streaming blobs + auto-pack for dict/list/numpy)
• ColumnVault columnar layout inspired by Stanchion (typed chunks: i64, msgpack, vec:*) built on SQLite BLOB ranges
• write-back caches plus standalone CSB+Tree / SkipList containers for ordered/temporal metadata
• VectorKVault (sqlite-vec) for k-NN search living in the same .db

from kohakuvault import KVault, ColumnVault, VectorKVault, CSBTree
import numpy as np

DB = "test.db"

# Store media + metadata in one file
kv = KVault(DB, table="media")
kv["video:42"] = b"abcdefg" # raw bytes stay raw
kv["video:42:meta"] = {"fps": 60, "tags": ["tutorial", "gpu"]}  # auto MessagePack

# Incremental columnar logging (no file rewrites)
cols = ColumnVault(kv) # or ColumnVault(DB)
ids = cols.ensure("ids", "i64")
latency = cols.ensure("latency_ms", "f64")
embeddings = cols.ensure("embeddings", "vec:f32:384")
with cols.cache(cap_bytes=8 << 20):
    ids.append(len(ids))
    latency.append(12.3)
    embeddings.append(np.random.randn(384).astype(np.float32))

# Ordered metadata via embedded CSBTree
index = CSBTree()
index.insert(latency[-1], ids[-1])  # log-latency -> run-id

# Vector similarity search (sqlite-vec inside the same DB)
search = VectorKVault(DB, table="runs", dimensions=384, metric="cosine")
for idx, emb in enumerate(embeddings[:10]):
    search.insert(emb, str(idx).encode())
for rank, (rid, distance, run_id) in enumerate(search.search(embeddings[-1], k=3), 1):
    print(rank, distance, run_id.decode())

Why this exists

• Single-file SWMR – SQLite WAL lets one writer and many readers share a file. Parquet can’t append, DuckDB locks the DB, Lance spawns version files.
• Columnar layout, not SQL tables – ColumnVault stores chunks in col_chunks/_idx, Stanchion-style, so incremental appends don’t rewrite tables.
• Auto-packed KV everywhere – KVault (BoringDB-inspired) automatically serializes dict/list/numpy values and keeps raw bytes untouched; every key-value interaction goes through this pipeline.
• Write caches – Vault- and column-level caches batch inserts automatically.
• Standalone CSB+Tree / SkipList – Specialized containers for ordered metadata or range queries without leaving the process.
• Vector keys/search – vec:* dtypes store tensors, and VectorKVault (sqlite-vec) runs k-NN search inside the same DB.

If your workflow looks like “keep logging forever, but don’t spawn thousands of files”, KohakuVault is the tool.

Installation

pip install kohakuvault

For development (build the PyO3 extension, run tests):

pip install -e .[dev]

# everytime you change the rust code, you should run this command
maturin develop

Use cases / references

• Media vaults – Random-access blobs + metadata (tests/test_kv_headers.py, examples/basic_usage.py).
• Incremental ML logging – Fixed/var columns, vector dtypes, caches (tests/test_columnar.py, examples/columnar_demo.py).
• Vector-heavy retrieval – VectorKVault, CSBTree/SkipList, auto-packed metadata (tests/test_vector_kvault.py, examples/vector_search_numpy.py, examples/all_usage.py).

Performance notes

Benchmarks on an M1 Max (WAL on, cache enabled):

• KVault streaming + cache: 24K writes/s, 63K reads/s (examples/benchmark.py).
• ColumnVault extend (i64): 12.5M ops/s; slicing 100 elements hits 2.3M slices/s.
• DataPacker vector unpack: 35× faster than Python loops for 768-dim tensors.

Documentation

• Documentation index
• KVault Guide
• ColumnVault Guide
• Vector Storage & Search
• DataPacker Reference
• Architecture Deep Dive
• Structured Data Cookbook

Development workflow

pip install -e .[dev]
maturin develop

Formatting/linting/testing before PR:

black .
cargo fmt
cargo clippy
maturin develop --release
pytest

• PyO3 module: src/kvault-rust (use maturin develop if you prefer).
• Format Python with black.
• Keep docs updated and linked via docs/README.md.

License

Apache 2.0. See LICENSE.