KohakuVault
One-file SQLite datastore with:
KVaultkey-value storage inspired by BoringDB (streaming blobs + auto-pack for dict/list/numpy)TextVaultfull-text search with FTS5 BM25 ranking (ideal for RAG pipelines)ColumnVaultcolumnar 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, TextVault, 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
# Full-text search with BM25 ranking (great for RAG)
tv = TextVault(DB, table="docs")
tv.insert("Machine learning is a subset of AI", {"category": "tech"})
for doc_id, score, meta in tv.search("machine learning", k=5):
print(f"Doc {doc_id}: score={score:.4f}")
# 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 –
ColumnVaultstores chunks incol_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, andVectorKVault(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). - RAG pipelines – Full-text BM25 search + vector similarity in the same DB (
tests/test_text_vault.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):
KVaultstreaming + cache: 24K writes/s, 63K reads/s (examples/benchmark.py).ColumnVaultextend (i64): 12.5M ops/s; slicing 100 elements hits 2.3M slices/s.DataPackervector unpack: 35× faster than Python loops for 768-dim tensors.
Documentation
- Documentation index
- KVault Guide
- TextVault Guide – Full-text search with BM25 ranking
- 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(usematurin developif you prefer). - Format Python with
black. - Keep docs updated and linked via
docs/README.md.
License
Apache 2.0. See LICENSE.
Metadata
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| Tags | CPython 3.11 macOS 11.0+ ARM64 |
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| Tags | CPython 3.10 Linux glibc 2.28+ x86-64 |
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| Download URL | kohakuvault-0.9.0-cp310-cp310-macosx_11_0_arm64.whl |
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| Size | 3.3 MB |
| Tags | CPython 3.10 macOS 11.0+ ARM64 |
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