vectlite 0.1.2

Embedded vector store for local-first AI applications.

Python Binding

The first language target now has a concrete package layout:

• Rust extension crate in bindings/python/src/lib.rs
• Python package in bindings/python/vectlite
• packaging via maturin

Local Development

cd bindings/python
maturin develop
pytest

Install From PyPI

pip install vectlite

Release history lives in CHANGELOG.md.

TestPyPI Release

From the repo root:

./scripts/publish_testpypi.sh

Then upload with a TestPyPI token:

export TEST_PYPI_API_TOKEN="pypi-..."
UPLOAD=1 ./scripts/publish_testpypi.sh

The full flow is documented in docs/testpypi-release.md in the repository.

PyPI Release

From the repo root:

./scripts/publish_pypi.sh

Then upload with a PyPI token:

export PYPI_API_TOKEN="pypi-..."
UPLOAD=1 ./scripts/publish_pypi.sh

The full flow is documented in docs/pypi-release.md in the repository.

API

import vectlite

db = vectlite.open("knowledge.vdb", dimension=384)
with db.transaction() as tx:
    tx.upsert(
        "doc1",
        embedding,
        {"source": "notes", "priority": 10, "title": "Auth setup"},
        namespace="notes",
        sparse={"auth": 1.0, "sso": 0.5},
        vectors={"title": title_embedding, "body": body_embedding},
    )
    tx.upsert_many(
        [
            {
                "id": "doc2",
                "vector": other_embedding,
                "sparse": {"auth": 0.7},
                "metadata": {"source": "notes", "text": "billing and auth notes"},
            }
        ],
        namespace="notes",
    )

record = db.get("doc1")
results = db.search(
    query,
    k=5,
    filter={"source": {"$ne": "blog"}, "priority": {"$gte": 5, "$lte": 20}},
    namespace="notes",
    sparse={"auth": 1.0},
    vector_name="title",
    dense_weight=1.0,
    sparse_weight=1.0,
    fusion="rrf",
    rrf_k=30,
    fetch_k=20,
    mmr_lambda=0.3,
    explain=True,
    rerank=vectlite.rerankers.compose(
        vectlite.rerankers.text_match(),
        vectlite.rerankers.metadata_boost("source", {"notes": 0.2}),
    ),
)

debug = db.search_with_stats(
    query,
    k=5,
    namespace="notes",
    sparse={"auth": 1.0},
    vector_name="title",
    fusion="rrf",
    fetch_k=20,
    mmr_lambda=0.3,
)

db.compact()
print(db.wal_path)

Supported metadata/filter value types are:

• str
• int
• float
• bool
• None
• list
• dict

Supported filter operators in the MVP are:

• equality with {"field": "value"}
• {"field": {"$eq": "value"}}
• {"field": {"$contains": "auth"}}
• {"field": {"$gt": 5}}
• {"field": {"$gte": 5}}
• {"field": {"$lt": 20}}
• {"field": {"$lte": 20}}
• {"field": {"$ne": "value"}}
• {"field": {"$in": ["a", "b"]}}
• {"field": {"$nin": ["a", "b"]}}
• {"field": {"$exists": True}}
• {"$and": [...]}
• {"$or": [...]}
• {"$not": {...}}

Batch helpers available on Database:

• insert_many(records)
• upsert_many(records)
• delete_many(ids)

Durability helpers available on Database:

• transaction() for atomic batched writes
• wal_path to inspect the write-ahead log path
• compact() / flush() to checkpoint the snapshot and clear the WAL

Dense vector helpers available on Database:

• upsert(..., vectors={"title": [...], "body": [...]})
• search(..., vector_name="title")
• get(id)["vectors"]

Namespace helpers available on Database:

• every CRUD/search method accepts namespace=...
• search(..., all_namespaces=True)
• namespaces()

Text helpers available at package level:

• vectlite.upsert_text(db, id, text, embed, ...)
• vectlite.search_text(db, query, embed, ...)
• vectlite.search_text_with_stats(db, query, embed, ...)
• vectlite.sparse_terms(text)

Retrieval Quality

• sparse retrieval uses a real inverted index with BM25-style scoring
• fetch_k controls how many candidates are gathered before truncation
• mmr_lambda enables MMR diversification for dense, sparse, or hybrid search
• fusion="linear" and fusion="rrf" control dense+sparse score fusion
• rerank(query, results) can reorder the top candidates from Python
• rerank_k limits how many initial candidates are sent into the rerank hook
• vectlite.rerankers.text_match() boosts metadata text/title overlap
• vectlite.rerankers.metadata_boost(field, boosts) boosts metadata values
• vectlite.rerankers.compose(...) chains rerankers sequentially or with RRF
• db.search_with_stats(...) returns both results and search diagnostics
• explain=True adds per-result debug payloads with ranks, matched terms, and rerank traces
• lower mmr_lambda favors diversity more aggressively; higher values stay closer to raw relevance

ANN Behavior

• dense and hybrid search use HNSW indexes when enough points are present
• named vector spaces get their own dense ANN indexes
• ANN sidecars are persisted on disk and reloaded on open when the manifest still matches the record set
• sparse-only search remains exact but uses the inverted index instead of a full scan
• small collections stay on exact dense search to avoid ANN overhead and low-cardinality edge cases
• writes land in a crash-safe WAL first, then compact() checkpoints back into the .vdb snapshot
• the .vdb snapshot plus .wal are the source of truth; ANN sidecars are acceleration artifacts