This release is a pre-release and may not be stable for production use.
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)ColumnVaultbuffer-oriented arrays, tensors, and variable-width records using bounded SQLite BLOB blocks- 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
# One atomic committed KV batch; reads preserve input order
kv.put_many([("run:1", {"score": 0.9}), ("run:2", {"score": 0.8})])
assert kv.get_many(["run:2", "missing"])[0] == {"score": 0.8}
# 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 with bounded append buffers
cols = ColumnVault(kv) # shares the database file, not the KV transaction
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())
search.close()
cols.close()
kv.close()
Why this exists
- Embedded reader/writer access – SQLite WAL permits independent readers alongside one active writer, without a database service. Runtime
-wal/-shmsidecars are normal; use a consistent backup rather than copying a live main file alone. - Buffer-oriented columns – Fixed-width arrays and tensor batches stay contiguous. Variable records use payload/offset blocks. Small reads fetch the requested BLOB ranges instead of materializing whole chunks.
- Complete iteration – KV iteration is unlimited by default and paginated internally.
limitis an optional total cap;batch_sizecontrols query size. - 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 and native batches – Vault- and column-level caches batch inserts;
put_many()commits an explicit KV batch atomically. Pending/in-flight cache reads do not wait for SQLite flush I/O. - 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]
# Rebuild after changing native code; use release mode for performance tests.
maturin develop --release
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
Use append_array(), read_array(), and iter_batches() for numeric/tensor throughput. Scalar/list APIs remain convenient but include Python object conversion costs.
python examples/benchmark_columnar.py --rows 1000000
python examples/benchmark_kv.py --structured --repeats 5
python examples/benchmark_vector.py --repeats 5
python examples/benchmark_readers.py
python examples/kvault_demo.py
Compared with 0.9.0
Local Windows/Python 3.13 release builds, same workload per row; lower elapsed time is better. KV/vector numbers are medians of five trials; the original column comparison is a single warm-process run. WAL and synchronization settings were not weakened.
| Interface / workload | 0.9.0 | Current | Speedup |
|---|---|---|---|
| KV: 10k cached scalar writes including flush, 256-byte values | 25.493 ms | 19.643 ms | 1.30× |
| KV: 10k point reads | 47.298 ms | 30.019 ms | 1.58× |
| KV: iterate 10k key/value pairs | 49.668 ms | 5.297 ms | 9.38× |
| KV: four shared-handle readers, 40k reads | 529.256 ms | 111.759 ms | 4.74× |
| Vector: insert 2k 128-dimensional float32 vectors + metadata, including commits | 245.020 ms | 214.565 ms | 1.14× |
| Vector: 2k ID lookups | 21.704 ms | 18.399 ms | 1.18× |
| Vector: 200 exact cosine searches, top-10 | 116.300 ms | 97.538 ms | 1.19× |
| Vector: four readers, 800 searches | 481.983 ms | 276.748 ms | 1.74× |
| Column: append 1M i64 values from a list | 109.94 ms | 53.26 ms | 2.06× |
| Column: read 1M i64 values into a list | 42.75 ms | 18.74 ms | 2.28× |
| Column: 1,000 slices of 64 i64 values | 7,611.75 ms | 29.94 ms | 254× |
New KV batch APIs additionally write/read 10k raw values in 12.596/7.781 ms; these APIs did not exist in 0.9.0. The column slice gain removes pathological whole-BLOB materialization, not a 254× general database speedup. Vector search remains exact sqlite-vec search, not a new ANN algorithm. Concurrent rows compare equal total work across versions, not scaling relative to one reader. See methodology, limitations, and detailed results.
Storage compatibility: KV/vector formats are preserved. The new column format does not read or migrate the legacy col_* layout. See column storage design.
Documentation
- Changes and migration notes – breaking behavior, new APIs, and compatibility
- Documentation index
- KVault Guide
- TextVault Guide – Full-text search with BM25 ranking
- ColumnVault Guide
- Column Storage Design
- Performance Checks
- 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.
1 maintainer
Metadata
1 maintainer
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