Uni is an embedded database that combines a property graph (OpenCypher), vector search, and columnar storage (Lance) into a single engine. Designed for applications requiring local, fast, multimodal data access with object storage (S3/GCS/local) durability.
Part of The Rustic Initiative by Dragonscale Industries Inc.
- Embedded & Serverless: Runs as a library within your application — no server process.
- Property Graph: OpenCypher queries with MATCH, CREATE, WHERE, ORDER BY, LIMIT, and aggregations.
- Vector Search: K-NN similarity search (L2, cosine) with pre-filter and threshold support.
- Columnar Storage: Lance-backed persistence on local disk or object storage (S3/GCS).
- Graph Algorithms: PageRank, Louvain, shortest path, and more via the built-in algorithm library.
- Rust & Python: Native Rust crate and Python bindings (PyO3).
Add to your Cargo.toml:
[dependencies]
uni-db = "0.1.3"pip install uni-dbimport uni_db
# Open or create a database
db = uni_db.Database("./my_graph")
# Define schema
db.create_label("Person")
db.add_property("Person", "name", "string", False)
db.add_property("Person", "age", "int64", True)
db.create_scalar_index("Person", "name", "btree")
# Write data
db.execute("CREATE (p:Person {name: 'Alice', age: 30})")
db.execute("CREATE (p:Person {name: 'Bob', age: 25})")
db.flush()
# Query
results = db.query(
"MATCH (p:Person) WHERE p.age > $min RETURN p.name",
{"min": 28},
)
print(results) # [{'p.name': 'Alice'}]# Create schema with a vector property
db.create_label("Document")
db.add_property("Document", "text", "string", False)
db.add_property("Document", "embedding", "vector[128]", True)
db.create_vector_index("Document", "embedding", "cosine")
# Insert data
db.execute("CREATE (d:Document {text: 'hello world', embedding: [0.1, 0.2, 0.3]})")
db.flush()
# K-NN search
results = db.query("""
CALL uni.vector.query('Document', 'embedding', $vec, 10)
YIELD vid, distance
RETURN vid, distance
ORDER BY distance
""", {"vec": my_embedding})
# K-NN with pre-filter
results = db.query("""
CALL uni.vector.query('Document', 'embedding', $vec, 10, 'category = "tech"')
YIELD vid, distance
RETURN vid, distance
""", {"vec": my_embedding})import uni_db
db = await uni_db.AsyncDatabase.open("./my_graph")
await db.execute("CREATE (p:Person {name: 'Alice', age: 30})")
results = await db.query("MATCH (p:Person) RETURN p.name")pip install uni-pydanticfrom uni_pydantic import UniNode, UniSession, Field, Relationship, Vector
class Person(UniNode):
name: str
age: int | None = None
email: str = Field(unique=True, index="btree")
embedding: Vector[128] = Field(metric="cosine")
friends: list["Person"] = Relationship("FRIEND_OF", direction="both")
session = UniSession(db)
session.register(Person)
session.sync_schema()
alice = Person(name="Alice", email="alice@example.com")
session.add(alice)
session.commit()
adults = session.query(Person).filter(Person.age >= 18).order_by(Person.name).all()Apache 2.0 — see LICENSE for details.
Uni is developed by Dragonscale Industries Inc. as part of The Rustic Initiative.