vectlite 0.1.10

Embedded vector store for local-first AI applications.

vectlite

Embedded vector store for local-first AI applications.

vectlite is a single-file, zero-dependency vector database written in Rust with Python bindings. It gives you dense + sparse hybrid search, HNSW indexing, metadata filtering, transactions, and crash-safe persistence in a single .vdb file -- no server, no Docker, no network calls.

Installation

pip install vectlite

Requires Python 3.9+. Pre-built wheels are available for macOS (x86_64, arm64), Linux (x86_64, aarch64), and Windows (x86_64).

Quick Start

import vectlite

# Create or open a database
db = vectlite.open("knowledge.vdb", dimension=384)

# Insert records with vectors, metadata, and sparse terms
db.upsert("doc1", embedding, {"source": "blog", "title": "Auth Guide"})
db.upsert("doc2", embedding2, {"source": "notes", "title": "Billing"})

# Search with filters
results = db.search(embedding_query, k=5, filter={"source": "blog"})

# Clean up
db.compact()

Features

Core

• Single-file storage -- one .vdb file per database, portable and easy to back up
• Dense vectors -- cosine similarity with automatic HNSW indexing for large collections
• Sparse vectors -- BM25-scored inverted index for keyword retrieval
• Hybrid search -- dense + sparse fusion with linear or RRF strategies
• Rich metadata -- str, int, float, bool, None, list, dict values
• Crash-safe WAL -- writes land in a write-ahead log first, then checkpoint with compact()
• Transactions -- atomic batched writes with db.transaction()
• File locking -- advisory locks prevent corruption from concurrent access

Search & Retrieval

• Metadata filters -- MongoDB-style operators: $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $contains, $exists, $and, $or, $not
• Nested filters -- dot-path traversal (author.name), $elemMatch, $size on lists and dicts
• Named vectors -- multiple vector spaces per record (vectors={"title": [...], "body": [...]})
• Multi-vector queries -- weighted search across vector spaces in a single call
• MMR diversification -- mmr_lambda controls relevance vs. diversity trade-off
• Namespaces -- logical isolation with per-namespace or cross-namespace search
• Rerankers -- built-in text_match(), metadata_boost(), cross_encoder(), bi_encoder(), composable with compose()
• Observability -- search_with_stats() returns timings, BM25 term scores, ANN stats, and per-result explain payloads

Data Management

• Physical collections -- vectlite.open_store() manages a directory of independent databases
• Bulk ingestion -- bulk_ingest() with deferred index rebuilds for fast imports
• Snapshots -- db.snapshot(path) creates a self-contained copy
• Backup / Restore -- db.backup(dir) and vectlite.restore(dir, path) for full roundtrips
• Read-only mode -- vectlite.open(path, read_only=True) for safe concurrent readers
• Text analyzers -- configurable tokenizer pipeline with stopwords, stemming, and n-grams

Usage

Hybrid Search with Reranking

import vectlite

db = vectlite.open("knowledge.vdb", dimension=384)

# Upsert with dense + sparse vectors
db.upsert(
    "doc1",
    dense_embedding,
    {"source": "docs", "title": "Auth Setup", "text": "How to configure SSO..."},
    sparse=vectlite.sparse_terms("How to configure SSO authentication"),
)

# Hybrid search with reranking
results = db.search(
    query_embedding,
    k=10,
    sparse=vectlite.sparse_terms("SSO authentication"),
    fusion="rrf",
    filter={"source": "docs"},
    explain=True,
    rerank=vectlite.rerankers.compose(
        vectlite.rerankers.text_match(),
        vectlite.rerankers.metadata_boost("source", {"docs": 0.5}),
    ),
)

for result in results:
    print(result["id"], result["score"])

Bulk Ingestion (Recommended for Large Imports)

For ingesting more than a few hundred records, use bulk_ingest() instead of calling upsert() in a loop. It writes records in WAL batches and rebuilds indexes only once at the end, making it orders of magnitude faster.

records = [
    {
        "id": f"doc{i}",
        "vector": embeddings[i],
        "metadata": {"source": "corpus", "chunk": i},
        "sparse": vectlite.sparse_terms(texts[i]),  # optional
    }
    for i in range(len(texts))
]

count = db.bulk_ingest(records, batch_size=5000)
print(f"Ingested {count} records")

The records parameter is a list[dict] where each dict has keys:

• id (str, required) -- unique record identifier
• vector (list[float], required) -- dense embedding vector
• metadata (dict, optional) -- arbitrary metadata
• sparse (dict[str, float], optional) -- sparse terms from sparse_terms()
• vectors (dict[str, list[float]], optional) -- named vectors
• namespace (str, optional) -- namespace override per record

upsert_many() and insert_many() also accept the same list[dict] format and rebuild indexes once, but don't batch WAL writes internally.

Collections

store = vectlite.open_store("./my_collections")
products = store.create_collection("products", dimension=384)
products.upsert("p1", embedding, {"name": "Widget", "price": 9.99})

logs = store.open_or_create_collection("logs", dimension=128)
print(store.collections())  # ["logs", "products"]

Transactions

with db.transaction() as tx:
    tx.upsert("doc1", emb1, {"source": "a"})
    tx.upsert("doc2", emb2, {"source": "b"})
    tx.delete("old_doc")
# All operations commit atomically or roll back on exception

Text Helpers

# Handles embedding + sparse term generation for you
vectlite.upsert_text(db, "doc1", "Auth setup guide", embed_fn, {"source": "docs"})
results = vectlite.search_text(db, "how to authenticate", embed_fn, k=5)

Analyzers

analyzer = vectlite.analyzers.Analyzer().lowercase().stopwords("en").stemmer("english")
terms = analyzer.sparse_terms("How to authenticate users with SSO")
# Use with upsert: db.upsert("doc1", emb, meta, sparse=terms)

Snapshots & Backup

db.snapshot("/backups/knowledge_2024.vdb")  # Self-contained copy
db.backup("/backups/full/")                 # Full backup with ANN sidecars

restored = vectlite.restore("/backups/full/", "restored.vdb")

Read-Only Mode

ro = vectlite.open("knowledge.vdb", read_only=True)
results = ro.search(query, k=5)  # Reads work
ro.upsert(...)                    # Raises VectLiteError

Search Diagnostics

outcome = db.search_with_stats(query, k=5, sparse=terms, explain=True)

print(outcome["stats"]["timings"])       # {"dense_us": 120, "sparse_us": 45, ...}
print(outcome["stats"]["used_ann"])      # True
print(outcome["results"][0]["explain"])  # Detailed scoring breakdown

Filter Operators

Operator	Example	Description
$eq	{"field": {"$eq": "value"}}	Equal (also {"field": "value"})
$ne	{"field": {"$ne": "value"}}	Not equal
$gt / $gte	{"field": {"$gt": 5}}	Greater than (or equal)
$lt / $lte	{"field": {"$lt": 20}}	Less than (or equal)
$in / $nin	{"field": {"$in": ["a", "b"]}}	In / not in set
$contains	{"field": {"$contains": "auth"}}	Substring match
$exists	{"field": {"$exists": True}}	Field presence
$and / $or	{"$and": [{...}, {...}]}	Logical combinators
$not	{"$not": {...}}	Logical negation
$elemMatch	{"tags": {"$elemMatch": {"$eq": "rust"}}}	Match list elements
$size	{"tags": {"$size": 3}}	List length
dot-path	{"author.name": "Alice"}	Nested field access

Database Methods Reference

Write Methods

Method	Description
db.upsert(id, vector, metadata, sparse=..., vectors=...)	Insert or update a single record
db.insert(id, vector, metadata, sparse=..., vectors=...)	Insert a record (raises on duplicate id)
db.upsert_many(records, namespace=None)	Upsert a batch of records (single index rebuild)
db.insert_many(records, namespace=None)	Insert a batch (raises on duplicate ids)
db.bulk_ingest(records, namespace=None, batch_size=10000)	Fastest bulk import with batched WAL writes
db.delete(id, namespace=None)	Delete a single record
db.delete_many(ids, namespace=None)	Delete multiple records by id

Read Methods

Method	Description
db.get(id, namespace=None)	Get a single record by id
db.search(query, k=10, ...)	Search and return a list of results
db.search_with_stats(query, k=10, ...)	Search with detailed performance stats
db.count() or len(db)	Number of records in the database
db.namespaces()	List all namespaces
db.dimension	Vector dimension (property)
db.path	Database file path (property)
db.read_only	Whether the database is read-only (property)

Maintenance Methods

Method	Description
db.compact()	Fold WAL into snapshot and persist ANN indexes
db.flush()	Alias for compact()
db.snapshot(dest)	Create a self-contained .vdb copy
db.backup(dest_dir)	Full backup including ANN sidecar files
db.transaction()	Begin an atomic transaction (use as context manager)

How It Works

• Records are stored in a compact binary .vdb snapshot file
• Writes go through a crash-safe WAL (.wal) before being applied in memory
• compact() folds the WAL into the snapshot and persists HNSW sidecar files
• Dense search uses HNSW indexes (auto-built for collections above ~128 records)
• Sparse search uses an inverted index with BM25 scoring
• Hybrid fusion combines dense + sparse via linear combination or reciprocal rank fusion
• Advisory file locks (flock) prevent concurrent write corruption

Links

• Official Documentation
• GitHub Repository
• Issue Tracker
• Changelog

License

MIT