quanda-vector-db
A minimal, production-grade vector store for LLM / RAG applications written in Rust with Python bindings.
Features
- WAL pattern — inserts are O(1) file appends, no index rebuild per insert
- HNSW index via
instant-distance— approximate nearest-neighbour search - Upsert / delete — no duplicate IDs; delete is WAL-backed and crash-safe
- Dimension validation — stored in snapshot, enforced on every write and query
- Crash recovery — on restart, snapshot is loaded and the WAL is replayed
Installation
pip install quanda-vector-db
Quick start
from quanda_vector_db import VectorStore, VectorEntry
# Open (or create) a store backed by two files: store.snap + store.wal
store = VectorStore.open("store.snap", "store.wal")
# Upsert — replaces any existing entry with the same id
store.upsert(VectorEntry(id="doc1", embedding=[0.1, 0.2, 0.3], metadata="hello"))
store.upsert(VectorEntry(id="doc1", embedding=[0.9, 0.8, 0.7], metadata="updated"))
# Batch upsert — single WAL write for the whole batch (fast for 100 K+ vectors)
entries = [
VectorEntry(id=f"doc{i}", embedding=[i * 0.01] * 64, metadata=f"chunk {i}")
for i in range(10_000)
]
store.upsert_batch(entries)
# Compact: merge WAL → snapshot, rebuild HNSW index once
store.compact()
# Search: returns [(VectorEntry, distance), ...]
results = store.search(query=[0.5] * 64, top_k=5)
for entry, dist in results:
print(entry.id, entry.metadata, f"dist={dist:.4f}")
# Delete
store.delete("doc1")
# Inspection
print(store.len) # total live entries
print(store.dim) # embedding dimension (None if empty)
print(store.wal_pending) # ops not yet compacted
Write pattern for large datasets
store = VectorStore.open()
# 1. Insert fast — WAL-only, no HNSW rebuild
for batch in batches_of(my_vectors, size=1_000):
store.upsert_batch(batch)
# 2. Compact once — rebuild HNSW from all entries
store.compact()
# 3. Search many times against the stable index — fast
for query in queries:
results = store.search(query, top_k=10)
Storage files
| File | Role |
|---|---|
store.snap |
Compacted JSON snapshot {"dim": N, "entries": [...]} |
store.wal |
Append-only log of mutations {"op": "upsert"/"delete", "data": {...}} |
API reference
VectorEntry(id, embedding, metadata="", created_at="")
| Attribute | Type | Description |
|---|---|---|
id |
str |
Unique identifier |
embedding |
list[float] |
Vector |
metadata |
str |
Arbitrary string payload |
created_at |
str |
Timestamp (user-supplied) |
VectorStore
| Method / Property | Description |
|---|---|
VectorStore.new(snapshot_path, wal_path) |
Fresh empty store |
VectorStore.open(snapshot_path, wal_path) |
Load snapshot + replay WAL |
.upsert(entry) |
Insert or replace by ID (WAL append) |
.upsert_batch(entries) |
Batch upsert (single WAL write) |
.delete(id) |
Delete by ID (WAL append) |
.get(id) |
Point lookup, returns VectorEntry or None |
.search(query, top_k=10) |
ANN search, returns list[(VectorEntry, float)] |
.compact() |
Merge WAL → snapshot, rebuild HNSW |
.len |
Number of live entries |
.dim |
Embedding dimension or None |
.wal_pending |
Ops not yet compacted |
Development
Requires Rust stable and maturin:
pip install maturin
python -m venv .venv && source .venv/bin/activate
maturin develop --release
python -c "import quanda_vector_db; print(quanda_vector_db.__version__)"
License
MIT
1 maintainer
Release files for quanda-vector-db 0.1.0
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