uni-db 0.3.0

Python bindings for Uni Graph Database

uni-db: Python Bindings for Uni Graph Database

Python bindings for the Uni embedded graph database.

Part of The Rustic Initiative by Dragonscale Industries Inc.

Installation

pip install uni-db

Quick Start

import 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'}]

Schema Operations

# Labels and edge types
db.create_label("Person")
db.create_edge_type("KNOWS", ["Person"], ["Person"])
db.add_property("Person", "name", "string", False)   # nullable=False
db.add_property("Person", "age",  "int64",  True)    # nullable=True

# Indexes
db.create_scalar_index("Person", "name", "btree")
db.create_vector_index("Person", "embedding", "cosine")  # or "l2"

# Introspection
db.list_labels()         # ['Person', ...]
db.list_edge_types()     # ['KNOWS', ...]
db.get_label_info("Person")
db.get_schema()

Transactions

txn = db.begin()
txn.execute("CREATE (p:Person {name: 'Charlie'})")
txn.commit()   # or txn.rollback()

Bulk Loading

writer = db.bulk_writer()
writer.insert_vertices("Person", [
    {"name": "Alice", "age": 30},
    {"name": "Bob",   "age": 25},
])
writer.insert_edges("KNOWS", [
    (person_vids[0], person_vids[1], {}),   # (src_vid, dst_vid, properties)
])
writer.commit()

Vector Search

# Create a vector index
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")

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 (SQL WHERE expression)
results = db.query("""
    CALL uni.vector.query('Document', 'embedding', $vec, 10, 'category = "tech"')
    YIELD vid, distance
    RETURN vid, distance
""", {"vec": my_embedding})

# K-NN with distance threshold
results = db.query("""
    CALL uni.vector.query('Document', 'embedding', $vec, 10, NULL, 0.5)
    YIELD vid, distance
    RETURN vid, distance
""", {"vec": my_embedding})

YIELD columns: vid (integer vertex ID), distance (float).

Async API

import uni_db

# Open
db = await uni_db.AsyncDatabase.open("./my_graph")
# or: db = await uni_db.AsyncDatabase.temporary()

await db.execute("CREATE (p:Person {name: 'Alice', age: 30})")
results = await db.query("MATCH (p:Person) RETURN p.name")
await db.flush()

Query Utilities

# Parameterized queries
results = db.query(
    "MATCH (p:Person) WHERE p.name = $name RETURN p",
    {"name": "Alice"},
)

# Explain / profile
plan    = db.explain("MATCH (p:Person) RETURN p")
results, stats = db.profile("MATCH (p:Person) RETURN p")

Development

git clone https://github.com/rustic-ai/uni-db
cd uni-db/bindings/uni-db
uv sync --group dev
uv run maturin develop   # builds and installs the extension module
uv run pytest            # run tests

Links

• Documentation
• GitHub
• Issues

License

Apache 2.0